• TABLE OF CONTENTS
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 Title Page
 Acknowledgement
 Table of Contents
 List of Tables
 List of Figures
 Abstract
 Introduction
 Income effects of education at...
 Costs of education at ENA
 Summary, conclusions, and...
 Appendix
 Bibliography
 Biographical sketch














Title: Cost-benefit analysis of vocational agricultural education in El Salvador
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Permanent Link: http://ufdc.ufl.edu/UF00073368/00001
 Material Information
Title: Cost-benefit analysis of vocational agricultural education in El Salvador
Physical Description: 65 leaves : ill. ; 28 cm.
Language: English
Creator: Dearing, Jose
Publication Date: 1972
 Subjects
Subject: Agricultural education -- El Salvador   ( lcsh )
Vocational education -- El Salvador   ( lcsh )
Agricultural education -- Costs   ( lcsh )
Vocational education -- Costs   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: El Salvador
 Notes
Thesis: Thesis (M.S. in Agr.)--University of Florida, 1972.
Bibliography: Includes bibliographical references (leaves 63-64).
Statement of Responsibility: by Jose Dearing.
General Note: Typescript.
General Note: Vita.
 Record Information
Bibliographic ID: UF00073368
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 37660795

Table of Contents
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Tables
        Page v
        Page vi
    List of Figures
        Page vii
    Abstract
        Page viii
    Introduction
        Page 1
        Problem
            Page 1
        Organization of the presentation
            Page 2
        The escuela national de agricultura
            Page 3
            Page 4
        Review of previous studies
            Page 5
            Page 6
            Page 7
        Objectives of the study
            Page 8
        Hypotheses
            Page 9
        Problems in using cost-benefit analysis
            Page 10
            Page 11
    Income effects of education at ENA
        Page 12
        The sample
            Page 12
            Page 13
        Average annual differences in income
            Page 14
            Page 15
        The model for estimating income effects of educational investments
            Page 16
            Page 17
            Page 18
            Page 19
        Analysis and interpretation of results
            Page 20
            Page 21
            Page 22
            Page 23
            Page 24
            Page 25
            Page 26
            Page 27
        Income projection equations
            Page 28
            Page 29
        Income projections for ENA and high school graduates
            Page 30
            Page 31
            Page 32
            Page 33
            Page 34
            Page 35
    Costs of education at ENA
        Page 36
        Social costs
            Page 36
            Page 37
        Private costs to ndividuals
            Page 38
        Rates of return from investments in education at ENA
            Page 38
            Page 39
            Page 40
        Comparison of rates of return from investments in education in EL Salvador and other countries
            Page 41
            Page 42
            Page 43
            Page 44
        Adjustments of annual incomes and rates of return for mortality rates
            Page 45
            Page 46
            Page 47
            Page 48
        Comparison of investment in education at ENA with other types of social investments in El Salvador
            Page 49
            Page 50
    Summary, conclusions, and recommendations
        Page 51
        Page 52
        Suggestions for further studies
            Page 53
            Page 54
            Page 55
            Page 56
    Appendix
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
    Bibliography
        Page 63
        Page 64
    Biographical sketch
        Page 65
        Page 66
Full Text



7





Cost-Benefit Analysis of Vocational Agricultural
Education in El Salvador












By

JOSE DARING


A THESIS PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN AGRICULTURE


UNIVERSITY OF FLORIDA
1972













ACKNOWLEDGMENTS


I wish to express sincere appreciation to Dr. Bobby. R.

Eddleman, Chairman of the Supervisory Committee, for his guidance and

contributions in all phases of this study.

I also appreciate the constructive suggestions of the other

members of the committee, Dr. Woodrow W. McPherson, and Dr. David T.

Geithman.

I am grateful for the assistance I received from the Center

for Tropical Agriculture and International Programs through its

Director, Dr. Hugh L. Popenoe, and their representative in El Salvador,

Dr. Harry Peirce.

I would like to thank Susan Cecil for her assistance and

patience in typing the many drafts of the thesis.














TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ......................... ........... ....... ii

LIST OF TABLES ................... ....... .................. v

LIST OF FIGURES ......................... ....* .. ............ vii

ABSTRACT ................................................... viii


Chapter

I. INTRODUCTION ....................................... 1

Problem .......................................... 1
Organization of the Presentation ................. 2
The Escuela Nacional de Agricultura .............. 3
Review of Previous Studies ....................... 5
Objectives of the Study ......................... 8
Hypotheses ....................................... 9
Problems in Using Cost-Benefit Analysis ......... 10

II. INCOME EFFECTS OF EDUCATION AT ENA ................. 12

The Sample .................. .................... 12
Average Annual Differences in Income ............. 14
The Model for Estimating Income Effects
of Educational Investments .................... 16
Analysis and Interpretation of Results ........... 20
Income Projection Equations .................... 28
Income Projections for ENA and High
School Graduates .............................. 30

III. COSTS OF EDUCATION AT ENA ........................... 36

Social Costs ..................................... 36
Private Costs to Individuals ..................... 38
Rates of Return From Investments in
Education at ENA ...............o.... ... 38
Comparison of Rates of Return From
Investments in Education in El
Salvador and Other Countries .................. 41











TABLE OF CONTENTS-Continued


Chapter Page

Adjustments of Annual Incomes and Rates
of Return for Mortality Rates ................ 45
Comparison of Investment in Education at
ENA with Other Types of Social
Investment in El Salvador ..................... 49

IV. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ............ 51

Suggestions For Further Studies ..... .......... 53

APPENDIX .........................** .. ............... 57

BIBLIOGRAPHY .................................. .................... 63

BIOGRAPHICAL SKETCH ....................... .................. 65














LIST OF TABLES


Table Page

1. Average Yearly Income for ENA and High School
Graduates Adjusted to 1964 Colones .............. 16

2. Regression Coefficients of the Actual Variate
and Double-log Models for ENA and High
School Graduates .......................... *....... 21

3. Estimated Incomes and Income Differences for
ENA and High School Graduates by Year
After Graduation From ENA ....................... 31

4. Average Social Cost per Student for 3 Years of
Education at ENA by Type of Cost ................ 37

5. Average Private Cost per Student for 3 Years of
Education at ENA by Type of Cost ................ 39

6. Rates of Return to Schooling for Different
Levels of Schooling in Various Countries ........ 43

7. Probabilities of a 22-year Old Male Salvadorean
Surviving to Indicated Ages ..................... 47

8. Differences in Annual Incomes of ENA and High
School Graduates Adjusted by Survival
Rates ........................................... 48

9. Rates of Return on Investment in Vocational
Agricultural Education in El Salvador ........... 49

10. Matrix of Correlation Coefficients Among the
Independent Variables for the Actual
Variate Regression Equation for High
School Graduates ............................... ..... 58

11. Matrix of Correlation Coefficients Among the
Independent Variables for the Actual
Variate Regression Equation for ENA
Graduates ..........................* .. ..******** 59











LIST OF TABLES--Continued


Table Page


12. Matrix of Correlation Coefficients Among the
Independent Variables for the Double-log
Regression Equation for High School
Graduates ....................................... 60

13. Matrix of Correlation Coefficients Among the
Independent Variables for the Double-log
Regression Equation for ENA Graduates ........... 61

14. Mean Values for the Independent and Dependent
Variables Used in the Regression
Equations ................. .... .. ........ ....... 62















LIST OF FIGURES


Figure Page

1. Income projections for ENA and high school
graduates using the actual variate
model ...... ........ ....... ................ 33

2. Income projections for ENA and high school
graduates using the double-log
model ...... ...... ......*............********** 34









Abstract of Thesis Presented to the Graduate Council of the
University of Florida in Partial Fulfillment of the Requirements
for the Degree of Master of Science in Agriculture


COST-BENEFIT ANALYSIS OF VOCATIONAL AGRICULTURAL
EDUCATION IN EL SALVADOR


By

Josg Dearing

March, 1972


Chairman: B. R. Eddleman
Major Department: Food and Resource Economics

The study was conducted for the purpose of measuring costs and

returns to investments in a 3-year post high school vocational agri-

cultural school. The study consisted of (a) measurement of the income

effects of vocational agricultural education relative to high school

training only, taking into account, the income effects of other

characteristics which are correlated with income, and (b) calculation

of the rate of return to the Salvadorean society and to private

individuals of their investments in the education.

Social and private rates of return were estimated at 11.4 and

16.6, respectively. These rates of return did not take into consideration

the value of nonmonetary and nondirect benefits of education.

Returns to investment in vocational agricultural training were

higher than most other forms of investments that could have been made

by the Salvadorean government. For this reason, the education given

at the Escuela Nacional de Agricultura was considered to be an

economically viable form of investment for the Salvadorean government

and society.


viii















CHAPTER I

INTRODUCTION


Problem


It has been hypothesized that education plays a major role

in agricultural development. However, studies of the economics of

education in the agricultural sector have been based largely on

aggregate data from developed countries. Economic studies of

education in developing countries have focused primarily on the non-

agricultural sector while the agricultural sector has been virtually

ignored.

A problem presently being confronted by policy makers of

developing countries in Central America involves the allocation of

public funds for agricultural education among high schools, post high

school vocational agricultural training programs, and University

training in agriculture and agriculturally related fields. Spe-

cifically, questions are being raised as to which area of agricultural

training would likely result in the highest payoff to individuals and

to the society making a substantial part of the educational investments.

An effective constraint that limits the expansion in all 3 of these

training areas is the availability of public funds. In order to

maximize the returns from investments in agricultural education, it is

necessary that the rates of return from investments in each type of


- 1 -















CHAPTER I

INTRODUCTION


Problem


It has been hypothesized that education plays a major role

in agricultural development. However, studies of the economics of

education in the agricultural sector have been based largely on

aggregate data from developed countries. Economic studies of

education in developing countries have focused primarily on the non-

agricultural sector while the agricultural sector has been virtually

ignored.

