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The socioeconomic status of Asian Brazilians in 1980

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Title:
The socioeconomic status of Asian Brazilians in 1980 a comparation of Asians, whites and Afro-Brazilians
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Jirimutu, 1956-
Publication Date:
Language:
English
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xiv, 277 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Age groups ( jstor )
Asians ( jstor )
Educational levels ( jstor )
Employment statistics ( jstor )
Immigration ( jstor )
Income level ( jstor )
Manual labor ( jstor )
Mortality ( jstor )
Rural areas ( jstor )
Womens studies ( jstor )
Anthropology thesis Ph.D
Asians -- Economics conditions -- Brazil ( lcsh )
Asians -- Social conditions -- Brazil ( lcsh )
Blacks -- Economic conditions -- Brazil ( lcsh )
Blacks -- Social conditions -- Brazil ( lcsh )
Brazilians -- Statistics -- Brazil ( lcsh )
Brazilians -- Economic conditions -- Brazil ( lcsh )
Brazilians -- Social conditions -- Brazil ( lcsh )
Dissertations, Academic -- Anthropology -- UF
Whites -- Economic conditions -- Brazil ( lcsh )
Whites -- Social conditions -- Brazil ( lcsh )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1994.
Bibliography:
Includes bibliographical references (leaves 266-276).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Jirimutu.

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University of Florida
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Copyright Jirimutu. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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AKF7993 ( NOTIS )

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THE SOCIOECONOMIC STATUS OF ASIAN BRAZILIANS IN 1980:
A COMPARATION OF ASIANS, WHITES AND AFRO-BRAZILIANS














By
JIRIMUTU


DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY














ACKNOWLEDGEMENTS


I wish to thank Dr. H. Russell Bernard, my advisor, for his persistent
teachings in the scientific approach to anthropological inquiries and


positivistic attitude toward human knowledge.


I also appreciate his generous


support, academic and otherwise, and his genuine understanding and belief


in me during the course of my graduate studies.
have been entirely impossible if Dr. Charles H.


This dissertation would


Vood had not sparked my


interest in demographic studies when I took a seminar on population with


him.


Throughout my dissertation research, I have benefited tremendously


from his breadth of knowledge, mastery of computer skills and data analysis


techniques, and friendship.


He has my deepest gratitude for inentoring me in


every way possible, even after he moved to another university.


thanks go to Dr. Paul J.


My sincere


Maganrella for his academic support and personal


friendship, both of which are very important for a foreign graduate student


like me.


I thank Dr. Paul L. Doughty for serving on my committee and


offering his wisdom.


I appreciate Dr. Barbara Ann Zsembik for joining my


committee and offering her expertise as a demographer.


I am extremely


grateful to the Wenner-Gren Foundation for Anthropological Research for
providing me with financial support for the first three years of my graduate


school.


Finally, I sincerely thank my wife, Mingxin Zhang, and my son,





















. i


TABLE OF CONTENTS

pAag
ACKNOWLEDGEMENTS .........................................................................................ii


LIST OF TABLES..


LIST OF FIGURES


ABSTRACT


CHAPTERS


INTRODUCTION


Asian Immigrants in the United States...............
Research Design ...... ......................... .....................
Asian Immigrants in Brazil......................................

HISTORICAL OVERVIEW OF THE JAPANESE


EXPERIENCE IN BRAZIL.


Historical Background for the Japanese Migration to Brazil ................14
Japanese Immigration to Brazil ................................................................16
Social Characteristics and Social Mobility of the


Japanese Immigrants
Summary .......................


....... ................ ...................... .... ........4.... 1


FERTILITY DIFFERENTIALS AMONG ASIANS,
AND AFRO-BRAZILIANS ..........................................


WHITES
. .. ....... ...... ...........48


A Brief Review of Literature on Fertility Studies ..................................48
Fertility Differentials among Ethnic/Racial Groups
in M odern States ................................................ .. .... .. .......... ........................ 53


-- Fertility Differentials Among Asians,
Afro-Brazilians ..............................


Whites and


Summary


....... ...................................................................................................xii


0"...'...........0.........................O................... ......00.......0...0.* ..4 ..1


.......... .......o..................................... .B............... 14


__ _II ___~









Child Mortality Differentials and Life Expectancy


by Color Group ........................
Sum m ar y ..........................................


EDUCATIONAL ATTAINMENT OF


ASIANS,


WHITES


AND AFRO-BRAZILIANS .....................................

School Attendance Rate of Children Ages 6-16 ..
Educational Attainment of Men Ages 18-65 ......


Educational Attainment of Women Ages


18-65


Summary ....................................................................................

OCCUPATIONAL PROFILE OF ASIANS, WHITES AND


AFRO-BRAZILIANS ...


Occupational Profile of Men Ages 18-65 .....
Occupational Profile of Women Ages 18-65
Summary ...........................................................


MEAN INCOME OF
AFRO-BRAZILIANS


ASIANS, WHITES AND


Mean Monthly Income of Men Ages 18-65


Mean Monthly Income of Women Ages
Summary .....................................................


18-65


..188
..205
..221


SUMMARY


AND CONCLUSION


APPENDICES

A. BRAZILIAN RACIAL CATEGORIES AND THE CENSUSES.........247

B INDIRECT MEASURES OF CHILD MORTALITY .................................254

C LOGISTIC REGRESSION WITH SCHOOL ATTENDANCE
RATE OF CHILDREN AGES 6-16 AS THE DEPENDENT
VARIABLE, METROPOLITAN SAO PAULO, BRAZIL, 1980..........258

D OCCUPATIONAL CATEGORIES IN THE 1980 CENSUS.................264


...........................................................2


'I).. .... ....tQtt O........O.i... .'.....''''.''.. 8


t......................................89


....................................91


.o......o... ................1 .32


.... ....................... .. .......o..........187













LIST OF TABLES


Table

1.1


1.2


Page
Distribution of Amarelos Ages 15-65 by Place of Birth and
National Origin, Metropolitan Sao Paulo, Brazil (1980)...............13


Racial Composition of Brazil's Population,


Industry Distribution of Brazilian Males Aged 10 and


Over by Color, 1950..


........................ .......23


Employment Status of Brazilian males Aged 10 and Over


for All Industries and Agriculture by Color, 1950.


.................24


Proportion of Farmers Among Japanese Immigrants and
Descendants Aged 10 and Over in Labor Force by Sex,
Brazil, 1958..............................................................................


................25


Occupational Distribution of Japanese Immigrants and
Descendants Aged 10 and Over in Labor Force,


Brazil


1958..................................................................


Japanese Immigrants and Descendants Aged 10 and Over
in Labor Force by Industry, Brazil, 1958................................


.................28


A Comparison of Occupational Status of Prewar and
Postwar Non-Farming Japanese Immigrants....................................30

Agricultural Production of Japanese Brazilians in Sao Paulo


and Brazil by Crop, 1958.................


Japanese Immigrants and Descendants Aged 7 and Over
by Level of Education and Residence, 1958...................


...... ............36


Marital Status of the Japanese Population in Brazil


.............................27


............32


1940-1980 ........................13










Proportion of Traditional Families Among Japanese
Farmers and Non-Farmers in Brazil by Value of


Property Owned,


Mean Children Ever Born to Women of 15-49 Years of Age
By Age Group, Metropolitan Sao Paulo, Brazil (1980)...................58


Mean Children Ever Born to Women of 15-49 Years of Age
By Color Group, Metropolitan Sao Paulo, Brazil (1980)................59

Mean Children Ever Born to Women of 15-49 Years of Age


By Age and Color Groups, Metropolitan Sao Paulo,
Brazil (1980).......................................................................


Mean Children Ever Born to Women of 15-49 Years of Age
By Education, Age and Color Groups, Metropolitan


Sao Paulo, Brazil (1980)


Mean Children Ever Born to Women of 15-49 Years of Age
By Income, Age and Color Groups, Metropolitan


Sao Paulo, Brazil (1980)


Mean Children Ever Born to Women of 15-49 Years of Age
By Residence, Age and Color Groups, Metropolitan


Sao Paulo, Brazil (1980)


Mean Children Ever Born to Women of 15-49 Years of Age
By Residence, Education and Color Groups,


Metropolitan Sio Paulo,


Brazil (1980).........


Mean Children Ever Born to Women of 15-49 Years of Age
By Residence, Income and Color Groups,


Metropolitan S&o Paulo, Brazil (1980)............


Children Ever Born to Women Aged


Age,


20-49 Regressed on


Residence, Education, Income and Color...................................71


Social Indicators by Color Group, Metropolitan Sio Paulo,
Brazil (1980) ............................................................................


1958..... ........... ...............................


.....................62


S.......................................................... ...............63


............................................... ........ .............65


...... .... ................'. ...........66


......................................68


............82


..................... 40


.....................60









Metropolitan Sao Paulo, Brazil (1980) ...................................................87

Number of Children Ages 6-16 and the Percent in School


by Age, Metropolitan Sao Paulo, Brazil (1980)....


................... .........93


Number of Children Ages 6-16 and the Percent in School
by Color Group, Metropolitan Sho Paulo, Brazil (1980).................93

Distribution of Children Ages 6-16 and the Percent in School
by Income Level, Metropolitan Sao Paulo, Brazil (1980)...............94

Children Ages 6-16 and the Percent in School by Residence,


Metropolitan Sao Paulo,


Brazil (1980)............


Number of Children Ages 6-16 and the Percent in School


by Parents' Education,


Metropolitan Sao Paulo,


.............................96


Number of Children Ages 6-16 and the Percent in School


Sex,


Metropolitan S&o Paulo, Brazil (1980)


..................97


In-School Rate of Children Ages 6-16 and the Percent
in School by Color Groups, Metropolitan Sao Paulo,


Brazil (1980)............................ .......................


.... .... ................. .........98


In-School Rate of Children Ages 6-16 by Income and Color,


Metropolitan Sao Paulo,


In-School Rate of Children Ages 6-16 by Region,


Income


and Color, Metropolitan Sao Paulo, Brazil (1980)............................102


5.10


Logistic Regression of In-School Rate of Children Ages


6-16 on Mother's and Father's
Income, Residence and Color b


Education, Household
'y Age, Metropolitan


Sao Paulo, Brazil (1980)...................................... .... ............... ...........1

Mean Years of Schooling for Men Ages 18-65 by Color Group,


Metropolitan S8o Paulo,


Brazil (1980).......


........114


Mean Years of Schooling for Men Ages 18-65 by Age Group,


Ilo.rn-nn- a C,- PT.iii1,n Ir-,-1I (1Qfn\


................95


Brazil (1980).............................................................


Brazil (1980)........ ..................... .....................100


11A









Metropolitan S o Paulo,


Brazil (1980) ... .... .... .. ............ ...... .............116


Mean Years of Schooling for Men Ages 18-65 by Residence


and Color, Metropolitan Sao Paulo, Brazil (1980)


Mean


Years of Schooling for Men Ages 18-65 by Income and


Color, Metropolitan Sho Paulo,


Mean


Me


Brazil (1980)..


.........118


Years of Schooling for Women Ages 18-65 by Color Group,
tropolitan Sio Paulo, Brazil (1980)................................. ...............120


A


Mean


Years of Schooling for Women Ages 18-65 by


Metropolitan Sao Paulo, Brazil (1980)...


Age Group,
.........................121


5.19


Mean


Years of Schooling for Women Ages 18-65 by Residence,


Metropolitan Sio Paulo,


5.20


Brazil (1980)...


.....121


Mean Years of Schooling for Women Ages 18-65 by Age
and Color, Metropolitan Sao Paulo, Brazil (1980)........


Mean


Years of Schooling for Women Ages 18-65 by Residence


and Color, Metropolitan Sao Paulo,


Mean


Brazil (1980).


.......126


Years of Schooling for Women Ages 18-65 by Income


and Color, Metropolitan Sao Paulo, Brazil (1980) ........................126


Occupational Distribution of Men Ages 18-65 by Color Group,


Metropolitan Sao Paulo, Brazil (1980)........


........134


Top Five Occupations in the Category of Unskilled/Personal
Service by Color, Metropolitan Sho Paulo, Brazil (1980).....


Occupational Distribution of Men Ages 18-65 by Residence,


Metropolitan Sao Paulo, Brazil (1980)


......0.............. ..137


Occupational Distribution of Men Ages 18-65 by Age Group,
Metropolitan Sio Paulo, Brazil (1980)...................................


..........138


Occupational Distribution of Men Ages 18-65 by Income,


Metropolitan Sao Paulo, Brazil (1980)


................. ............ ...... 140


.................117









Paulo, Brazil (1980)............................................................. ....... ..... ...144

Occupational Distribution of Men Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980).........................145

Proportion of White vs. Blue Collar Occupations of Men
Ages 18-65 by Age and Color, Metropolitan Sfo Paulo,


Brazil (1980)...................


.............147


6.10


Occupational Distribution of Men Ages 18-65 by Age
and Color, Metropolitan Sio Paulo, Brazil (1980)........................... 149


Occupational Distribution of Men Ages 18-65 by Income
and Color, Metropolitan Sio Paulo, Brazil (1980)............................ 151

Occupational Distribution of Men Ages 18-65 by Education
and Color, Metropolitan Sao Paulo, Brazil (1980).........................157


Occupational Distribution of Women Ages 18-65 by Color


Group, Metropolitan Sao Paulo, Brazil (1980)


..... ........ ...... .... .. .1


Occupational Distribution of Women Ages 18-65 by Residence,


Metropolitan S&o Paulo, Brazil (1980)....


Occupational Distribution of Women Ages 18-65 by
Metropolitan S&o Paulo, Brazil (1980).....................


Occupational Distribution of Women Ages


.........................160

Age Group,
.........................162


18-65 by Income


Level, Metropolitan S&o Paulo, Brazil (1980)...


............ ... .............164


Occupational Distribution of Women Ages 18-65 by Education,


Metropolitan S&o Paulo, Brazil (1980).............


.....166


6.18


Occupational Distribution of Women Ages 18-65 by Residence


and Color, Metropolitan Sio Paulo, Brazil (1980).


.......168


Occupational Distribution of Women Ages 18-65 by


Age


and Color, Metropolitan Sao Paulo, Brazil (1980).........................171


Occupational Distribution of Women Ages 18-65 by Income
and 'nolnr Mfatrnrnlifan <3 Pmin Rr2711 (1QRNf 171


6.14









Metropolitan So Paulo, Brazil (1980) ................................ .......189

Mean Monthly Income of Men Ages 18-65 by Age Group,


Metropolitan Sao Paulo, Brazil (1980)........


...............190


Mean Monthly Income of Men Ages 18-65 by Residence,


...........................................190


Metropolitan Sao Paulo, Brazil (1980)..


Mean Monthly Income of Men Ages 18-65 by Education,


Metropolitan Sao Paulo, Brazil (1980)...


............. ............................. 191


Mean Monthly Income of Men Ages 18-65 by Occupation,
Metropolitan Sio Paulo, Brazil (1980)......................................... .......193

Mean Monthly Income of Men Ages 18-65 by Age
and Color, Metropolitan Sio Paulo, Brazil (1980)........................194

Mean Monthly Income of Men Ages 18-65 by Residence
and Color, Metropolitan S&o Paulo, Brazil (1980)........................ 196

Mean Monthly Income of Men Ages 18-65 by Education
and Color, Metropolitan Sao Paulo, Brazil (1980)........................197

Mean Monthly Income of Men Ages 18-65 by Occupation
and Color, Metropolitan Sao Paulo, Brazil (1980)......................... 199


Monthly Income of Men Ages 18-65 Regressed on Age,


Education


, Residence and Color, Metropolitan


SAo Paulo, Brazil (1980)...............


Mean Monthly Income of Women Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980). .......................................


Mean Monthly Income of Women Ages 18-65 by
Metropolitan S&o Paulo, Brazil (1980)..................


Age Group,
..............................206


Mean Monthly Income of Women Ages 18-65 by Residence,


Metropolitan Sao Paulo, Brazil (1980).


Mean Monthly Income of Women Ages 18-65 by Education,


NI a%,-dHar^ cn P.guln fl,.^rwfl 0 0fo\


.....205


..0.0.........................207


*I ItJ


/ r. fn


.............................204









and Color, Metropolitan Sao Paulo, Brazil (1980)........................211


7.17


Mean Monthly Income of Women Ages


18-65 by Residence


and Color, Metropolitan Sao Paulo, Brazil (1980).... ......................212


Mean Monthly Income of Women Ages 18-65 by Education
and Color, Metropolitan S&o Paulo, Brazil (1980).........................213


Mean Monthly Income of Women Ages 18-65 by Occupation


and Color, Metropolitan Sao Paulo, Brazil (1980).......


Monthly Income of Women Ages


18-65 Regressed on


Age, Education, Residence and Color, Metropolitan
Slo Paulo, Brazil (1980).......................................................................220


.. ...............2












LIST OF FIGURES


Figure


Page


In-School Rate of Children Ages 6-16 by Age and Color Groups,


Metropolitan SAo Paulo, Brazil (1980)..............................


.......99


In-School Rate of Urban Children Ages 6-16 by Income and
Color Group, Metropolitan Sao Paulo, Brazil (1980).........


In-School Rate of Rural Children Ages 6-16 by Income and
Color Group, Metropolitan Slo Paulo, Brazil (1980)........


Effects of Father'


and Mother's Education on Children'


In-School Rate, Metropolitan Sao Paulo, Brazil (1980).


Effects of Household Income and Urban Residency
on Children's In-School Rate, Metropolitan
Sio Paulo, Brazil (1980).......................................................................111

The Odds of Afro-Brazilian and Asian Children Being in
School Against Those of White Children,
Metropolitan Sao Paulo, Brazil (1980)................................................112


Mean


Years of Schooling by Sex and Color Group,


Metropolitan Sio Paulo, Brazil (1980)


Mean


Years of Schooling by Sex and Age Group,


Metropolitan Sao Paulo, Brazil (1980)


Mean


Years of Schooling by Sex and Residence,


Metropolitan Sao Paulo, Brazil (1980) ............... ............... .....................123


.............................122


.............................................122













Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy


THE SOCIOECONOMIC STATUS OF ASIAN BRAZILIANS IN 1980:


A COMPARISON OF


ASIANS,


WHITES AND


AFRO-BRAZILIANS


Jirimutu
April 1994


Chairman:


Major Department:


I use the 3%


H. Russell Bernard


Anthropology


sample data of Metropolitan Sao Paulo from the 1980


Brazilian Census to examine the socioeconomic standing of Asian Brazilians,


relative to whites and Afro-Brazilians in Brazil.


Operationally


socioeconomic standings of the three color groups are measured in terms of


fertility level,


child mortality level and the life expectancy rate associated with


it, educational attainment (school attendance rate of children ages 6-16 and
years of school completed by men and women age 18-65), occupational profile


and mean monthly income of men and women ages 18-65.


The results of


these measurements indicate that in 1980 Asian Brazilians lead both whites









in Brazil


, the presence of ethnic enclaves and economies, ownership of small


business, continued heavy investment in education through several
generations, the family characteristics that facilitate stability and capital

accumulation, and the cultural values, such as hard work, industriousness,


emphasis on education,


obligation and loyalty to family and kin group.


This


shows that Asian immigrants in Brazil have experienced similar, if not more,
success in upward social mobility as have Asian immigrants in the United

States.













CHAPTER 1
INTRODUCTION


Asian Immigrants in the United States


Asian immigrants in the United States have long attracted the

attention of social scientists because it is widely recognized that they have


done very well,


over time


, compared to many other immigrant groups.


They


are said to have achieved parity with or even surpassed the majority whites


in socioeconomic standing (Bell,


1985; Bonacich & Modell,


1980


Chiswick,


1980; Hirschman, 1983; Hirschman & Wong, 1981


Hirschman & Wong, 1986; Jiobu,


Hirschman & Wong, 1984;


1976; Jiobu, 1990; Kitano, 1974; Kitano &


Daniels, 1988; Montero,


1981


Montero &


Tsukashima


1977


Nee & Sanders,


1985; Nee & Wong, 1985; Petersen, 1971; Ro,

Asian Americans have been called a


se, 1985; Wong, 1980; Wong, 1982).

"model minority" (Newsweek,


1982) and "America's


super minority" (Ramirez, 1986),


and hailed as


"America's


greatest success story" (Bell,


1985).


Indeed


, Asian Americans,


especially Japanese Americans and Chinese Americans,


have achieved great


success in terms of labor force participation, income and education.


In 1979,


95% of Asian Americans (the six largest groups within Asian Americans
which include Chinese, Filipino, Japanese, Asian Indians, Korean and

Vietnamese) had a median family income of $23.600. compared to the average









advances in income and education for Asian Americans.


Their median


family income in 1989 was $41,583 compared with the national average of


$35,225, and 38% of Asian Americans had graduated with a bachelor's


degree


or higher, compared with 20% of the total population (Bureau of the Census,
1993).


However


, the Asian success story has been exaggerated to some extent


because the statistics on median family income does not reflect the entire


picture of the socioeconomic status of Asian Americans.


have noted


As many researchers


, the higher median family income of Asian Americans is mainly


due to their larger average family size (3.8 persons for Asian American


families vs. 3.2 persons for all U


families in 1989),


higher proportion of


families with three or more workers (19.8% for Asian American vs.


the total population),


13% for


geographical locations (Asian Americans are highly


concentrated in California and New York, where the average income is
higher, relative to the rest of the country), and higher educational attainment.
In fact, the mean personal income of Asian Americans in 1989 was slightly


lower than the national average: Per capital income for


Asian Americans in


1989 was $13,806, compared with the national per capital income of $14,143


(Bureau of the Census, 1993).


Nonetheless, there is no doubt that compared


to many other immigrant groups, most Asian Americans have overcome the
disadvantages that immigrant groups typically confront in the United States.

Social scientists have devoted considerable effort to understanding the


factors that explain the relative success of Asian immigrants.


Some have


stressed the role of


"middleman minorities"


for various Asian groups in the









social mobility (Li,


1977


Lyman, 1977; Nee & Sanders, 1985;


Takaki 1989).


Others have argued that the strength of kinship and family ties, and the


emphasis on education,


hard work and sacrifice for children are mostly


responsible for their success (Kitano, 1969; Newsweek, 1982; Petersen, 1971;


Schwartz


, 1971)


The first two arguments are structural explanations while the third


type is cultural.


The structural arguments mainly examine the relationship


between the minority in question and the society at large in terms of
occupational structure, economic status and the role of ethnic organizations


in the economic, social and political arena.


Cultural arguments either focus


on the cultural characteristics of the minorities themselves or seek
similarities between the cultural values of the dominant society and the

minorities and to attribute the success of minorities to these cultural traits.

Nee and Wong (1985:282) argued that both the cultural and structural

explanations were ahistorical because they "fail to capture the dynamic nature

of immigrant groups as they respond to historical situations and changing


economic structures."


For them, the cultural argument was a form of circular


reasoning and failed to include two important variables that were essential
for the upward mobility of immigrants and their descendants; 1)


"immigrants'


willingness to endure hardship for economic gains" and 2) "the


socioeconomic background at the time of immigration" (1985:283).
They maintained that the cultural characteristics of Asian Americans


reflected the influence of neo-Confucianism,


which emphasized


legitimacy of status attainment through education and membership and


"the









the time of immigration as crucial for "the creation of opportunities for


upward mobility."


Without the necessary human capital to generate


resources, the cultural characteristics of immigrants would have much


smaller impact on their socioeconomic


standing.


Therefore


both the cultural


characteristics and the socioeconomic background of immigrants were
essential in understanding their success in the new country.

Nee and Wong (1985:286) also criticized the structural argument for
"failing to deal with the changing economic condition of the expanding


market economy in North America."


They maintained that the


socioeconomic attainments of immigrant and ethnic groups are the result of
"continuous change and transformations of both cultural attributes and labor


market conditions" (1985:287).


The formation of household production units,


they argued, facilitated the social and economic mobility of Japanese


Americans.


The profit from the household production units in turn served


as the capital for further development of small businesses.


Nee and Wong


particularly stressed the importance of the family bond in the socioeconomic


attainment of


Asian Americans:


Cheap labor generated by household units allowed these ethnic
businesses to be competitive in the dominant society; formation
of family businesses coincided with the development of an


enclave economy,


which opened ethnically controlled avenues


for socioeconomic mobility, and provided a stable environment
for family life and the socialization and education of an
upwardly mobile second generation. (1985:287-288)


Nee and Wong (1985) used a "supply-demand" versvective, which









and the socioeconomic background prior to and after immigration on the
supply side, and put the structural constraints and opportunity structures
created by the development of the capitalist economy on the demand side.
Theories of middleman minorities and of ethnic enclaves are often


applied in the literature of Asian Americans.
ethnic group relations, such as Blalock (1967),


Drawing on earlier works on
Bonacich (1973) and Bonacich


and Modell (1980) argued that certain minorities in multiethnic societies
occupy a middle status between the dominant group and the subordinate


groups, acting as buffers between elites and masses.


These middleman


minorities usually occupy an intermediate niche in the economic system and
tend to concentrate in certain occupations, such as traders, moneylenders and


shopkeepers.


Middleman minorities therefore provide goods and services to


both the elites and the masses.


belonging nowhere,


Because of their unique social position of


they tend to develop strong in-group solidarity and form


their own separate and distinct community.


Two often-cited examples of


middleman minorities are Jews in feudal and early modern Europe and
Chinese in Southeast Asia (also called overseas Chinese) (Bonacich and


Modell


, 1980).


Bonacich and Modell (1980) applied the theory of middleman


minorities to the experience of Japanese Americans.


They argued that


Japanese Americans, particularly the issei, or the first generation, exhibited
many of the traits of a middleman minority; they "formed a highly organized,


internally solidary community,"
nonindustrial family businesses"


"concentrated in self-employment and


and "faced severe hostility from the









"Ethnic enclaves are a distinctive economic formation, characterized by the
spatial concentration of immigrants who organize a variety of enterprises to

serve their own ethnic market and the general population" (Portes and Bach,


1985:203).


The presence of immigrants with sufficient capital to create new


opportunities for economic growth and an extensive division of labor are two


fundamental traits of economic enclaves.


Ethnic enclaves also require a large


number of immigrants with business skills and a large pool of low-wage


labor.


The Cubans in Miami and Koreans in Los Angeles are contemporary


examples of ethnic enclaves (Portes and Manning, 1986).


Some ethnic groups


are highly entrepreneurial, possess capital, and therefore develop ethnic
economies that consist of many small businesses, some of which interface


with the majority economy (Portes and Jensen, 1987).


Within this enclave,


ethnic workers do not have to compete with the majority workers and are
usually not directly subject to discrimination by the dominant group.
Therefore, they can climb the socioeconomic ladder relatively free of racial

and ethnic discrimination.
This does not mean that everyone is equal in an ethnic enclave. On the
contrary, ethnic employers exploit ethnic workers, especially recent arrivals,


and make huge profit from cheap labor.


On the relationship between the


employers and workers in ethnic enclaves, Jiobu (1990:171) stated that "to the


extent that workers rely on enclave employment,


their income


, and by


implication their socioeconomic standing, will be suppressed.


other hand


But on the


, suppressing the income of workers raises the income (and


socioeconomic standing) of ethnic employers."









more comparative research on the fate of Asian immigrants in other


countries, such as Brazil,
Asians (mostly Japanese)


a country that has received a large contingent of

. In contrast to the vast literature on Afro-Brazilians,


the literature, especially recent studies,


on the Asian population in Brazil is


remarkably small


In this dissertation


, I examine whether Asian immigrants


have experienced the same socioeconomic success in Brazil as they have in


the United States.


Specifically,


I compare Asian Brazilians to whites and


Afro-Brazilians in Brazil in terms of quality of life.




Research Design


Dependent Variables


The data for this study are the 3


sample of Metropolitan Sao Paulo


from the 1980 Brazilian Census.


Conceptually


quality of life can be measured


by success or failure at various crucial periods of the life course:


surviving childhood,


Operationally


giving birth,


acquiring an education, finding a job and getting paid.


the dependent variables in this study are fertility level (mean


children ever born to women ages 15-49),


child mortality rate and the life


expectancy rate associated with it, school attendance rate for children ages 6-16


and educational attainment of men and women ages


18-65


, occupational


profile of men and women ages
and women ages 18-65. Taken t


18-65, and mean monthly income of men


together, these variables provide us with a









Color Groups


The 1980 Brazilian census used four categories for racial classification;


branco, pardo, preto and amarelo, or white,


brown


, black and yellow.


In this


study, I use three color groups (whites,
instead of the four in the 1980 census.


Afro-Brazilians and Asian Brazilians),

In other words, I have combined the


census categories of black (preto) and brown (pardo) into a single category


called

"Asian


"Afro-Brazilians,"


Brazilians"


and have replaced "yellow" (amarelo) with the term


or simply "Asians."


My decision of combining the categories of brown and black into a


single category is based on two things; the focus of th


study and the findings


of a number of studies on racial inequalities in Brazil (Hasenbalg, 1985;


Hasenbalg and Huntington,


1982; Lovell,


1989; Silva,


1978; Silva,


1985; Wood,


1990; Wood and Carvalho, 1988; Wood and Lovell


1989


Wood and Lovell,


1992).


First, since the focus of this study is Asian Brazilians, I could have


compared them to the rest of the population as a whole or to all of the racial


categories used in the 1980 census.


In my view, though, the position of Asian


Brazilians in Brazilian society is most clearly shown by comparing them to
whites and Afro-Brazilians since we know from the literature that there are


significant differences among these groups.


Second


, the above studies found


that although there are differences between blacks and mulattos in
socioeconomic standing, they are much closer to one another than to whites


and there are substantial differences between whites and nonwhites.


In other


words


, there is a major dividing line between whites and non-whites.


Ths


Thus,









Independent


Variables


In addition to color group, the most important independent variables


in this study are age,


most chapters,


residence, educational level, and income level.


I treat age as an ordinal variable consisting of three categories


(18-25


, 26-39 and 40-65 years old).


the dependent variables.


This eliminates the general effect of age on


In regression analyses,


age is treated as an interval


variable.


Residence is a dichotomous variable: urban or rural.


Educational attainment is measured by years of schooling completed.


In descriptive analyses, I generally treat this a
of five levels: 1) no schooling at all, 2) one to I


s an ordinal variables consisting

four years of schooling, 3) five to


eight years of schooling, 4) nine to eleven years of schooling and 5) twelve or


more years of schooling.


However, years of schooling is treated as an interval


variable in regression analyses.
Mean monthly income refers to the sum of either household or
individual income from different sources, such as occupation, income in


kind, retirement (social security), rent, gifts,


capital and others,


during the


period of twelve months preceding the census.


1980 (one minimum wage


= 4,150 cruzeiros),


Based on the minimum wage

mean monthly income is


classified into four levels in descriptive analyses:


wage (zero to 4,150 cruzeiros),


51-8,300 cruzeiros),


1) up to one minimum


2) between one and two minimum wages


3) between two and three minimum wages (8,301-


12,450 cruzeiros),


4) above three minimum wages (above


2,450 cruzeiros).


However, in regression analyses mean monthly income is treated as an









Organizations of the Chapters


Chapter


2 provides an historical overview of Japanese migrations to


Brazil and of the Japanese experience in Brazil from their arrival at the turn


of the century to the late 1950s.


Chapter 3 starts with a review of fertility


theories and of racial/ethnic differentials in fertility.


fertility differences by color, age,


I then examine the


educational level, income level and


residence before comparing the fertility differentials among Asians, whites


and Afro-Brazilians, controlling for the other variables.


Finally


I conduct a


multivariate regression analysis to examine the association of fertility and the
other variables.
In Chapter 4, I discuss major determinants of mortality and


racial/ethnic differences in mortality in multiethnic societies.


Then


describe some key socioeconomic indicators of Asian, white and Afro-
Brazilian women and use indirect measures to calculate child mortality rate


for the three color groups.


On the basis of the mortality level for each group, I


calculate the life expectancy rate for each of the three groups and discuss the


implications of these rates.


Finally, I examine the association between the


major socioeconomic indicators and child mortality, using the


Tobit


regression procedure.


Chapter


5 has three sections.


The first section compares Asian,


white


and Afro-Brazilian children ages 6-16 in terms of in-school rate by age,


residence, parents'


educational level and income level.


I then use logistic


regression to measure the effects of these variables on racial differences in









differences in educational attainment.


In the third section, I repeat the same


analysis for the measurement of educational attainment of women ages


18-65.


In Chapter 6, I describe occupational profiles of men and women ages


18-65 separately by color, age, residence,


educational level and income level


and the effects of these variables on racial differences in occupational

distribution.


Chapter


7 describes mean monthly income of men and women


separately by age, residence, occupation and educational level.


I examine the


racial differences in mean income, controlling for the other variables.

In Chapter 8, the concluding chapter, I summarize the main findings of

the study and discuss the implications of my findings in the light of relevant

literature on the experience of Asian immigrants in the United States.




Asian Immigrants in Brazil


Amarelo has been used as one of the four racial categories in the


Brazilian Censuses since 1940


refers.


, and there is little ambiguity as to whom it


Amarelo is designated for people with yellow skin color, who are


either immigrants from Asia or their descendants.


Unlike other racial


categories, there has been very little movement in and out of amarelo.


is probably because of Asians'

intermarriage with other racia

percent of the total population


This


distinct physical features and their lack of

1 groups. Though they comprise less than one

in Brazil, the amarelos are a very stable group


I









records and recent estimates indicate that most of them are of Japanese


descent (Dwyer and Lovell,


1990; Suzuki,


1981


Tsuchida, 1978).


For instance,


of 242,320 amarelos censused in 1940, 99% were Japanese and only


were


Chinese (Tsuchida


,1978).


At the time, Japanese and Chinese were the only


two groups to which the category of amarelo was applied.


By 1980,


not much had changed.


I examined the data on the place of


birth and national origin of Asians (amarelo) aged 15-65, using the 3


sample


data of Sao Paulo from the


1980 Brazilian Census.


The overwhelming


majority of Asians in Brazil are still either Japanese immigrants or their


descendants.


by birth,


The data show that 67


6.8% are naturalized Brazilians and


those who are Brazilian citizens by birth, 92.1


of Asians in the sample are Brazilians


6% are foreign nationals.

were born in Sao Paulo,


followed by


6% from Parana and


from other places.


Meanwhile, of


Asians who were born in foreign countries, 88.1


are from Japan, followed by


from Korea,


countries (see


from China and the remaining


Table 1.1).


2.6% from other


Therefore, we can say with certainty that amarelos,


or "yellow people," are predominantly of Japanese descent, and the Japanese
experience in Sao Paulo constitutes the major part of Asian experience in

Brazil.


The percent of amarel
during the 1940s and 1950s.


in the Brazilian population was


It increased slightly to 0.


steady at 0.6


from the 1960s to the


1980s (see


Table 1.2).


According to the 1980 Brazilian census,


the total


population of amarelos is 673,000. Thri
amarelos as Asian-Brazilians or simply


oughout this stu

Asians, which,


I will refer to


I think, is a more









Paulo and comprised


of the state'


total population (FIBGE 1981).


That is


why I chose the sample data of So Paulo to study

relationships to whites and Afro-Brazilians.


Table 1.1


Asian Brazilians


and their


Distribution of Amarelos Ages


15-65 by Place of Birth and National Origin,


Metropolitan Sio Paulo,


Brazil (1980)


National Origin (


Place of Birth


Brazilian


Naturalized


Foreign


Brazil


Sao Paulo


3,492


Parana
Other


Total


100.0


Foreign
Japan
Korea


3,791


100.0


1,603


China
Other


Total

Total


100.0

100.0


80.7
86.0
45.1
70.8


54.9
29.2
20.9

6.8


----
---


1,819


5,610


67.6


Source: Weighted 3
Census.


sample data of Metropolitan Sao Paulo,


1980 Brazilian


Table 1.2


Racial Composition of Brazil'


Population,


1940-1980


Race 1940 1950 1960 1980
N % N % N% N %

White 26,172 63.5 32,028 61.7 42,838 61.0 64,540 54.2
Brown 8,744 21.2 13,786 26.5 20.706 29.5 46,233 38.8
Black 6,036 14.6 5,692 11.0 6,117 8.7 7,047 5.9
Yellow 242 0.6 329 0.6 483 0.7 673 0.7
N]iccina- Al) N1 1AQ 0 A'7 nA 1 1'7 n A













CHAPTER


HISTORICAL OVERVIEW
OF THE JAPANESE EXPERIENCE IN BRAZIL




Historical Background for the Japanese Migration to Brazil


The overseas migration of Japanese did not start until the Meiji


Restoration of 1868
Meiji Era (1868-191


After that, industrialization and urbanization during the


2) led to massive overseas migration in the late nineteenth


and early twentieth centuries.


Urbanization encroached on agricultural


families and wound up depriving them of access to their land (Ito-Adler,


1987).


Rapid population growth in the rural areas, which exceeded the


industrial growth, also contributed to the massive migration of farmers both


to urban areas in Japan and overseas.


Some analysts (Tsuchida,


1978; Reichl,


1988) argued that the Japanese government considered overseas migration as
a viable option for the increasing problem of surplus rural population.

The first important destinations outside Asia of Japanese emigrants


were Australia (1883),


Hawaii (1885) and Canada (1891).


Reichl (1988:22)


wrote


, "only those Anglo-Saxon countries were sanctioned for emigration


prior to the Russo-Japanese War in 1905 because they


'offered better economic


opportunities than other countries of immigration'


(Tsuchida


, 1978:27).


.I









Sao Paulo and a number of private Japanese emigration companies.
However, Brazil soon became the most important destination for Japanese


immigrants: they became the second largest group (16.8
groups to Brazil during the period from 1924-1941, only


immigrants (33.1%).


%) of all immigrant
after the Portuguese


In fact, by 1938 the Japanese population in Brazil grew to


95,116


, which was the second largest overseas Japanese population,


after that


in Manchuria (233,842), then a colony of Japan (Normano and Gerbi, 1943).
For the period 1950-1955, the Japanese population in Brazil was estimated at


373,000,


making Brazil the country that had the largest Japanese population


outside of Japan,


1959).


followed by the United States (326,376) (Fujii and Smith,


1968, the total number of the Japanese and their descendants in


Brazil was estimated at more than 615,000,


which was 50


of all Japanese


immigrants and their descendants residing in foreign countries.


By then,


United States was a distant second (Sims, 1972).
The serious labor shortage and underpopulation in Brazil in the late
nineteenth and early twentieth centuries were other major factors in the


large-scale emigration of Japanese to Brazil.


Smith (1972:118) cited two major


motivating forces of the Brazilian government for seeking immigrants.
first was "the creation of a small-farming class in the population." The


second was "the ensuring of what Brazil'


upper classes considered an


adequate and cheap labor supply to perform the manual work on the coffee,


cotton


, and sugar plantations of the nation," after the abolition of slavery in


1888.


The Brazilian government preferred Europeans to Asiatic people,









century.


However, several events in Europe and Japan at the turn of the


century had major impacts on the immigration wave to Brazil.
In 1902 the Italian government, in response to reports of mistreatment


of Italian colonos


on plantations in Sao Paulo,


temporarily banned the


subsidized migration of Italian laborers to Brazil. Although Italian laborers
continued to come in small numbers following the ban, they were far too few

to satisfy the growing demand for rural labor on the plantations of SAo Paulo


(Holloway,


1980).


In 1888, Australia prohibited Japanese immigration.
growing anti-Japanese sentiment in North America and t


Agreement"


There was also


between the United States and Japan in 1907 limited


immigration from Japan severely (Reichl,


shortage in Brazil,


1988).


lack of access to Australia and the U


Thus, a severe labor


and Japan'


increasingly overcrowded rural areas created a perfect climate for Japanese


migration to Brazil.


As Normano and Gerbi (1943:45) described it


, "Japan's


land hunger coincided with Brazil'


population hunger."


Japanese Immigration to Brazil


Japanese migration to Brazil can be separated into four time periods,


according to the volume and nature of migration,


and characteristics of


immigrants: 1) 1908-1923,


2) 1924-1941,


3) 1952-1958, 4) 1959-late 1960s.


The Period 1908-1923


"Gentlemen's









their maritime passage.


On the other hand, private Japanese companies were


mostly responsible for the emigration business and the Japanese government


primarily played a coordinating role for most of the time.


The volume of


immigrants during this period was relatively small except the years 1917-1919.


The majority of immigrants were farmers in family units,


Brazilian


as was required by


government.


The first group of Japanese immigrants, consisting of 781 individuals


(158 families),


arrived by ship in the port of Santos,


Sao Paulo, in 1908.


They


came as colonos (contract laborers) under a contract between Japan and the
state of Slo Paulo. During the next fifteen years, Japanese immigrants


continued to come


, though in small numbers.


The total number of Japanese


immigrants from 1908 to 1923 was 32,266, constituting only


of all the


immigrants to Brazil for the time period (Fujii and Smith,


1959).


However,


the period 191


1919 was the peak for the influx of Japanese immigrants,


representing 12.9%,


28.3


Brazil for the three years.


immigrants to Brazi


and 8.4%, respectively, of all the immigrants to

This dramatic increase in the number of


was mostly due to the establishment of the Kaigai Kogyo


-Kabushik (Overseas Development Company), or K.K.K.


Compared to other


Ky> ^ I>


groups, the number of Japanese immigrants was relatively small,


but their


successful beginning was very important to the future of Japanese emigration
to Brazil.


The Period 1924-1941


I *A .









Japanese emigration to Portuguese-speaking America, mainly Brazil, and


away from the earlier destinations in North America.


Both Normano and


Gerbi (1943) and Fujii and Smith (1959) noted that in 1924, the Emigration


Council


, headed by Minister of Foreign Affairs Shidehara, sent a new mission


to South America to explore possible destinations for large-scale emigration.
As a result, the Japanese government decided to concentrate her emigration

effort on Brazil and soon established the Overseas Development Company, a
centralized and highly rationalized management of emigration to Brazil. TJ1

Japanese government also provided subsidies to the company for its

emigration efforts.


Meanwhile


, in 1923 the state of Sao Paulo stopped the policy of giving


subsidies to immigrants from Japan.


The proportion of Japanese immigrants


(of all immigrants to Brazil) increased dramatically from 2.8% in 1924 to


53.2% in 1933, and then steadily decreased to 5.6


II broke out.


in 1941, when World War


This slowdown in the pace of Japanese immigration was also


caused by the Immigration Legislation of 1934 in Brazil, which aimed to
restrict the entry of immigrants annually to two percent of the total entries of


the previous fifty years.


However, the percentage of Japanese among all


immigrants during this period was


previous period,


16.8%, much higher than the


due to the decline of European immigrants.


in the


The total


number of Japanese immigrants to Brazil during the 33 years from 1908 to


1941


was estimated at 190,000 (Fujii and Smith,


1959).


The Period 1952-1958









Brazil (due to Japan's


involvement in the war) and also because Brazil


adopted a quota system to restrict all foreign immigrants.


After 1952


, Japanese


immigration to Brazil resumed,


1960s.


although at a much lower rate, until the late


It is worth noting that during the four years from 1953 to 1956,


Japanese immigration sped up rapidly and the Amazon region received a
larger proportion of the total of approximately 14,000 immigrants. The

annual proportion of Japanese immigrants of all the immigrants rose steadily


from 2.4


in 1953 to a postwar high of 11.0% in 1956.


In 1958, an important census was conducted by a special commission of
Japanese immigrants with financial support from the Japanese colony in

Brazil, the Brazilian government, the Japanese government, the Population

Council of New York and various private enterprises. In commemoration of
the fiftieth anniversary of Japanese immigration to Brazil, the census

provided valuable information on the Japanese immigrants and their


descendants.


The census organizers planned to cover "information not only


on the present situation of immigrants and their descendants,


but on the


immigrants'


background in Japan,


their initial conditions in Brazil, and the


changes they had undergone in the 50 year period" (Suzuki,


1965:117).


project was, in fact, a monumental work on various aspects of Japanese
immigrants and their descendants in Brazil.

According to the 1958 Japanese self-census, there were a total of 429,413


Japanese, of whom 32.3


were immigrants and 67.


were their


descendants.


Meanwhile


44.9


of the Japanese resided in urban areas and


55.1% lived in rural areas.


Proportionally


slightly fewer immigrants (42.9


*









The Period from 1959 to the Late 1960s


No statistics are available on the number of Japanese immigrants to

Brazil during the period from 1958 to the late 1960s, when large-scale


immigration from Japan to Brazil virtually stopped.


Nor is there any


consensus among researchers on the actual number of immigrants for this


period.


Sims (1972) reported one interesting feature of the Japanese migration


to Brazil during this period:


the Brazilian government authorized two


Japanese-Brazilians to import immigrants from Japan to certain areas in


Brazil and set them certain quotas as well.


For example,


"the late Mr.


Yasutaro Matsubara was authorized to settle 4,000 Japanese families in central


Brazil (southern Brazil was approved later) and Mr. Kotaro


Tsuji was


authorized to settle 5,000 Japanese families in the Amazon region" (Sims,


1972:246).


These quotas remained effective until 1966, when the "Japanese-


Brazilian Joint Committee" was established and the quotas were abolished.
Japanese agencies, governmental and private, continued to provide subsidies


to immigrants, especially those bound for Brazil during this period.


Suzuki


(1981) estimated the total influx for the period from 1952 to the late 1960s at

50,000, while Smith (1979) estimated it at 60,000.


All tolled


, during the 50 years from 1908 to 1958, about 240,000 Japanese


migrated to Brazil and the majority of them settled in the state of Sao Paulo


(Fujii and Smith,

the total amarelo


1959; Suzuki,


1981).


population was 329,082,


1950 Brazilian census reported that


and 84


of them resided in the


state of Sao Paulo.


The 1958 census of the Japanese community "reported that









Social Characteristics and Social Mobility of the Japanese Immigrants


Japanese immigrants were brought to Brazil primarily as farm laborers.
As a result, the majority of them were at the bottom of the social hierarchy


when they started their new lives in the new country.


Here I will focus on


the initial social status, as marked primarily by their occupations, of the
Japanese immigrants and the changes in the distributions of industries and


occupations for them during their first fifty years in Brazil.


Then I will


review their initial educational status and how that changed through the


years.


I wil


also review some demographic characteristics of the Japanese


immigrants that are closely associated with their social mobility.


Occupational Distribution and Mobility


The occupational distribution of immigrants to Brazil first and
foremost reflected the Brazilian immigration policy at the time, i.e., the
creation of a small-farming class and the provision of a supply of cheap labor

for plantation owners.


During the prewar period from 1908 to 1941,


98.8% of the Japanese


immigrants to Brazil were classified as farmers, whereas only 59.6% of all


immigrants to Brazil were farmers.


The proportions of farmers among the


arger immigrant groups are 78.6% Spaniards,


49.0


Italians and 47


Portuguese (Fujii and Smith,


1959).


During the postwar period from 1954 to


1956


, the percentage of farmers among Japanese immigrants dropped to about









Suzuki (1981) reported that 94% of all family heads started as farmers,
of whom were at the lowest status as colonos primarily on coffee


plantations (90%).


However, in 1958, the proportion of farmers among the


Japanese immigrants dropped to about 61


dropped to only


of the total farmers.


% and the proportion of colonos
The majority of the former colonos


went to large urban centers to work as craftsmen and unskilled laborers.
The 1950 Brazilian Census provided the first systematic information on


the distribution of industry by racial group.


Smith (1972) included a very


detailed table on the distribution of industry for males 10 years of age and


over by color, based on the 1950 census data.


Let me briefly summarize the


industry distribution of the amarelos and the standing of this group relative
to the other races described in Smith (1972).

The 1950 census included eleven categories of industries, but the
distribution of industry for the amarelos was highly concentrated in the
following four categories: agriculture (which included forestry and fishing)


(69.0


service (10.2


wholesale/retail trade (10.1


(which included construction and processing) (6.0%).


) and manufacturing
The proportions for the


remaining industries were, in descending order: transportation (which


included communication and storage) (2.3


finance (which included


insurance and real estate) (0.9%),


industries (0.


liberal professions (0.6%),


and social activities (0.4).


extractive


The total number of people who


were engaged in


"public administration, legislation and justice" and


"national defense and public security" was so small (90 and 138 respectively)


that they were omitted in the percentages for the original tabulation.











Table


Industry Distribution of Brazilian Males Aged 10 and Over by Color, 1950


Industry


Total (%)


White


Negroes


Yellow


Pardos


Agriculture
Extractive


64.6


70.0


69.9


Manufacturing
Wholesale


Finance


Service
Transportation
Liberal Profession
Social Activities
Public Ad.
National Defense


0.04


0.09


All Industries


100.0


100.0


100.0


100.0


100.0


Source: Table XI in Smith


, 1972, pp. 94-95.


We can also look at the proportions of employers, employees, self-


employed workers,


and family workers by race and see the differences among


racial groups.


Table


illustrates the proportions of different employment


statuses by color for all industries and agriculture, the most important


industry in 1950.


In both all industries and agriculture, the amarelos,


compared to the other groups, have the highest proportions of employers


(11.8


and 10.8% respectively) and the highest proportions of family workers


(29.6


and 38


respectively).


Expectedly, they have the lowest proportion


of employees (23.


in all industries and 15.4


in agriculture) among the









Table


Employment Status of Brazilian Males Aged 10 and Over
for All Industries and Agriculture by Color, 1950


Industry


Total(


White


Negroes


Yellow


Pardos


Industries


Employers
Employees
Own Account


46.3


23.7


36.3


Workers


Family
Total


32.0


Workers


100.0


Agriculture
Employers
Employees
Own Account


3.4
34.5


31.0
16.9
100.0


4.5
31.9


13.5
100.0


34.9
29.6
100.0

10.8


48.3


18.5
100.0

2.0
34.2


Workers


Family
Total


Workers


37.4
24.7
100.0


37.4


100.0


32.0


100.0


35.3
38.5


100.0


39.8
24.0
100.0


Source:


Table XI in Smith


, 1972, Pp. 94-95.


1958 Japanese self-census provided valuable information on many


aspects of their lives as a social group.


Tables


2.3 and 2.4 are calculated and


abbreviated from Table


7 in Suzuki (1965) to give more focused analysis on


the occupational distribution of the Japanese immigrants and their


descendants in 1958.


Table


3 shows that the proportions of farmers among


men and women in the labor force for the total population are approximately


the same


for men and


57.7%


for women.


Nevertheless


there are


noticeable differences between the immigrants and descendants and also


between the two


sexes


for the immigrants.


The proportion of farmers for the


male immigrants is 60.3


, while that for the male descendants is only 54.0%.


The difference in the proportion of farmers by


sex for the immigrants is










Table 2.3
Proportion of Farmers among Japanese Immigrants and Descendants


Aged 10 and Over in Labor Force by Sex,


Brazil


1958


Immigrant
Status


Males


Total


Farmers(


NF*(


Total


Females
Farmers(


NF*(%)


7,893


42.4


33,224


57.7


42.3


Immigrants

Descendants


67,518

50,375


60.3

54.0


39.7

46.0


11,492

21,732


66.6

53.0


33.4

47.0


Source:


*NF


Table


7 in Suzuki


, 1965


Population Index, 31:2,
= nonfarmers


, "Japanese Immigrants in Brazil,"
p.135.


Note:


There were three categories,


"farmers"


nonfarmerss"


and "farmers


and nonfarmers," in the original table.


For convenience and clarity,


the first and third categories are combined into "farmer" here, and the
second category remains the same.


Table 2.4 indicates the overall occupational distribution,


including the


most important one, farmer, for the population as a whole and for


immigrants and descendants separately.


Since the category of farmer here


excludes those farmers who had nonfarming jobs, not like the one used in


Table 2.3


, the percentages of farmers for all three groups are consistently a


little bit lower than those in


(less than 1


Table


However, the variations are minimal


) and the basic pattern remains the same.


The exact


percentages of farmers for the total population, immigrants,


and descendants


are 56.0


58.6


, respectively.


flnr i-he frk4til nnnIII n it r-tnL. innc iin-


-In rw n f Ct 4-rplr ^t nn^iy'^i/r


nW~lltl; lfl- nn ll/









remaining 0.4


under the category of "other" belongs to occupations


classified as


"fishermen."


"miners," "quarrymen"


"unqualified


laborers"


in the census.


For the immigrant group, the order remains the same for all


the occupations, except that the order for "clerical" and


"transportation / communication"


is reversed:


1) salesmen (17


craftsmen (10.4


3) service (5


.2%),


transportation/communication (2.0


4) professional (4.2

0) and 6) clerical (1


occupations within the category of "other"


account for 0.6


The three


of the


immigrants.
There are some interesting changes in the occupational distribution for


the descendant group.


First, the percentages for both salesmen and craftsmen


rank first and are identical to one another.


Second, the proportion of clerical


workers exceeds that of professionals, with the others more or less in the


same order as those for the other two groups.
proportions for the occupations are as follows:


More specifically,


salesmen and craftsmen


(14.4%), service (5.4%),


clerical (5.2%)


, professionals (4.6


) and


transportation/communication (2.4%).


The remaining 0.4% is distributed


among the three occupations mentioned above.
The overall trend in the changes of occupational distribution from the


immigrant to descendant group can be summarized as follows:


1) The


proportion of farmers and salesmen decreased from the immigrant to

descendant group; 2) there were large increases in the proportions of

craftsmen and clerical workers among the descendants, and 3) there was a

slight increase in the proportions of transportation/communication,









Table 2.4
Occupational Distribution of Japanese Immigrants and Descendants


Aged 10 and Over in the Labor Force, Brazil,


1958


Occupation


All (%)


Immigrant Status
Immigrants (%)


Descendants (%)


Farmer


56.0


58.6


53.2


Professional/
Technical
Clerical


Sales


Transportation
Crafts


Service
Other


Number


150,170


78,585


71,585


Source:


Table


7 in Suzuki


1972


, "Japanese Immigrants in Brazil,"


Population Index,


Note:


31:2, p.135.


There were ten occupations in the original table, in addition to the


seven listed here. Due to the


space limit and the insignificance of the


three categories


"fishermen," "miners, quarrymen" and


"unqualified


laborers," they are combined under the category of "other" in this table.


Suzuki (1981) described the change in the employment status of

Japanese immigrants and their descendants by classifying them into two


broad categories: independent persons and employed persons.


For farmers,


colonos and sharecroppers were considered employed persons and tenant

farmers and land-owning farmers were regarded as independent persons. For

nonfarmers, employed persons included employees and independent persons


included emnlovers and the


self-emnloved.


Suzuki wrote.


"Whereas the









The distribution of industries in Table

picture from a slightly different perspective.

original table from Suzuki (1965), but I have


2.5 provides us with a similar

There were ten categories in the

listed here only six of them,


which


, by the way


cover almost 99.0


of all industries.


Needless to say,


agriculture has the highest proportion of workers for all three groups:


for all


,59.8% for immigrants and 54.3


for descendants.


Apart from


agriculture,


for both the immigrant and descendant groups, the order of the


industries with the highest to lowest proportion of workers is the same.

Therefore, the order of industries for the total population is the same as well.


They are, in descending order, trade (17.5%),


service (13.3


manufacturing


transportation (2.4%) and finance, insurance, real estate (1.4


remaining 1.0% under the category of "other" belongs to the


industries of


"government," fisheri


es" and "mining."


They are not listed here because


they are negligible in terms of percentage.


Table


Japanese Immigrants and Descendants Aged 10 and Over
in the Labor Force by Industry, Brazil, 1958


All (


Agriculture
Manufacturing
Trade
Finance
Transportation
Service


Immigrant Status
) Immigrants (%)
59.8


Descendants (


54.3


Other


Number


Source


Tahlo


150,170


7 inSzk


*A A L t&L.'At *t A .tLALC LJ I UJ J


78,585


. "Taanese Immigrants in Brazil."


Industry


:


I









Although the proportions of industries have exactly the same ordering

for both the immigrant and descendant groups, there are variations in the

exact proportions of all industries for the two groups. Apart from a decrease

in the proportions of farmers, the descendants have increases of various

degrees in the proportions of workers for all the industries except that of


trade


which has a loss of 1


(18.3


for immigrants to 16.6


descendants).


The two biggest increases of workers occur in the industries of


service and manufacturing for this group; the former increases by 4


(from


11.3% for immigrants to 15.


for descendants) and the latter by 1


(from


for immigrants to 8.0


for descendants).


The agricultural status of postwar migrants and their descendants in


1958 is described in Sims (1972).


The study, based on a survey of 4,268


Japanese farmers who arrived in Brazil during the period 1952-58, showed


that of the total sample, 51


were colonos,


16.8% had become owner-


farmers, 15.4


had become renters, 16% had been reduced to sharecroppers


and 0.6


had become farm administrators.


In comparing the prewar and


postwar migrants in terms of ownership of land, Sims noted that "the private

ownership of land was slightly more common among the prewar migrants


(22%) than their postwar successors (16.8%)"


(1972:250).


However, one


important fact about the prewar Japanese migrants was that only 1.3


them were still colonos by


1958.


Another significant characteristic of the


Japanese farming community in the postwar period was that family workers


took up 59.3


of all the farmers


, "revealing the dependence of the farm


families upon their own kin" (Sims,


1972:250).









By the late 1950s, among nonfarmers, craftsmen constituted less than a


quarter, the proportion of salesmen increased from 8


service workers accounted for 10


to more than 50


Unskilled laborers used to account for


almost a quarter of the total nonfarming Japanese population, but by the late

1950s they had virtually disappeared (Suzuki 1981).

The occupational status of nonfarming Japanese Brazilians was

described in Sims (1972), who compared the prewar and postwar groups (see


Table 2.6).


There were striking differences between the prewar and postwar


migrants in terms of occupational status; 81


working for themselves, i.e.,


of the prewar migrants were


they were either employers or self-employed,


while only 28.5% of the postwar group were doing so.


By the same token,


nearly two thirds of the postwar migrants were employees or working for


others, whereas only


16.4% of the prewar group were so.


The only advantage


of the postwar group over the other was their higher proportion of managers


vs. 2.


) due to an increased level of education and more diverse


backgrounds among the postwar immigrants.


Table


A Comparison of Occupational Status
of Prewar and Postwar NonFarming Japanese Immigrants


Occupational
Status
Self-employed
Employers
Employees
Managers


Prewar Immigrants


Postwar Immigrants


59.8


63.9


Source: Sims (1972)









in Brazil.


The most obvious change was the sharp decrease in the proportion


of farmers, from over 95


in the beginning decades of immigration to about


56.0% in the late fifties.


Second


, the percentage of colonos in the prewar


Japanese migrants decreased from about 80


for the period before 1941 to


in 1958.


Third


, the amarelos,


who were overwhelmingly made up of


people of Japanese origin,


exceeded all other racial groups in the proportion of


employers.


Fourth


, they maintained the tradition of working as family units,


which had advantages over individual workers in terms of utilizing human
and capital resources.


Diversification of Agricultural Crops and High Productivity


Japanese-Brazilians are also considered to be "the first to move toward


the diversification of crops in the Slo Paulo coffee-lands"


1990:187).


(Dwyer and Lovell,


In addition to coffee, the Japanese owner-farmers produced cotton,


rice, potatoes and other new crops.


A survey of 35,871 Japanese Brazilian


farm families in 1958 revealed that the largest number of Japanese farmers


grew coffee: 17.6% in Sao Paulo and 27.5


in the nation as a whole.


Vegetable


was the second largest crop, with a farming population of 13%.


third with a farming population of


Cotton ranked


The majority of both the vegetable


growers and cotton growers were in Sao Paulo (Sims,


1972).


The above study also described the employment status of the Japanese-


Brazilian farmers in


1958.


Seventy-five percent of the coffee growers,


43.9


the cotton growers,


about 50% of the poultry raisers and nearly one-third of









It was not only the high proportions of the Japanese farmers in the
above agricultural sectors that is important, but also their production that is

more important in terms of their contribution to the agricultural


development of Brazil.


By the late


1930s, the Japanese-Brazilians accounted


for more than 50% of the cotton produced in the state of Sao Paulo (James,


1937, cited in Dwyer and Lovell,


1990) and were responsible for 80


of the


vegetable production in the suburban area of Sao Paulo city (Makabe,


In 1958, Japanese-Brazilians,


1981).


with only one percent of the total farming


population,


produced about 62


of the tomatoes


of the peanuts,


27%


the potatoes, about 12


of the eggs, and about 12


of the cotton produced in


Brazil.


They were also responsible for about 93% of the tomatoes, 92% of the


tea, 68% of the potatoes, 43


of the peanuts, 37% of the eggs, 36% of the


peppermint,


27%


of the cotton


of the banana produced in the state of Sao


Paulo (see


Table 2.7).


Table


Agricultural Production of Japanese-Brazilians
in SAo Paulo and Brazil by Crop, 1958


Crop % of the Brazilian Total % of the Sao Paulo Total

Tomatoes 61.7 93.3
Peanuts 39.1 42.8
Potatoes 27.0 67.9
Eggs 11.6 37.0
Cotton 11.6 26.8
Coffee 5.9 7.1
Banana 5.3 21.8
Fruits 2.9 --.
Rice 2.3 8.1
rT- n 1








Studies on the social mobility of the Japanese Brazilians in the last two


decades are extremely rare in the English language publications.


study available is Dwyer and Lovell (1990),


One such


"Earning Differentials Between


Whites and Japanese:


The Case of Brazil"


This study uses a sample of 272


white males and 242 Japanese males ages 18-64,


1980 census of Brazil.


from the 0.8% sample of the


The main findings of this study are: (1) the average


earnings of Japanese males are 61% higher than that of whites; (2) 48% of
Japanese males have more than nine years of schooling compared to 24%


white males; (3) only 51


of the Japanese are workers whereas


of the


whites are workers; (4) three times as many Japanese as whites are employers


and 32


of Japanese versus 19% of whites are self employed.


These findings


suggest that Japanese-Brazilians have surpassed whites in terms of many
important social indicators.


Educational Status


Educational status of a population is usually measured by its literacy
(illiteracy) level and the percentages of people who have received elementary,


secondary and higher education among its literate people.


There is ample


evidence that from the very beginning, Japanese-Brazilians fared very well in
terms of educational status among the various immigrant groups and among
the racial groups as well.
The literacy rate of the Japanese immigrants was one of the highest
among all immigrant groups through time. For the prewar period 1908-1941,









literacy rate was then measured in terms of the ability to read and write in the


native languages of immigrants,


not in Portuguese, the official language of


Brazil.

However, according to the 1940 and 1950 census, the illiteracy rate for

the yellow people was the lowest among the four racial groups (Smith,

1972:490):


Race


1940


1950


Yellow
White
Pardos
Negroes


34%
47%
71%
79%


It is also worthwhile to point out that the illiteracy rate declined by 50%

among the yellow people, while its rate of decline was not as great among the

other three groups, especially among pardos and Negroes.
The 1958 Japanese Self-Census indicated that of all Japanese residing in


Brazil aged


7 and over, the illiteracy rate was only


2.5%;


in urban areas


and 3.8


in rural areas.


The census also provided information on this subject


for immigrants and descendants separately: the illiteracy rate for all


immigrants was 1


with 1


in urban areas and nearly


in rural


areas.


Interestingly, the illiteracy rate for descendants was slightly higher than


that for immigrants: 3.2%

4.1% in rural areas (Suzuk

Sims (1972) reported


for all descendants, with


in urban areas and


1972).

he result of a 1962 survey of 151,701 newspaper


readers over 14 years of age to show the literacy rates in both Portuguese and


I









computation, that "at least


.4% of the community surveyed read


Portuguese, while a minimum of


were literate in Japanese in


1962"


(1972:258).

According to the 1950 census, the proportions of people who completed


elementary schooling was much higher among the


Yellow population than


was the case nationwide (


vs. 17.9


) (Smith and Fujii, 1959).


proportions of people who attended different levels of schooling among the

Japanese immigrants and their descendants in 1958 were described in Suzuki

(1965).


Table


2.8 offers a


ummary of the above information:


In urban areas


67.3


of the people aged


7 and over attended primary school,


29.2%


attended


secondary school, and 0.


attended college, while in rural areas, the


corresponding figures were 82.6%,


11.8% and 0.8%.


When immigrants and


descendants were compared,


areas


example,


the latter did better than the former in urban


whereas the former did better than the latter in rural areas.


the percentages for primary and secondary schooling among the


urban immigrants were


.3 and


counterparts were 62.6 and 33.9.


21.0, while the same percentages for their

On the other hand, proportionately, more


rural immigrants attended secondary school (14.


counterparts (9.9).


