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 Introduction
 Conceptual framework
 Data and methods
 Results
 Conclusions and implications for...
 Notes
 References
 Appendix














Title: Rural nonfarm employment, gaines and losses in human capital from migration and implications for rural women
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Table of Contents
    Introduction
        Page 1
    Conceptual framework
        Page 2
        Page 3
    Data and methods
        Page 4
        Page 5
    Results
        Page 6
        Page 6a
        Page 7
        Page 7a
    Conclusions and implications for rural women
        Page 8
        Page 8a
        Page 9
        Page 10
    Notes
        Page 11
    References
        Page 12
        Page 13
    Appendix
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
Full Text

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'" RURAL NONFARM EMPLOYMENT, GAINS AND LOSSES IN HUMAN
CAPITAL FROM MIGRATION AND IMPLICATIONS FOR RURAL WOMEN


Introduction

Human resources have long been regarded as a primary
determinant of economic development both in more and less
developed countries (Denison. 1960; Schultz, 1980; Becker,
1967). In his Nobel address, Schultz (1980:640) emphasizes
the role of human capital in economic growth and develop-
ment:
The decisive factors of production in improving the
welfare of poor people are not space, energy, and
cropland; the decisive factor is improvement in
population quality.
A similar argument was presented by Denison (1960) who
attributed to human capital increases 23% of the GNP growth
per employed person in the US from 1950-1962, and 6% for
eight Western European countries.


Human capital refers to the health, level of education,
and occupational status of individuals, which vary with age
and gender. Since Denison's pioneering work, improvements
in human capital levels are generally seen to enhance regional
or national growth (Hicks, 1980).


L Oftentimes, however, is regional economic development
adversely affected by the outmigration of individuals with
higher human capital levels. Referred to as 'migration )
selectivity', this process draws primarily males and the >
better trained and educated, younger and more innovative
individuals from the population. Much research has been
done on the effect of selective migration on both origin
and destination areas (Lipton, 1982; Schultz, 1982; Okun
and Richardson, 1961; Myrdal, 1957; Connell et al. 1.976).
Typically, it is found that while the private gains from
migration may be high, the social costs to the origin may


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also be high (Schuh, 1982:162), particularly for the
women and children who are often left behind:

-More and harder work by women and children will be
needed to replace absent young men; extra women's
work, especially if seasonally concentrated, can
endanger health, nourishment and care of babies
before as well as after childbirth; and extra
children's work may stunt growth and detract from
schooling. (Lipton, 1982:208).

The concern with retaining a more favorable human resource
balance is therefore of obvious importance. This study
focuses on the role of rural nonfarm employment opportunities
in stemming selective outmigration from rural areas, and
in attracting inmigrants with higher human capital levels.
The analysis uses Costa Rican data on individual migrants.

It is hypothesizes that when comparing rural areas,
those which lack rural nonfarm employment opportunities will
tend to loose valuable human resources by outmigration.
By contrast, those areas with dynamic, abundant rural
nonfarm employment, will attract .such individuals. When
comparing economic sectors, nonfarm employment will
attract higher human capital levels than agriculture, for
example, and will experience a net gain in human capital
from migration, or at least a smaller loss of human capital
by outmigration.

This paper proceeds with the conceptual framework
which links selective migration and rural nonfarm employ-
ment. Then, data, methods and empirical findings are
discussed. This is followed by a section on the implica-
tions of this research for rural women and women-sensitive
development strategies.


Conceptual Framework

As defined here, rural nonfarm employment refers to
the many small-scale, labor-intensive manufacturing, commerce
and service activities in rural areas. That rural nonfarm


- 2 -









employment ought to reduce human capital losses from
outmigration and stimulate human capital gains from
inmigration, can be inferred from the Theory of the House-
hold, a long-standing economic construct. Based on
Chayanov's (1925) Theory of Peasant Households and modified
by Mellor (1976), Evenson (1977) and Nakajima (1969), this
holds that individuals who are younger and better educated
would have greater earnings power in the nonfarm labor
market. Such individuals would prefer nonagricultural
occupations over farming, and and would seek this employ-
ment by migrating to the cities. As pointed out by
Schneider-Sliwa (1982), however, nonfarm employ-
ment in situ provides a real alternative to outmigration
and an incentive for inmigration to individuals with higher
human capital levels: Rural nonfarm employment may be
preferred over urbanward migration because of high monetary
and psychological costs associated with the move and the
high risk of unemployment in cities.

If in fact, rural nonfarm employment reduces selective
outmigration and/or attracts inmigrants with human resource
characteristics that are relatively desirable for economic
development, that ought to be manifested in several ways:

When comparing rural areas with significant nonfarm
employment to those lacking nonfarm enterprise, the areas
with nonfarm employment should have consistently 'better'
inmigrants (even though overall rural areas may loose out
to the cities). Accordingly, in areas with rural nonfarm
employment, human capital gains from inmigration should
offset the loss from outmigration to a greater degree.
Similarly, rural areas with significant nonfarm sector would
loose to a smaller degree valuable human resources through
outmigration than would areas lacking nonfarm enterprise.


- 3 -








When comparing farm and rural nonfarm sectors directly
in terms of their labor force, one would expect persons in
nonfarm occupations to have consistently higher human capital
levels than those in agriculture. Accordingly, rural non-
farm activities should gain more than the agricultural
sector from inmigration, and loose fewer valuable human
resources through outmigration.

Data and Methods

To examine the validity of these expectations, data
from the Costa Rican Census of Population (1973) were used.
The data consist of 58,425 individual records; of those,
10,785 represent migrants and 47,640 non-migrants. Both
these comprise the labor force portion of a sample of
197,926 persons, provided by Centro Latinamericano de
Demografia (CELADE).

The human capital dimension was represented by three
variables: age, measured as number of years since birth;
education, measured as number of years of school attendance;
and occupational status, an indicator based upon Treiman's >.w
(1977) international scale of occupational prestige. This
ranks occupations from 1 to 100, where higher numbers
indicate greater prestige for that occupation. ,v

The geographic unit of analysis were cantons, of which A
there are 79 in Costa Rica. Urban cantons contain province /
capitals and/or comprise the San Jose metropolitan area; ,
the remaining sixty-two cantons are classified as rural
by the census.

