metadata: authors: 0: author: Psacharopoulos, George
1: author: Ying Chu Ng
count: Latin America,Caribbean
docna: 0: docna: Earnings and education in Latin America :
            assessing priorities for schooling investments
docty: Policy Research Working Paper
subtopic: Primary Education,Secondary Education,Economics of Education,Educational Sciences
teratopic: Gender
abstracts: cdata!: The authors use household survey data
            for 18 Latin American countries to assess earnings
            differentials by level of education, and to assess how these
            differentials changed in the 1980s.  Introducing the cost of
            education allows them to estimate private and social rates
            of return on investments on education across several
            dimensions:  by gender, by level of education, by sector of
            employment, by nature of the secondary school curriculum,
            and over time.  The results show that, in most countries,
            the earnings premium received by graduates of higher
            education decreased in the 1980s.  Investment in primary
            education shows the highest rate of return among all levels
            considered - and is still the number one investment priority
            in most countries.
repnme: repnme: Earnings and education in Latin America :
            assessing priorities for schooling investments
display_title: Earnings and education in Latin
            America : assessing priorities for schooling investments
listing_relative_url: /research/1992/12/699496/earnings-education-latin-america-assessing-priorities-schooling-investments
topicv3: Primary Education m1326139 480,Education m1327048 470,Secondary Education m1327058 481,Economics of Education m1327511 1540,Returns to Education m1327513 2174
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Gender
origu: Latin America and Caribbean: Technical Department
no_of_pages: 101
model_extractions: <!-- Start of picture text --> Policy Researchl<br>Education and Employment<br><!-- End of picture text --> WPS **1056**

- This papera product of the Technical Department, Latin America and the Caribbean Region is part of a larger effort in the department to document the role of education in the region's development efforts. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Liliana Longo, room 14-187, extension 39244 (December 1992, 93 pages).

Psacharopoulos and Ng use household survey dzta for 18 Latin American countries to assess earnings differentials by level of education, and to assess how these differentials changed in the 1980s.

Introducing the cost of education allows them to estimate private and social rates of return on investments on education across several dimensions: by gender, by level of education, by sector of employment, by nature of the secondary school curriculum, and over time.

The results show that, in most countries, the earnings premium received by graduates of higher education decreased in the 1980s. Investment in primary education shows the highest rate of return among all levels considered - and is still the number one investment priority in most countries.

The Policy Research Working Paper Series disseminates the findings of work under way in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
# EARNINGS AND EDUCATION IN LATIN AMERICA: ASSESSING PRIORITIES FOR SCHOOLING INVESTMENTS

George Psacharopoulos Ying Chu Ng Technical Department Latin America and the Caribbean Region The World Bank
# **TAxi OF CONTW8**

|**I.**Introduction .. .. .. . ...,,,,,.,,,,.,<br>.........<br>....<br>v.<br>.1<br>.|
|---|
|II. Methodology ...................................<br>..<br>..<br>.<br>**3**<br>The Full Discounting or Elaborate Method**................**<br>3<br> <br>|
|The Earnings Function Method**...............................**<br>**6**<br>|
|The Short-cut Method .<br>.........<br>. .....<br>.<br>,......<br>. ...<br>8|
|III.Empirical Findings<br>.....<br>.............<br>.....<br>. **8**<br> <br>|
|Basic Earnings Functions ...................................<br>9<br>|
|Overtime Changes in Relative Earnings<br>**..**<br>14<br>|
|Overtime Returns to Education**.......................**<br>**.....**<br>20|
|Extended Earnings Functions ....<br>..<br>.............<br>......<br>.<br>.<br>25<br>|
|SecondaryGeneral<br>versus**Vocational**<br>**Education**<br>**...**<br>**.**<br>**...........**<br>28<br>Private Returns to Education<br>**...................**<br>29<br>|
|Social Returns to Education**....**<br>**...................**<br>**......**<br>31|
|IV. Conclusion ...<br>..<br>.<br>..<br>.<br>.<br>.<br>.<br>.......<br>.<br>37|
|References .......<br>39|
|Annex<br>1. Survey Data Description**.0.00000.....0.0.0.41**|
|Annex2. Variable Definitions ..<br>..<br>....<br>0.0......<br>.44|
|Annex3.BasicEanings Functions .....<br>.............<br>0...<br>48|
|Annex 4. Extended Earnings Functions**.....**<br>**.**<br>**............**<br>**77**|
# **LIST** OF **TABLES**

|1. Mean Earnings, Years of Schooling and Mincerian Returns to Education: Entire<br>Sample, circa 1989 ...........<br>;.......<br>10|
|---|
|2. Mean Earnings, Years of Schooling and Mincerian Returns to Education: by Gender,<br>circa 1989<br>....<br>..<br>12|
|3. Mean Eamings, Years of Schooling and Mincerian Retums to Education: by Sector,<br>circa 1989..<br>13|
|4. Over Time Earnings Differentials by Educational Level_.15_|
|5. Over Time Educational Attainment of the Labor Force.18|
|6. Mean Earnings, Years of Schooling and Mincerian Returns to Education: Entire<br>Sample, circa 1980<br>...............<br>.21|
|7. Mean Earnings, Years of Schooling and Mincerian Returns to Education: by Gender,<br>circa 1980<br>..<br>22|
|8.Mean Earnings, Years of Schooling and Mincerian Returns to Education: by Sector,<br>circa 1980 .24|
|9. Mean Earnings and Mincerian Returns to Education: by Level of Education ......<br>26|
|10. Private Retums to Education by Level of Education: Full Method .30|
|11. Social Direct Unit Cost per Year by Level of Education .33|
|12. Social Returns to Education by Level of Education: Full Method.<br>35|
|13. Index of Public Subsidization of Education by Level<br>......<br>38|
# Lwsr **OF FIGURES**

1. Age - Earnings Profiles by Level of Education, Brazil 1989 ................ **_5_** 2. Time Trend of Earnings Differentials by Level of Education in Four Countries .... 17

2. Time Trend of Earnings Differentials by Level of Education in Four Countries ....
# I. INTRODUCrION

The study of the relationship between earnings and education has been the cornerstone of the economics of education. There are several reasons why this relationship has been investigated extensively throughout the world since "human capital" was established in the economic growth and development literature in the early 1960s (see Schultz, 1961).

First, differences in mean earnings between graduates of successive levels of education reflect the premium associated with educational investment. This premium is definitely "private", in the sense that it accrues to the person who undertook the investment. Under certain conditions, however, this premium can also be used as a proxy for the higher social productivity of the graduate, e.g. as evidenced by earnings differentials in the competitive sector of the economy. Thus, earnings differentials by level of education provide an expedient, empirical way of documenting first order relative scarcities in the market for graduates in a given society, and may provide a guide for educational investments.

Second, the above earnings premium can be combined with the cost (either private or social) of investing in different levels of education, thus leading to a cost-benefit analysis of investment in schooling, which is very similar to traditional cost-benefit analysis in other sectors of the economy. Since the 1960s there has been an immense literature devoted to the profitability of investments in human capital. Estimates of such profitability are better known as "rates of return to investment in education" (for a review see Psacharopoulos, 1985a).
-2-

Third, and beyond the above efficiency considerations, the earnings premium associated with different levels of education leads to equity assessments in a given society, e.g. how does the provision of education, and at what level, contributes to poverty alleviation or a more equal income distribution?

The purpose of this paper is to present evidence on the relationship between earnings and education in Latin America for the latest year possible, and to discuss the implications of the findings for school investment priorities in the region. The analysis is necessarily macro, in the sense that a large number of countries has been covered, using a consistent methodology, although at the expense of performing more detailed within-country analyses. Thus the findings presented here should be considered as indicative regarding educational investment priorities in the region, pending more detailed country-specific work.

This study improves upon the previous cross-country studies by applying a consisten. mrethodology of estimation!'. Moreover, the database comes from representative national data sets of the countries in question. The data also allow for the study of time trends of rates of return to cducation, as well as assessing the profitability of different types of the secondary school curriculum.