) than did their


The proportions of people who attended college for all


groups was less one percentage.
Suzuki (1981:65) noted that "a relatively high educational level in

comparison to that of the society on a whole would seem to lessen handicaps


affecting foreign immigrants in their struggle for a better life."


The high









Table 2.8


Japanese Immigrants and Descendants Aged


7 and Over


by Level of Education and Residence, 1958


Residence


Total


Primary


Secondary


Higher Ed.


Urban


Immigrants
Descendants

Rural


Immigrants
Descendants


160,796
58,972
101,824

189,565
77,610
111,955


67.3
75.3
62.6

82.6
81.2
83.7


29.2
21.0
33.9

11.8


Source: Suzuki, 1965


Demographic Characteristics


The most distinctive demographic feature of Japanese immigration to


Brazil was "family immigration," which

imposed by the Brazilian government.


h was the direct result of a regulation

According to this regulation, an


immigrant family must have at least three capable laborers who were above


fifteen years of age.


Consequently, about 95


of the Japanese immigrants


between 1908-1941 and 80',

groups, as compared to 64


between 1954-1956 came to Brazil in such family

and 54% of the total immigrant population in


these two time periods (Fujii and Smith,


1959).


As a correlate of the high proportion of family units among the

Japanese immigrants, the percentage of married people was also high among


4Knnwv eit-I1 l4- nrA~n Anrrns~nn ,.y44, 4-le^n Cd aC i-I, n nrandn C., 4


nC{ C -n / nmrf^'^ffcn* C^^y ^4 ^nn^~


*









33.5% and 2.1


The proportions of singles and widowed increased by 8.4%


and 0.4, whereas the proportion of married decreased by 8.8%.


This was


probably resulted from the relaxation of family unit rule applied to Japanese

immigrants during the late fifties.

Table 2.9 illustrates the marital status of the Japanese population aged


15 and over by sex and generation in 1958.


of the males and 35.9


For the whole population, 44.5%


of the females were single, while 52.3% of the males


and 56.6% of the females were married.


The proportion of married people


was up by more than ten percent from 42.3% in the period 1908-1941


. The


percentage of married people among the immigrants was even higher due to

the fact that most of the immigrants were adults and had become parents or


grandparents by 1958.


The proportion of married people for the total


population was heavily affected by that of the immigrants since they were still


the majority at that time.


In contrast, the percentage of married people


among the second generation of Japanese was far lower than that for the


immigrants,


due to their relatively young age.


Table 2.9
Marital Status of the Japanese Population in Brazil
by Sex and Generation, 1958


Immigrant
Status


Males


Sin.


Mar.


Sep.


Females


Wid.


Sin.


Mar.


Sep.


Wid.


All Japanese
Immigrants


44.5


35.9


79.4


56.6
79.9


Generation


35.2


3rd & 4th









However, one common element among all groups, immigrants and

descendants alike, was that proportionately more women were married than


men; 56.6


vs. 52


for all Japanese, 79.9


21.4% for the second generation.


% vs. 79.4 for immigrants, and
This was largely caused by the fact


that women married at younger ages than men did in general


. Therefore, the


sexual differences in the percentage of married people among different groups

was a main indicator of the mean age at marriage for the groups concerned.

Family type and structure are known to correlate with the


socioeconomic well-being of a particular group.


Suzuki (1981) showed a


positive correlation between the Japanese family structure and the
improvement of their economic status by comparing the frequency of family

types with their economic status expressed in terms of the employment status


of the family head and property ownership (see Table


2.10).


He found out that


among the land-owning farmers, 36


were "lineal"


were three-generation families and


and "lineal and collateral" families; among the tenant


farmers, the corresponding figures were 21


and 24%.


In contrast, the


percentages of three-generation families and "lineal" and "lineal and


collateral" families among the sharecroppers and colonos were


respectively.


16%


Therefore, we can conclude that more independent


farmers tend to have extended (three-generation) families and lineal or
lineal/collateral families than employed farmers (sharecroppers and colonos).

This, in turn, suggests that three-generation families, and lineal and

lineal/collateral families may have a positive effect on the employment


status, i.e.


, whether being an independent or employed person.









and employers (13%,


This suggests that larger families may not be an


advantage for non-agricultural workers.


On the other hand


there was a


positive association between the value of property owned in both rural and

urban areas with three-generation families and lineal and lineal/collateral


families.


In other words, the proportion of three-generation families and


lineal and lineal/collateral families increased with the increase in value of


property owned.


According to Suzuki (1981),


in rural areas, the proportions


of three-generation families for those who owned no property


low property,


medium property and high property were 18%,


28%


42% and 53%


respectively, whereas for non-farmers in urban areas, those proportions were


24%


37%


respectively.


The same pattern remained for lineal


and lineal/collateral families (see


Table


Table 2.10
Proportion of Traditional Families among Japanese Heads of Family


by Employment Status for Farmers and Non-Farmers in Brazil,


1958


Employment Status Three-Generation Lineal and Lineal/Collateral
Families (%) Families (%)
Farmers
Landowners 36 40
Tenant Farmers 21 24
Sharecroppers 16 20
Colonos 10 11
Non-Farmers
Employees 29 31
Self-Employed 23 27
Employers 13 17


Source: Suzuki


1981









cooperation among family members.


Such cooperation is effected,


inter alia,


through family labor, i.e.,


family members work without wages in an


establishment operated by the head or another family member"(1981:69).


Table


Proportion of Traditional Families among Japanese Farmers and


Non-Farmers in Brazil by Value of Property Owned,


1958


Value of Property Three-Generation Lineal and Lineal/Collateral
Owned Families (%) Families
Farmers
None 18 21
Low 28 32
Medium 42 46
High 53 57
Non-Farmers
None 17 26
Low 24 27
Medium 35 36
High 37 37


Source: Suzuki


1981


Closely related to the family type and marital status of an immigrant


group is its


ratio, which is even more important when there are relatively


few inter-groups marriages.


During the period 1908-1941,


sex ratio of


128:100 among Japanese immigrants was significantly lower than that of any


other major immigrant groups ( 146 for Spaniards,


175 for Germans, 183 for


Italians


, and 208 for Portuguese).


However


sex ratio of the Japanese


immigrants rose to 1


for the period from 1954 to 1956 due to the relaxation


of the regulation on family groups.









Japanese immigrants were 12 years of age or younger


compared to 23


among the total immigrants,


during the period 1908 to 1941


(Fujii and Smith,


1959)


1950 Brazilian Census indicated some changes in some of the


demographic characteristics


of the amarelo population.


For example,


over


of the amarelo population were under twenty years of age, and the


ratio for them dropped from 128 to 110.8.


The fertility ratio (number of


children under five years of age per 100 women aged 15-49) for amarelos in
1950 was 79.6, the highest among the four major racial groups (65.3 for white,


55.6 for Negro, and 69.2 for brown).


On the other hand


, the proportion of


Asians in the Brazilian population remained at 0.6% from 1940 to


1950


(Smith


, 1972).


This may be due to the pause in Japanese migration to Brazil


during the period 1942 to 1952.

The 1958 Japanese self-census offered information on the changes of


the characteristics of the Japanese population at the time:


sex ratio was


108, a decrease of 2.8 from 110.8 in 1950; the number of people under 15


of age was 40.5% of the total population,


years


indicating that the population


became younger than it was eight years ago; and rural residents accounted for


about 55% and the urban residents 45


from rural areas (Suzuki,


, showing large volumes of exodus


1972).


Summary


Japanese migration to Brazil started at the turn of the century because


sex









during the period of initial industrialization and urbanization in Japan. Al
the same time, Japan faced strong resistance against overseas emigration in


countries like Australia, the United States, Canada and Peru.


By contrast,


Brazil sought after Japanese farm laborers because of a severe labor shortage


on coffee plantations after the abolishment of slavery


in 1888, and


particularly, in 1902 after the Italian government ceased subsidizing the
migration of its agricultural laborers to Brazil.

Japanese immigrants were subsidized by the state of Sao Paulo from
1908 to 1923 and then by various Japanese emigration agencies, both private


and governmental,


up to the late 1960s.


During the period 1908-1941,


approximately 190,000 Japanese immigrants came to Brazil. After a ten-year


pause from 1942-1952 due to World War II,


the migration wave continued at


a much lower rate until it virtually stopped in the late 1960s.


The total


number of immigrants during this period was estimated at 50,000-60,000


(Smith


, 1979; Suzuki,


1981).


The 1958 Japanese self-census indicated that the


Japanese population in Brazil at the time was 429,413, of whom 32.3% were


immigrants and 67.


were their descendants.


The majority of the Japanese immigrants were farmers and started as


colonos on coffee plantations in the state of Sao Paulo.


They rose from the


lowest and least privileged status of colonos to the middle class status in both
rural and urban areas through their hard work during the 50 years after their


first arrival in Brazil.


The experience of the Japanese population during the


1960s and 1970s is proof of their continued success in upward social mobility.
The most cited reasons for the success story of Japanese immigrants are









explanation focuses on the adaptive ability of Japanese, and their traditional


values and characteristics.


It seems to me that the former is mostly related to


external factors, and the latter to internal factors,


from the viewpoint of the


Japanese immigrants.

In comparing the experiences of Japanese immigrants in Canada and

Brazil, Makabe (1981) concludes that the major reason for the success of

Japanese immigrants in Brazil was the lack of economic competition from the

native Brazilians and other immigrant groups and hence the lack of

unfavorable differential treatment in wages because they occupied different


labor market.


He also notes that "ownership of land, which was the highest


achievement to be attained for the immigrants,


became possible relatively


easily and quickly" (1981:800).

In contrast, Dwyer and Lovell (1990) explain the Japanese success

mostly in terms of their adaptive ability, and their cultural values and


characteristics.


They point out three major reasons for their success: (1)


"second generation Japanese-Brazilians quickly learned the language,


business practices, and legal system of Brazil";

placed a great deal of emphasis on education"


(1990:188).


(2) "Japanese immigrants

(3) they were very industrious


This mostly cultural explanation is similar to the one used for the


explanation of the socioeconomic achievement of


(Bell, 1985; Kitano, 1969; Newsweek, 1982


Asian-Americans in the


Petersen, 1971).


These two types of explanation are very important in understanding

the Japanese experience in Brazil, and they are complementary rather than


mutually exclusive.


However


there were also other factors that contributed


,


J


v









industrialization in Sao Paulo, the continued close connections with the

home country and financial and technological assistance from the home

country, the establishment of agricultural cooperatives and ethnic enclaves,
and lack of overt racial discrimination by the Brazilian society,


By human capital, I refer to the educational level,


knowledge of


farming and technological skills the Japanese immigrants and their


descendants possessed.


As shown above, the educational status of the


Japanese population was the highest among the four census racial groups and


they had advanced knowledge of intensive agriculture.


These attributes


translated into better adaptation to the new environment and greater


efficiency and higher productivity


which would certainly result in higher


economic profits.
The Japanese immigrants benefited tremendously from, as well as


contributed to,


"the remarkable economic and demographic growth of SAo


Paulo attributable to the coffee industry and industrialization" (Tsuchida,


1978).


The labor shortage on coffee plantations brought them to Brazil in the


first place, and the urbanization and industrialization in the state of Sao
Paulo offered them the opportunity of pioneering vegetable farming and the


poultry industry.


The development of the textile industry in Sao Paulo in the


early 1930s created a huge domestic market for cotton,


and Japanese Brazilians


dominated the cotton industry from the outset due to their expertise in
growing cotton.

Meanwhile, Japan's importation of large quantities of cotton in the
mid 1930s from Sao Paulo also helped the Japanese Brazilian cotton growers.









already enabled the Japanese to solidify their economic base in such a way that
they and their descendants in Portuguese America could securely stand on
their own feet in total isolation from their mother country" (1978:311).
The fact that the Japanese immigrants maintained close ties with and


received financial and technical support from their home country


especially


in the early years,


was very important to their success in Brazil.


The Japanese


government and private companies financed various colonization projects
and provided information, technical assistance in farming, and even
improved seeds, which greatly promoted land ownership and increased


agricultural productivity among the Japanese immigrants (Tsuchida,


1978).


Another important feature of the Japanese immigrants in Brazil was
that from the outset, they established their own ethnic enclaves in the form


of agricultural cooperatives and larger community settlements.


Normano


and Gerbi described the Japanese in the following way:

The Japanese live almost completely isolated from the native


element in Brazil.


The population of their centers varies from


three hundred to six or seven thousand


in cities


, towns, and


large fazendas, but always they remain in atmosphere and
surroundings completely Japanese (1943:39).


Their agricultural cooperatives facilitated the transportation and the
marketing of their products, and the ethnically homogeneous communities


provided them


"with adequate educational opportunities, medical care,


technical assistance


loans


, and above all, a sense of security" (Tsuchida,


1978:313).


There is no doubt that these ethnic associations played a major role









racial discrimination by the dominant society as were their counterparts in


North America (Daniels,


1977


Daniels, 1988; Kitano and Daniels, 1988; Lee,


1989).


At least, there was


no overt discrimination against them in the


economic sphere so that they were able to demonstrate fully their valuable
assets and compete on an equal footing with others for land ownership,


property and social mobility.


Brazil


On the subject of anti-Japanese sentiment in


Tsuchida wrote:


Devoid of any serious economic conflict between the Japanese
community and the dominant society, charges against this
ethnic minority centered around racial desirability and the
intangible threat of Japanese imperialism. Anti-Japanese
agitation was restricted to a small circle of intellectuals who
advocated Japanese exclusion, on ideological ground, rather than
economic and political reasons (1978:321).


On the other hand


, the Japanese immigrants didn't compete with the natives


for occupations then considered more favorable, such as commerce.

apparently avoided possible conflicts in their economic activities. T


first engaged in coffee growing, then pioneered cotton,


They


They were


vegetable and fruit


farming, all of which were much needed by the Brazilian society


. In other


words, they had their own labor market, and were not in direct competition

with the dominant society.

However, this does not imply that Brazil has been a racial democracy,


as some scholars have advocated.


In fact, there is a body of literature that


indicates the scope of racial inequalities in Brazil (Hasenbalg, 1985; Lovell,


1989; Lovell and Dwyer, 1988


Silva, 1978; Silva,


1985; Wood and de Carvalho,


r









They managed to rise from the bottom of the society and achieve middle class
status within the first fifty years of their arrival mainly by hard work, assets in
human capital, a traditional practice of working as family units, demographic


factors (relatively balanced


sex ratio and younger age structure), collective


efforts and ethnic unity, strong support from the home country, a favorable
economic situation in Brazil and a lack of overt discrimination against them,
especially in the economic and political arenas.













CHAPTER 3
FERTILITY DIFFERENTIALS


AMONG


ASIAN


WHITES


AND


AFRO-BRAZILIANS


A Brief Review of Literature on Fertility Studies


Human fertility behavior is the subject of study in many disciplines of


the social sciences, and various theories on fertility


have been put forward.


Some of the major fields of study that deal with human fertility are


demography, sociology, economics, anthropology


psychology and biology.


Each discipline tends to focus on slightly different aspects of human fertility
behavior and differs somewhat in its approaches due to its distinct theoretical


orientations and scopes of study.


However, there are many things that


fertility studies have in common.
The economic theory of fertility is perhaps the most influential among


competing theories.


The most important works of this school of thought are


Leibenstein (1957),


Becker (1960),


Easterlin (1969) and Schultz (1973).


applying the economic theory of consumer behavior to childbearing


decisions


, they regarded human fertility as a result of rational decision based


on an effort to "maximize satisfaction, given a range of goods,


their pri


and his own tastes and income"


(Easterlin


, 1975:54).


In other words


"children









things being equal, higher income usually results in higher fertility rate; (2)

an increase in the price of children relative to other goods results in lower

fertility.

Counter to the first hypothesis, cross-cultural and cross-sectional
demographic data generally show that higher income groups tend to have


fewer children compared to lower income groups in a country.


Similarly


aggregate data show that more affluent and developed societies tend to have


lower fertility rates than their less developed counterparts.


noted


It should be


, however, that these studies may not represent an adequate test of the


economic theory of fertility (which predicts a positive correlation between


income and fertility).


The reason is that aggregate data on fertility rates by


income classes do not measure what economists refer to as the "pure income


effect."


That is, the effect of income after controlling for contraceptive


knowledge and other determinants of fertility behavior.
The second hypothesis is valid and supported by some historical


demographic data.


Yet it offers little insight to differential fertility among


various sub-populations of a society if we assume that "the price of children
relative to other goods" is, more or less, the same for all the people in the


same region at a certain period of time.


Moreover, the economic theory of


fertility analysis leaves little room for the role of sociocultural factors and

other institutional constraints in the fertility decisions and behaviors of
individuals, who live in a complex social context and are bound to be

influenced by many external factors.

In addition, Easterlin (1975) and Todaro (1981) have applied









includes both subjective and objective costs, as well as the time and money
required to learn about and use specific techniques for limiting fertility.

The sociological theory of fertility is mainly represented by Davis and


Blake (1956)


, Davis (1959)


, Freedman (1962),


and Hawthorne (1970).


In this


approach, observed level of fertility is seen as the outcome of the interaction
among biological processes, societal group factors and individual behavior


(Robinson and Harbison, 1980). Social
considerable attention in this approach,
dynamic than the economic approach.


norms about family size are given
and it is broader in scope and more
In an attempt to bridge the gap


between the economic theory and sociological


proposed a new "general theory"


theory,


Caldwell (1976, 1978)


of fertility, which states that


"fertility


behavior in both pre-transitional and post-transitional societies is
economically rational within the context of socially determined economic
goals and within bounds largely set by biological and psychological factors"


(1978:553).


Recent development of this approach is reflected in the


examination of the socioeconomic and proximate determinants of fertility.


(Easterlin, 1983


Standing, 1983; Menken, 1987)


The psychological approach to fertility focuses on individual-level
processes and places emphasis on psychological variables and measures.

Fishbein (1972) argued that human fertility behavior was determined by


people'


intentions, the normative beliefs regarding fertility


and the personal


attitudes toward the importance of these norms.


approach,


here.


Unlike the sociological


norms affect fertility through personal attitudes and intentions


Other works of this orientation include Jaccard and Davidson (1976),









the other factors, economic and social,


affect fertility through individual


attitudes and intentions.
Anthropologists usually study fertility behavior in terms of the

determinants of social and cultural differences within an evolutionary


framework.


Barlett (1980) identified three approaches to fertility within


anthropology: the ecological approach, the cognitive approach and the


statistical aggregate approach.


Chagnon (1968) and Harris (1974) applied the


ecological approach to explain the practice of female infanticide among the
Yanomamo, and concluded that female infanticide was an effective way of


limiting the overall fertility of the group.


Cognitive anthropologists (e.g.,


Marshall


, 1972a and Quinn,


1975) stressed the importance of individual-level


decision making, and attempted to build models for the decision-making


process.


The third approach, the statistical aggregate approach,


people do, not what people say they do" (Barlett,


1980:168).


"stresses what


More specifically,


in this approach,


"an anthropologist observes behavior, records outcomes,


and then analyzes the patterns in the outcomes to construct a statistical


profile of people who choose different options" (Barlett, 1980:168).


Since most


anthropological studies have dealt with relatively homogeneous societies in
the past, they tended to assume that shared values and traditions and societal


norms govern individuals'


behavior


, which in turn determine their fertility.


In short, most anthropological approaches to fertility tend to focus on cultural
patterns.


However


, there is another approach to fertility in anthropology that


stresses the role of material conditions or factors directly related to material









explain the fertility behavior of preindustrial societies. Handwerker (1986)
criticized the cultural approach to fertility as tautological, and offered a


materialist explanation to fertility transition.


Handwerker argued,


cannot identify specific behavioral patterns and the ideas they presuppose


independent of one another.


'explain'


behavior by reference to those ideas


therefore constitutes a covert tautology" (1986:14).


transition occurs "when personal material


According to him, fertility


well-being is determined less by


personal relationships than by formal education and skill training."
Handwerker further explained:

This transformation occurs when changes in opportunity
structure and the labor market increasingly reward


educationally-acquired skills and perspectives,


for these changes


have the effect of sharply limiting or eliminating the expected
intergenerational income flows both from children, and from
the social relationships created by or through the use of children.
(1986:3)

In terms of the relationship between education and fertility, Handwerker

offered an insightful analysis:


education or literacy itself can have no important effect on


fertility.


The linkage between education and fertility is


contingent on opportunity structure, and will turn on the issue
of how material well-being can best be created and maintained,
and how educationally acquired skills and perspectives fit, or do


not fit, into this process.


(1986:18)


The above approaches not only differ in theoretical orientation,


also in unit of analysis.


Both economic and psychological approaches to









which may be an extended family, a clan, a social class or the society as a


whole.


Even when individuals are the focus of attention,


they are situated


within the sociocultural context and regarded as members of a social group,
rather than as isolated individuals acting on their own.




Fertility Differentials among Ethnic/Racial Groups in Modern States


It is well documented in the literature of demography and ethnic/racial
studies that in multiethnic/racial societies, various ethnic/racial groups


reproduce at different rates.


For example, Rindfuss and Sweet (1977) reported


different fertility rates for whites, blacks,


American Indians, Mexican


Americans, Chinese Americans and Japanese Americans in the United States


for the period 1955-1969.


These ethnic/racial groups in the United States


continued to reproduce at different rates for the 1970s (Bean and Marcum,


1978) and 1980s (1980 census, cited in Farley and Allen,


1989).


Fertility


differentials among ethnic/racial groups in Canada were reported in Halli


(1987) and Halli et al.


(1990),


and ethnic fertility differentials in China have


been documented in the Chinese censuses since


1950.


Racial variations in


fertility rate in Brazil are also reported in the Brazilian censuses since


spite of the differences in ethnic/racial composition,


1950.


social and political


system and economic structure among these countries, one common element


about fertility rate is almost universal,


, fertility rate seems to vary along


ethnic/racial lines, as well as alone economic. educational. religious and









better understanding of the factors responsible for differential fertility rates


among groups.


Furthermore, an examination of these factors reveals, among


other things, the nature of relationships between different social groups, be


they racial,


cultural, or economic, or a combination of the above,


in terms of


access to education, level of employment and income, and ultimately the
level of well-being.
Within the larger theoretical framework of fertility research in general,
studies of differential fertility among various subgroups of a population


(Goldscheider & Uhlenberg, 1969; Sly,


1970; Bean & Wood, 1974; Roberts &


Lee, 1974; Gurak, 1978; Gurak, 1980; Johnson & Nishda, 1980; Bean &


Swicegood,


1985) suggest three approaches.


They are the cultural (or sub-


cultural) approach,


the structural (or social characteristics) approach and the


minority group status approach.
The cultural (or subcultural) approach emphasizes the role of values,


norms and ideology in determining a group's


fertility behavior (Goldscheider


& Uhlenberg, 1969).


In this approach,


one "searches for determinants of


demographic variation in the history and cultural traditions of different


subpopulations" (Frisbie and Bean,


1978:2).


Furthermore,


"even when groups


are similar socially, demographically, and economically, minority group
membership will continue to exert an effect on fertility" (Rindfuss & Sweet,


1977:113).


This approach reflects Schermerhorn'


definition of an ethnic


group:


"A collectivity within a larger society having real or putative common


ancestry, memories of a shared historical past, and a cultural focus on one or
more symbolic elements defined as the epitome of peoplehood"









the higher fertility of Mexican Americans stems from the
persistence of cultural norms and values supporting large
families, such as familism---a constellation of norms and values
giving overriding importance to the collective needs of the
family as opposed to the individual---or adherence to the
pronatalist positions of the Catholic church, including
prescriptions against certain forms of birth control.


The social characteristics (or structural) approach does not deny the


possible validity of the subcultural approach,


but it argues that differences in


social status, such as education, occupation and income, account for most or


all fertility differences among sub-groups.


'structural'


This approach also "implies that


assimilation with respect to education, occupation and income


will lead to the elimination of fertility differences between majority and


minority groups" (Bean & Swicegood 1985:7).


The social characteristics


approach has its grounding in the assimilation theory first put forward by


It draws heavily from the idea of "structural assimilation,"


one of seven dimensions of assimilation that Gordon identified


sometimes referred to as "the assimilationist theory."
approach, fertility differentials are attributed to social,


economic characteristics of various groups.


and is


According to this

demographic and


When these factors are


controlled, differences in fertility should disappear.


The minority group status approach was first proposed by Goldscheider
and Uhlenberg (1969), and was thereafter tested and applied in various

studies, such as Sly (1970), Roberts and Lee (1974), Johnson and Nishda (1980)

and Bean and Swicegood (1985). The basic assumption of this approach is that
-- -m -i -


Gordon (1964).









insecurity that accompanies minority group status.


Those minority members


who are in higher socioeconomic standing tend to aspire to greater social


mobility and therefore feel greater insecurity and marginality.


In order to


overcome the feeling of insecurity and the potential obstacles to greater

success, these members are likely to lower their fertility to secure their already


achieved status.


Goldscheider and Uhlenberg (1969) used this approach to


explain the lower fertility rate of highly educated black women as compared


to similar white women.


More recently, Halli (1987) applied this approach to


the fertility of Asian groups in Canada.

Although these approaches differ in focus and have different
theoretical orientations, in my opinion, they actually complement rather


than contradict each other.


They all contribute to the explanation of the


complex causes of differential fertility among various racial/ethnic and/or


socioeconomic groups.


However, it is crucial to test these approaches against


empirical data to determine the most important factors) by examining the
associations between fertility rate and the possible biological, sociocultural


and economic factors.


Specifically, it is important to determine the degrees to


which major independent variables contribute to the fertility level of a


population as a way to


assess


the validity of the competing theories of human


reproduction.


Fertility Differentials Among Asians,


Whites and


Afro-Brazilians









are the main causes for the differences?


The hypothesis tested here is that


socioeconomic status (defined by income level and educational attainment),
rather than color, is the best predicator (but not the only) for differential


fertility among different social groups.


Thus, when income and education are


controlled, color will contribute relatively little to subgroup differences in


fertility level.


It is also assumed that household income and mother's


educational level is negatively correlated with women's


fertility level; i.e.,


higher the income and educational levels are, the lower the fertility level is.


However


, I do not assume that socioeconomic status alone accounts for all


the differences in fertility of various groups.


Therefore, I expect that even


after controlling for the socioeconomic differences, some differences will
remain in the fertility levels of different color groups, although the amount

of variance in fertility explained by ethnic status will be relatively small.

The data set used here consists of women 15-49 years of age only since


we are only concerned with fertility level.


The dependent variable is fertility


level


, and the independent variables are place of residence,


color, age,


education, and mean income.


Fertility level is here defined by the mean


number of children ever born to women of a cohort classified by either color,


age, educational level or income level.


Following the conventional method,


women are divided into either seven age groups (15-19, 20-24, 25-29,


39, 40-45 and 45-49) or four age groups (1


30-34


, 20-29, 30-39, and 40-49) for


descriptive analysis.


In what follows


, I describe the characteristics of the sample data,


compare the mean fertility level by age group, color, educational level,









multivariate regression analyses to examine the relationships among the


variables.


The main findings are summarized at the end of the chapter.


Table 3.1 shows the mean number of children ever born to women in


seven age groups and the standard deviations from the means.

the proportions of each age group relative to the whole sample.


number of children ever born for the total sample is 1.89,


It also shows

The mean


with the expected


increase from the lower to higher age groups.


Table 3.1
Mean Children Ever Born to Women of 15-49 Years of Age
by Age Group, Metropolitan Sao Paulo, Brazil (1980)


A.e Group


Mean


Std Dev


Cases


0.77


20-24
25-29
30-34
35-39
40-44
45-49


3.28


4.17


1.56
1.98
2.46
2.91
3.20


39,916
38,968
33,482
26,925
21,916
19,163
16,283


20.3
19.8
17.0
13.7
11.1


Total


196,654


100.0


Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.


The mean number of children by color group is shown in


Afro-Brazilian women have the highest mean (2.18),


(1.82) and Asians third (
statistically significant.


Table 3.2.


with whites second


1.44). Given the sample size, these differences are
The color composition of the women in the sample









Table 3.


Mean Children Ever Born to Women of 15-49 Years of Age
by Color Group, Metropolitan Sao Paulo, Brazil (1980)


Color Group


Mean


Std Dev


Cases


White


147,786
44,365


Afro-Brazilian


Asian


75.3
22.6


4,045


Total


196,195


100.0


Source: Weighted 3% sample data of Metropolitan Sio Paulo, 1980 Brazilian
Census.


Table 3.3 illustrates the mean number of children ever born to women


by age group and color.


Here I still use five-year intervals for age groups in


order to obtain a more detailed picture of the fertility behaviors of the three


color groups.


At every age level,


Asian women have the lowest mean


number of children


, Afro-Brazilian women have the highest mean number


of children, and the mean number of children for white women is above that


of Asians but below that of Afro-Brazilians.


Expectedly, the age group of 1


for all three color groups has very few children,


particularly


Asian


, who, on


average, have only 0.008 children.


Furthermore, the mean number of


children for Asian women ages 20-24 and


25-29


are extremely low; only 0.18


and 0.69 respectively.


In contrast, the fertility levels of whites and Afro-


Brazilians are much higher than that of Asians in these two age groups.
fertility differences among the color groups decrease for older age groups,


the basic Pattern still remain.


In sum. Asian women not only have fpwpr









Table 3.3
Mean Children Ever Born to Women of 15-49 Years of Age
by Age and Color Groups, Metropolitan Sao Paulo, Brazil (1980)


Age Group


Mean


Asian


White


Afro-Brazilian


15-19
20-24
25-29
30-34
35-39
40-44
45-49


0.01*


0.77


3.28


4.17


0.18
0.69
1.56
2.35
2.89
3.51


0.73
1.59
2.39
3.11
3.62
3.93


0.15
0.95
1.95
2.96
4.01
4.81
5.30


Total


Source: Weighted 3


sample data of Metropolitan Sao Paulo,


Census.
*The actual value is 0.008.


In Table 3.4


1980 Brazilian


we see the fertility differences among the three color


groups, controlling for both educational level and age.


There are two


interesting observations to make here with regard to the fertility level of the


three color groups by educational level.


First, fertility differences among the


color groups for women with no schooling are very small (3.63 for Asians,


3.84 for whites and 3.96 for Afro-Brazilians).


Second


, the fertility levels of


Afro-Brazilians at all educational levels, except for the one of no schooling,

are the lowest among the three color groups.

It may seem surprising for Afro-Brazilians to have lower fertility levels

than those of whites and Asians at all educational levels but the first (no


schooling).


This suggests that education may have greater negative imPact on









the disproportionate distribution of Afro-Brazilians in educational level.


Because over 20


of Afro-Brazilians have no schooling, compared to 9.9


whites and 3


of Asians


, their overall fertility level is still higher than


those of whites and Asians, despite their lower fertility levels at all the other
levels.


When the three color groups are compared by age group within the


same educational level


, Asians have children at older ages than do whites at


all levels, and whites have children at older ages than do Afro-Brazilians at


levels of less than 9 years of schooling.


For example, Asian women between


ages 15 and 19 rarely have children at all educational levels, while the mean
number of children for white and Afro-Brazilian women ages 15-19 with less


than


5 years of schooling is more than 0.20.


Furthermore, the mean number


of children for Asian women between ages 20 and 29 ranges from 0.22 to 0.96,
while the mean for white women of the same age group ranges from 0.36 to

2.07, and that for Afro-Brazilian women of the same age group ranges from


0.17


to 2.


At higher educational levels (9 or more years of schooling),


however, Afro-Brazilian women have fewer children than do white women
in all age groups and Asian women in most age groups (see Table 3.4).

The fertility levels of the three color groups, controlling for income


and age, are shown in Table 3.5.


First


between income and fertility level,


fertility levels.


we see the negative association

, lower income groups have higher


The mean number of children for women from the lowest to


the highest income level are


,1.19, and 1.16, respectively.


difference between the fertility level of women in the first and second income










Table 3.4
Mean Children Ever Born to Women of 15-49 Years of Age
by Education, Age and Color Groups Metropolitan Sio Paulo, Brazil (1980)


Years of School


Total


Asian


White


Afro-Brazilian


Zero Years
15-19
20-29
30-39
40-49


Years
15-19
20-29
30-39
40-49


Years
15-19
20-29
30-39
40-49


3.89
0.29
2.09
4.22
5.55


2.30
0.21
1.55
3.02
3.83


0.92
0.08
0.99
2.16
2.70


1 Years
15-19
20-29
30-39
40-49


- Years
15-19
20-29
30-39
40-49


0.81
0.00
0.35
1.40
1.98


3.63
0.00
0.66
3.20
4.35

2.55
0.02
0.96
2.39
3.32


1.18
0.01
0.64
1.94
3.04

0.73
0.00
0.36
1.63
2.52

0.61
0.00
0.22
1.08
1.66


3.84
0.28
2.07
4.07
5.37

2.32
0.21
1.54
2.96
3.70

0.95
0.07
1.01
2.14
2.60

0.77
0.02
0.56
1.80
2.32

0.85
0.00
0.36
1.43
2.02


3.96
0.29
2.12
4.44
5.94

2.26
0.20
1.60
3.29
4.51

0.78
0.09
0.94
2.28
3.45

0.49
0.02
0.40
1.35
1.79

0.55
0.20
0.17
1.15
1.24


Source: Weighted 3% sample data of Metropolitan Sao Paulo,
Census.


1980 Brazilian









What's surprising about the distribution of income levels is that over

two thirds (68.8%) of the women ages 15-49 belong to the lowest income level,


and over five sixths (85.7%) are in the bottom two income levels.


This is a


vivid description of the labor force participation and the economic status of

the women under study here.


Table 3.5
Mean Children Ever Born to Women of 15-49 Years of Age
by Income, Age and Color Groups, Metropolitan Sao Paulo, Brazil (1980)


Income


Level


Total


Asian


White


Afro-Brazilian


1 MW
15-19
20-29
30-39
40-49


2MW
15-19
20-29
30-39
40-49


2.19
0.14
1.55
3.20
4.39


1.25
0.04
0.62
2.61
3.57


1.85
0.00
0.75
2.45
3.47

0.74
0.01
0.13
1.44
2.70


2.11
0.13
1.49
3.05
4.14

1.17
0.03
0.55
2.52
3.41


2.48
0.18
1.74
3.80
5.50

1.48
0.06
0.77
2.83
3.99


3MW
15-19
20-29
30-39
40-49


Above 3
15-19
20-29
30-39
40-49


1.19
0.04
0.44
1.97
2.95


MW


0.87
0.00
0.17
1.00
2.27


1.11
0.03
0.40
1.88
2.80


1.17
0.03
0.42
1.49


1.60
0.06
0.63
2.37
3.54


1.27
0.23
0.48
1.68
2.63









At every income level,


the fertility level for Asian is lower than that of


whites, which is in turn consistently lower than that of Afro-Brazilians.
However, the gaps between the means for Asians and those for whites in

every income group are much bigger than those between the means for

whites and those for Afro-Brazilians, indicating again that Asians are


significantly different from the other two groups,


as far as fertility is


concerned


, even when income is controlled.


More importantly,


this shows


that, after controlling for income,


three color groups.


there are still fertility variations among the


When both income and age are controlled, the mean


number of children for Afro-Brazilian women is higher than that for white


women at all income levels and in all age groups,


and the mean number of


children for white women is higher than that for Asian women at all income
levels and in all age groups.

Table 3.6 shows the fertility differences among the three color groups,


controlling for residence and age.


As expected, women in rura


areas have a


much higher fertility level than their urban counterparts.


level for rural women is 42


In fact, the fertility


more than that for urban women (2.58 for rural


women vs.


1.81 for urban women).


However, because rural women comprise


only 9.6% of the population of women, their high fertility level has little
impact on the fertility of the total population.

Color differences remain much the same in all age groups as well,


controlling for residence.


after


The pattern shown here conforms to the general


pattern exhibited by the data so far, i.e.,


average than do whites,


Asians have fewer children on


who in turn have fewer children on average than do









level for rural women, whether


Asian,


white or


Afro-Brazilian


consistently higher than that for urban women.


Table 3.6
Mean Children Ever Born to Women of 15-49 Years of Age


by Residence,


Age and Color Groups,


Metropolitan SAo Paulo,


Brazil (1980)


Residence


Total


Asian


White


Afro-Brazilian


Urban


15-19
20-29
30-39
40-49


3.83


1.07
2.60
3.60


1.34
3.28
4.84


0.42
1.85
3.08


Rural


0.17


20-29
30-39
40-49


Source: Weighted 3
Census.


Table 3.7 illu


0.18
1.66
3.81
5.34


2.94
0.17
1.95
4.59
6.45


sample data of Metropolitan Sao Paulo,


states color differentials in fertility


0.00
0.44
2.62
3.88


1980 Brazilian


controlling for both


residence and education.


Again, in both urban and rural areas, color


differences in fertility for women with no schooling are very small.


mean number of children for


Asian,


category in urban areas are 3


white and Afro-Brazilian women of this


and 3.85, respectively


the three color groups in rural areas are 4.


4.22 and 4.


r, whereas those for

, respectively.


In urban areas, the fertility level of Afro-Brazilians with any schooling
ah,"nvam nno Vosr 1C lnxAror fbsan neil- nnl'iz fhci ,I- 1'thni ,,..1r,.,if n,. ,,..n. -,,.., ,,,..4









whites at the levels of 1-4 and 5-8 years of schooling (2


and 1.21 for Asians


vs. 2.32 and 0.97 for whites).


For the top two educational level


(9-11 and 12 or


more years of schooling)


Asians; 0.


whites have slightly higher fertility level than


and 0.85 for whites and 0.73 and 0.62 for Asians.


Table 3.7
Mean Children Born to Women of 15-49 Years of Age
by Residence, Education and Color, Metropolitan Sao Paulo, Brazil (1980)


Residence


Mean


Asian (


White


Afro-Brazilian (


Urban


Zero


3.77


1.39 (100.0)
3.51 (2.3)


(21.8)


1.74 (100.0)


3.73
2.32


(8.4)
(43.5)


2.09 (100.0)
3.85 (18.7)


2.25


1 (15.5)


9-11


0.74


Rural


Zero


2.29
0.72
0.60


0.73 (21.5)
0.62 (15.6)

1.81 (100.0)
4.25 (4.9)
2.74 (45.4)
0.94 (23.1)
0.65 (19.4)


0.16 (7.3)

1.44


Total


0.97 (23.1)


(16.1)


0.85 (8.8)

2.49 (100.0)
4.22 (24.0)
2.26 (60.9)


(9.8)


0.60 (4.2)
0.87 (1.1)

1.82


0.79 (21.8)
0.48 (6.1)
0.55 (1.4)

2.94 (100.0)
4.51 (35.5)
2.35 (54.0)
0.67 (8.7)


(1.7)


0.49 (0.09)

2.18


Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.


Note:


The percentages in brackets are the proportions of people belonging to


various educational level


within color groups.


The patterns in rural areas are quite different; the fertility level of
Asian is higher than that of whites at all levels except at the level of 12 or









levels (about 85


of whites and 90


of Afro-Brazilians


vs. about 50


Asians),


their overall fertility levels are still higher than that of Asians.


people with no schooling at all,


the mean number of children for Afro-


Brazilians (4.51) is the highest among the three groups (4.


4.22 for whites).


for Asians and


Of those with 1-4 years of schooling, Asians have the highest


fertility level,


2.74, compared to


2.35 for Afro-Brazilians and


26 for whites.


At the levels of 5-8 and 9-11


years of schooling, Afro-Brazilians have the


lowest mean (0.67 and 0.52),


but they account for only


less than 10% of their


rural population.


The low fertility level of whites and Afro-Brazilians with


twelve or more years of schooling (0.87 for the former and 0.49 for the latter)
does not contribute much to their overall fertility level because they account


for only about 1


of their respective populations.


fertility level of Asians with 12


On the other hand


or more years of schooling (0.16),


which is


substantially lower than that for the two other groups, affects their overall


fertility level since they account for more than


of Asian in rural areas.


Considering the overall mean fertility level for each group, it appears


that there are two causes for the unpredicted distribution:


1) proportionally


Asian women are over-represented in the top two educational levels (about


46%),


compared to whites and Afro-Brazilians (about 13% and


respectively);
schooling (18.


2) Afro-Brazilians are over-represented in the category of no


in urban areas and 35


in rural areas).


Thus


the effect of


education seems to be different for the three groups.


In particular, education


seems to have greater negative impact on the fertility level of Afro-Brazilians


than on that of whites and Asians. If ti


his is true, the results here then









counterparts.


It also suggests that one's educational level is an important


factor in determining one's


fertility level,


regardless of residence and color.


Table 3.8 describes the color differentials in fertility,


controlling for


residence and income simultaneously.


As shown above


, fertility levels for all


groups in urban areas are lower than those in rural areas, and Asians have

lower fertility levels than whites in every income level, who in turn have

lower fertility levels than Afro-Brazilians, in both urban and rural areas.


Table 3.8
Mean Children Ever Born to Women of 15-49 Years of Age
by Residence, Income and Color, Metropolitan SAo Paulo, Brazil (1980)


Residence


Mean


Asian


White


Afro-Brazilian


Urban


MW


2 MW


To 3 MW
Above 3 MW


1.74
2.04


2.09
2.40


0.76
0.62
0.88


Rural


MW


2 MW


To 3 MW
Above 3 MW


2.67


2.57


0.57
0.22


2.94
3.04
1.91
3.64
2.92


Total


Source: Weighted 3% sample data of Metropolitan Sio Paulo, 1980 Brazilian
Census.


To find out the degree of association between fertility level and the


independent variables,


while controlling for some or all the other variables,


.I cn ',et- nC( tyn. tr 1n r r-n-l r erF en, ,^ ..^ 4-n' fl.4 ,- .11 1l..r k 1


'~ I n1- /Inrn n^ 4'/ ^> nlr~ rtr r tl-^I/^-









whites is considered as the reference group, against which the other two color


groups are compared.


Age and years of schooling are treated as interval


variables without modifications, but income is treated as an interval variable


with modifications such that the minimum wage in


1980 (4,150 cruzeiros) is


used as the unit of income, instead of the original unit (one cruzeiros) in the

census.

In order to compare the effects of various variables on fertility level, a


total of seven regression models are developed.


The first model measures the


effects of age and residence, the second one measures the effects of not only

age and residence but also the socioeconomic variables, education and


income.


The third model measures the effects of age, residence and color, and


the fourth one, the complete model, measures the effects of all the variables


examined here.


Models 5-7 are developed solely to examine whether


education and income have different effects on different color groups.

Based on the findings in the previous descriptive analysis, I first build a


regression model with only age and residence.


This model tells us three


things: 1) One unit of increase in age increases the mean number of children


by 0.1461,


with residence included in the model


2) being in rural areas


increases the mean number of children by 0.8294,


model with the two variables explains 36.


the tota


with age considered; 3) this


(the R-square for the model) of


variation in fertility for all the people in the sample data (see Model


(1) in Table 3.9).

To examine the cumulative effects of age, residence and socioeconomic
variables on fertility, education and income are entered into the existing









variables in Model 1; 2) when education and income are introduced into the


model


, the coefficient of age decreases slightly, but the coefficient of rural


areas (as opposed to urban areas) decreases dramatically by more than 50%,
suggesting a relatively high degree of covariation between residence,

education and income; 3) the negative signs of the coefficients of education

and income indicate a negative correlation between education and fertility,


and between income and fertility.


More specifically,


one year of increase in


schooling reduces the mean number of children by 0.2838,


and an increase of


one minimum wage in average income reduces the mean number of

children by 0.0997.


In order to measure the effects of color


, and to compare them to those


of education and income, Model 3 is obtained by adding the dummy variables
representing Afro-Brazilians and Asians (whites is the reference group) into


the first model.


There are several things to point out here:


First, unlike in


Model


, there are little changes in the coefficients for age and rural areas in


Model 3, compared to Model 1


indicating that variations in age and residence


do not contribute much to the color differences in fertility.


Second


when


Afro-Brazilians and Asians are compared to whites, they both differ
significantly from whites; the positive sign of the coefficient for Afro-
Brazilians indicates a higher fertility level than that of whites, and the

negative sign for the coefficient of Asians indicates a lower rate relative to


whites.

average,


Specifically


controlling for age and residence, Afro-Brazilians, on


have 0.4974 more children than do whites


, and Asians, on average,


have 0.6241 fewer children than do whites.


Third, a mere increase of 0.97% in









better than the first model without the color variables in explaining the total

variations in fertility for the sample data.


Table 3.9
Children Ever Born to Women Aged 20-49


Regressed on Age, Residence, Education,


Independent
Variables


Income and Color


Models


AB*


Age


.1461


.1306


.1473


.1230


.1669


.1125


Residence
Urban (Reference)


Rural


.8294


.3635


.8261


.3793


.3449


.4694


.2070


Education


-.2838


Income**


-.2720


-.0997


-.0992


-.2795

-.0851


-.2272

-.2771


-.2080

-.0809


Color
Whites (Reference)


Afro-Brazilians


Asians


.4974
-.6241


.1827
-.2476


.3674


.4351


.3771


.4364


.4382


.4400


.4996


Constant


.4024


-.6340


-2.5355


-.7518


-.4902


-1.445


-.7650


Note:


= Whites


= Afro-Brazilians


and A


= Asians


**The unit of income is the minimum wage in


P-value for all coefficients


1980 (4,150 cruzeiros).


.000.


As expected,


the fourth model


, the complete model with all the









slightly

from 43


. Meanwhile, the R-square in Model 4 increase only 0.13


p.51


in Model


0.13% more of the tota


variables in Model


to 43.64


2. This indicates that the color variables explain only

l variation in fertility that is not explained by the other

However, compared to Model 3, the coefficients of


Afro-Brazilians and Asians (as opposed to whites) drop significantly from


0.4974 to 0.1827 for the former and from -0.6241 to -0.2476 for the latter.


This


suggests that when the effects of age, residence, education, income and color

are measured simultaneously, Afro-Brazilians and Asians differ less from


whites in fertility level.


To put it differently,


most of the fertility differences


between whites and Afro-Brazilians


and between whites and Asians are due


to factors other than color.


Models 5


, 6 and 7 are developed to test the hypothesis that education


and income have different effects on different color groups.


regression analysis for each of the three group,


I run a separate


with the variables of age,


residence, education and income; Model 5 is for whites,


Model 6 is Afro-


Brazilians and Model


7 is for Asians.


Thus, we can measure the effect of the


same variable on different color group by comparing the coefficients of this
variable across Models 5, 6 and 7.


First


the coefficients of education in the three models show that


education does have different effects on the fertility levels of the three color


groups, but not in the order I expected.


Specifically


the coefficients of


education in Models


(-0.2795 for whites,


2272


for Afro-Brazilians and


-0.2080 for Asians) tell us that the (negative) effect of education is greater for


whites than it is for Afro-Brazilians. and it is the least for Asians.


In other









Second, the (negative) effect of income on fertility level is much greater
for Afro-Brazilians than it is for whites and Asians, as indicated by the


coefficients of income the three models.


We can interpret the coefficients of


income in the following way; a one unit of increase in income, i.e., an
increase of 4,150 cruzeiros, results in a reduction of 0.2771 in the mean


number of children for Afro-Brazilians


while it results in a reduction of


0.0851 and 0.0809 in the mean number of children for whites and Asians,


respectively.


Finally, the R-squares in Models 5 and 6 (0.4382 and 0.4400) are


very similar, but they are somewhat different from the R-square in Model


(0.4996).


This


indicates that the variables in the models explain


approximately the same amount of variance in fertility for whites and Afro-
Brazilians, but they explain slightly more of the variation in fertility for

Asians.


In addition


, we see the coefficient of age for Afro-Brazilians in Model 6


(0.1669) is considerably higher than those for whites (0.1125) and Asians


(0.1230).


This indicates that age has greater positive impact on the fertility


level of Afro-Brazilians than that of whites or


Asians


which confirms the


conclusion from the descriptive analysis that Afro-Brazilians have children at


younger ages than do the other two groups.


We also notice that the


coefficient of the dummy variable, rural areas, for Afro-Brazilians in Model 6


(0.4694) is much higher than that for whites (0.3449) in Model


Asians (0.2070) in Model


5 and that for


. This suggests that the gap between the fertility


level of urban and rural residents is bigger for Afro-Brazilians than it is either
for whites or Asians, which is consistent with the result of the descriptive









Summary


The descriptive analyses of the sample data show that the fertility level

of Brazilian women varies by age, color, education, income and place of


residence.


When age is controlled,


Asians have the lowest mean number of


children and Afro-Brazilians have the highest mean,


with whites in between


min every age group.
When education and age are controlled simultaneously, the existing

patterns of fertility differences among the three color groups change

completely; the fertility level of Afro-Brazilians is the lowest among the three


group, except at the level of no


are minimal.


schooling, even where the color differences


This indicates that fertility is associated with more with


education than with color.


On the other hand


, Asians have children at older


ages than do whites, and whites have children at older ages than do Afro-
Brazilians at the educational levels of less than 9 years of schooling. At


higher educational level


, it is just the opposite; Afro-Brazilians have fewer


children than do whites in all age groups, and do Asians in most age groups.

Color differences in fertility narrow a great deal when income and age


are controlled simultaneously.


The change in fertility level is most


pronounced between the first and second income level for all three color


groups.


However, in spite of the decreasing gaps among the three groups at


higher income levels, Asian fertility level is the lowest,


in the middle


white fertility level is


, and Afro-Brazilian fertility level is the highest at all age levels.


When residence and age are controlled simultaneously


c


color differences in


l









When both residence and education are controlled


there are some


interesting changes in the fertility differences among the three color groups.


In urban areas


, Asian fertility level exceeds that of whites at the educational


levels of 1-4 and 5-8 years of schooling, while the opposite is true at higher


levels for these two groups.


As before, the fertility level of Afro-Brazilians is


the lowest among the three groups, except at the level of no schooling. In
rural areas, Asians have the highest mean number of children among the

three groups, except at the level of no schooling, where Afro-Brazilians have

the highest mean. Nonetheless, the overall mean number of children for
Asians is still the lowest in both urban and rural areas due to their much
higher concentration at higher educational levels than the other two groups.

Color differences in fertility do not change much when both residence and

income are controlled.
The multivariate regression models show quantitatively the effects of


various independent variables on fertility level.


Model 1 in


Table 3.9 tells us


that both age and rural areas (as opposed to urban areas) are positively


associated with fertility level,


though the impact of the latter is much greater


and age and residence account for 36.7% of the total variation in fertility for


the sample.


The variables representing socioeconomic status, education and


income, explain 6.8% more of the variation in fertility,


with education


having greater negative impact than income on fertility (see Model 2 in


Table


3.9).


Specifically,


the mean number of children reduces by 0.2838 with one


unit (year) increase in schooling, and reduces by 0.0997 with one unit (4,150
cruzeiros) increase min average income.









different from whites in terms of fertility; on average, the mean number of
children for Afro-Brazilians is 0.4974 higher than that of whites and the mean


number of children for Asians is 0.6241 lower than that of whites


controlling for age and residence.


after


A comparison of the R-square values in


Models


2 and 3 indicates that socioeconomic status


education and


income


, has far greater impact than does color on fertility.


Model 4


, the complete model with all the variables, is very similar to


Model


to Model


, which does not include the dummy variables for color.


, the R-square increases only 0.13


Compared


in Model 4, suggesting that the


negligible effect of color on fertility


the model.


after controlling for the other variables in


However, the large decreases in the coefficients of the dummy


variables for color from Model 3 to Model 4 indicate that controlling for
education and income, the three color groups do not differ as much as they

did before these variables were controlled.

These findings support the social characteristics approach because in
general groups with higher educational attainment and higher income have


lower fertility levels.


For example, Asians have the highest educational


attainment (a mean of 6.65 years of schooling) and highest mean income
(7,261 cruzeiros) among the three group, and their fertility level is the lowest.


Likewise


, Afro-Brazilians have the lowest educational attainment (a mean of


3.23 years of schooling) and lowest mean income (3,030 cruzeiros) among the


three color groups, hence the highest fertility level.


The overall educational


level and mean income of whites rank second (4.9 years of schooling and


4,783 cruzeiros)


and therefore


, their overall fertility level is above that of









In addition, the fact that Asians differ more from whites than do Afro-
Brazilians suggests that cultural factors, such as religion and values and


norms on fertility behavior might be at work.


Unfortunately,


since the


census data do not allow us to examine the effect of cultural factors on


fertility


I can not address this issue empirically


In order to adequately


examine the complex causes of differential fertility outcomes among various
social groups, we need to conduct qualitative, as well as quantitative, research,

and consider a range of factors that are relevant to the problem.














CHAPTER 4
CHILD MORTALITY DIFFERENTIALS


AMONG


ASIANS,


WHITES AND


AFRO-BRAZILIANS


Major Determinants of Mortality and Racial/Ethnic Differentials in Mortality


It is well known in the demographic literature that the mortality rate of


a population is determined not only by biological factors (e.g.,


age,


sex and


some genetic differences) and environmental factors (e.g.,


climate and natural


resources) but also by


socioeconomic factors (e.g.,


income, education and


occupation) and cultural factors (e.g.,


group,


membership in different racial/ethnic


religious affiliation, and customs and practices related to health status).


In other words, mortality rate of a population is the result of the interplay
between the biological, environmental, socioeconomic and cultural

conditions of the society in which the population in question live at a


particular time period.


On the relationship among the above factors,


Vallin


(1980:27) pointed out that:


There is growing evidence that, within a framework of biological


constraints (progressive aging of the body,


limited life-span), and


taking into account the geographical context that may modify
these constraints, the main differences in mortality are of
socioeconomic and cultural origin.










(1) public health services, which influence mortality regardless
of individual behavior (such as spraying insecticides that control


malaria)


(2) health and environmental services that reduce the


costs of health care but require some individual responses (e.g.,


the availability of clean water)


(3) and an array of individual


characteristics (such as income, which affects health through
nutrition and housing, and education) associated with the speed
and efficiency with which individuals respond to health services
and environmental threats (1992:709).


Because mortality is the result of the interaction among these complex
factors, it is an important indicator of quality of life of a population or a sub-


population that has its distinctive characteristics.


Similarly, infant and child


mortality rates provide a summary measure of the quality of life of a

population, especially in developing countries, since they are also very


sensitive to the conditions of the above factors


endogenouss" and "exogenous"
childhood, respectively. Infant


in addition to the


causes that are particular to infancy and early
and child mortality rate is therefore used as a


fairly reliable index of social and public health conditions throughout the
world.

In modern societies that are marked by socioeconomic differences, we
see a great deal of variation among various social groups in terms of


mortality rate.


When socioeconomic differences are largely based on


racial/ethnic group affiliation, as they are in many societies, mortality


differentials vary along racial/ethnic lines as well.


For example, in the


United State, blacks have had a higher mortality rate than whites since 1940,
_. -- n -l 1 U 1. A i t r _- *









analyzing race differences in adult mortality


with controls for


sociodemographic factors,


Rogers (1992) found that:


1) The demographic


variables, race,


age and


appear to be related significantly to mortality


when no other variables are controlled


, 2) when family size and marital


status or socioeconomic status is controlled separately


mortality reduces considerably


age,


racial differences in


3) when all of the sociodemographic variables,


marital status, family size and income, are controlled


simultaneously, race differences in mortality are eliminated.


Thus


it is the


sociodemographic factors,


not race itself, that are the real causes of mortality


differentials between whites and blacks in the United States.

Wood and Lovell (1992) examined racial inequality in child mortality


and life expectancy in Brazil, using the 1950 and 1980 Brazilian Census.


They


found that although the life expectancy for whites and nonwhites increased


by more than 18 and 19 years, respectively


from 1950 to 1980


them remained about the same over the 30-year period:


outlived nonwhites by


, the gap between


"In 1950


whites


years; in 1980, the comparable figure was 6


.7 years"


(1992:721)


. Farley and Allen (1989:47) reported the difference in infant


mortality between whites and blacks in the U


during 1980s:


"black children


are about twice as likely as white children to die before attaining their first


birthday."


They also described the differences in the life span between whites


and blacks in 1980; the life expectancy of white men (70


.7 years) was seven


years longer than that of blacks (63.


years),


and the life expectancy of white


women (78.1 years) was


5.8 years longer than that of black women (72.3 years).


The purpose of this chapter is to use child mortality as a measure to









children


, and (B) to find out whether differences are due exclusively to


socioeconomic standing.


If skin color continues to explain variences in child


mortality, as I expect, then the findings suggest (although do not directly test)
that cultural factors may be at work.




Child Mortality Differentials and Life Expectancy by Color Group


In this chapter, I first describe a few of the major socioeconomic
indicators for Brazilian women of the three color groups, and then measure
child mortality level of each group, using the indirect methods developed by


Brass (Brass et al.


1968) and Trussell and Preston (Trussel and Preston 1982)


(See Appendix B).


Finally,


I will analyze the association between the


socioeconomic indicators and mortality level by applying the


Tobit regression


procedure.
The sample data used here for mortality measurement of Brazilian


women consist of households with women aged 20-29,


birth.


with at least one live


The variables selected as indicators of socioeconomic status of Brazilian


women are the educational attainment of both the wife and husband,
monthly household income, participation in the social security system and

presence of piped water in the house. The importance of parental education

(especially mother's) and household income on child mortality is widely


documented in the literature


The educational attainment of the wife and


husband is here measured by the number of years of school completed by









Chapter


1 for details).


Whether or not a household participate in the social


security system is an indicator of access to public health facilities because
membership in the social security system entitles people to medical services


(Wood and Lovell 1992).


Presence or absence of running water in the house


is an important indicator of housing quality, which has a significant effect on


child mortality (Merrick 1985, Wood and Lovell 1992).


Table 4.1 shows


marked differences in socioeconomic indicators of the three groups:



TABLE 4.1
Social Indicators by Color Group, Metropolitan Sao Paulo, Brazil (1980)*

Total Afro White Asian
Social Indicator (1) (2) (3) (4)


Mother'
Father's


Education**
Education**


Household


Income***


with social Security
with Piped Water


28,773
86.8
81.1


21,588
83.3
68.5


31,276
88.3
86.4


72,227
88.9
98.0


*The data include households with women aged 20-29 years, with at least one
live birth.
**Average years of school completed
**In 1980 Cruzeiros


Afro-Brazilian
three populations. T


women have the lowest educational attainment of the


'he average years of schooling among Afro-Brazilians


(3.2) is about a year below the comparable figure for white women (4.1), and


over two years below that of Asian wnmon (3 51


Tha camo ntfmorn hnlra1c Fnr









among white households is about 45 percent higher than the income earned

by Afro-Brazilians; Asian households, on the other hand, enjoy an income
level that is 335 percent higher than that of Afro-Brazilians and 231 percent


higher than that of whites.


In contrast, the differences in the percent of


households having the social security system is the smallest among the three


color groups; 83.3


of Afro-Brazilian households, 88.3% of white households


and 88.9% of Asian households.
Because the level of mortality of a population or a subpopulation is

determined by the combined effects of such factors as income, housing,
education and access to medical care, I expect to find corresponding
differences in the survival probabilities of children born to white, Asian and


Afro-Brazilian


women.


Advances in indirect techniques of estimating the probability of death
in the early childhood years have greatly enhanced the scope and accuracy of


mortality research.


Traditional measures of the death rate rely on vital


registration statistics.


The alternative approach,


developed by William Brass


(Brass et al.


1968), measures mortality indirectly from survey or census data.


In the Brass method


, the proportion of children surviving to mothers in


different age groups (20-24; 25-29 and 30-34),


correction factor


multiplied by the appropriate


, yields estimates of the probability of death by exact ages 2,


and 5.


In the following,


I estimate child mortality level for


Asians, whites


and Afro-Brazilians, using the Brass method.


A detailed description of the


Brass method by Wood and Lovell (1992) is included in Appendix 4.1.
Table 4.2 shows the estimates of mortality among children born to









two, mortality among Afro-Brazilian children --


116 per thousand -- is the


highest of all three groups (82 and 51 per thousand for whites and Asians


respectively).

Brazilians is


In fact, the probability of death by age two among Afro-

.41 times higher than the comparable figure for white children,


2.27


times higher than the estimate for Asian children.


The same pattern


holds for the probability of death between birth and ages 3 and 5 among the


three groups, i.e.,


of whites


the mortality estimate of Afro-Brazilians is higher than that


, which is in turn higher than that of Asians.


Table 4.2 also presents eo values, the average number of years expected


at birth.


They are calculated from the three estimates of child mortality


using model life tables (e.g.,


Coale and Demeny 1983).


The eO estimates


indicate an expectation of life of 59.14 years for Afro-Brazilian,


years for


whites, and


12 years for Asians.


In other words


based on the child


mortality levels of the sample data,


Asians are expected to live 6.35 more


years than whites, who are, in turn, expected to live 6.63 more years than


Afro-Brazilians.


These measures are interpreted as the life expectancy at birth


associated with the levels of infant and child mortality estimated among the


children born to women


0 to 34 years of age who declare themselves to be of


a given skin color in the census interview.


The mortality differentials shown in


Table 4


.2 raise an important


question: If we control for the major determinants of racial inequality (the


variables presented in


Table 4.1),


do the children of Asian women continue to


experience lower death rates compared to the children born to white or Afro-
Brazilian mothers? If the mother's skin color is no longer statistically









with the mortality of her children, the results indicate that additional factors

are at work.


TABLE 4.2
Measures of Child Mortality by Color Group,
Metropolitan Sao Paulo, Brazil (1980)


Mortality Afro-
Measure* Total Braizilian White Asian


290 .116 .082 .051
390 .126 .087 .054
5%0 .134 .093 .056

e 59.14 65.77 72.12

Mortality Ratio 1.09 1.39 .96 .49


*xo0 is the probability of death between age 0 and exact age
number of years of life expected, associated with the xq0 v


life table).


The mortality ratio is the mean value of the ratio of actual to


x. e0 is the average
alues (south model


expected proportion dead among children of women with at least one live
birth.


To simultaneously control for the several independent variables it is


necessary to apply multivariate techniques.


Rather than relying on mortality


rates for groups of women by age, as in the Brass method noted above, we
need, as a dependent variable, a measure of the mortality experience for each


woman in the sample.


calculating just such a measure.


Trussell and Preston (1982) proposed a method for


Trussell-Preston technique provides a


vl~ /^








population as a whole should be 1.00.


Indeed, the estimate of the average


mortality ratio for metropolitan SAo Paulo is 1.09, as shown at the bottom of


Table 4.


Among Afro-Brazilians,


the mortality ratio is


, indicating that


the actual number of deaths substantially exceeds the expected number.


the other hand


, the mortality ratio of .96 for whites is slightly below the


expected number, and the mortality ratio of .49 for Asians is about half that of


the total population.


In effect, the mortality ratio confirms the racial


differentials in child mortality estimated in terms of


x90 and eO values in


Table 4.2.

The mortality ratio for individual women is of additional value


because it permits the use of regression analysis.


Appendix 4.1,


For the reasons discussed in


Tobit regression procedure is the appropriate in this case.


The results of regressing the mortality ratio on the various social indicators


are given in


Table 4.3.


The model refers to the population of all women 20 to


29 years of age in metropolitan Sao Paulo.


The negative signs for the


coefficients indicate that maternal and paternal levels of education reduce


mortality, as does income, membership in the social security system,


presence of piped water in the home.

The numbers given in parenthe

coefficients are measures of elasticity.


and the


Table 4.3 below the regression


On the basis of these estimates, we can


conclude that a one percent increase in mother's education reduces mortality


13.5 percent.


Father's educational attainment and household income also


reduce mortality


, respectively.


but to a lesser degree, as indicated by elasticities of


Similarly


.095 and


net of the effects of the other variables in the


,




Full Text
THE SOCIOECONOMIC STATUS OF ASIAN BRAZILIANS IN 1980:
A COMPARATION OF ASIANS, WHITES AND AFRO-BRAZILIANS
By
JIRIMUTU
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1994

ACKNOWLEDGEMENTS
I wish to thank Dr. H. Russell Bernard, my advisor, for his persistent
teachings in the scientific approach to anthropological inquiries and
positivistic attitude toward human knowledge. I also appreciate his generous
support, academic and otherwise, and his genuine understanding and belief
in me during the course of my graduate studies. This dissertation would
have been entirely impossible if Dr. Charles H. Wood had not sparked my
interest in demographic studies when I took a seminar on population with
him. Throughout my dissertation research, I have benefited tremendously
from his breadth of knowledge, mastery of computer skills and data analysis
techniques, and friendship. He has my deepest gratitude for mentoring me in
every way possible, even after he moved to another university. My sincere
thanks go to Dr. Paul J. Maganrella for his academic support and personal
friendship, both of which are very important for a foreign graduate student
like me. I thank Dr. Paul L. Doughty for serving on my committee and
offering his wisdom. I appreciate Dr. Barbara Ann Zsembik for joining my
committee and offering her expertise as a demographer. I am extremely
grateful to the Wenner-Gren Foundation for Anthropological Research for
providing me with financial support for the first three years of my graduate
school. Finally, I sincerely thank my wife, Mingxin Zhang, and my son,
Jeffrey Wuhantu, for their unconditional support and love, without which I
could not have completed this dissertation. I dedicate this work to them.