The first task of the empirical analysis was to divide
these rural cantons into those with significant rural
nonfarm employment and those lacking nonfarm enterprise.
This was done by a factor analysis employing variables
from the 1973-75 Costa Rican Censuse of Manufacturing,
Commerce and Service, using rural cantons as units of


- 4 -









1
observation. The factor scores thus derived characterized
the economic structure of rural cantons; the factor
scores were then used in Ward's Hierarchical Grouping
Algorithm, which minimizes within and maximizes between
group variance. This yielded the breakdown of rural
cantons with significant nonfarm sectors and those without
or little such employment.2

Following this classification of areas, individual
out, in and non-migrants were cross-tabulated by type of
area (urban or rural, rural with significant nonfarm
employment and rural without or little nonfarm employment),
and within these areas, by their sector of employment
(agriculture or nonfarm, i.e. manufacturing, commerce or
service). For all migrant groups, present sector of
employment was used, in and non-migrants'were crosstabulated
by their present area of residence. Due to data limitations,
outmigrants were tabulated'by the type of area they came from.

Mean values for age, education and occupational status
were calculated for each group of migrants in each type of
area and sector of employment. Mean differences were then
calculated for these to determine which areas and economic
sectors experienced net gains or losses in human capital
from in or outmigration. Finally, tests of significance
were performed for these mean differences, where the null
hypothesis that the mean differences are due to chance was
tested against the alternative, that the mean differences
were not random. Results are shown on Tables 1,2 and 3.

Tables 1A,2A and 3A present the means for age,
educational level and occupational status of out, in and
nonmigrants tabulated by type of area and economic sector.
Tables 1B,2B and 3B show the mean differences between
migrant groups. So, for example, can one see from Table
1B that inmigrants to urban areas who are employed in


- 5 -









agriculture are on average 3.46 years younger than the
urban nonmigrant population in that sector. Tables 1C,2C
and 3C present the differences in mean age, education and
occupational status between economic sectors. So, for
example, can one see from Table 1C that there is a 3.9 years'
age difference among the urban nonmigrant population found
in agriculture versus manufacturing.

Results
Age:

Considering first mean age (Table:1A),- shows that
migration is clearly an age selective process, since
whatever the area or economic sector, in and outmigrants
tend to be younger than the stayer (=nonmigrant) population.
What could have retained or attracted the young is evident
when one considers the stayer population. In both types
of rural areas the younger stayers are found in non-
agricultural occupations. For example, the average age
for nonmigrants in manufacturing is between 30 and 31 years,
whereas for those in agriculture, it is 34-35 years. The
attraction of nonfarm employment is also evident when
considering the inmigrants. Among rural areas, those with
significant nonfarm employment tend to attract even younger
inmigrants than those areas with little nonfarm enterprise.

That the younger are attracted by nonfarm employment
is further underscored by the mean age differences (Table
1B). The urban areas, for example, which provide primarily
nonfarm employment, record a larger age gap between
inmigrants and stayers in nonfarm sectors than is found in
rural areas. This suggests that the younger migrants see
their opportunity clearly in the nonfarm (here urban) labor
market rather than in the farm sector. Among rural areas,
those with nonfarm enterprise show the greatest mean age
difference between in and nonmigrants indicating that among
migrants to rural areas, the younger people are clearly
drawn to nonfarm employment. Similarly, there is a smaller
mean age difference between outmigrants and stayers in


- 6 -









.TABLE 1. AGE


A.. MEAN VALUES a


Scoaemic
Sector


Agriculture


Rural with Signifi-
COCnt onfarm lural witb Little
Urb.e tural bployent Nlaof ar btlormeat
Out In mNo Out la non Out In nOn Out In non
Mix. Mit. Ml. MNi. Wit. MNt. Mat. Nit. Mtt. Mi.. Mit. MLt.


31.94 31.75 35.21 31.64 31.70 34.30 31.77 33.06 34.20 31.53 31.17 34.42
650 656 3611 2286 2659 154%0 1019 601 8221 1261 1761 7191


Msalacturig 29.88 29.03 31.31 27.66 29.94 30.51 26.83 28.68 29.79 28.63 33.33 31.69
700 1051 4182 604 342 1826 335 233 1268 26) 99 153

Coerce 31.22 30.08 36.85 28.91 30.63 33.91 28.18 29.46 33.06 29.9 31.66 35.23
663 997 3575 644 389 1881 393 19 1176 251 190 693

Service 30.29 28.44 34.17 26.51 28.43 31.57 26.51 27.73 31.49 26.49 29.1 31.62
1279 2151 6387 1587 928 3488 979 476 2225 606 44 124


B. MEAN DIFFERENCES BETWEEN MIGRANT GROUPS IN A GIVEN

SECTOR OF EMPLOYMENT AND TYPE OF AREA b

rtesL *wth lieifi-
aent oofart lural with Little
Tb__ Itlrt _____lot1" ieet R 1o2eBr 3meltr-
Ia Nit. Is Nit. o Nils. I n g. m Mis. e it. I Hit. ol NiL. 0os H6n. to Mit. In HNi. lOe Nig.
lMmie Niw Nimeu tMim NMiun Mie Nii Ni.s MiinMm Miie mi Mi men inu NMimes
Beter NI NiL. OIt Nig. aOt Rig. ee kNi. Out Ni|. Out Nig. .e NLi. Out MNi. Out Mig. ms Nig. Out Nil. Out NiS.


4gTi- -3.66
ettere *5.62


aitd-- -2.28
tri-*s -1.70
-ee

Comerce -4.77
-10.21
me


-.19 3.27 -.60 .06 2.66 -1.14 1.29 2.43 -3.25 -.36 2.89
-.54 5.39 -8.42 .15 8.27 -1.89 1.78 5.03 -8.80 -.72 6.68
w e ewe ewe o* m0ne

-.11 1.43 -.57 2.28 2.91 -1.11 1.79 2.90 1.64 4.70 3.06
-1.54 3.13 -.40 2.07 2.64 -1.33 1.8 4.11 1.24 3.39 3.38


-1.14 3.63 -3.26 1.74 5.00 -3.61 1.27 4.80 -3.37 1.70 5.27
-1.31 6.60 -4.49 2.16 8.39 -3.86 1.22 12.16 -3.06 1.35 5.16
e u e n n m m e ee


Service -.73 -1.85 3.88 -3.14 1.92
-18.65 -4.45 10.33 -7.35 4.24
Mwe ew" Mae 00 m1


5.06 -3.76
14.18 -6.79
*** ***


1.22 4.98 -2.44 2.69 5.13
2.02 11.02 -3.00 3.90 8.77
e m w n"e m" me


C. .MEAN DIFFERENCES BETWEEN ECONOMIC SECTORS WITHIN

MIGRANT GROUPS IN A GIVEN TYPE OF AREA b


CeograpkLc ou0t itrantse
Arue A-* A-C A-$6 -C c C-S


Orbae 2.06 .72 1.69 -1.34 -.l1 .93
3.14 .99 2.68 -2.14 -.74 1.58


Rtel 3.98 2.73 S.13 -1.21 1.15 2.40
7.33 4.80 12.63 -1.87 2.14 4.25
me e w C # me **w

rtel with 4.88 3.59 5.21 -1.29 .38 1.67
Sigificant 6.50 4.70 9.15 -1.49 .54 2.33
efo tam me` me m ae
mIloymat