The next section presents the methodology of estimation, followed by a section on the evidence. The implications of the findings are discussed in the concluding section.

_I/_ Previous studies on the returns to education in Latin America are listed in the Peferences.
-3-

# II. **MErHODOLOGY**

Estimates of the profitability of investment in education can be arrived at using different methods. The method adopted by various authors is often dictated by the nature of the available data. In what follows we briefly describe the available methods as background to the estimates ,we present later in this paper.

# (1) The Full Discountinlg or Elaborate Method

This method amounts to finding the discount rate (r) that equates a stream of benefits to a stream of costs at a given point in time: where (Yb-Ya)t is the earnings differential between a more educated person (subscript b) and a less educated person (subscript a, the control group). Cb represents the direct costs of schooling consisting of tuition and fees, books, etc., and Y. denotes the student's foregone earnings or indirect costs.

On the assumption that the direct costs of acquiring the next higher level of education are roughly the same as the income one would earn from part-time or summer job during the study, equation (1) can be simplified to:
-4-

(2)

*"1 t r)' S(Y,)S(l _+r)_

There is an important asymmetry between computing the returns to primary education and those to the other levels. Primary school children, mostly aged 6 to .2 years, do not forego earnings during the entire length of their studies. In the empirical analysis that follows we have assumed only two years of foregone earnings for primary school children.

The resulting r by solving the above equation can be considered to be a "private" rate of return. When the direct cost paid by the individual is replaced by the true full cost of someone's education (paid by the state in most countries) one can arrive at a "social" rate of return. Private rates of return are used to explain people's behavior in seeking education of different levels and types, and as distributive measures of the use of public resources. Social rates of return, on the other hand, can be used to set investment priorities for future educational investments.

The discoundng of actual net age-earnings profiles is the most appropriate method of estimating the returns to education because it takes into account the most important part of the -- one early earning history of the individual. But this method is very thirsty in terms of data must have a sufficient number of observations in a given age-educational level cell for constructing "well-behaved" age-earnings profiles. (An example of such profile is given in Figure 1.) This has been rarely the case in the early days of the economics of education, and thus researchers have resorted to less data-demanding methods.
u <!-- Start of picture text --> iXf<br>< Co<br>%.~~~~~~~<br>0~~<br>o~~ o<br>- ~ ~ r ¢ D<br><!-- End of picture text -->
**_-6-_**

# (2) The Earnings Function Method

This method is derived from Mincer (1974) and involves the fitting of a function specified as: where S is the number of years of schooling of individual i, and EX and EX<sup>2</sup> are years of experience and its square, respectively. Often weeks-worked or hours-worked are added as independent variables to this function as compensatory factors. We call the above a "basic earnings function." In this semi-log specification the coefficient of S **(O)** can be interpreted as the average private rate of return to one additional year of schooling, regardless of the educationa **l** evel this year of schooling refers to.

The earnings function method can be used to estimate returns to education at different levels by converting the continuous years of schooling variable (S) into a series of dummy variables, say PRIM, SEC, UNIV, to denote the fact that a person has completed the corresponding level of education, and that of course there are also people in the sample with no education in order to avoid matrix singularity.

Then, after fitting the following "extended earnings function": (4)
the private rate of return to different levels of education can be derived from the following formulas: <!-- Start of picture text --> r(PRJM = S<br>SPRIM<br>r(m TU SSEC-S<br>t = S-<br><!-- End of picture text --> where SpRIM, SsEc, and SU,V stand for the total number of years of schooling for each successive level of education (primary education completed, secondary education completed, and university education completed, respectively). Again, care has to be taken regarding the foregone earnings of primary school-aged children. In the empirical analysis that follows we have assigned only two years of foregone earnings to this group.

Although convenient, because it requires less data, this method is slightly inferior to the previous one as it in fact assumes flat age-earnings profiles for different levels of education (see Psacharopoulos ard Layard, 1979).
**-8** -

# (3) The Short-cut Method

This estimation method is based on a simple formula: <!-- Start of picture text -->
*=  S(t*-,)<br><!-- End of picture text -->

where **_Y_** refers to the mean earnings of an individual with the subscripted educational level, _k_ is the higher educational level in the comparison, and AS represents the difference in years of schooling between k and the control group.

Although this method is very easy to use, it is by definition very inferior relative to, any of the other methods described above. The weakness of the method lies in the abstraction from the fact that age-earnings profiles are concave (see Figure 1), and that the discounting process (in estimating the true rate of return) is very sensitive to the values of the early ages entering the calculation. Hence we will not use this method in the empirical analysis that follows.

# **IH.** EMPIRCAL **FmDIGS**

The data used in this analysis come from a series of Household Surveys conducted in the region around 1989. For some countries the same survey was also conducted around 1980. Details on the nature of the surveys appear in Annex 1. Variable definitions appear in Annex 2.
-9-

# A. Basic Earnings Functions

As a way of summarizing the data, we first fit basic earnings functions (see Annex 3). Table 1 shows the resulting average rate of return, along with the mean years of schooling of the sample. In comparing mean years of schooling across counties, it should be noted that the countries with the highest mean years of schooling, Bolivia and Peru, refer to urban samples.

Among the eighteen countries, twelve of them have an average return to schooling of 10 percent or above. The mean of years of schooling ranges from 4.3 to 10.1 years. The lowest mean years of schooling are found in Guatemala and Brazil, while Bolivians and Peruvians generally have more than 10 years of schooling because of the urban nature of the samples. It is not surprising, therefore, to find that the average private rate of return to schooling in Bolivia and Peru is relatively lower than that in Guatemala and Brazil.
- 10 -

Table.it Mean Earnings, Years of Schooling and Mlncerian Returns to Education: Entlre Sample circa 1989

|Country|Survey<br>Year|Earnings<br>(in local<br>currency)|Mean<br>Years of<br>Schooling|Rate of<br>Return (%)y|
|---|---|---|---|---|
|Argentina|1989|7,456|9.1|10.3|
|Bolivia|1989|364|10.1|7.1|
|Brazil|1989|6,969|5.3|14.7|
|Chile|1989|40,275|8.5|12.0|
|Colombia|1989|53,643|8.2|14.0|
|Costa Rica|1989|16,346|6.9|10.9|
|Dominican Republic|1989|652|8.8|9.4|
|Ecuador|1987|27,313|9.6|10.8|
|El Salvador|1990|899|6.9|9.7|
|Guatemala|1989|242|4.3|14.9|
|Honduras|1989|453|6.5|17.6|
|Jamaica|1989|5,886|7.2|28.8|
|Mexico|1984|81,029|6.6|14.1|
|Panama|1989|292|9.2|13.7|
|Paraguay|1990|239,861|9.1|11.5|
|Peru|1990|9,912|10.1|8.1|
|Uruguay|1989|145,840|9.0|9.7|
|Venezuela|1989|6,894|9.1|8.4|

Source: Annex 3. Notes: I' Coefficient of the years-of-schooling variable in the basic earnings function, times 100.
- 11 -

The earnings functions have been fitted to male and female sub-samples in order to examine differences in returns to education by gender. As seen in Table 2, working females generally attain more education than their male counterparts in all countries except Ecuador, El Salvador, Paraguay, and Peru. This, however, does not gives females an advantage over males in acquiring better pay in absolute terms. The mean earnings of each country shows that males in fact earn more than females2'. However, the rate of return being a reli concept, this does not prevent females realizing a higher payoff than men to their schooling investment in fifteen out of the eighteen cases reported in Table 2.

Table 3 presents the results of an alternative partitioning of the sample into public and private employment. As found elsewhere, public sector employees have more years of schooling than their private sector counterparts, and also a lower rate of return on their schooling investments (Psacharopoulos, 1983).