TABLE OF CONTENTS
page
ACKNOWLEDGEMENTS ii
LIST OF TABLES v
LIST OF FIGURES xii
ABSTRACT xiii
CHAPTERS
1 INTRODUCTION 1
Asian Immigrants in the United States 1
Research Design 7
Asian Immigrants in Brazil 11
2 HISTORICAL OVERVIEW OF THE JAPANESE
EXPERIENCE IN BRAZIL 14
Historical Background for the Japanese Migration to Brazil 14
Japanese Immigration to Brazil 16
Social Characteristics and Social Mobility of the
Japanese Immigrants 21
Summary 41
3 FERTILITY DIFFERENTIALS AMONG ASIANS, WHITES
AND AFRO-BRAZILIANS 48
A Brief Review of Literature on Fertility Studies 48
Fertility Differentials among Ethnic/Racial Groups
in Modern States 53
- Fertility Differentials Among Asians, Whites and
Afro-Brazilians 56
Summary 74
4 CHILD MORTALITY DIFFERENTIALS AMONG ASIANS,
WHITES AND AFRO-BRAZILIANS 78
Major Determinants of Mortality and Ethnic/Racial
Differentials in Mortality 78
m

Child Mortality Differentials and Life Expectancy
by Color Group 81
Summary 89
5 EDUCATIONAL ATTAINMENT OF ASIANS, WHITES
AND AFRO-BRAZILIANS 91
School Attendance Rate of Children Ages 6-16 91
Educational Attainment of Men Ages 18-65 113
Educational Attainment of Women Ages 18-65 119
Summary 127
6 OCCUPATIONAL PROFILE OF ASIANS, WHITES AND
AFRO-BRAZILIANS 132
Occupational Profile of Men Ages 18-65 133
Occupational Profile of Women Ages 18-65 158
Summary 179
7 MEAN INCOME OF ASIANS, WHITES AND
AFRO-BRAZILIANS 187
Mean Monthly Income of Men Ages 18-65 188
Mean Monthly Income of Women Ages 18-65 205
Summary 221
8 SUMMARY AND CONCLUSION 230
APPENDICES
A BRAZILIAN RACIAL CATEGORIES AND THE CENSUSES 247
B INDIRECT MEASURES OF CHILD MORTALITY 254
C LOGISTIC REGRESSION WITH SCHOOL ATTENDANCE
RATE OF CHILDREN AGES 6-16 AS THE DEPENDENT
VARIABLE, METROPOLITAN SÁO PAULO, BRAZIL, 1980 258
D OCCUPATIONAL CATEGORIES IN THE 1980 CENSUS 264
REFERENCES 266
BIOGRAPHICAL SKETCH 277
IV

LIST OF TABLES
Table Page
1.1 Distribution of Amarelos Ages 15-65 by Place of Birth and
National Origin, Metropolitan Sao Paulo, Brazil (1980) 13
1.2 Racial Composition of Brazil's Population, 1940-1980 13
2.1 Industry Distribution of Brazilian Males Aged 10 and
Over by Color, 1950 23
2.2 Employment Status of Brazilian males Aged 10 and Over
for All Industries and Agriculture by Color, 1950 24
2.3 Proportion of Farmers Among Japanese Immigrants and
Descendants Aged 10 and Over in Labor Force by Sex,
Brazil, 1958 25
2.4 Occupational Distribution of Japanese Immigrants and
Descendants Aged 10 and Over in Labor Force,
Brazil, 1958 27
2.5 Japanese Immigrants and Descendants Aged 10 and Over
in Labor Force by Industry, Brazil, 1958 28
2.6 A Comparison of Occupational Status of Prewar and
Postwar Non-Farming Japanese Immigrants 30
2.7 Agricultural Production of Japanese Brazilians in Sao Paulo
and Brazil by Crop, 1958 32
2.8 Japanese Immigrants and Descendants Aged 7 and Over
by Level of Education and Residence, 1958 36
2.9 Marital Status of the Japanese Population in Brazil
by Sex and Generation, 1958 37
2.10 Proportion of Traditional Families Among Japanese Heads
of Family by Employment Status for Farmers and
Non-Farmers in Brazil, 1958 39
v

2.11 Proportion of Traditional Families Among Japanese
Farmers and Non-Farmers in Brazil by Value of
Property Owned, 1958 40
3.1 Mean Children Ever Born to Women of 15-49 Years of Age
By Age Group, Metropolitan Sao Paulo, Brazil (1980) 58
3.2 Mean Children Ever Born to Women of 15-49 Years of Age
By Color Group, Metropolitan Sao Paulo, Brazil (1980) 59
3.3 Mean Children Ever Born to Women of 15-49 Years of Age
By Age and Color Groups, Metropolitan Sao Paulo,
Brazil (1980) 60
3.4 Mean Children Ever Born to Women of 15-49 Years of Age
By Education, Age and Color Groups, Metropolitan
Sao Paulo, Brazil (1980) 62
3.5 Mean Children Ever Born to Women of 15-49 Years of Age
By Income, Age and Color Groups, Metropolitan
Sao Paulo, Brazil (1980) 63
3.6 Mean Children Ever Born to Women of 15-49 Years of Age
By Residence, Age and Color Groups, Metropolitan
Sao Paulo, Brazil (1980) 65
3.7 Mean Children Ever Born to Women of 15-49 Years of Age
By Residence, Education and Color Groups,
Metropolitan Sao Paulo, Brazil (1980) 66
3.8 Mean Children Ever Born to Women of 15-49 Years of Age
By Residence, Income and Color Groups,
Metropolitan Sao Paulo, Brazil (1980) 68
3.9 Children Ever Born to Women Aged 20-49 Regressed on
Age, Residence, Education, Income and Color 71
4.1 Social Indicators by Color Group, Metropolitan Sao Paulo,
Brazil (1980) 82
4.2 Measures of Child Mortality By Color Group, Metropolitan
Sao Paulo, Brazil (1980) 85
4.3Mortality Ratio for Children Ever Born to Women 20-29
Years of Age Regressed on Social Indicators,
vi

Metropolitan Sao Paulo, Brazil (1980) 87
5.1 Number of Children Ages 6-16 and the Percent in School
by Age, Metropolitan Sao Paulo, Brazil (1980) 93
5.2 Number of Children Ages 6-16 and the Percent in School
by Color Group, Metropolitan Sao Paulo, Brazil (1980) 93
5.3 Distribution of Children Ages 6-16 and the Percent in School
by Income Level, Metropolitan Sao Paulo, Brazil (1980) 94
5.4 Children Ages 6-16 and the Percent in School by Residence,
Metropolitan Sao Paulo, Brazil (1980) 95
5.5 Number of Children Ages 6-16 and the Percent in School
by Parents' Education, Metropolitan Sao Paulo,
Brazil (1980) 96
5.6 Number of Children Ages 6-16 and the Percent in School
by Sex, Metropolitan Sao Paulo, Brazil (1980) 97
5.7 In-School Rate of Children Ages 6-16 and the Percent
in School by Color Groups, Metropolitan Sao Paulo,
Brazil (1980) 98
5.8 In-School Rate of Children Ages 6-16 by Income and Color,
Metropolitan Sao Paulo, Brazil (1980) 100
5.9 In-School Rate of Children Ages 6-16 by Region, Income
and Color, Metropolitan Sao Paulo, Brazil (1980) 102
5.10 Logistic Regression of In-School Rate of Children Ages
6-16 on Mother's and Father's Education, Household
Income, Residence and Color by Age, Metropolitan
Sao Paulo, Brazil (1980) 108
5.11 Mean Years of Schooling for Men Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980) 114
5.12 Mean Years of Schooling for Men Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980) 114
5.13 Mean Years of Schooling for Men Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980) 115
5.14Mean Years of Schooling for Men Ages 18-65 by Age and Color,
vn

Metropolitan Sao Paulo, Brazil (1980) 116
5.15 Mean Years of Schooling for Men Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980) 117
5.16 Mean Years of Schooling for Men Ages 18-65 by Income and
Color, Metropolitan Sao Paulo, Brazil (1980) 118
5.17 Mean Years of Schooling for Women Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980) 120
5.18 Mean Years of Schooling for Women Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980) 121
5.19 Mean Years of Schooling for Women Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980) 121
5.20 Mean Years of Schooling for Women Ages 18-65 by Age
and Color, Metropolitan Sao Paulo, Brazil (1980) 124
5.21 Mean Years of Schooling for Women Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980) 126
5.22 Mean Years of Schooling for Women Ages 18-65 by Income
and Color, Metropolitan Sao Paulo, Brazil (1980) 126
6.1 Occupational Distribution of Men Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980) 134
6.2 Top Five Occupations in the Category of Unskilled/Personal
Service by Color, Metropolitan Sao Paulo, Brazil (1980) 135
6.3 Occupational Distribution of Men Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980) 137
6.4 Occupational Distribution of Men Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980) 138
6.5 Occupational Distribution of Men Ages 18-65 by Income,
Metropolitan Sao Paulo, Brazil (1980) 140
6.6 Occupational Distribution of Men Ages 18-65 by Education,
Metropolitan Sao Paulo, Brazil (1980) 142
6.7A Comparison of White vs. Blue Collar Occupations of Men
Ages 18-65 by Residence and Color, Metropolitan Sao
viu

Paulo, Brazil (1980)
144
6.8 Occupational Distribution of Men Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980) 145
6.9 Proportion of White vs. Blue Collar Occupations of Men
Ages 18-65 by Age and Color, Metropolitan Sao Paulo,
Brazil (1980) 147
6.10 Occupational Distribution of Men Ages 18-65 by Age
and Color, Metropolitan Sao Paulo, Brazil (1980) 149
6.11 Occupational Distribution of Men Ages 18-65 by Income
and Color, Metropolitan Sao Paulo, Brazil (1980) 151
6.12 Occupational Distribution of Men Ages 18-65 by Education
and Color, Metropolitan Sao Paulo, Brazil (1980) 157
6.13 Occupational Distribution of Women Ages 18-65 by Color
Group, Metropolitan Sao Paulo, Brazil (1980) 159
6.14 Occupational Distribution of Women Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980) 160
6.15 Occupational Distribution of Women Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980) 162
6.16 Occupational Distribution of Women Ages 18-65 by Income
Level, Metropolitan Sao Paulo, Brazil (1980) 164
6.17 Occupational Distribution of Women Ages 18-65 by Education,
Metropolitan Sao Paulo, Brazil (1980) 166
6.18 Occupational Distribution of Women Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980) 168
6.19 Occupational Distribution of Women Ages 18-65 by Age
and Color, Metropolitan Sao Paulo, Brazil (1980) 171
6.20 Occupational Distribution of Women Ages 18-65 by Income
and Color, Metropolitan Sao Paulo, Brazil (1980) 175
6.21 Occupational Distribution of Women Ages 18-65 by Education
and Color, Metropolitan Sao Paulo, Brazil (1980) 178
7.1 Mean Monthly Income of Men Ages 18-65 by Color Group,
IX

Metropolitan Sao Paulo, Brazil (1980) 189
7.2 Mean Monthly Income of Men Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980) 190
7.3 Mean Monthly Income of Men Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980) 190
7.4 Mean Monthly Income of Men Ages 18-65 by Education,
Metropolitan Sao Paulo, Brazil (1980) 191
7.5 Mean Monthly Income of Men Ages 18-65 by Occupation,
Metropolitan Sao Paulo, Brazil (1980) 193
7.6 Mean Monthly Income of Men Ages 18-65 by Age
and Color, Metropolitan Sao Paulo, Brazil (1980) 194
7.7 Mean Monthly Income of Men Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980) 196
7.8 Mean Monthly Income of Men Ages 18-65 by Education
and Color, Metropolitan Sao Paulo, Brazil (1980) 197
7.9 Mean Monthly Income of Men Ages 18-65 by Occupation
and Color, Metropolitan Sao Paulo, Brazil (1980) 199
7.10 Monthly Income of Men Ages 18-65 Regressed on Age,
Education, Residence and Color, Metropolitan
Sao Paulo, Brazil (1980) 204
7.11 Mean Monthly Income of Women Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980) 205
7.12 Mean Monthly Income of Women Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980) 206
7.13 Mean Monthly Income of Women Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980) 207
7.14 Mean Monthly Income of Women Ages 18-65 by Education,
Metropolitan Sao Paulo, Brazil (1980) 208
7.15 Mean Monthly Income of Women Ages 18-65 by Occupation,
Metropolitan Sao Paulo, Brazil (1980) 209
7.16Mean Monthly Income of Women Ages 18-65 by Age
x

and Color, Metropolitan Sao Paulo, Brazil (1980) 211
7.17 Mean Monthly Income of Women Ages 18-65 by Residence
and Color, Metropolitan Sao Paulo, Brazil (1980) 212
7.18 Mean Monthly Income of Women Ages 18-65 by Education
and Color, Metropolitan Sao Paulo, Brazil (1980) 213
7.19 Mean Monthly Income of Women Ages 18-65 by Occupation
and Color, Metropolitan Sao Paulo, Brazil (1980) 215
7.20 Monthly Income of Women Ages 18-65 Regressed on
Age, Education, Residence and Color, Metropolitan
Sao Paulo, Brazil (1980) 220
XI

LIST OF FIGURES
Figure Page
5.1 In-School Rate of Children Ages 6-16 by Age and Color Groups,
Metropolitan Sao Paulo, Brazil (1980) 99
5.2 In-School Rate of Urban Children Ages 6-16 by Income and
Color Group, Metropolitan Sao Paulo, Brazil (1980) 103
5.3 In-School Rate of Rural Children Ages 6-16 by Income and
Color Group, Metropolitan Sao Paulo, Brazil (1980) 104
5.4 Effects of Father's and Mother's Education on Children's
In-School Rate, Metropolitan Sao Paulo, Brazil (1980) 109
5.5 Effects of Household Income and Urban Residency
on Children's In-School Rate, Metropolitan
Sao Paulo, Brazil (1980) Ill
5.6 The Odds of Afro-Brazilian and Asian Children Being in
School Against Those of White Children,
Metropolitan Sao Paulo, Brazil (1980) 112
5.7 Mean Years of Schooling by Sex and Color Group,
Metropolitan Sao Paulo, Brazil (1980) 122
5.8 Mean Years of Schooling by Sex and Age Group,
Metropolitan Sao Paulo, Brazil (1980) 122
5.9 Mean Years of Schooling by Sex and Residence,
Metropolitan Sao Paulo, Brazil (1980) 123
xu

Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
THE SOCIOECONOMIC STATUS OF ASIAN BRAZILIANS IN 1980:
A COMPARISON OF ASIANS, WHITES AND AFRO-BRAZILIANS
By
Jirimutu
April 1994
Chairman: H. Russell Bernard
Major Department: Anthropology
I use the 3% sample data of Metropolitan Sao Paulo from the 1980
Brazilian Census to examine the socioeconomic standing of Asian Brazilians,
relative to whites and Afro-Brazilians in Brazil. Operationally, the
socioeconomic standings of the three color groups are measured in terms of
fertility level, child mortality level and the life expectancy rate associated with
it, educational attainment (school attendance rate of children ages 6-16 and
years of school completed by men and women age 18-65), occupational profile
and mean monthly income of men and women ages 18-65. The results of
these measurements indicate that in 1980 Asian Brazilians lead both whites
and Afro-Brazilians in socioeconomic standing, even after controlling for the
independent variables. Their success is largely attributable to the favorable
social and economic situations they have experienced since their first arrival
Xlll

in Brazil, the presence of ethnic enclaves and economies, ownership of small
business, continued heavy investment in education through several
generations, the family characteristics that facilitate stability and capital
accumulation, and the cultural values, such as hard work, industriousness,
emphasis on education, obligation and loyalty to family and kin group. This
shows that Asian immigrants in Brazil have experienced similar, if not more,
success in upward social mobility as have Asian immigrants in the United
States.
xiv

CHAPTER 1
INTRODUCTION
Asian Immigrants in the United States
Asian immigrants in the United States have long attracted the
attention of social scientists because it is widely recognized that they have
done very well, over time, compared to many other immigrant groups. They
are said to have achieved parity with or even surpassed the majority whites
in socioeconomic standing (Bell, 1985; Bonacich & Modell, 1980; Chiswick,
1980; Hirschman, 1983; Hirschman & Wong, 1981; Hirschman & Wong, 1984;
Hirschman & Wong, 1986; Jiobu, 1976; Jiobu, 1990; Kitano, 1974; Kitano &
Daniels, 1988; Montero, 1981; Montero & Tsukashima, 1977; Nee & Sanders,
1985; Nee & Wong, 1985; Petersen, 1971; Rose, 1985; Wong, 1980; Wong, 1982).
Asian Americans have been called a "model minority" (Newsweek,
1982) and "America's super minority" (Ramirez, 1986), and hailed as
"America's greatest success story" (Bell, 1985). Indeed, Asian Americans,
especially Japanese Americans and Chinese Americans, have achieved great
success in terms of labor force participation, income and education. In 1979,
95% of Asian Americans (the six largest groups within Asian Americans
which include Chinese, Filipino, Japanese, Asian Indians, Korean and
Vietnamese) had a median family income of $23,600, compared to the average
of $20,800 for white families (the national average was $19,900); 35% of Asian
Americans age 25 and older had graduated from college, compared to 17% of
white adults (Gardner, et al., 1985). The 1990 census revealed further
1

2
advances in income and education for Asian Americans. Their median
family income in 1989 was $41,583 compared with the national average of
$35,225, and 38% of Asian Americans had graduated with a bachelor’s degree
or higher, compared with 20% of the total population (Bureau of the Census,
1993).
However, the Asian success story has been exaggerated to some extent
because the statistics on median family income does not reflect the entire
picture of the socioeconomic status of Asian Americans. As many researchers
have noted, the higher median family income of Asian Americans is mainly
due to their larger average family size (3.8 persons for Asian American
families vs. 3.2 persons for all U.S. families in 1989), higher proportion of
families with three or more workers (19.8% for Asian American vs. 13% for
the total population), geographical locations (Asian Americans are highly
concentrated in California and New York, where the average income is
higher, relative to the rest of the country), and higher educational attainment.
In fact, the mean personal income of Asian Americans in 1989 was slightly
lower than the national average: Per capita income for Asian Americans in
1989 was $13,806, compared with the national per capita income of $14,143
(Bureau of the Census, 1993). Nonetheless, there is no doubt that compared
to many other immigrant groups, most Asian Americans have overcome the
disadvantages that immigrant groups typically confront in the United States.
Social scientists have devoted considerable effort to understanding the
factors that explain the relative success of Asian immigrants. Some have
stressed the role of "middleman minorities" for various Asian groups in the
labor market and occupational concentration (Bonacich, 1973; Bonacich and
Modell, 1980; Kitano, 1974). Some have emphasized the importance of small
business ownership, ethnic economy and ethnic enclaves in their upward

3
social mobility (Li, 1977; Lyman, 1977; Nee & Sanders, 1985; Takaki 1989).
Others have argued that the strength of kinship and family ties, and the
emphasis on education, hard work and sacrifice for children are mostly
responsible for their success (Kitano, 1969; Newsweek, 1982; Petersen, 1971;
Schwartz, 1971).
The first two arguments are structural explanations while the third
type is cultural. The structural arguments mainly examine the relationship
between the minority in question and the society at large in terms of
occupational structure, economic status and the role of ethnic organizations
in the economic, social and political arena. Cultural arguments either focus
on the cultural characteristics of the minorities themselves or seek
similarities between the cultural values of the dominant society and the
minorities and to attribute the success of minorities to these cultural traits.
Nee and Wong (1985:282) argued that both the cultural and structural
explanations were ahistorical because they "fail to capture the dynamic nature
of immigrant groups as they respond to historical situations and changing
economic structures." For them, the cultural argument was a form of circular
reasoning and failed to include two important variables that were essential
for the upward mobility of immigrants and their descendants; 1)
"immigrants' willingness to endure hardship for economic gains" and 2) "the
socioeconomic background at the time of immigration" (1985:283).
They maintained that the cultural characteristics of Asian Americans
reflected the influence of neo-Confucianism, which emphasized "the
legitimacy of status attainment through education and membership and
obligation to an interdependent family and kinship unit" (1985:284), rather
than Protestant values, as suggested by Petersen (1971) and Kitano (1969).
Meanwhile, they regarded the socioeconomic background of immigrants at

4
the time of immigration as crucial for "the creation of opportunities for
upward mobility." Without the necessary human capital to generate
resources, the cultural characteristics of immigrants would have much
smaller impact on their socioeconomic standing. Therefore, both the cultural
characteristics and the socioeconomic background of immigrants were
essential in understanding their success in the new country.
Nee and Wong (1985:286) also criticized the structural argument for
"failing to deal with the changing economic condition of the expanding
market economy in North America." They maintained that the
socioeconomic attainments of immigrant and ethnic groups are the result of
"continuous change and transformations of both cultural attributes and labor
market conditions" (1985:287). The formation of household production units,
they argued, facilitated the social and economic mobility of Japanese
Americans. The profit from the household production units in turn served
as the capital for further development of small businesses. Nee and Wong
particularly stressed the importance of the family bond in the socioeconomic
attainment of Asian Americans:
Cheap labor generated by household units allowed these ethnic
businesses to be competitive in the dominant society; formation
of family businesses coincided with the development of an
enclave economy, which opened ethnically controlled avenues
for socioeconomic mobility, and provided a stable environment
for family life and the socialization and education of an
upwardly mobile second generation. (1985:287-288)
Nee and Wong (1985) used a "supply-demand" perspective, which
treated culture and structure as part of an integrated explanation, for the
differential timing of socioeconomic attainment for Chinese and Japanese
immigrants in California. They placed the cultural attributes of immigrants

5
and the socioeconomic background prior to and after immigration on the
supply side, and put the structural constraints and opportunity structures
created by the development of the capitalist economy on the demand side.
Theories of middleman minorities and of ethnic enclaves are often
applied in the literature of Asian Americans. Drawing on earlier works on
ethnic group relations, such as Blalock (1967), Bonacich (1973) and Bonacich
and Modell (1980) argued that certain minorities in multiethnic societies
occupy a middle status between the dominant group and the subordinate
groups, acting as buffers between elites and masses. These middleman
minorities usually occupy an intermediate niche in the economic system and
tend to concentrate in certain occupations, such as traders, moneylenders and
shopkeepers. Middleman minorities therefore provide goods and services to
both the elites and the masses. Because of their unique social position of
belonging nowhere, they tend to develop strong in-group solidarity and form
their own separate and distinct community. Two often-cited examples of
middleman minorities are Jews in feudal and early modern Europe and
Chinese in Southeast Asia (also called overseas Chinese) (Bonacich and
Modell, 1980).
Bonacich and Modell (1980) applied the theory of middleman
minorities to the experience of Japanese Americans. They argued that
Japanese Americans, particularly the issei, or the first generation, exhibited
many of the traits of a middleman minority; they "formed a highly organized,
internally solidary community," were "concentrated in self-employment and
nonindustrial family businesses" and "faced severe hostility from the
surrounding society" (1980:35).
Like the middleman minority theory, the ethnic enclave theory
examines the structural incorporation of immigrants into the host economy.

6
"Ethnic enclaves are a distinctive economic formation, characterized by the
spatial concentration of immigrants who organize a variety of enterprises to
serve their own ethnic market and the general population" (Portes and Bach,
1985:203). The presence of immigrants with sufficient capital to create new
opportunities for economic growth and an extensive division of labor are two
fundamental traits of economic enclaves. Ethnic enclaves also require a large
number of immigrants with business skills and a large pool of low-wage
labor. The Cubans in Miami and Koreans in Los Angeles are contemporary
examples of ethnic enclaves (Portes and Manning, 1986). Some ethnic groups
are highly entrepreneurial, possess capital, and therefore develop ethnic
economies that consist of many small businesses, some of which interface
with the majority economy (Portes and Jensen, 1987). Within this enclave,
ethnic workers do not have to compete with the majority workers and are
usually not directly subject to discrimination by the dominant group.
Therefore, they can climb the socioeconomic ladder relatively free of racial
and ethnic discrimination.
This does not mean that everyone is equal in an ethnic enclave. On the
contrary, ethnic employers exploit ethnic workers, especially recent arrivals,
and make huge profit from cheap labor. On the relationship between the
employers and workers in ethnic enclaves, Jiobu (1990:171) stated that "to the
extent that workers rely on enclave employment, their income, and by
implication their socioeconomic standing, will be suppressed. But on the
other hand, suppressing the income of workers raises the income (and
socioeconomic standing) of ethnic employers."
The debate on cultural vs. structural explanations for the relative
success of Asian Americans continues, but there are important aspects of the
issue that have received very little attention. One such aspect is the need for

7
more comparative research on the fate of Asian immigrants in other
countries, such as Brazil, a country that has received a large contingent of
Asians (mostly Japanese). In contrast to the vast literature on Afro-Brazilians,
the literature, especially recent studies, on the Asian population in Brazil is
remarkably small. In this dissertation, I examine whether Asian immigrants
have experienced the same socioeconomic success in Brazil as they have in
the United States. Specifically, I compare Asian Brazilians to whites and
Afro-Brazilians in Brazil in terms of quality of life.
Research Design
Dependent Variables
The data for this study are the 3% sample of Metropolitan Sao Paulo
from the 1980 Brazilian Census. Conceptually, quality of life can be measured
by success or failure at various crucial periods of the life course: giving birth,
surviving childhood, acquiring an education, finding a job and getting paid.
Operationally, the dependent variables in this study are fertility level (mean
children ever born to women ages 15-49), child mortality rate and the life
expectancy rate associated with it, school attendance rate for children ages 6-16
and educational attainment of men and women ages 18-65, occupational
profile of men and women ages 18-65, and mean monthly income of men
and women ages 18-65. Taken together, these variables provide us with a
measure of the quality of life for each of the three color groups (white, Afro-
Brazilian and Asian Brazilians) in Brazil.

8
Color Groups
The 1980 Brazilian census used four categories for racial classification;
branco, pardo, preto and amarelo, or white, brown, black and yellow. In this
study, I use three color groups (whites, Afro-Brazilians and Asian Brazilians),
instead of the four in the 1980 census. In other words, I have combined the
census categories of black (preto) and brown (pardo) into a single category,
called "Afro-Brazilians," and have replaced "yellow" (amarelo) with the term
"Asian Brazilians" or simply "Asians."
My decision of combining the categories of brown and black into a
single category is based on two things; the focus of this study and the findings
of a number of studies on racial inequalities in Brazil (Hasenbalg, 1985;
Hasenbalg and Huntington, 1982; Lovell, 1989; Silva, 1978; Silva, 1985; Wood,
1990; Wood and Carvalho, 1988; Wood and Lovell, 1989; Wood and Lovell,
1992). First, since the focus of this study is Asian Brazilians, I could have
compared them to the rest of the population as a whole or to all of the racial
categories used in the 1980 census. In my view, though, the position of Asian
Brazilians in Brazilian society is most clearly shown by comparing them to
whites and Afro-Brazilians since we know from the literature that there are
significant differences among these groups. Second, the above studies found
that although there are differences between blacks and mulattos in
socioeconomic standing, they are much closer to one another than to whites
and there are substantial differences between whites and nonwhites. In other
words, there is a major dividing line between whites and non-whites. Thus,
to compare Asians to the rest of the population would ignore the social reality
of the race relations in Brazil. I discuss the debate on the racial categories in
Brazil and the Brazilian censuses in Appendix A.

9
Independent Variables
In addition to color group, the most important independent variables
in this study are age, sex, residence, educational level, and income level. In
most chapters, I treat age as an ordinal variable consisting of three categories
(18-25, 26-39 and 40-65 years old). This eliminates the general effect of age on
the dependent variables. In regression analyses, age is treated as an interval
variable. Residence is a dichotomous variable: urban or rural.
Educational attainment is measured by years of schooling completed.
In descriptive analyses, I generally treat this as an ordinal variables consisting
of five levels: 1) no schooling at all, 2) one to four years of schooling, 3) five to
eight years of schooling, 4) nine to eleven years of schooling and 5) twelve or
more years of schooling. However, years of schooling is treated as an interval
variable in regression analyses.
Mean monthly income refers to the sum of either household or
individual income from different sources, such as occupation, income in
kind, retirement (social security), rent, gifts, capital and others, during the
period of twelve months preceding the census. Based on the minimum wage
in 1980 (one minimum wage = 4,150 cruzeiros), mean monthly income is
classified into four levels in descriptive analyses: 1) up to one minimum
wage (zero to 4,150 cruzeiros), 2) between one and two minimum wages
(4,151-8,300 cruzeiros), 3) between two and three minimum wages (8,301-
12,450 cruzeiros), 4) above three minimum wages (above 12,450 cruzeiros).
However, in regression analyses mean monthly income is treated as an
interval variable, with one minimum wage constituting one unit.

Organizations of the Chapters
Chapter 2 provides an historical overview of Japanese migrations to
Brazil and of the Japanese experience in Brazil from their arrival at the turn
of the century to the late 1950s. Chapter 3 starts with a review of fertility
theories and of racial/ethnic differentials in fertility. I then examine the
fertility differences by color, age, educational level, income level and
residence before comparing the fertility differentials among Asians, whites
and Afro-Brazilians, controlling for the other variables. Finally, I conduct a
multivariate regression analysis to examine the association of fertility and the
other variables.
In Chapter 4, I discuss major determinants of mortality and
racial/ethnic differences in mortality in multiethnic societies. Then, I
describe some key socioeconomic indicators of Asian, white and Afro-
Brazilian women and use indirect measures to calculate child mortality rate
for the three color groups. On the basis of the mortality level for each group, I
calculate the life expectancy rate for each of the three groups and discuss the
implications of these rates. Finally, I examine the association between the
major socioeconomic indicators and child mortality, using the Tobit
regression procedure.
Chapter 5 has three sections. The first section compares Asian, white
and Afro-Brazilian children ages 6-16 in terms of in-school rate by age, sex,
residence, parents' educational level and income level. I then use logistic
regression to measure the effects of these variables on racial differences in
school attendance rate. The second section describes differences in
educational attainment of men ages 18-65 by color, age, residence and mean
monthly income and the effects of these independent variables on the racial

differences in educational attainment. In the third section, I repeat the same
analysis for the measurement of educational attainment of women ages 18-65.
In Chapter 6,1 describe occupational profiles of men and women ages
18-65 separately by color, age, residence, educational level and income level
and the effects of these variables on racial differences in occupational
distribution.
Chapter 7 describes mean monthly income of men and women
separately by age, residence, occupation and educational level. I examine the
racial differences in mean income, controlling for the other variables.
In Chapter 8, the concluding chapter, I summarize the main findings of
the study and discuss the implications of my findings in the light of relevant
literature on the experience of Asian immigrants in the United States.
Asian Immigrants in Brazil
Amarelo has been used as one of the four racial categories in the
Brazilian Censuses since 1940, and there is little ambiguity as to whom it
refers. Amarelo is designated for people with yellow skin color, who are
either immigrants from Asia or their descendants. Unlike other racial
categories, there has been very little movement in and out of amarelo. This
is probably because of Asians’ distinct physical features and their lack of
intermarriage with other racial groups. Though they comprise less than one
percent of the total population in Brazil, the amarelos are a very stable group
in terms of racial classification.
Although there are no accurate data on the composition of amarela
(people of Asian origin in Brazil) by national/ethnic origin, both historical

records and recent estimates indicate that most of them are of Japanese
descent (Dwyer and Lovell, 1990; Suzuki, 1981; Tsuchida, 1978). For instance,
of 242,320 amarelos censused in 1940, 99% were Japanese and only 1% were
Chinese (Tsuchida, 1978). At the time, Japanese and Chinese were the only
two groups to which the category of amarelo was applied.
By 1980, not much had changed. I examined the data on the place of
birth and national origin of Asians (amarelo) aged 15-65, using the 3% sample
data of Sao Paulo from the 1980 Brazilian Census. The overwhelming
majority of Asians in Brazil are still either Japanese immigrants or their
descendants. The data show that 67.7% of Asians in the sample are Brazilians
by birth, 6.8% are naturalized Brazilians and 25.6% are foreign nationals. Of
those who are Brazilian citizens by birth, 92.1% were born in Sao Paulo,
followed by 5.6% from Parana and 2.2% from other places. Meanwhile, of
Asians who were born in foreign countries, 88.1% are from Japan, followed by
4.7% from Korea, 4.5% from China and the remaining 2.6% from other
countries (see Table 1.1). Therefore, we can say with certainty that amarelos,
or "yellow people," are predominantly of Japanese descent, and the Japanese
experience in Sao Paulo constitutes the major part of Asian experience in
Brazil.
The percent of amarelos in the Brazilian population was steady at 0.6%
during the 1940s and 1950s. It increased slightly to 0.7% from the 1960s to the
1980s (see Table 1.2). According to the 1980 Brazilian census, the total
population of amarelos is 673,000. Throughout this study, I will refer to
amarelos as Asian-Brazilians or simply Asians, which, I think, is a more
appropriate term. Asian-Brazilians are highly concentrated in the state of Sao
Paulo. The historical reasons for this are described in Chapter 2. In fact, in
1980 more than 75% of the total Asian population of Brazil resided in Sao

Paulo and comprised 2.3% of the state's total population (FIBGE 1981). That is
why I chose the sample data of Sao Paulo to study Asian Brazilians, and their
relationships to whites and Afro-Brazilians.
Table 1.1
Distribution of Amar el os Ages 15-65 by Place of Birth and National Origin,
Metropolitan Sao Paulo, Brazil (1980)
National Origin
(%)
Place of Birth
%
N
Brazilian
Naturalized
Foreign
Brazil
Sao Paulo
92.1
3,492
92.1
—
Parana
5.6
214
5.6
—
Other
2.2
85
2.2
—
Total
100.0
3,791
100.0
—
Foreign
Japan
88.1
1,603
—
19.3
80.7
Korea
4.7
86
—
14.0
86.0
China
4.5
82
—
54.9
45.1
Other
2.6
48
29.2
70.8
Total
100.0
1,819
20.9
79.1
Total
100.0
5,610
67.6
6.8
25.6
Source: Weighted 3% sample data of Metropolitan
Census.
Sao Paulo, 1980 Brazilian
Table 1.2
Racial Composition of Brazil's Population,
1940-1980
Race
1940
1950
1960
1980
N
%
N
%
N
%
N
%
White
26,172
63.5
32,028
61.7
42,838
61.0
64,540
54.2
Brown
8,744
21.2
13,786
26.5
20.706
29.5
46,233
38.8
Black
6,036
14.6
5,692
11.0
6,117
8.7
7,047
5.9
Yellow
242
0.6
329
0.6
483
0.7
673
0.7
Missing
42
0.1
108
0.2
47
0.1
517
0.4
Total
41.236
100.0
51.944
100.0
70.191
100.0
119.011
100.0
Figures are in thousands.
Source: Demographic Censuses 1940, 1950, 1960, 1980

CHAPTER 2
HISTORICAL OVERVIEW
OF THE JAPANESE EXPERIENCE IN BRAZIL
Historical Background for the Japanese Migration to Brazil
The overseas migration of Japanese did not start until the Meiji
Restoration of 1868. After that, industrialization and urbanization during the
Meiji Era (1868-1912) led to massive overseas migration in the late nineteenth
and early twentieth centuries. Urbanization encroached on agricultural
families and wound up depriving them of access to their land (Ito-Adler,
1987). Rapid population growth in the rural areas, which exceeded the
industrial growth, also contributed to the massive migration of farmers both
to urban areas in Japan and overseas. Some analysts (Tsuchida, 1978; Reichl,
1988) argued that the Japanese government considered overseas migration as
a viable option for the increasing problem of surplus rural population.
The first important destinations outside Asia of Japanese emigrants
were Australia (1883), Hawaii (1885) and Canada (1891). Reichl (1988:22)
wrote, "only those Anglo-Saxon countries were sanctioned for emigration
prior to the Russo-Japanese War in 1905 because they 'offered better economic
opportunities than other countries of immigration' (Tsuchida, 1978:27)."
Japanese emigration to South America did not start until 1903, when
they first came to Peru, Mexico and Argentina. Japanese immigration to
Brazil began late in 1908, after several years of negotiation between the state of

1 5
Sao Paulo and a number of private Japanese emigration companies.
However, Brazil soon became the most important destination for Japanese
immigrants: they became the second largest group (16.8%) of all immigrant
groups to Brazil during the period from 1924-1941, only after the Portuguese
immigrants (33.1%). In fact, by 1938 the Japanese population in Brazil grew to
95,116, which was the second largest overseas Japanese population, after that
in Manchuria (233,842), then a colony of Japan (Normano and Gerbi, 1943).
For the period 1950-1955, the Japanese population in Brazil was estimated at
373,000, making Brazil the country that had the largest Japanese population
outside of Japan, followed by the United States (326,376) (Fujii and Smith,
1959). By 1968, the total number of the Japanese and their descendants in
Brazil was estimated at more than 615,000, which was 50% of all Japanese
immigrants and their descendants residing in foreign countries. By then, the
United States was a distant second (Sims, 1972).
The serious labor shortage and underpopulation in Brazil in the late
nineteenth and early twentieth centuries were other major factors in the
large-scale emigration of Japanese to Brazil. Smith (1972:118) cited two major
motivating forces of the Brazilian government for seeking immigrants. The
first was "the creation of a small-farming class in the population." The
second was "the ensuring of what Brazil's upper classes considered an
adequate and cheap labor supply to perform the manual work on the coffee,
cotton, and sugar plantations of the nation," after the abolition of slavery in
1888.
The Brazilian government preferred Europeans to Asiatic people,
therefore European immigrants (mainly from Italy, Portugal and Germany)
composed most of the agricultural laborers who came to Brazil during the last
decade of the nineteenth century and the first two decades of the twentieth

century. However, several events in Europe and Japan at the turn of the
century had major impacts on the immigration wave to Brazil.
In 1902 the Italian government, in response to reports of mistreatment
of Italian colonos on plantations in Sao Paulo, temporarily banned the
subsidized migration of Italian laborers to Brazil. Although Italian laborers
continued to come in small numbers following the ban, they were far too few
to satisfy the growing demand for rural labor on the plantations of Sao Paulo
(Holloway, 1980).
In 1888, Australia prohibited Japanese immigration. There was also
growing anti-Japanese sentiment in North America and the "Gentlemen's
Agreement" between the United States and Japan in 1907 limited
immigration from Japan severely (Reichl, 1988). Thus, a severe labor
shortage in Brazil, lack of access to Australia and the U.S., and Japan's
increasingly overcrowded rural areas created a perfect climate for Japanese
migration to Brazil. As Normano and Gerbi (1943:45) described it, "Japan's
land hunger coincided with Brazil's population hunger."
Japanese Immigration to Brazil
Japanese migration to Brazil can be separated into four time periods,
according to the volume and nature of migration, and characteristics of
immigrants: 1) 1908-1923, 2) 1924-1941, 3) 1952-1958, 4) 1959-late 1960s.
The Period 1908-1923
The beginning period from 1908-1923 was characterized by the partial
subsidy provided by the state of Sao Paulo to the immigrants to help cover

17
their maritime passage. On the other hand, private Japanese companies were
mostly responsible for the emigration business and the Japanese government
primarily played a coordinating role for most of the time. The volume of
immigrants during this period was relatively small except the years 1917-1919.
The majority of immigrants were farmers in family units, as was required by
the Brazilian government.
The first group of Japanese immigrants, consisting of 781 individuals
(158 families), arrived by ship in the port of Santos, Sao Paulo, in 1908. They
came as colonos (contract laborers) under a contract between Japan and the
state of Sao Paulo. During the next fifteen years, Japanese immigrants
continued to come, though in small numbers. The total number of Japanese
immigrants from 1908 to 1923 was 32,266, constituting only 2.5% of all the
immigrants to Brazil for the time period (Fujii and Smith, 1959). However,
the period 1917-1919 was the peak for the influx of Japanese immigrants,
representing 12.9%, 28.3% and 8.4%, respectively, of all the immigrants to
Brazil for the three years. This dramatic increase in the number of
immigrants to Brazil was mostly due to the establishment of the Kaigai Kogyo
Kabushik (Overseas Development Company), or K.K.K. Compared to other
groups, the number of Japanese immigrants was relatively small, but their
successful beginning was very important to the future of Japanese emigration
to Brazil.
The Period 1924-1941
This period witnessed a steady increase in the number of immigrants
because of Japan's increased governmental financial support and
involvement in overseas emigration. This period also marked a shift of

Japanese emigration to Portuguese-speaking America, mainly Brazil, and
away from the earlier destinations in North America. Both Normano and
Gerbi (1943) and Fujii and Smith (1959) noted that in 1924, the Emigration
Council, headed by Minister of Foreign Affairs Shidehara, sent a new mission
to South America to explore possible destinations for large-scale emigration.
As a result, the Japanese government decided to concentrate her emigration
effort on Brazil and soon established the Overseas Development Company, a
centralized and highly rationalized management of emigration to Brazil. The
Japanese government also provided subsidies to the company for its
emigration efforts.
Meanwhile, in 1923 the state of Sao Paulo stopped the policy of giving
subsidies to immigrants from Japan. The proportion of Japanese immigrants
(of all immigrants to Brazil) increased dramatically from 2.8% in 1924 to
53.2% in 1933, and then steadily decreased to 5.6 % in 1941, when World War
II broke out. This slowdown in the pace of Japanese immigration was also
caused by the Immigration Legislation of 1934 in Brazil, which aimed to
restrict the entry of immigrants annually to two percent of the total entries of
the previous fifty years. However, the percentage of Japanese among all
immigrants during this period was 16.8%, much higher than the 2.5% in the
previous period, due to the decline of European immigrants. The total
number of Japanese immigrants to Brazil during the 33 years from 1908 to
1941 was estimated at 190,000 (Fujii and Smith, 1959).
The Period 1952-1958
During the ten years from 1942 to 1952, Japanese immigration to Brazil
virtually stopped mainly because anti-Japanese sentiment was very high in

Brazil (due to Japan's involvement in the war) and also because Brazil
adopted a quota system to restrict all foreign immigrants. After 1952, Japanese
immigration to Brazil resumed, although at a much lower rate, until the late
1960s. It is worth noting that during the four years from 1953 to 1956,
Japanese immigration sped up rapidly and the Amazon region received a
larger proportion of the total of approximately 14,000 immigrants. The
annual proportion of Japanese immigrants of all the immigrants rose steadily
from 2.4% in 1953 to a postwar high of 11.0% in 1956.
In 1958, an important census was conducted by a special commission of
Japanese immigrants with financial support from the Japanese colony in
Brazil, the Brazilian government, the Japanese government, the Population
Council of New York and various private enterprises. In commemoration of
the fiftieth anniversary of Japanese immigration to Brazil, the census
provided valuable information on the Japanese immigrants and their
descendants. The census organizers planned to cover "information not only
on the present situation of immigrants and their descendants, but on the
immigrants' background in Japan, their initial conditions in Brazil, and the
changes they had undergone in the 50 year period" (Suzuki, 1965:117). The
project was, in fact, a monumental work on various aspects of Japanese
immigrants and their descendants in Brazil.
According to the 1958 Japanese self-census, there were a total of 429,413
Japanese, of whom 32.3%, were immigrants and 67.7%, were their
descendants. Meanwhile, 44.9% of the Japanese resided in urban areas and
55.1% lived in rural areas. Proportionally, slightly fewer immigrants (42.9%)
resided in urban areas and slightly more immigrants (57.1%) lived in rural
areas. In contrast, 45.9% of the descendants lived in urban and 54.1% of them
resided in rural areas.

20
The Period from 1959 to the Late 1960s
No statistics are available on the number of Japanese immigrants to
Brazil during the period from 1958 to the late 1960s, when large-scale
immigration from Japan to Brazil virtually stopped. Nor is there any
consensus among researchers on the actual number of immigrants for this
period. Sims (1972) reported one interesting feature of the Japanese migration
to Brazil during this period: the Brazilian government authorized two
Japanese-Brazilians to import immigrants from Japan to certain areas in
Brazil and set them certain quotas as well. For example, "the late Mr.
Yasutaro Matsubara was authorized to settle 4,000 Japanese families in central
Brazil (southern Brazil was approved later) and Mr. Kotaro Tsuji was
authorized to settle 5,000 Japanese families in the Amazon region" (Sims,
1972:246). These quotas remained effective until 1966, when the "Japanese-
Brazilian Joint Committee" was established and the quotas were abolished.
Japanese agencies, governmental and private, continued to provide subsidies
to immigrants, especially those bound for Brazil during this period. Suzuki
(1981) estimated the total influx for the period from 1952 to the late 1960s at
50,000, while Smith (1979) estimated it at 60,000.
All tolled, during the 50 years from 1908 to 1958, about 240,000 Japanese
migrated to Brazil and the majority of them settled in the state of Sao Paulo
(Fujii and Smith, 1959; Suzuki, 1981). The 1950 Brazilian census reported that
the total amarelo population was 329,082, and 84% of them resided in the
state of Sao Paulo. The 1958 census of the Japanese community "reported that
there were about 430,000 persons of Japanese origin in Brazil, 94% of whom
resided in the southern part of Brazil, principally in the states of Sao Paulo
and Parana" (Makabe, 1981:790).

2 1
Social Characteristics and Social Mobility of the lapanese Immigrants
Japanese immigrants were brought to Brazil primarily as farm laborers.
As a result, the majority of them were at the bottom of the social hierarchy
when they started their new lives in the new country. Here I will focus on
the initial social status, as marked primarily by their occupations, of the
Japanese immigrants and the changes in the distributions of industries and
occupations for them during their first fifty years in Brazil. Then I will
review their initial educational status and how that changed through the
years. I will also review some demographic characteristics of the Japanese
immigrants that are closely associated with their social mobility.
Occupational Distribution and Mobility
The occupational distribution of immigrants to Brazil first and
foremost reflected the Brazilian immigration policy at the time, i.e., the
creation of a small-farming class and the provision of a supply of cheap labor
for plantation owners.
During the prewar period from 1908 to 1941, 98.8% of the Japanese
immigrants to Brazil were classified as farmers, whereas only 59.6% of all
immigrants to Brazil were farmers. The proportions of farmers among the
larger immigrant groups are 78.6% Spaniards, 49.0% Italians and 47.7%
Portuguese (Fujii and Smith, 1959). During the postwar period from 1954 to
1956, the percentage of farmers among Japanese immigrants dropped to about
86% from 98.8% in the previous period. However, it was still amazingly
high, when compared to the farmer percentage of 15.9% for all immigrants
during that time period (Fujii and Smith, 1959).

22
Suzuki (1981) reported that 94% of all family heads started as farmers,
78% of whom were at the lowest status as colonos primarily on coffee
plantations (90%). However, in 1958, the proportion of farmers among the
Japanese immigrants dropped to about 61% and the proportion of colonos
dropped to only 2% of the total farmers. The majority of the former colonos
went to large urban centers to work as craftsmen and unskilled laborers.
The 1950 Brazilian Census provided the first systematic information on
the distribution of industry by racial group. Smith (1972) included a very
detailed table on the distribution of industry for males 10 years of age and
over by color, based on the 1950 census data. Let me briefly summarize the
industry distribution of the amarelos and the standing of this group relative
to the other races described in Smith (1972).
The 1950 census included eleven categories of industries, but the
distribution of industry for the amarelos was highly concentrated in the
following four categories: agriculture (which included forestry and fishing)
(69.0%), service (10.2%), wholesale/retail trade (10.1%) and manufacturing
(which included construction and processing) (6.0%). The proportions for the
remaining industries were, in descending order: transportation (which
included communication and storage) (2.3%), finance (which included
insurance and real estate) (0.9%), liberal professions (0.6%), extractive
industries (0.5%), and social activities (0.4). The total number of people who
were engaged in "public administration, legislation and justice" and
"national defense and public security" was so small (90 and 138 respectively)
that they were omitted in the percentages for the original tabulation. The
percentage of amarelos in wholesale and service industries are the highest
among the four color groups, and the percentage of amarelos in agriculture in
about the same as the that of Negroes (see Table 2.1).

23
Table 2.1
Industry Distribution of Brazilian Males Aged 10 and Over by Color, 1950
Industry
Total (%)
White
Negroes
Yellow
Pardos
Agriculture
64.6
60.6
70.0
69.9
72.3
Extractive
3.2
2.1
3.9
0.5
5.6
Manufacturing
13.0
14.5
13.4
6.0
9.5
Wholesale
6.2
7.9
2.4
10.1
3.5
Finance
0.7
1.1
0.1
0.9
0.2
Service
5.3
6.0
4.0
10.2
3.9
Transportation
4.7
5.5
4.7
2.3
3.8
Liberal Profession
0.5
0.7
0.04
0.6
0.09
Social Activities
1.4
1.7
1.1
0.4
0.9
Public Ad.
1.6
2.0
0.9
—
0.9
National Defense
1.8
2.0
1.2
—
1.4
All Industries
100.0
100.0
100.0
100.0
100.0
Source: Table XI in Smith, 1972, pp. 94-95.
We can also look at the proportions of employers, employees, self-
employed workers, and family workers by race and see the differences among
racial groups. Table 2.2 illustrates the proportions of different employment
statuses by color for all industries and agriculture, the most important
industry in 1950. In both all industries and agriculture, the amarelos,
compared to the other groups, have the highest proportions of employers
(11.8% and 10.8% respectively) and the highest proportions of family workers
(29.6% and 38.5% respectively). Expectedly, they have the lowest proportion
of employees (23.7% in all industries and 15.4% in agriculture) among the
four groups. Thus Asians were overrepresented proportionally in the
ownership of businesses, and they were more likely to work as family units
than the other groups.

Table 2.2
Employment Status of Brazilian Males Aged 10 and Over
for All Industries and Agriculture by Color, 1950
Industrv
All Industries
Total(%)
White
Negroes
Yellow
Pardos
Employers
4.3
5.8
1.1
11.8
2.0
Employees
Own Account
46.7
46.3
58.1
23.7
36.3
Workers
32.0
31.0
27.3
34.9
36.3
Family Workers
17.0
16.9
13.5
29.6
18.5
Total
Agriculture
100.0
100.0
100.0
100.0
100.0
Employers
3.4
4.5
1.2
10.8
2.0
Employees
Own Account
34.5
31.9
48.3
15.4
34.2
Workers
37.4
37.4
32.0
35.3
39.8
Family Workers
24.7
26.2
18.5
38.5
24.0
Total
100.0
100.0
100.0
100.0
100.0
Source: Table XI in Smith, 1972, Pp. 94-95.
The 1958 Japanese self-census provided valuable information on many
aspects of their lives as a social group. Tables 2.3 and 2.4 are calculated and
abbreviated from Table 7 in Suzuki (1965) to give more focused analysis on
the occupational distribution of the Japanese immigrants and their
descendants in 1958. Table 2.3 shows that the proportions of farmers among
men and women in the labor force for the total population are approximately
the same, 57.6% for men and 57.7% for women. Nevertheless, there are
noticeable differences between the immigrants and descendants and also
between the two sexes for the immigrants. The proportion of farmers for the
male immigrants is 60.3%, while that for the male descendants is only 54.0%.
The difference in the proportion of farmers by sex for the immigrants is
equally pronounced: 60.3% for males and 66.6% for females. On the other
hand, there is little difference in the proportion of farmers for the
descendants: 54.0% for males vs. 53.0% for females.

25
Table 2.3
Proportion of Farmers among Japanese Immigrants and Descendants
Aged 10 and Over in Labor Force by Sex, Brazil, 1958
Immigrant
Males
Females
Status
Total
Farmers!%) NF*(%)
Total
Farmers(%)
NF*(%)
All
117,893
57.6
42.4
33,224
57.7
42.3
Immigrants
67,518
60.3
39.7
11,492
66.6
33.4
Descendants
50,375
54.0
46.0
21,732
53.0
47.0
Source: Table 7 in Suzuki, 1965, "Japanese Immigrants in Brazil,"
Population Index, 31:2, p.135.
*NF = nonfarmers
Note: There were three categories, "farmers", "nonfarmers" and "farmers
and nonfarmers," in the original table. For convenience and clarity,
the first and third categories are combined into "farmer" here, and the
second category remains the same.
Table 2.4 indicates the overall occupational distribution, including the
most important one, farmer, for the population as a whole and for
immigrants and descendants separately. Since the category of farmer here
excludes those farmers who had nonfarming jobs, not like the one used in
Table 2.3, the percentages of farmers for all three groups are consistently a
little bit lower than those in Table 2.3. However, the variations are minimal
(less than 1.2%) and the basic pattern remains the same. The exact
percentages of farmers for the total population, immigrants, and descendants
are 56.0%, 58.6% and 53.2%, respectively.
For the total population, the rankings of the occupations, excluding the
category of farmer, are 1) salesmen (16.0%), 2) craftsmen (12.3%), 3) service
(5.3%), 4) professional (which included technical, managerial and officials)
(4.4%), 5) clerical (3.4%) and 6) transportation/communication (2.2%). The

26
remaining 0.4% under the category of "other" belongs to occupations
classified as "fishermen," "miners," "quarrymen" and "unqualified laborers"
in the census. For the immigrant group, the order remains the same for all
the occupations, except that the order for "clerical” and
"transportation/communication" is reversed: 1) salesmen (17.4%), 2)
craftsmen (10.4%), 3) service (5.2%), 4) professional (4.2%), 5)
transportation/communication (2.0%) and 6) clerical (1.7%). The three
occupations within the category of "other" account for 0.6% of the
immigrants.
There are some interesting changes in the occupational distribution for
the descendant group. First, the percentages for both salesmen and craftsmen
rank first and are identical to one another. Second, the proportion of clerical
workers exceeds that of professionals, with the others more or less in the
same order as those for the other two groups. More specifically, the
proportions for the occupations are as follows: salesmen and craftsmen
(14.4%), service (5.4%), clerical (5.2%), professionals (4.6%) and
transportation/communication (2.4%). The remaining 0.4% is distributed
among the three occupations mentioned above.
The overall trend in the changes of occupational distribution from the
immigrant to descendant group can be summarized as follows: 1) The
proportion of farmers and salesmen decreased from the immigrant to
descendant group; 2) there were large increases in the proportions of
craftsmen and clerical workers among the descendants, and 3) there was a
slight increase in the proportions of transportation/communication,
professionals and service, which were considered nontraditional occupations
or occupations related to industrial and urban settings, among descendant.

27
Table 2.4
Occupational Distribution of Japanese Immigrants and Descendants
Aged 10 and Over in the Labor Force, Brazil, 1958
Immigrant Status
Occupation All (%) Immigrants (%) Descendants (%)
Farmer
Professional/
56.0
58.6
53.2
Technical
4.4
4.2
4.6
Clerical
3.4
1.7
5.2
Sales
16.0
17.4
14.4
Transportation
2.2
2.0
2.4
Crafts
12.3
10.4
14.4
Service
5.3
5.2
5.4
Other
0.4
0.6
0.4
Number
150,170
78,585
71,585
Source: Table 7 in Suzuki, 1972, "Japanese Immigrants in Brazil,"
Population Index, 31:2, p.135.
Note: There were ten occupations in the original table, in addition to the
seven listed here. Due to the space limit and the insignificance of the
three categories "fishermen," "miners, quarrymen" and "unqualified
laborers," they are combined under the category of "other" in this table.
Suzuki (1981) described the change in the employment status of
Japanese immigrants and their descendants by classifying them into two
broad categories: independent persons and employed persons. For farmers,
colonos and sharecroppers were considered employed persons and tenant
farmers and land-owning farmers were regarded as independent persons. For
nonfarmers, employed persons included employees and independent persons
included employers and the self-employed. Suzuki wrote, "Whereas the
number of independent persons rose from 14% in 1912 to 86% in 1958, the
employed decreased in just the opposite proportion" (1981:63).

28
The distribution of industries in Table 2.5 provides us with a similar
picture from a slightly different perspective. There were ten categories in the
original table from Suzuki (1965), but I have listed here only six of them,
which, by the way, cover almost 99.0% of all industries. Needless to say,
agriculture has the highest proportion of workers for all three groups: 57.2%
for all, 59.8% for immigrants and 54.3% for descendants. Apart from
agriculture, for both the immigrant and descendant groups, the order of the
industries with the highest to lowest proportion of workers is the same.
Therefore, the order of industries for the total population is the same as well.
They are, in descending order, trade (17.5%), service (13.3%), manufacturing
(7.2%), transportation (2.4%) and finance, insurance, real estate (1.4%). The
remaining 1.0% under the category of "other" belongs to the industries of
"government," "fisheries" and "mining." They are not listed here because
they are negligible in terms of percentage.
Table 2.5
Japanese Immigrants and Descendants Aged 10 and Over
in the Labor Force by Industry, Brazil, 1958
Immigrant Status
Industry
All (%)
Immigrants (%)
Descendants (%)
Agriculture
57.2
59.8
54.3
Manufacturing
7.2
6.5
8.0
Trade
17.5
18.3
16.6
Finance
1.4
1.1
1.8
Transportation
2.4
2.1
2.7
Service
13.3
11.3
15.5
Other
1.0
0.9
1.1
Number
150.170
78.585
71.585
Source: Table 7 in Suzuki, 1972, "Japanese Immigrants in Brazil,"
Population Index, 31:2.
Note: There were ten occupations in the original table, in addition to the six
listed here. Due to the space limit and the insignificance of the three
categories "fisheries," "mining" and "construction," they are combined
under the category of "other" in this table.

29
Although the proportions of industries have exactly the same ordering
for both the immigrant and descendant groups, there are variations in the
exact proportions of all industries for the two groups. Apart from a decrease
in the proportions of farmers, the descendants have increases of various
degrees in the proportions of workers for all the industries except that of
trade, which has a loss of 1.7% (18.3% for immigrants to 16.6% for
descendants). The two biggest increases of workers occur in the industries of
service and manufacturing for this group; the former increases by 4.2% (from
11.3% for immigrants to 15.5% for descendants) and the latter by 1.5% (from
6.5% for immigrants to 8.0% for descendants).
The agricultural status of postwar migrants and their descendants in
1958 is described in Sims (1972). The study, based on a survey of 4,268
Japanese farmers who arrived in Brazil during the period 1952-58, showed
that of the total sample, 51.2% were colonos, 16.8% had become owner-
farmers, 15.4% had become renters, 16% had been reduced to sharecroppers
and 0.6% had become farm administrators. In comparing the prewar and
postwar migrants in terms of ownership of land, Sims noted that "the private
ownership of land was slightly more common among the prewar migrants
(22%) than their postwar successors (16.8%)" (1972:250). However, one
important fact about the prewar Japanese migrants was that only 1.3% of
them were still colonos by 1958. Another significant characteristic of the
Japanese farming community in the postwar period was that family workers
took up 59.3% of all the farmers, "revealing the dependence of the farm
families upon their own kin" (Sims, 1972:250).
On the other hand, the nonfarming Japanese population continued to
grow, though not at a steady rate, as more and more people moved away from
farms to the urban areas in search of better opportunities in other professions.

30
By the late 1950s, among nonfarmers, craftsmen constituted less than a
quarter, the proportion of salesmen increased from 8% to more than 50%, and
service workers accounted for 10%. Unskilled laborers used to account for
almost a quarter of the total nonfarming Japanese population, but by the late
1950s they had virtually disappeared (Suzuki 1981).
The occupational status of nonfarming Japanese Brazilians was
described in Sims (1972), who compared the prewar and postwar groups (see
Table 2.6). There were striking differences between the prewar and postwar
migrants in terms of occupational status; 81% of the prewar migrants were
working for themselves, i.e., they were either employers or self-employed,
while only 28.5% of the postwar group were doing so. By the same token,
nearly two thirds of the postwar migrants were employees or working for
others, whereas only 16.4% of the prewar group were so. The only advantage
of the postwar group over the other was their higher proportion of managers
(7.6% vs. 2.7%) due to an increased level of education and more diverse
backgrounds among the postwar immigrants.
Table 2.6
A Comparison of Occupational Status
of Prewar and Postwar NonFarming Japanese Immigrants
Occupational
Status
Prewar Immigrants
(%)
Postwar Immigrants
(%)
Self-employed
59.8
22.1
Employers
21.2
6.4
Employees
16.4
63.9
Managers
2.7
7.6
Source: Sims (1972)
In sum, tremendous changes took place in the occupational
distribution of the Japanese population in Brazil during their first fifty years

in Brazil. The most obvious change was the sharp decrease in the proportion
of farmers, from over 95% in the beginning decades of immigration to about
56.0% in the late fifties. Second, the percentage of colonos in the prewar
Japanese migrants decreased from about 80% for the period before 1941 to
1.3% in 1958. Third, the amarelos, who were overwhelmingly made up of
people of Japanese origin, exceeded all other racial groups in the proportion of
employers. Fourth, they maintained the tradition of working as family units,
which had advantages over individual workers in terms of utilizing human
and capital resources.
Diversification of Agricultural Crops and High Productivity
Japanese-Brazilians are also considered to be "the first to move toward
the diversification of crops in the Sao Paulo coffee-lands" (Dwyer and Lovell,
1990:187). In addition to coffee, the Japanese owner-farmers produced cotton,
rice, potatoes and other new crops. A survey of 35,871 Japanese Brazilian
farm families in 1958 revealed that the largest number of Japanese farmers
grew coffee: 17.6% in Sao Paulo and 27.5% in the nation as a whole. Vegetable
was the second largest crop, with a farming population of 13%. Cotton ranked
third with a farming population of 7.3%. The majority of both the vegetable
growers and cotton growers were in Sao Paulo (Sims, 1972).
The above study also described the employment status of the Japanese-
Brazilian farmers in 1958. Seventy-five percent of the coffee growers, 43.9% of
the cotton growers, about 50% of the poultry raisers and nearly one-third of
the rice growers were owner-farmers, while 56.6% of the vegetable producers
and 45.1 % of the rice growers were renters. It was also noted that the colonos
were important proportionally in coffee, poultry and potato farming.