Iaral with 2.90 1.7 5.04 -.33 2.14 3.67
Little 3.62 1.77 8.47 -1.25 2.6% 3.79
ad arm m a .me Me **
mpiloymeec



Note:

a the top number in each cell

indicates the frequency


In M) irintC
A-H A-C A-S N-C


2.72 1.67 3.31 -1.05
4.20 2.47 53.4 -2.00

1.76 1.05 3.27 -.75
1.70 1.30 1.27 -.63


4.38 3.61 5.33 -.77
4.64 3.56 7.10 -.69



-2.16 -.49 1.99 1.67
-1.75 -.45 3.26 1.09


~__________Ron Ntirinnt
M-S C-S A-M A-C A-S N-C -IS C-9


.59 1.64 3.90 .66 1.14 -3.54 -2.8 .68
1.38 3.48 11.61 1.23 3.50 -10.99 -10.78 2.26


1.51 2.22 3.79 .39 2.73 -3.40 -1.06 2.36
1.42 ).04 3.76 1.07 7.44 -3.22 -1.04 5.79
me me m mew se

.95 1.72 4.41 1.16 2.71 -3.27 -1.70 1.57
1.07 1.78 10.75 2.50 8.25 -5.91 -3.74 3.17
S we me .me wee m me


A.15 2.46 2.73 -.81 2.80 -3.54 .07 3.61
3.19 2.24 4.52 -1.21 4.68 -*.13 .10 4.88
** ** ** ** ** *


is the mean value and the bottom number


b the top number in each cell is the mean difference, the second entry is

the z-score, = significant at .10, ** = .05 and ***= at .01 level.









rural cantons with significant nonfarm sector than in
cantons without. The above suggests then, that the
presence of rural nonfarm employment retains younger
migrants whereas the relative absence of such employment
is associated with a drain of younger persons out of these
areas.

The same picture emerges when one compares economic
sectors directly (Table 1C). In almost all cases, the
age of those in the nonagricultural work force is signifi-
cantly younger than the age of those in farming. On Table
1C, for example, the mean age difference indicated for rural
migrants under heading Agriculture minus Manufacturing (A-M),
shows that manufacturing attracts the younger inmigrants.
The mean difference is greater in rural areas with nonfarm
employment suggesting that such employment is particularly
attractive to the younger segment of the labor force. It
suggests furthermore a dynamic mechanism: there is an
improvement in the human capital balance and quality if
ii / ^ Ithe labor force in rural cantons with nonfarm employment.

Education

Inspection of Tables 2A,B and C indicates that nonfarm
employment tends to attract the more educated, with a mean
educational level of 5-8 years compared to 3-5 years for
persons in agriculture.

There were no systematic differences in education
between out and nonmigrants in a given area. Regarding
inmigrants, those to rural cantons with significant nonfarm
enterprise were better educated than inmigrants to primarily
agricultural cantons, and in both types of rural cantons,
inmigrants were better educated than the stayer population
in nonfarm sectors. Comparing educational levels by
economic sectors directly (Table 2C), confirmed that .nonfarm
sectors attract the better educated.

There was a difference in educational levels between
in and outmigrants. Among rural cantons, those with
7 -












TABLE 2. EDUCATIONAL LEVEL


A. MEAN VALUES a


Iconaoic
sector


Agricullure


Nawutfctui 8


Coevwrce


Service


Rural with Signift-
cant oelfTr" Rural with Little
trbqa Rural ImplorTeat n LarfT EbloTeO(t
Out la non Out to Noa Out In Non Out Is loa
Mit. t *M. MN. Ni Ni Nt.M Mi. M*i. Mit. M. a Mt. mit.


&.27 4.4A 3.33 3.22 3.21 3.16 3.15 3.07.252 3.27 3.27 3.04
650 656 3612 2287 :460 15450 1020 691 8222 1261 1766 7190

6.20 6.23 6.13 5.14 5.14 5.11 5.18 5.33 5.26 5.06 6.83 6.72
698 1049 4182 604 342 1826 135 1, 1248 265 9* 553

6.1 6.66 6.42 5.32 5.64 5.23 5.02 6.06 5.27 5.16 5.21 3.15
665 97 1571 646 38 IAst 191 11l 1176 217 190 691

7.82 6.98 7.58 6.77 8.43 6.74 6.53 8.03 6.82 7.14 .88 6.58
1287 2!1" 6386 )586 926 3486 174 47 2224 605 445 1247


B. MEAN DIFFERENCES BETWEEN MIGRANT GROUPS IN A
GIVEN SECTOR OF EMPLOYMENT AND TYPE OF AREA.b


Inral with Silnifi-
cast NofaerT
rbs Riurall apolor t --
Is Nil. It Nil. ose Nit. In Mi.. I i Non Nit. In Mis. In MI1. oi MNLI.
Ig ile Nlu i Mins inus minus Minus Minu Hiouns Minus Minus
aster se Mit. Oat Nis. Oat 5Mi. "a Mis. Oat Nit. Out Mig. 0on Nil. Out Mit. Out Ni.


.11 .I7 -.*4
.39 .86 -6.59


.10 .03 -.07
.12 .20 -.56


.05 -.01 .06 -.18
.88 -.13 -1.05 -1.83
0


.03 0.0
.19 0.0


*.01 .10 .23 0.00 -.23
-.65 1.21 3.24 0.00 -2.91
*s, --


.03 .07 .15 .00 .13 -.21 -.34
.24 .37 .66 .30 .42 -.61 -1.42


Coerce. -.36
-2.62


-.25 .11 .41 .32 -.09 .79 .64 -.19
-1.41 .82 2.53 1.73 -.72 3.66 2.62 -.93
1e en en


.06 .05 .01
.24 .18 .05


smiee -..60 -.U -.24
-6.12 -5.97 -1.94
*** ** e


1.69 1.66
11.15 91.9
en en


-.03 1.21 1.50 .29 2.30 1.74 -.53
-.256 6.19 .00 2.03 9.63 6.58 -2.75
*09 00, Owa ava


C. MEAN DIFFERENCES BETWEEN ECONOMIC SECTORS WITHIN
MIGRANT GROUPS IN A GIVEN TYPE OF AREA b


CGegraphic Out nitirlnt
Ares A-M A-C A-* N-C I-I C-S


toIn Mtrlnts on Mtllir t
A-M A-C A-S N-C N-S C-S A- A-C 4A- N-C -S C-*


UrDas -1.93 -2 32. .5 -.11 -1.62 -1.51 -1.79 -1.62 -1.54 .17 -.75 -.97 -2.80 -3.01 -4.25 -.29 -1.45 -1.1
-10.74 -11.10 -10.11 -.67 -9.95 -9.02 -10.23 -8.64 -15.24 1.03 -5.76 -6.04 -45.40 -45.9 -64.05 -4.62 -21.70 -16.0
S e 0 ` ** sen a n **e ** *vf* v e ** fe