2/ For a more detailed analysis of the male-femal **e** arnings differentials using the same samples and selectivity correction, see Psacharopoulos and Tzannatos (1992a) and (1992b).
- 12 -

Table 2. Mean Earnings, Years of Schooling and Mincerian Returns to Education: by Gender, circa 1989

|Country|Year|Ear<br>(ocal|nings<br>curr.)|Ye<br>Sch|ars of<br>ooling|Rate o<br>(pe|f Return<br>rcent)|
|---|---|---|---|---|---|---|---|
|||Males|Females|Males|Females|Males|Females|
|Argentina|1989|<br>8,563|5,629|8.7|9.8|10.7|11.2|
|Bolivia|1989|<br>402|270|9.5|_11.5_|7.3|7.7|
|Brazil|1989|7,151|6,640|_5.0_|5.9|15.4|.14.2|
|Chile|1989|43,878|31,373|8.2|9.4|12.1|13.2|
|Colombia|1989|60,592|43,124|8.1|8.3|14.5|12.9|
|Costa Rica|1989|17,283|13,928|6.4|8.1|_10.5_|13.5|
|Dominican Republic|1989|722|522|8.4|9.5|7.8|12.0|
|Ecuador|1987|32,049|19,360|9.7|9.5|9.8|11.5|
|El Salvador|1990|988|766|7.0|6.7|9.6|9.8|
|Guatemala|1989|256|207|4.0|4.8|14.2|16.3|
|Honduras|1989|488|384|6.1|7.1|17.2|19.8|
|Jamaica|1989|9,829|1,047|7.0|7.4|28.0|31.7|
|Mexico|1984|84,520|71,533|6.3|_7.5_|14.1|15.0|
|Panama|1989|312|258|8.6|10.1|12.6|17.1|
|Paraguay|1990|290,496|165,787|9.1|8.9|10.3|12.1|
|Peru|1990|11,482|6,937|10.2|9.8|8.5|_6.5_|
|Uruguay|1989|178,086|97,865|8.7|9.3|9.0|10.6|
|Venezuela|1989|7,858|6,067|7.9|10.2|8.4|8.0|

Source: Annex 3.
- 13 -

Table 3. Mean Earnings, Years of Schooling and Mincerian Returns to Education: by Employment Sector, **circa** 1989

|Country|Year|Ear<br>(loc|nings<br>alcurr.)|Years of|Schooling|Rate o<br>(pe|f Return<br>rcent)|
|---|---|---|---|---|---|---|---|
|||Private|Public|Private|Public|Private|<br>Public|
|Argentina|1989|7,155|8,463|_8.5_|11.C|11.1|8.9|
|Bolivia|1989|361|369|8.7|11.7|8.7|6.7|
|Brazil|1989|9,979|7,746|4.1|8.2|15.0|11.4|
|Chile|1989|38,232|58,044|8.1|12.3|11.4|11.2|
|Colombia|1989|50,279|80,116|7.8|11.2|13.7|11.9|
|Costa Rica|1989|14,245|26,079|6.2|10.1|9.3|8.5|
|Dominican Republic|1989|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Ecuador|1987|26,254|32,258|8.9|12.8|11.3|7.1|
|El Salvador|1990|835|1,215|6.1|10.9|9.4|6.2|
|Guatemala|1989|219|455|3.7|9.1|14.1|8.7|
|Honduras|1989|406|714|5.9|9.9|17.4|12.3|
|Jamaica|1989|6,443|3,140|6.9|8.5|24.9|16.0|
|Mexico|1984|73,448|108,503|5.7|9.7|15.4|8.0|
|Panama|1989|232|434|8.2|11.5|12.2|11.0|
|Paraguay|1990|,236,360|264,629|8.6|12.6|11.9|8.3|
|Peru|1990|8,002|10,295|9.6|12.5|9.0|9.0|
|Uruguay|1989|146,073|145,069|8.6|10.2|10.5|5.7|
|Venezuela|1989|6,700|7,037|7.3|_10.5_|9.7|-<br>6.6|

Source: Annex 3 n.a. = not available
- 14 -

# **B. Overtime Changes in Relative Earnings**

Before we **turn** to the discussion of the time trends of the rate of return to schooling, let us examine the behavior of earnings differentials in Latin American countries. Owing **to** the availability of data, calculation of earnings differentials over time is only allowed for twelve countries. Table 4 presents the earnings differentials in absolute and index values by level of education, and Table 5 provides the corresponding structure of the labor force by level of education.

N.

As seen in Table 4 and depicted in Figure 2, the relative earnings of individuals with either secondary or university education decreased over time in most conntries with the exception of Bolivia, Guatemala and Panama. This is expected since the number of workers as a percentage of the labor force with post-compulsory schooling increased during the same time period in all countries expect Hondurasl'. This implies a strong relationship between schooling and income distribution in society. Earnings differentials narrow as the educational attaintment of the labor force increases.

_I/_ The overtime comparability of the Honduras, Peru and Uruguay data sets is limited given the fact the later surveys refer only to urban areas.
- 15-

Table 4. Over Time Earnings Differentials by Educational Level

|||Earnings O|ocal curr.)|Index (Prim|. =100)|
|---|---|---|---|---|---|
|Country|Educational Level|||||
|<br>(Early-Late|<br>)|Early|Late|Early|Late|
|Argentina<br>|No Education|n.a.|2,724|n.a.|56|
|1980-89|Less than Primary|511|4,099|83|85|
||Primary|615|4,843|100|100|
||Secondary|976|7,757|159|160|
||University|2,381|13,231|387|272,|
|Bolivia|No Education|134,311|268|64|95|
|1986-89|Primary|208,855|281|100|100|
||Secondary|235,632|363|113|129|
||University|463,970|795|222|283|
|Brazil|No Education|7,399|5,257|69|74|
|1979-89|Primary<br>|10,791|7,145|100|100|
||Secondary|17,398|8,772|161|123|
||University|39,934|17,658|370|247|
|Colombia|No Education|3,690|24,004|65|65|
|1980-89|Primary|5,643|36,769|100|100|
||Secondary|12,719|57,529|225|156|
||University|31,413|145,487|557|396|
|Costa Rica|No Education|1,256|10,389|87|79|
|1981-89|Primary|1,437|13,222|100|100|
||Secondary|2,763|20,956|192|_158_|
||University|5,043|42,323|351|320|
|Guatemala|No Education|92|134|48|54|
|1986-89|Primary|192|246|100|100'|
||<br>Secondary|354|444|184|180|
||<br>University|762|1,096|-<br>397|446|
|Honduras|No Education|180|n.a.|59|n.a.|
|1986-89|Less than Primary|269|263|88|72|
||<br>Primary|307|363|100|100|
||<br>Secondary|647|700|211|193|
||University|1,667|1,853|543|_510_|
|||||Con|tinued<br>-|
- 16-

Table 4 cont'd.

|||Earnings (|local curr.)|Ind|ex|
|---|---|---|---|---|---|
|Country|Educational Level||<br>-||--- -|
|<br>(Early-Late)||Early|Late|Early|Late|
|Panama|No Education|115|167|70|93|
|1979-89|Primary|164|180|100|100|
||Secondary|279|335|170|186|
||<br>University|592|750|361|417|
|Paraguay|Less than Primary*|26,290|145,371|87|82|
|1983-90|Primary|30,064|177,895|100|100|
||Secondary|55,837|282,777|186|159|
||<br>University|112,866|599,067|375|337|
|Peru|No Education|2,661|6,593|36|89|
|1985-90|Primary|7,493|7,431|100|100|
||Secondary|10,432|9,014|139|121-|
||University|27,463|22,778|367|307|
|Uruguay|No Education|2,132|68,495|58|52|
|1981-89|Primary|3,646|132,583|100|100|
||Secondary|5,278|171,160|145|129|
||University|8,157|263,405|224|199|
|Venezuela|No Education|n.a.|3,778|n.a.|63|
|1981-89|Less than Primary|1,940|4,869|84|81|
||Primary|2,321|5,983|100|100|
||Secondary|3,367|8,013|145|134|
||University|6,138|13,802|264|231|

Source: Annex 3.

Notes: n.a. = not available.