32
It was not only the high proportions of the Japanese farmers in the
above agricultural sectors that is important, but also their production that is
more important in terms of their contribution to the agricultural
development of Brazil. By the late 1930s, the Japanese-Brazilians accounted
for more than 50% of the cotton produced in the state of Sao Paulo (James,
1937, cited in Dwyer and Lovell, 1990) and were responsible for 80% of the
vegetable production in the suburban area of Sao Paulo city (Makabe, 1981).
In 1958, Japanese-Brazilians, with only one percent of the total farming
population, produced about 62% of the tomatoes, 39% of the peanuts, 27% of
the potatoes, about 12% of the eggs, and about 12% of the cotton produced in
Brazil. They were also responsible for about 93% of the tomatoes, 92% of the
tea, 68% of the potatoes, 43% of the peanuts, 37% of the eggs, 36% of the
peppermint, 27% of the cotton, 22% of the banana produced in the state of Sao
Paulo (see Table 2.7).
Table 2.7
Agricultural Production of Japanese-Brazilians
in Sao Paulo and Brazil by Crop, 1958
Crop % of the Brazilian Total % of the Sao Paulo Total
Tomatoes
61.7
93.3
Peanuts
39.1
42.8
Potatoes
27.0
67.9
Eggs
11.6
37.0
Cotton
11.6
26.8
Coffee
5.9
7.1
Banana
5.3
21.8
Fruits
2.9
—
Rice
2.3
8.1
Tea
—
92.1
Peppermint
—
36.4
Source: Sims, 1972, p. 251

33
Studies on the social mobility of the Japanese Brazilians in the last two
decades are extremely rare in the English language publications. One such
study available is Dwyer and Lovell (1990), "Earning Differentials Between
Whites and Japanese: The Case of Brazil". This study uses a sample of 272
white males and 242 Japanese males ages 18-64, from the 0.8% sample of the
1980 census of Brazil. The main findings of this study are: (1) the average
earnings of Japanese males are 61% higher than that of whites; (2) 48% of
Japanese males have more than nine years of schooling compared to 24% for
white males; (3) only 51 % of the Japanese are workers whereas 75% of the
whites are workers; (4) three times as many Japanese as whites are employers
and 32% of Japanese versus 19% of whites are self employed. These findings
suggest that Japanese-Brazilians have surpassed whites in terms of many
important social indicators.
Educational Status
Educational status of a population is usually measured by its literacy
(illiteracy) level and the percentages of people who have received elementary,
secondary and higher education among its literate people. There is ample
evidence that from the very beginning, Japanese-Brazilians fared very well in
terms of educational status among the various immigrant groups and among
the racial groups as well.
The literacy rate of the Japanese immigrants was one of the highest
among all immigrant groups through time. For the prewar period 1908-1941,
the Japanese immigrants ranked second after the Germans, with a literacy rate
of 72.9% (compared to 87.2% for Germans, 59.6% for Italians and 43.1% for
Portuguese) (Fujii and Smith, 1959). However, it should be noted that the

34
literacy rate was then measured in terms of the ability to read and write in the
native languages of immigrants, not in Portuguese, the official language of
Brazil.
However, according to the 1940 and 1950 census, the illiteracy rate for
the yellow people was the lowest among the four racial groups (Smith,
1972:490):
Race
1940
1950
Yellow
34%
17%
White
47%
34%
Pardos
71%
69%
Negroes
79%
73%
It is also worthwhile to point out that the illiteracy rate declined by 50%
among the yellow people, while its rate of decline was not as great among the
other three groups, especially among pardos and Negroes.
The 1958 Japanese Self-Census indicated that of all Japanese residing in
Brazil aged 7 and over, the illiteracy rate was only 2.5%; 1.8% in urban areas
and 3.8% in rural areas. The census also provided information on this subject
for immigrants and descendants separately: the illiteracy rate for all
immigrants was 1.5%, with 1.2% in urban areas and nearly 1.8% in rural
areas. Interestingly, the illiteracy rate for descendants was slightly higher than
that for immigrants: 3.2% for all descendants, with 2.2% in urban areas and
4.1% in rural areas (Suzuki, 1972).
Sims (1972) reported the result of a 1962 survey of 151,701 newspaper
readers over 14 years of age to show the literacy rates in both Portuguese and
Japanese among the Japanese-Brazilians at the time. According to this survey,
51.8% of the sample read Brazilian periodicals regularly, with 60.6% for urban
residents and 40.5% for rural residents. Sims further concluded, by way of

35
computation, that "at least 22.4% of the community surveyed read
Portuguese, while a minimum of 75.1% were literate in Japanese in 1962"
(1972:258).
According to the 1950 census, the proportions of people who completed
elementary schooling was much higher among the Yellow population than
was the case nationwide (37.7% vs. 17.9%) (Smith and Fujii, 1959). The
proportions of people who attended different levels of schooling among the
Japanese immigrants and their descendants in 1958 were described in Suzuki
(1965).
Table 2.8 offers a summary of the above information: In urban areas,
67.3% of the people aged 7 and over attended primary school, 29.2% attended
secondary school, and 0.7% attended college, while in rural areas, the
corresponding figures were 82.6%, 11.8% and 0.8%. When immigrants and
descendants were compared, the latter did better than the former in urban
areas, whereas the former did better than the latter in rural areas. For
example, the percentages for primary and secondary schooling among the
urban immigrants were 75.3 and 21.0, while the same percentages for their
counterparts were 62.6 and 33.9. On the other hand, proportionately, more
rural immigrants attended secondary school (14.5%) than did their
counterparts (9.9). The proportions of people who attended college for all
groups was less one percentage.
Suzuki (1981:65) noted that "a relatively high educational level in
comparison to that of the society on a whole would seem to lessen handicaps
affecting foreign immigrants in their struggle for a better life." The high
literacy rate among Japanese migrants was indeed an important factor in their
successful adaptation to the Brazilian society and their rapid upward social
mobility from the initial status of colonos.

36
Table 2.8
Japanese Immigrants and Descendants Aged 7 and Over
by Level of Education and Residence, 1958
Residence
Total
Primary
Secondary
Higher Ed.
Urban
160,796
67.3
29.2
0.7
Immigrants
58,972
75.3
21.0
0.9
Descendants
101,824
62.6
33.9
0.5
Rural
189,565
82.6
11.8
0.8
Immigrants
77,610
81.2
14.5
0.8
Descendants
111,955
83.7
9.9
0.8
Source: Suzuki, 1965
Demographic Characteristics
The most distinctive demographic feature of Japanese immigration to
Brazil was “family immigration," which was the direct result of a regulation
imposed by the Brazilian government. According to this regulation, an
immigrant family must have at least three capable laborers who were above
fifteen years of age. Consequently, about 95% of the Japanese immigrants
between 1908-1941 and 80% between 1954-1956 came to Brazil in such family
groups, as compared to 64% and 54% of the total immigrant population in
these two time periods (Fujii and Smith, 1959).
As a correlate of the high proportion of family units among the
Japanese immigrants, the percentage of married people was also high among
them, and it gradually decreased with the fall of the proportion of family
units among the immigrants over the years. Fujii and Smith (1959) reported
that for the period 1908-1941, the percentages for single, married and widowed
people were 56.0%, 42.3%, and 1.7%; for the period 1954-56, they were 64.4%,

37
33.5% and 2.1%. The proportions of singles and widowed increased by 8.4%
and 0.4, whereas the proportion of married decreased by 8.8%. This was
probably resulted from the relaxation of family unit rule applied to Japanese
immigrants during the late fifties.
Table 2.9 illustrates the marital status of the Japanese population aged
15 and over by sex and generation in 1958. For the whole population, 44.5%
of the males and 35.9% of the females were single, while 52.3% of the males
and 56.6% of the females were married. The proportion of married people
was up by more than ten percent from 42.3% in the period 1908-1941. The
percentage of married people among the immigrants was even higher due to
the fact that most of the immigrants were adults and had become parents or
grandparents by 1958. The proportion of married people for the total
population was heavily affected by that of the immigrants since they were still
the majority at that time. In contrast, the percentage of married people
among the second generation of Japanese was far lower than that for the
immigrants, due to their relatively young age.
Table 2.9
Marital Status of the Japanese Population in Brazil
by Sex and Generation, 1958
Immigrant
Males
Females
Status
Sin.
Mar.
Sep.
Wid.
Sin.
Mar.
Sep.
Wid.
All Japanese
44.5
52.3
0.4
2.8
35.9
56.6
0.6
6.9
Immigrants
2nd
14.9
79.4
0.6
5.1
5.8
79.9
0.8
13.4
Generation
3rd & 4th
78.3
21.4
0.1
0.1
64.1
35.2
0.3
0.4
Generation
98.5
1.5
—
—
95.8
4.1
—
—
Source: Suzuki, 1965
Note: Sin.=Single, Mar.=Married, Sep.=Separated, Wid.=Widowed

38
However, one common element among all groups, immigrants and
descendants alike, was that proportionately more women were married than
men; 56.6% vs. 52.3% for all Japanese, 79.9% vs. 79.4 for immigrants, and
35.2% vs. 21.4% for the second generation. This was largely caused by the fact
that women married at younger ages than men did in general. Therefore, the
sexual differences in the percentage of married people among different groups
was a main indicator of the mean age at marriage for the groups concerned.
Family type and structure are known to correlate with the
socioeconomic well-being of a particular group. Suzuki (1981) showed a
positive correlation between the Japanese family structure and the
improvement of their economic status by comparing the frequency of family
types with their economic status expressed in terms of the employment status
of the family head and property ownership (see Table 2.10). He found out that
among the land-owning farmers, 36% were three-generation families and
40% were "lineal" and "lineal and collateral" families; among the tenant
farmers, the corresponding figures were 21% and 24%. In contrast, the
percentages of three-generation families and "lineal" and "lineal and
collateral" families among the sharecroppers and colonos were 16%, 20% and
10%, 11% respectively. Therefore, we can conclude that more independent
farmers tend to have extended (three-generation) families and lineal or
lineal/collateral families than employed farmers (sharecroppers and colonos).
This, in turn, suggests that three-generation families, and lineal and
lineal/collateral families may have a positive effect on the employment
status, i.e., whether being an independent or employed person.
However, the pattern for non-farmers was just the opposite;
employees had higher proportions of three-generation families (29%) and
lineal, lineal/collateral families (31%) than did the self-employed (23%, 27%)

39
and employers (13%, 17%). This suggests that larger families may not be an
advantage for non-agricultural workers. On the other hand, there was a
positive association between the value of property owned in both rural and
urban areas with three-generation families and lineal and lineal/collateral
families. In other words, the proportion of three-generation families and
lineal and lineal/collateral families increased with the increase in value of
property owned. According to Suzuki (1981), in rural areas, the proportions
of three-generation families for those who owned no property, low property,
medium property and high property were 18%, 28%, 42% and 53%
respectively, whereas for non-farmers in urban areas, those proportions were
17%, 24%, 34% and 37% respectively. The same pattern remained for lineal
and lineal/collateral families (see Table 2.11).
Table 2.10
Proportion of Traditional Families among Japanese Heads of Family
by Employment Status for Farmers and Non-Farmers in Brazil, 1958
Employment Status Three-Generation Lineal and Lineal/Collateral
Families (%)Families (%)
Farmers
Landowners
36
40
Tenant Farmers
21
24
Sharecroppers
16
20
Colonos
10
11
Non-Farmers
Employees
29
31
Self-Employed
23
27
Employers
13
17
Source: Suzuki, 1981
As for the advantage of extended family and lineal and lineal/collateral
family, Suzuki maintained, the "characteristics imply cohesion and active

40
cooperation among family members. Such cooperation is effected, inter alia,
through family labor, i.e., family members work without wages in an
establishment operated by the head or another family member"(1981:69).
Table 2.11
Proportion of Traditional Families among Japanese Farmers and
Non-Farmers in Brazil by Value of Property Owned, 1958
Value of Property
Owned
Three-Generation
Families (%)
Lineal and Lineal/Collateral
Families
Farmers
None
18
21
Low
28
32
Medium
42
46
High
53
57
Non-Farmers
None
17
26
Low
24
27
Medium
35
36
High
37
37
Source: Suzuki, 1981
Closely related to the family type and marital status of an immigrant
group is its sex ratio, which is even more important when there are relatively
few inter-groups marriages. During the period 1908-1941, the sex ratio of
128:100 among Japanese immigrants was significantly lower than that of any
other major immigrant groups ( 146 for Spaniards, 175 for Germans, 183 for
Italians, and 208 for Portuguese). However, the sex ratio of the Japanese
immigrants rose to 157 for the period from 1954 to 1956 due to the relaxation
of the regulation on family groups.
As would be predicted from the above data, the age composition of the
Japanese was younger than that for other immigrant groups because more
families usually mean more children. For example, about 30% of the

41
Japanese immigrants were 12 years of age or younger, compared to 23%
among the total immigrants, during the period 1908 to 1941 (Fujii and Smith,
1959).
The 1950 Brazilian Census indicated some changes in some of the
demographic characteristics of the amarelo population. For example, over
50% of the amarelo population were under twenty years of age, and the sex
ratio for them dropped from 128 to 110.8. The fertility ratio (number of
children under five years of age per 100 women aged 15-49) for amarelos in
1950 was 79.6, the highest among the four major racial groups (65.3 for white,
55.6 for Negro, and 69.2 for brown). On the other hand, the proportion of
Asians in the Brazilian population remained at 0.6% from 1940 to 1950
(Smith, 1972). This may be due to the pause in Japanese migration to Brazil
during the period 1942 to 1952.
The 1958 Japanese self-census offered information on the changes of
the characteristics of the Japanese population at the time: The sex ratio was
108, a decrease of 2.8 from 110.8 in 1950; the number of people under 15 years
of age was 40.5% of the total population, indicating that the population
became younger than it was eight years ago; and rural residents accounted for
about 55% and the urban residents 45%, showing large volumes of exodus
from rural areas (Suzuki, 1972).
Summary
Japanese migration to Brazil started at the turn of the century because
of Japan's internal problems of rural over-population and the
impoverishment of agricultural workers. This was caused by loss of land,
heavy taxation and detrimental competition with foreign farm products

42
during the period of initial industrialization and urbanization in Japan. At
the same time, Japan faced strong resistance against overseas emigration in
countries like Australia, the United States, Canada and Peru. By contrast,
Brazil sought after Japanese farm laborers because of a severe labor shortage
on coffee plantations after the abolishment of slavery in 1888, and
particularly, in 1902 after the Italian government ceased subsidizing the
migration of its agricultural laborers to Brazil.
Japanese immigrants were subsidized by the state of Sao Paulo from
1908 to 1923 and then by various Japanese emigration agencies, both private
and governmental, up to the late 1960s. During the period 1908-1941,
approximately 190,000 Japanese immigrants came to Brazil. After a ten-year
pause from 1942-1952 due to World War II, the migration wave continued at
a much lower rate until it virtually stopped in the late 1960s. The total
number of immigrants during this period was estimated at 50,000-60,000
(Smith, 1979; Suzuki, 1981). The 1958 Japanese self-census indicated that the
Japanese population in Brazil at the time was 429,413, of whom 32.3% were
immigrants and 67.7% were their descendants.
The majority of the Japanese immigrants were farmers and started as
colonos on coffee plantations in the state of Sao Paulo. They rose from the
lowest and least privileged status of colonos to the middle class status in both
rural and urban areas through their hard work during the 50 years after their
first arrival in Brazil. The experience of the Japanese population during the
1960s and 1970s is proof of their continued success in upward social mobility.
The most cited reasons for the success story of Japanese immigrants are
of two types; economic and cultural. The economic explanation emphasizes
the split labor market (Bonacich, 1972a) and lack of economic competition
from native Brazilians and other immigrant groups in Brazil. The cultural

explanation focuses on the adaptive ability of Japanese, and their traditional
values and characteristics. It seems to me that the former is mostly related to
external factors, and the latter to internal factors, from the viewpoint of the
Japanese immigrants.
In comparing the experiences of Japanese immigrants in Canada and
Brazil, Makabe (1981) concludes that the major reason for the success of
Japanese immigrants in Brazil was the lack of economic competition from the
native Brazilians and other immigrant groups and hence the lack of
unfavorable differential treatment in wages because they occupied different
labor market. He also notes that “ownership of land, which was the highest
achievement to be attained for the immigrants, became possible relatively
easily and quickly" (1981:800).
In contrast, Dwyer and Lovell (1990) explain the Japanese success
mostly in terms of their adaptive ability, and their cultural values and
characteristics. They point out three major reasons for their success: (1)
“second generation Japanese-Brazilians quickly learned the language,
business practices, and legal system of Brazil"; (2) “Japanese immigrants
placed a great deal of emphasis on education"; (3) they were very industrious
(1990:188). This mostly cultural explanation is similar to the one used for the
explanation of the socioeconomic achievement of Asian-Americans in the
U.S. (Bell, 1985; Kitano, 1969; Newsweek, 1982; Petersen, 1971).
These two types of explanation are very important in understanding
the Japanese experience in Brazil, and they are complementary rather than
mutually exclusive. However, there were also other factors that contributed
to the success of Japanese-Brazilians, and they could come under either of the
two types of explanation. To name just a few important ones, the human
capital of the immigrants, the favorable economic growth and

44
industrialization in Sao Paulo, the continued close connections with the
home country and financial and technological assistance from the home
country, the establishment of agricultural cooperatives and ethnic enclaves,
and lack of overt racial discrimination by the Brazilian society,
By human capital, I refer to the educational level, knowledge of
farming and technological skills the Japanese immigrants and their
descendants possessed. As shown above, the educational status of the
Japanese population was the highest among the four census racial groups and
they had advanced knowledge of intensive agriculture. These attributes
translated into better adaptation to the new environment and greater
efficiency and higher productivity, which would certainly result in higher
economic profits.
The Japanese immigrants benefited tremendously from, as well as
contributed to, "the remarkable economic and demographic growth of Sao
Paulo attributable to the coffee industry and industrialization" (Tsuchida,
1978). The labor shortage on coffee plantations brought them to Brazil in the
first place, and the urbanization and industrialization in the state of Sao
Paulo offered them the opportunity of pioneering vegetable farming and the
poultry industry. The development of the textile industry in Sao Paulo in the
early 1930s created a huge domestic market for cotton, and Japanese Brazilians
dominated the cotton industry from the outset due to their expertise in
growing cotton.
Meanwhile, Japan’s importation of large quantities of cotton in the
mid 1930s from Sao Paulo also helped the Japanese Brazilian cotton growers.
As Tsuchida put it, "cotton growing was instrumental in transforming small
Japanese-owned farms into economically viable production units in Paulista
agriculture" (1978:324). He even stated that "the cotton boom in the 1930s had

4 5
already enabled the Japanese to solidify their economic base in such a way that
they and their descendants in Portuguese America could securely stand on
their own feet in total isolation from their mother country” (1978:311).
The fact that the Japanese immigrants maintained close ties with and
received financial and technical support from their home country, especially
in the early years, was very important to their success in Brazil. The Japanese
government and private companies financed various colonization projects
and provided information, technical assistance in farming, and even
improved seeds, which greatly promoted land ownership and increased
agricultural productivity among the Japanese immigrants (Tsuchida, 1978).
Another important feature of the Japanese immigrants in Brazil was
that from the outset, they established their own ethnic enclaves in the form
of agricultural cooperatives and larger community settlements. Normano
and Gerbi described the Japanese in the following way:
The Japanese live almost completely isolated from the native
element in Brazil. The population of their centers varies from
three hundred to six or seven thousand, in cities, towns, and
large fazendas, but always they remain in atmosphere and
surroundings completely Japanese (1943:39).
Their agricultural cooperatives facilitated the transportation and the
marketing of their products, and the ethnically homogeneous communities
provided them "with adequate educational opportunities, medical care,
technical assistance, loans, and above all, a sense of security" (Tsuchida,
1978:313). There is no doubt that these ethnic associations played a major role
in the success of the Japanese immigrants.
Finally, the Japanese immigrants would not have achieved what they
did in such a short time if they had been subjected to the severe systematic

46
racial discrimination by the dominant society as were their counterparts in
North America (Daniels, 1977; Daniels, 1988; Kitano and Daniels, 1988; Lee,
1989). At least, there was no overt discrimination against them in the
economic sphere so that they were able to demonstrate fully their valuable
assets and compete on an equal footing with others for land ownership,
property and social mobility. On the subject of anti-Japanese sentiment in
Brazil, Tsuchida wrote:
Devoid of any serious economic conflict between the Japanese
community and the dominant society, charges against this
ethnic minority centered around racial desirability and the
intangible threat of Japanese imperialism. Anti-Japanese
agitation was restricted to a small circle of intellectuals who
advocated Japanese exclusion, on ideological ground, rather than
economic and political reasons (1978:321).
On the other hand, the Japanese immigrants didn't compete with the natives
for occupations then considered more favorable, such as commerce. They
apparently avoided possible conflicts in their economic activities. They were
first engaged in coffee growing, then pioneered cotton, vegetable and fruit
farming, all of which were much needed by the Brazilian society. In other
words, they had their own labor market, and were not in direct competition
with the dominant society.
However, this does not imply that Brazil has been a racial democracy,
as some scholars have advocated. In fact, there is a body of literature that
indicates the scope of racial inequalities in Brazil (Hasenbalg, 1985; Lovell,
1989; Lovell and Dwyer, 1988; Silva, 1978; Silva, 1985; Wood and de Carvalho,
1988). Here, I only intend to show that one of the reasons for the Japanese
success was that they experienced far less discrimination than did their
counterparts in North America and some other South American countries.

47
They managed to rise from the bottom of the society and achieve middle class
status within the first fifty years of their arrival mainly by hard work, assets in
human capital, a traditional practice of working as family units, demographic
factors (relatively balanced sex ratio and younger age structure), collective
efforts and ethnic unity, strong support from the home country, a favorable
economic situation in Brazil and a lack of overt discrimination against them,
especially in the economic and political arenas.

CHAPTER 3
FERTILITY DIFFERENTIALS
AMONG ASIANS, WHITES AND AFRO-BRAZILIANS
A Brief Review of Literature on Fertility Studies
Human fertility behavior is the subject of study in many disciplines of
the social sciences, and various theories on fertility have been put forward.
Some of the major fields of study that deal with human fertility are
demography, sociology, economics, anthropology, psychology and biology.
Each discipline tends to focus on slightly different aspects of human fertility
behavior and differs somewhat in its approaches due to its distinct theoretical
orientations and scopes of study. However, there are many things that
fertility studies have in common.
The economic theory of fertility is perhaps the most influential among
competing theories. The most important works of this school of thought are
Leibenstein (1957), Becker (1960), Easterlin (1969) and Schultz (1973). By
applying the economic theory of consumer behavior to childbearing
decisions, they regarded human fertility as a result of rational decision based
on an effort to "maximize satisfaction, given a range of goods, their prices,
and his own tastes and income" (Easterlin, 1975:54). In other words, "children
are viewed as a special kind of good, and fertility is seen as a response to the
consumer's demand for children relative to other goods" (Easterlin 1975:54).
Two conclusions implied in the economic approach of fertility are: (1) Other
48

things being equal, higher income usually results in higher fertility rate; (2)
an increase in the price of children relative to other goods results in lower
fertility.
Counter to the first hypothesis, cross-cultural and cross-sectional
demographic data generally show that higher income groups tend to have
fewer children compared to lower income groups in a country. Similarly,
aggregate data show that more affluent and developed societies tend to have
lower fertility rates than their less developed counterparts. It should be
noted, however, that these studies may not represent an adequate test of the
economic theory of fertility (which predicts a positive correlation between
income and fertility). The reason is that aggregate data on fertility rates by
income classes do not measure what economists refer to as the "pure income
effect." That is, the effect of income after controlling for contraceptive
knowledge and other determinants of fertility behavior.
The second hypothesis is valid and supported by some historical
demographic data. Yet it offers little insight to differential fertility among
various sub-populations of a society if we assume that "the price of children
relative to other goods" is, more or less, the same for all the people in the
same region at a certain period of time. Moreover, the economic theory of
fertility analysis leaves little room for the role of sociocultural factors and
other institutional constraints in the fertility decisions and behaviors of
individuals, who live in a complex social context and are bound to be
influenced by many external factors.
In addition, Easterlin (1975) and Todaro (1981) have applied
microeconomic theory to the study of fertility. In this approach, fertility is
determined mainly by three factors; 1) the demand for children, 2) the
potential output of children, and 3) the cost of fertility regulation, which

50
includes both subjective and objective costs, as well as the time and money
required to learn about and use specific techniques for limiting fertility.
The sociological theory of fertility is mainly represented by Davis and
Blake (1956), Davis (1959), Freedman (1962), and Hawthorne (1970). In this
approach, observed level of fertility is seen as the outcome of the interaction
among biological processes, societal group factors and individual behavior
(Robinson and Harbison, 1980). Social norms about family size are given
considerable attention in this approach, and it is broader in scope and more
dynamic than the economic approach. In an attempt to bridge the gap
between the economic theory and sociological theory, Caldwell (1976, 1978)
proposed a new "general theory" of fertility, which states that "fertility
behavior in both pre-transitional and post-transitional societies is
economically rational within the context of socially determined economic
goals and within bounds largely set by biological and psychological factors"
(1978:553). Recent development of this approach is reflected in the
examination of the socioeconomic and proximate determinants of fertility.
(Easterlin, 1983; Standing, 1983; Menken, 1987)
The psychological approach to fertility focuses on individual-level
processes and places emphasis on psychological variables and measures.
Fishbein (1972) argued that human fertility behavior was determined by
people's intentions, the normative beliefs regarding fertility, and the personal
attitudes toward the importance of these norms. Unlike the sociological
approach, norms affect fertility through personal attitudes and intentions
here. Other works of this orientation include Jaccard and Davidson (1976),
and Friedman et al. (1976). In general, the psychological approach to fertility
assumes that human fertility behavior is rational and purposeful, and that all

the other factors, economic and social, affect fertility through individual
attitudes and intentions.
Anthropologists usually study fertility behavior in terms of the
determinants of social and cultural differences within an evolutionary
framework. Barlett (1980) identified three approaches to fertility within
anthropology: the ecological approach, the cognitive approach and the
statistical aggregate approach. Chagnon (1968) and Harris (1974) applied the
ecological approach to explain the practice of female infanticide among the
Yanomamo, and concluded that female infanticide was an effective way of
limiting the overall fertility of the group. Cognitive anthropologists (e.g.,
Marshall, 1972a and Quinn, 1975) stressed the importance of individual-level
decision making, and attempted to build models for the decision-making
process. The third approach, the statistical aggregate approach, "stresses what
people do, not what people say they do" (Barlett, 1980:168). More specifically,
in this approach, "an anthropologist observes behavior, records outcomes,
and then analyzes the patterns in the outcomes to construct a statistical
profile of people who choose different options" (Barlett, 1980:168). Since most
anthropological studies have dealt with relatively homogeneous societies in
the past, they tended to assume that shared values and traditions and societal
norms govern individuals' behavior, which in turn determine their fertility.
In short, most anthropological approaches to fertility tend to focus on cultural
patterns.
However, there is another approach to fertility in anthropology that
stresses the role of material conditions or factors directly related to material
conditions of people in examining their fertility behavior. For example,
Harris and Ross (1987) focused on the material benefits and costs of child
rearing, as well as other fertility regulatory measures, in their attempt to

52
explain the fertility behavior of preindustrial societies. Handwerker (1986)
criticized the cultural approach to fertility as tautological, and offered a
materialist explanation to fertility transition. Handwerker argued, "we
cannot identify specific behavioral patterns and the ideas they presuppose
independent of one another. To 'explain' behavior by reference to those ideas
therefore constitutes a covert tautology" (1986:14). According to him, fertility
transition occurs "when personal material well-being is determined less by
personal relationships than by formal education and skill training."
Handwerker further explained:
This transformation occurs when changes in opportunity
structure and the labor market increasingly reward
educationally-acquired skills and perspectives, for these changes
have the effect of sharply limiting or eliminating the expected
intergenerational income flows both from children, and from
the social relationships created by or through the use of children.
(1986:3)
In terms of the relationship between education and fertility, Handwerker
offered an insightful analysis:
education or literacy itself can have no important effect on
fertility. The linkage between education and fertility is
contingent on opportunity structure, and will turn on the issue
of how material well-being can best be created and maintained,
and how educationally acquired skills and perspectives fit, or do
not fit, into this process. (1986:18)
The above approaches not only differ in theoretical orientation, but
also in unit of analysis. Both economic and psychological approaches to
fertility focus primarily on individuals and tend to ignore the social, cultural
and external factors beyond the individual level. In contrast, in sociological
and anthropological approaches, the unit of analysis is usually the group,

53
which may be an extended family, a clan, a social class or the society as a
whole. Even when individuals are the focus of attention, they are situated
within the sociocultural context and regarded as members of a social group,
rather than as isolated individuals acting on their own.
Fertility Differentials among Ethnic/Racial Groups in Modern States
It is well documented in the literature of demography and ethnic/racial
studies that in multiethnic/racial societies, various ethnic/racial groups
reproduce at different rates. For example, Rindfuss and Sweet (1977) reported
different fertility rates for whites, blacks, American Indians, Mexican
Americans, Chinese Americans and Japanese Americans in the United States
for the period 1955-1969. These ethnic/racial groups in the United States
continued to reproduce at different rates for the 1970s (Bean and Marcum,
1978) and 1980s (1980 census, cited in Farley and Allen, 1989). Fertility
differentials among ethnic/racial groups in Canada were reported in Halli
(1987) and Halli et al. (1990), and ethnic fertility differentials in China have
been documented in the Chinese censuses since 1950. Racial variations in
fertility rate in Brazil are also reported in the Brazilian censuses since 1950. In
spite of the differences in ethnic/racial composition, social and political
system and economic structure among these countries, one common element
about fertility rate is almost universal, i.e., fertility rate seems to vary along
ethnic/racial lines, as well as along economic, educational, religious and
generational lines.
The study of differential fertility rates among various subgroups of a
population is an important part of demographic studies because it leads to

54
better understanding of the factors responsible for differential fertility rates
among groups. Furthermore, an examination of these factors reveals, among
other things, the nature of relationships between different social groups, be
they racial, cultural, or economic, or a combination of the above, in terms of
access to education, level of employment and income, and ultimately the
level of well-being.
Within the larger theoretical framework of fertility research in general,
studies of differential fertility among various subgroups of a population
(Goldscheider & Uhlenberg, 1969; Sly, 1970; Bean & Wood, 1974; Roberts &
Lee, 1974; Gurak, 1978; Gurak, 1980; Johnson & Nishda, 1980; Bean &
Swicegood, 1985) suggest three approaches. They are the cultural (or sub¬
cultural) approach, the structural (or social characteristics) approach and the
minority group status approach.
The cultural (or subcultural) approach emphasizes the role of values,
norms and ideology in determining a group's fertility behavior (Goldscheider
& Uhlenberg, 1969). In this approach, one "searches for determinants of
demographic variation in the history and cultural traditions of different
subpopulations" (Frisbie and Bean, 1978:2). Furthermore, "even when groups
are similar socially, demographically, and economically, minority group
membership will continue to exert an effect on fertility" (Rindfuss & Sweet,
1977:113). This approach reflects Schermerhorn's definition of an ethnic
group: "A collectivity within a larger society having real or putative common
ancestry, memories of a shared historical past, and a cultural focus on one or
more symbolic elements defined as the epitome of peoplehood"
(Schermerhorn, 1970:12). Bean and Swicegood (1985:6) explained the higher
fertility of Mexican Americans with the subcultural approach:

55
the higher fertility of Mexican Americans stems from the
persistence of cultural norms and values supporting large
families, such as familism—a constellation of norms and values
giving overriding importance to the collective needs of the
family as opposed to the individual—or adherence to the
pronatalist positions of the Catholic church, including
prescriptions against certain forms of birth control.
The social characteristics (or structural) approach does not deny the
possible validity of the subcultural approach, but it argues that differences in
social status, such as education, occupation and income, account for most or
all fertility differences among sub-groups. This approach also "implies that
'structural' assimilation with respect to education, occupation and income
will lead to the elimination of fertility differences between majority and
minority groups" (Bean & Swicegood 1985:7). The social characteristics
approach has its grounding in the assimilation theory first put forward by
Gordon (1964). It draws heavily from the idea of "structural assimilation,"
one of seven dimensions of assimilation that Gordon identified, and is
sometimes referred to as "the assimilationist theory." According to this
approach, fertility differentials are attributed to social, demographic and
economic characteristics of various groups. When these factors are
controlled, differences in fertility should disappear.
The minority group status approach was first proposed by Goldscheider
and Uhlenberg (1969), and was thereafter tested and applied in various
studies, such as Sly (1970), Roberts and Lee (1974), Johnson and Nishda (1980)
and Bean and Swicegood (1985). The basic assumption of this approach is that
the fertility of high socioeconomic status members of some groups (minority
groups) is lower than that of their majority counterparts, even though
fertility for the group as a whole exceeds that of the majority, because of the

56
insecurity that accompanies minority group status. Those minority members
who are in higher socioeconomic standing tend to aspire to greater social
mobility and therefore feel greater insecurity and marginality. In order to
overcome the feeling of insecurity and the potential obstacles to greater
success, these members are likely to lower their fertility to secure their already
achieved status. Goldscheider and Uhlenberg (1969) used this approach to
explain the lower fertility rate of highly educated black women as compared
to similar white women. More recently, Halli (1987) applied this approach to
the fertility of Asian groups in Canada.
Although these approaches differ in focus and have different
theoretical orientations, in my opinion, they actually complement rather
than contradict each other. They all contribute to the explanation of the
complex causes of differential fertility among various racial/ethnic and/or
socioeconomic groups. However, it is crucial to test these approaches against
empirical data to determine the most important factor(s) by examining the
associations between fertility rate and the possible biological, sociocultural
and economic factors. Specifically, it is important to determine the degrees to
which major independent variables contribute to the fertility level of a
population as a way to assess the validity of the competing theories of human
reproduction.
Fertility Differentials Among Asians. Whites and Afro-Brazilians
In this section, I examine the fertility levels of the three color groups in
metropolitan Sao Paulo, Brazil by asking the following questions: Do these
color groups have different fertility levels? If so, how do they differ and what

57
are the main causes for the differences? The hypothesis tested here is that
socioeconomic status (defined by income level and educational attainment),
rather than color, is the best predicator (but not the only) for differential
fertility among different social groups. Thus, when income and education are
controlled, color will contribute relatively little to subgroup differences in
fertility level. It is also assumed that household income and mother's
educational level is negatively correlated with women's fertility level; i.e., the
higher the income and educational levels are, the lower the fertility level is.
However, I do not assume that socioeconomic status alone accounts for all
the differences in fertility of various groups. Therefore, I expect that even
after controlling for the socioeconomic differences, some differences will
remain in the fertility levels of different color groups, although the amount
of variance in fertility explained by ethnic status will be relatively small.
The data set used here consists of women 15-49 years of age only since
we are only concerned with fertility level. The dependent variable is fertility
level, and the independent variables are place of residence, color, age,
education, and mean income. Fertility level is here defined by the mean
number of children ever born to women of a cohort classified by either color,
age, educational level or income level. Following the conventional method,
women are divided into either seven age groups (15-19, 20-24, 25-29, 30-34, 35-
39, 40-45 and 45-49) or four age groups (15-19, 20-29, 30-39, and 40-49) for
descriptive analysis.
In what follows, I describe the characteristics of the sample data,
compare the mean fertility level by age group, color, educational level,
income level and residence, and compare the fertility levels of the three color
groups, controlling for the other variables. Finally, I conduct a series of

58
multivariate regression analyses to examine the relationships among the
variables. The main findings are summarized at the end of the chapter.
Table 3.1 shows the mean number of children ever born to women in
seven age groups and the standard deviations from the means. It also shows
the proportions of each age group relative to the whole sample. The mean
number of children ever born for the total sample is 1.89, with the expected
increase from the lower to higher age groups.
Table 3.1
Mean Children Ever Born to Women of 15-49 Years of Age
by Age Group, Metropolitan Sao Paulo, Brazil (1980)
Age Group
Mean
Std Dev
Cases
%
15-19
0.12
0.41
39,916
20.3
20-24
0.77
1.09
38,968
19.8
25-29
1.66
1.56
33,482
17.0
30-34
2.50
1.98
26,925
13.7
35-39
3.28
2.46
21,916
11.1
40-44
3.85
2.91
19,163
9.7
45-49
4.17
3.20
16,283
8.3
Total
1.89
2.35
196,654
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
The mean number of children by color group is shown in Table 3.2.
Afro-Brazilian women have the highest mean (2.18), with whites second
(1.82) and Asians third (1.44). Given the sample size, these differences are
statistically significant. The color composition of the women in the sample
data is also indicated in Table 3.2; whites constitute 75.3%, Afro-Brazilians
22.6%, and Asians 2.1% of the sample population.

59
Table 3.2
Mean Children Ever Born to Women of 15-49 Years of Age
by Color Group, Metropolitan Sao Paulo, Brazil (1980)
Color Group
Mean
Std Dev
Cases
%
White
1.82
2.22
147,786
75.3
Afro-Brazilian
2.18
2.75
44,365
22.6
Asian
1.44
1.85
4,045
2.1
Total
1.89
2.35
196,195
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Table 3.3 illustrates the mean number of children ever born to women
by age group and color. Here I still use five-year intervals for age groups in
order to obtain a more detailed picture of the fertility behaviors of the three
color groups. At every age level, Asian women have the lowest mean
number of children, Afro-Brazilian women have the highest mean number
of children, and the mean number of children for white women is above that
of Asians but below that of Afro-Brazilians. Expectedly, the age group of 15-19
for all three color groups has very few children, particularly Asian, who, on
average, have only 0.008 children. Furthermore, the mean number of
children for Asian women ages 20-24 and 25-29 are extremely low; only 0.18
and 0.69 respectively. In contrast, the fertility levels of whites and Afro-
Brazilians are much higher than that of Asians in these two age groups. The
fertility differences among the color groups decrease for older age groups, but
the basic pattern still remain. In sum, Asian women not only have fewer
children on the average but also have children at older ages than do white
women, who in turn have fewer children and have children at older than do
Afro-Brazilian women.

60
Table 3.3
Mean Children Ever Born to Women of 15-49 Years of Age
by Age and Color Groups, Metropolitan Sao Paulo, Brazil (1980)
Age Group
Mean
Asian
White
Afro-Brazilian
15-19
0.12
0.01*
0.11
0.15
20-24
0.77
0.18
0.73
0.95
25-29
1.66
0.69
1.59
1.95
30-34
2.50
1.56
2.39
2.96
35-39
3.28
2.35
3.11
4.01
40-44
3.85
2.89
3.62
4.81
45-49
4.17
3.51
3.93
5.30
Total
1.89
1.44
1.82
2.18
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*The actual value is 0.008.
In Table 3.4, we see the fertility differences among the three color
groups, controlling for both educational level and age. There are two
interesting observations to make here with regard to the fertility level of the
three color groups by educational level. First, fertility differences among the
color groups for women with no schooling are very small (3.63 for Asians,
3.84 for whites and 3.96 for Afro-Brazilians). Second, the fertility levels of
Afro-Brazilians at all educational levels, except for the one of no schooling,
are the lowest among the three color groups.
It may seem surprising for Afro-Brazilians to have lower fertility levels
than those of whites and Asians at all educational levels but the first (no
schooling). This suggests that education may have greater negative impact on
the fertility behavior of Afro-Brazilian women than on that of white and
Asian women. I will test this hypothesis in the multivariate regression
analyses later in this chapter. However, the main reason is, in my opinion,

61
the disproportionate distribution of Afro-Brazilians in educational level.
Because over 20% of Afro-Brazilians have no schooling, compared to 9.9% of
whites and 3.2% of Asians, their overall fertility level is still higher than
those of whites and Asians, despite their lower fertility levels at all the other
levels.
When the three color groups are compared by age group within the
same educational level, Asians have children at older ages than do whites at
all levels, and whites have children at older ages than do Afro-Brazilians at
levels of less than 9 years of schooling. For example, Asian women between
ages 15 and 19 rarely have children at all educational levels, while the mean
number of children for white and Afro-Brazilian women ages 15-19 with less
than 5 years of schooling is more than 0.20. Furthermore, the mean number
of children for Asian women between ages 20 and 29 ranges from 0.22 to 0.96,
while the mean for white women of the same age group ranges from 0.36 to
2.07, and that for Afro-Brazilian women of the same age group ranges from
0.17 to 2.12. At higher educational levels (9 or more years of schooling),
however, Afro-Brazilian women have fewer children than do white women
in all age groups and Asian women in most age groups (see Table 3.4).
The fertility levels of the three color groups, controlling for income
and age, are shown in Table 3.5. First, we see the negative association
between income and fertility level, i.e., lower income groups have higher
fertility levels. The mean number of children for women from the lowest to
the highest income level are 2.19, 1.25, 1.19, and 1.16, respectively. The
difference between the fertility level of women in the first and second income
levels is most pronounced (.83), and the differences among the upper three
levels are not as obvious.

62
Table 3.4
Mean Children Ever Bom to Women of 15-49 Years of Age
by Education, Age and Color Groups , Metropolitan Sao Paulo, Brazil (1980)
Years of School
Total
Asian
White
Afro-Brazilian
Zero Years
3.89
3.63
3.84
3.96
15-19
0.29
0.00
0.28
0.29
20-29
2.09
0.66
2.07
2.12
30-39
4.22
3.20
4.07
4.44
40-49
5.55
4.35
5.37
5.94
1-4 Years
2.30
2.55
2.32
2.26
15-19
0.21
0.02
0.21
0.20
20-29
1.55
0.96
1.54
1.60
30-39
3.02
2.39
2.96
3.29
40-49
3.83
3.32
3.70
4.51
5-8 Years
0.92
1.18
0.95
0.78
15-19
0.08
0.01
0.07
0.09
20-29
0.99
0.64
1.01
0.94
30-39
2.16
1.94
2.14
2.28
40-49
2.70
3.04
2.60
3.45
9-11 Years
0.73
0.73
0.77
0.49
15-19
0.02
0.00
0.02
0.02
20-29
0.53
0.36
0.56
0.40
30-39
1.75
1.63
1.80
1.35
40-49
2.31
2.52
2.32
1.79
12+ Years
0.81
0.61
0.85
0.55
15-19
0.00
0.00
0.00
0.20
20-29
0.35
0.22
0.36
0.17
30-39
1.40
1.08
1.43
1.15
40-49
1.98
1.66
2.02
1.24
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

63
What's surprising about the distribution of income levels is that over
two thirds (68.8%) of the women ages 15-49 belong to the lowest income level,
and over five sixths (85.7%) are in the bottom two income levels. This is a
vivid description of the labor force participation and the economic status of
the women under study here.
Table 3.5
Mean Children Ever Born to Women of 15-49 Years of Age
by Income, Age and Color Groups, Metropolitan Sao Paulo, Brazil (1980)
Income Level
Total
Asian
White
Afro-Brazilian
To 1 MW
2.19
1.85
2.11
2.48
15-19
0.14
0.00
0.13
0.18
20-29
1.55
0.75
1.49
1.74
30-39
3.20
2.45
3.05
3.80
40-49
4.39
3.47
4.14
5.50
To 2 MW
1.25
0.74
1.17
1.48
15-19
0.04
0.01
0.03
0.06
20-29
0.62
0.13
0.55
0.77
30-39
2.61
1.44
2.52
2.83
40-49
3.57
2.70
3.41
3.99
To 3 MW
1.19
0.59
1.11
1.60
15-19
0.04
0.00
0.03
0.06
20-29
0.44
0.14
0.40
0.63
30-39
1.97
0.98
1.88
2.37
40-49
2.95
2.32
2.80
3.54
Above 3 MW
1.16
0.87
1.17
1.27
15-19
0.05
0.00
0.03
0.23
20-29
0.42
0.17
0.42
0.48
30-39
1.48
1.00
1.49
1.68
40-49
2.21
2.27
2.18
2.63
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

64
At every income level, the fertility level for Asian is lower than that of
whites, which is in turn consistently lower than that of Afro-Brazilians.
However, the gaps between the means for Asians and those for whites in
every income group are much bigger than those between the means for
whites and those for Afro-Brazilians, indicating again that Asians are
significantly different from the other two groups, as far as fertility is
concerned, even when income is controlled. More importantly, this shows
that, after controlling for income, there are still fertility variations among the
three color groups. When both income and age are controlled, the mean
number of children for Afro-Brazilian women is higher than that for white
women at all income levels and in all age groups, and the mean number of
children for white women is higher than that for Asian women at all income
levels and in all age groups.
Table 3.6 shows the fertility differences among the three color groups,
controlling for residence and age. As expected, women in rural areas have a
much higher fertility level than their urban counterparts. In fact, the fertility
level for rural women is 42% more than that for urban women (2.58 for rural
women vs. 1.81 for urban women). However, because rural women comprise
only 9.6% of the population of women, their high fertility level has little
impact on the fertility of the total population.
Color differences remain much the same in all age groups as well, after
controlling for residence. The pattern shown here conforms to the general
pattern exhibited by the data so far, i.e., Asians have fewer children on
average than do whites, who in turn have fewer children on average than do
Afro-Brazilians, whether they are in urban or rural areas. At the same time,
place of residence has a uniform effect on all three groups; the mean fertility

level for rural women, whether Asian, white or Afro-Brazilian, is
consistently higher than that for urban women.
65
Table 3.6
Mean Children Ever Born to Women of 15-49 Years of Age
by Residence, Age and Color Groups, Metropolitan Sao Paulo, Brazil (1980)
Residence
Total
Asian
White
Afro-Brazilian
Urban
1.81
1.74
2.09
1.39
15-19
0.11
0.10
0.15
0.01
20-29
1.12
1.07
1.34
0.42
30-39
2.73
2.60
3.28
1.85
40-49
3.83
3.60
4.84
3.08
Rural
2.58
2.49
2.94
1.81
15-19
0.17
0.18
0.17
0.00
20-29
1.71
1.66
1.95
0.44
30-39
3.96
3.81
4.59
2.62
40-49
5.54
5.34
6.45
3.88
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Table 3.7 illustrates color differentials in fertility, controlling for both
residence and education. Again, in both urban and rural areas, color
differences in fertility for women with no schooling are very small. The
mean number of children for Asian, white and Afro-Brazilian women of this
category in urban areas are 3.51, 3.73 and 3.85, respectively, whereas those for
the three color groups in rural areas are 4.25, 4.22 and 4.51, respectively.
In urban areas, the fertility level of Afro-Brazilians with any schooling
above one year is lower than not only that of their white counterparts and
also that of their Asian counterparts. This is surprising considering that the
overall fertility level for Afro-Brazilians is much higher than those of the
other two groups. Interestingly enough, Asian fertility level exceeds that of

66
whites at the levels of 1-4 and 5-8 years of schooling (2.52 and 1.21 for Asians
vs. 2.32 and 0.97 for whites). For the top two educational levels (9-11 and 12 or
more years of schooling), whites have slightly higher fertility level than
Asians; 0.77 and 0.85 for whites and 0.73 and 0.62 for Asians.
Table 3.7
Mean Children Born to Women of 15-49 Years of Age
by Residence, Education and Color, Metropolitan Sao Paulo, Brazil (1980)
Residence
Mean
Asian (%)
White (%)
Afro-Brazilian (%)
Urban
1.81
1.39 (100.0)
1.74(100.0)
2.09(100.0)
Zero
3.77
3.51 (2.3)
3.73 (8.4)
3.85 (18.7)
1-4
2.30
2.52 (21.8)
2.32 (43.5)
2.25 (52.1)
5-8
0.93
1.21 (15.5)
0.97 (23.1)
0.79(21.8)
9-11
0.74
0.73(21.5)
0.77(16.1)
0.48 (6.1)
12+
0.82
0.62 (15.6)
0.85 (8.8)
0.55(1.4)
Rural
2.58
1.81 (100.0)
2.49 (100.0)
2.94 (100.0)
Zero
4.31
4.25 (4.9)
4.22 (24.0)
4.51 (35.5)
1-4
2.29
2.74 (45.4)
2.26 (60.9)
2.35 (54.0)
5-8
0.72
0.94 (23.1)
0.72 (9.8)
0.67 (8.7)
9-11
0.60
0.65 (19.4)
0.60 (4.2)
0.52 (1.7)
12+
0.75
0.16 (7.3)
0.87 (1.1)
0.49 (0.09)
Total
1.89
1.44
1.82
2.18
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Note: The percentages in brackets are the proportions of people belonging to
various educational levels within color groups.
The patterns in rural areas are quite different; the fertility level of
Asian is higher than that of whites at all levels except at the level of 12 or
more years of schooling, and the fertility level of Afro-Brazilian is lower than
that of white at all levels except the two lowest levels. However, since more
whites and Afro-Brazilians are concentrated at the two lower educational

67
levels (about 85% of whites and 90% of Afro-Brazilians vs. about 50% of
Asians), their overall fertility levels are still higher than that of Asians. For
people with no schooling at all, the mean number of children for Afro-
Brazilians (4.51) is the highest among the three groups (4.25 for Asians and
4.22 for whites). Of those with 1-4 years of schooling, Asians have the highest
fertility level, 2.74, compared to 2.35 for Afro-Brazilians and 2.26 for whites.
At the levels of 5-8 and 9-11 years of schooling, Afro-Brazilians have the
lowest mean (0.67 and 0.52), but they account for only less than 10% of their
rural population. The low fertility level of whites and Afro-Brazilians with
twelve or more years of schooling (0.87 for the former and 0.49 for the latter)
does not contribute much to their overall fertility level because they account
for only about 1% of their respective populations. On the other hand, the
fertility level of Asians with 12 or more years of schooling (0.16), which is
substantially lower than that for the two other groups, affects their overall
fertility level since they account for more than 7% of Asian in rural areas.
Considering the overall mean fertility level for each group, it appears
that there are two causes for the unpredicted distribution: 1) proportionally,
Asian women are over-represented in the top two educational levels (about
46%), compared to whites and Afro-Brazilians (about 13% and 7%,
respectively); 2) Afro-Brazilians are over-represented in the category of no
schooling (18.7% in urban areas and 35.5% in rural areas). Thus, the effect of
education seems to be different for the three groups. In particular, education
seems to have greater negative impact on the fertility level of Afro-Brazilians
than on that of whites and Asians. If this is true, the results here then
support the minority status hypothesis, which assumes that highly educated
minority members tend to have fewer children than their majority

68
counterparts. It also suggests that one's educational level is an important
factor in determining one's fertility level, regardless of residence and color.
Table 3.8 describes the color differentials in fertility, controlling for
residence and income simultaneously. As shown above, fertility levels for all
groups in urban areas are lower than those in rural areas, and Asians have
lower fertility levels than whites in every income level, who in turn have
lower fertility levels than Afro-Brazilians, in both urban and rural areas.
Table 3.8
Mean Children Ever Born to Women of 15-49 Years of Age
by Residence, Income and Color, Metropolitan Sao Paulo, Brazil (1980)
Residence
Mean
Asian
White
Afro-Brazilian
Urban
1.81
1.39
1.74
2.09
To 1 MW
2.12
1.80
2.04
2.40
To 2 MW
1.24
0.76
1.15
1.46
To 3 MW
1.18
0.62
1.10
1.55
Above 3 MW
1.16
0.88
1.16
1.26
Rural
2.58
1.81
2.49
2.94
To 1 MW
2.67
2.11
2.57
3.04
To 2 MW
1.68
0.57
1.61
1.91
To 3 MW
1.98
0.22
1.56
3.64
Above 3 MW
1.78
0.43
1.87
2.92
Total
1.89
1.44
1.82
2.18
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
To find out the degree of association between fertility level and the
independent variables, while controlling for some or all the other variables, I
use a set of multivariate regression analyses. The categorical variables,
residence and color, are treated as a set of dummy variables. The dummy
variable of urban areas is treated as the reference group and the dummy
variable of rural areas is compared to it. Similarly, the dummy variable of

69
whites is considered as the reference group, against which the other two color
groups are compared. Age and years of schooling are treated as interval
variables without modifications, but income is treated as an interval variable
with modifications such that the minimum wage in 1980 (4,150 cruzeiros) is
used as the unit of income, instead of the original unit (one cruzeiros) in the
census.
In order to compare the effects of various variables on fertility level, a
total of seven regression models are developed. The first model measures the
effects of age and residence, the second one measures the effects of not only
age and residence but also the socioeconomic variables, education and
income. The third model measures the effects of age, residence and color, and
the fourth one, the complete model, measures the effects of all the variables
examined here. Models 5-7 are developed solely to examine whether
education and income have different effects on different color groups.
Based on the findings in the previous descriptive analysis, I first build a
regression model with only age and residence. This model tells us three
things: 1) One unit of increase in age increases the mean number of children
by 0.1461, with residence included in the model; 2) being in rural areas
increases the mean number of children by 0.8294, with age considered; 3) this
model with the two variables explains 36.7% (the R-square for the model) of
the total variation in fertility for all the people in the sample data (see Model
(1) in Table 3.9).
To examine the cumulative effects of age, residence and socioeconomic
variables on fertility, education and income are entered into the existing
model. We can interpret Model 2 in the following way: 1) A significant
increase from 36.7% to 43.5% in the R-Square indicates that education and
income explain 6.8% of the variance in fertility that is unexplained by the

70
variables in Model 1; 2) when education and income are introduced into the
model, the coefficient of age decreases slightly, but the coefficient of rural
areas (as opposed to urban areas) decreases dramatically by more than 50%,
suggesting a relatively high degree of covariation between residence,
education and income; 3) the negative signs of the coefficients of education
and income indicate a negative correlation between education and fertility,
and between income and fertility. More specifically, one year of increase in
schooling reduces the mean number of children by 0.2838, and an increase of
one minimum wage in average income reduces the mean number of
children by 0.0997.
In order to measure the effects of color, and to compare them to those
of education and income, Model 3 is obtained by adding the dummy variables
representing Afro-Brazilians and Asians (whites is the reference group) into
the first model. There are several things to point out here: First, unlike in
Model 2, there are little changes in the coefficients for age and rural areas in
Model 3, compared to Model 1, indicating that variations in age and residence
do not contribute much to the color differences in fertility. Second, when
Afro-Brazilians and Asians are compared to whites, they both differ
significantly from whites; the positive sign of the coefficient for Afro-
Brazilians indicates a higher fertility level than that of whites, and the
negative sign for the coefficient of Asians indicates a lower rate relative to
whites. Specifically, controlling for age and residence, Afro-Brazilians, on
average, have 0.4974 more children than do whites, and Asians, on average,
have 0.6241 fewer children than do whites. Third, a mere increase of 0.97% in
the R-square for Model 3 suggests that the color variables account for only less
than 1% of the total variations in fertility that is not explained by age and
residence. In other words, the third model with the color variables is no

better than the first model without the color variables in explaining the total
variations in fertility for the sample data.
7 1
Table 3.9
Children Ever Born to Women Aged 20-49
Regressed on Age, Residence, Education, Income and Color
Independent
Models
Variables
(1)
(2)
(3)
(4)
(5)
W *
(6)
AB*
(7)
A*
Age
.1461
.1306
.1473
.1317
.1230
.1669
.1125
Residence
Urban (Reference)
Rural
.8294
.3635
.8261
.3793
.3449
.4694
.2070
Education
-.2838
-.2720
-.2795
-.2272
-.2080
Income**
-.0997
-.0992
-.0851
-.2771
-.0809
Color
Whites (Reference)
Afro-Brazilians
Asians
.4974
-.6241
.1827
-.2476
R2
.3674
.4351
.3771
.4364
.4382
.4400
.4996
Constant
-2.4024
-.6340
-2.5355
-.7518
-.4902
-1.445
-.7650
Note:
*W = Whites, AB =
Afro-Brazilians,
and A =
Asians
**The unit of income is the minimum wage in 1980 (4,150 cruzeiros).
P-value for all coefficients < .000.
As expected, the fourth model, the complete model with all the
variables in it, is very similar to the second model. Compared to the second
model, the coefficients of age and rural areas (as opposed to urban areas)
increase slightly, while the coefficients of education and income decrease

72
slightly. Meanwhile, the R-square in Model 4 increase only 0.13% to 43.64
from 43.51% in Model 2. This indicates that the color variables explain only
0.13% more of the total variation in fertility that is not explained by the other
variables in Model 2. However, compared to Model 3, the coefficients of
Afro-Brazilians and Asians (as opposed to whites) drop significantly from
0.4974 to 0.1827 for the former and from -0.6241 to -0.2476 for the latter. This
suggests that when the effects of age, residence, education, income and color
are measured simultaneously, Afro-Brazilians and Asians differ less from
whites in fertility level. To put it differently, most of the fertility differences
between whites and Afro-Brazilians, and between whites and Asians are due
to factors other than color.
Models 5, 6 and 7 are developed to test the hypothesis that education
and income have different effects on different color groups. I run a separate
regression analysis for each of the three group, with the variables of age,
residence, education and income; Model 5 is for whites, Model 6 is Afro-
Brazilians and Model 7 is for Asians. Thus, we can measure the effect of the
same variable on different color group by comparing the coefficients of this
variable across Models 5, 6 and 7.
First, the coefficients of education in the three models show that
education does have different effects on the fertility levels of the three color
groups, but not in the order I expected. Specifically, the coefficients of
education in Models 5-7 (-0.2795 for whites, -0.2272 for Afro-Brazilians and
-0.2080 for Asians) tell us that the (negative) effect of education is greater for
whites than it is for Afro-Brazilians, and it is the least for Asians. In other
words, a one unit increase in education reduces the mean number of children
by 0.2795 for whites, by 0.2272 for Afro-Brazilians and by 0.2080 for Asians.

73
Second, the (negative) effect of income on fertility level is much greater
for Afro-Brazilians than it is for whites and Asians, as indicated by the
coefficients of income the three models. We can interpret the coefficients of
income in the following way; a one unit of increase in income, i.e., an
increase of 4,150 cruzeiros, results in a reduction of 0.2771 in the mean
number of children for Afro-Brazilians, while it results in a reduction of
0.0851 and 0.0809 in the mean number of children for whites and Asians,
respectively. Finally, the R-squares in Models 5 and 6 (0.4382 and 0.4400) are
very similar, but they are somewhat different from the R-square in Model 7
(0.4996). This indicates that the variables in the models explain
approximately the same amount of variance in fertility for whites and Afro-
Brazilians, but they explain slightly more of the variation in fertility for
Asians.
In addition, we see the coefficient of age for Afro-Brazilians in Model 6
(0.1669) is considerably higher than those for whites (0.1125) and Asians
(0.1230). This indicates that age has greater positive impact on the fertility
level of Afro-Brazilians than that of whites or Asians, which confirms the
conclusion from the descriptive analysis that Afro-Brazilians have children at
younger ages than do the other two groups. We also notice that the
coefficient of the dummy variable, rural areas, for Afro-Brazilians in Model 6
(0.4694) is much higher than that for whites (0.3449) in Model 5 and that for
Asians (0.2070) in Model 7. This suggests that the gap between the fertility
level of urban and rural residents is bigger for Afro-Brazilians than it is either
for whites or Asians, which is consistent with the result of the descriptive
analysis.

Summary
The descriptive analyses of the sample data show that the fertility level
of Brazilian women varies by age, color, education, income and place of
residence. When age is controlled, Asians have the lowest mean number of
children and Afro-Brazilians have the highest mean, with whites in between
in every age group.
When education and age are controlled simultaneously, the existing
patterns of fertility differences among the three color groups change
completely; the fertility level of Afro-Brazilians is the lowest among the three
group, except at the level of no schooling, even where the color differences
are minimal. This indicates that fertility is associated with more with
education than with color. On the other hand, Asians have children at older
ages than do whites, and whites have children at older ages than do Afro-
Brazilians at the educational levels of less than 9 years of schooling. At
higher educational levels, it is just the opposite; Afro-Brazilians have fewer
children than do whites in all age groups, and do Asians in most age groups.
Color differences in fertility narrow a great deal when income and age
are controlled simultaneously. The change in fertility level is most
pronounced between the first and second income level for all three color
groups. However, in spite of the decreasing gaps among the three groups at
higher income levels, Asian fertility level is the lowest, white fertility level is
in the middle, and Afro-Brazilian fertility level is the highest at all age levels.
When residence and age are controlled simultaneously, color differences in
fertility remain much the same for all age groups; Asians have fewer children
on average than do whites, who in turn have fewer children than do Afro-
Brazilians.

When both residence and education are controlled, there are some
interesting changes in the fertility differences among the three color groups.
In urban areas, Asian fertility level exceeds that of whites at the educational
levels of 1-4 and 5-8 years of schooling, while the opposite is true at higher
levels for these two groups. As before, the fertility level of Afro-Brazilians is
the lowest among the three groups, except at the level of no schooling. In
rural areas, Asians have the highest mean number of children among the
three groups, except at the level of no schooling, where Afro-Brazilians have
the highest mean. Nonetheless, the overall mean number of children for
Asians is still the lowest in both urban and rural areas due to their much
higher concentration at higher educational levels than the other two groups.
Color differences in fertility do not change much when both residence and
income are controlled.
The multivariate regression models show quantitatively the effects of
various independent variables on fertility level. Model 1 in Table 3.9 tells us
that both age and rural areas (as opposed to urban areas) are positively
associated with fertility level, though the impact of the latter is much greater,
and age and residence account for 36.7% of the total variation in fertility for
the sample. The variables representing socioeconomic status, education and
income, explain 6.8% more of the variation in fertility, with education
having greater negative impact than income on fertility (see Model 2 in Table
3.9). Specifically, the mean number of children reduces by 0.2838 with one
unit (year) increase in schooling, and reduces by 0.0997 with one unit (4,150
cruzeiros) increase in average income.
Model 3 in Table 3.9 shows that color accounts for only 0.97% of the
total variation in fertility that is not explained by age and residence.
However, it also shows that Afro-Brazilians and Asians are significantly

different from whites in terms of fertility; on average, the mean number of
children for Afro-Brazilians is 0.4974 higher than that of whites and the mean
number of children for Asians is 0.6241 lower than that of whites, after
controlling for age and residence. A comparison of the R-square values in
Models 2 and 3 indicates that socioeconomic status, i.e., education and
income, has far greater impact than does color on fertility.
Model 4, the complete model with all the variables, is very similar to
Model 2, which does not include the dummy variables for color. Compared
to Model 2, the R-square increases only 0.13% in Model 4, suggesting that the
negligible effect of color on fertility, after controlling for the other variables in
the model. However, the large decreases in the coefficients of the dummy
variables for color from Model 3 to Model 4 indicate that controlling for
education and income, the three color groups do not differ as much as they
did before these variables were controlled.
These findings support the social characteristics approach because in
general groups with higher educational attainment and higher income have
lower fertility levels. For example, Asians have the highest educational
attainment (a mean of 6.65 years of schooling) and highest mean income
(7,261 cruzeiros) among the three group, and their fertility level is the lowest.
Likewise, Afro-Brazilians have the lowest educational attainment (a mean of
3.23 years of schooling) and lowest mean income (3,030 cruzeiros) among the
three color groups, hence the highest fertility level. The overall educational
level and mean income of whites rank second (4.9 years of schooling and
4,783 cruzeiros), and therefore, their overall fertility level is above that of
Asians and below that of Afro-Brazilians. (The above values for mean years
of schooling and mean income are obtained from the same data set and
described in later chapters.)

77
In addition, the fact that Asians differ more from whites than do Afro-
Brazilians suggests that cultural factors, such as religion and values and
norms on fertility behavior might be at work. Unfortunately, since the
census data do not allow us to examine the effect of cultural factors on
fertility, I can not address this issue empirically. In order to adequately
examine the complex causes of differential fertility outcomes among various
social groups, we need to conduct qualitative, as well as quantitative, research,
and consider a range of factors that are relevant to the problem.