-1.92 -2.10 -3.55 -.31 -1.63 -1.46 -1.93 -2.43 -5.22 -.50 -3.29 -2.79 -1.5 -2.07 -3.51 -.12 -1.63 -1.3
-1I.37 -17.43 -32.09 -1.15 -10.98 -10.02 -12.21 -15.30 -35.64 -2.37 -16.32 -13.80 -31.44 -31.44 -51.88 -1.39 -18.36 -16.3
e en en en *v en *n en ** en S *- eue en e00


tural with -2.03 -1.27 -3.38 -.24 -1.35 -1.11 -2.26 -2.94 -4.9 -.73 -2.70 -1.97 -2.01 -2.02 -3.57 -.01 -1.56 -1.5
lSigtiflct -*1.29 -14.08 -23.83 -I.19 -7.21 -6.04 -11.41 -10.35 -24.52 -2.76 -10.84 -7.36 -27.08 -24.41 -42.9 -.10 -16.80 -13.1
Ntonfar men en we 6n *n e a e- a e *a- _0 en
Aloymeet
l.ral with -1.79 -1.89 -3.17 -.10 -2.08 -1.98 -1.'8 -1.96 -5.16 -.16 -4.01 -1.67 -1.64 -21.1 -3.54 -.41 -1.86 -1.4
Little -9.11 -10.30 -21.43 -.41 -8.58 -8.41 -'.27 -8.28 -21.84 -.9 -11.25 -12.02 -14.79 -9.l51 -28.93 *2.84 *1l.4 -9.0
ostlarn e m en e m .a n *- wn em en
kp1oywes


Note:

a the top number in each cell is the mean and the bottom number the frequency
b
the top number in each cell is the mean difference, the second number is

the z-score and the thtrd entry indicates:**significant at .10,
**=at .05 and ***=at the .01 level.


Agri-
culture



tourism


lural with Little
Nontl.r meyUelp
I Mit. In Mil. e&s ~i.
Ninus Minus HMlue
os Nils. Out Nit. Out Nis.


lural









nonfarm employment record that inmigrants were better educated
than outmigrants, thus there was a net gain in this human
resource. Areas lacking nonfarm employment, on the other
hand show a slipping away, or, at best, status quo of well
educated people. Hence, one can only conclude that promoting
the nonfarm sector in rural areas would upgrade their human
capital base.

Occupational Status' ''

Tables 3A,B and C show noticeable differences in
occupational status between agriculture and nonfarm employ-
ment, with nonfarm sectors recording higher scale values;
manufacturing and.commerce, for example, scored 35-36,
service 39-42, compared to 30-32 for agriculture.

Regarding differences in occupational status between
in, out and nonmigrants for a given economic sector, they
were generally not significant and only some details are
noteworthy. In rural cantons with significant nonfarm
development, inmigrants to all nonfarm sectors were of
higher occupational status than either out or nonmigrants).
Furthermore, with the exception of commerce, this obser-
vation also held for cantons with little nonfarm enterprise.
The implication seems to be, then, that even in less developed
rural cantons, nonfarm activity gives rise to an increase
in human capital as measured by occupational status.


Conclusions and Implications for Rural Women

In summary, selective migration, and its consequences
for sending and receiving areas, seems to be very much a
function of the type of employment in a rural area. Rural
nonfarm employment attracts individuals with human capital
attributes deemed desirable for economic development. Further,
the rural nonfarm sector, while not completely offsetting
selective outmigration to urban areas, does stem the


- 8 -












TABLE 3. OCCUPATIONAL STATUS


A. MEAN VALUES a


LoIsic
Setor


Articulltre


lNesfacturing


Service


tural with Stinlfl-
coat Ronftr luret with Little
Urbea Rural ETontorent Ponfanr Em.olosnt
Out In on Out In Mon Out In Moo Out In loe
wit. pit. Nit. N N. i. Ni. i. it. Nit. M. Nit. Nit.


32.32 32.09 11.33 30.10 31.61 11.32 30.64 30.99 31.27 30.92 31.11 31.38
650 656 3612 2287 2466 15451 1020 691 8222 1261 1766 7191

37.33 36.81 36.35 14.22 36.09 36.02 33.97 35.S7 36.29 34.39 37.29 31.39
700 1051 4182 604 342 1826 133 23) 1268 265 99 553

36.35 31.61 16.41 34.87 16.51 13.Al )4.92 37.04 131.0 34.64 315.94 36.35
665 997 1573 646 189 18il 193 193 1176 251 190 693

42.37 19.63 41.69 38.96 4.09 39.29 38.34 43.67 39.14 39.86 46.67 39.52
1279 2151 6387 1287 928 3489 919 416 2225 606 444 1246


B. MEAN DIFFERENCES BETWEEN MIGRANT GROUPS IN A
GIVEN SECTOR OF EMPLOYMENT AND TYPE OF AREA


lural with Sisifi-
ecst lomfarw tural with Little
Otrbq Itrtlt t iorT nt Mfaers emLo mtm
It Nil. In Nil. alm N1i. tI MNi. Is Nis. ou NMit. In Nis. In Ntx. Ion Nig. In Nig. Is Nit. m -Mig.
Ietsie s Nlu Mmils Nism Nioni Misse minus Nilus NMise NMius Mious Niles NMisL
Setr wig. .OBI Ni. O.t Mit. -oa Nil. 0ot Mis. Out Nig. l" Nit. Oat Nil. 0ot Mit. ot Mig. Out Mli. Oae Nis.


&Stu- .74 -.23 -.99
-mltee 1.1 -.44 -2.81


Itc- .29 -.72- -.98
tuning .64 -1.31 -2.15



C rc -.2
-2.22
*0


-.25 .27 .52 -.28 .35 .63 -.27
-1.95 1.47 3.74 -1.64 1.40 3.31 -1.60
* e*


.19 .44
.751 2.30
we


.07 1.87 1.8 -.72 1.60 2.32 1.90 2.90 1.0
.12 2.86 4.02 -1.01 1.88 3.91 1.89 2.67 1.4
ow en ne


-.34 .26 .74 1.68 .94 1.15 2.06 .52 -.41 1.30 1.71
-1.03 .59 1.43 2.132..34 2.1 2.8 .06 4 1.42 2.4
te S a owe


S rties -2.06 -2.94 -.S
-5.77 -S.77 -I.96
-o we s


3.8 4.15 .31 4.53 5.33 .80 7.13 6.81 -.34
10.60 10.22 .84 6.03 6.51 1.55 8.79 7.51 -.48
0e *e -* .0s es*


C. MEAN DIFFERENCES BETWEEN ECONOMIC SECTORS WITHIN
MIGRANT GROUPS IN A GIVEN TYPE OF AREA D


CGorTaphic Out mitrsuat
Ares A-M A-C A-I NC M-S C-S


In Migrant eon Mitgrant
A- A A- A-S N-C N-S C-S A-M A-C A-5 -C In- C.