* Due to the sample size, individuals with no education or less-than-primary education are grouped into less-than-primary education.
> - 17 -

# Figurie 2. Time Trend of Earnings Differentials by Level of Education In Four Countries

<!-- Start of picture text --> O Lug fm MmP a Sftm°<br>a imiverity<br>400 ,<br>200.<br>jjo _ _ _ _ -j0 _ _<br>0<br>lgho Yh<br>Cduobb4 It"<br>N om E*gatla a S,ec Y<br>0 UseoIYUdvet<br>I00~~~~~~~~~~~~~~~~~~0<br>- 400- Soo<br>200,<br>_ _ _ _ 010_ _<br>0<br>Wb Y<br><!-- End of picture text --> <!-- Start of picture text --> o No *t1a Sw Y<br>a LrlvmsIty<br>~~~~~~~~~~~~~~~~~400<br>200<br>_ _ _ _ _ _<br>~~~~~~~~~~~~~~~~~~~0<br>19gh YW<br>V-u=t, IN49<br>O Lm Then Ptiwy a 9Sm1cn'y<br>200<br>1 *,<br>0<br>toh jg<br><!-- End of picture text -->
- 18 -

Table S. Over **Time** Educational Attainment of the Labor **Force**

|||Attainme|nt**(X)**|Index||
|---|---|---|---|---|---|
|Countr|Educational Level|||||
|y<br>(Early-Late)||Early|Late|Early|Late|
|Argentina|No Education|n.a.|1.4|n.a.|4|
|1980-89|Less than Primary|19.9|13.6|_55_|42|
||<br>Primary|36.1|321|100|100|
||Secondary|11.6|14.0|32|44|
||<br>University|3.7|6.8|10|21|
|Bolivia|No Education|4.9|2.1|104|39|
|1986-89|Primary|4.7|5.4|100|100|
||<br>Secondary|27.1|17.9|577|331|
||University|8.9|10.5|189|194|
|Brazil|No Education|19.2|22.7|202|295|
|1979-89|Primary|9.5|7.7|100|100|
||<br>Secondary|9.4|12.4|99|161|
||<br>University|4.1|6.3|43|82|
|Colombia|No Education|4.2|2.4|20|12|
|1980-89|Primary|20.9|19.3|100|100|
||<br>Secondary|10.2|19.5|49|101|
||<br>University|5.6|9.2|27|48|
|Costa Rica|No Education|6.0|_5.5_|19|16|
|1981-89|Primary|32.4|34.7|100|100|
||<br>Secondary|9.9|12.2|31|35|
||<br>University|3.7|_5.0_|11|14|
|Guatemala|No Education|30.3|31.0|194|200'|
|1986-89|Primary|15.6|_15.5_|100|100|
||<br>Secondary|7.9|6.1|51|39|
||<br>University|2.0|2.4|13|15|
|Honduras|No Education|11.9|n.a.|52|n.a.|
|1986-89|Less than Primary|26.4|41.1|115|146|
||<br>Primary|22.9|28.1|100|100|
||<br>Secondary|19.1|14.8|83|53|
||University|4.8|4.1|21|15|

Continued -
- 19-

Table **5** cont'd.

|||Attainme|nt (%)|Index||
|---|---|---|---|---|---|
|Country|Educational Level|-||||
|<br>(Early-Late)||Early|Late|Early|Late|
|Panama|No Education|4.0|3.4|14|13|
|1979-89|Primary|28.1|25.2|100|100|
||Secondary|18.4|21.1|**65**|84|
||University|5.8|9.2|21|37|
|Paraguay|Less than Primary*|23.9|**15.0**|86|**_59_**|
|1983-90|Primary|27.9|25.3|100|100|
||Secondary|14.9|23.5|**53**|93|
||University|**_5.9_**|6.4|21|25|
|Peru|No Education|11.1|19.4|**65**|96|
|1985-90|Primary|17.1|20.2|100|100|
||Secondary|32.4|36.2|189|179|
||University|**_5.6_**|10.0|33|50|
|Uruguay|No Education|1.6|0.9|**_5_**|2|
|1981-89|Primary|30.2|51.6|100|100|
||Secondary|4.7|13.0|16|25|
||University|8.3|5.7|27|11|
|Venezuela|No Education|n.a.|2.8|n.a.|11|
|1981-89|Less than Primary|22.0|8.8|67|34|
||Primary|32.8|25.6|100|100|
||Secondary|9.2|10.0|28|39|
||University|4.1|13.1|13|**_51_**|

Notes: n.a. = not available.

> ***** Due to the sample size, individuals with no education or **less-dhan-primary** education are grouped into less-than-primary education.
-20-

# C. Overtime Returns to Education

Among the eighteen Latin America countries, eight country data sets allow us to examine the change in returns to education over the past decade and of comparing the results shown in Table 6 to those in Table 1. There exists a slight declining trend in the returns to education, while the ten year difference in the returns to education in Argentina and Panama is only marginal. (See also Psacharopoulos (1987), Psacharopoulos and Velez (1988) and Riveros (1990), on the experiences of other countries).

The behavior of rates of return to schooling by gender over time demonstrates an interesting pattern (Table 7). In the earlier period females did not have such a clear advantage over men in the returns they realized on their schooling investment; while in the early eighties, only three out of the eight countries show that the average rate of return to males is greater than their female counterparts.
- 21-

Table 6. Mean Earnings, Years of Schoolng and Mincerian Returns to Education: Entire Sample, chrca 1980

|Country|Year|Earnings<br>local curr.)|Years of<br>Schooling|Rate of<br>Return(m )|
|---|---|---|---|---|
|Argentina|1980|735,000|7.9|9.3|
|Brazil|1979|10,244|5.3|11.4|
|Colombia|1980|8,401|6.9|18.6|
|Costa Rica|1981|1,925|6.7|16.8|
|Panama|1979|231|_8.5_|13.0|
|Paraguay|1983|42,203|8.2|11.6|
|Uruguay|1981|4,375|_8.5_|10.3|
|Venezuela|1981|2,619|7.3|11.8|

Source: Annex 3.
-22-

**:ble 7.** Mean Earings, Yem of Schooling and Mincerian Returns to Education: by Gender, circa 1980

|Country|Year|Earnings (|ocal curr.)|Years of|Schooling|Rate of|Return(%)|
|---|---|---|---|---|---|---|---|
|||Males|Females|Males|Females|Males|<br>Females|
|Argentina|1980|1,234Q9O|0 333,000|8.1|7.8|8.0|9.8|
|Brazil|1979|10,943|6,813|_5.2_|_5.5_|11.8|8.9|
|Colombia|1980|10,124|5,626|7.0|6.8|18.6|17.3|
|Costa Rica|1981|1,539|3,334|_5.9_|_9.5_|14.8|10.4|
|Panama|1979|193|281|7.3|10.0|13.4|10.5|
|Paraguay|1983|51,043|28,678|8.4|7.7|10.5|11.7|
|Uruguay|1981|4,549|4,318|9.9|8.0|7.3|10.5|
|Venezuela|1981|2,545|2,799|6.7|8.7|12.1|10.9|

Source: Annex 3.
- 23 -

Unlike previous researchi', the results of the present study for the time trend in rate of return to education by gender is mixed. Although the overall average schooling for both males and females increased over time, the figures presented in Table 7 strongly support an obvious declining trend in the average rate of return for males, but not in the case of females. In five out of the eight countries studied, an increase over time in average years of schooling of females is associated with a higher rate of return. This interesting result implies that the "race" between technology and education is more relevant in the case of males than their female counterparts.