CHAPTER 4
CHILD MORTALITY DIFFERENTIALS
AMONG ASIANS, WHITES AND AFRO-BRAZILIANS
Major Determinants of Mortality and Racial/Ethnic Differentials in Mortality
It is well known in the demographic literature that the mortality rate of
a population is determined not only by biological factors (e.g., age, sex and
some genetic differences) and environmental factors (e.g., climate and natural
resources) but also by socioeconomic factors (e.g., income, education and
occupation) and cultural factors (e.g., membership in different racial/ethnic
group, religious affiliation, and customs and practices related to health status).
In other words, mortality rate of a population is the result of the interplay
between the biological, environmental, socioeconomic and cultural
conditions of the society in which the population in question live at a
particular time period. On the relationship among the above factors, Vallin
(1980:27) pointed out that:
There is growing evidence that, within a framework of biological
constraints (progressive aging of the body, limited life-span), and
taking into account the geographical context that may modify
these constraints, the main differences in mortality are of
socioeconomic and cultural origin.
Wood and Lovell, citing Birdsall (1980), maintained that the level of
mortality in a population is the result of the interaction of three sets of factors:
78

79
(1) public health services, which influence mortality regardless
of individual behavior (such as spraying insecticides that control
malaria); (2) health and environmental services that reduce the
costs of health care but require some individual responses (e.g.,
the availability of clean water); (3) and an array of individual
characteristics (such as income, which affects health through
nutrition and housing, and education) associated with the speed
and efficiency with which individuals respond to health services
and environmental threats (1992:709).
Because mortality is the result of the interaction among these complex
factors, it is an important indicator of quality of life of a population or a sub¬
population that has its distinctive characteristics. Similarly, infant and child
mortality rates provide a summary measure of the quality of life of a
population, especially in developing countries, since they are also very
sensitive to the conditions of the above factors, in addition to the
"endogenous" and "exogenous" causes that are particular to infancy and early
childhood, respectively. Infant and child mortality rate is therefore used as a
fairly reliable index of social and public health conditions throughout the
world.
In modern societies that are marked by socioeconomic differences, we
see a great deal of variation among various social groups in terms of
mortality rate. When socioeconomic differences are largely based on
racial/ethnic group affiliation, as they are in many societies, mortality
differentials vary along racial/ethnic lines as well. For example, in the
United State, blacks have had a higher mortality rate than whites since 1940,
when the comparable data between whites and blacks became first available
(Farley and Allen 1989). Although the gap between the mortality rate of
whites and blacks had reduced somewhat up until the early 1980s, it has
unfortunately been on the rise in the second half of the 1980s. After

80
analyzing race differences in adult mortality, with controls for
sociodemographic factors, Rogers (1992) found that: 1) The demographic
variables, race, age and sex, appear to be related significantly to mortality
when no other variables are controlled, 2) when family size and marital
status or socioeconomic status is controlled separately, racial differences in
mortality reduces considerably, 3) when all of the sociodemographic variables,
age, sex, marital status, family size and income, are controlled
simultaneously, race differences in mortality are eliminated. Thus, it is the
sociodemographic factors, not race itself, that are the real causes of mortality
differentials between whites and blacks in the United States.
Wood and Lovell (1992) examined racial inequality in child mortality
and life expectancy in Brazil, using the 1950 and 1980 Brazilian Census. They
found that although the life expectancy for whites and nonwhites increased
by more than 18 and 19 years, respectively, from 1950 to 1980, the gap between
them remained about the same over the 30-year period: "In 1950, whites
outlived nonwhites by 7.5 years; in 1980, the comparable figure was 6.7 years"
(1992:721). Farley and Allen (1989:47) reported the difference in infant
mortality between whites and blacks in the U. S. during 1980s: "black children
are about twice as likely as white children to die before attaining their first
birthday." They also described the differences in the life span between whites
and blacks in 1980; the life expectancy of white men (70.7 years) was seven
years longer than that of blacks (63.7 years), and the life expectancy of white
women (78.1 years) was 5.8 years longer than that of black women (72.3 years).
The purpose of this chapter is to use child mortality as a measure to
compare the social wellbeing of Asians, whites and Afro-Brazilians in Brazil.
As in other analyses in this study, the objective is to determine (A) if children
born to Asian women have lower mortality than white and Afro-Brazilian

81
children, and (B) to find out whether differences are due exclusively to
socioeconomic standing. If skin color continues to explain variences in child
mortality, as I expect, then the findings suggest (although do not directly test)
that cultural factors may be at work.
Child Mortality Differentials and Life Expectancy by Color Group
In this chapter, I first describe a few of the major socioeconomic
indicators for Brazilian women of the three color groups, and then measure
child mortality level of each group, using the indirect methods developed by
Brass (Brass et al. 1968) and Trussell and Preston (Trussel and Preston 1982)
(See Appendix B). Finally, I will analyze the association between the
socioeconomic indicators and mortality level by applying the Tobit regression
procedure.
The sample data used here for mortality measurement of Brazilian
women consist of households with women aged 20-29, with at least one live
birth. The variables selected as indicators of socioeconomic status of Brazilian
women are the educational attainment of both the wife and husband,
monthly household income, participation in the social security system and
presence of piped water in the house. The importance of parental education
(especially mother's) and household income on child mortality is widely
documented in the literature. The educational attainment of the wife and
husband is here measured by the number of years of school completed by
them, and described as mother's and father's education in the following
discussion. The variable, monthly household income, refers to the sum of
both husband's and wife's income (in cruzeiros) from various sources (see

82
Chapter 1 for details). Whether or not a household participate in the social
security system is an indicator of access to public health facilities because
membership in the social security system entitles people to medical services
(Wood and Lovell 1992). Presence or absence of running water in the house
is an important indicator of housing quality, which has a significant effect on
child mortality (Merrick 1985, Wood and Lovell 1992). Table 4.1 shows
marked differences in socioeconomic indicators of the three groups:
TABLE 4.1
Social Indicators by Color Group, Metropolitan Sao Paulo, Brazil (1980)*
Social Indicator
Total
(1)
Afro
(2)
White
(3)
Asian
(4)
Mother's Education**
3.9
3.2
4.1
5.5
Father's Education**
4.0
3.4
4.3
5.5
Household Income***
28,773
21,588
31,276
72,227
% with social Security
86.8
83.3
88.3
88.9
% with Piped Water
81.1
68.5
86.4
98.0
*The data include households with women aged 20-29 years, with at least one
live birth.
’"'Average years of school completed
***In 1980 Cruzeiros
Afro-Brazilian women have the lowest educational attainment of the
three populations. The average years of schooling among Afro-Brazilians
(3.2) is about a year below the comparable figure for white women (4.1), and
over two years below that of Asian women (5.5). The same pattern holds for
father's education, monthly household income, and the percent of homes
with internal plumbing (a measure of water and housing quality). Average
household income shows the largest variation: the average monthly income

83
among white households is about 45 percent higher than the income earned
by Afro-Brazilians; Asian households, on the other hand, enjoy an income
level that is 335 percent higher than that of Afro-Brazilians and 231 percent
higher than that of whites. In contrast, the differences in the percent of
households having the social security system is the smallest among the three
color groups; 83.3% of Afro-Brazilian households, 88.3% of white households
and 88.9% of Asian households.
Because the level of mortality of a population or a subpopulation is
determined by the combined effects of such factors as income, housing,
education and access to medical care, I expect to find corresponding
differences in the survival probabilities of children born to white, Asian and
Afro-Brazilian women.
Advances in indirect techniques of estimating the probability of death
in the early childhood years have greatly enhanced the scope and accuracy of
mortality research. Traditional measures of the death rate rely on vital
registration statistics. The alternative approach, developed by William Brass
(Brass et al. 1968), measures mortality indirectly from survey or census data.
In the Brass method, the proportion of children surviving to mothers in
different age groups (20-24; 25-29 and 30-34), multiplied by the appropriate
correction factor, yields estimates of the probability of death by exact ages 2, 3
and 5. In the following, I estimate child mortality level for Asians, whites
and Afro-Brazilians, using the Brass method. A detailed description of the
Brass method by Wood and Lovell (1992) is included in Appendix 4.1.
Table 4.2 shows the estimates of mortality among children born to
white, Asian and Afro-Brazilian mothers. The 2^0 value refers to the
probability of death by age two, the 3^0 and 5^0 values refer to the probability
of death by age three and five, respectively. The estimates show that by age

84
two, mortality among Afro-Brazilian children -- 116 per thousand -- is the
highest of all three groups (82 and 51 per thousand for whites and Asians
respectively). In fact, the probability of death by age two among Afro-
Brazilians is 1.41 times higher than the comparable figure for white children,
and 2.27 times higher than the estimate for Asian children. The same pattern
holds for the probability of death between birth and ages 3 and 5 among the
three groups, i.e., the mortality estimate of Afro-Brazilians is higher than that
of whites, which is in turn higher than that of Asians.
Table 4.2 also presents e° values, the average number of years expected
at birth. They are calculated from the three estimates of child mortality, by
using model life tables (e.g., Coale and Demeny 1983). The e° estimates
indicate an expectation of life of 59.14 years for Afro-Brazilian, 65.77 years for
whites, and 72.12 years for Asians. In other words, based on the child
mortality levels of the sample data, Asians are expected to live 6.35 more
years than whites, who are, in turn, expected to live 6.63 more years than
Afro-Brazilians. These measures are interpreted as the life expectancy at birth
associated with the levels of infant and child mortality estimated among the
children born to women 20 to 34 years of age who declare themselves to be of
a given skin color in the census interview.
The mortality differentials shown in Table 4.2 raise an important
question: If we control for the major determinants of racial inequality (the
variables presented in Table 4.1), do the children of Asian women continue to
experience lower death rates compared to the children born to white or Afro-
Brazilian mothers? If the mother's skin color is no longer statistically
significant after controlling for key social indicators, we can conclude that the
observed differences in child survival are due to differences in socioeconomic
standing. Alternatively, if the mother's skin color continues to be associated

with the mortality of her children, the results indicate that additional factors
are at work.
85
TABLE 4.2
Measures of Child Mortality by Color Group,
Metropolitan Sao Paulo, Brazil (1980)
Mortality
Measure*
Total
Afro-
Braizilian
White
Asian
2%
.116
.082
.051
3%
.126
.087
.054
5%
.134
.093
.056
e°
59.14
65.77
72.12
Mortality Ratio
1.09
1.39
.96
.49
*xclo is the probability of death between age 0 and exact age x. e° is the average
number of years of life expected, associated with the x^o values (south model
life table). The mortality ratio is the mean value of the ratio of actual to
expected proportion dead among children of women with at least one live
birth.
To simultaneously control for the several independent variables it is
necessary to apply multivariate techniques. Rather than relying on mortality
rates for groups of women by age, as in the Brass method noted above, we
need, as a dependent variable, a measure of the mortality experience for each
woman in the sample. Trussell and Preston (1982) proposed a method for
calculating just such a measure. The Trussell-Preston technique provides a
mortality index that is based on the ratio of actual to expected mortality for
every woman who has experienced at least one live birth. The procedures for
estimating the expected number of deaths are described by Wood and Lovell
(1992) (see Appendix B). The mean value of the mortality ratio for the

86
population as a whole should be 1.00. Indeed, the estimate of the average
mortality ratio for metropolitan Sao Paulo is 1.09, as shown at the bottom of
Table 4.2. Among Afro-Brazilians, the mortality ratio is 1.39, indicating that
the actual number of deaths substantially exceeds the expected number. On
the other hand, the mortality ratio of .96 for whites is slightly below the
expected number, and the mortality ratio of .49 for Asians is about half that of
the total population. In effect, the mortality ratio confirms the racial
differentials in child mortality estimated in terms of x^O and e() values in
Table 4.2.
The mortality ratio for individual women is of additional value
because it permits the use of regression analysis. For the reasons discussed in
Appendix 4.1, the Tobit regression procedure is the appropriate in this case.
The results of regressing the mortality ratio on the various social indicators
are given in Table 4.3. The model refers to the population of all women 20 to
29 years of age in metropolitan Sao Paulo. The negative signs for the
coefficients indicate that maternal and paternal levels of education reduce
mortality, as does income, membership in the social security system, and the
presence of piped water in the home.
The numbers given in parentheses in Table 4.3 below the regression
coefficients are measures of elasticity. On the basis of these estimates, we can
conclude that a one percent increase in mother’s education reduces mortality
by 13.5 percent. Father's educational attainment and household income also
reduce mortality, but to a lesser degree, as indicated by elasticities of .095 and
.072, respectively. Similarly, net of the effects of the other variables in the
equation, belonging to the social security system and the presence of running
water inside the house are associated with a 22.8 and a 35.2 percent reduction
in infant and child mortality.

87
TABLE 4.3
Mortality Ratio for Children Ever Born to Women
20-29 Years of Age, Regressed on Social Indicators
Metropolitan Sao Paulo, 1980
Independent Variable
Tobit Coefficient
(elasticity)
Mother's Years of School
-.580
(-.135)
Father's Years of School
-.408
(-.095)
Household Income (log)
-.307
(-.072)
Social Security
Yes
-.928
(-.228)
No*
Piped Water
Yes
-1.421
(-.352)
No*
Color
Afro-Brazilian
1.010
(.242)
Asian
-3.185
(-.578)
White*
—
-2 Log Likelihood
88,778
% w/a Value of Zero
85.9
Sample Size
21,015
*They are the reference categories.

88
The coefficients for the two color groups, shown at the bottom of the
table, are of particular interest. The results indicate that being Afro-Brazilian
increases the probability of death by 24.2 percent. Being Asian, in contrast,
reduces the probability of death by 57.8 percent. In other words, Afro-
Brazilian children die at a higher rate than do whites children even after
statistically removing the effects of the major covariates of mortality:
education, income, access to medical care, and housing quality. Similarly, net
of the effects of socioeconomic standing, being Asian improves the chances of
child survival (compared to white children). Therefore, I conclude that some
of the differences in the life chances of Asian, white and Afro-Brazilian
children are due to factors other than observed differences in socioeconomic
standing.
Because the data at hand do not permit further empirical analyses, we
can only speculate what these additional factors might be. For example,
cultural differences could play a role, particularly in explaining the low
mortality among Asians. Similarly, other findings regarding the black and
mulatto population suggest that unmeasured forms of discrimination against
Afro-Brazilians may account for their high mortality levels. Additional
considerations include such variables as residential location within the city
(associated with higher costs, greater distances from health care facilities, and
higher environmental risks), and possible differences in the strength of the
social networks to which families belong (thereby influencing the
household's ability to deal with illness and emergency). If these observations
are necessarily conjectural, the findings nonetheless provide quantitative
estimates of the degree to which the historical legacies of African and Asian
immigration to Brazil have led to marked differences in the life chances of
children born in the metropolitan area of Sao Paulo.

89
Summary
The different experiences of the Portuguese, African and Asian
populations in Brazil have led to marked differences in socioeconomic
standing. Afro-Brazilians are at the bottom, whites are in the middle, and
Asians are at the top. It would be worthwhile to explore in detail why such
differences exist among the three color groups, especially considering the
different historical backgrounds of whites and Asians, and their present
positions in the social and political structures of the country. The purpose
here however, has been to demonstrate the correlation between skin color
and life chances among the three groups.
A comparison of child mortality and life expectancy among the three
groups shows that there are obvious differences among them. As predicted,
the Afro-Brazilian population has the highest mortality measures, and
therefore the lowest life expectancy; whites have lower mortality levels than
the Afro-Brazilians, and higher life expectancy than them; Asians have the
lowest mortality levels and highest life expectancy among the three groups.
Since we already know the differences in socioeconomic standing of these
groups, we can conclude that life chances and socioeconomic status are highly
associated with one another. However, we can not be sure whether the
observed differences in child survival are completely due to differences in
social indicators.
The Tobit regression analysis provides us with a deeper understanding
of the relationships between child mortality and the social indicators
examined here, and between child mortality and skin color: The variables
indicating socioeconomic standing are all negatively correlated with mortality
ratio. In descending order of importance, the variables are piped water, social

security, mother's education, father's education and household income.
Nevertheless, significant differences remain among the three groups, even
after controlling for the variables of social indicators. The result that being
Afro-Brazilian increases the probability of death by 24.2 percent while being
Asian reduces the probability of death by 57.8 percent indicate clearly that
factors other than those investigated here are at work here. Some of these
factors may be family size and structure, cultural differences (i.e., customs and
practices associated with child-rearing and fertility rate and pattern), social
support network, various forms of discrimination against Afro-Brazilians
indirectly contributing to their high mortality levels, and some other
socioeconomic variables that are not examined here, such as residential
location within the city.
Finally, as the findings of this study have quantified the importance of
the key social indicators examined here in relation to life chances, they have
practical implications on how to improve the life chances of the people in
low socioeconomic standing, particularly of those in metropolitan Sao Paulo,
Brazil. The study also begs further investigation into the question of child
mortality differentials, a major measurement of quality of life, among
different racial/ethnic groups in modern nation states.

CHAPTER 5
EDUCATIONAL ATTAINMENT
OF ASIANS, WHITES AND AFRO-BRAZILIANS
Having analyzed the differences in fertility and mortality, I now turn to
examine the next key social indicator, educational attainment, for the three
color groups in question. In modern societies, educational attainment of a
population or a subgroup is directly linked to its quality of life. Thus, it is
essential for us to measure and compare the educational levels of Asian-
Brazilians, whites and Afro-Brazilians to find out where each group stands in
terms of educational achievement in the Brazilians society. In the first
section, I measure the school attendance rate of children ages 6-16, in the
second section I focus on the educational attainment of men ages 18-65, and
in the third sections, I examine the educational attainment of women age 18-
65. Finally, I discuss the findings at the end of the chapter.
School Attendance Rate of Children Ages 6-16
The goal of this section is to test the hypothesis that, other things being
equal, Asian children are more likely to attend school compared to white and
Afro-Brazilian children. To test this hypothsis, 1 selected children ages 6-16
from the 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian Census
for the measurement of school attendance.

92
Most children start school at age seven across Brazil, but in Sao Paulo,
the most industrialized part of Brazil, children tend to start school earlier
than in the other parts of Brazil. Based on the PNAD-1985 sample, Levison
(1991) reported that 59% of 7-14 year olds in first grade began school at the age
of seven, 24% of them at age six, 11% at age eight, and 5% between ages nine
and fourteen. Thus, I include six-year olds in my calculation in order to
obtain an accurate picture of school attendance by children of different color
groups.
The school attendance rate of the entire sample is 71.5%, with rather
stable high rates in the middle and lower rates at both ends of the age groups.
Specifically, the in-school rate for six-year olds is 13% and the rates for
children ages 7-14 are consistently between 70% and 90%, with peaks at ages
nine and ten. The rates for 15 and 16-year olds drops to 62% and 52.5%. Table
5.1 shows the total number of children in each age group, the percent of each
age group, and the average percentages of children in school for each age
group. As expected, the attendance rate is low among children aged 6, then
rises among children 8-12 years of age, only to drop off in the older ages (14-
16).
Table 5.2 shows the considerable differences in school attendance
among children of the three color groups. Asian children have the highest
percent in school, 88.7%, followed by white children, 73.6%, and Afro-
Brazilian children, 64.7%. The total number of children (168,064) in Table 5.2
is slightly lower than that in Table 5.1 (168,666), probably because a small
number of people did not use any of the racial categories in the census.
However, the number of children from each color group in the sample is
proportional to the size of each group in the data, except that there are
relatively fewer Asian children due to their relatively low fertility rate.

93
Table 5.1
Number of Children Ages 6-16 and the Percent in School
by Age, Metropolitan SSo Paulo, Brazil (1980)
Aee Group
N
6
15,474
7
15,676
8
15,362
9
15,205
10
15,296
11
14,620
12
15,004
13
14,740
14
15,478
15
16,217
16
15,595
Total
168,666
%
% in School
9.2
13.0
9.3
70.2
9.1
87.2
9.0
91.1
9.1
91.2
8.7
89.2
8.9
84.3
8.7
78.7
9.2
70.4
9.6
62.0
9.2
52.5
100.0
71.5
Source: Weighted 3% sample data of Metroplitan Sao Paulo, 1980 Brazilian
Census.
Table 5.2
Number of Children Ages 6-16 and the Percent in School
by Color Group, Metropolitan Sao Paulo, Brazil (1980)
Color Group
N
%
% in School
Asian
2,716
1.6
88.7
White
122,608
73.0
73.6
Afro-Brazilian
42,739
25.4
64.7
Total
168,064
100.0
71.6
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

94
The school attendance rate varies not only by color group but also by
income level, as shown in Table 5.3. Mean monthly income was defined and
divided into four income levels in Chapter 1. The total number of children
(144,476) in Table 5.3 differs slightly from the previous ones mainly because
these data are generated based on parents with children ages 6-16, instead of
selecting children directly out the data. Similarly, the overall school
attendance rate for this subset of data (72.8%) is slightly higher than the
previous ones (71.5% Table 5.1 and 71.6% in Table 5.2) precisely because it
excludes those children who were either orphans or didn't live in
households headed by parents with income of some sort.
The school attendance rates for the four income levels are 52.8%,
59.2%, 68.9%, and 81.4% respectively. Note that the difference between the
first and second income levels is smaller than the differences among the
other levels. This suggests that an average income of two minimum wages,
though it has a positive effect, does not affect the in-school rate as much as
the higher levels of income.
Table 5.3
Distribution of Children Ages 6-16 and the Percent in School
by Income Level, Metropolitan Sao Paulo, Brazil (1980)
Income
N
%
% in School
To 1 MW*
8,753
6.1
52.8
To 2 MW
29,726
20.6
59.2
To 3 MW
27,310
18.9
68.9
Above 3 MW
78,687
54.4
81.4
Total
144,476
100.0
72.8
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*MW = minimum wage

95
Other factors contributing to school attendance rate are residence,
parents' education and gender. As is true across the developing world, urban
residents in Brazil have higher educational levels than their rural
counterparts. Since the data in this study are for the metropolitan area of Sao
Paulo, the majority of the people (86.1%) are urban residents and only 13.9%
are the rural residents. Nonetheless, as Table 5.4 shows, the school
attendance rate of rrural children is much lower rate than that for urban
children; 54.2% for rural children and 74.4% for urban children.
Table 5.4
Children Ages 6-16 and the Percent in School
by Residence, Metropolitan Sao Paulo, Brazil (1980)
Residence
N
%
% in School
Urban
145,201
86.1
74.4
Rural-
23,465
13.9
54.2
Total
168,666
100.0
71.6
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
The impact of parents' education on their children's attendance in
school is well documented. The sample data indicate that whether or not a
parent had any schooling as well as how many years of schooling a parent had
makes a lot of difference in whether a child is in school or not. Parents'
education is here measured separately by the average number of years of
school attended by the father and mother. Based on the Brazilian educational
system, five educational levels are identified (see Chapter 1). Although there
are no large differences between the impact of father’s and that of mother's
schooling on school attendance rate of their children, they are still listed

96
separately here to show exactly what impact each had on their children.
Overall, mother's schooling has a slightly more positive impact than father's
schooling at all but the highest level (12 or more years of schooling).
As shown in Table 5.5, the school attendance rates for children with
mothers or fathers who had no schooling are very similar and quite low
(about 59%) probably because people tend to marry those with a similar level
of education. In contrast, the in-school rate increases considerably at each
higher level of education: More than 72% of children with parents who had
one to four years of schooling are in school, about 82% of children with
fathers or mothers with 5-8 years of schooling are in school, and about 85% of
children whose fathers or mothers had 9-11 years of schooling are in school.
The difference between the top two levels of schooling is not very big; 85.6%
vs. 88.3% for father's schooling and 86.8% vs. 87.0% for mother's schooling.
Table 5.5
Number of Children Ages 6-16 and the Percent in School
by Parents' Education, Metropolitan Sao Paulo, Brazil (1980)
Parents' Schooling
N
%
(%) in School
Father's Schooling
Zero (years)
29,896
20.3
58.9
1-4
87,126
59.1
72.6
5-8
14,454
9.8
81.9
9-11
7,924
5.4
85.6
12+
8,019
5.4
88.3
Total
147,419
100.0
72.3
Mothers' Schooling
Zero (years)
36,835
25.5
59.6
1-4
84,389
58.4
75.3
5-8
12,583
8.7
82.3
9-11
6,758
4.7
86.8
T2+
3,989
2.7
87.0
Total
144,554
100.0
72.7
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

97
There is a slight difference in school attendance between male and
female children. On the whole, male children have a slightly higher in¬
school rate than female children, 72.3% vs. 70.8%. These figures are less than
one percent plus or minus the overall mean, 71.6%. Compared to some other
developing countries, this degree of gender equality in school attendance for
children ages 6-16 is amazingly high. The school attendance rates by gender is
shown in Table 5.6:
Table 5.6
Number of Children Ages 6-16 and the Percent in School
by Sex, Metropolitan Sao Paulo, Brazil (1980)
Sex
N
%
% in School
Male
84,281
50.1
72.3
Female
83,783
49.9
70.8
Total
168,064
100.0
71.6
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
I have shown above the variations of in-school rate by age group, color
group, income group, place of residence, parents' schooling and gender. The
question now is whether the three color groups still differ from one another
within the same age group, income group, place of residence, parents’
schooling and gender. I constructed a series of crosstabulations with these
variables and the results show a consistent and clear pattern of difference
among the three color groups. Proportionately more Asian children are in
school than white children, who in turn have a higher school attendance rate
than Afro-Brazilian children in almost every category (see Table 5.7).

98
Table 5.7
In-School Rate of Children Ages 6-16 by Age and Color Groups,
Metropolitan Sao Paulo, Brazil (1980)
Age -
Sample (%)
Asian
White
Afro-Brazilian
6
13.0
21.5
14.1
9.4
7
70.2
94.9
74.2
58.0
8
87.2
96.2
89.7
79.5
9
91.1
99.2
92.5
86.8
10
91.2
98.0
92.6
87.3
11
89.2
95.9
90.4
85.6
12
84.4
96.1
85.3
81.0
13
78.7
95.1
80.5
72.4
14
70.4
95.7
72.8
61.8
15
62.0
92.8
64.9
51.1
16
52.5
85.2
56.0
39.6
Total
71.5
88.7
73.6
64.7
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Specifically, at age 6, 21.5% of Asian children are in school, compared to
only 14.1% of white children and 9.4% of Afro-Brazilian children. At age 7,
almost 95% of Asian children are in school, whereas 74.2% of white children
and only 58% of Afro-Brazilian children are in school. Most importantly,
from age 7 to 15, more than 90% of Asian children are in school at all times
(for ages 7-14, over 95% of them are in school), and even at age 16, 85.2% of
them are still in school. In contrast, over 90% of white children are in school
only at ages 9-11, and the in-school rate for Afro-Brazilian children never
reaches 90% at any age. More than 80% of white children are in school only at
ages 8-13, and more than 80% of Afro-Brazilian children are in school only at
ages 9-12. The in-school rate drops drastically from age 14 on for white
children and it does so from age 13 on for Afro-Brazilian children. The onset

99
of leaving school starts at age 16 for Asian children, at age 14 for white
children, and at age 13 for Afro-Brazilian children.
Figure 5.1 shows graphically the result of cross tabulation of school
attendance rate by age and color group. At every age, the school attendance
rate for Asian children is not only far above the overall sample mean but also
significantly higher than that for white children, whose rate is slightly above
the overall mean. On the other hand, the school attendance rate for Afro-
Brazilian children is consistently lower than the sample mean, let alone the
rates for the other two groups. The differences among the groups are greater
at younger ages (6 and 7), and at older ages (14-16).
100
90
80
70
o 60
o
X
CO 50
.5
* 40
30
20
10
0
6 7 8 9 10 11 12 13 14 15 16
Age
Figure 5.1 In-School Rate Children Ages 6-16 by Age and Color Groups,
Metropolitan Sao Paulo, Brazil (1980)

100
As shown in Table 5.8, when income is controlled, the in-school rates
still differ by color group; between 81.8% and 89.7% of Asian children for all
income groups are in school, compared to between 52.9% and 82.6% for white
children and between 51.9% and 74.9% for Afro-Brazilian children. In other
words, inter-group variation is greater than intra-group variation even when
income is controlled. However, at the lower two income levels, white and
Afro-Brazilians are very similar, indicating the greatly diminished effect of
factors other than parents' income on the school attendance rate of their
children. It is safe to say that for whites and Afro-Brazilians with an average
income of two minimum wages or less, the importance of color is minimal.
Rather, it is the average income that matters most in determining whether
more or fewer children are in school. On the other hand, at the upper two
income levels, considerable difference still remains between whites and Afro-
Brazilians, indicating that income alone can not explain the difference.
Table 5.8
In School Rate of Children Ages 6-16 by Income and Color Groups,
Metropolitan Sao Paulo, Brazil (1980)
Income
Mean (%)
Asian
White
Afro-Brazilian
To 1 MW*
52.8
81.8
52.9
51.9
To 2 MW
59.2
87.3
59.8
57.7
To 3 MW
68.9
84.6
70.4
65.0
Above 3 MW
81.5
89.7
82.6
74.9
Total
71.6
88.7
73.6
64.7
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*MW = minimum wage

101
The three color groups differ not only when income is controlled, but
also when place of residence is controlled. In urban areas, the in-school rates
for Asian, white and Afro-Brazilians are 89.1%, 76.6% and 67%, respectively,
while the corresponding figures in rural areas are 88.7%, 73.6% and 64.7%.
Next, I will examine the color differences when both place of residence and
income are controlled simultaneously.
Table 5.9 shows the in-school rate of children ages 6-16 by place of
residence, income level and color group. Among urban residents, when
income is controlled, differences still exist among the three groups, though
the gap among the groups reduces somewhat, especially between white and
Afro-Brazilian children in the lower two income groups. However, Asian
children remain significantly different from the other two at every income
level. For example, at the first income level (up to one minimum wage), the
in-school rate for Asian children is about 20% higher than that for white and
Afro-Brazilian children, and at the second income level (up to two minimum
wages), it is more than 20% higher than that of the other two groups. At the
third income level (up to three minimum wages), the in-school rate for
Asian children is more than 12% higher than that of white children and
more than 18% higher than that of Afro-Brazilian children. At the fourth
income level, the gap among the three group becomes smaller but
proportionately, 6.2% and 14.6% more Asian children are in school than
white and Afro-Brazilian children, respectively.
On the other hand, the gap between white and Afro-Brazilian children
narrows considerably at the lower half of the income strata, when place of
residence and income are controlled. For instance, the difference between the
in-school rate of white children and that of Afro-Brazilian children at the
lower two income levels is only about 3%, compared to the difference of more

102
than 10% between the two when place of residence and income are not
controlled simultaneously. This suggests that both place of residence and
income play an important role in determining whether a child is in school or
not because the gap between the two groups narrows a great deal when they
are controlled. In other words, most of the difference can be explained by
residence and income.
Table 5.9
In-School Rate of Children Age 6-16 by Region, Income
and Color Groups, Metropolitan Sao Paulo, Brazil (1980)
Residence
Sample (%)
Asian
White
Afro-Brazilian
Urban
74.4
89.1
76.6
67.0
To 1 MW*
57.7
76.8
58.6
55.6
To 2 MW
63.4
87.6
64.2
61.5
To 3 MW
70.5
84.8
72.4
66.3
Above 3 MW
82.3
89.9
83.7
75.3
Rural
54.2
86.7
54.7
50.5
To 1 MW
45.3
95.6
44.8
45.7
To 2 MW
49.4
86.9
50.2
46.8
To 3 MW
59.4
83.8
60.0
56.0
Above 3 MW
69.0
88.1
67.8
68.7
Total
71.6
88.7
73.6
64.7
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*MW = minimum wage
At the two upper income levels in urban areas, significant differences
still remain between white and Afro-Brazilian children in terms of school
attendance rates. The gaps between white and Afro-Brazilian children at the
third and fourth levels are 6.1% and 8.4%. In other words, the gap between
the two groups widens as the average income grows. Figure 5.2 graphically
illustrates the differences among the three groups at various income levels:

103
o
o
A
o
C/5
S
1MW 2 MW 3 MW 3+MW Overall
Income Group
§¡f Sample
11 Asian
| White
Afro-Brazilian
Figure 5.2 In-School Rate of Urban Children Ages 6-16 by Income
and Color Groups, Metropolitan Sao Paulo, Brazil (1980)
In rural areas, while the school attendance rate for Asian children still
remains much higher than that of the other two groups, the gap between the
latter two further narrows. Particularly noteworthy is the fact that at the first
income level (up to one minimum wage), Afro-Brazilian children exceed
white children by 0.9%, and at the fourth level (above three minimum wages)
they are behind white children by only 0.9%. This indicates that parents'
income is even more important in explaining the difference of the in-school
rates for white and Afro-Brazilian children in rural areas than it is in urban
areas. In rural areas, there is less variation between whites and Afro-
Brazilians within the same income group. This may be partly due to the fact
that the overall in-school rate among rural residents is quite low for both
groups (54.7% for whites and 50.5% for Afro-Brazilians). The differences
among the three groups in rural areas are shown in Figure 5.3:

104
o
o
-tí
o
en
.5
éP
100
90
80
70
60
50
40
30
20
10
0
1MW 2MW 3 MW 3+MW Overall
Income Group
Sample
Asian
White
Afro-Brazilian
Figure 5.3 In-School Rate of Rural Children Ages 6-16 by Income
and Color Groups, Metropolitan Sao Paulo, Brazil (1980)
In order to measure the precise impact of the independent variables
(father's and mother's schooling, income, place of residence, and color) on
the dependent variable (in-school rate), I ran a series of logistic regressions.
This method is appropriate because the dependent variable is a dichotomy
(whether or not a child is in school). The purpose of running logistic
regression analysis here is to measure the impact of the various independent
variables. Given the wide range of variations in in-school rate at different
ages described in the previous analyses, I ran a separate test at each age.
Rather than simply treating age as another independent variable, this method
both controls for the age of child and, at the same time, enables us to clearly
see the way in which the effect of each of the independent variables change
across the various age groups.

105
In the 1980 Brazilian Census, father's and mother's schooling were
coded as 1,2, 3,4, 5,6 and 7, of which codes 1-4 refer to the actual years of
schooling, code 5 refers to five to eight years of schooling, code 6 refers to nine
to eleven years of schooling, and code 7 refers to twelve or more years of
schooling. In this logistic regression, I recoded 5 and 6 to reflect the real mean
years of schooling for each code, so that code 5 equals 6.5 years of schooling,
and code 6 equals 10 years of schooling. I assigned a value of 12 (meaning 12
years of schooling) to code 7.
Household income is treated as an interval variable in this logistic
regression analysis. According to the minimum wage standard (one
minimum wage = 4,150 cruzerios) in 1980, household income is recoded into
twenty six levels, i.e., Level 1 = 0-4,150 cruzerios, Level 2 = 4,151-8,300
cruzerios,..., Level 25 = 99,601-103,750 cruzerios and Level 26 = 103,751
cruzerios and up. In other words, the unit of income is one minimum wage,
up till 103,750 cruzerios, and any income beyond that is considered as a
separate level.
As the requirement of logistic regression model, if an independent
variable consists of only two categories (e.g., urban and rural for place of
residence), or if it is a categorical variable (e.g., white, Afro-Brazilian and
Asian for color in this analysis), one of them must be treated as the reference
category to which the other(s) is (are) compared. Thus, the logistic regression
model includes six independent variables, mother’s schooling, father's
schooling, household income, residing in urban areas (vs. residing in rural
areas), being Afro-Brazilian (vs. being white) and being Asian (vs. being
white).
Before I discuss the results of the analysis, I should emphasize that we
are not measuring the cumulative effect of all the independent variables

106
together on the dependent variable here. Instead, we are measuring the effect
of each independent variable separately, assuming that all other things are
equal. For example, when we compare Asians to whites, we only need to
look at the row values for Asians at various ages because whites are treated as
the reference group in this analysis. Another important thing to keep in
mind is that the values in Table 5.10 represent the amount of change in the
dependent variable for a one-unit change in the independent variable. A
one-unit change refers to being in one or the other category in the case of
nominal variables, or a one-unit increase if the variable in question is an
interval variable. For example, place of residence and color are nominal
variables, and father's and mother's schooling and household income are
interval variables in this analysis. The sign in front of the values indicates
whether belonging to one or the other group, or a one-unit increase in an
independent variable has a positive or negative impact on the dependent
variable.
The interpretation of logistic regression coefficients is, however, not as
straightforward as in multiple linear regression. They can be expressed in
terms of the odds of an event occurring. Thus, the logistic equation can be
written in terms of odds as:
Prob. (event)
— gBo gBiXl eBpXp
Prob. (no event)
This formula can be further modified as the following;
Prob. (event) 1
Odds = =
Prob. (no event) 1 + e-z
Fortunately, we no longer have to calculate the odds by hand since they are
automatically generated and given in the column Exp(B) of the computer

107
printout for SPSS. If a coefficient is positive, this factor will be greater than 1,
and if a coefficient is negative, this factor will be less than 1. A factor of more
than 1 indicates an increase in the odds, and a factor of less than 1 indicates
the opposite. The main results of the regression analyses are given in Table
5.10, and the complete results are presented in Appendix C.
In Table 5.10, the regression coefficients for the independent variables
are listed by age level, and the Exp(B) values for coefficients are listed below
each coefficient in parentheses. The constant in each regression model is
given in the last column. Significance level (p-value) for all coefficients are
less than 0.05 unless it is marked with *, in which case it is 0.05 or greater.
The category of rural residence is treated as the reference category, to which
urban residence is compared, and the category of white is treated as the
reference group, to which Afro-Brazilian and Asian are compared.
As shown in Table 5.10, the coefficients of all the independent
variables, except the two dummy variables presenting Asians and Afro-
Brazilians, are significantly different from 0 at a significance level of 0.05. The
coefficient of the dummy variable, Afro-Brazilians, at age 12, and the
coefficients of the dummy variable, Asians, at ages 6, 8,10 and 11 are not
significantly different from 0, meaning that they do not differ significantly
from whites at these age levels in terms of in-school rate. In other words, at
age twelve, whites and Afro-Brazilians are not significantly different, and at
ages 6, 8,10 and 11 whites and Asians are not significantly different.

108
Table 5.10
Logistic Regression of In-School Rate of Children Ages 6-16
on Mother's and Father's Education, Household Income, Residence
and Color by Age, Metropolitan Sao Paulo, Brazil (1980)
Regression Coefficients for Independent Variables
Age
FEd
MEd
Income
Urban
Afro-B
Asian
Constant
6
.06
(1.06)
.02
(1.03)
.30
(1.03)
-.24
(.79)
-.22
(.80)
.27*
(1.31)
-2.17
7
.11
(1.12)
.13
(1.14)
.09
(1.10)
.33
(1.39)
-.35
(.71)
1.40
(4.04)
-.44
8
.14
(1.15)
.18
(1.20)
.12
(1.12)
.56
(1.74)
-.39
(.68)
.21*
(1.23)
.33
9
.10
(1.11)
.17
(1.19)
.11
(1.12)
.58
(1.78)
-.30
(.74)
1.67
(5.30)
.89
10
.14
(1.15)
.16
(1.18)
.08
(1.08)
.69
(1.99)
-.25
(.78)
.78*
(2.18)
.93
11
.12
(1-12)
.12
(1.13)
.12
(1.12)
.99
(2.70)
-.15
(.86)
.12*
(1.13)
.43
12
.10
(1.10)
.12
(1.13)
.14
(1.14)
1.09
(2.98)
.07*
(1.07)
.89
(2.44)
-.22
13
.13
(1.13)
.13
(1.14)
.09
(1.10)
1.21
(3.37)
-.15
(.86)
1.05
(2.85)
-.64
14
.13
(1-13)
.12
(1.13)
.12
(1.12)
1.11
(3.04)
-.19
(.83)
1.59
(5.72)
-1.11
15
.13
(1.13)
.13
(1.14)
.08
(1.09)
1.02
(2.76)
-.19
(.83)
1.74
(5.72)
-1.37
16
.09
(1.09)
.13
(1.14)
.06
(1.06)
1.05
(2-86)
-.39
(.68)
1.16
(3.20)
-1.57
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Notes:
1. The categories of rural and white are treated as reference groups in this
analysis.
2. The values in parentheses are the Exp(B) values, which indicate the
change of odds for an event occurring.
3. P-values <.05, unless marked with *.

109
Now consider the impact of father's and mother's schooling,
household income and urban residence on the dependent variable, in-school
rate. The first two columns in Table 5.10 show that except at ages six and
fourteen, mother's schooling has either slightly more positive impact than,
or the same positive impact as father's schooling on the in-school rate of
children ages 6-16. For instance, at age seven, a one-year increase in father's
schooling results in an increase in in-school rate by a factor of 1.12 and one
year increase in mother’s schooling leads to an increase in the dependent
variable by a factor of 1.14 . For most ages, the difference between the
coefficient of father's schooling and that of mother's schooling is less than .04,
which suggests that a one-unit of increase in either father's or mother's
schooling would result in almost the same effect on the in-school rate. The
effect of father's and mother’s schooling on their children's in-school rate is
compared in Figure 5.4:
Age
F — Father's Ed
m — Mother's Ed
Figure 5.4 Effect of Father's and Mother's Schooling on Children's
In-School Rate, Metropolitan Sao Paulo, Brazil (1980)

no
The positive coefficients of household income (the third column in
Table 5.10) shows that it has a positive effect on the dependent variable for all
ages. The Exp(B) values in parentheses below the coefficients indicate that a
one-unit increase (i.e., 4,150 cruzerios) in household income increases the
chance of being in school by a factor of 1.03-1.14 for all age levels. The general
pattern of the degree of impact is that it gradually increases from age six to
twelve, and gradually decreases thereafter. This pattern is illustrated in
Figure 5.5, along with the effect of urban residence.
Urban residency has an increasingly more positive correlation with in¬
school rate as age increases, except for the negative value at age six, which
may be ignored because only 13% of all six-year olds in the sample were in
school. The increasingly positive values of coefficients for the category of
urban residence illustrate the great advantage of residing in urban areas as
opposed to residing in rural areas.
Another trend of the impact of urban residency on in-school rate is that
it gradually increases from age seven to age thirteen, when it reaches the
highest point, and then decreases slightly at ages 14-16. Even at the last three
age levels, the coefficients of urban residency are still 1.11,1.02, and 1.05,
respectively, which in turn translate into high Exp(B) values of 3.04, 2.76 and
2.86. We can interpret these Exp(B) values as follows: All things being equal,
the odds of urban children being in school, as opposed to rural children,
increases by a factor of 39% at age 7, 74% at age 8, 78% at age 9,99% at age 10,
170% at age 11,198% at age 12,237% at age 13,204% at age 14,176% at age 15
and 1.05 at age 16. Figure 5.5 shows the effects of household income and
urban residency on in-school rate.

Figure 5.5 Effect of Household Income and Urban Residence
on In-School Rate, Metropolitan SSo Paulo, Brazil (1980)
Asian children outperformed white children by a large margin, except
at ages 6, 8,10 and 11, where they are not significantly different from whites.
It is possible that the extremely small number of Asian children, relative to
white children, at these age levels is a main factor for the resulting
significance level of greater than 0.05. The values of Exp(B) in Table 5.10
show that being Asian, as opposed to being white, increases the odds of being
in school by 304% at age 7,430% at age 9,144% at age 12,185% at age 13,472%
at ages 14 and 15 and 220% at age 16.
Compared to white children, Afro-Brazilian children consistently do
worse,-except at age twelve, where they are not significantly different from
white cildren. However, the difference between them is not as great as the
one between Asians and whites. The school attendance rates of Afro-
Brazilian children ages 6-16 are 14-32% lower than those of white children

112
(see Table 5.1 ). Figure 5.6 illustrates the odds of Afro-Brazilian and Asian
children being in school, compared to that of white children.
Age
B —Afro-Brazilian
B —Asian
Figure 5.6 The Odds of Afro-Brazilian and Asian Children vs. White
Children Being in School, Metropolitan Sao Paulo, Brazil (1980)
The pattern of Afro-Brazilian children's in-school rate at different age
levels, compared to that of white children, may be indicative of the
relationship between the two groups across time. For example, children who
were sixteen years old in 1980 were bom in 1964, and children who were six
years old in 1980 were born in 1974. If we add six years, which is the
minimum age for starting school, to the lower and upper limits for birth
years, we get the period, 1970-1980, during which these children were in
school. If we take in-school rate as one indication of the race relations
between whites and Afro-Brazilians, we can make the following observation
from Figure 5.6. The relation between whites and Afro-Brazilians improved
from 1970 to 1974, declined from 1975 to 1978, and improved again in 1979

and 1980. Of course, the validity of this observation has to be tested by other
data for the same time periods.
113
Educational Attainment of Men Ages 18-65
In order to measure the educational attainment of adults, I selected
men and women ages 18-65 from the 3% sample data of Sao Paulo in the 1980
Brazilian Census. Preliminary analysis of the data shows a great deal of
differences between the educational attainment of men and women. Since
the main focus here is not the gender difference, but the color difference, I
will discuss men and women separately, and offer brief comparisons between
the educational attainment of men and women when necessary. Educational
attainment is measured by the mean years of schooling a person had. I earlier
discussed the coding for years of schooling in the 1980 Brazilian census.
The total number of men ages 18-65 in the sample data is 211,063 and
99.6% are identified by the racial classification system in the 1980 census. Of
the latter group, 74.6% are white, 23.2% are Afro-Brazilian, and 2.3% are
Asian. The mean years of schooling for the sample is 4.93 years, with a great
deal of variations among the three color groups; 7.44 years for Asians, 5.3
years for whites, and 3.5 years for Afro-Brazilians. A comparison of the mean
years of schooling among the three groups is shown in Table 5.11.
The data also show that the mean years of schooling for men ages 18-65
vary by age, place of residence. For analytical purpose, I divided men into
three age groups; they are ages 18-25, ages 26-39, and ages 40-65. It turned out
that each of the three age groups consists of about one third of the adult male
population in the sample, with the first group slightly underrepresented

114
(29.9%) and the second group slightly over-represented (36.7%). As expected,
the mean years of schooling decreases from younger to older groups, and the
mean years of schooling for the oldest group (ages 40-65) is substantially lower
than those for the two younger groups. The means for the three groups are
described in Table 5.12.
Table 5.11
Mean Years of Schooling for Men Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980)
Color
Mean
Std Dev
Cases
%
Asian
7.44
3.76
4,761
2.3
White
5.30
3.60
156,826
74.6
Afro-Brazilian
3.50
2.79
48,716
23.2
Total
4.93
3.54
210,303
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Table 5.12
Mean Years of Schooling of Men Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980)
Age
Mean
Std Dev
Cases
%
18-25
5.95
3.30
63,038
29.9
26-39
5.16
3.57
77,525
36.7
40-65
3.76
3.37
70,500
33.4
Total
4.93
3.54
211,063
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

115
Place of residence is another important factor in the variation of
schooling of men ages 18-65. Urban men, on the average, have 2.33 more
years of schooling than rural men. However, since only about 11% of men
reside in rural areas, the overall educational level for men is not affected very
much by the extremely low educational level of rural men (see Table 5.13).
Table 5.13
Mean Years of Schooling for Men Ages 18-65
by Residence, Metropolitan Sao Paulo, Brazil (1980)
Residence
Mean
Std Dev
Cases
%
Urban
5.18
3.55
188,141
89.1
Rural
2.85
2.63
22,922
10.9
Total
4.93
3.54
211,063
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
I have shown above (Tables 5.11-5.13) that mean years of schooling for
men ages 18-65 vary by color group, age group, place of residence. I will now
examine whether the three color groups differ in terms of schooling when
age, place of residence and income are controlled separately. If the differences
among the three groups disappear when these variables are controlled, we
can conclude that differences in educational attainment are mainly caused by
these factors. On the other hand, if the people from three color groups who
are in the same category still differ from one another, we would need to
examine factors other than those examined here.
First of all, the data indicate that age has a uniform effect on the level
of education for the three color groups. Younger age groups have more years
of schooling than do older age groups, regardless of color. Thus, color

116
differences remain when age is controlled. For example, in the first age group
(ages 18-25), Asians, on average, have 3.05 more years of schooling than
whites, who have 1.86 more years of schooling than Afro-Brazilians. The
same pattern among the three groups persists in the two older age groups.
The ratios of mean years of schooling between Asians and Whites and
between Asians and Afro-Brazilians for the three age groups are presented in
the last two columns of Table 5.14. These ratios are better indicators of
amount of change between two means because they measure relative changes
of the two, not merely the absolute increase or decrease in them separately.
We see in Table 5.14 that the gap in the mean years of schooling between
Asian and white men widens from older to younger ages, and the gap
between Asian and Afro-Brazilian men narrows from older to younger ages.
Table 5.14
Mean Years of Schooling for Men of Ages 18-65
by Age and Color, Metropolitan Sao Paulo, Brazil (1980)
Ratio
Age
Sample
Asian
White
Afro-Brazilian
A/W*
A/AB
18-25
5.95
9.43
6.38
4.52
1.48
2.09
26-39
5.16
8.37
5.60
3.54
1.49
2.36
40-65
3.76
5.44
4.08
2.26
1.33
2.41
Total
4.93
7.44
5.30
3.50
1.40
2.13
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asian/White, A/AB = Asian/Afro-Brazilian.
When place of residence is controlled, the gap among the three groups
in urban areas reduces slightly, but the gap among them in rural areas not

1 17
only remains but also widens considerably. The ratio between the mean years
of schooling for Asians and that for whites in urban areas decreases slightly
from 1.40 before place of residence is controlled to 1.37 after it is controlled.
And the ratio between Asians and Afro-Brazilians in urban areas drops from
2.16 before residence is controlled to 2.09 after it is controlled.
On the other hand, the ratio between Asians and whites in rural areas
increases from 1.40 before place of residence is controlled to 1.84 after it is
controlled. Similarly, the ratio between Asians and Afro-Brazilians in rural
areas increases from 2.16 before the control of residence to 2.45 after its
control. On the average, urban Asians have 2.09 more years of schooling than
urban whites, who in turn have 1.91 more years of schooling than urban
Afro-Brazilians. In rural areas, Asians have 2.48 more years of schooling than
whites, who have 0.75 more years of schooling than Afro-Brazilians. The
color differences by place of residence are described in Table 5.15.
Table 5.15
Mean Years of Schooling for Men Ages 18-65
by Residence and Color, Metropolitan Sao Paulo, Brazil (1980)
Residence
Sample
Asian
White
Afro-Brazilian
Ratio
A/W* A/AB*
Urban
5.18
7.67
5.58
3.67
1.37
2.09
Rural
2.84
5.45
2.97
2.22
1.84
2.45
Total
4.93
7.44
5.30
3.50
1.40
2.13
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asian/White, A/AB = Asian/Afro-Brazilian.

118
When Asians are compared to the other two groups in terms of mean
years of schooling, the gap between them widens at the two lower income
levels and narrows at the two upper income levels. For example, the ratio
between Asians and whites without controlling income is 1.40, while the
ratios between them at the first and second income levels are 1.68 and 1.73
respectively. The ratio between Asians and Afro-Brazilians without
controlling income is 2.13, whereas the ratio between them at the first and
second income levels are 2.69 and 2.19, respectively.
However, the data also show that income does have a positive effect on
mean years of schooling for people with an average income of above two
minimum wages. The ratios between Asians and whites at the third and
fourth income levels are respectively 1.43 and 1.16, compared to 1.40 when
income is not controlled, and the ratios between Asians and Afro-Brazilians
at these income levels are 1.80 and 1.65, as compared to 2.13 when income is
not controlled.
Table 5.16
Mean Years of Schooling for Men Ages 18-65 by Income
and Color, Metropolitan Sao Paulo, Brazil, 1980
Ratio
Income
Total
Asian
White
Afro-Brazilian
A/W*
A/AB
To 1 MW**
4.14
7.56
4.50
2.81
1.68
2.69
To 2 MW
3.48
6.41
3.70
2.93
1.73
2.19
To 3 MW
4.19
6.33
4.43
3.51
1.43
1.80
Above 3 MW
6.43
7.74
6.68
4.68
1.16
1.65
Total
4.93
7.44
5.30
3.50
1.40
2.13
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asian/White, A/AB = Asian/Afro-Brazilian.
**MW = minimum wage

119
We can also compare directly the mean years of schooling with and
without controlling income. When income is not controlled, Asians have
2.14 more years of schooling whites, who have 1.80 more years of schooling
than Afro-Brazilians. When income is controlled, Asians lead whites by 3.06
and 2.71 years, and whites lead Afro-Brazilians by 1.69 and 0.77 years at the
first and second income levels. At the two upper income levels, Asians lead
whites by 1.90 and 1.06 years, and whites lead Afro-Brazilians by 0.92 and 2.0
years (See Table 5.16). This indicates that income is positively associated with
the educational level of people who have an average income of above two
minimum wages, although it is not so for people with lower income.
Educational Attainment of Women Ages 18-65
The total number of women ages 18-65 in the sample is 211,175, and
99.7% of them are racially identified in the 1980 census. Of those racially
identified, whites constitute 76.5%, Afro-Brazilians 21.4%, and Asians 2.1%.
The mean years of schooling for the sample is 4.58 years, which is slightlt
lower than that of men (4.93 years). Just as with men, there are significant
variations in mean years of schooling among women by color group; 6.65
years for Asians, 4.90 years for whites, and 3.23 years for Afro-Brazilians.
Table 5.17 illustrates the mean years of schooling for Asians, whites and Afro-
Brazilians.
When compared to men of the same color group, women of all three
groups have fewer years of schooling as well. Asian women, on average,
have 6.65 years of schooling, compared to 7.44 years for Asian men, white
women have 4.90 years of schooling, compared to 5.3 years for white men,

120
and Afro-Brazilian women have 3.23 years of schooling, compared to 3.5 years
for Afro-Brazilian men. Note, too, that the ratio between mean years of
schooling for men and women of the same color group is approximately the
same for all three groups; 1.11 for Asians, 1.08 for whites and 1.08 for Afro-
Brazilians. This suggests that the degree of gender differences in terms of
schooling is about the same across the three groups (see Figure 5.7).
Table 5.17
Mean Years of Schooling for Women Ages 18-65
by Color Group, Metropolitan Sao Paulo, Brazil (1980)
Color Group
Mean
STD DEV
Cases
%
Asian
6.65
3.85
4,509
2.1
White
4.90
3.61
160,952
76.5
Afro-Brazilian
3.23
2.88
45,028
21.4
Total
4.58
3.55
210,488
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Women in the sample are also divided into the three age groups (18-25,
26-39, and 40-65 years) as men are for analytical purpose. The general trend
here is the same as with the men's data, i.e., younger women have more
years of schooling than older women. For instance, women ages 18-25 have
an average schooling of 6.05 years, compared to 4.81 years for women ages 26-
39 and 3.06 years for women ages 40-65 (see Table 5.18).
Although the mean years of schooling for women in the two older age
groups (4.81 and 3.06 years) are lower than those for men of the same age
groups (5.16 and 3.76 years), women ages 18-25 have .10 years than do men of
the same ages (6.05 vs. 5.95). This indicates that women ages 18-25 are slightly
better educated than their male counterparts, although they, as a whole, still

lag behind men in educational attainment. Figure 5.8 compares men and
women in terms of mean years of schooling by age group.
121
Table 5.18
Mean Years of Schooling for Women Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980)
Age
Mean
STD DEV
Cases
%
18-25
6.05
3.39
62,536
29.6
26-39
4.81
3.55
76,422
36.2
40-65
3.06
3.06
72,217
34.2
Total
4.58
3.55
211,175
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Educational level of women also varies a great deal by place of
residence. Urban women have much more schooling than their rural
counterparts; 4.79 years for urban women, compared to only 2.51 years for
rural women. Due to the small proportion of women in rural areas (9.2%),
the overall mean years of schooling for women is not affected much by the
extremely low mean for rural women, as shown in Table 5.19.
Table 5.19
Mean Years of Schooling for Women Ages 18-65
by Residence, Metropolitan Sao Paulo, Brazil (1980)
Residence
Mean
STD DEV
Cases
%
Urban
4.79
3.57
191,782
90.8
Rural
2.51
2.60
19,393
9.2
Total
4.58
3.55
211,175
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

122
8
Sample Asian White Afro-Brazilian
Color Group
H Male
H Female
Figure 5.7 Mean Years of Schooling by Sex and Color,
Metropolitan Sao Paulo, Brazil (1980)
7
m
Sample 18-25 26-39 40-65
Age Group
^ Male
[H Female
Figure 5.8 Mean Years of Schooling by Sex and Age Group,
Metropolitan Sao Paulo, Brazil (1980)

123
Gender difference in schooling is also noticeable in both urban and
rural areas; 5.18 years for men compared to 4.79 years for women in urban
areas, and 2.85 years for men compared to 2.51 years for women in rural areas.
The ratio between the means of men and that of women in rural areas (1.90)
is slightly bigger than the ratio between the means of men and women in
urban areas (1.81), indicating a slightly greater degree of inequality between
men and women in rural areas (see Figure 5.9 below).
o>
I5
o
o4
(f)
°3

§2
>-
§1
o
Rural
Sample Urban
Place of Residence
| Male
Ü Female
Figure 5.9 Mean Years of Schooling by Sex and Residence,
Metropolitan Sao Paulo, Brazil (1980)
We already know from Tables 5.17-5.19 that there are considerable
differences in mean years of schooling for women by color, age and place of
residence. Now I will examine whether the differences in schooling among
women of the three color groups become smaller or larger when age and
residence are controlled.

124
The data show that the difference in mean years of schooling between
Asian and white women is actually bigger at the two younger age groups than
the difference between the two when age is not controlled. The overall ratio
between the two groups is 1.36 , while the ratios between the two at ages 18-25
and 26-39 are 1.47 and 1.45, respectively. Younger Asian women (those under
40) have made greater progress in educational attainment than white women
of the same age groups. However, the ratio between older Asian and white
women ages 40-65 (1.24) is lower than the ratio between the two (1.36) when
age is not controlled. This shows that the gap between Asian and white
women is widening from older to younger ages (see Table 5.20).
Table 5.20
Mean Years of Schooling for Women Ages 18-65
by Age and Color, Metropolitan Sao Paulo, Brazil (1980)
Age
Total
Asian
White
Afro-Brazilian
A/W
Ratio
* A/AB*
18-25
6.05
9.46
6.45
4.59
1.47
2.06
26-39
4.81
7.55
5.19
3.24
1.45
2.33
40-65
3.06
4.13
3.34
1.67
1.24
2.47
Total
4.58
6.65
4.90
3.23
1.36
2.06
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asian/White, A/AB = Asian/Afro-Brazilian.
"When Asian women are compared to Afro-Brazilian women within
each age level, there is no change in the gap between the two at the first age
level (ages 18-25), and the gap widens at the two older age groups. This
pattern is just the opposite of the one between Asian and white women. In
other words, the gap in schooling between Asian and Afro-Brazilian women

125
has been narrowing from generation to generation. The ratio between the
mean years of schooling for Asian women ages 40-65 and that for Afro-
Brazilian women of the same ages is 2.47 years, whereas the ratio between the
two at the second age level reduces to 2.33 years, and the ratio at the youngest
age level drops to 2.06 years, which is exactly the ratio between the two before
age is controlled. The difference between Asian and Afro-Brazilian women is
still much bigger than the one between Asian and white women, but the
main issues here are the effect of age on the differences in schooling among
the color groups and the general patterns from older to younger age groups.
When place of residence is controlled, the differences between Asians
and the other two groups become smaller in urban areas, and bigger in rural
areas. Specifically, the ratio between Asian and white women in urban areas
is 1.33, compared to 1.36 when residence is not controlled, and the ratio
between Asian and Afro-Brazilian women is 2.02, which is slightly lower
than 2.06 before residence is controlled. In rural areas, the educational level
for Asian women is much higher than those for white and Afro-Brazilian
women. For example, rural Asian women, on the average, have 5.03 years of
schooling, which is close to 5.12 years for urban white women, and 1.65 years
more than that for urban Afro-Brazilian women. In a word, color differences
still remain after place of residence is controlled, as shown in Table 5.21.
Finally, let's look at what happens to the color differences in schooling
of women when income is controlled. With the exception of the ratio
between the mean years of schooling for Asians and that for whites at the
second income level, all the ratios between Asians and whites and between
Asians and Afro-Brazilians are either smaller than or equal to those ratios
among the three color groups before income is controlled. More importantly,
the gap between them narrows with the increase of income, indicating a

126
strong positive correlation between income and schooling. For example, the
gap between the mean years of schooling for Asian and white women
narrows from 1.44 years at the lowest income level to only 0.38 years at the
highest income level.
Table 5.21
Mean Years of Schooling for Women Ages 18-65
by Residence and Color, Metropolitan Sao Paulo, Brazil (1980)
Residence
Total
Asian
White
Afro-Brazilian
Ratio
A/W* A/AB*
Urban
4.79
6.83
5.12
3.38
1.33
2.02
Rural
2.51
5.03
2.61
1.91
1.93
2.63
Total
4.58
6.65
4.90
3.23
1.36
2.06
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asian/White, A/AB = Asian/Afro-Brazilian.
Table 5.22
Mean Years of Schooling for Women Ages 18-65
by Income and Color, Metropolitan Sao Paulo, Brazil (1980)
Income
Total
Asian
White
Afro-Brazilian
Ratio
A/W* A/AB*
To 1 MW*
3.76
5.46
4.02
2.69
1.36
2.03
To 2 MW
4.71
7.25
5.05
3.82
1.44
1.90
To 3 MW
6.64
8.19
6.90
5.41
1.19
1.51
Above 3 MW
8.96
9.46
9.08
7.22
1.04
1.31
Total
4.58
6.65
4.90
3.23
1.36
2.06
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asian/White, A/AB = Asian/Afro-Brazilian.
**MW = minimum wage

127
The gap between the mean years of schooling for Asian and Afro-
Brazilian women have reduced from 2.77 years at the lowest income level to
2.24 years at the highest income level. Although the rate of decrease is very
small, -it is steady and indicates the increasingly positive correlation between
the two. The educational level of Afro-Brazilian women with income of
above three minimum wages is still considerably lower than those of Asians
and whites, but their average years of schooling has a significant increase of
4.53 years from the lowest to highest income level. Table 5.22 presents a
comparison of the mean years of schooling for women of the three color
groups with income controlled.
Summary
The data on children show that in Metropolitan Sao Paulo, Brazil,
more than 80% of children ages 8-12 are in school, with peaks (more than
90%) at ages 9 and 10, but the number of children in school decreases from age
13 until it drops to 52.5% at age 16. When children of the three color groups
are compared, Asian children have the highest percentage in school, 88.7%,
followed by 73.6% for white children and 64.7% for Afro-Brazilian children.
When children of different income, place of residence and parental
education are compared separately, there are marked differences; a higher
percent of children from higher income levels, urban areas, and with parents
who have more years of schooling are in school. In other words, income,
urban residency and parental education have positive impact on the school
attendance rate of children. A slightly higher percentage of male than female

128
children are in school (72.3% for males vs. 70.8% for females). When the
school attendance rate is measured, controlling for income and place of
residence separately, Asian children do better than white children, who do
better than Afro-Brazilian children at every age. This indirectly indicates that
Asians put more emphasis on education than the other two groups.
The logistic regression results quantitatively show the effects of each of
the independent variables examined here on the dependent variable, in¬
school rate, and the changes in the odds of being in school for a one-unit
increase in the independent variable. From ages 6 to 16, the coefficients of all
independent variables, except a few of the dummy variables (Asians at ages 6,
8,10 and 11, and Afro-Brazilians at age 12), are statistically significant. In
general, father's and mother education and income have about the same
positive effect across ages on whether or not a child is in school. Except at age
6, urban residency has an increasingly more positive effect than does rural
residency on in-school rate. In fact, the odds of being in school for urban
children increase by a factor of more than 2.0 from ages 11-16. This indicates
the great advantages of urban areas over rural areas in terms to access to
school: Most importantly, being Asian, compared to being white (the
reference group here), increases the odds of being in school at most ages by a
factor of more than two and in some cases by as much as a factor of more than
five. In contrast, being Afro-Brazilian decreases the odds of being in school at
all ages but 12 by a factor of about .20 and in some cases by a factor of more
than .30.
The educational attainment of Brazilian men varies a great deal by
color, age group and place of residence. The mean years of schooling for
Asians is 7.44, compared to 5.3 years for whites and 3.5 years for Afro-
Brazilians; the mean years of schooling for the age group 18-25 is 5.95, while

129
the comparable figures for the age groups 26-39 and 40-65 are 5.16 and 3.76,
respectively; urban residents, on average, have 5.18 years of schooling, as
opposed to 2.85 years for rural residents.
When age is controlled, the differences in educational attainment
among Asians and the other two groups widen a little in most cases; for the
age groups 18-25 and 26-39, the ratios between the mean years of schooling for
Asians and whites (1.48 and 1.49) are bigger than it is before the control of age
(1.40); the same ratios between Asians and Afro-Brazilians for the age groups
26-39 and 40-65 (2.36 and 2.41) are also bigger than it is before the control of age
(2.13). This indicates that age is not a causal factor for the color differences in
education.
Neither is place of residence a major factor for the color differences in
education because the same patterns of differences remain among the three
groups in both urban and rural areas. Although there are slight decreases in
the ratios between the mean years of schooling for Asians and whites and
Asians and Afro-Brazilians in urban areas, there are considerable increases in
the same ratios among them in rural areas.
When income is controlled, the color differences in education narrow
at the two higher income levels, but they widen at the two lower income
levels. This indicates that income is positively correlated with education at
higher higher income levels, but not at lower income levels. This is probably
because income is most likely the result rather than the cause of educational
level.
There are significant variations in the educational attainment of
Brazilian women by color, age group and place of residence, just as the case
with their men counterparts. The mean years of schooling for Asian women
is 6.65 while those for white and Afro-Brazilian women are 4.90 and 3.23,

130
respectively; the mean years of schooling for the age groups 18-25, 26-39 and
40-65 are, respectively, 6.05, 4.81 and 3.06; urban women, on average, have 4.79
years of schooling, compared to 2.51 years of schooling for rural women; the
mean years of schooling for the four income levels are, in ascending order,
3.76,4.71, 6.64 and 8.96.
Although the educational attainment of women as a whole is lower
than that of men and the same holds true within each of the three color
groups, women outperform men in a few categories. For instance, the mean
years of schooling for women ages 18-25 (6.05 years) is slightly higher than
that men of the same age group (5.95 years) and the mean years of schooling
for women at all income levels, except the lowest, exceed the corresponding
figures for men of the same income levels. However, as more than two
thirds of women belong to the lowest income level, their overall educational
level (4.58 years) is still lower than that of men (4.93). This reflects the
economic status of Brazilian women as well as their educational level.
When age is controlled, the color differences in education still remain
for most age groups. For example, the ratio between the mean years of
schooling for Asian and white women widens for the two younger age
groups, and the ratio between Asian and Afro-Brazilian women widens for
the two older age groups. This suggests that the mean years of schooling for
both Asian and Afro-Brazilian women have increased at a faster rate than
that of white women over the years. When place of residence is controlled,
the color differences in education reduce slightly in urban areas, but increase
considerably in rural areas, suggesting a minimal role of residence in the
color differences in education.
The educational differences among Brazilian women of the three color
groups reduce considerably, controlling for income, with one exception. All

131
the ratios among the three groups, except for the ratio between Asians and
whites at the income level of up to two minimum wages, become smaller
and smaller with the increase of income. At the highest income level of
above three minimum wages, the mean years of schooling for Asian and
white women become almost identical (9.46 for Asians and 9.08 for whites),
and the ratio between Asian and Afro-Brazilian women reduce to 1.31 from
2.06 before the control of income. In brief, this suggests that the association
between income and education, particularly at higher income levels, is
greater for women than it is for men in Brazil.