A problem presently being confronted by policy makers of

developing countries in Central America involves the allocation of

public funds for agricultural education among high schools, post high

school vocational agricultural training programs, and University

training in agriculture and agriculturally related fields. Spe-

cifically, questions are being raised as to which area of agricultural

training would likely result in the highest payoff to individuals and

to the society making a substantial part of the educational investments.

An effective constraint that limits the expansion in all 3 of these

training areas is the availability of public funds. In order to

maximize the returns from investments in agricultural education, it is

necessary that the rates of return from investments in each type of


- 1 -






- 2 -


agricultural education be known for hoth the individuals receiving

the education and the society making the public investment.

In this study estimates are made of the costs and returns

to human capital created by investment in 3 years of post high school

vocational agricultural education in San Salvador. The study

involves a cost-benefit analysis for a selected group of graduates of

the Escuela Nacional de Agricultura (ENA) in El Salvador.


Organization of the Presentation


The remaining sections of Chapter I provide a description of

the history and organization of the Escuela Nacional de Agricultura,

a brief review of previous studies on the economics of education, an

explanation of the objectives of this study and the hypotheses to be

tested, and a discussion of some problems in using cost-benefit

analysis to evaluate educational investments.

Chapter II provides information on collection of data to

estimate the income effects of educational investments, the analytic

framework used in the study, and the empirical estimates of the

relationships between incomes of ENA and high school graduates and

the variables hypothesized to affect income levels. In Chapter III

an evaluation of costs and estimates of the rates of return to

educational investments at ENA are presented. Finally, Chapter IV

contains the major conclusions derived from the analysis.







-3-


The Escuela Nacional de Agricultural


The Escuela Nacional de Agricultura "Roberto Quinones"

provides the only post high school agricultural vocational training

in El Salvador. ENA was founded on August 28, 1956, under the

Ministry of Agriculture and Livestock.

The school is located in the Valley of San Andres, at 475

meters above sea level. It contains an area of 115 hectares on the

west side of the Pan-American Highway, midway between the cities of

San Salvador and Santa Ana.

ENA confers the degree of Perito Agricola (Agricultural

Expert) after the successful completion of 3 years of required

classroom and field training. The school currently admits some 110

students annually and expects to graduate about 90 Peritos Agricolas

each year. The school's present enrollment is about 250 students.

Students are required to spend mornings doing field and

laboratory work. Formal classroom study is conducted in the afternoons.

The 3-year program of studies is divided into 9 academic trimesters of

15 weeks each. Approximately 1 month of vacation time is allowed

each year.

Forty-seven subjects are required and these are grouped into

6 departments which include Basic Science, Agricultural Mathematics,

Agricultural Economics, Horticulture, Agronomy and Zootechnia. The


IThe National School of Agriculture.






-4-


faculty now consists of 6 professors who are also department heads,

15 assistant professors, and 9 instructors.

The School has graduated 558 persons with the Peritos Agricolas

degree since its founding. The following summary shows the number of

graduates by years:


Years Number of Graduates

1959 42
1960 40
1961 42
1962 34
1963 70
1964 25
1965 39
1966 52
1967 44
1968 81
1969 89


Most of ENA's 558 graduates have been Salvadoreans but a few

students from other Central American countries have been admitted in

past years. About 80 percent of the Salvadorean graduates now work

for the government in such institutions as the Extension Service,

Administration de Bienestar Campesino, the Instituto de Colonizacion

Rural, and others. The'remaining graduates are employed by large

farms, private agribusiness firms, or are self-employed.






-5-


Review of Previous Studies


The years following World War II have provided substantial

evidence of the benefits derived from investing in "human capital."

The nations that recuperated most rapidly from that destructive

conflict were those where the population possessed education and skills

for use of the latest industrial and agricultural techniques. Germany

and Japan could be considered examples of recuperation made possible

by the availability of "human capital."

The recognition of this fact resulted in an increase in the

number of economic studies, during the 1950s, concerning human capital

investments in its different forms, such as improvements in education,

health, diets, medical services, etc. Blaugh's bibliography contains

792 items of reference of which only about 60 were published before

1955.1

The unexplained, increases in production observed over time in

studies by different economists also contributed to the growing

interest in investments in education. After eliminating all produc-

tivity increases attributable to increased use of land, capital, or

labor, the only remaining agent was man himself and the improvements

in his efficiency obtained through investments in human capital.

A review of the literature concerning these human capital

investments indicated that the major part of the work of economists


1Mark Blaugh, A Selected Annotated Bibliography in the
Economics of Education, Pergamon Press, London, 1966.






-6-


in this area has dealt with the. impact of education on economic

growth and development. The approaches to this problem varied from a

general defense of education as a gestator of inventions and inno-

vations in society by "classical" and "neoclassical" economists, to

attempts at empirical measurement of residuals and rates of return

during the 1950s and 1960s.

The pioneering quantitative work in the field came from

S. G. Strumilin1 who in 1925 tried to analyze the economic effect of

education. At the micro-economic level there were studies attempting

to relate earnings to years of schooling. The works of Walsh2 and

Clark3 in the 1930s and Friedman and Kuznets4 in the 1940s prepared

the ground for many of the studies that appeared in the 1950s

and 1960s particularly those of Schultz,S Becker,6 Blaugh,7 and


1S. G. Strumilin, "The Economic Significance of National
Education," reprinted in The Economics of Education, edited by E.A.G..
Robinson and John Vaizey, S. Martin Press, New York, 1965.

2j. Walsh, "Capital Concept Applied to Man," Quarterly Journal
of Economics, Vol. 49, 1935, pp. 255-285.

3H. Clark, Life Earnings in Selected Occupations in the
United States, Harper and Brothers, New York, 1937.

4M. Friedman and S. Kuznets, Income for Independent
Professional Practice, National Bureau of Economic Research, New York,
1946.

5Theodore W. Schultz, The Economic Value of.Education,
Colombia University Press, New York, 1963, pref..viii.

6G. S. Becker, Human Capital, A Theoretical and Empirical
Analysis, with Special Reference to Education, National Bureau of
Economic Research, New York, 1964.

7Blaugh, 1966.







-7-


others.1 Studies on the benefits of education in general in several

countries in Latin America have been made by Carnoy (1967).2 in Mexico,

Harberger and Selowsky (1966)3 in Chile, and Franco Camacho (1964)4

in Colombia.

In general these studies concluded that:

1. Rates of return from investment in education, however
measured, compare favorably with rates of return from other
types of capital investment.
2. Wherever relative earnings reflect the free interplay of
market forces, one may expect that more education with its
corresponding higher earnings reflects higher productivity
from the national point of view.

3. Earnings from educational investments increase with age of
the education recipient over time, but at a decreasing rate.


1For examples, see: G. Stigler and D. Blank, The Demand and
Supply of Scientific Personnel, National Bureau of Economic Research,
New York, 1957; E. Haveman and P. S. West, They Went to College,
Harcourt, Brace and Co., New York, 1952; Dael Wolfle and J. G. Smith,
"Occupational Value of Education for Superior High School Graduates,"
Journal of Higher Education, Vol. 27, 1956, pp. 201-213; H. S.
Houthaker, "Education and Income," Review of Economics and Statistics,
Vol. 41, 1959, pp. 24-28; H. P. Miller, "Annual and Life Time Income
in Relation to Education, 1929-1959," American Economic Review, Vol. 50,
1960, pp. 962-986.

2Martin Carnoy, "Rates of Return to Schooling in Latin America,"
The Journal of Human Resources, Vol. 11, Number 3, Summer 1967.

3Arnold C. Harberger and Marcelo Selowsky, "Key Factors in the
Economic Growth of Chile," presented to the conference at Cornell
University on the Next Decade of Latin American Development, Cornell
University Press, Ithaca, New York, April 1966.

4Guillermo Franco Camacho, Return to Investment in Education
in Colombia, Centro de Estudios sobre Desarrollo Economico, Universidad
de los Andes Bogota, Colombia, July 1964.







-8-


4. Rates of return to individuals and to Latin American countries
making public investments in education are generally higher
for 5 to 8 years of formal schooling than for any other level
of education.

These previous studies of both physical and human capital have

contributed to the present knowledge of rates of return from various

types of educational investments. However, specific information is

very limited on the productivity of investments in post high school

vocational education. One very good study in this area was done by

Carroll (1966)1 on the costs, returns, and value of human capital

created by North Carolinians who invested in 2 years of technical

industrial education at the post high school level.


Objectives of the Study


The objectives of this study are:

1. To estimate costs and returns from investments in education
for a group of graduates of the Escuela Nacional de
Agriculture (ENA).

2. To calculate rates of return to individuals and to the
Salvadorean society from investments in vocational agri-
cultural education in El Salvador.

3. To compare the rates of return from investments in
vocational agricultural education in El Salvador with
previously determined rates of return from investments
in education in other Latin American countries, and with
other forms of capital investment in El Salvador.


1AdgerB. Carroll, Value of Human Capital Created by Investment
in Technical Education, Ph.D. dissertation, North Carolina State
University, Raleigh, North Carolina, 1966.







-9-




Hypotheses


It may be assumed that there is a direct relation between the

level of education of an individual and his level of income, i.e.,

the higher his level of educational accomplishment the higher his

income and vice versa. But questions have been raised by the

Salvadorean government concerning the marginal net income benefits

of investing in agricultural vocational training when compared to a

high school level of education. Is the difference between a high

school graduate's income and an ENA graduate's income sufficient to

repay the investment required to obtain the level of education that

ENA provides? If so, what is the net income benefit to an individual

and to the Salvadorean society that is attributable to differences

in these levels of education? How large is this income difference

and is it constant throughout the productive life of the individual

receiving the education from ENA?

To gain insight into these questions, three hypotheses were

proposed for testing in this study:

1. Individuals with the vocational agricultural education
provided by ENA earn higher incomes than similar persons
possessing a high school degree without any post high school
education.

2. Income earnings from vocational agricultural education
increase with age of the education recipient over time but
at a decreasing rate.