The relatively higher educational attainment of public employees has not changed during the past decade (Table 8). The behavior of the average rate of return, however, has changed during the 1980s. In early 1980s, the average rates of return for the public employees in Argentina, Costa Rica, Panama, Uruguay and Venezuela were higher than those of individuals who worked in the private sector. Towards the end of the decade, the opposite is observed. This may be due to the economic crisis and fiscal constraints during the period.

g/ See Psacharopoulos (1987), Psacharopoulos and Steier (1988), and Riveros (1990).
-24 -

**Table S.** Mean Earnings, **Years** of Schooling and Mincerlan Returns to Education: by Sector of Employment, **circa** 1980

|Country|Year|Earnings|(local curr.)|Years of|Schooling|Rate of R|eturn (%)|
|---|---|---|---|---|---|---|---|
|||Private|Public|Private|Public|Private|Public|
|Argentina|1980|657,000|1,319,000|7.7|9.5|6.3|9.7|
|Colombia|1980|10,374|3,442|7.7|_5.0_|16.7|14.0|
|Costa Rica|1981|1,990|1,744|6.3|7.7|15.6|20.6|
|Panama|1979|250|198|7.8|9.6|12.3|15.7|
|Paraguay|1983|40,880|51,151|7.6|11.9|12.0|9.2|
|Uruguay|1981|5,331|2,748|8.3|8.7|9.1|11.9|
|Venezuela|1981|2,895|1,898|7.1|7.8|11.2|14.0|

Source: Annex 3.
- _25_ -

# D. Extended Earnings Functions

Table 9 reports the results of fitting the extended earnings functions in order to disaggregate the retums to education by level of schooling. As shown in this table, all countries except Chile, Costa Rica, Panama, Paraguay and Peru are found to have the highest rate of return in primary education compared to other educational levels. This result is consistent with studies by Psacharopoulos (1972, 1981, 1985a, 1988). Consequently, primary education remains the most profitable investment in human capital, at least from the individual point of view.
- 26 -

**Table 9.** Mean Earings and Mincerlan Retums to Educatlon: by Level of Education

|Country|Year||Earning|s Oocal curr.)||R|ate of Retum (%|)|
|---|---|---|---|---|---|---|---|---|
|||No Ed.|Primary|Secondary/<br>Gen-Voc|<br>Univ.|Primary|Secondary/<br>Gen-Voc|<br>Univ.|
|Argentina|1989|2,724|4,843|7,757<br>7,321"<br>7,915F|13,231|17.90|11.32<br>12.30(<br>10.98W|15.40|
|Bolivia|1989|268|281|363<br>351w<br>398W|795<br>|19.40|7.60<br>6.63W<br>10.43#|17.40|
|Brazil|1989|_5,257_|7,145|8,772|17,658|49.55|16.40|25.75|
|Chile|1989|21,253|28,451|48,309<br>48,165"<br>77,109k'|150,269|16.65|9.45<br>9.43w<br>13.08Wf|16.70|
|Colombia|1989|24,004|36,769|57,529|145,487|28.35|12.22|19.92|
|Costa Rica|1989|10,389|13,222|20,956<br>20,89(0<br>21,365k|42,323|17.05|11.44<br>11.78W<br>12.25'|17.60|
|Dom. Rep.|1989|376|_506_|668<br>669"<br>656k|1,677|24.50|10.78<br>10.83W<br>10.28W|13.98|
|Ecuador|1987|12,931|18,725|30,101|64,584|27.25|11.37|11.40|
|El Salvador|<br>1990|495|741|1,180|2,458|23.75|11.35|8.93|
|Guatemala|1989|134|246|444|1,096|46.55|15.02|18.38|
|Honduras|1989|263|363|700<br>693",<br>1,091W|1,853<br>|23.30L'|<br>19.94<br>19.80W<br>28.10Y|19.90|
|Jamaica|1989|301|4,285|6,604|7,473|23.45|38.27|9.68|
|Mexico|1984|18,613|54,558|100,393<br>101,402W<br>93,182k'|173,304<br>|51.85|12.40<br>12.42"<br>12.33kv|12.95|
|Panama|1989|167|180|335<br>342"<br>274W|750|11.80|14.25<br>15.02w<br>9.86w<br>Co|18.12<br>ntinued<br>-|
- 27 -

Table 9 cont'd.

|Country|Year||Earnings|(local curr.)||Ra|te of Retun (%|)|
|---|---|---|---|---|---|---|---|---|
|||No Ed.|Primary|Secondary/<br>Gen-Voc|Univ.|Primary|Secondary/<br>Gen-Voc|Univ|
|Paraguay|1990|145,371|177,895|282,777|599,067|10.801'|11.82|13.77|
|Peru|1990|6,593|7,431|9,014<br>9,013"<br>9,036|22,778|9.90|4.14<br>4.04p<br>6.362'|13.28|
|Uruguay|1989|68,495|132,583|171,160<br>173,317WV<br>168,486'|263,405<br>|30.55|8.98<br>8.151'<br>10.15k|14.05|
|Venezuela|1989|3,778|5,983|8,013<br>7,672W<br>9,994'|13,802|17.55|9.66<br>8.9421<br>13.10(|8.12|

Source: Annex 4.

Notes: ' The rate of retum is with respect to less-than-primary education. Earnings for individuals of secondary general education.

**E** amings for individuals of secondary vocational education.

> SJ Rate of return to secondary generl education with respect to pinmy education.

> J' Rate of return to secondary vocational education with respec **t** o primary education.
- 28 -

On the other hand, only three of the countries demonstrate a declining trend of rates of return by level of education (El Salvador, Honduras and Venezuela). The "law of diminishing returns" in investment in human capital in Latin America is found wamting.

# E. Secondary General versus Vocational Education

Ten of the data sets distinguish the type of secondary education attended. It has been argued that secondary vocational education provides individuals with potentially a better match between skills acquired in school and the world of work. Previous studies have shown that empirical evidence on this issue is ambiguous5'.

We use both the earnings function and elaborate methods to estimate rates of return to secondary general or secondary vocational education with respect to primary education. For both methods, the results are very similar. Six out of the eleven countries show that the rate of return to secondary vocational education is higher than that of secondary general education (Tables 9 and 10). Moreover, in seven out of eleven countries, the private return to secondary general education does not differ from that of secondary vocational education&'. The results suggest that individuals who graduated from a vocational track of the secondary level generally have a relatively higher rate of return to their investment in education.

S/ See Bellew and Moock (1990), Psacharopoulos (1985a, 1985b, 1987), Psacharopoulos and Velez (1992), and Velez and Psacharopoulos (1987).

f/ Rates of return to vocational and general secondary graduates do not differ in Dominican Republic and Mexico.
- -29

# F. Private Returns to Education

We now use the full discounting method described earlier to compute more accurate returns to education for each level and type of schooling. As shown in Table 10, among the eighteen countries, in only about half of them does primary education remain the most profitable investment by the end of the 1980s. The number of countries whose rate of return to secondary education with respect to primary education is the lowest is eight when the elaborate method is adopted. This provides stronger evidence that higher education is worthwhile to attain from an individual point of view. While El Salvador, Jamaica, Paraguay and Venezuela demonstrate a declining trend of rates of return by educational level, rates of return at each successive level increases the higher the level of education in Argentina, Chile and Honduras.
- 30 -

**Table 10.** Private Returns to Education by **Level** of Education: **FuU** Method (percent)

|Country|Year|Primary||Seconda|ry|University|
|---|---|---|---|---|---|---|
||||All|General|Vocational||
|Argentina|1989|10.14|14.16|15.69|13.42|14.92|
|Bolivia|1989|9.84|8.12|7.08|8.51|16.42|
|Brazil|1989|36.61|5.13|n.a.|n.a.|28.17|
|Chile|1989|9.7|12.91|12.91|8.59|20.69|
|Colombia|1989|27.69|14.66|n.a.|n.a.|21.66|
|Costa Rica|1989|12.18|17.60|16.72|17.08|12.93|
|Dominican Rep.|1989|85.11|15.11|14.98|negative|<br>19.43|
|Ecuador|1987|17.09|17.16|n.a.|n.a.|12.67|
|El Salvador|1990|18.90|14.51|n.a.|n.a.|9.50|
|Guatemala|1989|33.75|17.85|n.a.|n.a.|22.22|
|Honduras|1989|20.84"|23.29|22.08|26.78|25.94|
|Jamaica|1989|20.44|15.65|n.a.|n.a.|n.a.|
|Mexico|1984|21.57|15.13|_15.05_|15.77|21.74|
|Panama|1989|5.71|21.03|21.02|32.55|20.99|
|Paraguay|1990|23.741'|14.64|n.a.|n.a.|13.73|
|Peru|1990|13.22|6.26|6.18|negative|39.65|
|Uruguay|1989|27.80|10.31|10.09|10.29|12.80|
|Venezuela|1989|36.32|14.58|14.13|15.38|10.96|

n.a. = not available Notes: 1' The rate of return is with respect to less-than-primary education.
# - - 31

# G. Social Returns to Education

In order to provide insight on the issues of resource allocation among different levels of education we present the social rates of return by educational level. Given the availability of information on unit costs of different levels of schooling, as shown in Table 11, we calculate the social rates of return to education for fourteen Latin American countries using the elaborate method discussed earlier.