CHAPTER 6
OCCUPATIONAL PROFILE
OF ASIANS, WHITES AND AFRO-BRAZILIANS
In this chapter, I examine another key indicator of socioeconomic
status, occupational distribution, of men and women ages 18-65 in
Metropolitan Sao Paulo, Brazil. In modern societies, the type of occupation
people have is very much dependent upon the level of education they have
received, and their income is very much associated with their occupations.
Thus, the occupational profile of a social group tells us a great deal about their
status vis-a-vis other groups in the society. The main objective of this chapter
is to find out if there are any occupational differences among the three color
groups, and if there are, whether they are due to some other factors, such as
differences in residence and educational level.
In what follows, I describe the mean income of men and women
separately because the main focus here is on the color groups and there are
vast income differences between men and women in Brazil. The first part of
the chapter deals with the occupational distribution of men and the second
part deals with that of women, with brief comparisons of the occupational
distributions of men and women. In each part, I first describe the
occupational distribution of the sample by color, place of residence, age group,
educational level and income level, and then compare the occupational
distributions of the three color groups by residence, age group, educational
and income levels. The findings are summarized at the end of the chapter.
132

133
The 1980 Brazilian Census used hundreds of codes for classifying
respondent's primary occupation in the twelve months period preceding the
census. I have reclassified the occupations into six major categories;
1. managerial/administrative, 2. professional/technical, 3. clerical, 4.
transportation/communications, 5. transformative, and 6. unskilled/personal
service (See Appendix D for details). The first three categories consist of white
collar occupations, and the last three categories blue collar occupations. For
comparison and clarity, I list the percentage of blue collar occupations, in
addition to the percentages of individual categories, in the tables that follow.
Occupational Profile of Men Ages 18-65
There are a total of 167,294 cases in the sample data for men ages 18-65,
of which 99.8%, are racially classified. There are many variations among the
three color groups in terms of occupational distribution, as shown in Table
6.1. While 67.4% of the sample have blue collar occupations, only 47.2% of
Asians, 63.1% of whites and 83.6% of Afro-Brazilians have blue collar
occupations. Furthermore, within the category of white collar occupations,
there are proportionally more Asians than whites and Afro-Brazilians who
are in the first two categories, i.e., managerial/administrative and
professional/technical. For instance, almost 39% of Asians are in these
occupations, compared to about 22% of whites and about 7% of Afro-
Brazilians. The proportions of Asians and whites who are in the third
category, occupations related to clerical work, are about the same (14% and
14.5%), whereas only about 9% of Afro-Brazilians have clerical occupations.

134
Table 6.1
Occupational Distribution of Men Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980)
Ratio
Occupation
Total
Asian
White
Afro-B*
A/W**
A/AB
%
%
%
%
Managerial/
Administrative
10.5
19.9
12.2
3.3
1.63
6.03
Professional/
Technical
8.8
18.9
10.0
3.9
1.89
4.85
Clerical
13.3
14.0
14.5
9.1
0.97
1.54
Blue Collar***
67.3
47.2
63.1
83.7
0.75
0.56
Transportation/
Communication
8.3
5.4
8.3
8.6
0.65
0.63
Transformative
35.7
14.5
31.9
50.6
0.45
0.29
Unskilled/
Personal Service
23.3
27.3
22.9
24.5
1.19
1.11
%
100.0
100.0
100.0
100.0
N of Cases
166,986
4067
124,711
38,208
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians/Whites ratio, A/AB = Asians/Afro-Brazilians ratio
***6106 collar percentages are the sum of transportation/communication,
transformative, and unskilled/personal service categories.
Within the broad category of blue collar occupations, the color
differences are most obvious in the distributions of transformative and
unskilled/personal service occupations. On the one hand, there are
proportionally more Afro-Brazilians (50.6%) than whites (31.9%) and Asians
(14.5%) who are in transformaitve occupations. On the other hand, higher
percentage of Asians (27.3%) than whites (22.9%) and Afro-Brazilians (24.5%)
are classified as being engaged in unskilled/personal service occupations.

135
Further cross tabulation of each occupation in the category of
unskilled/personal service by color group reveals that Asians differ from the
other two in specific occupational concentration. Table 6.2 lists the top five
individual occupations within the category of unskilled/personal service for
the three color groups. The number one occupation for Asians (29.2%) is
"self-employed small business", while the number one occupation for whites
(35.1%) and Afro-Brazilians (45.6%) is "other jobs in agriculture and fishing."
The high concentration of Asian Brazilians in small business has a striking
similarity with the case of Japanese immigrants in the United States. Asians
are also heavily concentrated in the occupations of "autonomous producers
in agriculture and fishing " (28.7%), "mobile sellers" (people who go from
market to market to sell things) (13.8%) and "other jobs in agriculture and
fishing" (13.4%).
Table 6.2
Top Five Occupations in the Category of Unskilled/Personal Service
by Color Group, Metropolitan S3o Paulo, Brazil (1980)
Occupation
Asians
Whites
Afro-Brazilians
Self-employed small
%
%
%
business
Autonomous producers
29.2 (1)
13.5 (3)
5.2 (5)
in agriculture & fishing
28.7 (2)
16.3 (2)
6.6 (4)
Mobile sellers
13.8 (3)
1.8
1.4
Other jobs in agr./fishing
13.4 (4)
35.5 (1)
45.6 (1)
Laundry/ironing
2.6 (5)
0.3
0.5
Domestic security guards
0.4
4.5 (5)
7.2 (3)
Domestic cleaning service
0.5
6.0 (4)
8.9 (2)
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

136
In contrast, the second, third and fourth most popular occupations for
whites are "autonomous producers in agriculture and fishing" (16.3%), "self-
employed small business (13.5%) and "domestic cleaning service" (6.0%), and
those for Afro-Brazilians are "domestic cleaning services" (8.9%), "domestic
security guards" (7.2%) and "autonomous producers in agriculture and
fishing" (6.6%). Another striking difference between Asians and the other
two groups is that a considerable number of Asians are in the "laundry and
ironing business," while a considerable number of whites and Afro-Brazilians
are in the "domestic security and cleaning business."
The occupational distribution of men varies a great deal by residence,
as shown in Table 6.3. The percentage of white collar occupations is much
higher for urban residents than it is for urban residents, and the distribution
of blue collar occupations is much higher in rural areas than it is urban areas.
Specifically, of urban residents, 11% have managerial/administrative
occupations, 9.9% have professional/technical occupations, and 14.8% have
clerical occupations, compared to the corresponding figures for rural
residents, 6.9%, 1.1% and 2.3%. Clerical is the most popular white collar
occupation for urban residents, while managerial/administrative is the most
popular white collar occupation among rural residents. However, both the
percentage and the absolute number of people who are managers and
administrators are far greater in urban areas than in rural areas (see Table 6.3).
Within blue collar occupations, the highest percentage of urban people
(38.7%) have transformative occupations, followed by those occupations in
unskilled/personal service (16.7%) and in transportation/communications
(8.8%). In comparison, the highest percentage of rural people (71.1%) have
unskilled/personal service occupations, followed by transformative (14.1%)
and transportation/communications (4.5%).

137
Table 6.3
Occupational Distribution of Men Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Total
Urban
Rural
U/R Ratio*
%
%
%
Managerial/
Administrative
10.5
11.0
6.9
1.59
Professional/
Technical
8.8
9.9
1.1
9.00
Clerical
13.3
14.8
2.3
6.43
Blue Collar
67.4
64.2
89.7
0.72
Transportation/
Communication
8.3
8.8
4.5
1.96
Transformative
35.8
38.7
14.1
2.74
Unskilled/
Personal Service
23.3
16.7
71.1
0.23
%
100.0
100.0
100.0
N of Cases
167,294
146,957
20,337
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*U/R Ratio = ratio between the percentages of urban and rural areas.
There are also differences in the occupational distribution of men by
age group. These differences are, however, not at the broad level of white
collar vs. blue collar, but at the level of the six major occupational categories
used in this study. If we look at the percentages of men with blue collar
occupations across the three age groups in Table 6.4, we see that they are about
the same; 67.4% for men ages 18-25, 66.3% for men ages 26-39, and 68.7% for
men ages 40-65. However, within white collar occupations, the three age
groups differ considerably due to factors closely related to age, such as
education and experience. For example, a higher percentage of the youngest
group have clerical jobs, and a higher percentage of the oldest group have

138
managerial/administrative jobs. The middle-age group is almost evenly
distributed among the three occupations. The age differences are less
pronounced for blue collar workers, though they are noticeable, especially
between men below and above 40 years of age: A higher percentage of people
under 40 have transformative occupations, and a higher percentage of people
above 40 have occupations in the category of unskilled/personal service. To
some degree, this reflects the requirements and demands of different
occupations in the labor force.
Table 6.4
Occupational Distribution of Men Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Total
18-25
26-39
40-65
Managerial/
%
%
%
%
Administrative
Professional/
10.5
4.1
11.7
15.0
Technical
8.8
6.4
10.9
8.4
Clerical
13.3
22.1
11.1
7.9
Blue Collar
Transportation/
67.4
67.4
66.3
68.7
Communication
8.3
6.1
9.9
8.3
Transformative
Unskilled/
35.8
41.1
37.3
28.9
Personal Service
23.3
20.3
19.1
31.6
%
100.0
100.0
100.0
100.0
N of Cases
167,294
48,176
66,693
52,425
Source; Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Naturally, occupational distribution is highly correlated with average
income: White collar jobs produce higher wages and blue collar jobs produce

139
less money. Therefore, people with higher income are expected to be
concentrated in white collar occupations, especially in the first two categories,
managerial/administrative and professional/technical, and people with
lower income are expected to be concentrated in blue collar occupations,
especially in the category of unskilled/personal service.
The data show exactly this relation between occupation and income
(see Table 6.5). Of those who are in the first three income groups (1-3
minimum wages), very few are in the first two categories of white collar
occupations. In other words, the majority of people who belong to these two
occupational categories earn an income of above three minimum wages. The
percentage of clerical workers increases from 6.6% for the first income group,
to 12.3% for the second income group, to 15.2% for the third income group,
and then decreases slightly to 14.1% for the fourth income group. This
suggests that clerical jobs are most popular among people who have an
average income of 2-3 minimum wages, and they tend to become less
common among people with even higher wages.
The percentage of blue collar occupations decreases with the increase of
average income; 90.3% for the first income group, 83.7% for the second
income group, 79.1% for the third income group, and 49.9% for the fourth
income group. The contrast between the first three and the last income group
is particularly sharp. In other words, while 80-90% of people with an average
income of less three minimum wages are blue collar workers, less than 50%
of people who have an average income of above three minimum wages are
blue collar workers.
Within blue collar occupations, the percentage of people in
unskilled/personal service varies the most across income levels. It drops
from 66.2% at the first income level to 34.3% at the second income level, to

140
17.2% at the third level, and finally to 12.6% at the fourth income level. This
indicates the plain fact that occupations in this category are least desirable in
terms of monetary reward. On the other hand, the high concentration of
transformative occupations at the second and third income level (42.3% and
48.1% respectively) shows that people in these occupations are considerably
better off than those in the category of unskilled/personal service. They are
still blue collar jobs, however, and tend to be less popular among people
whose average income is above three minimum wages (only 28.9%). Table
6.5 describes the occupational distribution of men ages 18-65 by income group
Table 6.5
Occupational Distribution of Men Ages 18-65 by Income,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Total
1MW*
2MW
3MW
3+MW
Managerial/
%
%
%
%
%
Administrative
Professional/
10.5
1.3
2.0
3.8
20.0
Technical
8.8
1.7
2.0
3.9
16.0
Clerical
13.3
6.6
12.3
15.2
14.1
Blue Collar
Transportation/
67.4
90.3
83.7
79.1
49.9
Communication
8.3
2.4
7.0
11.7
8.5
Transformative
Unskilled/
35.7
21.7
42.3
48.1
28.9
Personal Service
23.3
66.2
34.3
17.2
12.6
%
100.0
100.0
100.0
100.0
100.0
N of Cases
167,048
12,594
44,591
33,243
76,619
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*MW = minimum wage

141
Educational attainment, as measured by mean years of schooling, is
also closely associated with the occupational distribution of men ages 18-65.
In general, average years of schooling is positively correlated with the
percentage of white collar workers and negatively correlated with the
percentage of blue collar occupations. In other words, as the average years of
schooling go up, the percentage of white collar occupations increases and the
percentage of blue collar occupations decreases.
First of all, there are dramatic increases in the percentage of white
collar occupations and sharp decreases in the percentage of blue collar
occupations from lower to higher educational levels, especially at higher
levels of 5-8 years, 9-11 years and 12 or more years of schooling. For example,
93.8% of men who have no schooling and 83.6% of men who have 1-4 years
of schooling are blue collar workers, compared to 60% of men who have 5-8
years of schooling, 28% for those who have 9-11 years and only 6.3% for those
who have 12 or more years of schooling have blue collar occupations.
Within the category of white collar occupations, the first two,
managerial/administrative and professional/technical, have a positive and
linear correlation with educational level, while the third one, clerical, has a
positive, but nonlinear correlation with educational level. The percentages of
the first two categories go up with increasing average years of schooling and
are most popular among people with 12 or more years of schooling. In
contrast, the percentage of clerical occupations reaches its highest point
(35.1%) at the level of 9-11 years of schooling and then drops back to 15.8% at
the level of 12 or more years of schooling. Thus, people with 9-11 years of
schooling are more likely to have clerical occupations than any other
educational groups.

142
Table 6.6
Occupational Distribution of Men Ages 18-65
by Education, Metropolitan Sao Paulo, Brazil (1980)
Occupation
Total
0
1-4
5-8
9-11
12+ (vears)
%
%
%
%
%
%
Managerial/
Administrative 10.5
2.9
6.8
10.9
19.7
29.6
Professional/
Technical
8.8
0.8
2.8
6.3
17.3
48.2
Clerical
13.2
2.5
6.9
22.8
35.1
15.8
Blue Collar
67.5
93.8
83.6
60.0
28.0
6.3
Transportation/
Communication 8.3
4.2
11.9
8.6
2.7
0.3
Transformative
35.8
40.4
44.2
37.7
16.2
2.7
Unskilled /Personal
Service
23.4
49.2
27.5
13.7
9.1
3.3
%
100.0
100.0
100.0
100.0
100.0
100.0
N of Cases
166,679
19,805
83,483
29,957
19,024
14,410
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
For people with less than 9 years of schooling, transformative
occupations are most prevalent; 40.4% for men with no schooling, 44.2% for
men with 1-4 years of schooling, 37.7% for men with 5-8 years of schooling.
Occupations in the category of unskilled/personal service are highly
concentrated among people with less than 5 years of schooling: 49.2% for
people with no schooling at all and 27.5% for people with 1-4 years of
schooling. Accordingly, the percentage of unskilled/personal service
occupations decreases sharply at higher educational levels; 13.7% for people
with 5-8 years of schooling, 9.1% for people with 9-11 years of schooling and
only 3.3% for people with 12 or more years of schooling. In contrast,
occupations in transportation/communications are more prevalent among

143
people with 1-4 years of schooling (11.9%) and those with 5-8 years of
schooling (8.6%) than among those with less or more schooling.
In Table 6.1 and Tables 6.3-6.6,1 have described the variances in
occupational distribution of men ages 18-65 by color group, place of residence,
age, income group and educational level. In the following, I will examine if
the color differences in occupational distribution still remain when these
independent variables are controlled. If most of the color differences
disappear after controlling for these variables, we could conclude that they are
largely due to factors other than color. Otherwise, it is safe to say to that color,
along with the other independent variables examined here, does play a role
in the uneven occupational distribution of men ages 18-65.
Let's first look at the occupational distribution of the three color groups
when place of residence is controlled, i.e., in urban and rural areas separately.
The data show that in urban areas, while there are slight decreases in the
proportion of blue collar occupations for all color groups, the color differences
still remain much the same. Compared to urban areas, the proportion of blue
collar occupations have significant increases for all color groups and the color
differences have narrowed somewhat in rural areas, but the three groups are
still significantly different from one another. Table 6.7 compares the
proportions of white vs. blue collar occupations of men in the entire sample,
and in urban and rural areas separately.
Table 6.8 describes the occupational distribution of the three color
group in greater detail. In urban areas, Asians exceed whites and Afro-
Brazilians by a large margin in the first two categories of white collar
occupations and whites exceed the other two groups in clerical occupations.
Specifically, the ratio between the proportions of Asians and whites who have
managerial/administrative occupations is 1.50, and the ratio between Asians

144
and Afro-Brazilians is 5.79. In the category of professional/technical
occupations, the ratio between Asians and whites is 1.84, and the ratio
between Asians and Afro-Brazilians is 4.79. However, in the category of
clerical occupations, the percentage of whites (16.2%) exceeds that of Asians
(15.1%) by 1.1% and that of Afro-Brazilians (10.2%) by 6%. On the other hand,
Afro-Brazilians lead the other two groups in the first two categories of blue
collar occupations, and Asians have the highest percentage in the category of
unskilled/personal service. It is worth noting that as many as 55.1% of Afro-
Brazilians and 34.5% of whites are engaged in transformative occupations,
and as many as 23.3% of Asians have unskilled/personal service occupations.
The high concentration of urban Asians in this category is understandable
since it includes such occupations as "self-employed small business people,"
"autonomous producers in agriculture and fishing," and "mobile sellers." As
shown in Table 6.2, over 70% of Asians who have blue collar occupations
belong to one of the above three occupations.
Table 6.7
A Comparison of White vs. Blue Collar Occupations of Men
by Residence and Color, Metropolitan Sao Paulo, Brazil (1980)
Occupation
Sample
%
White Collar
Total
32.6
Urban
35.8
Rural
10.3
Blue Collar
Total
67.4
Urban
64.2
Rural
89.7
Asian
White
Afro-Brazilian
%
%
%
52.8
36.9
16.4
55.6
40.5
17.9
29.0
11.1
6.0
47.2
63.1
83.6
44.4
59.5
82.1
71.0
88.9
94.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

145
Table 6.8
Occupational Distribution of Men Ages 18-65
by Residence and Color, Metropolitan Sao Paulo, Brazil (1980)
Ratio
Residence
Sample
Asian
White AB*
A /W**
A/AB
Urban
Managerial/
%
%
%
%
Administrative
Professional/
11.0
19.7
13.1
3.4
1.50
5.79
Technical
9.9
20.6
11.2
4.3
1.84
4.79
Clerical
14.8
15.1
16.2
10.2
0.93
1.48
Blue Collar
Transportation/
64.2
44.4
59.5
82.1
0.75
0.54
Communication
8.8
5.7
8.8
9.2
0.65
0.62
Transformative
Unskilled/
38.7
15.4
34.5
55.1
0.45
0.28
Personal Service
Total
Rural
Managerial/
16.7
100.0
23.3
100.0
16.2
100.0
17.8
100.0
1.44
1.31
Administrative
Professional/
6.9
21.5
7.8
2.9
2.76
7.41
Technical
1.1
3.7
1.1
0.9
3.36
4.11
Clerical
2.3
3.7
2.3
2.2
1.61
1.68
Blue Collar
Transportation/
89.7
71.0
88.9
94.0
0.79
0.76
Communication
4.5
2.2
4.6
4.5
0.48
0.49
Transformative
Unskilled/
14.1
5.6
12.5
19.8
0.45
0.28
Personal Service
Total
71.1
100.0
63.2
100.0
71.8
100.0
69.7
100.0
0.88
0.91
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*AB = Afro-Brazilians
**A/W = Asians/whites, A/AB = Asians/Afro-Brazilians

146
In rural areas, Asians lead the other two groups in every category of
white collar occupations, but the most striking difference among them is in
the percentage of managerial/administrative occupations. For instance, as
many as 21.5% of rural Asians have occupations in this category, compared to
only 7.8% for whites and 2.9% for Afro-Brazilians. On the other hand, unlike
in urban areas, the percentage of Asians who are in the category of
unskilled/personal service (63.2%) is the lowest, compared to the
corresponding figures for whites and Afro-Brazilians, 71.8% and 69.7%.
When age is controlled, the color differences in some occupational
categories narrow considerably within the same age group, indicating the
important role of age in occupational distribution. Meanwhile, the data also
tell us that age alone can only explain some of the vast variations among the
three color groups. If we look at the proportions of white vs. blue collar
occupations in different age groups, we see different patterns for the three
color groups (see Table 6.9): For both Asians and whites, the age group of 26-
39 years old has the highest percentage of white collar occupations (61.1% for
Asians and 38.3% for whites), while for Afro-Brazilians, it is the age group of
18-25 years old that has the highest percentage of white collar occupations
(19.5%). In other words, the proportion of white collar occupations increases
from the first age group to the second age group and then decreases for the
third age group for Asians and whites, whereas it decreases from younger to
older age groups for Afro-Brazilians. Obviously, the distribution of blue
collar occupations for the three groups has the opposite pattern.
Now let's look at the occupational distribution for the three age groups
one by one (see Table 6.10). For the age group of 18-25 years old, clerical
occupations account for the highest percentage of Asians (33.3%), while
transformative occupations account for the highest percentage for whites

147
(37.1%) and Afro-Brazilians (53.3%). Another striking difference between
Asians and the other two groups is the high concentration of Asians in the
category of professional and technical occupations (19.4%), as opposed to only
7.3% for whites and 3.1% for Afro-Brazilians. This is particularly significant,
considering the age range (18-25 years) of this group. It shows that Asians, as a
group, have a much better start in occupation, mainly due to their higher
educational attainment. When it comes to unskilled/personal service
occupations, the three groups have about the same percentages (22% for
Asians, 21.5% for Afro-Brazilians and 19.8% for whites).
Table 6.9
A Comparison of White vs. Blue Collar Occupations of Men
by Age and Color, Metropolitan Sao Paulo, Brazil (1980)
Occupation
Sample
%
White Collar
Total
32.6
18-25
32.6
26-39
33.7
40-65
56.8
Blue Collar
Total
67.4
18-25
67.4
26-39
66.3
40-65
43.2
Asian
White
Afro-Brazilian
%
%
%
52.8
36.9
16.3
58.4
36.8
19.5
61.1
38.3
16.2
42.2
35.4
12.6
47.2
63.1
83.7
41.6
63.2
80.5
38.9
61.7
83.8
57.8
64.6
87.4
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
For the age group of 26-39, the primary occupation for Asians is not
clerical any more, but professional/technical (26.3%), which is closely
followed by managerial/administrative (22.2%), while the primary
occupations for whites and Afro-Brazilians are still transformative (32.8% for
whites and 54.1% for Afro-Brazilians). In other words, almost half of Asians

148
in this age group have occupations in either managerial/administrative or
professional/technical, while only 26% of whites and 8.9% of Afro-Brazilians
do so. This shows the success of Asians in this age group in terms of upward
mobility through occupational recruitment. On the other hand, the color
differences in the proportions of clerical occupations and unskilled/personal
service occupations reduce further, even becoming almost identical in some
cases. For example, 12.5% of Asians and 12.3% of whites have clerical
occupations, and the range of difference in the percentage of
unskilled/personal service occupations for all three groups is only 0.4%.
For the age group of 40-65 years old, the difference between Asians and
whites in almost all categories diminishes a great deal, while the difference
between Asians and Afro-Brazilians diminishes slightly in most categories
but widens in a few categories. For instance, while the percentage of Asians
who have managerial/administrative occupations increases a mere 1.4%
from the age group of 26-39 to the age group of 40-65, the percentage of whites
with the same occupations increases 3.5% from 13.7% to 17.2%. Although the
percentages of professional/technical occupations for both Asians and whites
drop from the preceding age group, the gap between them narrows
considerably due to a greater drop on the part of Asians. For example, the
proportion of Asians in this category drops drastically by more than 50% from
26.3% at the previous age group to only 11.2% at this age group, while the
proportion of whites decreases moderately from 12.3% to 9.4%. Similar
changes happen to the percentages of clerical occupations for Asians and
whites, only this time whites exceed Asians in the proportions of clerical
occupations (7.4% for Asians vs. 8.7% for whites). The gap between Asians
and whites narrows in two of the three blue collar occupations, and widens in
the category of unskilled/personal service.

149
Table 6.10
Occupational Distribution of Men Ages 18-65
by Age and Color, Metropolitan Sao Paulo, Brazil (1980)
Aee Group
Ages 18-25
Managerial/
Sample
%
Asian
%
White
%
Afro-B*
%
A /W**
A/AB
Administrative
Professional/
4.1
5.8
5.0
1.5
1.16
3.87
Technical
6.4
19.4
7.3
3.1
2.66
6.26
Clerical
22.1
33.3
24.5
14.9
1.36
2.23
Blue Collar
Transportation/
67.5
41.6
63.2
80.5
0.66
0.52
Communications
6.1
2.3
6.3
5.7
0.37
0.40
Transformative
Unskilled/Personal
41.1
17.3
37.1
53.3
0.47
0.32
Service
Ages 26-39
Managerial/
20.3
22.0
19.8
21.5
1.11
1.02
Administrative
Professional/
11.7
22.2
13.7
4.1
1.62
5.41
Technical
10.9
26.3
12.3
4.6
2.14
5.72
Clerical
11.1
12.5
12.3
7.4
1.02
1.69
Blue Collar
Transportation/
66.3
38.9
61.7
83.8
0.63
0.46
Communications
9.9
5.0
9.9
10.6
0.51
0.47
Transformative
Unskilled/Personal
37.3
14.5
32.8
54.1
0.44
0.27
Service
Ages 40-65
Managerial/
19.1
19.4
19.0
19.1
1.02
1.02
Administrative
Professional/
15.0
23.6
17.2
4.4
1.37
5.36
Technical
8.4
11.2
9.4
3.6
1.19
3.11
Clerical
7.9
7.4
8.7
4.6
0.85
0.66
Blue Collar
Transportation/
43.2
57.8
64.6
87.4
0.89
0.66
Communications
8.3
7.1
8.1
9.2
0.88
0.77
Transformative
Unskilled/Personal
28.9
13.1
26.3
41.9
0.50
0.31
Service
31.6
37.6
30.2
36.4
1.25
1.03
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*AB = Afro-Brazilians
**A/W = Asians/whites ratio, A/AB = Asians/Afro-Brazilians ratio

150
When Asians and Afro-Brazilians are compared, the difference
between them in terms of the distribution of white vs. blue collar occupations
is the smallest in the age group of 40-65 and the biggest in the age group of 26-
39. If we look at the distribution of the first two categories of white collar
occupations for the two groups, we find the same result, i.e. the gap between
Asians and Afro-Brazilians is slightly smaller in the age group of 40-65 than
in the other two age groups (see Table 6.10).
When income is controlled, the difference in occupational distribution
between Asians and whites becomes much smaller at the two ends of income
levels, and larger at the two middle income levels (see Table 6.11). This can
be seen from both the actual percentages and the ratio of the percentages of
Asians vs. whites who have blue collar occupations at the four income levels.
For instance, at the income level of up to one minimum wage, 85.1% of
Asians and 89.1% of whites have blue collar occupations. These percentages
translate into a ratio of 0.96 between Asians and whites, indicating a high
degree of similarity between the two. At the income level of above three
minimum wages, 40.6% of Asians and 46.4% of whites have blue collar
occupations, which results in a ratio of 0.88, also a high degree of similarity.
On the other hand, at the two middle income levels, the ratios between
the two are 0.81 and 0.80, respectively, which are still pretty high but lower
than the previous rates. When Asians and Afro-Brazilians are compared, the
difference between them tend to become larger in most categories as income
grows. For example, the ratios between these two groups in terms of the
percentage of blue collar occupations from lower to higher income levels are
0.91, 0.74, 0.71 and 0.56 (see Table 6.11).

151
Table 6.11
Occupational Distribution of Men Ages 18-65
by Income and Color, Metropolitan Sao Paulo, Brazil (1980)
Income Level
Sample
Asian
White
AB*
A/W**
A/AB
One MW
%
%
%
%
Man/Adm
1.3
1.4
1.6
0.6
0.88
2.33
Prof/Tech
1.7
4.4
2.0
1.1
2.20
4.00
Clerical
6.6
9.1
7.4
4.7
1.23
1.94
Blue Collar
90.3
85.1
89.1
93.5
0.96
0.91
Trans/Com
2.4
2.2
2.5
2.1
0.88
1.05
Transform
21.7
15.4
19.3
27.8
0.80
0.55
Unskilled
66.2
67.5
67.3
63.6
1.00
1.06
Two MW
Man/Adm
2.0
3.5
2.4
1.1
1.46
3.18
Prof/Tech
2.0
6.4
2.2
1.4
2.91
4.57
Clerical
12.3
24.1
13.9
8.5
1.73
2.84
Blue Collar
83.6
66.0
81.5
88.9
0.81
0.74
Trans/Com
7.0
5.8
7.6
5.9
0.76
0.98
Transform
42.3
25.1
38.4
51.2
0.65
0.49
Unskilled
34.3
35.1
35.5
31.8
0.99
1.10
Three MW
Man/Adm
3.8
8.9
4.4
2.2
2.02
4.05
Prof/Tech
3.9
7.3
4.2
2.9
1.74
2.52
Clerical
15.2
23.9
16.9
10.5
1.41
2.28
Blue Collar
77.0
59.8
74.4
84.3
0.80
0.71
Trans/Com
11.7
9.1
12.1
10.9
0.75
0.83
Transform
48.1
22.6
44.2
58.8
0.51
0.38
Unskilled
17.2
28.1
18.1
14.6
1.55
1.92
Above Three MW
Man/Adm
20.0
24.8
21.8
8.1
1.14
3.06
Prof/Tech
16.0
23.1
16.9
8.7
1.37
2.66
Clerical
14.1
11.6
14.9
10.2
0.78
1.14
Blue Collar
50.0
40.6
46.4
73.0
0.88
0.56
Trans/Com
8.5
5.0
8.1
12.2
0.62
0.41
Transform
28.9
11.8
25.9
50.3
0.46
0.23
Unskilled
12.6
23.8
12.4
10.5
1.92
2.27
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*AB = Afro-Brazilians
**A/W = Asians/whites ratio, A/AB = Asians/Afro-Brazilians ratio

152
As for the distributions of the six major occupations for the three color
groups, they exhibit different patterns at various income levels. In other
words, some occupations vary very little across color groups at certain income
levels and some vary a great deal. For instance, at the first two income levels,
the three color groups have very similar distributions of occupations of
transportation/communications and unskilled and personal service (see
Table 6.11). At the third income level, they also have very similar
proportions of transportation and communications occupations; 9.1% for
Asians, 12.1% for whites and 10.9% for Afro-Brazilians. Furthermore, Asians
and whites are very similar in the proportion of occupations in
managerial/administrative at the lowest and highest income levels. On the
other hand, Asians are far too over-represented in professional/technical
occupations at all income levels and far too under-represented in
transformative occupations than the other two groups (see Table 6.11).
When educational level is controlled, the differences among the three
color group in most occupational categories reduce considerably, suggesting a
strong positive association between education and occupational distribution.
First of all, the gap among the three group in the proportion of
professional/technical, transportation/communications and
unskilled/personal service occupations has become much smaller within the
same educational level, as shown in Table 6.12.
For those with no schooling at all, almost the same percentage of
whites (0.7%) and Afro-Brazilians (0.8%) have professional/technical
occupations. Although Asians fare a little better in this regard, with only
1.6%, the extremely low percentages for all groups show that
professional/technical occupations require a much higher level of education,
and the differences in these occupations are largely due to varying degrees of

153
education, rather than to race or color. The distribution of
transportation/communications occupations for the three groups at this
educational level are also very close, 5.1% for Asians, 4.5% for whites and
3.7% for Afro-Brazilians. These low percentages for all groups suggests again
that education may be a major factor here. Another similarity between
Asians and whites is that almost equal percentages of each group (53.2% of
Asians and 52.7% of whites) have occupations in the category of
unskilled/personal service.
At the second educational level (1-4 years of schooling), the gaps in the
ratio of blue collar occupations among the three groups reduce slightly from
what they are at the previous educational level; the ratio between Asians and
whites is 0.88 and the ratio between Asians and Afro-Brazilians is 0.81. Again,
the three groups have almost the same percentage of people whose
occupations are in transportation and communications; 10.6% for Asians,
12.2% for whites and 11.1% for Afro-Brazilians. The gaps among them in
professional/technical occupations also narrow from the previous level; 2.7%
for Asians, 2.9% for whites and 2.4% for Afro-Brazilians. In addition, about
the same percentage of Asians (7.2%) and whites (7.4%) have clerical
occupations. Meanwhile, Asians continue to lead the other two by a big
margin in the categories of managerial/administrative (18% for Asians vs. 8%
for whites and 2.8% for Afro-Brazilians) and unskilled/personal service
occupations (43.4% for Asians vs. 28.7% for whites and 23.3% for Afro-
Brazilians).
At the educational level of 5-8 years of schooling, the gaps in the ratio
of blue collar occupations among the three groups, especially the ratio
between Asians and whites, continue to drop from the second level. As at the
two previous levels, about the same proportion of people from all three

154
groups have transportation/communications occupations. Furthermore,
Asians and whites have become very similar in their distribution of
managerial/administrative and professional/technical occupations,
indicating a strong effect of education for these two groups. Although the gap
between Asians and Afro-Brazilians still exists in most categories, the gap
between them has narrowed considerably in such categories as clerical and
unskilled/personal service.
There are dramatic reductions in the percentage of people who have
blue collar occupations for all groups at the educational level of 9-11 years of
schooling. Specifically, 26.2% of whites, 36.2% of Asians and 39.4% of Afro-
Brazilians now have blue collar occupations, compared to 50-70% at the
previous educational level. This is a clear indication of the impact of
increased years of schooling on the occupational distribution of all three
groups. Furthermore, for the first time, the percentage of
managerial/administrative occupations for whites (21%) surpasses that of
Asians (20.4%), and the percentage of clerical occupations for Afro-Brazilians
(35.8%) surpasses that of whites (35.6%). The occupational distribution of
Asians and whites continue to become very much alike, except in the
categories of clerical and unskilled/personal service.
At the educational level of 12 or more years of schooling, the
percentage of blue collar occupations further drops to less than 10% for Asians
and Afro-Brazilians, and less than 6% for whites. On the other hand, there is
a tremendous gain for whites and Afro-Brazilians in the percentage of
managerial/administrative occupations and marked increases for all three
groups in the percentage of professional/technical occupations. For instance,
the percentage of managerial/administrative occupations for whites increases
by more than 8% from 21% to 30.4%, the same percentage for Afro-Brazilians

155
increases by more than 10% from 8.3% to 19.1%, and the percentage for Asians
increases by 5% from 20.4% to 25.5%. Similarly, the percentage of
professional/technical occupations for the three groups jumps by more than
20-30%, resulting in 51.9% for Asians, 40% for whites and 47.2% for Afro-
Brazilians. It is particularly important to point out that among people with 12
or more years of schooling, there are proportionally more Afro-Brazilians
than whites who have professional/technical occupations. This again
demonstrates that education has the most positive effect on the distribution
of professional/technical occupations. In contrast, the same amount of
increase in education results in less gain for Asians. This may have to do
with their relatively high concentration in these occupations to start with at
lower educational levels.
In sum, controlling for education has reduced the color differences in
occupational distribution to a great extent, but there are still visible
differences, especially in the managerial/administrative and
professional/technical categories occupations. Meanwhile, although
education level is positively correlated with white collar occupations and
negatively correlated with blue collar occupations, its degree of impact on
different groups is different in some cases. For instance, while the proportion
of whites and Afro-Brazilians with managerial/administrative occupations
almost doubles at each higher educational level, the increase of Asians in this
category at each higher educational level is much less pronounced, and it
even decreases at one level (5-8 years). In other words, increase in education
has more positive effect on whites and Afro-Brazilians than on Asians as far
as the distribution of managerial/administrative occupations is concerned.
Second, the data show that education is most closely associated with
the distribution of professional/technical occupations, and seems to have a

156
uniform positive effect on all three groups. This can be seen in the amount
of increases in the percentage of professional/technical occupations for each
of the three groups at all educational levels in Table 6.12. Third, the
percentages of transportation/communications occupations for all groups are
very similar, when educational level is controlled, suggesting that these
occupations have more to do with education than with race or color. Fourth,
the percentages of whites and Afro-Brazilians with clerical occupations are
much higher than that of Asians at the levels of more than four years of
schooling. Finally, the main difference in the distribution of blue collar
occupations between Asians on the one hand and whites and Afro-Brazilians
on the other is that proportionally higher percentages of Asians have
unskilled/personal service occupations than do the others, and
proportionally higher percentages of whites and Afro-Brazilians have
transformative occupations than do Asians. Therefore, we could conclude
that with the control of education, occupational differences among the three
groups diminish greatly, and whites and Afro-Brazilians are very similar,
especially at higher educational levels, but Asians continue to be
overrpresented in managerial/ administrative occupations at most
educational levels.

157
Table 6.12
Occupational Distribution of Men Ages 18-65
by Education and Color, Metropolitan Sao Paulo, Brazil (1980)
Years of Schooling
Zero Years
Sample
%
Asian
%
White
%
AB
%
A/W
A/AB
Man/Adm
2.9
17.7
3.6
1.4
4.92
12.6
Prof/Tech
0.8
1.6
0.7
0.8
2.29
2.00
Clerical
2.5
4.4
2.6
2.1
1.69
2.10
Blue Collar
93.8
76.2
92.9
95.7
0.82
0.79
Trans/Com
4.2
5.1
4.5
3.7
1.13
1.38
Transform
40.4
17.9
35.7
48.4
0.50
0.37
Unskilled
49.2
53.2
52.7
43.6
1.01
1.22
1-4 Years
Man/Adm
6.8
18.0
8.0
2.8
2.25
6.43
Prof/Tech
2.8
2.7
2.9
2.4
0.93
1.13
Clerical
6.9
7.2
7.4
5.6
0.97
1.29
Blue Collar
83.6
72.1
81.8
89.2
0.88
0.81
Trans/Com
11.9
10.6
12.2
11.1
0.87
0.95
Transform
44.2
18.1
40.9
54.8
0.44
0.33
Unskilled
27.5
43.4
28.7
23.3
1.51
1.86
5-8 Years
Man/Adm
10.9
15.3
12.6
4.6
1.21
3.33
Prof/Tech
6.3
7.1
6.7
4.6
1.06
1.54
Clerical
22.8
17.3
23.7
20.1
0.73
0.86
Blue Collar
50.0
60.3
57.0
70.7
1.06
0.85
Trans/Com
8.6
7.1
8.8
8.2
0.81
0.87
Transform
37.7
23.6
34.7
50.1
0.68
0.47
Unskilled
13.7
29.6
13.5
12.4
2.19
2.39
9-11 Years
Man/Adm
19.7
20.4
21.0
8.3
0.97
2.46
Prof/Tech
17.3
20.1
17.2
16.5
1.17
1.22
Clerical
35.1
23.4
35.6
35.8
0.66
0.65
Blue Collar
28.0
36.2
26.2
39.4
1.38
0.92
Trans/Com
2.7
2.4
2.6
3.6
0.92
0.67
Transform
16.2
13.1
15.1
27.2
0.87
0.48
Unskilled
9.1
20.7
8.5
8.6
2.44
2.41
12+ Years
Man/Adm
29.6
25.5
30.4
19.1
0.84
1.34
Prof/Tech
48.2
51.9
40.0
47.2
1.30
1.10
Clerical
15.8
13.2
15.6
24.3
0.85
0.54
Blue Collar
6.3
9.4
5.9
9.5
1.59
0.99
Trans/Com
0.3
0.3
0.3
0.1
1.00
3.00
Transform
2.7
3.2
2.5
5.7
1.28
0.56
Unskilled
3.3
5.9
3.1
3.7
1.90
1.59
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Census.

158
Occupational Profile of Women Ages 18-65
Compared to men, far fewer women have occupations listed in the
1980 Census. Of the total of 211,744 women in the sample, only 74,053, or
about 35%, participate in the formal labor force. Of these 74,053 cases, 73,871,
or 99.7%, are classified by color, 73,906, or 99.8% are identified by income and
73,724, or 99.6% are identified by educational level. These slight differences in
the total number of cases are the result of varying number of unidentified
cases with a certain variable. Thus, in the following, the total number of cases
differ slightly, depending on what classification scheme is used.
Table 6.13 describes the occupational distribution of women in the
sample data as a whole and also by color group. Note the striking differences
between men's and women's occupational distribution: 1) the percentage of
blue collar workers is much lower for women than for men (56.6% for
women and 67.3% for men); 2) there are proportionally more women than
men who have occupations in the categories of professional /technical and
clerical (14.6% and 24.6% for women, as opposed to 8.8% and 13.3% for men);
3) occupations in the category of transportation/communications are almost
non-existent for women (only 0.2%); 4) the percentage of transformative
occupations is much lower for women than for men (17.5% for women and
35.7% for men); 5) there are proportionally more women than men who have
unskilled/personal service occupations (38.9% for women and 23.3% for
men). The same differences remain between men and women of the same
color groups as well (see Table 6.1 and 5.13).
When women of the three color groups are compared, Asians do much
better than do whites, who in turn do much better than do Afro-Brazilians.
In other words, Asians lead whites and whites lead Afro-Brazilians in the

159
percentage of white collar occupations. Predictably, the opposite pattern holds
for the three groups, as far as the distribution of blue collar occupations is
concerned, i.e., Afro-Brazilians lead whites, who in turn lead Asians in the
percentage of blue collar occupations. The ratios between the percentage of
each occupational category for Asians and that for whites and Afro-Brazilians
are listed in the last two columns of Table 6.13. The gaps among the three
group in the percentage of blue collar occupations are significant; 34.1% for
Asians, 49.2% for whites and 80.1% for Afro-Brazilians.
Table 6.13
Occupational Distribution of Women Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Sample
Asian
%
%
Management/
Administrative
4.2
8.4
Professional/
Technical
14.6
20.1
Clerical
24.6
37.5
Blue Collar
56.6
34.1
Transportation/
Communication
0.2
0.1
Transformative
17.5
11.4
Unskilled/
Personal Service
38.9
22.6
%
100.0
100.0
N of Cases
73,871
1,749
Ratio
White
Afro-B*
A/W**
A/AB
%
%
5.0
1.4
1.68
6.00
17.6
5.5
1.14
3.65
28.2
13.0
1.33
2.88
49.2
80.1
0.69
0.43
0.2
0.2
0.50
0.50
16.8
19.9
0.68
0.57
32.2
60.0
0.70
0.38
100.0
100.0
53,720
18,402
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians/whites; A/AB = Asians/Afro-Brazilians.

160
Residential differences in the occupational distribution of women are
shown in Table 6.14. The data show that compared to rural women, urban
women are over represented in every major occupational category but the last
one, unskilled/personal service, where the percentage of rural women more
than doubles that of their urban counterparts. As a result, 87.7% of rural
women have blue collar occupations, as opposed to 54.9% of urban women.
Meanwhile, the huge ratios between the percentage of urban vs. rural women
in all three white collar occupational categories tell us that residence plays an
important role in the occupational distribution of women in Brazil.
Table 6.14
Occupational Distribution of Women Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980)
Occupation 1
Sample
Urban
Rural
U/R Ratio*
%
%
%
Management/
Administrative
4.2
4.4
1.1
4.00
Professional/
Technical
14.6
15.2
4.4
3.45
Clerical
24.6
25.6
6.9
3.71
Blue Collar
56.6
54.9
87.7
0.63
Transportation/
Communication
0.2
0.2
0.1
2.00
Transformative
17.5
17.7
12.5
1.42
Unskilled/
Personal Service
38.9
36.9
75.1
0.49
%
100.0
100.0
100.0
N of Cases
74,053
70,155
3,898
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*U/R Ratio = Urban /Rural Ratio

161
Compared to men’s data, the gap between urban and rural residents
narrows in some categories and widens in some others. For example, in the
categories of professional/technical and clerical occupations, the ratios
between urban and rural residents in women's data (3.45 and 3.71) are much
smaller than those in men's data (9.0 and 6.43). On the other hand, the ratio
between the percentage of women with managerial/administrative
occupations in urban areas and those in rural areas is as big as 4.0, while the
corresponding figure for men is only 1.59, indicating that there are
proportionally more men than women with managerial/administrative
occupations in rural areas.
In categories of the blue collar occupations, the gaps between urban and
rural residents in transformative and unskilled/personal service occupations
are smaller for women (1.42 and 0.49) than they are for men (2.74 and 0.23),
although the percentages of women in these two categories in rural areas
(12.5% and 75.1%) are about the same as those of their men counterparts
(14.1% and 71.1%). This is due to the fact that the percentage of men with
transformative occupations in urban areas (38.7%) is substantially higher than
that of their women counterparts (23.4%) and the percentage of women in the
category of unskilled/personal service occupations in urban areas (36.9%) is
substantially higher than that of their men counterparts (16.7%).
Unlike with men's data, there are considerable differences in the
percentage of blue collar occupations for women of the three age groups;
50.1% for the age group of 18-25 years old, 55.9% for the age group of 26-39
years old and 68.6% for the age group of 40-65 years old. In other words, the
percentage of blue collar occupations increases from younger to old age
groups. However, the increase in the proportion of blue collar occupations
for the two older age groups is caused mainly by the dramatic decrease in the

162
percentage of clerical occupations. The percentage of clerical occupations
drops from 39.2% for the first age group to 19.2% for the second age group,
and then again declines to 9.2% for the third age group. As a result, the
percentage of unskilled/personal service occupations increases sharply from
younger to older age groups ; 29.4%, 39.0% and 54.7% for the first, second and
third age groups, respectively.
Table 6.15
Occupational Distribution of Women Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Sample
18-25
26-39
40-65
Management/
%
%
%
%
Administrative
Professional/
4.2
2.0
5.0
6.5
Technical
14.6
8.7
19.9
15.7
Clerical
24.6
39.2
19.2
9.2
Blue Collar 56.6
Transportation/
Communication 0.2
Transformative 17.5
Unskilled/
Personal Service 38.9
% 100.0
N of Cases 74,053
50.1
55.9
68.6
0.1
0.3
0.2
20.6
16.6
13.7
29.4
39.0
54.7
100.0
100.0
100.0
28,513
28,546
16,994
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
As I mentioned above, although the number of women who have
occupations listed in the census is only about 35% of the total women in the
sample, the proportions of them with professional/technical and clerical
occupations are much greater than the corresponding proportions for their

163
men counterparts in every age group. The percentage of women who have
professional/technical occupations for the three age groups are 8.7%, 19.9%
and 15.7% respectively, as opposed to 6.4%,10.9% and 8.4% for their men
counterparts. Similarly, 39.2%, 19.2% and 9.2% of women in the three age
groups have clerical occupations, compared to 22.1%, 11.1% and 7.9% of men.
In a word, the data show that compared to men, women are more
likely to have clerical occupations, especially at ages 18-25, and more likely to
have professional/technical occupations, especially at ages 26-39, if they
happen to have white collar occupations. On the other hand, men are more
likely than women to have managerial/administrative occupations at all age
groups (see Table 6.4). As far as the blue collar occupations are concerned,
men are more concentrated in transportation/communications and
transformative occupations, while women are more concentrated in
unskilled/personal service occupations (see Table 6.15).
As expected, the data on women show that income is, in general,
positively correlated with the proportion of white collar occupations and
negatively correlated with the proportion of blue collar occupations. The
basic patterns of the distribution of each major occupational category for
women are very similar to those for men. For example, the correlation
between income and the proportion of the first two categories of white collar
occupations is positive and linear, meaning that the proportions of these
occupations increase from lower to higher income levels, as shown in Table
6.16. Similarly, the highest percentage of clerical occupations is found among
women with an average income of up to three minimum wages, just as the
case with their men counterparts. And the proportion of blue collar
occupations declines sharply from lower to higher income levels for both
men and women.

164
Table 6.16
Occupational Distribution of Women Ages 18-65 by Income Level,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Sample
%
1MW*
%
2MW
%
3MW
%
3+MW
%
Management/
Administrative
4.2
0.5
1.3
3.9
13.1
Professional/
Technical
14.6
2.7
6.6
17.4
38.9
Clerical
24.6
8.7
23.4
40.9
33.5
Blue Collar
56.6
88.0
68.9
37.8
14.4
Transportation/
Communication
0.2
0.0
0.3
0.3
0.1
Transformative
Unskilled/
17.5
12.5
28.0
18.7
4.7
Personal Service
38.9
75.5
40.5
18.8
9.6
%
100.0
100.0
100.0
100.0
100.0
N of Cases
73,906
18,265
27,879
10,815
16,947
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*MW = Minimum Wage
However, there are some differences between men and women too.
For instance, the percentage of professional/technical occupations for women
increases sharply to 17.4% at the third income level, and the percentage of
clerical occupations for women increases dramatically to 23.4% at the second
income level. In comparison, the percentage of professional /technical for
men at the same income level is only 3.9%, and the percentage of clerical
occupations for men at the same income level is only 12.3%. In fact, the
percentages of these two categories for men at the highest income level are
still lower than those of women at lower income levels. Furthermore, the
rate of decline for the percentage of blue collar occupations is much faster for

165
women than it is for men; a decline of 73.6 percentage point from 88% at the
income level of one minimum wage to 14.4% at the income level of above
three minimum wages for women, compared to a decline of 40.4 percentage
points from 90.3% at the income level of one minimum wage to 49.9% at the
income level of above three minimum wages for men. In a way, this suggests
some kind of discrimination against women as far as income is concerned
because higher percentages of white collar occupations for women have not
translated into higher average income for them.
The occupational distribution for women varies a great deal by
educational level, especially at the higher levels, as shown in Table 6.17.
While 97.7% of women with no schooling at all and 85.3% of women with 1-4
years of schooling have blue collar occupations, only 50.2% of women with 5-
8 years of schooling and 10.6% of women with 9-11 years of schooling do so.
As for women with more than 12 years of schooling, less than 2% of them
have blue collar occupations. This clearly indicates that education is one of
the most important factors determining whether Brazilian women have
white or blue collar occupations.
If we look at the occupational distributions across educational levels,
we find that different occupations are more prevalent at different educational
levels. For women with no schooling, the overwhelming majority of them
(86.3%) have unskilled/personal service occupations. For women with 1-4
years of schooling, unskilled/personal service occupations are still the
primary ones, but a considerable number of them (27.1%) are engaged in
transformative occupations. There are some fundamental changes at the
level of 5-8 years of schooling: clerical occupations becomes the primary
occupations, with 36.9%, and almost equal number of women have
occupations in the categories of transformative (24.4%) and

166
unskilled/personal service (25.6%). The percentage of clerical occupations
continues to grow to as much as 60% for women with 9-11 years of schooling,
and professional/technical occupations have become the second biggest
occupations, with 22.5% of women having them. At the highest level of 12 or
more years of schooling, nearly 60% of women have professional/technical
occupations, and over one third of them are still engaged in clerical
occupations.
To sum up, unskilled/personal service occupations are the primary
occupations for women with less than five years of schooling, clerical
occupations are the primary ones for women with 5-11 years of schooling, and
professional /technical occupations are the primary occupations for women
with 12 or more years of schooling.
Table 6.17
Occupational Distribution of Women Ages 18-65
by Education, Metropolitan Sao Paulo, Brazil (1980)
Occupation Sample
0
1-4
5z8
9-11
12+ (vears)
%
%
%
%
%
%
Management/
Administrative
4.2
0.5
2.5
4.7
6.9
8.3
Professional/
Technical
14.6
0.7
3.8
8.3
22.5
58.9
Clerical
24.6
1.1
8.5
36.9
60.0
30.8
Blue Collar
56.6
97.7
85.3
50.2
10.6
1.9
Transportation/
Communications
0.2
0.1
0.3
0.2
0.1
0.0
Transformative
17.5
11.3
27.1
24.4
4.5
0.6
Unskilled/
Personal Service
38.9
86.3
57.9
25.6
6.0
1.3
%
100.0
100.0
100.0
100.0
100.0
100.0
N of Cases
73,724
8,319
29,762
13,493
12,655
9,496
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

167
Compared to the occupational profile of men with the same education,
women with less than five years of schooling lead their men counterparts in
the percentage of blue collar occupations and women with more than five
years of schooling lead men in the percentage of white collar occupations. It
is particularly interesting to see that of people with more than four years of
schooling, there are proportionally more men than women who have
managerial/administrative occupations, and there are proportionally more
women than men who have either professional/technical and clerical
occupations (see Table 6.6 and 5.17).
I have described above the differences in occupational distribution of
women ages 18-65 by color, residence, age group, income level and
educational level separately so far. In the following, I will examine whether
color differences in occupational distribution remain when the other
variables are controlled separately. In other words, I want to find out how
much of the color differences, if any, result from the differences in factors
other than race, such as place of residence, age, income and educational level.
When the three color groups are compared in terms of occupational
distribution controlling for residence, i.e., in urban and rural areas separately,
there are some minor changes in most of the occupational categories in urban
areas, and there are significant changes in most of the categories in rural
areas. For example, the ratios between the percentage of blue collar
occupations for Asians vs. whites and that for Asians vs. Afro-Brazilians in
urban areas are 0.68 and 0.40, compared to 0.69 and 0.43 before controlling for
residence. And the ratios between Asians and whites in most occupational
categories drop slightly while the ratios between Asians and Afro-Brazilians
increase slightly (see Table 6.13 and 6.18).

168
Table 6.18
Occupational Distribution of Women of Ages 18-65
by Residence and Color, Metropolitan Sao Paulo, Brazil, 1980
Ratio
Residence
Urban
Management/
Sample
%
Asian
%
White
%
Afro-B*
%
A/W**
A/AB
Administrative
Professional/
4.4
8.7
5.2
1.4
1.67
6.21
Technical
15.2
20.9
18.2
5.7
1.15
3.67
Clerical
25.6
38.2
29.3
13.6
1.30
2.81
Blue Collar
Transportation/
54.9
32.2
47.3
79.8
0.68
0.40
Communication
0.2
0.1
0.2
0.2
0.50
0.50
Transformative
Unskilled/
17.7
11.5
17.1
20.4
0.67
0.56
Personal Service
Rural
Management/
36.9
20.6
30.0
58.6
0.69
0.35
Administrative
Professional/
1.1
3.9
1.3
0.2
3.00
19.5
Technical
4.4
8.7
5.3
1.6
1.64
5.44
Clerical
6.9
27.7
7.3
3.7
3.79
7.49
Blue Collar
Transportation/
87.7
59.7
86.1
94.6
0.69
0.63
Communication
0.1
0.0
0.1
0.0
0.00
0.00
Transformative
Unskilled/
12.5
10.6
12.7
12.3
0.83
0.86
Personal Service
75.1
49.1
73.3
82.3
0.67
0.60
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians/Whites, A/AB = Asians/Afro-Brazilians

169
On the other hand, the differences among the three color groups,
especially those between Asians and Afro-Brazilians, increase dramatically in
rural areas after controlling for residence. For instance, while the ratio
between the percentages of blue collar occupations for Asians and whites
remains the same in rural areas as it is in urban areas, Asians with
managerial/administrative occupations are three times as many as whites
and Asians with clerical occupations are 3.79 times as many as whites. The
ratio between Asians and Afro-Brazilians in the percentage of blue collar
occupations increased from 0.43 before the control of residence to 0.63 in rural
areas after controlling for residence. The biggest differences between Asians
and Afro-Brazilians are found in the distribution of white collar occupations:
The percentage of Asians with managerial/administrative occupations are
19.5 times that of Afro-Brazilians, the percentage of Asians with
professional/technical occupations are 5.44 times that of Afro-Brazilians, and
the percentage of Asians with clerical occupations are 7.49 times that of Afro-
Brazilians, as opposed to 6.00, 3.65 and 2.88, the corresponding ratios between
them before controlling for residence. In short, the data suggest that
controlling for residence does not reduce the color differences in the
occupational distribution for Brazilian women. Therefore, it is safe to
conclude that the existing differences among the three color groups are not
due to the residential variations among these groups.
Table 6.19 describes the occupational distribution of Asian, white and
Afro-Brazilian women controlling for age. First of all, let's look at some of
the major characteristics of each color group in the same age group. In the age
group of 18-25, Asians and whites are highly concentrated in clerical
occupations (63% and 45.1%, respectively), whereas Afro-Brazilians are
heavily concentrated in unskilled/personal service occupations (48%). In the

170
age group of 26-39, although clerical occupations are still the primary
occupations for Asians (32.8%), they are not as dominant as they are in the
first age group, and the percentage of professional/technical occupations
increases to 27%. For whites and Afro-Brazilians, unskilled/personal service
occupations have become the primary occupations (32.2% for whites and
61.2% for Afro-Brazilians). However, the main difference between whites
and Afro-Brazilians is that more than 50% of white women have white collar
occupations, while less than 20% of Afro-Brazilian women have white collar
occupations. In the age group of 40-65, unskilled/personal service
occupations are the primary occupations for all three groups, but their actual
percentages vary as much as 40% and consequently their percentages for
white collar occupations vary accordingly. Asians and whites in this age
group have similar occupational distribution, especially with respect to white
collar ones (see Table 6.19).
As far as the changes in the degree of color variations in each age group
are concerned, we can compare the ratios in the last two columns of Table
6.19, where age is controlled, with the last two columns of Table 6.13, where
age is not controlled. We find that in the two younger age groups, 18-25 and
26-39, the color differences have narrowed to some degree in most of the
occupational categories, and in the age group of 40-65, the color differences
have widened, except for the differences between Asians and whites in the
white collar occupations, which have narrowed considerably. This indicates
that in spite of considerable differences in occupational distribution existing
among the three color groups , there is more equality for women under the
age of forty than those over forty. In other words, it suggests that in the
aggregate, some progress had been made with regard to racial equality in the
few decades preceding 1980, even though it is far from adequate.

171
Table 6.19
Occupational Distribution of Women Ages 18-65
by Age and Color, Metropolitan Sao Paulo, Brazil (1980)
Ratio
Age Group
Sample
%
Asian
%
White
%
Afro-B*
%
A/W**
A/AB
Ages 18-25
Man/Adm
2.0
3.5
2.4
0.8
1.46
4.38
Prof/Tech
8.7
11.3
10.4
3.8
1.09
2.97
Clerical
39.2
63.0
45.1
21.3
1.40
2.96
Blue Collar
50.1
22.3
42.1
74.2
0.53
0.30
Transp/Comm
0.1
0.0
0.1
0.2
0.00
0.00
Transformative
20.6
7.4
19.0
26.0
0.38
0.28
Unskld / Personal
29.4
14.9
23.0
48.0
0.65
0.31
Ages 26-39
Man/Adm
5.0
10.0
5.8
1.9
1.72
5.26
Prof/Tech
19.9
27.0
23.7
8.0
1.14
3.38
Clerical
19.2
32.8
22.0
9.8
1.49
3.35
Blue Collar
55.9
30.2
48.4
80.3
0.62
0.38
Transp/Comm
0.3
0.0
0.2
0.3
0.00
0.00
Transformative
16.6
10.7
16.0
18.8
0.67
0.57
Unskld / Personal
39.0
19.5
32.2
61.2
0.61
0.32
Ages 40-65
Man/Adm
6.5
11.7
8.0
1.4
1.46
8.36
Prof/Tech
15.7
18.4
19.3
4.3
0.95
4.28
Clerical
9.2
11.7
10.9
3.4
1.07
3.44
Blue Collar
68.6
58.2
61.8
90.9
0.94
0.64
Transp/Comm
0.2
0.3
0.2
0.1
1.50
3.00
Transformative
13.7
18.4
14.5
10.4
1.29
1.77
Unskld/Personal
54.7
39.6
47.0
80.4
0.84
0.49
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians/Whites, A/AB = Asians/Afro-Brazilians

172
When income is controlled, the color differences in occupational
distribution generally become smaller, except in a few categories and/or at
some income levels. For example, at the income level of up to one
minimum wage, the gaps between Asians and whites and between Asians
and Afro-Brazilians become wider in most occupational categories. The most
striking differences between Asians on the one hand, and whites and Afro-
Brazilians on the other are in the distributions of white collar occupations, as
shown in the first three rows of the last two columns of Table 6.20. Although
only 3% of Asians have managerial/administrative occupations, it is 4.28
times that of whites and 30 times that of Afro-Brazilians, as opposed to the
ratio of 1.68 between Asians and whites and the ratio of 6.00 between Asians
and Afro-Brazilians before controlling for income. The gap between Asians
and whites in professional/technical and clerical occupations widens a little
bit, while the gap between Asians and Afro-Brazilians in these two categories
has widened considerably. For instance, the ratios between Asians and Afro-
Brazilians in these two categories have increased from 3.65 and 2.88 to 5.75
and 5.51, respectively. On the other hand, the gap between the three groups
in the percentage of blue collar occupations has narrowed considerably; the
ratio between Asians and whites is now 0.84, compared to 0.69 without
controlling for income, and the ratio between Asians and Afro-Brazilians is
now 0.74, as opposed to 0.43 before.
At the income level of up to two minimum wages, the color
differences have narrowed further in varying degrees in almost all
occupational categories (see Table 6.20). Furthermore, whites have surpassed
Asians in the percentage of professional /technical occupations (7.9% for
whites vs. 5.5% for Asians). As the proportion of Asians with clerical
occupations jumps to 44.8%, the gap between Asians and the other two

173
groups in this category has widened somewhat. Consequently, the gaps in the
percentage of blue collar occupations among the three groups have widened a
little bit from what they are at the previous income level. Nevertheless, the
proportions of whites and Afro-Brazilians with transformative occupations
have hecome almost identical (27.9% for whites and 28.4% for Afro-
Brazilians), which is also close to the percentage of Asians, 22.4%.
At the income level of up to three minimum wages, the color
differences, especially the gap between Asians and whites, continue to narrow
in most occupational categories. Whites and Asians have become more
similar in the distribution of professional/technical and clerical occupations.
For example, 16.5% of Asians, as opposed to 18.7% of whites, are classified in
professional/technical occupations and 50.6% of Asians, as opposed to 43.4%
of whites, are classified in clerical occupations. Meanwhile, there are
significant increases in the percentage of Afro-Brazilians with the above
occupations so that the gap between them and the other two groups reduces
greatly. Specifically, for Afro-Brazilians, the percentage of
professional/technical occupations increases to 12.6%, which is more than 4
times of the percentage (3.8%) at the previous income level, and the
percentage of clerical occupations increases to 29.6%, which is more two times
of the percentage (12.7%) at the previous income level. In blue collar
occupations, Asians and whites become more similar, and the gap between
Afro-Brazilians the other two widens, in spite of the sharp decrease of more
than 20% in the proportions of unskilled/personal service occupations.
At the income level of above three minimum wages, whites finally
surpass Asians in the percentage of white collar occupations. It turns out that
both Asians and whites have the same proportion of their members in
managerial/administrative occupations, 13.4%, and almost the same

174
proportion of their members in clerical occupations, 34.9% for Asians and
33.6% for whites. However, there are proportionally more whites than
Asians with professional/technical occupations; 40.3% of whites vs. 32.4% of
Asians. At the same time, Afro-Brazilians have made tremendous progress
in white collar occupations as well, as shown in Table 6.20. The ratios
between Asians and Afro-Brazilians in all three categories of white collar
occupations are now between 1.10 and 1.35. In addition, the gap between
Asians and Afro-Brazilians in the proportion of blue collar occupations has
also narrowed from the previous income levels so that Asians are now
ranked second after Afro-Brazilians (30.9% for Afro-Brazilians, 19.3% for
Asians and 12.6% for whites).
In sum, when educational level is controlled, the color differences in
most occupational categories tend to become smaller with the increase of
education, although they are surprisingly big at the lower educational levels.
Meanwhile, some occupations, such as professional/technical and clerical, are
found to be more closely associated with educational level than others, and
therefore, are more sensitive to the change of educational level. Of all the
independent variables examined here, education is the most important factor
in determining the occupational distribution of men and women in
Metropolitan Sao Paulo, Brazil. In the following, I will describe the findings
in Table 6.20 one educational level at a time to see how education affects the
occupational distribution of the three color groups.