3. The rates of return to both private individuals and the
Salvadorean government from investments in vocational
agricultural education are at least equivalent to rates of
return on physical capital and, therefore, investments in
vocational agricultural education in El Salvador are
economically viable.






- 10 -


Problems in Using Cost-Benefit Analysis


Many different kinds of objections have been raised by

several authors on the appropriateness of cost-benefit analysis as a

tool for evaluating the returns to educational investments. In

general, these objections can be grouped as follows:

1. The assumption that differences in income between the less
and the more education are due to a given training or school
is unrealistic since many of the more affluent members of
society possessing a high school degree may have received
additional training or schooling not related to the
agricultural training at ENA. In this study, all ENA
or high school graduates who had received any kind of
additional schooling and training, other than these two
sources, were eliminated from consideration. Thus, the
observed differences in income are free of the distortions
that these factors could have introduced.

2. Differences in socioeconomic background with implications of
better employment and salary opportunities for the members
of the more affluent families are potentially as important
as levels of education in accounting for differences in
income. It was recognized in this study that socioeconomic
differences could be a major source of variation in income.
Thus, to account for this influence two variables were
introduced in the study. Measures of the father's
educational level and the father's income at the time of the
son's graduation from ENA were used to represent the social
and economic position of the family.

3. The difference in income may be more attributable to
differences in intelligence, motivation, or ability than to
differences in education. A person's intelligence,
motivation, and ability are recognized as important elements
of income potential, perhaps just as important as education
may be. In order to adjust for these differences, a variable
included in the study was the final results on nationally
administered examinations at the end of the high school
training.

4. Within the scope of most studies of the benefits of education,
it is impossible to adequately measure the benefits because
the indirect and nonmonetary benefits of education are
unmeasurable. Some measures of indirect costs, specifically,







- 11 -


the foregone income earning opportunities of the ENA graduate
during the 3 years of ENA schooling, are included in this
study. Nonmonetary effects or benefits of education were not
considered, mainly due to difficulties in measuring these
types of educational benefits. Although most of the non-
monetary benefits arising from education are not directly
measurable, an application of the "residual" concept as
employed by Denison (1964)1 when measuring the contribution
of education to economic growth could be considered as.a
possible approach for providing a rough estimate of these
benefits. However, the "residual" approach was not utilized
in this study because the main concern was with the costs
and returns of education at ENA rather than the contribution
of the educational investment to economic growth of San
Salvador.































1See Edward E. Denison, "Measuring the Contribution of
Education (and the Residual) to Economic Growth" in the Residual
Factor and Economic Growth, OECD, Paris, 1964.














CHAPTER II

INCOME EFFECTS OF EDUCATION AT ENA


The Sample


The collection of data for the study was carried out during

the period October through December 1970. It was begun with the

initial selection of ENA graduates. Although in the early years of

the institution the graduates of ENA may or may not have completed

high school, the initial selection was made from the group that

possessed a high school degree. This was done in order to select

high school classmates of the ENA graduates for a paired comparison

in the study.

After the initial selection, an additional set of restrictions

was used to eliminate possible biases in the study. The final

selection of individuals for the study were those who satisfied the

following criteria:

1. Each participant could have only the high school or the
high school plus ENA education. Persons with additional
schooling or training other than on the job training were
eliminated.

2. No women were selected since ENA has no women graduates.
Also women with a high school education in San Salvador are
generally paid less than their male classmates.

3. No physically disabled persons were included. Their
opportunity for employment is necessarily limited. Consequently,
their income may not be comparable to a person who is not
physically disabled.


- 12 -














CHAPTER II

INCOME EFFECTS OF EDUCATION AT ENA


The Sample


The collection of data for the study was carried out during

the period October through December 1970. It was begun with the

initial selection of ENA graduates. Although in the early years of

the institution the graduates of ENA may or may not have completed

high school, the initial selection was made from the group that

possessed a high school degree. This was done in order to select

high school classmates of the ENA graduates for a paired comparison

in the study.

After the initial selection, an additional set of restrictions

was used to eliminate possible biases in the study. The final

selection of individuals for the study were those who satisfied the

following criteria:

1. Each participant could have only the high school or the
high school plus ENA education. Persons with additional
schooling or training other than on the job training were
eliminated.

2. No women were selected since ENA has no women graduates.
Also women with a high school education in San Salvador are
generally paid less than their male classmates.

3. No physically disabled persons were included. Their
opportunity for employment is necessarily limited. Consequently,
their income may not be comparable to a person who is not
physically disabled.


- 12 -







- 13 -


4. Persons that were employed by relatives or in family-type
enterprises were eliminated. In Latin American countries,
family members are often paid much higher salaries than
nonfamily employees in similar types of employment.
Substantial upward biases could have been introduced into
the study if the income data for persons employed .by
relatives were included.

5. Each high school graduate selected as a paired observation
had to have final results on the graduating high school
examinations comparable to those of his classmate who
attended ENA. The Ministry of Education in San Salvador
gives final examinations to every high school graduate in
the country and the results are entered in the Ministry
records. These results were available for selecting paired
observations among the high school and ENA graduates. In
cases where no adequate high school classmate was found or
was not available for interviewing, the ENA graduate was
dropped from the study.

6. The ENA graduates had to be natives of El Salvador. This
was done to reduce the costs of conducting the study by
eliminating other students who had graduated from ENA and
were back in their countries of origin.

A stratified random sample of ENA graduates was initially

selected from records kept by ENA administrators. The stratification

was done on the basis of the year of graduation in order to obtain a

time series of income data encompassing the years 1959 through 1969.

Records from ENA also gave information about the high school which

each selected ENA graduate attended and the year of graduation from

high school.

The different high schools were visited, and names of at

least 3 classmates of each ENA graduate selected for the study were

obtained for possible comparison. Three high school graduates were

selected for possible comparison in order to have replacements if the

first choice could not be located or did not fulfill the prespecified

criteria. Each of these 3 prospective high school graduates had







- 14 -


obtained grades on the final examinations that were very close to those

of the ENA graduate. Also, their ages were as close as possible to

the age of the ENA graduate. Thus, differences between 2 of the

variables that were included in the study, age, and final grades,

were minimized in order to eliminate their effects on the difference

in the paired subjects incomes.

The ENA graduates were then interviewed to obtain data on the

variables included in the study. Whenever possible, the locations of

their previously selected high school classmates were obtained from

the ENA graduate. Once the ENA graduates had been interviewed then

the high school classmates were contacted. Those who did not fit the

prespecified criteria were eliminated from consideration and a

replacement was chosen.

Even though this process of selection minimized the differences

in age and final grades between individuals from the same high school

class, there remained sufficient variation in grades and ages among

the graduates from different high schools to justify using the 2

variables in the regression analyses. At the end of the survey, 53

pairs of individuals were suitable for the study.

Average Annual Differences in Incomel

Average annual incomes for both ENA and high school graduates

were computed from the survey data for each year. The difference


IIncome is defined as salaries or wages plus fringe benefits
such as employer sponsored insurance programs, living facilities and/or
transportation which are provided free or at reduced cost, paid vaca-
tions, paid sick leave, etc. These fringe benefits were measured in
terms of what the individuals in the study would have been paying for
them if they were not available as part of their incomes, and added to
their salaries or wages to obtain total income.







- 15 -


between the 2 estimates obtained for each year measures the average

difference in mean annual income. ENA graduates first entered the

labor market in 1958. Thus, the average income estimates covered a

period of 11 years with 1969 as the ending year,

Average income on a yearly basis for both ENA and high school

graduates as well as the annual differences are shown in Table 1. An

income difference to the advantage of the ENA graduate started

immediately upon graduation and initial employment. In every case the

first year income of the ENA graduate corresponds with the fourth

year income of the high school graduate. This initial difference was

262 colones at the end of the ENA graduate's first year of employment.1

This may be considered a small difference, but the gap between average

annual incomes widened considerably in the following years. At the

end of the second year the difference was 951 colones, and this

difference widened to a maximum of 3,228 colones at the end of the

seventh year after graduation from ENA.

The drop in the ENA graduates' average income in the eighth

year, i.e., 1966, can be attributed to a drop in wholesale prices of

several agricultural commodities which specifically affected the

incomes of 14 ENA graduates included in the study. These individuals

were working for private industry on a salary plus commission basis.

The reductions in their incomes due to a drop in sales of agricultural


1All incomes are measured in 1964 constant Salvadorean colones
with approximately 2.5 colones equalling $1.00 U.S.






- 16 -


Table 1. Average Yearly Income for ENA and
Adjusted to 1964 Colones


High. School Graduates


Year ENA High School Difference

1 3,727 3,165 262
2 4,394 3,443 951
3 5,177 3,554 1,623
4 6,102 3,915 2,187
5 6,743 4,100 2,643
6 7,390 4,409 2,981
7 8,240 5,012 3,228
8 7,907 5,209 2,697
9 8,249 5,598 2,651
10 8,727 5,861 2,865
11 8,769 6,329 2,440



commodities and purchases of agricultural inputs were enough to more

than offset the income gains of the other 39 ENA graduates in the

study. Details of the drop in agricultural prices in El Salvador

during 1966 may be found in the summary of a study by Robert R. Nathan

Associates (1969).1


The Model For Estimating Income
Effects of Educational Investments


The analytical model expresses income as a function of a

number of factors or variables in addition to educational level. The

primary idea in regressing each group's income on these variables was


1Robert R. Nathan Associates, Inc., Agricultural Sector
Analysis for El Salvador Summary, December 1969.







- 17 -


to obtain the net relation between income and level of education after

taking into account the effects of other variables which were hypothe-

sized to affect income.

The general form of the model is:


Yt = f(G,A,E ,Sf ,IfM,T) [1]


where

Y = annual income of the ENA or high school graduate.

G = scores on final high school examinations administered
nationally in El Salvador (an approximate measure of
ability, motivation and intelligence).

A = age of the individual when he first enters the job
market (a measure of the aggregate effects of life
experience on income).