From Table 12, it is obvious that social rates of return are lower than the private rates of return presented in Table 10 for the corresponding countries. This is consistent with previous findings.Y In addition, in ten out of the fourteen countries the social rates of return to investment in primary education is the highest. This result is consistent with studies by Fiszbein and Psacharopoulos (1991), Gomez-Castellanos and Psacharopoulos (1990), Kugler and Psacharopoulos (1989), Psacharopoulos and Velez (1992), and Riveros (1990). It also suggests that investment in primary education is still the number one priority for most of the Latin American countries.

With the lowest social rate of return to secondary education, countries such as Argentina, Bolivia, Brazil, Colombia, Mexico and Uruguay may have reached the equilibrium at this particular level of education. As seen from the estimates of social returns to both primary and university education for these six countries, with the exception of Argentina, it is profitable to 7/ See Gomez-Castellanos and Psacharopoulos (1990), Fiszbein and Psacharopoulos (1991), Kugler and Psacharopoulos (1989), Psacharopoulos (1981, 1985a, 1988), Psacharopoulos and Velez (1992), and Riveros (1990).
- 32 -

devote resources not only to primary education, but also to higher education. Results for Argentina, Brazil, Chile Honduras, Mexico and Uruguay indicate that on efficiency grounds, investments in higher and primary education are both profitable and that expansion of secondary education is an alternative for Chile, Costa Rica and Honduras.
- 33 -

Table 11. Social **Direct** Unit **Cost** per Year by Level of Education

||Education||Unit Cost/Ye|ar|Source|
|---|---|---|---|---|---|
|Country/Year|Level|$US|Local Curr|.<br>Index||
|Argentina<br>1989|Primary<br>Secondary<br>- General<br>- Vocational<br>University|142<br>310<br>577|18,759<br>41,157<br>76,603|100<br>219<br>408|Inflated from 1985 figures.<br>Kugler and Psacharopoulos<br>(1989).|
|Bolivia<br>1990|Primary<br>Secondary<br>- General<br>- Vocational<br>University|72<br>101<br>504|228<br>320<br>1598|100<br>140<br>701|Bolivia Central Bank, Memoria<br>1990. UDAPE, "Basic<br>Statistics on the University<br>System," 1992. Exchange rate<br>3.17.|
|Brazil<br>1989|Primary<br>Secondary<br>- General<br>- Vocational<br>Uk.versity|243<br>434<br>3,174<br>5,911|689<br>1,231<br>8,996<br>16,752|100<br>179<br>1,306<br>2,431|Reports from Human Resources<br>Operations Division, Country<br>Department I, Latin America<br>and Caribbean Region Office,<br>World Bank.Y1|
|Chile<br>1989|Primary<br>Secondary<br>- General<br>- Vocational<br>University|181<br>173<br>169<br>192<br>1,006|48,280a<br>46,201'<br>45,090<br>51,200<br>268,829|100<br>96<br>93<br>106<br>_557_|Inflated from 1987 figu'res,<br>World Bank estimates by<br>McMeekin.|
|Colombia<br>1990|Primary<br>Secondary<br>- General<br>- Vocational<br>University|80<br>191<br>1013|44,080<br>95,860<br>509,020|100<br>217<br>1155|Fundacion para la Educacion<br>Superior y Desarrollo,<br>communication to the World<br>Bank, August 11, 1992.|
|Costa Rica<br>1989|Primary<br>Secondary<br>- General<br>- Vocational<br>University|144<br>222<br>423<br>1,541|11,750<br>18,133<br>34,476<br>125,614|100<br>154<br>293<br>1,069|Inflated from 1986 figures in<br>1978 constant prices. Cota<br>Rica Public Sector Expenditure<br>Review, Report No. 7877-CR,<br>Latin America and the<br>Canbbean Regional Office, The<br>World Bank (1989).|

Continued -
34 Table 11 cont'd.

||Education<br>l|US|Unit Cost/Ye<br>Ll C|ar<br> <br>Id|Source|
|---|---|---|---|---|---|
|Country/Year|Leve|$|oca urr.|<br>nex||
|Ecuador<br>1987|Primary<br>Secondary<br>- General<br>- Vocational<br>University|97<br>218<br>652|16,500<br>37,200<br>111,200|100<br>225<br>674|Gomez-Castellanos and<br>Psacharopoulos (1990).|
|El Salvador<br>1990|Primary<br>Secondary<br>- General<br>- Vocational|63<br>66|502<br>533|100<br>106|Fundacidn Miguel Kast (1990).|
||University|227|1,852|364||
|Honduras<br>1989|Primary<br>Secondary<br>- General<br>- Vocational|115<br>185|229<br>369|100<br>161|Deflated from 1990 figures.<br>Honduras Social Sector<br>Programs by W. McGreevey,<br>The World Bank, 1990.|
||University|1,233|2,465|1,076||
|Jamaica<br>1989|Primary<br>Secondary<br>- General<br>- Vocational<br>University|127<br>323<br>6,452|729<br>1,854<br>37,062|100<br>254<br>5,084|Inflated from 1987 figures.y<br>Access. frality and Efficiency in<br>Caribbean Education: A<br>Regional Study. Population and<br>Human Resources Division,<br>Country Dept. Ell, Latin<br>American and Caribbean<br>Regional Office, The World<br>Bank, 1992.|

Continued -
- 35 -

Table 11 cont'd.

||Educational<br>||Unit Cost/Yea<br>|r<br> <br>|Source|
|---|---|---|---|---|---|
|Country/Year|Level|USS|Local Curr.|<br>Index||
|Mexico<br>1984|Primary<br>Secondary<br>- General<br>- Vocational<br>University|135<br>589<br>1,035|22,663<br>98,846t<br>173,658|100<br>436<br>766|Based on SEP (1985), p.8.|
|Paraguay<br>1990|Primary<br>Secondary<br>- General<br>- Vocational|50<br>144|61,623<br>176,259|100<br>286|Public Ex=<br>1ditue Reviews The<br>Social S<br>n,Report No.<br>10193-PA,TheWorld Bank,<br>1992.|
||University|710|872,685|1,416||
|Uruguay<br>1989|Primary<br>Secondary|256|154,900|100|Psacharopoulos<br>and VElez<br>(1992).|
||- General|306|185,100|119||
||- Vocational|450|272,200|176||
||University|614|371,500|240||
|Venezuela|Primary|198|7,668|100|Fiszbein and Psacharopoulos|
|1989|Secondary|347|13,420|175|(1991).|
||<br>- General|315|12,170|159||
||- Vocational|471|18,198|237||
||University|1,625|62,795|819||

# Source: Annex 1.

Notes: Y Primary education unit cost comes from Public Sendina on Social Prosram=,while secondary educadon unit costs come from Secondary Education and Trainina in Bmzil: Adaotin to New Economic Realiis. llhzr Educaion Reform in Brazil provides the unit cost of higher education. Information for both pnmary and secondary education has to be inflated to 1989 current prices.

Unit cost for nine years of primary education.

I Weighted average of academic and vocational tracks _(4.5:_ I ratio, respectively from Unesco, Statisti Xmuboic, 1990) for 3 years of secondary education.