175
Table 6.20
Occupational Distribution of Women Ages 18-65
by Income and Color, Metropolitan Sao Paulo, Brazil (1980)
Income Level
Sample
Asian
White
AB*
Ratio
%
%
%
%
A/W**
A/AB**
One MW
Man/Adm
0.5
3.0
0.7
0.1
4.28
30.0
Prof/Tech
2.7
4.6
3.7
0.8
1.24
5.75
Clerical
8.7
21.5
11.0
3.9
1.95
5.51
Blue Collar
88.0
70.8
84.6
95.1
0.84
0.74
Transp/Com
0.0
0.0
0.0
0.0
0.00
0.00
Transform
12.5
18.5
14.5
8.7
1.28
2.13
Unskilled
75.5
52.3
70.1
86.4
0.75
0.61
Two MW
Man/Adm
1.3
2.0
1.5
0.7
1.33
2.86
Prof/Tech
6.6
5.5
7.9
3.8
0.70
1.45
Clerical
23.4
44.8
27.9
12.7
1.61
3.53
Blue Collar
68.9
47.6
62.8
82.8
0.76
0.57
Transp/Com
0.3
0.0
0.3
0.3
0.00
0.00
Transform
28.0
22.4
27.9
28.4
0.80
0.79
Unskilled
40.5
25.2
34.6
54.1
0.73
0.47
Three MW
Man/Adm
3.9
6.5
4.1
2.5
1.59
2.60
Prof/Tech
17.4
16.5
18.7
12.6
0.88
1.31
Clerical
40.9
50.6
43.4
29.6
1.17
1.71
Blue Collar
37.8
26.4
33.7
62.1
0.78
0.43
Transp/Com
0.3
0.4
0.3
0.3
1.33
1.33
Transform
18.7
11.1
17.6
30.9
0.63
0.36
Unskilled
18.8
14.9
15.8
30.9
0.94
0.48
Above Three MW
Man/Adm
13.1
13.4
13.4
9.9
1.00
1.35
Prof/Tech
38.9
32.4
40.3
27.4
0.80
1.18
Clerical
33.5
34.9
33.6
31.8
1.04
1.10
Blue Collar
14.4
19.3
12.6
30.9
1.53
0.62
Transp/Com
0.1
0.0
0.1
0.2
0.00
0.00
Transform
4.7
4.7
4.2
10.4
1.12
0.45
Unskilled
9.6
14.6
8.3
20.3
1.76
0.72
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians/Whites, A/AB = Asians/Afro-Brazilians

176
For people with no schooling at all, Asians differ significantly from the
other two groups, who are very similar, in most of the occupational
categories. For example, as many as 12.1% and 8.6% of Asians have
managerial/administrative and professional/technical occupations, while
less than 1% of both whites and Afro-Brazilians have these occupations.
Consequently, about 98% of whites and Afro-Brazilians have blue collar
occupations, compared to only 76.7% of Asians who do so. Why such high
percentage of Asians with no schooling have these white collar occupations
remains to be explored. For one thing, the fact that very few Asians have no
schooling at all, relative to large numbers of whites and Afro-Brazilians with
no schooling, may have contributed to this extremely skewed picture.
At the level of 1-4 years of schooling, the color differences reduce
considerably in most occupational categories, except in
managerial/administrative occupations, where Asians are still highly
concentrated, relative to the other two groups. This again may be due to the
fact that most Asians have more than four years of schooling and when a
small number of them with 1-4 years of schooling have
managerial/administrative occupations, they result in a relatively big
proportion. The impact of education is reflected in the distribution of both
white and blue collar occupations for all three groups. For instance, both
whites and Afro-Brazilians have surpassed Asians in the percentage of
professional/technical occupations, and the proportions of clerical,
transformative and unskilled/personal service occupations for Asians and
whites have become very similar; 12.7% vs. 10.2%, 26.3% vs. 29.0% and 50.7%
vs. 53.1% for Asians and whites, respectively. Furthermore, all three groups
have exactly the same proportion (0.3%) of their workforce in
transportation/communications occupations. Although Afro-Brazilians

177
continue to fall behind Asians in two of the white collar occupations, the gap
between them has reduced.
For people with 5-8 years of schooling, whites rank first, followed by
Asians and Afro-Brazilians, in the percentage of white collar occupations,
although Asians still have the lead in the percentage of
managerial/administrative occupations. The proportion of Afro-Brazilians
with professional/technical occupations (7.4%) continue to be slightly higher
than that of Asians (6.9%), while their proportion in
managerial/administrative occupation (1.9%) is still far below that of Asians
(9.7%). Meanwhile, there is a rapid increase in the proportion of clerical
occupations for Afro-Brazilians (from 4.5% to 24.2%), which is mainly the
result of increased education. However, Afro-Brazilians are still
overrepresentated in the blue collar occupations since the majority of them
have not received more than five years of schooling.
At the level of 9-11 years of schooling, the proportions of white collar
occupations for both whites and Afro-Brazilians exceed that of Asians,
although Asians still have the lead in managerial/administrative and clerical
occupations over the other two. It is particularly important to note that there
are substantial increases in the proportions of professional/technical
occupations on the part of whites and Afro-Brazilians. This again is an
indication that these occupations are more closely associated with education
than with color. Conversely, there are substantial decreases in the percentage
of blue collar occupations for all three groups, suggesting the strong negative
association between education and blue collar occupations. In fact, only less
than 10% of whites, and less than 20% of Asians and Afro-Brazilians are now
engaged in blue collar occupations at this educational level.

178
Table 6.21
Occupational Distribution of Women Ages 18-65
by Education and Color, Metropolitan Sao Paulo, Brazil (1980)
Years of Schooling
Sample
Asian
White
Afro-B
A/W
A/AB
Zero Years
%
%
%
%
Man/Adm
0.5
12.1
0.6
0.3
20.2
40.3
Prof/Tech
0.7
8.6
0.9
0.4
9.56
21.5
Clerical
1.1
2.7
1.2
1.0
2.25
2.70
Blue Collar
97.7
76.7
97.2
98.4
0.79
0.78
Transp/Com
0.1
0.0
0.1
0.1
0.00
0.00
Transform
11.3
19.9
13.4
8.5
1.48
2.34
Unskilled
86.3
56.8
83.7
89.8
0.68
0.63
1-4 Years
Man/Adm
2.5
8.0
3.1
1.0
2.58
8.00
Prof/Tech
3.8
1.9
4.4
2.6
0.43
0.73
Clerical
8.5
12.7
10.2
4.5
1.25
2.82
Blue Collar
85.3
77.3
82.4
91.8
0.94
0.84
Transp/Com
0.3
0.3
0.3
0.3
1.00
1.00
Transform
27.1
26.3
29.0
23.0
0.91
1.14
Unskilled
57.9
50.7
53.1
68.5
0.95
0.74
5-8 Years
Man/Adm
4.7
9.7
5.6
1.9
1.73
5.11
Prof/Tech
8.3
6.9
8.7
7.4
0.79
0.93
Clerical
36.9
32.4
41.5
24.2
0.78
1.34
Blue Collar
50.2
51.0
44.2
66.5
1.15
0.77
Transp/Com
0.2
0.0
0.2
0.1
0.00
0.00
Transform
24.4
21.4
22.0
31.2
0.97
0.69
Unskilled
25.6
29.6
22.0
35.2
1.35
0.84
9-11 Years
Man/Adm
6.9
8.6
7.4
3.0
1.16
2.87
Prof/Tech
22.5
12.9
23.7
17.8
0.54
0.72
Clerical
60.0
62.1
60.0
59.3
1.03
1.05
Blue Collar
10.6
16.4
8.9
19.8
1.84
0.83
Transp/Com
0.1
0.0
0.0
0.1
0.00
0.00
Transform
4.5
3.7
3.9
8.8
0.95
0.42
Unskilled
6.0
12.7
5.0
10.9
2.54
1.17
12+ Years
Malt /Adm
8.3
7.3
8.4
7.3
0.87
1.00
Prof/Tech
58.9
49.3
60.1
47.2
0.82
1.04
Clerical
30.8
39.8
29.7
42.7
1.34
0.93
Blue Collar
1.9
3.6
1.8
2.8
2.00
1.29
Transp/Com
0.0
0.0
0.0
0.0
0.00
0.00
Transform
0.6
0.6
0.6
1.5
1.00
0.40
Unskilled
1.3
3.0
1.2
1.3
2.50
2.30
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Census.

179
Finally, for those with 12 or more years of schooling, most of the color
differences have disappeared, i.e., the occupational profiles of the three
groups are very similar: Blue collar occupations account for less than 4% for
all three groups, and Asians and Afro-Brazilians have similar proportions of
white collar occupations as well; 7.3% of for both Asians and Afro-Brazilians
have managerial and administrative occupations; 49.3% of Asians and 47.2%
of Afro-Brazilians have professional/technical occupations; 39.8% of Asians
and 42.7% of Afro-Brazilians have clerical occupations. In comparison,
whites lead the other two groups in managerial/administrative and
professional/technical occupations, with 8.4% and 60.1%, respectively. This
suggests that beyond 12 years of schooling, whites have advantages over both
Asians and Afro-Brazilians in terms of access to more privileged white collar
occupations, even if they have the same amount of education. To put it
differently, although there is an increasing degree of parity among color
groups at higher educational levels, Asians and Afro-Brazilians are still
disadvantaged, relative to whites, in terms of access to better occupations
beyond twelve years of schooling.
Summary
The occupational distribution of men ages 18-65 varies a great deal by
color. The percentage of Asians with white collar occupations (52.8%) is
substantially higher than that of whites (36.9%), let alone that of Afro-
Brazilians (16.3%). More importantly, nearly 39% of Asians have occupations
in either managerial/administrative or professional/technical categories,
compared to about 22% for whites and about 7% for Afro-Brazilians.

Accordingly, Afro-Brazilians have the highest percentage of blue collar
occupations (83.7%), with the majority of them in transformative occupations
(50.6%). The percentage of blue collar occupations for whites (63.1%) is
substantially lower than that of Afro-Brazilians (83.7%), but substantially
higher than that of Asians (47.2%).
Urban residents have a much higher percentage of white collar
occupations (35.8%) than do rural residents (10.3%). The difference between
them is particularly big in the categories of professional/technical and clerical
occupations; the percentages of these two occupations for urban residents are
9.00 and 6.43 times those of rural residents. They also have very different
distributions of blue collar occupations; most of urban blue collar workers
have transformative occupations, while the overwhelming majority of rural
blue collar workers have unskilled/personal service occupations.
The three age groups are very similar in the distribution of white vs.
blue collar occupations, but they differ in the concentration of specific
occupations, especially within white collar occupations. For instance,
younger age groups have a higher percentage of clerical occupations, and
older age groups have a higher percentage of managerial/administrative
occupations. The middle age group is almost evenly divided among the three
white collar occupations.
The average income of men is highly correlated with their
occupational distribution. Therefore, the prestigious occupations of
managerial/administrative and professional/technical categories are highly
concentrated at the income level of above three minimum wages (20% and
16%, respectively), and the low status occupations of unskilled/personal
service and transformative categories are heavily concentrated at the lower
income levels.

181
The occupational distribution of men varies the most by educational
level. The percentage of blue collar occupations decreases dramatically from
lower to higher levels of education, and the percentages of
managerial/administrative and professional/technical occupations increase
substantially from lower to higher level of education. For example, the
percentage of blue collar occupations decreases from 93.8% at the level of no
schooling to 83.6% at the level of 1-4 years of schooling, to 60% at the level of
5-8 years of schooling, to 28% at the level of 9-11 years of schooling, and
finally to 6.3% at the level of 12 or more years of schooling. Meanwhile, the
percentages of managerial/administrative and professional/technical
occupations increase by more than 50% at each higher level, and finally reach
29.6% and 48.2%, respectively, at the level of 12 or more years of schooling.
The occupational differences among the three color groups still remain
much the same when place of residence is controlled. In both urban and
rural areas, Asians do significantly better than do whites and Afro-Brazilians:
In urban areas, the ratio between the percentages of blue collar occupations for
Asians and whites is 0.75, and the same ratio between Asians and Afro-
Brazilians is 0.54; the corresponding ratios in rural areas are 0.79 and 0.76. In a
word, place of residence does not contribute much to the occupational
differences among the three color groups.
Different age groups of the three color groups have slightly different
occupational distributions, but the color differences remain about the same
across age groups. For Asians and whites, the age group of 26-39 has the
highest percentage of white collar occupations, while for Afro-Brazilians, the
age group of 18-25 does so. In general, the differences among the three color
groups are smaller at the age group of 40-65, and bigger at the other two age
groups in most occupational categories.

182
As expected, the occupational difference among the three color groups
reduce considerably when income is controlled. Specifically, the difference
between Asians and whites is smaller at the two ends of income level but
larger at the two middle income levels, while the difference between Asians
and Afro-Brazilians becomes bigger when income increases. The ratios
between Asians and whites at the four income levels are, respectively, 0.96,
0.81, 0.80 and 0.88, and the ratios between Asians and Afro-Brazilians are,
respectively, 0.91, 0.74, 0.71 and 0.56. On the other hand, the three color
groups have similar percentages of unskilled/personal service occupations at
the two lowest income levels, and similar percentages of
transportation/communications occupation at the third income level. This
suggests that the distribution of these occupations have less to do with color.
When educational level is controlled, the occupational differences
among the three color groups reduce considerably, especially at higher levels
of education. This is particularly obvious in the distribution of white collar
occupations for Asians and whites. At the levels of 5-8 and 9-11 years of
schooling, Asians and whites have similar distributions of
managerial/administrative and professional/technical occupations.
Meanwhile, the gap between Asians and Afro-Brazilians in most
occupational categories is smaller than it is before the control of education.
However, the degree of impact of education on different color groups are not
uniform, i.e., education seem to have greater positive impact on whites and
Afro-Brazilians than it does on Asians.
There are some differences between the occupational distributions of
men and women in metropolitan Sao Paulo, Brazil. First of all, only about
35% of women in the sample data have occupations listed in the census.
Second, there are proportionally more women who have clerical and

183
professional/technical occupations than do men, and consequently fewer
women have blue collar occupations. Nonetheless, the percentage of
unskilled/personal service occupations for women is much higher than it is
for men because transportation/communications is almost non-existent and
transformative occupations are less popular among women than they are
among men.
Despite the above differences, the basic patterns in the occupational
distribution of men are also present in that of women. In other words, there
are similar occupational differences among women by color, residence, age
group, income and education. For example, 65.9% of Asian women, as
opposed to 50.8% of whites and 19.9 of Afro-Brazilians, have white collar
occupations, and 54.9% of urban women have blue collar occupations while
87.7% of rural women do so. Unlike men's data, there are considerable
differences among the three age groups of women in the percentage of blue
collar occupations; 50.1% of women ages 18-25, 55.9% of women ages 26-39
and 68.6% of women ages 40-65 are blue collar workers.
There are sharp decreases in the percentage of blue collar occupations
for women from lower to higher income levels; 88%, 68.9%, 37.8% and 14.4%,
respectively, for the four income levels. It is particularly important to note
that the percentage of professional/technical occupations for women
increases to 17.4% at the third income level and again to 38.9% at the fourth
income level, which are much higher than those for men at the same income
level. The occupational differences of women by educational level are the
greatest; nearly 98% women with no schooling, 85.3% of women with 1-4
years of schooling, and about 50% of women with 5-8 years of schooling have
blue collar occupations while less than 2% of women with 12 or more years of
schooling do so. Moreover, the percentage of professional/technical

184
occupations for women jumps to 22.5% at the level of 9-11 and 58.9% at the
level of 12 or more years of schooling. Compared to men, women have a
higher percentage of blue collar occupations when they have less than five
years of schooling but they have a higher percentage of white collar
occupations when they have more than 5 years of schooling. Most of the
white collar occupations are in clerical and professional /technical categories,
and the percentage of women with managerial/administrative occupations is
far lower than that of men at all educational levels.
When place of residence is controlled, there are minor changes in the
occupational distribution of the three color groups in urban areas, but the
color differences widen in rural areas. For instance, the percentage of Asians
with managerial/administrative occupations is 3 times that of whites and 19.5
times that of Afro-Brazilians, the percentage of Asians with
professional/technical occupations is 1.64 times that of whites and 5.44 times
that of Afro-Brazilians, and the percentage of Asians with clerical occupations
is 3.79 times that of whites and 7.49 times that of Afro-Brazilians. Thus, the
existing differences among the three groups are not due to the residential
variations among these groups.
When age is controlled, the differences among the three groups in the
percentages of white vs. blue collar occupations are smaller in older age
groups. The ratio between the percentage of blue collar occupations for
Asians and whites decreases sharply from 0.94 at the oldest age group to 0.53
at the youngest age group. The same ratio between Asians and Afro-
Brazilians drops from 0.64 to 0.30. At the same time, the differences between
Asians and whites in white collar occupations are biggest at the age group of
26-39, and the differences between Asians and Afro-Brazilians are smaller at
younger age groups.

185
The occupational differences among the three color groups become
smaller in most cases, when income is controlled. Moreover, the gaps among
them narrow considerably as income increases, especially in white collar
occupations. For example, at the highest income level, the percentage of
managerial/administrative occupations for Asians (13.4%) is exactly the same
as that of whites, compared to a ratio of 4.28 between them at the lowest
income level. Similarly, the ratio between the percentages of
managerial/administrative occupations for Asians and Afro-Brazilians drops
from 30.0 at the lowest income level to 1.35 at the highest income level. At
the income levels of more than two minimum wages, whites surpass Asians
in professional/technical occupations and the gap between Asians and Afro-
Brazilians in this regard narrows considerably. It turns out that the
percentage of blue collar occupations for Asians (19.3%) is higher than that of
whites (12.6%) at the highest income level.
When educational level is controlled, the differences among the three
color groups become smaller in most cases, particularly at higher educational
levels. The occupational differences between Asians and the other two
groups are the biggest at the level of no schooling; more than 20% of Asians
have managerial/administrative and professional occupations, compared to
less than 1% of both whites and Afro-Brazilians. However, at 1-11 years of
schooling, although Asians continue to lead in the percentage of
managerial/administrative occupations, both whites and Afro-Brazilians
exceed Asians in the percentages of professional/technical occupations. At
the level of 12 or more years of schooling, whites finally surpass Asians, and
Afro-Brazilians have caught up with Asians in the percentage of
managerial/administrative occupations as well. In fact, at higher educational
levels, Asians and Afro-Brazilians are more similar in many aspects, and at 12

186
or more years of schooling, the three groups have very similar occupational
profiles. To conclude, education is highly correlate with occupation and
explains most of the occupational differences among the three color groups.
Nonetheless, Asians have a great advantage over the other two at the level of
no schooling, and whites have advantages over the other two in terms of
access to more privileged occupations beyond 11 years of schooling.

CHAPTER 7
MEAN INCOME OF ASIANS, WHITES AND AFRO-BRAZILIANS
In this chapter, I examine the mean income, the most important
indicator of socioeconomic standing, of men and women ages 18-65 in
Metropolitan Sao Paulo, Brazil. Since the main focus here is on the mean
income of the three color groups and there are vast differences in the mean
income of men and women, I discuss men and women separately. The first
part of the chapter deals with men's mean income and the second part deals
with women's mean income, with brief comparisons between the two when
necessary. The hypothesis tested here is that the mean income of a color
group is mainly a function of its educational level, occupational distribution,
age structure and residential location. In other words, after controlling for
these factors, color itself does not contribute much to the explanation of the
income differences among the color groups.
In both sections, I first describe the mean income by color, age group,
educational level, place of residence and occupation, and then examine the
color differences in mean income by age group, educational level, residence
and occupation separately to see how much of the differences are due to
factors other than color. Finally, I use regression analyses to measure and
compare the effect of each of the independent variables on mean income,
while controlling for one or more variables simultaneously.
The 1980 Brazilian Census recorded monthly income of individuals
from various sources, such as income from occupation, kind, retirement
187

188
(social security), rent, gift and capital, in the twelve-month period preceding
the census. In this study, I calculated a new variable, "mean income," from
the sum of income from all sources included in the census. Thus, "mean
income" here refers to the overall monthly income of an individual. To put
the mean income in perspective, we need to keep in mind that the minimum
wage in 1980 in Brazil was 4,150 cruzeiros, which was the equivalent of U.S.
$74.70-(Cz$5,552.8 = $100, in 1980). Furthermore, it is also helpful to use the
minimum wage as a unit of mean income and think of mean income in
terms of minimum wages when different groups are compared.
Mean Income of Men Ages 18-65
The sample data of men ages 18-65 show that the three color groups
differ substantially in mean income, as described in Table 7.1. On average,
Asians have a monthly income of Cz$35,492, whites Cz$21,lll and Afro-
Brazilians Cz$l0,775. In other words, the mean monthly income of Asians is
more than 1.5 times that of whites, more than 3 times that of Afro-Brazilians,
and the mean income of whites is more than 2 times that of Afro-Brazilians.
We can also compare the average incomes of the three color groups with the
average income of the sample in order to see how each group fares relative to
the entire sample. The average monthly incomes of Asians (Cz$35,492) and
whites (Cz$21,lll) are higher than the average income of the sample
(Cz$19,047), while the average monthly income of Afro-Brazilians (Cz$l 0,775)
is much lower than that of the sample. Of particular interest are the big gaps
between Asians and the sample, and between Afro-Brazilians and the sample.
The mean income of Asians is 1.86 times higher than that of the sample,

whereas the mean income of Afro-Brazilians is only 56.6% of the mean
income of the entire sample.
189
Table 7.1
Mean Monthly Income of Men Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980)
Color Group
Mean
Std Dev
Cases
%
Asian
35,492
73,458
4,778
2.3
White
21,111
43,616
157,046
74.6
Afro-Brazilian
10,775
11,174
48,670
23.1
Total
19,047
39,947
210,493
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
When the three age groups are compared, the mean monthly income
of the youngest group is much lower than those of the two older groups, as
shown in Table 7.2. Specifically, the mean monthly income of the age group
of 18-25 (Cz$9,410), is only 43.7% of the mean income of the age group of 26-39
(Cz$21,538), and only 37.7% of the mean income of the age group of 40-65
(Cz$24,946). Similarly, while the mean income of the youngest age group is
far below the sample mean (Cz$l 9,047), the average incomes of the two older
groups are above the sample mean. It is normal for people under 26 to have
such a low mean income because most of them either do not have
permanent jobs, or have just started their careers and lack work experience.
The income difference between urban and rural residents is also quite
obvious; the mean monthly income of urban residents (Cz$20,104) is almost
twice as much as that of their rural counterparts (Cz$l0,349) (see Table 7.3).
Since urban residents constitute about 90% of the sample, the mean monthly

190
income of the entire sample (Cz$l9,047) is not greatly affected by the
extremely low mean of rural residents. Furthermore, rural residents don't
need as much money as urban dwellers do because living expenses, such as
the cost of housing and food, are much cheaper in rural areas.
Table 7.2
Mean Monthly Income of Men Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980)
Age Group
Mean
Std Dev
Cases
%
18-25
9,410
6,264
62,956
29.9
26-39
21,538
33,186
77,324
36.7
40-65
24,946
57,769
70,213
33.4
Total
19,047
39,947
210,493
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Table 7.3
Mean Monthly Income of Men Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980)
Residence Mean
Std Dev
Cases
%
Urban
Rural
20,104
10,349
40,497
33,866
187,685
22,808
89.2
10.8
Total
19,047
39,947
210,493
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

191
The impact of education on income is predictably very great, as shown
in Table 7.4. People with no schooling have a mean monthly income of only
Cz$8,152, compared to a mean of Cz$l3,978 for people with 1-4 years of
I
schooling, a mean of Cz$l7,948 for people with 5-8 years of schooling, a mean
of Cz$25,940 for people with 9-11 years of schooling, and a mean of Cz$58,451
for people with 12 or more years of schooling. In minimum wages, the mean
monthly income of people with no schooling is less than two minimum
wages, that of people with 1-4 years of schooling is more than three
minimum wages, that of people with 5-8 years of schooling is more than four
minimum wages, that of people with 9-11 years of schooling is over six
minimum wages, and the mean monthly income of people with 12 or more
years of schooling is more than fourteen minimum wages. Although each
higher educational level results in an increase of more than one minimum
wage for the first three levels, the effect of 9-11 years of schooling and 12 or
more years of schooling is extremely great, suggesting the importance of
education beyond nine years of schooling as far as income is concerned.
Table 7.4
Mean Monthly Income of Men Ages 18-65 by Education,
Metropolitan Sao Paulo, Brazil (1980)
Years of Schooling
Mean
Std Dev
Cases
%
Zero
8,152
13,870
27,153
12.9
1-4
13,978
29,842
104,287
49.6
5-8
17,948
31,579
36,949
17.6
9-11
25,940
42,634
24,177
11.5
12+
58,451
82,060
17,565
8.4
Total
19,017
39,720
210,132
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.

192
We can also compare the ratios between the mean incomes at various
educational levels. People with 1-4 years of schooling have 1.71 times the
income of those with no schooling, people with 5-8 years of schooling have
1.28 times the income of those with 1-4 years of schooling, people with 9-11
years of schooling have 1.45 times the income of those with 5-8 years of
schooling, and people with 12 or more years of schooling have 2.25 time the
income of those with 9-11 years of schooling.
The relationship between income and occupation is illustrated in Table
7.5. The occupations in the first two categories (managerial/administrative
and professional/technical) enjoy much higher mean income than the others
and they constitute the top level of the income strata, with clerical and
transportation and communications occupations in the middle and with
transformative and unskilled/personal service occupations at the bottom.
Furthermore, while the mean monthly incomes of people with
managerial/administrative occupations (Cz$55,7669) and with
professional/technical occupations (Cz$45,932) is well above the sample mean
(Cz$21,220), the mean monthly income of people with all the other
occupations is well below the sample mean (see Table 7.5). The huge gap in
income between people with managerial/administrative and
professional/technical occupations and those with the other occupations
suggests that the former belong to the upper class, while the latter belong
either to the lower middle class or lower class.
There is no real middle class to speak of, according to this occupational
classification. This is clear when we examine the ratios between the mean
income of each of the occupational categories and the mean income of the
entire sample. The mean monthly income of managerial/administrative and
professional/technical occupations are 2.63 and 2.16 times of the mean

193
income of the entire sample (Cz$21,220). In contrast, the mean income of
clerical occupations (Cz$18,059) and that of transportation/communications
occupations (Cz$16,192) are 85.1% and 76.3%, respectively, of the sample
mean, and the mean monthly incomes of unskilled/personal service
occupations is only 58% of the sample mean.
Table 7.5
Mean Monthly Income of Men Ages 18-65 by Occupation,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Mean
Std Dev
Cases
%
Managerial/
Administrative
55,769
106,083
17,608
10.5
Professional/
Technical
45,932
51,461
14,678
8.9
Clerical
18,059
21,119
22,167
13.3
Transportation/
Communication
16,192
15,764
13,888
8.3
Transformative
12,996
9,763
59,711
35.7
Unskilled/
Personal Service
12,501
23,846
38,996
23.3
Total
21,220
43,477
167,048
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Table 7.6 shows that the color differences in mean income at ages 18-25
are substantially smaller, while they are slightly bigger in most other cases,
compared to what they are before the control of age group. For example,
within the age group of 18-25, the difference between the mean monthly
income of Asians and whites is only Cz$786, and the difference between the
mean monthly income of whites and Afro-Brazilians is Cz$l,915. Although
the difference between Asians and Afro-Brazilians (Cz$2,701) is a little bigger

194
than the above two figures, it is substantially smaller than the difference
between the two before controlling for age group, which is Cz$24,717. This is
also reflected in the ratios between the mean monthly incomes of Asians and
whites, and of Asians and Afro-Brazilians shown in the last two columns of
Table 7.6.
Table 7.6
Mean Monthly Income of Men Ages 18-65 by Age and Color,
Metropolitan Sao Paulo, Brazil (1980)
Ratio
Age Group
Total
Asian
White
Afro-Brazilian
A/W*
A/AB
18-25
9,410
10,675
9,889
7,974
1.08
1.34
26-39
21,538
42,238
23,826
12,543
1.77
3.37
40-65
24,946
43,119
27,681
11,675
1.56
3.69
Total
19,047
35,492
21,111
10,775
1.68
3.29
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asians/whites, A/AB = Asians/Afro-Brazilians.
For the age group of 26-39, the income differences among the three
groups increases slightly. For example, the ratios between Asians and whites
and between Asians and Afro-Brazilians are now 1.77 and 3.37, respectively,
compared to 1.68 and 3.29 before controlling for age group. For the age group
of 40-65, the difference between Asians and whites is slightly smaller but the
gap between Asians and Afro-Brazilians is wider than they are before the
control of age. This can be seen from the changes in the ratios between these
groups; the ratio between Asians and whites reduces to 1.56 from 1.68, and the
ratio between Asians and Afro-Brazilians increases to 3.69 from 3.29. To sum

195
up, when age group is controlled, the color differences in mean income
reduce substantially for the age group of 18-25, but increase slightly for the
other age groups.
Table 7.7 shows that when residence is controlled, the color differences
in income reduce slightly in urban areas but increase substantially in rural
areas. For example, The ratios between Asians and whites (1.57) and between
Asians and Afro-Brazilians (3.11) in urban areas are smaller than those before
controlling for residence. However, mainly due to a moderate increase of
Cz$3,738 in the mean income of rural Asians and a sharp decrease of about
50% in the mean income of both whites and Afro-Brazilians in rural areas,
the differences among them have widened considerably, resulting in a ratio
of 3.63 between Asians and whites, and a ratio of 5.74 between Asians and
Afro-Brazilians. On the whole, residence does not seem to play a major role
in explaining the income differences between Asians and the other two racial
groups.
When educational level is controlled, the differences in mean income
between Asians and the other two groups are wider at lower levels of
educafion than they are before the control of education, but they decrease
considerably at higher levels of education, as shown in Table 7.8. In other
words, the income differences between Asians and whites, and between
Asians and Afro-Brazilians become smaller at each higher educational level
than they are at the previous levels. In fact, at the level of 9-11 years of
schooling, the mean monthly incomes of Asians (Cz$27,649) and whites
(Cz$26,984) are very close, and at the level of 12 or more years of schooling,
the mean income of whites (Cz$59,972) surpasses that of Asians (Cz$53,573).
This clearly demonstrates the importance of education beyond 12 years in
terms of monetary reward, particularly for Asians and whites.

196
Table 7.7
Mean Monthly Income of Men Ages 18-65 by Residence and Color,
Metropolitan Sao Paulo, Brazil (1980)
Ratio
Aee Group
Total
Asian
White
Afro-Brazilian
A/W*
A/AB
Urban
20,104
35,110
22/353
11,292
1.57
3.11
Rural
10,349
38,848
10,707
6,769
3.63
5.74
Total
19,047
35,492
21,111
10,775
1.68
3.29
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asians/whites, A/AB = Asians/Afro-Brazilians
However, for Afro-Brazilians, the same amount of education does not
result in the same amount of increase in their mean income as it does for
Asians and whites. The mean income of Afro-Brazilians with 9-11 years of
schooling (Cz$16,202) is only 58.6% that of Asians and 60% that of whites with
the same education. Similarly, the mean income of Afro-Brazilians with 12
or more years of schooling is only 54.5% that of whites and 61% that of Asians
in the same category. This is clear evidence of systematic discrimination
against Afro-Brazilians in the Brazilian society. Nonetheless, it does not
negate the importance of education, especially education beyond 12 or more
years, because the mean income of both whites and Afro-Brazilians with 12 or
more years of schooling more than doubles (and the mean income of Asians
nearly doubles) what they are at the level of 9-11 years of schooling.
On the other hand, the gap between the mean monthly income of
whites and Afro-Brazilians widens with the increase of education. For
example, the ratio between the mean income of whites and Afro-Brazilians
with no schooling is 1.11, while it increases to 1.40, 1.62, 1.67 and 1.84,

respectively, at the following higher educational levels. This suggests that
whites receive increasingly higher returns for their investment in education,
relative to Afro-Brazilians, with the increase of education.
Table 7.8
Mean Monthly Income of Men Ages 18-65 by Education and Color,
Metropolitan Sao Paulo, Brazil (1980)
Years of
Schooling
Total
Asian
White
Afro-Brazilian
Ratio
A/W* A/AB*
None
8,152
29,842
8,259
7,437
3.61
4.01
1-4
13,983
32,043
14,877
10,623
2.67
3.43
5-8 -
17,949
29,730
19,280
11,934
1.75
2.63
9-11
25,939
27,649
26,984
16,202
1.02
1.71
12+
58,487
53,573
59,972
32,676
0.89
1.64
Total
19,025
35,492
21,111
10,775
1.68
3.29
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asians/whites, A/AB = Asians/Afro-Brazilians.
When the color groups are compared in terms of mean monthly
income within each of the six major occupational categories, Asians and
whites are similar in most cases, and Afro-Brazilians continue to lag behind
the other two in every category, suggesting the existence of discrimination
against Afro-Brazilians in all major occupational categories (see Table 7.9).
The differences between Asians and whites are greater in blue collar
occupations than in white collar ones, as shown in the last but one column of
Table 7.9. Specifically, the highest ratio between the mean income of Asians
and that of whites (2.50) is found in unskilled/personal service occupations,
and the lowest ratio (1.05) is found in professional/technical occupations. A

198
ratio of 2.50 between Asians and whites in this instance means that the mean
income of Asians is 2.5 times that of whites. The main reason for the bigger
income difference between Asians and whites in unskilled/personal service
occupations is that a higher percentage of Asians in this category are engaged
in more profitable occupations, such as "self-employed small business,"
"autonomous producers in agriculture and fishing" and "mobile sellers," as
described in Chapter 6. The lower income disparity between Asians and
whites with professional/technical occupations is probably due to the similar
characteristics and qualifications of all people in this category, regardless of
color.
The biggest income disparity between Asians and Afro-Brazilians is
also found in unskilled/personal service occupations, followed by
occupations in managerial/administrative and professional/technical, and
the smallest disparity is found in transformative occupations. The mean
income of Asians with unskilled/personal service occupations (Cz$32,890) is
more than 4 times that of Afro-Brazilians (Cz$8,109) with the same
occupations; the mean income of Asians with managerial/administrative
occupations (Cz$69,619) and with professional/technical occupations
(Cz$51,022) are, respectively, 2.79 and 2.27 times of those of Afro-Brazilians
(Cz$24,943 and Cz$22,515). The main reason for the relatively high mean
income of Asians with unskilled/personal service occupations is described
above.* The bigger disparity between the mean incomes of Asians and Afro-
Brazilians in managerial/administrative and professional/technical
occupations may be related to the fact that more Asians are at the higher end
and a disproportionate number of Afro-Brazilians are at the lower end of the
two occupational categories. This is an illustration of the disadvantaged
status of Afro-Brazilians, even when they obtain white collar occupations.

199
Table 7.9
Mean Monthly Income of Men Ages 18-65 by Occupation and Color,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Total
Asian
White
Afro-B*
Ratio
A/W** A/AB*’
Management/
Administrative
55,771
69,619
57,572
24,943
1.21
2.79
Professional/
Technical
45,946
51,022
48,416
22,515
1.05
2.27
Clerical
18,057
22,398
19,046
12,223
1.18
1.83
Transportation/
Communication
16,198
27,081
16,792
13,594
1.61
1.99
Transformative
12,999
20,160
13,779
11,176
1.46
1.80
Unskilled/
Personal Service
12,502
32,890
13,144
8,109
2.50
4.06
Total
19,025
35,492
21,111
10,775
1.68
3.29
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians /whites, A/AB = Asians/Afro-Brazilians.
When whites and Afro-Brazilians are compared, controlling for
occupation, the disparity between them are generally greater in white collar
occupations than in blue collar occupations. The mean incomes of whites
with managerial/administrative occupations (Cz$57,572) and with
professional/technical occupations (Cz$48.416) are, respectively, 2.31 and 2.15
times that of Afro-Brazilians with the same occupations (Cz$24,943 and
Cz$22,515). The smallest disparity between the two is found in
transformative and transportation/communications occupations (only 1.23
and 1.24), suggesting a lesser degree of discrimination against Afro-Brazilians
in these occupations.

200
In order to measure and compare the effects of the independent
variables on the mean income of men ages 18-65, while controlling for one or
more variables simultaneously, I developed five separate regression models
(see Table 7.10). Model 1, which has only one variable, age, tells us two
things: 1) The coefficient of age means that a one-year increase in age results
in an increase of Cz$561 in mean income for the sample, and 2) the R of the
model indicates that age explains only 2.34% of the variation in income for
the sample. Based on this information, we can estimate that a 25-year old
man would have a mean income of Cz$15,984 (Y = 1,929 + (25 x 561) = 15,984),
and a 40-year old man would have a mean income of 24,399 cruzeiros (Y =
I,929 + (40 x 561) = 24,399). However, these estimates may not be reliable since
age accounts for only 2.3% of the total variation in income.
Model 2 has two variables, age and education. The coefficient of
education in this model shows that education has great positive effect on
income, and the coefficient of age increases considerably, compared to the first
model. Specifically, the coefficients of education and age can be interpreted as
follows; a one-year increase in education results in an increase of Cz$6,856 in
mean income, and a one-year increase in age amounts to an increase of
Cz$864 in mean income. More importantly, the R in Model 2 increases to
II.65%, a gain of more than 9% from Model 1. This means that education
accounts for more than 9% of the total variation in income that is not
explained by age. According to this model, a 25-year old man with 4 years of
schooling would have a mean income of Cz$14,207 (Y = -34,817 + (25 x 864) +
(4 x 6,856) = 14,207).
Model 3 has three variables, age, education and residence. Since
residence is a dichotomous variable (urban vs. rural), urban area is treated as
the reference, to which rural area is compared. The coefficient of the dummy

201
variable for rural areas indicates that the mean income of rural residents is
Cz$2/190 less than that of urban residents, after controlling for age and
education. At the same time, we see that the coefficient of age remains
almost the same as in Model 2 (864 in Model 2 vs. 862 in Model 3) and the
coefficient of education decreases slightly from 6,856 in Model 2 to 6,761 in
ModeL.3, where residence is introduced. Furthermore, there is almost no
change in the R from Model 2 to Model 3, suggesting that after controlling
for age and education, residence explains very little of the variation in
income. According to this model, a 25-year old man with 4 years of schooling
in a rural area would have a mean income of Cz$l 2,281 (Y = -34,098 + (25 x
861) + (4 x 6,761) -2,190 = 12,281), while the same person in a urban area would
have a mean income of Cz$14,471, which is the mean income of a rural
person (Cz$12,281) plus the income difference between urban and rural
residents (Cz$2,190).
Model 4 includes the dummy variables for the color groups, in
addition to those already in Model 3. Whites are treated as the reference
group here. There are slight decreases in the coefficients of age and education,
indicating similar degrees of decreases in the effects of these variables on
mean income, when the color variables are introduced into the model.
However, the absolute value of the coefficient of residence increases from
-2,190 in Model 3 to -2,513 in Model 4, suggesting a greater income disparity
between urban and rural residents, after controlling for age, education and
color. The negative coefficient of the dummy variables for Afro-Brazilians
means that the mean income of Afro-Brazilians is Cz$3,709 less than that of
whites, and the positive coefficient of Asians means that the mean income of
Asians is Cz$7,913 more than that of whites, other things being equal. A mere
increase of .0019 in the R2 from Model 3 to Model 4 indicates that the color

202
variables are responsible for only about 2% of the variation in income that is
unexplained by age, education and residence. According to this model, the
average income of 40-year old Afro-Brazilians with 4 years of schooling in
rural areas would be Cz$21,700 (Y = -31,430 + (40 x 836) + (4 x 6,478) - 2,513 -
3,709 = 21,700), and the average income of Asians with the same qualifications
would be Cz$33,322 (Y = -31,430 + (40 x 6,478) - 2,513 + 7,913 = 33,322).
Model 5 includes the dummy variables for occupations, in addition to
the variables already in Model 4. As expected, we see substantial decreases in
the coefficients of age, education, and the color variables, suggesting that the
effects of these variables on income reduce greatly after the dummy variables
for occupational categories are introduced into the model. Specifically, the
coefficient of age reduce by about 24% to 638; the coefficient of education
reduce by 32% to 4,429; the coefficient of Afro-Brazilians (as opposed to
whites) reduce by about 39% to -2,277; the coefficient of Asians (as opposed to
whites) reduce by about 29% to 6,459. The coefficient of rural areas (as
opposed to urban areas), on the contrary, increases slightly from 2,513 in
Model 4 to 2,572 in Model 5, suggesting a slightly bigger income disparity
between urban and rural areas, after controlling for occupation.
The category of clerical occupations is treated as the reference group in
Model'S because the mean income of this category is the closest to the sample
mean according to the descriptive analysis. However, it turns out that, when
age, education, residence and color are controlled simultaneously, clerical
occupations have the lowest mean income. Specifically, controlling for the
other independent variables, the average incomes of people with
managerial /administrative, professional/technical,
transportation/communications, transformative, and unskilled/personal
service occupations are, respectively, 31,532, 20,521, 1,364, 1,140 and 899

203
2
cruzeiros higher than that of people with clerical occupations. The R of
Model 5 increases by .0475 to .1664 in Model 5 from .1189 in Model 4. This
means that the occupational variables account for about 5% of the total
variation that is unexplained by the other variables in Model 4.
We can also estimate the mean income of Afro-Brazilians and Asians
with various occupations, based on the information in Model 5. For instance,
if an 40-year old Afro-Brazilian man with 4 years of schooling in rural areas
has a managerial/administrative occupation, his mean income would be
Cs$47,029 (Y = -22,890 + (40 x 638) + (4 x 4,429) - 2,572 - 2,277 + 31,532 = 47,029),
while an Asian with the same qualifications would have a mean income of
Cz$55,765 (Y = -22,890 + (40 x 638) + (4 x 4,429) - 2,572 +6,459 + 31,532 = 55,765).
The mean income of a white man in the same situation would be Cz$49,306,
which is the mean income of either Afro-Brazilians or Asians plus or minus
the difference between them and whites. Similarly, while the mean income
of an Asian who has 4 years of schooling, lives in rural areas and has an
unskilled/personal service occupation would be Cz$25,132, the mean income
of a 40-year old Afro-Brazilian with the same qualifications would be
Cz$16,396, and the mean income of a white man in the same situation would
be Cz$18,673.

204
Table 7.10
Monthly Income of Men Ages 18-65 Regressed on Age, Education,
Residence, and Color, Metropolitan Sao Paulo, Brazil (1980)
Independent
Model
Variable 1
2
3
4
5
Age 561
864
861
836
638
Education
6,856
6,761
6,478
4,429
Residence
Urban*
Rural
-2,190
-2,513
-2,572
Color
White*
Afro-Brazilian
Asian
-3,709
7,913
-2,277
6,459
Occupation
Man/Adm**
Prof/Tech**
Clerical*
Transp/Comm**
Transformative
Unskilled/PS**
31,532
20,521
1,364
1,140
***899
R2 .0234
.1165
.1168
.1189
.1664
Constant 1,959
-34,817
-34,098
-31,430
-22,890
*These are the reference groups, to which the other variable(s) of the same
category are compared.
**These occupations are abbreviated. They are:
man/adm = managerial/administrative
prof/tech = professional/technical
transp/comm = transportation/communications
unskilled/PS = unskilled/personal service
***P-value = .0160. P-values for all the other coefficients < .01.

205
Mean Income of Women Ages 18-65
The sample data of women ages 18-65 show that the mean monthly
income of Brazilian women is only Cz$4,461, which is slightly more than one
minimum wage (Cz$4,150) and less than one fourth of the average income of
their men counterparts (Cz$19/047). However, there are many variations by
color, age, educational level, residence and occupation. As Table 7.11 shows,
Asian women's mean monthly income is Cz$7,261, compared to Cz$4,783 for
white women and Cz$3,030 for Afro-Brazilian women. In other words, the
mean monthly income of Asian women is more than 1.5 times that of white
women and about 2.4 times that of Afro-Brazilian women.
Table 7.11
Mean Monthly Income of Women Ages 18-65 by Color Group,
Metropolitan Sao Paulo, Brazil (1980)
Color Group
Mean
Std Dev
Cases
%
Asian
7,261
14,233
4,517
2.1
White
4,783
13,854
160,971
76.5
Afro-Brazilian
3,030
5,186
45,015
21.4
Total
4,461
12,552
210,503
100.0
Source: Weighted 3% sample data of metropolitan Sao Paulo, 1980 Brazilian
Census.
As shown in Table 7.12, the age differences in income among women
are much smaller than they are among men (see Table 7.2), probably due to
the extremely low average income for all women. The mean income of
women ages 18-25 is only Cz$3,855, which is below one minimum wage
(Cz$4,150), and the mean income of the two older age groups are Cz$4,901 and

206
Cz$4,507, respectively. Interestingly, it is the age group of 26-39, not the age
group of 40-65, that has the highest mean income. This is mainly because
women ages 26-39 were better educated than those ages 40-65 and they have
been in the labor force long enough to develop some seniority of pay. In
comparison, the mean income of men increases from younger to older age
groups (see Table 7.2), and the oldest age group has the highest mean income.
Table 7.12
Mean Monthly Income of Women Ages 18-65 by Age Group,
Metropolitan Sao Paulo, Brazil (1980)
Age Group
Mean
Std Dev
Cases
%
18-25
3,855
8,346
62,668
29.7
26-39
4,901
11,389
76,496
36.2
40-65
4,507
16,192
71,971
34.1
Total
4,456
12,538
211,134
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
The income difference between urban and rural residents is quite big as
well, in spite of the relatively low mean income for all women. On the
average, urban women have an income of Cz$4,797, which is more than four
times the mean income of rural women (Cz$l,082), as shown in Table 7.13.
This indicates that the vast majority of rural women do not have income of
their own, and they are at the bottom of the society in terms of income.
Just like men's mean income, women’s mean income is closely
correlated with the amount of education they have received, i.e., women who
received more years of schooling have higher income. The mean income of
women with no schooling is only Cz$l,563, which is less than 40% of one

207
minimum wage; the mean income of women with 1-4 years of schooling is
only Cz$2/606/ which is about 63% of one minimum wage; and the mean
income for women with 4-8 years of schooling is Cz$4,576, which is just above
one minimum wage. On the other hand, the mean income of women with
9-11 years of schooling (Cz$8,657) is almost double the mean income of those
with 4-8 years of schooling, and the mean income of women with 12 or more
years of schooling (Cz$17,635) is more than double that of those with 9-11
years of schooling. The increase of mean income, especially at higher
educational levels (more than 9 years of schooling), illustrates the strong
positive impact of education on women's average income.
Table 7.13
Mean Monthly Income of Women Ages 18-65 by Residence,
Metropolitan Sao Paulo, Brazil (1980)
Residence
Mean
Std Dev
Cases
%
Urban
4,797
13,024
191,767
90.8
Rural
1,082
4,667
19,367
9.2
Total
4,456
12,538
211,134
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Meanwhile, a comparison of men's and women's mean income by
educational level reveals that the same amount of education does not result
in the same amount of income for men and women, and there is a huge gap
between the mean income of men and women with the same education. For
example, at the first two educational levels, men's income is more than five
times that of women's and at the three higher levels, men's income is more
than three times that of women's. More specifically, the mean income of

208
men without any schooling (Cz$8,152) is 1.78 times higher than the mean
income of women with 5-8 years of schooling (Cz$4,576) and almost as high as
the mean income of women with 9-11 years of schooling (Cz$8,657); the mean
income of men with 5-8 years of schooling (Cz$l7,948) is higher than the
mean income of women with 12 or more years of schooling (Cz$l7,635). This
shows that there is a great deal of disparity between the income of men and
women in Brazil, even if they have the same amount of education.
Table 7.14
Mean Monthly Income of Women Ages 18-65 by Education,
Metropolitan Sao Paulo, Brazil (1980)
Years of
Schooling
Mean
Std Dev
Cases
%
Zero
1,563
4,923
36,664
17.4
1-4
2,606
6,668
102,314
48.6
5-8
4,576
9,500
33,084
15.7
9-11
8,657
22,399
24,262
11.5
12+
17,635
25,097
14,246
6.8
Total
4,448
12,543
210,570
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
The mean income of women in the six major occupational categories
are listed in Table 7.15. The distribution of income by occupation shows three
distinctive levels of income: the top level is made up of
managerial/administrative and professional/technical occupations, with a
mean income of Cz$29,670 and Cz$19,405, respectively; the middle level is
made up of clerical and transportation/communications occupations, with a
mean income of Cz$l 1,995 and Cz$9,690, respectively; and the bottom level is

209
made up of the remaining two occupational categories, with a mean income
of Cz$6,936 for transformative occupations and Cz$5,717 for
unskilled/personal service occupations. The top level of income is about two
to three times the average income of the sample (Cz$l0,488), the middle level
of income is about the sample mean, and the bottom level of income is far
below the sample mean.
Table 7.15
Mean Monthly Income of Women Ages 18-65 by Occupation,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Mean
Std Dev
Cases
%
Managerial/
Administrative
29,670
40,105
3,093
4.2
Professional/
Technical
19,405
17,776
10,805
14.6
Clerical
11,995
15,462
18,205
24.6
Transportation/
Communication
9,690
7,126
139
0.2
Transformative
6,936
4,608
12,923
17.5
Unskilled/
Personal Service
5,717
7,860
28,742
38.9
Total
10,488
15,454
73,906
100.0
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
Compared to men's income with the same occupations, women have
much lower income in all categories: In managerial/administrative
occupations, women's income is 53.2% of men's; in professional/technical
occupations, women's income is 42.2% of men's; in clerical occupations,
women's income is 66.4% of men; in transportation/communications
occupations, women's income is 59.8% of men's; in transformative

210
occupations, women's income is 53.4% of men's; and in unskilled/personal
service occupations, women' income is 45.7% of men's. Relatively speaking,
the gap between men's and women's income is the biggest in
professional/technical occupations and the smallest in clerical occupations.
The bigger disparity in professional/technical occupations may be related to
the fact that this category covers occupations with a wide range of income,
and more men than women have higher-paying jobs. This is, however, no
justification for the bigger disparity because men and women in this category
tend to have more comparable educational levels and a higher percent of
women (14.6% of all women vs. 8.9% of all men) have these occupations. On
the other hand, the smaller disparity between men's and women's income in
clerical occupations are mainly due to the smaller range of income associated
with these occupations, and a higher percent of women (24.6% of all women)
than men (13.3% of all men) who have these occupations.
So far, I have described the income differences of Brazilian women by
color, age, education, residence and occupation. In the following, I will
describe the income differences among the three color groups by age group,
educational level, residence and occupational category. Table 7.16 compares
the mean income of women by age group and color group. We see a mixed
result here; the gap between the mean income of Asians and whites widens
for the two younger age groups and narrows for the age group of 40-65, and
the gap between the mean income of Asians and Afro-Brazilians widens at
the age of 26-39 and narrows for the other two age groups.
Specifically, the ratio between the mean income of Asians and whites
increases from 1.52 before controlling for age to 1.66 and 1.83 for the first two
age groups, but decreases to 1.07 for the age group of 40-65. Meanwhile, the
ratio between the mean income of Asians and Afro-Brazilians decreases from

211
2.40 before the control of age to 2.15 for the age group of 18-25 and to 1.97 for
the age group of 40-65, but increases to 2.98 for the age group of 26-39. In other
words, the color differences in income are the biggest for people ages 26-39
and the smallest for people above 40.
Table 7.16
Mean Monthly Income of Women Ages 18-65 by Age and Color,
Metropolitan Sao Paulo, Brazil (1980)
Ratio
Age Group
Sample
Asian
White
Afro-Brazilian
A/W*
A/AB
18-25
3,858
6,692
4,034
3,118
1.66
2.15
26-39
4,908
9,618
5,260
3,226
1.83
2.98
40-65
4,513
5,266
4,912
2,673
1.07
1.97
Total
4,461
7,261
4,783
3,030
1.52
2.40
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asians/whites, A/B = Asians/Afro-Brazilians.
Table 7.17 show that the income difference between Asians and whites
increases slightly in both urban and rural areas, and the difference between
Asians and Afro-Brazilians increases slightly in urban areas, but decreases
considerably in rural areas. For instance, the ratio between the mean income
of Asians and whites increases by 0.01 to 1.53 in urban areas, and the ratio
between the two increases by 0.16 to 1.68 in rural areas; the ratio between the
mean incomes of Asians and Afro-Brazilians increases from 2.40 before
controlling for residence to 2.43 in urban areas, but decrease sharply to 1.55 in
rural areas (see Table 7.17).

212
It is particularly important to point out that the mean income of rural
Afro-Brazilians (Cz$l,135) actually surpasses that of rural whites (Cz$l,046).
This tells us that the income difference between whites and Afro-Brazilians is
much smaller in rural areas than it is in urban areas, and Afro-Brazilian
women fare a little better than their white counterparts in rural areas.
Table 7.17
Mean Monthly Income of Women Ages 18-65 by Residence and Color,
Metropolitan SSo Paulo, Brazil (1980)
Ratio
Residence
Total
Asian
White
Afro-Brazilian
A/W*
A/AB
Urban
4,803
7,872
5,152
3,236
1.53
2.43
Rural
1,083
1,762
1,046
1,135
1.68
1.55
Total
4,461
7,261
4,783
3,030
1.52
2.40
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*A/W = Asians/whites, A/AB = Asians/Afro-Brazilians.
As shown in Table 7.18, the income differences between the three color
groups decrease significantly when educational level is controlled,
particularly at higher levels. This indicates a strong positive effect of
education on women’s income. For people with no schooling, the mean
income of Asians is, respectively, 1.71 and 1.47 times the mean income of
whites and Afro-Brazilians; for those with 1-4 years of schooling, the mean
income of Asians is, respectively, 1.3 and 1.24 times of that of whites and
Afro-Brazilians. At the level of 5-8 years of schooling, the ratio between
Asians and whites further reduce to 1.18, while the ratio between Asians and
Afro-Brazilians increases slightly to 1.33; At the level of 9-11 years of

213
schooling, the ratios between Asians and whites and between Asians and
Afro-Brazilians are, respectively, 0.93 and 1.25. At the highest level of 12 or
more years of schooling, the mean income of Asians and whites are almost
identical (Cz$l 7,788 for Asians and Cz$l7,728 for whites), and the mean
income of Afro-Brazilians (Cz$16,228) is more than 90% of that of Asians.
Table 7.18
Mean Monthly Income of Women Ages 18-65 by Education and Color,
Metropolitan Sao Paulo, Brazil (1980)
Years of
Schooling
Total
Asian
White
Afro-Brazilian
Ratio
A/W* A/AB*
None
1,563
2,518
1,469
1,717
1.71
1.47
1-4
2,609
3,331
2,569
2,689
1.30
1.24
5-8
4,576
5,495
4,665
4,128
1.18
1.33
9-11
8,665
8,283
8,887
6,818
0.93
1.25
12+
17,669
17,788
17,728
16,228
1.00
1.09
Total
4,453
7,261
4,783
3,030
1.52
2.40
Source: Weighted 3% sample data of Metropolitan S3o Paulo, 1980 Brazilian
Census.
*A/W = Asians/whites, A/AB = Asians/Afro-Brazilians.
On the other hand, if we compare the mean incomes of whites and
Afro-Brazilians by educational level, we find a somewhat different pattern;
the mean income of Afro-Brazilians is slightly more than that of whites at the
first two educational levels, and the mean income of whites is more than that
of Afro-Brazilians at the remaining higher levels. This suggests that at five or
more years of schooling, whites enjoy a more favorable monetary return than
do Afro-Brazilians for the same amount of education. In short, although
Afro-Brazilians fare a little better than do whites at less than five years of

214
schooling, they fall behind whites at higher educational levels. However, the
gap between whites and Afro-Brazilians at the level of 12 or more years of
schooling is narrower than it is at the two previous levels. This again
suggests that Afro-Brazilians face more discrimination at 5-11 years of
schooling, but once they have 12 or more years of schooling, they face less
discrimination in the job market.
When the mean incomes of Asian, white and Afro-Brazilian women
are compared, controlling for occupation, Asian women lead white women
in most categories, and Afro-Brazilians continue to fall behind both Asians
and whites in all categories. White women have advantage over Asian
women in two of the six categories. As Table 7.19 shows, the mean income of
Asian women with managerial/administrative occupations (Cz$29,645) is
97% of that of white women with the same occupations (Cz$30,663), and the
mean income of Asians with transportation/communications occupations
(Cz$9,000) is 85% of the mean income of whites with the same occupations
(Cz$l 0,596).
The biggest gap between the mean income of Asians and whites is
found in unskilled/personal service occupations, where the mean income of
Asians is 1.91 times that of whites. And the gap between them in
professional/technical occupations is the second biggest; Asians' mean
income is 1.35 times that of whites. The occupations where the mean income
of Asians and whites are most similar are managerial/administrative and
clerical ones, with a ratio of 0.97 between the two in the former and a ratio of
1.12 between the two in the latter. On the whole, the income disparity
between Asians and whites is bigger in blue collar occupations than in white
collar ones.

215
The income disparity between Asians and Afro-Brazilians is the biggest
in unskilled/personal service occupations, followed by professional/technical
and managerial/administrative occupations. For example, the mean income
of Asians in these three occupational categories are, respectively, 2.38, 2.08 and
1.53 times that of Afro-Brazilians. The smallest income disparity between
Asians and Afro-Brazilians is in transportation/communications and
transformative occupations, where Asians’ mean income is 1.24 times that of
Afro-Brazilians in the former and 1.38 times that of Afro-Brazilians in the
latter. In general, we can say that the income disparity between Asians and
Afro-Brazilians is bigger in white collar occupations than in blue collar ones,
with the exception of unskilled/personal service occupations.
Table 7.19
Mean Monthly Income of Women Ages 18-65 by Occupation and Color,
Metropolitan Sao Paulo, Brazil (1980)
Occupation
Total
Asian
White
Afro-B*
Ratio
A/W** A/AB**
Managerial/
Administrative
29,690
29,645
30,663
19,288
0.97
1.53
Professional/
Technical
19,406
26,847
19,827
12,894
1.35
2.08
Clerical
11,999
13,798
12,373
9,146
1.12
1.51
Transportation/
Communications
9,690
9,000
10,596
7,276
0.85
1.24
Transformative
6,937
9,108
7,031
6,587
1.29
1.38
Unskilled/
Personal Service
5,717
11,642
6,104
4,900
1.91
2.38
Source: Weighted 3% sample data of Metropolitan Sao Paulo, 1980 Brazilian
Census.
*Afro-B = Afro-Brazilians
**A/W = Asians/whites, A/AB = Asians/Afro-Brazilians.

216
As with men's data, I ran five separate regression analyses to measure
and compare the effects of the independent variables on the mean income of
women ages 18-65 (see Table 7.20). Model 1 measures the effect of age on the
mean income of women. It shows that age has a positive (though small)
effect on the income of women and that age explains only 1.61% (R of the
model) of the total variance in women's income. Specifically, the coefficient
of age means that a one-year increase in age amounts to an increase of Cz$181
in income. For instance, according to this model, the average income of 25-
year old women is estimated at Cz$9,328 (Y = 4,803 + (25 x 181) = 9,328), and
the average income of 40-year old women is estimated at Cz$l2,043 (Y = 4,803
+ (40 x 181) = 12,043). However, these estimates may not be reliable since age
accounts for only 1.61% of the variation in income.
Model 2, which includes two variables, age and education, shows that
education has tremendous positive effect on the income of women and the
effect of age almost doubles, when education is introduced into the model.
Specifically, the coefficient of age and education can be interpreted as follows;
a one-year increase in age results in an increase of Cz$346 in income, and a
one-year increase in schooling amounts to an increase of Cz$2,885 in income.
More importantly, the R in Model 2 increases to .1536, a gain of .1375 from
Model 1. This means that education explains more than 13% of the variance
in income that is not explained by age. According to this model, 25-year old
women with 4 year of schooling would have a mean income of Cz$7,709 (Y =
-12481 + (25 x 346) + (4 x 2,885) = 7,709).
Model 3 has a new variable, residence, in addition to age and
education. Since residence is a dichotomous variable (urban vs. rural), urban
area is treated as the reference, to which rural area is compared. We see very
little changes in the coefficients of age and education, suggesting little

217
covariation between age, education and residence. In another words, they are
independent of one another, as far as their impact on income is concerned.
The negative coefficient of rural area indicates that the average income of
rural women is Cz$l,491 less than that of urban women, other things being
equal. There is almost no change in the R of Model 3. This means that with
age and education already controlled, residence explains virtually no
additional variation in income. In other words, Model 2 is as good as Model 3
in explaining the total variance of income, if we are not concerned with the
income difference between urban and rural residents. This model estimates
that the mean income of 25-year old women with 4 years of schooling would
be Cz$6,311 in a rural area, and Cz$7,802 in urban areas.
Model 4 includes the dummy variables for the color groups, in
addition to age, education and residence. When the color variables are
introduced, the coefficients of age and education reduce slightly, indicating
minor decreases in the effects of these variables on income. However, the
coefficient of residence (rural) increases somewhat, suggesting the increased
negative effect of rural areas on income. In another words, the income
difference between urban and rural women increases, controlling for age,
education and color simultaneously. The negative coefficient of Afro-
Brazilians means that their mean income is Cz$l,615 less than that of whites,
and the positive coefficient of Asians means that their mean income is
Cz$2,292 more than that of whites, other things being equal. There is a mere
increase of .0025 in the R from Model 3 to Model 4. This indicates that the
color variables explain only .25% of the variation in income that is not
explained by age, education and residence. Thus, we can say that Model 3,
which does not include the color variables, is just as good as Model 4, which
includes the color variables, in explaining the variation in income of women.

218
According to this model, the mean income of 25-year old Afro-Brazilian
women with 4 years of schooling would be Cz$4,990 in rural areas, and
Cz$6,641 in urban areas; the mean income of Asians with the same
qualifications is Cz$8,897 in rural areas and Cz$l0,548 in urban areas; the
mean income of white women with the same qualifications would be
Cz$6,605 in rural areas and Cz$8,256 in urban areas.
Model 5, the full model, has all the variables, including the
occupational variables. The coefficients of age, education, residence (rural
areas) and Afro-Brazilian in Model 4, reduce considerably in Model 5, while
the coefficient of Asian drops slightly in Model 5. These big reductions in
coefficients mean that when the occupational variables are introduced into
the model, the effects of age, education, residence and being Afro-Brazilian on
mean income reduce significantly. To be specific, the effect of age decreases by
about 21% (336 in Model 4 vs. 264 in Model 5); the effect of education drops by
73.3% (2732 in Model vs. 1730 in Model 5); the (negative) effect of residence
(rural areas) reduces by about 21% (-1651 in Model 4 vs. -1301 in Model 5); the
(negative) effect of being Afro-Brazilian (as opposed to being white) reduces by
about 45% (-1615 in Model 4 vs. -889 in Model 5). In contrast, the effect of
being Asian, as opposed to being white, reduces only by 3% (2292 in Model 4
vs. 2214 in Model 5), after the introduction of the occupational variables into
the model.
The coefficients of the occupational variables in Model 5 show the
differences between the mean incomes of these occupations and that of
clerical occupations, which are treated as the reference group. For example,
other things being equal, the mean incomes of managerial/administrative
and professional/technical occupations are, respectively, Cz$15,201 and
Cz$4,454 more than that of clerical occupations, while the mean incomes of

219
transportation/communications, transformative and unskilled/personal
service occupations are, respectively, Cz$725, Cz$2,657 and Cz$3,069 less than
that of clerical occupations. Note that the p-value for the coefficient of
transportation/communications occupations is .5371. This suggests that the
difference between the mean income of this category and that of the reference
group (clerical occupations) is not significantly different. Finally, we see an
increase of .0535 in the R of Model 5, compared to Model 4. This indicates
that the occupational variables explain more than 5% of the variance that is
not explained by the other variables in model. Thus, the full model with all
the variables explains 21% of the total variation in income. According to this
model, the average income of 25-year old Afro-Brazilian women who have 4
years of schooling and a managerial/administrative occupation would be
Cz$22,088 in rural areas and Cz$23,389 in urban areas, while the average
income of Asian women with the same qualifications would be Cz$23,413 in
rural areas and Cz$24,714 in urban areas, and the mean income of white
women in the same situation would be Cz$21,199 in rural areas and Cz$22,500
in urban areas.