E = number of years in present employment (a measure of the
P effects of work experience in the present job).

Sf = years of schooling completed by the father (a measure of
the position of the subject's family in the social scale
in El Salvador).

I = father's income as measured by the annual income at time
of the subject's entrance into the labor market (another
variable indicating the possibility of the parent
achieving a high income and social status in the absence
of any significant level of education).

M = migration variable, quantified in terms of the kilometers
between a subject's place of employment and the high
school from which he graduated (a form of investment in
human capital since it is expected to indicate differences
in a person's earning power).

T = a time trend variable for either the ENA graduate's or the
high school graduate's income (measured in terms of the
number of years after graduation from ENA for each type
of subject). The coefficient of this variable is a







- 18 -


measure of the annual incremental increase in the ENA
graduate's or the high school graduate's income taking
into account the effects of the variables, other than
education, that were hypothesized as affecting the
time stream of income.


Empirical Forms of the Model

In order to determine appropriate statistical procedures for

measuring the relationships given by the analytical model, it is

necessary to specify the model in the form of precise mathematical

equations and to state the statistical assumptions concerning the

distributions of all variables. Economic theory seldom specifies

the exact functional form of economic relationships. Therefore, there

was no a priori basis for selecting a particular algebraic form for

the relationships of equation 1. Hence, 3 alternative functional forms

were chosen, namely a linear relationship in the actual variates

and the double-log form and a quadratic relationship in the actual

variates form. It was felt that these 3 mathematical forms of the

analytical model would provide meaningful information.

Implicit in the double-log form is the assumption that income

is an increasing function, at a decreasing rate, of age when age is

measured as time in terms of the year of employment after graduation.

The quadratic form was included to determine if a major portion of the

income benefits of the ENA education had been captured within the 11-

year range of the data and, consequently, whether a narrowing of the

income differences between ENA graduates and high school graduates






- 19 -


in later years could be expected. For the quadratic form, the partial

regression coefficient for the second degree term of the time trend

variable was not statistically significant and the absolute value was

near 0 for both ENA graduates and high school graduates. Additionally,

the sign of this coefficient was positive for high school graduates.

The quadratic form of the model was eliminated from further consider-

ation in the analysis.

The actual variate form of the analytical model may be stated

in the following mathematical form:


Yt = bo + blG + b2A + b3E + b4Sf + bIf + b6M + b7Tt + ut [2]


where the variables in the model are those previously defined in

equation 1, b0 is a constant term, b, through b7 are estimates of the

parameters associated with the independent variables and u is a random

error term.

The log linear form of the analytical model may be mathematically

stated as follows:


anYt = Anc + cnG + cnG + cnA + c3knE + ci4nSf + c5+nIf + c6gnM


+ c7ynTt + nnvt [3]


where the variables in the model are the same as previously defined.and

the parameters have similar interpretations as those for the actual

variate form of the model given in equation 2.







- 20 -


Income is the dependent variable of interest in both the

actual variate and double-log forms of the model. All variables to

the right of the equality signs are assumed to be predetermined, and

hence are not affected by the dependent variable. Classical linear

regression requires that there are no exact linear relationships among

the observed values of the predetermined variables. There is no

reason to expect this condition to be violated for the independent

variables. The random error terms, u and v, are assumed to be

normally, and independently distributed with 0 mean and finite,

constant variance.


Analysis and Interpretation of Results


Estimates of the parameters of both mathematical forms of the

model were obtained by running separate regressions for ENA graduates

and high school graduates. The estimated regression coefficients and

their associated standard errors for each form of the model and level

of education are presented in Table 2.

The regression coefficients for the grade variable had

negative signs for ENA graduates for both the actual variate and

double-log models. These results indicate that ENA graduates with

lower scores on their final high school examinations obtained higher

starting salaries and experienced more rapidly increasing annual

salaries from their jobs after graduating from ENA.

The signs of.the regression coefficients for the age variable

were also negative for ENA and high school graduates for both forms







- 21 -


Table 2. Regression Coefficients of the Actual Variate and Double-log
Models for ENA and High School Graduates


Actual Variate Double-log
Independent
Variables ENA High School ENA High School


G (grades)


A (age)


E (seniority)


Sf (father's
education)

I (father's
income)

M (migration)


T (time trend)


Intercept

R2 (Coefficient
of multiple
determination)

F Value


-4.01
(3.14)


-111.96
(87.78)

108.43*
(50.47)

321.59**
(45.31)

.02
(.04)

-9.26
(8.63)
**
532.57
(53.31)

4,945.00


30.689**


2.22
(1.64)

-62.93*
(36.84)

-8.55
(20.58)

113.41 **
(22.85)

-.01
(.02)

-2.31
(5.42)

320.96**
(26.04)

2,156.70

.41


-.2552
(,2540)

-.6701"*
(.2647

.1284**
(.0286

.3125**
(.0437)

.0317
(.0367)

-.0219
(.0202)

.3335**
(.0290)

10.7860

.52


27.630** 39.132*


aNumbers in parentheses are standard errors of
coefficients.

*Significant at .05 level.


the regression


**Significant at .01 level.


.1842
(.2077)

-.2353
(.1731

-.0106
(.0244)

.1136
(.0332)

.0549*
(.0290)

-.0107
(.0187)

.2659**
(.0225)

6.8574

.52



25.198*







- 22 -


of the model. This variable was measured as the age of the individual

when he,first entered the job market. The results indicate that older

individuals received lower starting salaries and lower income increases

in subsequent years.

The negative signs for the regression coefficients of the

grade and age variables require further explanation. The sign of the

regression coefficients for the grade variable was positive for high

school graduates. This result indicates that high school graduates

with the higher academic performance as measured by the grade

variable, were able to obtain the higher paying positions. Thus,

ability, motivation, and intelligence were recognized by employers in

their selection of employees and in the salaries paid to these

employees.

The sign of the regression coefficients for the grade variable

was negative for ENA graduates. There are two reasons why this is

happening.

1. Persons with lower grades who entered ENA immediately after
its opening are now obtaining high salaries due to their
10 or 11 years of work experience. The data show that
average grades and, thus, the quality of the students have
been improving over the years but this does not offset the
fact that in the short time span of 11 years covered by the
data, the people with lower grades who entered ENA initially
are now receiving higher salaries, i.e., even if we now have
a positive relation of high grades to high salaries it is
not enough to offset the initial low grades and present high
salaries of some initial graduates.

2. Salaries could be interpreted more as a function of performance
at ENA rather than at high school, i.e., some individuals with
low high school grades indicating lower levels of academic






- 23 -


achievement than other high school graduates were better at
learning to work with their hands in an agricultural
environment such as the one offered at ENA. This better
performance at ENA and higher working ability is reflected
in the higher salaries that they are now receiving working
in agriculture.

To discuss the negative regression coefficients for the age

variable on both forms of the model and for both the ENA and high

school graduates, the data averages must be analyzed. The actual

data averages showed that average incomes for ENA and high school

graduates were increasing during the period 1958 to 1969. It also

showed that the average age at graduation for the ENA and high school

graduates was decreasing during the same period.

The decrease in age for both groups was caused by the fact

that when ENA started operating in 1956, the first groups of students

entering the school were older on the average than the normal high

school graduate. They had probably realized after several years of

employment that they could use the opportunity that ENA was offering

to improve their education and decided to enter ENA.

Upon pairing these older ENA graduates with their high school

classmates, and in order to eliminate discrepancies in ages, the high

school graduates were chosen from the same age groups as the ENA

graduates, i.e., high school graduates older than the average.

The combination of the two factors of higher initial ages to

both the ENA and high school graduates in the study with the increasing

incomes resulted in the negative regression coefficients for ages that

can be seen in Table 2.






- 24 -


The coefficients of the age variable were statistically

significant at the 5 percent level for high school graduates using

the actual variate form of the model, and at the 1 percent level

for ENA graduates using the double-log form. Additionally, the other

estimates of the regression coefficients for the grade and age

variables were generally larger than their associated standard errors,

as shown in Table 2. Therefore, the grade and age variables were

included in the income projection models and were used to adjust the

intercept value.

The signs of the regression coefficients for seniority in the

present employment were positive for ENA graduates and negative for

high school graduates for both the actual variate and the double-log

forms of the model. The parameters for the high school level of

education were not statistically significant and the associated

standard errors were more than double the estimates of the parameters,

in size. For ENA graduates, the regression coefficients were

statistically significant at the 5 percent level or above. The high

school education is of a general nature and not directed toward any

specific field such as the education given at ENA. Thus, a higher

"changeover" rate in employment was expected for the high school

graduates which implies a less important seniority factor for them

than for the ENA graduates.

The signs of the regression coefficients for father's education

were as expected, and the coefficients were all statistically







- 25 -


significant at the 1 percent level. These results indicate that the

higher the father's level of education, the higher the income levels

obtained by both ENA and high school graduates.

Father's income did not have as strong an effect on a person's

income as did father's education. The regression coefficient was

statistically significant at the 5 percent level for high school

graduates using the double-log form of the model and the sign of

the coefficient was positive. This regression coefficient was

significant because with the double-log form for ENA and high school

graduates, we have a diminishing average return for graduates' incomes

associated with their fathers' income, which means that fathers' income

level was more influential in terms of initial salary than what it was

as years passed when the productivity of the graduates became more

important in terms of income advantages.

The regression coefficient of the fathers' income was not

significant in the actual variate form of the model since it assumes

a constant effect through time of the father's income. The effect

on the graduates' income of their fathers' income was not of a constant

nature. It was not having the same effect 5, 10, or 15 years after

graduation as it was having initially at the time of their entering

the labor market.

The negative signs for the regression coefficients of the

migration variable were unexpected. Migration was quantified as the






- 26 -


number of kilometers between a.person's place of employment and the

high school from which he graduated. Migration was expected to be

positively related to income. This expectation was based on the

assumption that persons in the age category of 20 to 30 years migrate

in search for higher-paying jobs or a larger number of job oppor-

tunities. Studies by Sjaastad (1961)1 have indicated that migration

of this type generally should result in higher earnings.