Orkas -5.22 -1.U -10.26 1.38 -5.04 -6.42
-4.67 -7.21 -19.37 2.34 -8.60 -11.07
.00 wev e** g ow e

tsral -3.42 -4.07 -8.14 -.65 -4.72 -4.07
-6.29 -10.81 -22.11 -1.24 -9.09 -8.29
asg egg *4` e**

airal with -1.30 -4.34 -7.70 -1.01 -4.37 -3.36
ligifictast -5.87 -9.27 -16.47 -1.68 -6.40 -51.3
oaf aim e* eg* oe gg *
lploymt

MSrte with -3.47 -3.72 -8.94 -.25 -5.47 -5.22
Little -5.69 -5.87 -1*4.9 -.30 -.75 -4.30
>lof m m are
nloye


-4.7; -3.52 -7.S4 1.20 -2.82 -4.02 -1.22 -5.08 -10.36 .14 -*.14 -3.
-8.88 -4.89 -15.07 2.51 -6.10 -9.0' -26.23 -26.34 -49.21 .59 -20.26 -21.


-5.02 -5.48 -14.02 -.46 -9.00 -8.54 -4.70 -4.49 -7.97 .21 -3.21 -1.
-4.23 -11.10 -27.52 -.65 -12.43 -12.43 -21.18 -23.14 -33.61 .72 -4.98 -it.
e** *n **e *e **e *of* *g -

-4.38 -6.05 -12.68 -1.47 -8.10 -6.63 -5.02 -4.23 -7.87 .79 -2.83 -I.
-6.71 -8.69 -17.82 -1.55 -4.64 -.85 -18.82 -18.14 -26.87 2.26 -7.27 *-.



-6.18 -4.81 -15.56 1.35 -9.38 -10.73 -4.01 -4.97 -8.14 -.94 -4.13 -1.
-4.61 *6.88 -21.48 1.18 -8.09 -10.9? -9.87 -14.94 -20.10 -1.80 -7.21 -4.
S e.. ..*g en e e a en m e o


Note:

athe top number in each cell is the mean value and the bottom number

indicates the frequency.
b
the top number in each cell is the mean difference, the second is the

z-score, and *=significant at .10, **=at .05 and *** = at .01 level.









exodus of people with valuable human resource characteristics.
As such, rural nonfarm employment provides a means for
retaining/fostering the growth of a higher quality labor
force, which contributes to overall economic growth and
development.

In stemming selective outmigration, the rural nonfarm
sector has furthermore special implications for rural
women and women-sensitive development strategies. As noted
above in the quotation by Lipton, selective outmigration
with its preponderance of males, may affect women and J
children particularly seriously. The absence of males
implies that more women and children must be brought into
tha labor force. The benefits of this additional and
harder labor, however, may be limited since this
'mixture of tired man-hours by established members of the
work force and inexperienced man-hours by new members of
the work force ... pushes down rural output per worker and
per man-hour so that these ... may end up lower than before
outmigration' (Lipton, 1982:196). Several serious and
damaging consequences may be observed, as summarized in
Lipton (1982): first, families with male absences may shift
from labor-intensive field crops to pasture, tree-crops
and continuous cropping (McEvoy, 1971; Dussauze-Ingrand, 1974)
and such shifts may reduce caloric output per acre. This
adds to the already serious problems of malnutrition
among rural women and children.

Furthermore, female-headed households may be forced
to/sell out and join the ranks of landless day laborers
or slum dwellers in the cities. Clearly, then, the
creation of rural nonfarm employment opportunities would be
a women -sensitive approach to development (in addition to
fostering overall economic growth). Two points are note-
worthy in this regard: first, rural nonfarm employment
seems to be attracting/retaining males (even though our
analysis was not sex-specific), hence it lightens women's
burdens associated with male absences. Furthermore, the


- 9 -









rural nonfarm sector also employs females. Thus, it helps
women directly by addressing the

key to women's problems: the lack of control over
money, which is the single most important factor
retarding rural development, and which contributes
to the low status of women and increases their
motivation for frequent childbearing (Dixon, 1982:14;
see also Ahmad, 1976 and Safilios-Rothschild, 1970).

We conclude, then, that rural nonfarm employment through
its positive impact on migration selectivity and the human
resource balance, can both promote rural economic growth
and integrate women better into development.


- 10 -











NOTES


1. Appendix I indicates variables used in factor analyses,
factor loadings, other relevant statistics and a description
of factors and groupings thus derived. Further detail is
presented in Schneider-Sliwa (.1982).


2. The grouping algorithm used for this breakdown is
described in Veldman (1967).


- 11 -









REFERENCES:


Ahmad, P.
1976. 'The Role of Women in Cooperative Development in
Bangladesh', in: International Cooperative Alliance,
Regional Conference on the Role of Women in Cooperative
Development. Kuala Lumpur 21-28 July, 1975. New Delhi,
International Cooperative Alliance.

Becker, G.
1975. Human Capital: A Theoretical and Empirical Analysis
with Special Reference to Education. National Bureau of
Economic Research, Columbia University Press, New York.

Chayanov, A.V.
1925, 1966. The Theory of Peasant Economy. Edited by
D. Thorner, B.Kerblay and R.E.F. Smith. Homewood, Ill.
Published for the American Economic Association by R.D.
Irwin.

Connell, J, B. DasGupta, R. Laishley and M.Lipton.
1976. Migration from Rural Areas. Delhi, Oxford University
Press.

Denison, E.F.
1962. The Sources of Economic Growth in the United States.
New York, Committee for Economic Development.

Dixon, R.
1982. Rural Women at Work. Johns Hopkins University Press,
Baltimore.

Dussauze-Ingrand, E.
1974. 'L'Emigration Sarakollaise'. in S.Amin (ed.) Modern
Migration in Western Africa. Oxford University Press,
London.

Evenson, R.E.
1977. 'On the New Household Economics', Journal of Agricul-
tural Economics and Development, Vol. 41.

Lipton, M.
1982.'Migration from Rural Areas of Poor Countries: The
Impact on Rural Productivity and Income Distribution', in
R.H.Sabot (ed.) Migration and the LaborMarket in Developing
Countries. Westview Special Studies in Social, Political
and Economic Development. Boulder, Colorado.