220
Table 7.20
Monthly Income of Women Ages 18-65 Regressed on Age,
Education, Residence, and Color, Metropolitan Sao Paulo, Brazil (1980)
Independent
Variable
Model
1
2
3
4
5
Age
181
346
344
336
264
Education
2,885
2,854
2,732
1,730
Residence
Urban*
—
—
Rural
-1,491
-1,651
-1,301
Color
White*
Afro-Brazilian
-1,615
-889
Asian
2,292
2,214
Occupation
Man/Adm**
15,201
Prof/Tech’
Hfr
4,454
Clerical*
—
Transp/Com**
***-725
Transformative
-2,657
Unskilled/PS**
-3,069
R2
.0161
.1536
.1540
.1565
.2100
Constant
4,803
-12,481
-12,214
-11,072
-4,443
’•'These are the reference groups, to which the other variable(s) of the same
category are compared.
**These occupations are abbreviated. They are:
man/adm = managerial/administrative
prof/tech = professional/technical
transp/comm = transportation/communications
unskilled/PS = unskilled/personal service
***P-value = .5371. P-values for all the other coefficients <.0000.

221
Summary
The mean monthly income of men ages 18-65 in metropolitan Sao
Paulo, Brazil varies by color, age, residence, education and occupation. The
mean income of Asian men (Cz$35,492) is 1.5 times that of whites (Cz$21,lll)
and more than 3 times that of Afro-Brazilians (Cz$l0,775). The age
differences in income are expectedly obvious too; the age group of 18-25 have
the lowest mean income (Cz$9,410), the age group of 26-39 have a mean
income of Cz$21,538, and the age group of 40-65 have the highest mean
income (Cz$24,946). The mean income of urban residents (Cz$20,104) is
almost twice as much as that of rural residents (Cz$l 0,349).
The educational differences in income are huge, ranging from a mean
of Cz$8,152 for those with no schooling to a mean of Cz$58,451 for those with
12 or more years of schooling. However, the income differences are not very
big at lower educational levels (below 8 years of schooling) but they become
extremely big at higher educational levels; the mean monthly income of
those with 12 or more years of schooling (Cz$58,451) is 2.25 times that of those
with 9-11 years of schooling.
Similarly, there are huge income differences among people with
different occupations. The mean monthly income of those with
managerial/administrative occupations (Cz$55,769) and those with
professional/technical occupations (Cz$45,932) are, respectively, 4.46 and 3.67
times that of those with unskilled/personal service occupations (Cz$12,501).
Meanwhile, the mean monthly incomes of those with clerical occupations
(Cz$18,059), transportation/communications occupations (Cz$16,192), and
transformative occupations (Cz$l2,996) are below the sample mean
(Cz$21,220).

222
When age group is controlled, the income differences among the three
color groups reduce substantially in the age group of 18-25, but increase in
most other cases. For the age group of 18-25, the difference between the mean
income of Asians and that of whites is only Cz$786 and the same difference
between Asians and Afro-Brazilians is Cz$2,701. The color differences in
income are the greatest for the age group of 26-39; the mean incomes of
Asians, whites and Afro-Brazilians are, respectively, Cz$42,238, Cz$23,826 and
Cz$l2,543. In each age group, Asians have the highest mean income and
Afro-Brazilians have the lowest mean, with whites in the middle.
The income differences among the three color groups reduce slightly in
urban areas, but increase substantially in rural areas, when residence is
controlled. In urban areas, the mean incomes of Asians, whites and Afro-
Brazilians are, respectively, Cz$35,110, Cz$22,353 and Cz$l 1,292. In contrast,
the mean income of rural Asians (Cz$38,848) is 3.63 times that of rural whites
(Cz$l0,707) and 5.74 times that of rural Afro-Brazilians (Cz$6,769).
When educational level is controlled, the income differences between
Asians and the other two groups are wider at lower levels of education (below
8 years of schooling), but decrease considerably at higher levels of education.
At the level of 9-11 years of schooling, Asians and whites have about the
same mean income (Cz$27,649 for Asians and Cz$26,984 for whites), and at
the level of 12 or more years of schooling, the mean income of whites
(Cz$59,972) is even higher that of Asians (Cz$53,573). The ratios between the
mean income of Asians and Afro-Brazilians at 9-11 years of schooling and at
12 or more years of schooling reduce to 1.71 and 1.64 from a ratio of 3.29 before
the control of education. Unfortunately, the gaps between Afro-Brazilians
and the other two groups beyond 9 years of schooling are still too big; the
mean income of Afro-Brazilians is around 60% of those of whites and Asians.

223
This is clear evidence of systematic discrimination against Afro-Brazilians in
Brazil.
Controlling for occupation, the income differences among the three
color groups reduce considerably, except for the difference between Asians
and the other two in unskilled/personal service occupations. Due to the fact
that a higher proportion of Asians with unskilled/personal service
occupations have more profitable jobs, such as "self-employed small
business," "autonomous producers in agriculture and fishing" and "mobile
seller," their mean income (Cz$32,890) is much higher than that of whites
(Cz$13,144) and that of Afro-Brazilians (Cz$8,109). Apart from the category of
unskilled/personal service occupations, the income difference between
Asians and whites is smaller in white collar occupations than it is in blue
collar occupations, and the income difference between Asians and Afro-
Brazilians is smaller in blue collar occupations than it is in white collar ones.
Although the income differences among the color groups become
smaller after the control of occupation, Asians still have the highest mean
income and Afro-Brazilians have the lowest mean income, with the mean
income of whites in the middle, in every category. In particular, the mean
income of Afro-Brazilians with managerial/administrative occupations is
only 35.8% of that of Asians and 43.3% of that of whites. The mean income of
Afro-Brazilians with professional/technical occupations is only 44.1% of that
of Asians and 46.5% of that of whites. These huge income disparities between
Afro-Brazilians and the other groups once again illustrate the disadvantaged
status of Afro-Brazilians, even when they manage to obtain good jobs.
The regression models quantitatively show the effect of each of the
independent variables on men's income. The effect is expressed in three
ways; 1) the nature (positive or negative) of the effect, 2) the amount of

224
change (in cruzeiros) per one unit increase in the independent variables (or as
a result of belonging to one of the categories when the independent variable
is a categorical one), and 3) the amount of variation in income the
independent variables explain.
Age, education, being Asian (as opposed being white) and having
occupations other than clerical ones have positive effects on men's income,
while residing in rural areas (as opposed to urban areas) and being Afro-
Brazilian have negative effects on men's income. The effects of the
independent variables on income vary from model to model, depending on
the number and type of variables included in the model. The coefficients of
the independent variables in Model 5 (Table 7.10) indicate the effect of each
independent variable, while controlling for the others: A one-year increase
in age results in an increase of Cz$638, a one-year increase in schooling
amounts to an increase of Cz$4,429, and residing in rural areas (as opposed to
urban areas) results in an decrease of Cz$2,572 in mean income. Other things
being equal, being Afro-Brazilian (as opposed to being whites) results in a
decrease of Cz$2,277 and being Asian (as opposed to being whites) results in
an increase of Cz$6,459 in mean income. The mean incomes of
managerial/administrative, professional/technical,
transportation/communications, transformative and unskilled/personal
service occupations are, respectively, Cz$31,532, Cz$20,521, Cz$l,364, Cz$l,140
and Cz$899 more than that of clerical occupations (the reference group).
The R in the regression models tell us the amount of variation in
men's income each independent variable explains; age explains 2.34%,
education 9.31%, residence .02%, the color variables .21%, and the
occupational variables 4.96% of the total variation in income. Thus, we can
conclude that of the variables examined here, education is the most

225
important factor in determining men's income, occupation is the second and
age is the third important factor. Residence and skin color have very little
impact on income, when the other variables are controlled.
Women ages 18-65 in metropolitan Sao Paulo, Brazil have a mean
monthly income of Cz$4,461, which is less than one fourth of the average
income of men (Cz$19,047). Nonetheless, similar income differences by color,
age, residence, education and occupation remain among women as well. The
mean income of Asian women is more than 1.5 times that of white women
and about 2.4 times that of Afro-Brazilians. The income difference by age
group for women is very small, compared to men's data, mainly because of
the extremely low average income of women; the mean incomes of the three
age groups are, respectively, Cz$3,855, Cz$4,901 and Cz$4,456. On the other
hand, the income difference between urban and rural women is quite big; the
mean income of urban residents (Cz$4,797) is more than 4 times that of rural
residents (Cz$l,082). This is probably the result of the vast majority of rural
women having no income of their own.
The educational differences in income for women are very small at the
levels of below 9 years of schooling, but they are extremely big at higher
levels. The mean income of those with 9-11 years of schooling (Cz$8,657) is
almost twice that of those with 5-8 years of schooling (Cz$4,576), and the
mean income of those with 12 or more years of schooling (Cz$17,635) is more
than two times that of those with 9-11 years of schooling. It is also important
to point out that there is a huge gap between the mean income of men and
women with the same education; at the levels of no schooling and 1-4 years
of schooling, men's income is more than 5 times that of women's, and at the
higher levels, men's income is more than 3 times that of women's.

226
The mean incomes of the six major occupational categories are clearly
divided into three levels; the top level of income (Cz$29,670 for
managerial/administrative occupations and Cz$19,405 for
professional/technical occupations) is about 2 to 3 times the sample mean
(Cz$l 0,488), the middle level of income (Cz$l 1,995 for clerical occupations and
Cz$9,690 for transportation/communications occupations) is about the same
as the sample mean, and the bottom level of income (Cz$6,936 for
transformative occupations and Cz$5,717 for unskilled/personal service
occupations) is far below the sample mean.
There is also a great deal of income disparity between men and women
with the same occupation. In general, women's income is between 42-66% of
men's income within the same occupational category, with the smallest
disparity in clerical occupations (66.4%) and the biggest disparity in
professional/technical occupations (42.2%). The bigger disparity in
professional/technical occupations may be related to the fact that this category
covers occupations with a wide range of income, and more men than women
have higher-paying jobs. And the smaller disparity in clerical occupations are
perhaps due to the smaller range of income associated with these occupations,
and a higher percent of women (24.6%) than men (13.3%) who have these
occupations.
When age group is controlled, the income difference between Asians
and whites increases for the age groups of 18-25 and 26-39 but decreases
considerably for the age group of 40-65, and the income difference between
Asians and Afro-Brazilians widens for the middle age group but narrows for
the other two age groups. In other words, the color differences in income are
the biggest for people ages 26-39 and the smallest for people above 40.
Although age accounts for some of the differences, especially for people above

227
40, it is not a major factor for the color differences in income. Controlling for
residence, the color differences in income remain about the same in urban
areas, but narrow generally in rural areas.
The income differences among the three color groups decrease
significantly when educational level is controlled, particularly at higher
levels of education. This indicates a strong positive effect of education on
women's income. With the exception of the income difference between
Asians and whites at the level of no schooling, the income differences among
the three color groups at all levels of education are smaller than they are
before the control of educational level. In particular, the mean incomes of
the three groups at the level of 12 or years of schooling (Cz$17,788 for Asians,
Cz$l 7,728 for whites and Cz$l 6,228 for Afro-Brazilians) are very similar, and
the mean incomes of Asians and whites at the level of 9-11 years of schooling
(Cz$8,283 for Asians and Cz$8,887 for whites) are very close as well.
When occupational category is controlled, the color differences in
income become much smaller. However, Asian women continue to lead
white women in most occupational categories and Afro-Brazilians continue
to fall behind Asians and whites in all categories. The income differences
between Asians and the other two groups are the biggest in
unskilled/personal service occupations; the ratio between Asians and whites
is 1.91 and the ratio between Asians and Afro-Brazilians is 2.38. The second
biggest income differences between Asians and the other two groups are
found in professional/technical occupations, where the ratio between Asians
and whites is 1.35 and the ratio between Asians and Afro-Brazilians is 2.08.
However, whites lead Asians in the categories of managerial/administrative
and transportation/communications occupations, with a mean income of
Cz$30,663, as opposed to a mean of Cz$29,645 for Asians, in the former, and

228
with a mean income of Cz$l 0,596, as opposed to a mean of Cz$9,000 for
Asians, in the latter.
The regression models on women's data show that age, education,
being Asian (as opposed to being white) and having occupations in
managerial/administrative, professional/technical category (as opposed to
clerical occupations) have positive effects on women's mean income, while
residing in rural areas (as opposed to urban areas), being Afro-Brazilian and
having transportation/communications, transformative and
unskilled/personal services occupations (as opposed to clerical occupations)
have negative effects on their mean income. Specifically, as the coefficients of
the independent variables indicate in Model 5 of Table 7.20, a one-year
increase in age results in an increase of Cz$264, a one-year increase in
schooling results in an increase of Cz$l,730, residing in rural areas (as
opposed to urban areas) results in a decrease of Cz$l,301. Other things being
equal, being Afro-Brazilian (as opposed to being white) results in a decrease of
Cz$899, and being Asian (as opposed to being white) results in an increase of
Cz$2,214 in mean income. While controlling for the other variables, the
mean incomes of managerial/administrative and professional occupations
are Cz$l5,201 and Cz$4,454 more than that of clerical occupations (the
reference group), but the mean income of transportation/communications,
transformative and unskilled/personal service occupations are, respectively,
Cz$725, Cz$2,657 and Cz$3,069 less than that of clerical occupations.
The R in the regression models show the amount of variation in
income each independent variable explains. Age explains 1.61%, education
13.75%, residence .04%, the color variables .25% and the occupational
variables explain 5.35% of the total variation in income. Therefore, we can
conclude that of the independent variables examined here, education is the

229
most important factor in determining women's income, occupation is the
second and age is the third important factor. The effects of residence and skin
color are minimal after the control of the other variables.

CHAPTER 8
SUMMARY AND CONCLUSION
Japanese immigrants began to arrive in Brazil as the turn of the
century and continued to come in sizable numbers through the 1960s, except
for the ten-year pause from 1942-1952. The majority of the Japanese
immigrants were farmers and started as colonos on coffee plantations in the
state of Sao Paulo. By the late 1950s, they rose as a group from the lowest and
least privileged status of colonos to middle class status through their hard
work.
The experience of the Japanese Brazilians from the late 1950s to 1980 is
proof of their continued success in upward social mobility. As I have shown
throughout this study, Asian Brazilians (the majority of whom are of
Japanese descent) fare better than do whites and Afro-Brazilians in all key
social indicators examined here.
The fertility level of a population (or a subgroup) in modern societies is
usually associated with its overall well-being. The data show that the fertility
level (mean number of children) of Asian Brazilian women is 1.44, compared
to 1.82 and 2.18 for white and Afro-Brazilian women. Controlling for age,
Asians, still have the lowest fertility level at all age levels. When income and
age, residence and age, and residence and income are controlled, Asian
Brazilians continue to have the lowest fertility level. However, when both
educational level and age are controlled, Afro-Brazilians have the lowest
fertility level at all levels except for the level of no schooling. On the one
230

231
hand, this is the result of the higher fertility level of Afro-Brazilian women
with no schooling (over 20% of the total). On the other hand, this indicates
the importance of education in reducing fertility for Afro-Brazilian women.
The multivariate regression models show that age and residence
account for 36.7% and education and income account for 6.8% of the total
variance in fertility, while the dummy variables for color account for only
0.97% of the total variance in fertility. All the independent variables together
(age, residence, education, income and color) explain 43.6% of the total
variation in fertility. Controlling for the other variables, the effects of the
independent variables on the fertility level of Brazilian women are as
follows: a one-year increase in age results in an increase of .1317 in mean
number of children; residing in rural areas (as opposed to urban areas) results
in an increase of .3793; a one-year increase in education results in a decrease of
.2720; a one-unit increase (Cz$4,150) in mean monthly income results in a
decrease of .0992; being Afro-Brazilian (as opposed to being white) results in
an increase of .1827, while being Asian (as opposed being white) results in a
decrease of .2476.
The regression models also show the differential effects of education
and income on the three color groups. Education has greater negative effect
on the fertility level of whites than that of Afro-Brazilians and Asians, and
income has greater negative effect on the fertility level of Afro-Brazilians
than that of whites and Asians. Specifically, a one-year increase in education
reduces the mean number of children by .2795 for whites, compared with
.2272 for Afro-Brazilians and .2080 for Asians, and a one-unit increase in
mean monthly income reduces the mean number of children by .2771 for
Afro-Brazilians, compared to .0851 for whites and .0809 for Asians.

232
The estimates based on the Brass Method show that the three color
groups have different child mortality measures. The mortality estimates
among Afro-Brazilian, white and Asian children are, respectively, 116, 82 and
51 per thousand by age two, 126, 87 and 54 per thousand by age three, and 134,
93 and 56 per thousand by age five. Based on the child mortality levels,
Asians have an life expectancy of 72.12 years, compared to an life expectancy
of 65.77 years for whites and 59.14 years for Afro-Brazilians. In other words,
Asians are expected to live 6.35 more years than whites, who are, in turn,
expected to live 6.63 more years than Afro-Brazilians.
The Tobit Regression analysis provides us with a deeper understanding
of the relationship between child mortality and the social indicators, and
between child mortality and skin color. The variables indicating
socioeconomic status are all negatively correlated mortality ratio, with piped
water having the greatest negative impact (-0.352) and household income
having the least negative impact (-0.072). The differential effects of some of
the variables are due to differences in scale of the variables; e.g., household
income is an interval variable, while piped water is a nominal one. Of
particular interest to us is the relationship of skin color to mortality. The
Tobit regression results show that after controlling for the variables of social
indicators, being Afro-Brazilian increases the probability of death by 24.2%
while being Asian reduces the probability of death by 57.8%, compared to
being white. These differences are significant, although there may be other
structural and cultural factors at work.
In this study, I have measured educational attainment from three
aspects; the school attendance rate for children ages 6-16, the mean years of
schooling for men ages 18-65 and women age 18-65. There are huge
differences in these three aspects among the three color groups.

233
The school attendance rate for children ages 6-16 differ greatly by color
group: 88.7% of Asian children are in school, while 73.6% of white children
and 64.7% of Afro-Brazilian children are in school. There are also marked
differences in school attendance rate by age, income level, residence and
parental education. Controlling for age, income, residence and parental
education, Asian children still do much better than the other two groups,
suggesting indirectly that Asians put more emphasis on education than do
the other two groups.
The results of logistic regression analyses show quantitatively the
effects of the independent variables on the in-school rate of children ages 6-16.
In general, father's and mother's education and household income have
about the same positive effects across ages on whether or not a child is in
school. For example, a one-unit increase in these variables increase the odds
of being in school by 10-15% in most cases. Urban residency has an
increasingly more positive effect on in-school rate, except at age 6. In fact, the
odds of being in school for urban children (compared to rural children)
increase by more than 100% between ages 11 and 16. The logistic regression
analyses also indicate that being Afro-Brazilian, compared to being white,
reduces the odds of being in school by 14-32%, except at age 12, while being
Asian, compared to being white, increases the odds of being in school by 140-
470% for most ages. However, the significance level (p-value) for the
coefficients of the dummy variables for Afro-Brazilian at age 12 and for
Asians at ages 6, 8,10 and 11 are above .05, indicating that they are not
significantly different from whites at these ages in school attendance rate.
The educational attainment of men ages 18-65 vary a great deal by color
group, as well as by age group, residence and income level: Asian men, on
average, have 7.44 years of schooling, compared to 5.3 years for whites and 3.5

234
years for Afro-Brazilians. The educational differences among the three color
groups do not reduce in most cases, after controlling for age or residence.
This indicates that the color differences in education are related to factors
other than the differences in age and residence of these groups. However,
when income is controlled, the color differences in education become bigger
at the levels of below two minimum wages and smaller at the levels of above
two minimum wages. This suggests that income above two minimum wages
has positive effect on education while lower income does not. The bigger
differences among the color groups at lower levels of income are more than
likely related to factors other than income.
The educational attainment of women ages 18-65 vary by color, as well
as by age group, residence and income: The mean years of schooling of Asian
women is 6.65, compared to a mean of 4.90 years for whites and a mean of 3.23
years for Afro-Brazilians. Although the educational level of women as a
whole is lower than that of men and the same holds true within the
respective color groups, women outperform men in a few categories. For
instance, women age 18-25, on average, have 6.05 years of schooling,
compared to a mean of 5.95 years for men of the same age group. Also, the
mean years of schooling of women with a mean monthly income of at least
one minimum wage exceed those of men. Unfortunately, over two thirds of
women have mean incomes of less than one minimum wage.
When age group or residence is controlled, the color differences in
education remain about the same, i.e., the educational level of Asian women
is higher than that of white women, who in turn have a higher educational
level than Afro-Brazilian women. This suggests that the differences in age
and residence are not causal factors for the color differences in education.
However, when income is controlled, the educational differences among the

235
three groups reduce considerably in most cases. In particular, at the highest
income level of above three minimum wages, Asian and white women have
very similar levels of schooling (9.46 years for Asians and 9.08 years for
whites), and the ratio between Asian and Afro-Brazilian women reduce to
1.31 from 2.06 before the control of income. This indicates a stronger
correlation between education and income for women than for men in
Brazil.
The occupational profile of men ages 18-65 varies a great deal by color,
as well as by age group, residence, income and educational levels. Fifty-two
percent of Asian men have white collar occupations, compared with 36.9% of
whites and 16.3% of Afro-Brazilians. More importantly, nearly 39% Asian
men have either managerial/administrative or professional/technical
occupations, as opposed to about 22% of whites and about 7% of Afro-
Brazilians. The distribution of white vs. blue collar occupations is very
similar for the three age groups. As expected, the percentage of white collar
occupations is much higher for urban residents (35.8%) than for their rural
counterparts (10.3%), and it increases from lower to higher income and
educational levels. For instance, the percentages of white collar occupations
for men from the lowest to the highest income levels are, respectively, 9.7%,
16.3%, 20.9% and 50.1%, and those for men from the lowest to highest
educational levels are 6.2%, 16.4%, 40%, 72% and 93.7%, respectively.
The occupational differences among the three color groups remain
much the same, controlling for age group or residence, i.e., Asian men are
over represented in white collar occupations than are whites, who, in turn,
do better than Afro-Brazilians. As expected, the occupational differences
among the three color groups decrease considerably when income is
controlled. However, the rates of decrease vary with color groups and income

236
levels. For instance, after the control of income, the difference between
Asians and whites in the proportion of blue collar occupations are smaller at
the two ends of income level than it is at the middle two income levels.
Meanwhile, the difference between Asians and Afro-Brazilians in the
proportion of blue collar occupations is the smallest at the lowest income
level, gets increasingly bigger at the two middle levels, and finally becomes
the same as before it is the control of income.
When education is controlled, the occupational differences among the
three color groups reduce considerably, particularly at higher levels of
education. This is particularly obvious in the distribution of white collar
occupations for Asians and whites. At the levels of 5-8 and 9-11 years of
schooling, Asians and whites have similar percentages of
managerial/administrative and professional/technical occupations.
Meanwhile, the gap between Asians and Afro-Brazilians in most
occupational categories reduces considerably; 36.2% of Asians vs. 39.4% of
Afro-Brazilians have blue collar occupations at the level of 9-11 years of
schooling and 9.5% of both groups have blue collar occupations at the level of
12 or more years of schooling.
The occupational distribution of women as a whole differs from that of
men in several respects. First, only about 35% of women in the sample have
occupations listed in the census. Second, proportionally more women than
men have clerical and professional/technical occupations, and consequently
fewer women have blue collar occupations. Third, the percentage of
unskilled/personal service occupations is much higher for women than it is
for men because the other two categories of blue collar occupations are far less
popular among women.

237
Despite the above differences, women share the basic patterns in the
occupational distribution of men, i.e., there are differences by color, age group,
residence, income and educational levels. Asian women, as their men
counterparts, have the highest percentage of white collar occupations, 65.9%,
compared to 50.8% of white women and 19.9% of Afro-Brazilian women.
Unlike men's data, there are considerable differences among women of the
three age groups in the percentage of blue collar occupations; 50.1% of women
ages 18-25, 55.9% of women ages 26-39 and 68.9% of women ages 40-65 have
blue collar occupations. As expected, the percentage of blue collar occupations
is much lower for urban women (54.9%) than for rural women (87.7%), and it
decreases sharply with the increase of income and educational levels. For
instance, the percentages of blue collar occupations for women from the
lowest to the highest income levels are, respectively, 88%, 68.9%, 37.8% and
14.4% and those for women from the lowest to the highest educational levels
are 98%, 85.3%, 50%, 10.6% and 1.9%, respectively.
When age group or residence is controlled, the color differences in
occupational distribution have very little changes. This indicates that the
color differences in occupational distribution are not due to the variations in
the age structures or residential locations of these groups. However, the
occupational differences among the three color groups reduce considerably,
when income is controlled, especially at higher levels of income. Although
Asians have the highest percentage of white collar occupations at the first
three income levels, whites surpass them at the highest income level (above
three minimum wages). The gaps among the three color groups are much
smaller at the highest income level as well. For example, the percentages of
managerial/administrative occupations for Asians and whites are the same

238
(13.4%), and the percentages of clerical occupations for all three groups are
very close (34.9% for Asians, 33.6% for whites and 31.8% for Afro-Brazilians).
The occupational differences among the three color groups reduce
considerably, when educational level is controlled, especially at higher levels
of education. At the levels of less than 4 years of schooling, Asian women
have the highest percentage of white collar occupations, but whites have the
highest percentage of white collar occupations above 4 years of schooling.
Nonetheless, Asians lead the other two group in the proportion of
managerial/administrative occupations at all levels, except at the level of 12
or more year of schooling, where whites have the highest percentage.
Interestingly, at the levels of 1-4, 5-8 and 9-11 years of schooling, Afro-
Brazilians have a higher percentage of professional/technical occupations
than do Asians. While whites lead the other two groups in every category of
white collar occupation at the highest educational level, Asian and Afro-
Brazilians have a very similar distribution of white collar occupations; 7.3%
of both group have managerial/administrative occupations, 49.3% of Asians
vs. 47.2% of Afro-Brazilians have professional/technical occupations, and
39.8% of Asians vs. 42.7% of Afro-Brazilians have clerical occupations.
The mean monthly income of men ages 18-65 in metropolitan Sao
Paulo, Brazil varies by color, age, residence, education and occupation. The
mean monthly income of Asians (Cz$35,492) is 1.5 times that of whites
(Cz$21,lll) and more than 3 times that of Afro-Brazilians (Cz$10,775). As
expected, older people have higher mean incomes than younger people,
urban residents have higher mean incomes than rural residents, people with
more years of schooling have higher mean incomes than people with fewer
years of schooling, and people with white collar occupations have higher
mean incomes than people with blue collar occupations.

239
When age group is controlled, the income differences among the three
color groups reduce substantially for the age group of 18-25, but increase for
the other age groups. This is mainly because most people ages 18-25 have
low-paying entry level jobs and the range of income difference for these jobs
is usually small. The income differences among the three color groups are
the biggest for the age group of 26-39; the mean monthly incomes of Asians,
whites and Afro-Brazilians are, respectively, Cz$42,238, Cz$23,826 and
Cz$12,543. Nevertheless, Asians have the highest mean income in each age
group. The income differences among the color groups reduce slightly in
urban areas, but increase substantially in rural areas, when residence is
controlled. Asians still maintain a significant lead in both urban and rural
areas.
Interestingly, when education is controlled, the income differences
between Asians and the other two groups are wider at lower levels of
education (below 8 years of schooling), but decrease considerably at higher
levels. For example, Asian and whites have comparable incomes at the level
of 9-11 years of schooling (Cz$27,649 for Asians and Cz$26,984 for whites), and
whites have a higher mean income than do Asians at 12 or more years of
schooling (Cz$59,972 for whites and Cz$53,573 for Asians). Although the
income gap between Asians and Afro-Brazilians are also smaller at levels of
more than 9 years of schooling than it is before the control of education, the
mean income of Afro-Brazilians is still around 60% of those of whites and
Asians.
When occupation is controlled, the income differences Asians and the
other two groups reduce considerably, except in unskilled/personal service
occupations, where the mean income of Asians is substantially higher than
that of the other two. In general, the income difference between Asians and

240
whites is smaller in white collar occupations and the income difference
between Asians and Afro-Brazilians is smaller in blue collar occupations.
Still, Asians maintain the highest mean income in every occupational
category.
The results of the regression analyses show that of the independent
variables examined here, education explains the most, 9.31%, of the total
variation in men's income, followed by occupation (4.96%) age (2.34%), color
(.21%) and residence (.02%). Specifically, controlling for the other variables, a
one-year increase in age results in an increases of Cz$638, a one-year increase
in schooling amounts to an increase of Cz$4,429, and residing in rural areas
(as opposed to urban areas) results in a decrease of Cz$2,572 in mean income.
Other things being equal, being Afro-Brazilian (as opposed to being white)
reduces one's mean income by Cz$2,277, and being Asian (as opposed to being
white) increases one's mean income by Cz$6,459. In other words, even after
controlling for the other independent variables, the three color groups still
have significantly different mean incomes. There may be some other factors
contributing to the color differences in income, such as total hours worked.
The mean monthly income of women ages 18-65 in metropolitan Sao
Paulo, Brazil is Cz$4,461, which is less than one fourth of the average
monthly income of men (Cz$19,047). Nonetheless, there are similar income
differences by color, age, residence, education and occupation among women
as well. Asian women have the highest mean income (Cz$7,261), which is
more than 1.5 times that of white women and about 2.4 times that of Afro-
Brazilian women. The income differences of women by age group are very
small, mainly due to their extremely low average income, but the income
difference between urban and rural women is quite big (Cz$4,797 for urban

241
women vs. Cz$l,082 for rural women) probably because the majority of rural
women have no income of their own.
The income differences of women by education are very small at the
levels of below 9 years of schooling, but they are extremely great at higher
levels. The mean income of women with 9-11 years of schooling (Cz$8,657) is
almost twice that of those with 5-8 years of schooling, and the mean income
of women with 12 or more years of schooling (Cz$l 7,635) is more than 2 times
that of those with 9-11 years of schooling. The income differences of women
by occupation are quite obvious and show three distinct levels; the top level
(managerial/administrative and professional/technical occupation) with a
mean income of 2-3 times that of the sample mean, the middle level (clerical
and transportation/communications occupations) with a mean income about
the sample mean, and the bottom level (transformative and
unskilled/personal service occupations) with a mean income far below the
sample mean.
When age or residence is controlled, the income differences among the
three color groups either increase or decrease a little, but not much, indicating
the minimal role of age and residence in the income variations of the color
groups. On the other hand, when education is controlled, the income
differences among the color groups reduce significantly, especially at higher
levels of education. For example, the mean incomes of Asians and whites at
9-11 years of schooling are very close (Cz$8,283 for Asians and Cz$8,887 for
whites), and the three color groups have similar mean incomes at the level of
12 or more years of schooling; Cz$17,788 for Asians, Cz$17,728 for whites and
Cz$16,228 for Afro-Brazilians. The income differences among the three color
groups are very small, when occupation is controlled. However, the mean
income of Asian women is higher than that of white women in most

242
categories, and the mean income of Afro-Brazilians continue to fall behind
those of Asians and whites in all categories.
The results of the regression analyses on women's income show that of
the independent variables examined here, education explains the most,
13.75%, of the variation in their income, followed by occupation (5.35%), age
(1.61%), color (.25%) and residence (.04%). Specifically, controlling for the
other variables, a one-year increase in age results in an increase of Cz$264, a
one-year increase in schooling amounts to an increase of Cz$l,730, and
residing in rural areas (as opposed to urban areas) results in a decrease of
Cz$l,301 in mean monthly income. Other things being equal, being Afro-
Brazilian (as opposed to being white) reduces one's mean income by Cz$899,
and being Asian (as opposed to being white) increases one's mean income by
Cz$2,214. This indicates that there are still significant income differences by
color, even after controlling for the other variables. However, it is possible
that some other variables that are not examined in this study are partly or
mainly responsible for the color differences in income. Thus, we need to
explore the issue in a broader context to find out the real causes of the color
differences.
The findings of this study show that Asian immigrants in Brazil have
experienced similar, if not more, success in upward social mobility as Asian
immigrants in the United States have. Then, we need to ask why Asian
immigrants in both the United States and Brazil have been so successful,
relative to other minority groups and even the majority whites. The
literature on the Asian experience in both countries suggests that they share
some common characteristics, which have contributed to their success.
First, Asian Brazilians and the more successful Asian groups, such
Japanese and Chinese, in the U.S., have had or still have their ethnic

243
enclaves. As discussed in Chapter 1, ethnic enclaves generally promote the
development of ethnic economies thereby benefiting all people within the
enclaves, although ethnic employers get a much bigger share of the profit.
Therefore, ethnic minorities usually benefit from the presence of ethnic
enclaves and ethnic economies in the long run because ethnic economies
counter "the hostility of the host society by creating economic opportunities
in family and other kin-based economic enterprises" (Hirschman and Wong,
1986:174).
Second, the ownership of small business is very important to the
success of many Asians in both Brazil and the U.S. In family-owned small
businesses, family members usually work as a unit, pool their resources
together so that they can minimize the cost and achieve higher returns on
their investment. Nee and Sanders (1985) noted that small business
ownership provided Asian Americans with ethnically controlled avenue of.
economic mobility.
Third, the success of Asian Americans can be attributed to certain
family characteristics associated with them, namely a high proportion of
families with both husband and wife, a high proportion of families with three
or more workers, and a low rate of family dissolution. According to the 1990
U.S. census, 82% of Asian families have both husband and wife, compared to
the national average of 79%, and 20% of Asian families have three or more
worker per family, compared to the national average of 13% (U.S. Bureau of
the Census, 1993). Similarly, more successful Asian groups have lower family
dissolution rates. For example, Japanese and Chinese American women had
lower family dissolution rates than did white women in 1979: For women
ages 25-64, 11.5% of native-born Chinese women and 8.5% of native-born
Japanese women were either divorced or separated, compared to 12.5% of

244
native-born white women; 4.2% of foreign-born Chinese women and 9.5% of
foreign-born Japanese women were either divorced or separated, compared to
9.6% of foreign-born white women (U.S. Commission of Civil Rights, 1988).
Similar family characteristics are also found among Asian Brazilians.
Fourth, both in Brazil and the U.S., Asian immigrants have invested
heavily on human capital, mainly in the form of education, and have
achieved tremendous success in educational attainment. Some attribute their
extraordinary educational achievement solely to their cultural values that
revere scholarship and learning, while others argue that Asians have
invested heavily in education because they have realized that education is the
main channel for social mobility for them, after experiencing considerable
occupational discrimination. As a result of their higher educational
attainment, they are over represented in white collar occupations, such as
managerial, professional, technical, sales and service, and hence have higher
average income. At the same time, we must note that most Asian Americans
have lower returns on their educational investment than do similarly
educated whites.
Finally, certain cultural values, such as hard work, industriousness,
emphasis on education, the obligation and loyalty to family and kin group,
sacrifice for children and delayed gratification, of Asian groups have also
contributed indirectly to their success. Without the persistent reinforcement
of these cultural values from generation to generation, Asian immigrants
and their descendants could not have achieved what they have achieved
today.
In sum, the success of Asian immigrants in both Brazil and the United
States are related to factors at the community, household and individual
levels. At the community level, ethnic enclaves have provided a favorable

245
atmosphere not only for ethnic employers and entrepreneurs to develop
ethnic economies and to make profits, but also for ethnic workers, especially
new comers, to obtain employment, overcome their language barriers and
cultural shock, and to gradually secure means of social mobility. At the
household level, the ownership of small business and family members
working together in these small businesses are crucial elements to the success
of Asian groups. Also important are a higher proportion of families with
married couples and having more family members in the labor force. It is
very difficult to have a high proportion of families with married couples and
have more persons working within a family without the loyalty and
obligation to family and sacrifice for children. At the individual level, higher
educational attainment, professional and technological skills associated with
many foreign-born Asian immigrants in recent years, and adherence to the
cultural values of hard work, industriousness and delayed gratification are
vital to the upward social mobility of Asian groups.
While we focus on the success of Asian immigrants in both Brazil and
the U.S., we must be aware that the stereotype of Asian Americans as "model
minority" is partly based on misleading statistics and does not fit many
groups within the Asian American population. Furthermore, it has negative
consequences with regard to race relations (Commission on Civil Rights,
1992): 1) It may lead people to ignore the real social and economic problems
of many Asian groups who are less successful; 2) it diverts public attention
from the existence of discrimination against more successful Asian
Americans (e.g., "glass ceiling" in employment and discriminatory
admissions policies in colleges and universities); 3) it puts undue pressure on
young Asian Americans to succeed in school and in their careers; 4) it may be
used to discredit other minorities for failing to succeed.

246
Based on the historical experience of Asian Brazilians and the
assessment of their socioeconomic status in 1980, relative to whites and Afro-
Brazilians, I expect Asian Brazilians to have achieved further improvements
in socioeconomic standing in 1990s. The up-coming 1990 Brazilian census
will allow us to test this hypothesis, and examine the changes, if any, in the
relationships among the various color groups in Brazil. Meanwhile, there is
also a great need for more cross-cultural studies, both quantitative and
qualitative, on the present conditions of certain Asian immigrant
populations (e.g., Japanese and Chinese) throughout the world to see if they
have achieved similar successes as their counterparts in Brazil and the United
States have. If so, we then need to examine the factors that are associated
with their success, and compare them to what we already know from the
literature so that we will have a better understanding of why certain
ethnic/racial minorities can achieve greater level of success in modern
societies.

APPENDIX A
BRAZILIAN RACIAL CATEGORIES AND THE CENSUSES
Researchers on Brazilian race relations have noted that Brazilians use
hundreds of racial terms to identify themselves (Degler 1971; Harris 1964a;
Harris 1970; Harris et al. 1993; Harris and Kottak 1963; Sanjak 1971). For
example, Harris (1964a) elicited forty different racial types, using a set of
portraits with different skin tones, hair textures, and nasal and lip widths.
The respondents in the 1976 National Household Survey (Pesquisa Nacional
de Amostragem por Domicilios, PNAD) provided nearly 200 racial terms in
the open-ended question on race (Andrews 1991). Recently, Kottak (1992)
reported that his respondents in a national survey of racial classification used
a total of 36 terms to describe racial categories.
By contrast, the Brazilian censuses of 1940,1950 and 1980 offered
respondents just four categories—white (branco), mulatto (pardo), black (preo)
and yellow (amarelo)—to identify themselves racially. Obviously, the census
has greatly simplified the complex system of racial classification used by
Brazilian people, and there has been a debate on whether the census
categories accurately reflect people's self-perception of their race, or more
accurately, the major distinctions among the many racial terms. The crucial
question is the extent to which the census scheme departs from people's self¬
classification if they are allowed other options.
Of course, it is impossible to include hundreds of racial terms in a
census, but the main focus of the recent debate is on whether pardo, a term
used in the censuses, is more appropriate or salient than moreno, which has
247

248
not been used in the censuses, for people who are neither white (branco) nor
black (preto). Harris et al. (1993) and (Kottak, 1992) argue that moreno is more
salient than pardo, and that the use of pardo in the censuses has distorted the
data on the racial composition of Brazilians. Using pardo, they say, produces
overestimates of the number of whites and blacks and underestimates of the
number of people who belong to the intermediate categories between white
and black.
The 1976 National Household Survey (Pesquisa Nacional de
Amostragem por Domicilios, PNAD) addressed this issue. The survey
included two items on race; the first was an open-ended item which
permitted respondents to use whatever term they wished and the second was
the standard fourfold classification. Soares and Silva (1987) reported the
result of the cross-classification of the racial terms by free and forced choice in
the survey: 96.7% of those who identified as branco in the free choice chose
branco under the forced option; 94.0% of those who identified themselves as
pardo in the free choice chose pardo under the forced option; 89.3% of those
who identified themselves as preto chose preto under the forced option. In
addition, almost 40% of the respondents used three other racial labels for self-
identification, claro (light-skinned), moreno claro (tan), and moreno (brown).
Of the three labels, moreno (brown) accounted for 86.7 percent, and 66.1% of
those who identified themselves as moreno chose pardo under the forced
choice. Soares and Silva summarized the findings as follows:
But just 7 (terms) out of the 190 accounted for 95 percent of all
answers. These seven included the four standard labels in the
precoded question plus claro (light-skinned), moreno (brown),
and moreno claro ( light brown or tan). Moreover, two of the
designation, branco and moreno, accounted for 76 percent of all
answers. (1987:167)

249
Based on these observations, Soares and Silva argued that most of the racial
labels used by Brazilians "do not amount to a social phenomenon with
possible political significance" (1987:168) because they are either statistically
insignificant or derivations of individual idiosyncrasies.
Wood and Lovell (1992:708) also commented on the results of the 1976
National Household Survey:
Analyses of the open-ended item showed that, despite the wide
range of terms, the four categories (white, black, mulatto and
yellow) accounted for about 57.1 percent of the responses. Three
additional classifications (which range from light to dark brown)
proved to be important: clara (2.5 percent); morena clara (2.8
percent); and morena (34.4 percent). Further analyses found that
nearly all of the people who declared themselves morena in the
open-ended question properly classified themselves as pardos
when confronted with the pre-coded options. The four-category
scheme thus accounted for approximately 95 percent of all
responses (Oliveira, Porcaro and Costa 1981). Analysts in the
Census Bureau concluded that the forced-choice method,
although not perfect, was sufficiently reliable to be used in the
1980 enumeration.
Recently, Harris et al. (1993) tested the census assumption that pardo
and moreno are more or less equivalent. Using a sample of 253 people from
the town of Rio de Contas, they asked respondents to identify themselves
racially first by free choice and then by two sets of forced racial categories. One
of the sets was the "pardo option" (the four-category scheme of the Brazilian
census) and the other was the "moreno option" (also a four-category scheme
where pardo is replaced by moreno). Harris et al. found that when people
were given the forced choices, they were twice as likely to classify themselves
as moreno than as pardo. Furthermore, only 37.4% of those who identified
themselves as moreno in the moreno option shifted to pardo, when they
were given the pardo option, and 31.3% shifted to branca and 27.8% shifted to

250
preto. Using the asymptotic approximation technique known as the delta
method (Agresti 1990), they concluded that they can be 95% confident that
with the moreno option, randomly selected respondents are between 25.64%
and 59.56% less likely to self identify as branco as opposed to with the pardo
option. They also concluded that they can be 95% confident that respondents
chosen at random, when given the moreno option, are between 34.08% and
68.92% less likely to identify as preto then are those presented with the pardo
option (Harris et al. 1993). In other words, the number of whites and blacks
falls dramatically (40-50% on average), if the moreno option is used instead of
the pardo option.
If the above conclusions were correct at the national level, there would
be dramatic changes in the racial composition of Brazil. In fact, Harris et al.
say that "there is no reason to believe that similar confusion does not reign
for the nation as a whole" (1993:459). Unfortunately, there has been no such
study at the national level that directly addresses the problem of pardo vs.
moreno in the census and its impact on not only the racial composition of
Brazil as a whole, but also race relations and studies on racial inequalities.
Therefore, the conclusions of the study by Harris et al. (1993), though
important, remains to be tested at the national level before it can be accepted.
Another way of approaching this problem is to examine the stability of
self-identification over time. Although direct estimates of the magnitude of
color reclassification do not exist, one study devised an indirect technique to
address this topic. Following the same logic that demographers use to study
net migration, Wood (1990) took the number of men and women aged 10 to
29 years in 1950 (in standard five-years categories) and applied survival rates
derived from a series of race-specific life tables to project the size of each
cohort 30 years ahead. The projections, by sex and age, produced estimates of

251
the number of people expected in each color category in 1980 if no
reclassification occurred during the period. The difference between the size of
the projected population and the actual number enumerated in the 1980
census thus provided a crude estimate of the degree to which people
"migrated" from one color classification to another.
The findings showed that around 38% of the men and women who
declared themselves black in 1950 changed their identity to mulatto in 1980.
Among mulattoes, the actual population exceeded the projected number by
around 36 percent as a consequence of the "migration" of blacks into the
mulatto category. For whites, on the other hand, the actual size of population
was very similar to the projected size, suggesting little movement in or out of
that color classification (Wood 1990). These findings suggest that the "black"
self-designation has been unstable over time, and especially subject to the
circularity between color self-identification and socioeconomic standing. On
the other hand, the "mulatto" designation was far more stable and less subject
to the circularity bias. Based on these observations, Wood (1990) suggested
that there were compelling reasons to collapse black and mulatto into a single
nonwhite category when we use Brazilian census data to study racial
inequality.
A number of studies on racial inequalities in Brazil (Hasenbalg 1985;
Hasenbalg and Huntington 1982; Lovell 1989; Silva 1978 and 1985; Wood 1990;
Wood and Carvalho 1988; Wood and Lovell 1989 and 1992) adopted the
dichotomous system of racial classification, whites vs. nonwhites, in
analyzing the Brazilian race relations. Wood and Carvalho explained both
the practical and substantive reasons for collapsing the "black" and "brown"
into a single "nonwhite" category:

In practical terms, the number of blacks is very small, a factor
that limits our ability to crossclassify the data in meaningful
ways. Moreover, the substantive findings of research by Silva
(1985) and Hasenbalg (1985) show little differences between the
two, thus allowing us to treat them as a single group. (1988:270)
Silva (1978) analyzed the income differentials between whites and
non whites, using the 1960 Brazilian Census and Silva (1985) did a follow-up
study on the same topic, using the 1976 National Household Survey. Both
studies concluded that blacks and mulattos seemed to display strikingly
similar profiles and there were substantial differences in economic
attainment between whites and non whites. Hasenbalg (1985) also used the
1976 National Household Survey to examine the structural relations and
unequal exchange between whites and nonwhites. On the relationship
among the racial groups, Hasenbalg maintained that "the mulatto group
occupies an intermediate position between blacks and whites in all the
dimensions considered, although its position is always closer to the black
than to the white group" (1985:28). After examining the geographic
distribution, literacy rate, educational level, distribution in the economic
sector, mean monthly income, intergenerational occupational mobility,
occupational distribution by educational level and income returns on
education by race, Hasenbalg concluded that
nonwhites are exposed to a cycle of cumulative disadvantage in
terms of intergenerational social mobility and the process of
status attainment. To be born nonwhite in Brazil usually means
to be born into low-status families. The chances of escaping from
the disabilities inherent in a low social position are considerably
smaller for nonwhites than for whites of the same background.
As compare to whites, nonwhites suffer a competitive
disadvantage in all phases of the process of intergenerational
transmission of social inequalities. (1985:40)

253
The findings of Lovell's research (1989) on racial inequalities and the
Brazilian labor market were consistent with the findings by Silva (1978 and
1985) and Hasenbalg (1985); i.e., although there were differences between
blacks and mulattos in the labor market, the major dividing line fell between
whites and non whites.
Based on the empirical findings in the above studies and the main
focus of my dissertation, which is Asian Brazilians, I adopt the three-way
classification scheme. In other words, the categories of white and yellow
remain the same, except that I use the label of Asian Brazilian for yellow, and
brown (pardo) and black (preto) Brazilians are combined into a single category
of Afro-Brazilians. I could have compared Asian Brazilians to the rest of the
population as a whole or to all of the racial categories used in the census. In
my view, though, the position of Asians in Brazilian demography is most
clearly shown by comparing them to whites and nonwhites in general.

APPENDIX B
INDIRECT MEASURES OF CHILD MORTALITY
The following are from Wood and Lovell (1992:710-711).
The Brass Method
The equations for the Brass method are of the form:
q(a) = d(j) • G(j)
where
q(a) = the probability that a child will die before age a,
d(j) = the proportion dead among children ever born to women in age
category j
(where j signifies age groups 15-19, 20-24, 25-29, 30-34), and
G(j) = multiplier corresponding to women in age category j.
The multiplier G(j) adjusts for the age pattern of fertility. This is
necessary because the age pattern of childbearing determines the distribution
of the children of a group of women by length of exposure to the risk of dying.
The younger the onset of childbearing, the older the average age of children
born to women in age j. In the Brass method, the multiplier is selected
according to the values of P(l)/P(2), where P(l) is the average parity (average
number of children ever born) reported by women in ages 15-19 and P(2) is
the average parity reported by women in ages 20-24. Estimates of q(l) are
generally inaccurate and are therefore discarded. The estimates used here are
for q(2), q(3) and q(5) that correspond to the child mortality experience of
254

255
women aged 20-24, 25-29 and 30-34, respectively. Subsequent extensions of
the method, such as the one proposed by Trussell (1975) rely on the same
relationships that Brass identified and used to such good effect.
The Trussel and Preston Technique
Trussell and Preston (1982) further suggested a method for analyzing
mortality differentials that, instead of translating survival ratios into rates for
groups of women, adjusts the ratio of dead children to the number ever born
for each woman. The goal of the Trussell-Preston procedure was to construct
an index of child mortality which could be treated as the dependent variable
using multivariate statistical techniques. We refer to the index as the child
mortality ratio because it is the ratio of observed to expected deaths. The
index of child mortality for woman i in age category j, Mij, is thus:
Di
M ij=
Ni • EPDj
where
Di = number of dead children for woman i
Ni = number of births to woman i
EPDj= expected proportion of dead children for a woman in age category j
(in this case, 20-24 and 25-29).
To derive EPDj, Brass's mortality estimation procedure, q(a)=d(j)G(j), is
inverted, yielding: EPDj=q(a)/G(j). To apply the Brass equation to the problem
at hand, Trussell and Preston imposed a "standard" mortality function qs (a)
(in this study, from the "South" model in the Coale-Demeny family of life

256
tables) and converted the standard into the expected proportion dead by
rewriting the previous equation as EPDj= qs (a)/G(j).
In the Trussell/Preston method, the multipliers, G(j) are estimated
from a regression involving average parities (Pj) for all women in age groups
15-19 (PI), 20-24 (P2), 25-29 (P3). The constant term, a(j), and the regression
coefficients, b(j) and c(j), are taken from the National Academy of Sciences
Manual on Demographic Techniques (NAS 1981) (again from the "South"
model values).
The probability that a child will die is partly a function of how long he
or she has been exposed to the risk of death. But it is unappealing to insert an
additive term into a regression analysis. Such a procedure implies that the
duration effect acts independently of the effects of the covariates on the
cumulative chances of death. A more reasonable assumption is that the
effects of being located in an unfavorable environment tend to accumulate
the longer the child is exposed, suggesting an interaction between duration of
exposure and other covariates. The method Trussell and Preston (1982)
developed offers a procedure for estimating the covariates of child morality by
accounting for these interactions in the estimation of the child mortality
ratio, a dependent variable amenable to regression analysis.
The Pj/Pj+1 ratios account for differences in the duration of exposure
to death by adjusting the equation by the timing of childbearing, as follows:
G(j)= a(j) + b(j) (P(l)/P(2) + c(j) (P(l)/P(2)
The final form of the child mortality index for woman i in age category
j is thus:
Di
Mij =
Ni • qg(a)/G(j)

257
Because there are no negative values for the ratio, the mortality index
is truncated on the left at zero. Moreover, the value of M will be zero for the
majority of women because relatively few of them experience the death of
one or more children. Among those women who do experience child
mortality, there will be wide variability in the size of M. Under these
circumstances, ordinary least squares is inappropriate for estimating the
covariates of child mortality (see Dhrymes 1986; Maddala 1985). Tobin (1958)
first considered the problem, and proposed an iterative solution of the
maximum likelihood equations. The alternative method, which is a hybrid
of probit and ordinary least squares analysis, has come to be called the Tobit
model (from Tobin's probit). Because Tobit estimates are standardized by the
probability of observing a zero in the dependent variable, it is particularly
applicable to the mortality ratio due to the high proportion of women in our
sample who experienced no child mortality.

APPENDIX C
LOGISTIC REGRESSION WITH SCHOOL ATTENDANCE
OF CHILDREN AGES 6-16 AS THE DEPENDENT VARIABLE,
METROPOLITAN SÁO PAULO, BRAZIL, 1980
Age 6
-2 log likelihood
Chi-Square
10400.236
DF
13821
Significance
.0000
Model chi-square
291.263
6
.0000
Improvement
291.263
6
.0000
Goodness of fit
13832.428
13821
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.0553
.0111
24.9310
1
.0000
.0463
1.0568
Mothers' Ed
.0247
.0116
4.5613
1
.0327
.0155
1.0250
Income
.0287
.0056
26.1463
1
.0000
.0475
1.0291
Urban
-.2393
.0761
9.8999
1
.0017
-.0272
.7872
Afro-Brazilian
-.2192
.0682
10.3249
1
.0013
-.0279
.8032
Asian
.2681
.1699
2.4897
1
.1146
.0068
1.3075
Constant
-2.1833
.0727
902.5747
1
.0000
Age ,7
-2 log likelihood
Chi-Square
14867.199
DF
13924
Significance
.0000
Model chi-square
1967.561
6
.0000
Improvement
1967.561
6
.0000
Goodness of fit
14168.750
13924
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.1118
.1288
114.0737
1
.0000
.0816
1.1183
Mothers' Ed
.1288
.0110
136.5178
1
.0000
.0894
1.1374
Income
.0934
.0086
117.5861
1
.0000
0829
1.0979
Urban-
.3286
.0519
40.0840
1
.0000
.0476
1.3890
Afro-Brazilian
-.3492
.0438
63.6909
1
.0000
-.0605
.7052
Asian
1.3957
.3373
17.1202
1
.0000
.0300
4.0379
Constant
-.4369
.0530
67.9134
1
.0000
258

259
Age 8
Chi-Square
DF
Sienificance
-2 log likelihood
8702.992
13529
.0000
Model chi-square
1360.146
6
.0000
Improvement
1360.146
6
.0000
Goodness of fit
20201.435
13529
.0000
Independent
Variable
B
S.E.
Wald
DF
Sie.
R
Exp (B)
Fathers' Ed
.1427
.0160
79.6550
1
.0000
.0878
1.1533
Mothers' Ed
.1849
.0169
120.2116
1
.0000
.1084
1.2031
Income
.1175
.0148
63.1307
1
.0000
.0779
1.1247
Urban
.5558
.0643
74.6221
1
.0000
.0850
1.7432
Afro-Brazilian
-.3914
.0585
44.7251
1
.0000
-.0652
.6761
Asian
.2102
.3779
.3093
1
.5781
.0000
1.2339
Constant
.3316
.0684
23.5277
1
.0000
Age 9
Chi-Square
DF
Significance
-2 log likelihood
6909.496
13362
.0000
Model chi-square
806.721
6
.0000
Improvement
806.721
6
.0000
Goodness of fit
19373.151
13362
.0000
Independent
Variable
B
S.E.
Wald
DF
Sie.
R
Ext) (B)
Fathers' Ed
.1034
.0185
31.2931
1
.0000
.0616
1.1089
Mothers' Ed
.1724
.0201
73.3429
1
.0000
.0962
1.1881
Income
.1108
.0172
41.6041
1
.0000
.0716
1.1171
Urban
.5777
.0737
61.4235
1
.0000
.0878
1.7819
Afro-Brazilian
-.3022
.0682
19.6400
1
.0000
-.0478
.7392
Asian
1.6686
.7908
4.4520
1
.0349
.0178
5.3046
Constant
.8853
.0769
132.6641
1
.0000

260
Age 10
Chi-Square
DF
Significance
-2 log .likelihood
6733.702
13312
.0000
Model chi-square
762.755
6
.0000
Improvement
762.755
6
.0000
Goodness of fit
18104.201
13312
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.1357
.0194
48.7864
1
.0000
.0790
1.1453
Mothers' Ed
.1641
.0207
62.8661
1
.0000
.0901
1.1783
Income
.0782
.0161
23.5439
1
.0000
.0536
1.0813
Urban
.6887
.0736
87.5281
1
.0000
.1068
1.9912
Afro-Brazilian
-.2531
.0701
13.0344
1
.0003
-.0384
.7764
Asian
.7809
.5246
2.2158
1
.1366
.0054
2.1835
Constant
.9265
.0757
149.6128
1
.0000
Age 11
Chi-Square
DF
Significance
-2 log likelihood
7260.873
12613
.0000
Model chi-square
967.419
6
.0000
Improvement
967.419
6
.0000
Goodness of fit
16656.678
12613
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exd (B)
Fathers' Ed
.1177
.0181
42.3795
1
.0000
.0701
1.1249
Mothers' Ed
.1204
.0192
39.5122
1
.0000
.0675
1.1279
Income
.1176
.0159
54.9827
1
.0000
.0802
1.1247
Urban
.9936
.0677
215.6211
1
.0000
.1611
2.7010
Afro-Brazilian
-.1530
.0673
5.1748
1
.0229
-.0196
.8581
Asian
.1220
.3313
.1357
1
.7126
.0000
1.1298
Constant
.4335
.0703
38.0297
1
.0000

261
Age 12
-2 log likelihood
Model chi-square
Improvement
Goodness of fit
Independent
Variable
Chi-Square
9429.900
1431.302
1431.302
19529.348
B S.E.
DF
12795
6
6
12795
Wald DF
Significance
.0000
.0000
.0000
.0000
Sie. R
Exp (B)
Fathers' Ed
.0992
.0152
42.6702
1
.0000
.0612
1.1043
Mothers' Ed
.1209
.0630
55.2613
1
.0000
.0700
1.1286
Income
.1354
.0350
100.4199
1
.0000
.0952
1.1450
Urban
1.0919
.0586
347.2457
1
.0000
.1783
2.9800
Afro-Brazilian
.0716
.0591
1.4675
1
.2257
.0000
1.0742
Asian
.8915
.3786
5.5432
1
.0186
.0181
2.4387
Constant
-.2162
.0621
12.1118
1
.0005
Age 13
Chi-Square
DF
Significance
-2 log likelihood
10715.350
12435
.0000
Model chi-square
1833.625
6
.0000
Improvement
1833.625
6
.0000
Goodness of fit
16299.478
12435
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.1253
.0136
85.1108
1
.0000
.0814
1.1335
Mothers' Ed
.1320
.0144
84.2709
1
.0000
.0810
1.1411
Income
.0932
.0103
81.7693
1
.0000
.0797
1.0977
Urban
1.2146
.0580
439.1935
1
.0000
.1867
3.3690
Afro-Brazilian
-.1491
.0536
7.7436
1
.0054
-.0214
.8614
Asian
1.0485
.3402
9.4960
1
.0021
.0244
2.8533
Constant
-.6378
.0604
111.5067
1
.0000

262
Age 14
Chi-Square
DF
Significance
-2 log likelihood
12810.458
12800
.0000
Model chi-square
2396.473
6
.0000
Improvement
2396.473
6
.0000
Goodness of fit
19299.507
12800
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.1260
.0120
111.0053
1
.0000
.0847
1.1343
Mothers' Ed
.1209
.0128
89.7313
1
.0000
.0760
1.1285
Income
.1167
.0089
173.4523
1
.0000
.1062
1.1238
Urban
1.1110
.0556
398.6568
1
.0000
.1615
3.0372
Afro-Brazilian
-.1900
.0489
15.1170
1
.0001
-.0294
.8270
Asian
1.5917
.3522
20.4185
1
.0000
.0348
4.9121
Constant
-1.1109
.0585
361.0516
1
.0000
Age 15
Chi-Square
DF
Significance
-2 log likelihood
14531.148
13174
.0000
Model-chi-square
2601.369
6
.0000
Improvement
2601.369
6
.0000
Goodness of fit
16809.739
13174
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.1251
.0107
137.9042
1
.0000
.0891
1.1333
Mothers' Ed
.1301
.0114
130.6135
1
.0000
.0866
1.1390
Income
.0844
.0069
148.8979
1
.0000
.0926
1.0881
Urban
1.0160
.0583
303.8221
1
.0000
.1327
2.7622
Afro-Brazilian
-.1866
.0463
16.2771
1
.0001
-.0289
.8297
Asian
1.7447
.2813
38.4669
1
.0000
.0461
5.7239
Constant
-1.3688
.0597
524.9518
1
.0000

263
Age 16
Chi-Square
DF
Significance
-2 log likelihood
14351.446
12146
.0000
Model chi-square
2312.898
6
.0000
Improvement
2312.898
6
.0000
Goodness of fit
13264.903
12146
.0000
Independent
Variable
B
S.E.
Wald
DF
Sig.
R
Exp (B)
Fathers' Ed
.0897
.0098
83.8971
1
.0000
.0701
1.0938
Mothers' Ed
.1294
.0110
139.6501
1
.0000
.0909
1.1382
Income
.0521
.0056
112.8330
1
.0000
.0816
1.0609
Urban
1.0506
.0632
276.6914
1
.0000
.1284
2.8594
Afro-Brazilian
-.3858
.0480
64.5792
1
.0000
-.0613
.6799
Asian
1.1646
.2036
32.7036
1
.0000
.0429
3.2046
Constant
-1.5717
.0630
621.6624
1
.0000
Note: B = the regression coefficients of independent variables
S.E. = the standard error of regression coefficients
Wald = the Wald statistics
DF = degrees of freedom
Sig. = the significance level
R = the R statistics
Exp (B) = the odds ratio

APPENDIX D
1980 BRAZILIAN CENSUS OCCUPATIONAL CATEGORIES
Occupational
Category
Subcategory
Code
Management/
Administrative
Contractors, Employers
1-13
Administrators
20-40
Professional/
Technical
Technicians
50-52
Professionals (e.g., engineers, architects,
chemists, physicians, professors)
101-205
-
Lawyers, Clergy, Writers, Artists
Supervisors, Technicians in
211-293
Mineral Extraction, Transportation,
Textile, Power, Water industries
Pilots, Commanders, Navigators,
401-406
Captains
711-722
Transpiration Inspectors
761
Professional Athletes
831-834
Clerical
Office Workers
53-65
Cashiers
602-605
Commerce Workers
631-646
Clerks
741
Transformative
Mechanics, Metal Workers
411-431
Textile, Related Workers
441-479
Woodworkers
481-490
Electricians
491-499
Construction Workers
511-521
Food, Beverage Workers
Graphic Industry Workers,
Ceramic, Glass Workers,
531-545
Other Manufacturing Worker
551-589
264

Transportation/
Communications
Maritime Transportation 723-732
Railroad Workers 742-753
Railroad Construction Workers 762
Airline Workers 771-776
Unskilled/
Personal Service
Agricultural Workers
301-391
Sales Clerks
601
Street Vendors
611-621
Service Workers
801
Domestic Workers,
Other Service Workers
805-826
Doormen, Elevator Operators,
Building Security Workers,
Custodians
841-845

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BIOGRAPHICAL SKETCH
Jirimutu was born in Xilinhot, Inner Mongolia, P. R. China, on October
19, 1956. He graduated with a B.A. in English from Inner Mongolia Teachers'
University, P.R. China, in 1978. He taught English at Inner Mongolia
Teachers' University from 1978 to 1988. He received a TESL (Teaching
English as a Second Language) Certificate from Brigham Young University in
1989, an M.A. degree in anthropology from the University of Florida in 1991,
and a Ph.D. degree in anthropology from the University of Florida in 1994.
He has been warded a postdoctoral fellowship from the Andrew Mellon
Foundation for 1994-1995 to conduct research in anthropological demography
at the Population Research Center, the University of Texas at Austin.
277

I certify that I have read this study and that in my opinion it conforms
to acceptable standards of scholarly presentation and is fully adequate, in
scope and quality, as a dissertation for the degreepf Doctor of Philosophy.
ssell Bernard, Chair
Professor of Anthropology
1 certify that I have read this study and that in my opinion it conforms
to acceptable standards of scholarly presentation and is fully adequate, in
scope and quality, as a dissertation for the degree of Doctor of Philosophy.
^Charles H. Wood
Professor of Sociology
I certify that I have read this study and that in my opinion it conforms
to acceptable standards of scholarly presentation and is fully adequate, in
scope and quality, as a dissertation for the degree of Doctor of Philosophy.
Je V c^Jl9 —
Paul ]. Magnarelta-)
Professor of Anthropology
I certify that 1 have read this study and that in my opinion it conforms
to acceptable standards of scholarly presentation and is fully adequate, in
scope and quality, as a dissertation for the degree of Doctor of Philosophy^
1aa
Doughty
Distinguished Service Professor of Ariíhró^ology
I certify that I have read this study and that in my opinion it conforms
to acceptable standards of scholarly presentation and is fully adequate, in
scope and quality, as a dissertation for the degree of Doctor of Philosophy.
Barabara Ann Zsembik
Assistant Professor of Sociology
This dissertation was submitted to the Graduate Faculty of the
Department of Anthropology in the College of Liberal Arts and Sciences and
to the Graduate School and was accepted as partial fulfillment of the
requirements for the degree of Doctor of Philosophy.
April, 1994
Dean, Graduate School

UNIVERSITY OF FLORIDA I
3 1262 08556 7039




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