The reason for the negative sign on the migration coefficients

may be due to the small size of El Salvador where there are very

definite geographical limitations on migration. The ENA and high

school graduates with higher potential are employed close to their

hometowns and the rest of the graduates are forced to accept

employment away from their hometowns at lower salaries.

The regression coefficients for the trend in income had

positive signs and were statistically significant at the 1 percent

level for both ENA and high school graduates. The coefficients were

higher for ENA graduates than for high school graduates. Thus, ENA

graduates received higher increases in salary in any given year than

their high school classmates. These results indicate that the annual

income differences between ENA graduates and high school graduates

widened over the 11 years of employment after taking into account the


1See Larry A. Sjaastad, "Income and Migration in the United
States," Ph.D. dissertation, University of Chicago, 1961, p. 38.






- 27 -


effects of the other variables.on income. Thus, ENA graduates may

expect to earn higher incomes due to the additional education they

received at ENA when compared to their high school classmates.

In summary, the signs associated with the various coefficients

in the actual variate form of the model remained unchanged when the

double-log form was used for ENA graduates. The consistency in the

signs of the coefficients occurred also for high school graduates

with the exception of the father's income variable. The R2's for the

double-log forms of the regression were higher than for the actual

variate forms. This indicates that the double log form is a better

fit than the actual variate form, i.e., the double log function is

closer to the actual data than the actual variate function.

This better fit was caused by the fact that income through

time was found to be increasing for both the ENA and the high school

graduates but at a decreasing rate, i.e., a diminishing returns type.

of function. The double-log form depicts the diminishing returns

functions better than the actual variate form with its constant

increases assumption and thus the double-log form was a better

statistical fit.

On the basis of the F-test, the null hypothesis of no

relationship between the dependent variable and the independent

variables can be rejected at the 1 percent level for each of the

regressions.







- 28 -


Income Projection Equations


The results of the actual variate and double-log forms of

the model for the ENA and high school graduates were used in two ways:

1. To compare incomes estimated from the regression results
with average incomes obtained from sample data covering
the first 11 years of employment of the ENA graduates.

2. To make income projections over the productive life of the
average ENA and high school graduate, from age 21 to age 65
years. These income trends over time provided the basis for
estimating the stream of annual income benefits over the life
of the ENA graduate that is attributable to the education
received from ENA.

The general income projection equation for the actual

variate model was:


Y = bf + byT [4]


where b6 = b0 + fb X. for i = 1,2,...6, and X represents the

arithmetic mean of all the independent variables except the time

trend variable.

In order to obtain the trend effects of the post high school

education on the incomes of the ENA graduates, the product of the

arithmetic mean and the partial regression coefficients for the other

6 independent variables were summed algebraically and used to adjust

the intercept value.

The numerical projection equation in actual variate form for

ENA graduates was:

ENA 3,693.18 + 532.57 (T ) [5]
where t 1,2,...
where t = 1,2,...,44






- 29 -


For high school graduates, the numerical income projection

equation in actual variate form was:


YHSt 2,678.15 + 320.96 (T ) [6]



where t = 1,2,...,44

The general income projection equation for the double-log

form of the model was:


An Yt = nc + c7knT [7]


where enc = nco + 4 c.inX. for i = 1,2,...6, and X. is the geometric
i
mean of the independent variables except time trend. The values for

both the arithmetic means and the geometric means for all the

variables used in the regressions are presented in Table 14 (Appendix).

The numerical income projection equation for the double-log

form of the model for ENA graduates was:


n YENAt = 8.1731 + .3335 (An Tt) [8]


where t = 1,2,....,44.

For high school graduates the numerical income projection

equation was:


n Yt HS 7.9255 + .2659 (An T ) [9]
tt


where t = 1,2,....,44.







- 30 -


Income Projections for FNA and High School Graduates


Equation 5 and 8 for ENA graduates and equations 6 and 9 for

high school graduates were used to project incomes for an average

period of 44 years of employment after graduation from ENA. The

income projects from both models and the projected income differences

are given in Table 3.

An income advantage to the ENA graduates over their high

school classmates was indicated beginning with the first year of

employment of the ENA graduate for both types of projection models.

In the actual variate model this first year income advantage was

estimated to be 1,227 colones and for the double-log model it was 777

colones.

The regression coefficients for the time trend variable give

the expected average yearly increases in incomes for both groups.

In the actual variate model this income trend was estimated to be

533 colones for ENA graduates and 321 colones for high school

graduates. The difference of 212 colones is not much different from

the mean difference of 262 colones for the first year of employment

of the ENA graduate as shown in Table 1.

Existing information contradicted the possibility that this

annual increment of 212 colones to the income differences would remain

constant over the 44-year projection period. Information was obtained

from the Ministry of Agriculture and Livestock in El Salvador on





- 31 -


Table 3. Estimated Incomes and Income Differences for ENA and High
School Graduates by Year After Graduation From ENA


Actual Variate Double-log

Year ENA High School Diff. ENA High School Diff.


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44


4,226
4,758
5,291
5,823
6,356
6,889
7,421
7,954
8,486
9,019
9,551
10,084
10,617
11,149
11,682
12,214
12,747
13,279
13,812
14,345
14,877
15,410
15,942
16,375
17,007
17,540
18,073
18,605
19,138
19,670
20,203
20,735
21,268
21,801
22,333
22,866
23,398
23,931
24,463
24,996
25,529
26,061
26,594
27,126


2,999
3,320
3,641
3,962
4,283
4,604
4,925
5,246
5,567
5,888
6,209
6,530
6,851
7,172
7,493
7,814
8,134
8,455
8,776
9,097
9,418
9,739
10,060
10,381
10,702'
11,023
11,344
11,665
11,986
12,307
12,628
12,949
13,270
13,591
13,912.
14,233
14,554
14,875
15,196
15,517
15,838
16,158
16,479
16,800


1,227
1,438
1,650
1,861
2,073
2,285
2,496
2,708
2,920
3,131
3,343
3,554
3,766
3,978
4,189
4,401
4,612
4,824
5,036
5,247
5,459
5,670
5,882
5,994
6,305
6,517
6,829
6,940
7,151
7,363
7,575
7,787
7,998
8,210
8,421
8,633
8,845
9,056
9,268
9,480
9,691
9,903
10,114
10,326


3,544
4,466
5,113
5,628
6,063
6,449
6,783
7,092
7,376
7,640
7,886
8,119
8,338
8,547
8,746
8,936
9,118
9,295
9,464
9,627
9,785
9,937
10,086
10,230
10,370
10,507
10,640
10,770
10,897
11,020
11,254
11,315
11,376
11,451
11,603
11,712
11,819
11,925
12,028
12,131
12,231
12,330
12,427
12,522


2,767
3,327
3,706
4,000
4,245
4,456
4,643
4,811
4,963
5,105
5,236
5,358
5,474
5,583
5,685
5,784
5,878
5,969
6,055
6,138
6,218
6,296
6,370
6,443
6,513
6,582
6,648
6,713
6,776
6,837
6,897
6,955
7,012
7,068
7,123
7,177
7,229
7,281
7,331
7,381
7,429
7,477
7,524
7,570


777
1,139
1,407
1,627
1,817
1,987
2,140
2,281
2,412
2,535
2,651
2,760
2,865
2,964
3,061
3,152
3,240
3,326
3,409
3,489
3,567
3,641
3,716
3,787
3,857
3,925
3,992
4,057
4,121
4,183
4,358
4,360
4,364
4,382
4,480
4,535
4,590
4,644
4,697
4,750
4,801
4,853
4,903
4,952






- 32 -


public servants' salaries as related to seniority. Officials in the

Ministry indicated that government employees usually obtained

increases in salaries for periods up to about 25 years of employment,

after which time their salaries could be considered almost constant

until retirement age. The personnel files of several older employees

of the Ministry were analyzed and found to corroborate the fact that

salaries increased with age at a decreasing rate up to about 25 years

of employment, and then, with minor variations, became almost constant.

In the case of governmental employees in El Salvador the highest

average salary that could be expected after 25 years of seniority was

12,000 colones per year.

Based on this information, the salaries of ENA and high

school graduates were projected with the actual variate model

coefficients for 25 years and then held constant for remaining years

until retirement age was reached at 65 years. The results may be

seen in Figure 1.

Constant rates of increases in income differences between the

ENA and high school graduates do not occur with the double-log model.

Projected incomes of the two groups over the 44-year period, with

year one as the first year after graduation for the ENA member of

each pair, are shown in Figure 2.

The income projections using the double-log model were more

consistent with the information obtained from the Ministry of Agri-

culture than the projections using actual variates. The income

projection for ENA graduates corresponded more closely to the present









16
15
14
13
a 12 oo
1o 11 /
8 0 10


8.
1 10.
0 93
vieo

j 7
S 6
5


3
2
1
------------------------------------F.-------
I-
1 5 7 9 11 13 15 17 19 21 23 25 27 ... 44

Years of Employment



Figure 1. Income projections for ENA and high school graduates using the actual
variate model

















-. ---






- -


1 6 11 16 21 26 31 36


Years of Employment






Income projections for ENA and high school graduates using
model


the double-log


L^ J


o 10
0



M 8
'0


x 6


Figure 2.






- 35 -


maximum salary of government employees and the years at which those

high salaries were to be expected.

Twenty-five years after graduation from ENA when, according

to information obtained from the Ministry of Agriculture an employee

should be making at the most 12,000 colones annually measured in

current values, the graduates from ENA have projected annual earnings

of about 10,370 measured in 1964 colones.