McEvoy, F.D.
1971.'History, Tradition and Kinship Factors in Moders Subo
Labor Migration'. Ph.D. Dissertation, University of Oregon.
Eugene.
12 -










Mellor, J.
1976. The New Economics of Growth. Cornell University
Press, Ithaka.

Myrdal, G.
1957. Rich Lands and Poor, the Road to World Prosperity.
Harper, New York.

Nakajima, C.
1969. 'Subsistence and Commercial Family Farms: Some
Theoretical Models of Subjective Equilibrium'. in Wharton,C.
(ed.) Subsistence Agriculture and Economic Development.
Aldine Publishing Co. Chicago.

Safilios-Rothschild, C.
1970.'The Study of Family Power Structure, 1960-69'.
Journal of Marriage and the Family. Vol. 32, No 4.

Schneider-Sliwa, R.
1982. 'Rural Nonfarm Employment and Migration: Evidence
from Costa Rica'. Discussion Paper No 4 Studies on The
Interrelationships between Migration and Development in
Third World Settings. Department of Geography, The Ohio
State University.

Schuh, E.G.
1982. 'Outmigrayion, Rural Productivity, and the Distribution
of Income'. in R.H. Sabot (ed.) Migration and the Labor
Market in Developing Countries. Westview Special Studies
in Social, Political and Economic Development. Boulder,
Colorado.

Schultz, T.W.
1980. Transforming Traditional Agriculture. Yale University
Press, New Haven, Connecticut.

Treiman, D.J.
1977. Occupational Prestige in Comparative Perspective.
Academic Press, New York.

Veldman, D.J.
1967. 'Fortran Programming for the Behavioral Sciences.'
Chap. 12:308-317. Hierarchical Grouping Analysis. Holt.
Rinehart and Winston, New York.


- 13 -














Appendix I-1


Table A.1 Definitions, Means, and Standard Deviations for Agricultural Resource
Base Variables (n 62 rural cantons).


Variable Standard
Variable Name Definition x Deviation


Crop Specialization and Degree of Commercialism


Maize Production
Maize production per capital
Av. maize yield per ha
Z commercial maize prod.

Bean Production
Bean production per capital
Av. bean yield per ha
X commercial bean prod.

Banana Production
Banana production per capital
Av. banana yield per ha
% exported bananas

Coffee Production
Coffee production per capital
Av. coffee yield per ha
% coffee exported

Sugar Production
Sugar production per capital
Av. sugar yield per ha
% sugar exported

Z Cattle Ranches

Cocoa Production
Cocoa production per capital
Av. Cocoa yield per ha

Ownership of Land
hectares per farm
% ha permanently cultivated
% farms owned
ha owned per capital
farms managed by producer per capital
ha managed by producer per capital

Degree of Mechanization
Tractors
Z tractor using farms

Population Pressure on Land
Population pressure per ha
Population pressure per farm
Rural population
Dependency ratio


Information. Education and Amenities
% people without education
% people with secondary education
% houses with TV
% houses with radio
2 houses without toilet


Maize production in tons
Maize prod./rural population
Maize prod./# ha
Maize commercially cropped/maize prod.

Bean production
Bean prod./rural population
Bean prod./# ha
Beans commercially cropped/bean prod.

Banana production (1000 stems)
Banana prod./rural population
Banana prod./# ha
Bananas exported/banana production

Coffee production (1000 quintals)
Coffee prod./rural population
Coffee prod./# ha
Coffee exported/coffee prod.

Sugar production (1000 tons)
Sugar prod./rural population
Sugar prod./# ha
Sugar cane exported/sugar cane prod.

* cattle ranches/# farms

Cocoa production
Cocoa prod./rural population
Cocoa prod./# ha


# hectares/# farms
# ha permanently cultivated/# ha
# farms individually owned/# farms
# ha individually owned/rural population
# farms producer-managed/rural population
# ha producer-managed/rural population


# tractors
# tractor using farms/# farms


Population/# ha
Population/# farms
Rural population
(# people less than or equal to 12 years +
# people equal to or greater than 65)/
# people 13 to 64

# of people without any education/pop.
# of people with secondary education/pop.
# houses with TV/# of houses
# houses with radio/# of houses
# houses without toilet/# of houses


740539.93
48.81
315.00
.62

162875.88
10.87
72.05
.49


16.14
87.36
.65

4499560.75
350.98
2399.00
.95

31299.93
2.11
13.64
.90

.51

44301.38
5.42
40.07


9.29
.55
.94
23.20
2.33
20.24


70.41
.10


8.26
35.70
12813.66
.70


1137297.99
47.68
311.00
.14

292421.78
14.24
95.42
.19


52.58
257.83
.33

6750293.61
434.35
2953.00
.17

76195.32
4.53
27.84
.21

.14

183122.34
26.20
200.74


.44
.12
.04
20.00
1.01
17.11


100.99
.90


1.17
7.20
9814.46
.12
















Appendix 1-2

Table A.2 Rotated Factor Loading on Agricultural Variables.a


Comunalities

Variable Name Factor 1 Factor 2 Factor 3 Factor 4 of Variables



Crop Specialization and Degree of Commercialism

Maize Production 0.63038 0.45387 .65
Maize production per capital 0.80649 .72
Av. maize yield per ha 0.78689 .68
2 commercial maize prod. -0.31676 .09

Bean Production 0.64815 0.33918 .58
Bean production per capital 0.75029 .67
Av. bean yield per ha 0.73452 0.26616 .68
2 commercial bean prod. 0.43834 .16

Banana Production -0.76895 .49
Banana production per capital -0.75107 .47
Av. banana yield per ha -0.74332 .44
% exported bananas -0.29820 .14

Coffee Production 0.41379 .38
Coffee production per capital -0.58356 0.29554 .42
Av. coffee yield per ha -0.57305 0.28985 .40
2 coffee exported .03

Sugar Cane Production 0.88720 .76
Sugar production per capital 0.81832 .75
Av. sugar yield per ha 0.85580 .83
% Sugar Exported 0.30315 .09

% Cattle Ranches 0.61604 0.58057 .81

Cocoa Production -0.72217 .40
Cocoa production per capital -0.76794 .45
Av. cocoa yield per ha -0.76481 .41

Ownership of Land
hectares per farm -0.33231 0.45604 0.30461 .43
% ha permanently cultivated -0.32446 -0.40487 .46
% farms owned 0.56389 0.27180 .43
ha owned per capital 0.74469 .58
farms managed by producer per capital 0.65376 .63
ha managed by producer per capital 0.78405 .65

Degree of Mechanization
# tractors 0.77935 .65
% tractor using farms 0.70380 .54

Population Pressure on Land
Population pressure per ha -0.74661 0.32269 .68
Population pressure per farm -0.69287 0.48992 .80
Rural Population 0.29911 0.38176 0.45652 .40
Dependency Ratio 0.36215 0.39878 .35

Information. Education and Amenities
2 people without education 0.75432 -0.50468 .81
2 people with secondary education -0.75750 0.31136 .78
I houses with TV -0.83686 .87
2 houses with radio -0.38325 0.46307 0.42120 .54
% houses without toilet 0.88271 .83


Eigenvalue 11.25 5.12 3.81 3.36
Cumulative Variance Explained .27 .40 .49 .58


aFactor loadings between +0.25 and -0.25 are omitted from table.