FUnit cost refers to upper secondary education.
- 36 -

TIble 12. Social Returns to Education by Level of Education: Full Method (percent)

|Country|Year|Primary||Secondar|y|University|
|---|---|---|---|---|---|---|
||||All|General|Vocational||
|Argentina|1989|8.44|7.06|n.a.|n.a.|_7.55_|
|Bolivia|1989|9.31|7.31|n.a.|n.a.|13.13|
|Brazil|1989|_35.55_|5.08"'|n.a.|n.a.|21.44|
|Chile|1989|8.05|11.10|11.13|7.23|13.96|
|Colombia|1989|20.04|11.36|n.a.|n.a.|14.03|
|Costa Rica|1989|11.16|14.43W|<br>13.74|11.86|9.03|
|Ecuador|1987|14.71|12.73|n.a.|n.a.|9.87|
|El Salvador|1990|16.38|13.33|n.a.|n.a.|8.00|
|Honduras|1989|18.181|19.72|n.a.|n.a.|18.93|
|Jamaica|1989|17.73|7.92|n.a.|n.a.|n.a.|
|Mexico|1984|19.04|9.57|n.a.|n.a.|12.91|
|Paraguay|1990|20.30-'|12.74|n.a.|n.a.|10.84|
|Uruguay|1989|21.61|8.05"'|8.16|7.08|10.29|
|Venezuela|1989|23.41|10.18|9.99|10.42|6.16|

n.a. = not available.

Notes: LI Same cost as secondary general education.

**1'** The rate of return is with respect to less-than-primary education.
- 37 -

As suggested by Psacharopoulos (1988), results of vocationalization may not always match expectations. The estimates of social rates of return to general versus vocational secondary education shown in Table 12 demonstrate mixed results. Given the limitation of the data and the limited availability of unit cost estimates, the present study is not able to provide any conclusive argument on this issue.

By comparing the private and social rates of return by level of education, one can address the extent of public subsidization of education. Table 13 presents the index of subsidization of education by level, showing that in nearly all countries university education is the most heavily subsidized level of schooling. This finding points to the regressive allocation of public finds -- a finding common in the world today.

# IV. Conclusion

In this paper we have adopted a macro, although consistent, approach to study earnings differentials by level of education in 18 Latin American countries and used altemative methodologies to assess the private and social retums to investment in each level of education. The results document that primary education is still the number one investment priority in most countries, and that the earnings premium of higher education graduates has declined during the eighties. Given the macro nature of our findings, these should be used only as a starting point for more detailed within-country work in order to assess education investment priorities in the respective country.
- 38-

# Table 13. Index of Pubic Subsidization of Education by Level (percent)

|Country|Year|Primary|Secondary|Higher|
|---|---|---|---|---|
|Argentina|1989|17|50|49|
|Bolivia|1989|6|10|20|
|Brazil|1989|3|1|30|
|Chile|1989|17|14|33|
|Colombia|1989|28|22|35|
|Costa Rica|1989|8|18|30|
|Ecuador|1989|14|26|22|
|El Salvador|1989|13|8|16|
|Honduras|1989|13|17|27|
|Jamaica|1989|13|49|n.a.|
|Mexico|1989|12|37|41|
|Paraguay|1989|14|13|21|
|Uruguay|1989|22|22|20|
|Venezuela|1989|36|30|44|

## Source: Based on Tables 10 and 12.

n.a. - not available.

Note: The subsidization index is defined as the percent by which the private rate of return exceeds the social rate.
- 39 -

# REFEENCES

- Bellew, Rosemary and Peter Moock. 1990. "Vocational and Technical Education in Peru." Economics of Education Review 9(4):365-75.

- Bourgouignon, F. 1980. "The Role of Education in the Urban Labour Market During the Process of Development: The Case of Colombia." Paper presented at the VI World Congress of the International Economic Association, Mexico, August 1980.

- Gomez-Castellanos, Luisa and George Psacharopoulos. 1990. "Earnings and Education in Ecuador: Evidence from the 1987 Household Survey." Economics of Education Reiew 9(3):219-27.

- Kugler, Bernardo and George Psacharopoulos. 1989. "Earnings and Education in Argentina: An analysis of the 1985 Buenos Aires Household Survey.' Economics of Education 8(4):353-65.

- Mincer, J. 1974. Schooling. Experience and Earnings. New York: Columbia University Press.

- Fiszbein, Ariel and George Psacharopoulos. 1991. "A Cost Benefit Analysis of Educational Investment in Venezuela, 1989." Views from LATHR, Latin America and the Caribbean Technical Department, The World Bank.

- Psacharopoulos, George. 1972. "Rates of Return to Investment in Education Around the World." Comparative Education Review 16(1):54-67.

- Psacharopoulos, George and R. Layard, 1979. "Human Capital and Earnings: British Evidence and a Critique, "The Review of Economic Studies, Vol. LVI, No. 3, July 1979; 485503.

- Psacharopoulos, George. 1981. "Returns to Education: An Updated International Comparison." Comparative Education Review 17(3):321-41.

- Psacharopoulos, George. 1983. "Education and Private versus Public Sector Pay." IAour and SggipU 8(2):123-34.

- Psacharopoulos, George. 1985a. "Returns to Education: A Further International Update and Implications." The Journal of Human Resources 20(4):583-604.

- Psacharopoulos, George. 1985b. "An Evaluation of Curriculum Diversification in Colombia and Tanzania." Comparative Education Review 29(4):507-25.
-40 -

- Psacharopoulos, George. 1987. "Earnings and Education in Brazil: Evidence from the 1980 Census." Education and Training Department, Research Division, The World Bank.

- Psacharopoulos, George. 1988. "Education and Development: A Review." The World Bank Research Observer 3(l):99-116.

- Psacharopoulos, George. 1989. "Time Trends of the Returns to Education: Cross-National Evidence." Economics of Education Review 8(3):225-31.

- Psacharopoulos, George and Francis Steier. 1988. "Education and the Labor Market in Venezuela, 1975-84." Economics of Education Review 7(3):321-32.

- Psacha,opoulos, George, Ana-Maria Arriagada and Eduardo Velez. 1992. "Earnings and Education among Self-Employed Males in Colombia." Bulletin of Latin American Research 2(l):69-89.

- Psacharopoulos, George and Zafiris Tzannatos. 1992a. Women's Employment and Pay in Latin America: Overview and Methodology. Washington, DC: The World Bank.

- Psacharopoulos, George and Zafiris Tzannatos, eds. 1992b. Case Studies on Women's Employment and Pay in Latin America. Washington, DC: The World Bank.

- Psacharopoulos, George and Eduardo Velez. 1992. "Education and the Labor Market in Uruguay." Views from LATHR, I-atin America and the Caribbean Technical Department, The World Bank.

- Riveros, Luis A. 1990. "The Economic Return to Schooling in Chile. An Analysis of its Long*

- Term Fluctuations." Economics of Education Review 9(2): 111-21.

Schultz, T. W. 1961. "Investment in Human Capital." American Economic Review. March.

Tinbergen, Jan. 1975. Income Distribution: Analysis and Policies. Amsterdam: North-Holland.

Velez, Eduardo and George Psacharopoulos. 1987. "The External Efficiency of Diversified Secondary Schools in Colombia." Economics of Education Review 6(2):99-110.
- 41 -