A relevant comparison can be made between the maximum annual

salary paid by the government and the projected annual salary at the

end of 25 years of governmental employment by converting the 10,370

colones to current value terms in 1984. This was done by compounding

the 10,370 colones by 1 percent per year for 25 years where the 1

percent per year was the average annual increase in the consumer

price index for El Salvador during the period 1959 to 1969. The

resulting projected salary measured in current 1984 colones was

13,298 which can be compared to the 12,000 colones that represent

present restrictions on salaries of most governmental employees.

The income projections using the double-log model were used

for estimating the rates of return to individuals and the Salvadorean

government from investments in education at ENA.














CHAPTER III

COSTS OF EDUCATION AT ENA


Social Costs


Social costs of education at ENA were estimated from

information provided by ENA's staff and by the Ministry of Agriculture

and Livestock. The private costs to individuals were estimated with

data obtained from the sample survey of selected ENA graduates.

The social costs of education at ENA include the students'

foregone productivity while attending school plus the costs to the

government of providing and paying for the school's staff, facilities,

and supplies. The opportunity cost to society for foregone produc-

tivity of students was considered to be the salaries that the ENA

graduates' classmates had received during the 3 years that ENA

graduates were going to school. It was assumed that ENA graduates

could have been working as high school graduates during the 3 years

they were enrolled at ENA. Thus, the first 3 years of income for

high school graduates were taken as the income foregone to the ENA

graduates and to society.

High school graduates earned an average of 2,354 colones,

adjusted to constant 1964 colones by the consumer price index for

El Salvador,l during the corresponding first year of ENA schooling.


1International Financial Statistics, International Monetary
Fund, Vol. XXIV, Number 2, February 1971.


- 36 -














CHAPTER III

COSTS OF EDUCATION AT ENA


Social Costs


Social costs of education at ENA were estimated from

information provided by ENA's staff and by the Ministry of Agriculture

and Livestock. The private costs to individuals were estimated with

data obtained from the sample survey of selected ENA graduates.

The social costs of education at ENA include the students'

foregone productivity while attending school plus the costs to the

government of providing and paying for the school's staff, facilities,

and supplies. The opportunity cost to society for foregone produc-

tivity of students was considered to be the salaries that the ENA

graduates' classmates had received during the 3 years that ENA

graduates were going to school. It was assumed that ENA graduates

could have been working as high school graduates during the 3 years

they were enrolled at ENA. Thus, the first 3 years of income for

high school graduates were taken as the income foregone to the ENA

graduates and to society.

High school graduates earned an average of 2,354 colones,

adjusted to constant 1964 colones by the consumer price index for

El Salvador,l during the corresponding first year of ENA schooling.


1International Financial Statistics, International Monetary
Fund, Vol. XXIV, Number 2, February 1971.


- 36 -







- 37 -


The annual salary was 2,670 colones during the second year and 3,044

colones during the third year. The sum of these 3 annual incomes

gives an opportunity cost to the ENA graduate and society of 8,068

colones (Table 4).


Table 4. Average Social Cost per Student for 3 Years of Education at
ENA by Type of Cost


Foregone Annual School Facilities, Total
Year Income Staff and Supplies Costs


First 2,354 2,536 4,890

Second 2,670 2,536 5,206

Third 3,044 2,536 5,580

Total for 3
years 8,068 7,608 15,676


In addition to the opportunity costs associated with foregone

income to the students and society, social costs also include expend-'

itures by the Salvadorean government for providing the facilities,

staff, and supplies to ENA. To compute these costs ENA's budgets

were obtained from the Ministry of Agriculture and Livestock for the

academic years during 1959 to 1969. The total school budget for any

given year divided by the number of students attending school in that

year gives the average annual cost per student. These average costs

per year were adjusted by the consumer price index to obtain an

adjusted annual cost per student of 2,536 colones for the entire

period. This amount is composed of 1,248 colones for salaries, 728

colones for supplies, and 562 colones for facilities.






- 38 -


The total cost of providing school facilities, staff, and

supplies for one student during any 3-year period was estimated to

be 7,608 colones. This 3-year cost plus the average total foregone

income per student, previously estimated at 8,068 colones, gives an

estimated average social cost of providing the ENA education at

15,676 colones per student (Table 4).


Private Costs to Individuals


The costs which were borne by ENA students while attending

school are identified as private costs for purposes of this study.

These private costs were less than social costs as the costs of

providing the school facilities, staff, and supplies needed for ENA

operations were government financed and the students incurred no

tuition fees.

Average private costs were the sum of foregone income and the

private expenses incurred by the ENA student while attending school

(Table 5). These private expenses were all those expenses incurred

for special clothes needed at ENA, books, and transportation.


Rates of Return from Investments in Education at ENA


Following Becker (1964),1 rates of return were defined as

those interest rates at which the income, or earnings from investments

in human capital, would be sufficient to repay the investment costs

plus the interest, compounded annually, on that investment. In this

study, the social rate of return was obtained by finding the interest

1Becker (1964).






- 38 -


The total cost of providing school facilities, staff, and

supplies for one student during any 3-year period was estimated to

be 7,608 colones. This 3-year cost plus the average total foregone

income per student, previously estimated at 8,068 colones, gives an

estimated average social cost of providing the ENA education at

15,676 colones per student (Table 4).


Private Costs to Individuals


The costs which were borne by ENA students while attending

school are identified as private costs for purposes of this study.

These private costs were less than social costs as the costs of

providing the school facilities, staff, and supplies needed for ENA

operations were government financed and the students incurred no

tuition fees.

Average private costs were the sum of foregone income and the

private expenses incurred by the ENA student while attending school

(Table 5). These private expenses were all those expenses incurred

for special clothes needed at ENA, books, and transportation.


Rates of Return from Investments in Education at ENA


Following Becker (1964),1 rates of return were defined as

those interest rates at which the income, or earnings from investments

in human capital, would be sufficient to repay the investment costs

plus the interest, compounded annually, on that investment. In this

study, the social rate of return was obtained by finding the interest

1Becker (1964).







- 39 -


Table 5. Average Private Cost per Student for 3 Years of Education
at ENA by Type of Cost


Foregone Annual Total
Year Private Expenses Income Costs

First 446 2,354 2,800

Second 446 2,670 3,116

Third 446 3,044 3,490

Total for 3
years 1,338 8,068 9,406



rate at which the total costs, to the Salvadorean society, of the

education provided to a student at ENA was equal to the discounted

sum of the differences over time between the ENA and high school

graduates' incomes. The private rate of return was obtained using

the total private costs to the ENA student, both actual cost and

foregone income. These costs were also compared to the discounted

sum of the differences over time between the incomes of ENA and high

school graduates. To obtain these 2 rates of return, the estimated

difference in incomes (D ) was defined as follows:


Dt1 Y[ENA] VHSt [10]



where t = 1,2,3,...,44.

The discounting equation was:

44A
Costs = Present value of D = D 1[
St= t (1 + r)t
where r is the discount rate and t is time measured in years.






- 40 -


The expanded form of equation 11 may be written as:

A A A
DI D2 D44
Costs + -- + ... + [12]
(1+r)1 (l+r)2 (l+r)44


The market discount rate, r, was assumed to be the same for each year

in the 44 year period. Thus, it represents the average internal rate

of return on investment in education at ENA. The differences in

income each year between the ENA and high school graduates using the

double-log form of the model were given in Table 3 of Chapter II.

The sum of these income differences over the 44-year time span was

found to be 152,554 colones.

The social costs of three years' education at ENA have been

previously estimated at 15,676 colones. The market discount rate

that equates this value with the stream of income differences between

ENA and high school graduates over the 44-year period is an estimate

.of the social rate of return on public investment in education at ENA.

The social rate of return was found to be 13.4 percent.

This may be interpreted as the average annual rate of return from

the Salvadorean government's investment in 3 years of training for

students at ENA. The 13.4 percent annual return is a minimum estimate

of the social rate of return since it is based on the assumption that

ENA graduates earned annual incomes equivalent to the value of the

marginal product from their work effort. Additional increases in

national income due to increased agricultural productivity stemming

from 'the work efforts of the ENA graduates but not reflected in their






- 41 -


annual salaries, are not included in these computations. Given, the

salary structure of governmental agencies in San Salvador, it is

questionable as to whether governmental employees are earning salaries

equivalent to the value of their marginal productivities.

The private costs have been previously estimated to be 9,406

colones. Equating this figure with the stream of income difference

over the 44 years yields a private rate of return of 19.2 percent.

This return was higher than the social rate of return because social

costs were higher than private costs.


Comparison of Rates of Return From Investments in
Education in El Salvador and Other Countries


There are many difficulties in making comparisons of rates

of return to education between countries. These difficulties include

such things as different educational systems with variations in

requirements for the different education levels, different currencies;

but mainly different internal market conditions causing inflated or

deflated rates of return to educational investments. Recognizing

these as factors affecting the conclusions that may be drawn from

such comparisons, the estimates of social and private rates of

return to agricultural vocational education in El Salvador were

compared with rates of return to education in Mexico, Chile, Colombia,

and the United States.

Carnoy (1967)1 established a comparison between the results

of his study of the Mexican educational system and the results of


ICarnoy (1967).






- 42 -


other education studies in Latin American countries. Some of these

results as well as the rates of return to agricultural education in

El Salvador and to technical industrial education in the United

States are shown in Table 6.

The rates of return to agricultural education in San Salvador

compare very favorably with rates of return to education in other

Latin American countries. In comparison with Mexico's 12-to-13-year

level of education, the 12-to-14-year level of El Salvador produced

a higher social rate of return, 13.4 percent in El Salvador as

compared to 12.4 percent in Mexico. Also a higher private rate of

return in El Salvador (19.2 percent) was obtained compared to

Mexico (15.8 percent). The comparison with Chile was more difficult

as the Chilean study did not show a breakdown of post high school

education between vocational and college training. Nevertheless,

an interpolation was made between the Chilean's levels of education

.and corresponding rates of return to investments required to reach

those levels. Their 7-to-12-year level produced a rate of return of

17 percent. Their 13-to-17-year level, or what can normally be

considered as the college level, produced a rate of return of 12

percent. Thus the vocational education level in Chile corresponding

to about the first three years of college education would, assuming

the same proportional decreases, result in a social rate of return

of about 14 percent for vocational education. This estimate is very

close to the social rate of return of 13.4 percent in San Salvador.