Appendix I-3

Table A.3 Definitions, Means, and Standard Deviations for Variables Pertaining to the
Rural Nonfarm and Urban Sectors.


Variable Variable Urban Cantons (n 7) Rural Cantons (n 62)
Name Definition Standard Standard
x Deviation x Deviation


Industry
# Industrial Establishments
# Industrial Employees
Av. Employees per Ind. Establ.
Av. Indus. Wages per Person
I Ind. Estab. Open All Year
% Male Ind. Employees
2 Unpaid Indus. Employees
2 Indus. Empl. with Insurance
Av. Capital Input Industry

Capital/Output Industry

Capital/Labor Industry

% Ind. With Low Value of Prod.


2 Ind. With Med. Value of Prod.

2 Ind. With High Value of Prod.


Service
# Service Establishments
# Service Employees
Av. Empl. per Serv. Establ.
Av. Service Wages per Person
X Service Establ. Open All Year

Z Male Service Employees
% Unpaid Service Employees
2 Service Workers with Insurance
Av. Capital Input Service

Capital/Output Service


Capital/Labor Service

Commerce
# Commerce Establishments
# Commerce Employees
Av. Employees per Comm. Estab.
Av. Commercial Wages per Person
% Commerce Estab. Open All Year

% Male Commerce Employees
Z Unpaid Commerce Employees
Z Insured Commerce Employees
Av. Capital Input Commerce

Capital/Output Commerce


Capital/Labor Commerce

Population and Labor Force
2 Population without Insurance
Z Unemployed Work Age Pop.
Z White Collar Labor Force

% Office Labor Force

I Labor Force in Transportation

% Labor Force Artisans
I Labor Force with Casual
Employment
Z Labor Force Own Account
Z Labor Force Part-time
Employment
I Labor Force Full-time
Employment
% Labor Force in Low Wage Jobs

I Labor Force in Med. Wage Jobs

% Labor Force in High Wage Jobs

Urban Population
% Rural Population


i of industrial establishments 262.57
# of employees in industry 6154.00
# Ind. emp./# industrial establishments 22.14
Total indus. wage bill/# industrial empl. 12828.91
# indus. estab. open all year/# indus. estab. .94
# male indus. empl./# indus. employees .82
# unpaid indus. empl./# indus. employees .02
# insured indus. empl./# indus. empl. .83
(value of fixed assets plus necessary 2090982.23
purchases)/# indus. estab.
(value of fixed assets plus necessary 1.10
purchases)/value of indus. production
(value of fixed assets plus necessary 83007.15
purchases)/# indus. employees
(# ind. w/low values of production, .30
i.e., less than 50,000 colones)/
i industrial establishments
(# ind. w/med. value of prod. i.e., up .10
to 500,000 col.)/# industrial estab.
(# ind. w/high value of production, .20
i.e., greater than 500,000 col.)/
# industrial establishments


i of service establishments
# of employees in service
# service employees/# service eatabl.
Total serve. wage bill/# service empl.
I service establ. open all year/
# service establishments
# male service empl./# service empl.
# unpaid service empl./# service empl.
# insured service empl./ service empl.
(total value of fixed assets plus neces-
sary purchases)/# service establ.
(total value of fixed assets plus neces-
sary purch.)/total income from sales
and other sources
(total value of fixed assets plus neces-
sary purch.)/# service employees


562.71
2018.00
3.44
3848.76
.93

.65
.18
.60
145259.79

.51


42151.30


# of commerce establishments 831.00
# of employees in commerce 4223.71
* commerce empl./f commerce establ. 4.00
Total comm. wage bill/# comm. empl. 7014.20
# commerce establ. open all year/ .93
# commerce establ.
# male comm. empl./# comm. employees .67
* unpaid comm. empl./# comm. empl. .17
# insured comm. empl./# comm. empl. .21
(total value of fixed assets plus neces- 56141.23
sary purchases)/# commerce establ.
(total value of fixed assets plus neces- 15.01
sary purchases)/total income from sales
and other sources
(total value of fixed assets plus neces- 14493.90
sary purchases)/# commerce empl.


# of people without insur./total population
# unemployed/working age population
# persons in white collar occup./working
age population
# persons in office jobs/working age
population
# persons in transportation/working
age population
# artisans/working age population
# people w/casual empl./vorking age
population
# self-employed/working age population
# part-time employed/working age
population
# full-time employed/working age
population
# persons with low wage jobs/working
age population
# persons with med. wage jobs/working
age population
I persons with high wage jobs/working
age population
population residing in urban areas
rural population/total population


441.26
11044.31
6.89
2397.31
.04
.12
.01
.07
2696949.82

1.06

72100.13

.13


.04

.09



848.37
3219.16
.40
1195.07
.04

.14
.01
.49
24592.23

.09


16.66
201.40
11.51
8040.90
.94
.89
.22
.44
685272.54

.58

40470.46

.50


.05

.10



51.96
134.58
2.53
1772.91
.90

.70
.29
.52
102076.49

.51


19.14
292.61
16.18
8441.01
.20
.13
.80
.31
1528450.24

.75

35631.73

.28


.07

.11



48.65
132.41
.69
1169.87
.07

.13
.11
.49
64489.83

.11


4806.66 39260.15 13060.60


1248.48
7721.37
.97
2707.73
.05

.04
.05
.11
26841.06

21.92


68.19
203.75
2.61
2602.34
.92

.67
.29
.06
28690.39

49.69


66.68
285.81
1.02
3187.00
.05

.08
.09
.31
11459.30

41.62


8197.28 11417.58 4308.69


.50 .10
.04 .00
.11 .04

.06 .02

.03 .01

.18 .04
.08 .03

.12 .03
.03 .02

.09 .04

.27 .075

.31 .03

.14 .05

81314.42 14402.34
.43 .16


.70 .23
.03 .01
.05 .02

.02 .01

.02 .01

.10 .06
.04 .02

.20 .08
.02 .01

.22 .12

.34 .09

.19 .10

.06 .02

3078.67 2908.72
.81 .12














SAppendix 1-4

Table A.4 Rotated Factor Loadings for Nonagricultural Economic Activity Variables in
Rural Cantons (n 62) and in Rural plus Urban Cantons (a 69).a