- 43 -


Table 6. Rates of Return to
Various Countries


Schooling for Different Levels of Schooling in


Level of Education Private.Rate of Return Social Rate of Return
Years ----- ----------Percent-------------

Mexico (1963)
2- 4 21.1 17.3
5- 6 48.6 .37.5
7- 8 36.5 23.4
9-11 17.4 14.2
12-13 15.8 12.4
14-16 36.7 29.5

Chile (1959)
1- 6 -- 24.0
7- 9 29.0
7-12 -- 17.0
13-17 -- 12.0

Colombia (1961)
1- 5 20.0 --
6-11a 30.0
6-11b 19.0
12-17 19.0 --

U.S. (North Carolina-1964)
11-12 21.0 17.7

El Salvador (1970)
12-14 19.2 13.4


Sources: Mexico: Martin Carnoy, "Rates of Return to Schooling in Latin
America," The Journal of Human Resources, Vol. 11, Number 3, Summer 1967;

Chile: Arnold C. Harberger and Marcelo Selowsky, "Key Factors in the
Economic Growth of Chile," presented to the conference at Cornell University
on the Next Decade of Latin American Development, Ithaca, New York, Cornell
University Press, April 1966;

Colombia: Guillermo Franco Gamacho, Return to Investment in
Education in Colombia, Centro de Estudios sobre Desarrollo Economico,
Universidad de los Andes Bogota, Colombia, July 1964;

U.S.A.: Adger B. Carrol, "Value of Human Capital Created by
Investment in Technical Education," Ph.D. dissertation, North Carolina, 1966.

aTechnical or special secondary schooling.


bGeneral secondary schooling.







- 44 -


Without making the interpolation it can be seen from Table 6

that the Salvadorean society obtains a higher return on its investment

in vocational agricultural education than the Chilean society receives

from its investment in college education.

When the private rates of return to education in San Salvador

are compared with those in Colombia, it may be seen that the

Salvadoreans obtained a rate of return to investments in vocational

education comparable to the return that Colombians obtained from

investments in a college education. Colombians obtained a private

rate of return of 19 percent from their investments in college edu-

cation while the Salvadoreans obtained a private rate of return of

19.2 percent for their investments in vocational education.

In comparison with the United States, the Salvadoreans

obtained smaller social and private rates of return to their agricul-

tural vocational education. This difference in rates of return is

probably due to differences in job opportunities and to generally

higher salaries that a United States citizen has in comparison with

the rest of the world. Even so, the private rate of return to

vocational agricultural education in El Salvador of 19.2 percent is

not very different from the private rate of return of 21 percent from

vocational technical training in North Carolina. In general, it may

be concluded that the rates of return to agricultural education in

El Salvador compare favorably with those in other Latin American

countries and in the United States.






- 45 -


Adjustments of Annual Incomes and Rates
of Return for. Mortality Rates


Survival rates for ENA graduates were computed in order to

compensate for overestimation in incomes introduced by not considering

the death of some of the educational recipients before reaching age

65. Those ENA graduates not surviving to retirement age would be

expected to decrease both private and social rates of return from

educational investments. No data were available on the survival rates

of various age groups of people in El Salvador. Thus, in order to

calculate the expected survival rates for different age groups in

El Salvador an adjustment factor was established by comparing the

overall average death rates in El Salvador and the United States

during the last 10 years. According to the United Nations monthly

Bulletin of Statistics,1 for every 94.5 persons per 1,000 population

dying in the United States there were 115.4 persons dying in El

Salvador during the same period. This gives a ratio of 0.8188 deaths

in the United States to every death in El Salvador.

This ratio was used in adjusting the result of a study by the

United States Department of Health Education and Welfare (1964)2

showing the life expectancy of 100,000 United States citizens by age

groups. Multiplying the ratio between the United States and El


1United Nations, Monthly Bulletin of Statistics, December 1970.

2U.S. Department of Health, Education and Welfare, 1964,
Vital Statistics of the U.S. 1962, Vol. 11, Mortality, Part A. U.S.
Government Printing Office, Washington.






- 46 -


Salvador by the life expectancy of those 100,000 United States citizens,

an estimate of the life expectancy of the Salvadorean population by

age groups was obtained.

The survival rates for ENA graduates were calculated by taking

their average age upon entry in the labor market, 22 years, finding

the number of expected survivors of age 22 in the adjusted table of

life expectancy for El Salvador and dividing that number into the

number of adjusted expected survivors to age 23. The result was an

estimate of the probability that a Salvadorean 22 years of age would

survive to age 23. The same procedure was applied to each of the

different ages up to 65 years old or retirement age. The results are

shown in Table 7, indicating the probabilities of a 22-year old normal

Salvadorean male surviving to age 23, 24, ..., 65. It was assumed for

the purposes of the study, that the survival rates for high school

graduates were the same as for ENA graduates.

After the probabilities of survival to each age level were

calculated, the differences in income between ENA and high school

graduates were multiplied by the probability of surviving to that

age. By this procedure the estimated income stream with respect to

time was adjusted for one of the most important factors negatively

affecting the value of investments in human capital, the uncertainty

of human life. These adjusted differences in income are given in

Table 8.

Using the adjusted income differences in Table 8 and the

procedures.for estimating rates of return by equating costs with the









Table 7. Probabilities of 2
Table 7. Probabilities of a 22-year Old Male Salvadorean Surviving to Indicated
Ages

Age Proportion of Males Surviving Age Proportion of Males Surviving


22 .8229 44 .78

23 .7808
.8217 45 .779

24 .8206 46
25 .7730
.8193 47

26 .7709
.8189 48

27 .7628
.8180 49

28 ..7560
.8174 50

29 .7533
.8162 51

30 .7469
.8151 52

31 .7383
.8137

32 .7296
.8122 54

33 .7215
.8106 55

34 .7126
.8090

35 .6968
.8074 57

36 .6886
.8048 58

37 .6809
.8039 59

38 .6639
.8025 60

39 .6489
.7994

40 .6321
.7979 62

41 .6163
.7952 63

42 .5996
.7923 64

43 .5874
.7890 65


- 47 -





- 48 -


Table 8. Differences in Annual Incomes of ENA and
Graduates Adjusted by Survival Rates


High School


Year After Adjusted Income Year After Adjusted Income
Graduation Differences Graduation Differences


640

936

1,155

1,333

1,488

1,625

1,749

1,862

1,966

2,063

2,153

2,237

2,317

2,393

2,461

2,534

2,599

2,650

2,720

,2783

2,826

2,873


23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44


2,901

2,950

2,978

3,026

3,045

3,067

3,104

3,124

3,218

3,181

3,149

3,123

3,122

3,123

3,125

3,083

3,048

3,002

2,959

2,910

2,880

2,816






- 49 -


discounted stream of income differences, a social rate of return of

11.4 percent and a private rate of return of 16.6 percent from

investments in education at ENA were obtained.

Table 9 shows a comparison between the unadjusted and adjusted

social and private rates of return. The adjusted figures are

considered to be a more realistic reflection of the social and

private rates of return from investments in agricultural education

in El Salvador.


Table 9. Rates of Return on Investment in Vocational Agricultural
Education in El Salvador


Unadjusted Rate Adjusted Rate
Type of Investment of Return of Return
--------------Percent--------------

Social 13.4 11.4

Private 19.2 16.6



Comparison of Investment in Education at ENA with
Other Types of Social Investment in Et Salvador


The interest rate that the Salvadorean government obtains

from loans to cooperatives in El Salvador is 6 percent annually.

The interest rate on loans by the national credit institution,

Credisa, fluctuates between 9 and 11 percent annually depending on

the situation of the borrower. Industrial loans are made with the

general expectation of between 11.5 and 13.5 percent annual return

on investment. Commercial bank loans range between 9.5 percent and

10.5 percent annually.







- 50 -


When these different rates of return on investments and

interest rates on loans are compared with returns from educational

investments at ENA, it is evident that the adjusted social rate of

return of 11.4 percent annually compares very favorably with most

of them. Perhaps more important, the 16.6 percent adjusted annual

rate of return to private capital invested in education at ENA

compares very favorably with the rate of return on most other types

of private investments in San Salvador.

The Salvadorean Government must pay an annual rate of between

3.5 percent and 8.5 percent on loans from the United States Agency

for International Development, and an interest rate of about 3 per-

cent to 6 percent annually on development loan funds from other

international loan agencies such as the World Bank and the Inter-

American Development Bank. Thus, from the standpoint of the

Salvadorean society an annual rate of return of 11.4 percent from

. investments in education at ENA may be obtained from loans with

annual interest rates that do not exceed 8 percent. These results

indicate that public and private investments in education at ENA

are economically viable when compared with many of the other forms

of public and private investment in El Salvador.















CHAPTER IV

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS


The objectives of this study were:

1. To estimate cost and returns of investments in education
for a group of graduates of the Escuela Nacional de
Agriculture (ENA).

2. To calculate private and social rates of return on investment
in vocational agricultural education in El Salvador.

3. To compare the rates of return from vocational agricultural
education in El Salvador with previously determined rates
of return from educational investment in other countries.

A sample of 53 paired individuals was obtained in a survey

carried out during the period October through December 1970. Each

pair consisted of an ENA graduate and a high school graduate from the

same class and high school as the ENA graduate.

Observations on their respective annual incomes were

obtained covering a period of 11 years. These data were used to

estimate the average annual income for each group and the annual

income differences. The data also were used to estimate the param-

eters of an analytical model which expressed income as a function of

a number variables in addition to educational level. Each group's

income was regressed on a set variables hypothesized to affect

income levels. The net relation between annual incomes and the

level of education, free of the effects of the variables representing

age, results of final high school examinations, seniority, father's

education, father's income, and migration was obtained.