Comunality of
Factor 1 Factor 2 Factor 3 Factor 4 Variables
Variable Pame (a) (b) (a) (b) (a) (b) (a) (b) (a) (b)
n-69 n-62 n-69 n62 n-69 a-62 n-69 no62 n-69 n-62


Industry
# Industrial Establishment
# Industrial Employees 0.39651
Av. Employees per Indus. Estab. 0.42662
Av. Indus. Wages per Person
I Indus. Estab. Open All Tear
I Kale Indus Employees -0.37768
2 Unpaid Indus. Employees
I Indus. Employees with Insurance 0.49341
Av. Capital Input Industry 0.26939
Capital/Output Industry
Capital/Labor Industry
I Ind. With Low Value of Prod. -0.43828
2 Ind. With Ned. Value of Prod. 0.26363
I Ind. With nigh Value of Prod. 0.27438


0.44613


-0.35570

0.55346
0.29469

0.25602
-0.51823
0.31653
0.38481


0.89231 0.97893 .
0.35621 0.96937 0.69853
0.78635 0.73768
0.35184 0.40790


0.41142
0.92314
0.69015
0.51250
-0.35295
0.72463


0.38089
0.88889
0.74421
0.58943
-0.28350
0.71110


.97
.96
.77
.19
.01
.19
0.27293 .14
.48
.88
.59
.42
-0.26538 -0.27164 .43
0.28283 0.25270 .67
.19


Service
# Service Establishments
# Service Employees
Av. Employees per Service Estab.
Av. Service Wages per Person
I Service Establ. Open All Tear
I Kale Service Employees
2 Unpaid Service Employees
I Service Workers with Insurance
Av. Capital Iapot Service
Capital/Output Service
Capital/Labor Service


# Commerce Itablishments
# Commerce employees
Av. Employees per Comm. Estab.
Av. Commercial Wages per Person
I Commerce Etab. Open All Tear
Z Kale Coerce Employees
Z Unpaid Commerce Employees
Z Insured Comerce Employees
Av. Capital Input Commerce
Capital/Output Commerce
Capital/Labor Commerce


0.34193


0.34441

0.32660
-0.30102


0.89970 0.97883
0.90522 0.98108
0.25511
0.45511


0.74104 0.64737
0.51695 0.44889




0.84588 0.78641
0.44852 0.44099
0.52612 0.53536


0.94913 0.98780
0.89541 0.98478
0.52336 0.44248
0.35761


-0.41501 -0.51255 -0.38103 -0.25725

0.41480
-0.27728 -0.25930


0.36256

0.29303


0.49166
0.71088

-0.58089


0.54292
0.73461

-0.55865


.98
.98
.24
.17
0.61367 0.57926 .34
0.42220 0.44295 .23
.36
.04
.36
.13
.09


Population and Labor Force
Z Population without Insurance -0.47715
I Unemployed Work Age Pop. 0.63374
I White Collar Labor Force 0.73800
I Office Labor Force 0.84972
I Labor Force in Transportation 0.80014
I Labor Force Artisuan 0.82219
2 Labor Force vith Casual 0.76167
Employment
I Labor Force Own Account -0.80696
I Labor Force Part-time Employment 0.19359
I Labor Force Full-time Employment
I Labor Force in Low Wage Jobs
I Labor Force in led. Wage Jobs 0.85425
I Labor Force in nigh Wage Jobs 0.79429
Urban Population 0.42851
% Rural Population -0.78027


-0.58143
0.59807
0.71362
0.76083
0.79674
0.83242
0.67226

-0.85063
0.31997
-0.61756

0.82522
0.73291

-0.76175


-0.43395 -0.33568


0.42003
0.47865


0.31358 0.25508
0.27788


0.42399
0.79732 0.97528
-0.18501 -0.39580


0.42866
0.37624
0.52922

-0.26508


0.25680


.78
0.32995 .25
0.37755 .55
0.48986 .30
.76
.80
.96
.77


iSenvalus 14.44 12.49 6.53 5.87 3.78 4.10 2.87 3.44
Cumulative Variance Explained .31 .26 .45 .38 .57 .47 .60 .54

























Appendix 1-5


Table A.5 Dimensions of the Agricultural and Rural Nonfarm/Suall-
Scale Enterprise Sectors.a


Variable Name Used Factor Factor Score Definitions
in Regression Dimension (4) Observations (-) Observations

A. Rural Nonfarm Small-Scale Enterprise Sector

SSEI 1 Modern, large rural Less modern small-
nonfarm industry scale informal in-
in more urbanized dustry, commerce,
cantons. and service.

SSE2 2 General industrial,
commercial and service
activity in more
urbanized cantons.

SSE3 3 Capital intensive Less capital-
industry, commerce, intensive
service.

SSE4 4 Informal, casual,
self-employed, full-
time and part-time
commerce and service
activities.
and commercial beans.

B. Agricultural Sector

Agl Commercial and sub- Coffee, high popula-
aistence agricul- tion pressure.
ture, cattle, maize,
beans, high dependency
ratio, low educa-
tional levels.

Ag2 2 Cattle/dairy in- Bananas, some com-
dustry, some sugar mercial maize.
cane export, subsis-
tence farming of
beans, high population
pressure, somewhat
better educated
population.

Ag3 3 Sugar cane on
mechanized farms.

Ag4 4 Coffee in a mixed Cocoa cultiva-
agriculture setting, tion.
vith subsistence maize
and commercial beans.

aThese dimensions are based upon the analyses of selected variables
for 62 rural cantons. The analyses are described in Chapter IV.











Appendix 1-6


Table A.6 Classification of Rural Cantons.


Category A:


Well Established Commercial/Export Agriculture and
Well Developed Secondary and Tertiary Sector.


Group 1: Mixed commercial agriculture (coffee) and large
number of informal, self-employed jobs in service
and commerce in outlying areas of the Meseta
Central.

Group 2: Sugar cane, coffee and commercially cropped beans in
the center of the Meseta Central and general
industrial, commercial and service activity.

Group 3: Coffee and banana with some subsistence farming and
larger modern industry, commerce and service in the
Meseta Central.

Group 4: Coffee, cattle/dairy with some sugar cane and less
capital-intensive industrial, commercial and service
activities in the Meseta Central.


Category B:


Commercial/Export Agriculture with Few Job
Opportunities in Secondary and Tertiary Activities.


Group 5: Cocoa monoculture with few, if any, nonagricultural
activity in peripheral locations (Limon, Puntarenas).

Group 6: Commercial cattle/dairy with subsistence farming of
maize and very few, if any, kind of service and
commerce activities.

Group 7: Banana monoculture with few, if any, kind of service
and commerce activities.


Category C:


Highly Capital-Intensive Industry and Some Export
Agriculture.


Group 8: Capital-intensive industry and some coffee
cultivation.




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