- 51 -







- 52 -


Two empirical forms of the model were estimated, an actual

variate form and a double-log form. Results from the regressions

were used to make income projections over the expected employment

life of the ENA and high school graduates. The double-log form had

the best fit of the two empirical models used when judged on the

basis of the number of statistically significant variables, signs of

the regression coefficients and the R2's. Thus, the income projections

from the double-log model were used in estimating the rates of return

from educational investments at ENA.

The difference between the annual income projections for ENA

and high school graduates over their expected employment life was

considered as the income benefits of the education given at ENA.

These income benefits were compared with the social and private

costs of the ENA education in order to obtain the social and private

rates of return on investments in education at ENA. The estimated

social rate of return was 13.4 percent annually, and the estimated

private rate of return was 19.2 percent annually.

These rates of return were adjusted by estimated age-specific

survival ratios of male Salvadoreans. The adjusted social rate of

return was estimated to be 11.4 percent annually and the private

rate of return was 16.6 percent annually. The adjusted rates of

return were found to compare very favorably with rates of return

to investments in education in Mexico, Chile, Colombia, and the United

States.







- 53 -


The results of the study supported the first hypothesis,

stated in Chapter I, that graduates of ENA obtain higher incomes

throughout their lives than people with only a high school education.

Since the results using the double-log form of the model were judged

to be generally "better" than those from using the actual variate

form, the second hypothesis that ENA and high school graduates'

incomes increase with their age over time, but at a decreasing rate

was supported.

The rates of return to educational investments at ENA were

also compared with other types of investments that the Salvadorean

government could have made with public funds and with the different

interest rates paid by the Salvadorean government on foreign loans.

The results of these comparisons indicate that the annual rates of

return from public and private investments in education at ENA

compare favorably with rates of return from other forms of public

and private investment in San Salvador.


Suggestions for Further Studies


There are many unanswered questions in this study which call

for more extensive study. The returns from investment in vocational

agricultural education are probably realized faster than the returns

from investment in a four-or-five-year college education since the

nature of the training given at ENA is such that its graduates are

probably more "job oriented" than the average college graduate. This

study did not attempt to estimate the rates of return from investments






- 54 -


in a college education. Future studies to estimate the difference

in rates of return from investments in vocational agricultural

education in San Salvador and investment in five years of college

agricultural education are needed before decisions to expand the

program at ENA versus expanding the program at the college level of

agricultural education can be fully evaluated.

Similar studies to identify differences in rates of return

to schooling between college agricultural education and vocational

agricultural education are needed in other Central American countries.

For example, a vocational agricultural'school similar to ENA is now

being started in Costa Rica. Evaluation of the likely payoff to

society relative to a four-to-five-year college agricultural education

could be made if estimates of rates of return to college education in

Central America were available.

Even if considerable differences exist between El Salvador

and Costa Rica, the similarities in the agricultural orientation of

both countries and the level of income of their college agricultural

graduates may be enough to offset the weakness of such a comparison.

As mentioned previously, the estimated social rate of return

was based on the assumption that each person receives annual incomes

equivalent to the value of the marginal productivity of their work

effort. This probably does not occur for governmental employees in

San Salvador because of the rather rigid pay structures of government

agencies. The study did not consider the social value of any

additional agricultural productivity resulting from the efforts of







- 55 -


the ENA graduates that was not reflected in their annual salaries.

In order to include these social benefits in the estimate of the

social rate of return to investments in ENA, the concept used by

Patrick and Kehrberg (197R)1 in Brazil could be applied in further

studies by measuring the increases in farm and agriculturally oriented

businesses due to the efforts of the ENA graduates. Eighty percent

of the ENA graduates are working for the government as extension

agents, representatives of national credit and loan institutions, or

representatives of health and welfare agencies, while the rest are

working for agriculturally oriented businesses, or are self-employed.

With minor changes the conceptual model used in Brazil could be

applied to the situation in El Salvador.

In both countries the gross income per farm is dependent on

similar types of variables such as farmer's education, age, size of

his farm, annual operating expenses for purchased inputs, annual

inputs of fixed capital, amount of labor used, possible contacts

with government employees as extension agents, credit agents, health

and welfare agents, plus the contacts with representatives of agri-

business. These contacts with government agents and representatives

of agribusiness will undoubtedly increase the gross income of the

farm by bringing to the farmer the latest techniques and information

on fertilizers, seeds, prices of products produced on his farm,

information on credit and loans, welfare, etc.


lGeorge F. Patrick and Earl W. Kehrberg, "Costs and Returns
of Education in Five Agricultural Areas of Eastern Brazil," Purdue
University Agricultural Experiment Journal Paper, Purdue University,
Lafayette, 1971.




































APPENDIX













Table 10. Matrix of Correlation Coefficients Among the Independent Variables for the Actual
Variate Regression Equation for High School Graduates


Father's Father's
Age Seniority Education Income Migration Trend Income

Grades -0.11 -0.15 -0.09 0.15 -0.24 -0.09 0.002

Age 0.26 -0.15 -0.30 0.05 0.09 -0.07

Seniority -0.09 -0.01 -0.01 0.19 -0.08

Father's Education 0.23 0.12 0.07 0.28

Father's Income 0.10 -0.05 0.04

Migration -0.01 -0.02

Trend 0.58













Table 11. Matrix of Correlation Coefficients Among the Independent Variables for the Actual
Variate Regression Equation for ENA Graduates


Father's Father's
Age Seniority Education Income Migration Trend Income

Grades -0.10 0.03 -0.09 -0.08 -0.07 -0.10 -0.013

Age 0.36 -0.08 0.07 0.20 0.13 -0.004

Seniority -0.10 -0.04 0.06 0.24 0.16

Father's Education 0.38 -0.27 0.08 0.42

Father's Income -0.31 0.05 0.20

Migration 0.03 -0.15

Trend 0.52













Table 12. Matrix of Correlation Coefficients Among the Independent Variables for the Double-log
Regression Equation for High School Graduates


Father's Father's
Age Seniority Education Income Migration Trend Income

Grades -0.11 -0.01 0.10 0.19 -0.20 -0.07 0.03

Age 0.15 -0.16 -0.27 0.17 0.09 -0.08

Seniority 0.10 0.07 -0.14 0.17 0.09

Father's Education 0.29 -0.04 0.08 0.25

Father's Income -0.00 -0.01 0.16

Migration -0.06 -0.08

Trend 0.56













Table 13. Matrix of Correlation Coefficients Among the Independent Variables for the Double-log
Regression Equation for ENA Graduates


Father's Father's
Age Seniority Education Income Migration Trend Income

Grades -0.12 0.04 -0.09 -0.03 0.25 -0.08 -0.09

Age 0.31 -0.06 0.10 0.15 0.13 -0.006

Seniority -0.13 -0.04 -0.19 0.19 0.24

Father's Education 0.25 -0.06 0.14 0.39

Father's Income -0.31 0.07 0.17

Migration -0.03 -0.16

Trend 0.58







62 -













Table 14. Mean Values for the Independent and Dependent Variables Used in
the Regression Equations


Variable



G (grades)

A (age)

E (seniority)

Sf (father's education)

If (father's income)

M (migration)

T (time)

Y (income)


Actual Variate Model

ENA High School
-----Absolute Value----

538.89 537.96

20.47 20.54

4.46 7.88

8.59 6.94

6756.00 .5338.10

22.14 18.32

4.53 4.53

6107.00 4133.00


Double-log Model

ENA High School
------Logarithms-----

-6.2861 6.2844

3.0155 3.0176

1.2144 1.8938

2.0505 1.8240

2.6451 2.5704

2.6451 2.5704

1.2845 1.2845

8.6019 8.2674














BIBLIOGRAPHY


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Universidad de los Andes Bogota, Colombia, July 1964.

Carnoy, Martin. "Rates of Return to Schooling in Latin America,"
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Denison, Edward E. "Measuring the Contribution of Education (and
the Residual) to Economic Growth" in the Residual Factor and
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BIOGRAPHICAL SKETCH


The author was born in Havana, Cuba, March 17, 1936. He

graduated from Vedado High School in 1953 and in 1954 he entered the

Cuban Military Academy where he received a degree in Military Science

in 1958.

After participating, as a commissioned officer, in military

operations against Fidel Castro's rebels, the author left Cuba in

January, 1959, when the communist took control of the government. He

participated in the Bay of Pigs Invasion in April, 1961, where he

was captured and forced to spend 2 years in a communist prisoner-of-

war camp in Havana. Ransomed from Cuba, in 1963, he joined the

U. S. Marine Corps as a commissioned officer, 2nd Lieutenant. Upon

finishing Basic Officer's School at Quantico, Virginia, he transferred

to the U. S. Army Special Forces (Green Berets). He participated in

the operation in the Dominican Republic from May, 1965, to June,

1966, and went to the Republic of Vietnam as a Special Forces Captain

in December, 1967. He returned to the United States in December,

1968, and left the active service in March, 1969. In September, 1969,

he entered the graduate school at the University of Florida. He has

been a graduate assistant in the Department of Food and Resource

Economics since June, 1970.

The author was married to Carmina Dellunde y Garcia-Menocal,

August 10, 1963. They have 2 children, Joseph F., born October 17,

1965, and Christina, born October 2, 1966.


- 65 -











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 thesis for the degree of
Master of Science in Agriculture.



Bobby R. Eddleman, Associate Professor
Food and Resource Economics


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 thesis for the degree of
Master of Science in Agriculture.



Woodrow W. McPherson, Professor
Food and Resource. Economics


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 thesis for the degree of
Master of Science in Agriculture_ ..-.. -- ,

--: ------ t-i--i- ------..--*
David T. Geithman,- Asistant Professor
Economics



This thesis was submitted to the Dean of the College of Agriculture
and to the Graduate Council, and was accepted as partial fulfillment
of the requirements for the degree of Master of Science in Agriculture.


March, 1972


Dean, College of Agriculture




Dean, Graduate School




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