ANNEx 1. SURVEY DATA DuscrinoN

|Country/Date|Survey Name|Executing Agenoy|Geographical<br>Coverage|Number of<br>Households|
|---|---|---|---|---|
|Argentina<br>October 1980|Encuesta Permanente<br>de Hogares (EPH)|Instituto Nacional do<br>Estadistica y Censos|Metropolitan Ara|3,400|
|Argentina<br>May 1989|Encuesta Permanent.<br>do Hogares (EPH)|Intituto Nacional de<br>Estadistica y Censos|**Metropotitan**<br>Area|16,759|
|Bolivia<br>1986|EncuestsPermanent.<br>de Hogare (EPH)|Instituto Nacional de<br>Estadistica (INS)|La Paz, Coohabamba,<br>Cruro, y Santa Cruz|12,226|
|Bolivia<br>November 1989|Encuesta Integrada<br>de Hogarp (EIH)|Instituto Nacional de<br>Estadistica (INE)|17 urban centers with<br>10,000 or mor<br>inhabitants|37,864|
|8razil<br>November 1979|Pesquisa Nacional por<br>Amostra de Domicilios<br>(PNAD)|Fundacao Istituto Brashiro<br>de Geografia y Estadistica|Naional|88,975|
|Brazil<br>Fourth quarter 1989|Pesquisa Nacional por<br>Amostra de Domicilios<br>(PNAD)|Fundacao Instituto Bnuieiro<br>de Geografia y Eatadistics|National|70,777|
|Chile<br>Fourth quarter 1989|Encuesta Nacional del<br>Empleo (PIDEH)|Instituto Nacional de<br>Estadistica y Censos|National|32,456|
|Colombia<br>September 1980|Encuesta Nacional de<br>Hogares - Fuerza de<br>Trabajo (ENH)|Departamento<br>Administraivo Nacional<br>de Estadstica|Barranquilla, Bogota,<br>Bucaramanga, Call,<br>Manizales, Medellin y<br>pato|7,473|
|Colombia<br>September 1989|Encuesta Nacional de<br>Hogares - Fuerza de<br>Trabajo (ENH)|Deparumento<br>Administraivo Nacional<br>de Estadistica|Barranquilla, Bogota,<br>Bucaramanga, Ca}i,<br>Catagena, Manizales,<br>Medetlin y Pasto|17,949|
|Costa Rica<br>July 1981|Encuesta Nacional de<br>Hogars- Empleo y<br>Desempleo (ENH)|Direccion General de<br>Estadistica y Censos|National|6,604|
|Costa Rica<br>July 1989|Encueta de Hogares<br>de Propositos<br>Muldples (EHPM)|Direecion General de<br>Estadisdca y Censos|National|7,637|
|Dom. Republic<br>March 1989|Encuesta de Gasto<br>Social de las Familias|Banco Central de la<br>Republica Dominicas|National|799|
|Ecuador<br>November 1987|Encuesta Peiodica<br>sobro Empleo y<br>Desempleo|Instituto Nacional<br>do Empleo|Nadonal|5,558|

|Continued-|
|---|
-42-

ANNX 1 continued -

|Country/Date|Survey Name|Execouting<br>Agency|Goographical<br>Coverage|Number of<br>Houscholds|
|---|---|---|---|---|
|El Salvador<br>October 1990|Encuesta de Hogamos<br>de Proposizos<br>Multiples (EHPM)|Ministerio do Planificacion<br>y Coordinacion<br>del<br>Desarrolo Eoonomico<br>y Social|Urban|23,773|
|Guatemala<br>Oct 1986-Aug 1987|Encuesta Nacional<br>Soci-Detmografica<br>(ENSD)|Instituto Nacional de<br>Estadistica|National|9,660|
|Guatemala<br>Apr 4-July 24, 1989|Encuesta Nacional<br>Socio-Demografica<br>(ENSD)|Instituto Nacional do<br>Estadistica|National|10,934|
|Honduras<br>September 1986|Encuesta Continua<br>Sobre Fucrza<br>do Trbajo (ECSFT)|Direccion General do<br>Estadstica y Censos|16 major cities|8,650|
|Honduras<br>September 1989|Encues Pormanente<br>do<br>Hogares do Propositos<br>Multipls (EPHPM)|Direccion General de<br>Estadistica y Ccnsos|National|8,648|
|Jamaica<br>July 1989|Jamlaica Survey of<br>Living Conditions|Statistidcal<br>htitute<br>of Jamaica|National|2,725|
|Mexico<br>Third quartwr 1984|Encuesta Nacional do<br>Ingreso- Gasto de<br>los Hogarps (ENIGH)|Diroccion de Estadistica<br>de Corto Plaza|National|4,708|
|Panama<br>Sep 19-Oct 28. 1979|Encueta do Hogares,<br>Mano de Obra (EMO)|Direcocion<br>de EstAdistica<br>y Conso|National|8,593|
|P4nama<br>August 1989|Encuesta de Hogares,<br>Mano de Obra (EMO)|Diroocion de Estadistica<br>y Censos|National|8,817|
|Parguay<br>June-August 1983|Encuesta do Hogaes,<br>Mano de Obra (EMO)|Direcion General de<br>Estadistica y Coms|Metropolitan Area|1,002|
|Paraguay<br>June-August 1990|Encuesta d Hoepros,<br>Mane do Obra (EMO)|Direccion General do<br>Estadistica y Censos|Metopoltan Ar|1,000|
|Peru<br>July 1985-July 1986|Peru LSMS|Instituto Nacional de<br>_Estica_<br>Informatica|National|4,981|
|Peru<br>June-July 1990|Peru LSMS|Insdtuto Cuanto|Lima|1,385|
|Uruguay<br>Second half 1981|Encuest Nacional de<br>Hogas (ENH)|Direccion Generald<br>Estadistica y Caaos|Urban|9,506|

Continued -
-43

-

continued -

|Country/Date,|Survey Name|Execudng Agoney|Geogrphial<br>Covegp|Number of<br>Households|
|---|---|---|---|---|
|Uruguay<br>Second half 1989|Encuesta Nacional de<br>Hogarm (NH)|Direccion Goneral de<br>Estadistica y Consos|Urban|21,473|
|Venezuela<br>Second half 1981|Encuosta<br>do Hogares<br>por Muesta (EHM)|Oficina Centrl do<br>Estadisdca e Informatica|National|4S,421|
|Venezuela<br>Second half 1989|Encuasado Hogares<br>por Muestra (EHM)|Oficina Cental do<br>Estadistica e Informatica|Nadonal|61,385|
- 44 -

## ANNEX 2. VARLABLE DEFINIONS

All country data sets refer to individuals age 15 to 65, inclusive. Observations with missing values for education variables have been dropped from the analysis. All individuals have positive earnings from employment.

# 1. Education

The education variables are of two types. One is continuous in terms of years of schooling (S). The other is a series of educational dummy variables:

# NOSCHOOL No education.

- PRIMDROP Primary education incomplete.

- PRIM Primary education completed.

- SECDROP Secondary education, either general or vocational, incomplete

- SECGEN Secondary general education completed. In cases where information on the general/vocational split of secondary education is not available, this variable refers to both types of the curriculum.

- SECVOC Secondary vocational education completed.

- SECTEAC Teachers' college graduate.

- NOUNI Higher non-university education such as post-secondary education or technical institute.

- UNIVDROP University education incomplete.

- UNIV University education completed.

In the case of the Paraguay data sets (1983 and 1990), the sample size of those with noeducation (NOSCHOOL) is very small and hence we grouped this category with the less-thanprimary category ,PRIMDROP). In Peru's 1985 and 1990 data sets, the two education groups are defined in a different way:

# NOSCHOOL No education or primary education incomplete.

PRIM Primary education completed or secondary education incomplete.
- 45 -

# 2. Eamings

The earnings variable used in the study is the sum of monthly wage earnings and selfemployment income in the primary job in local currency with a few exceptions. In the 1990 El Salvador data, bonus earnings are included. Quarterly wage income is used for Mexico while income from the main job for the last week is used in the Peru 1985 data set.

# 3. Hours Worked

According to the over table information in the country data sets, there are three different time periods to which the hours worked variable refers to -- daily, weekly or yearly. In the 1984 Mexico data set, the hours worked information is not available.

|Daily<br>Daily hours worked in|Bolivia|1989|
|---|---|---|
|primary job|Jamaica|1989|
|Weekly|||
|Total hours worked per week|Argentina|1980, 1989|
||Panama|1979|
||Venezuela|1981|
|Weekly hours worked in|Bolivia|1986|
|primary job|Brazil|1979, 1989|
||Costa Rica|1981, 1989|
||Guatemala|1986, 1989|
||Honduras|1986|
||Panama|i989|
||Paraguay|1983, 1990|
||Uruguay|1981, 1989|
|Total hours worked last week|Dominican<br>Republic|1989|
||Ecuador|1987|
||El Salvado|r 1990|
||Peru|1990|
|Normal hours worked per week|Chile<br>Colombia|1989<br>1980, 1989|
||Honduras|1989|
||Venezuela|1989|
|Yearly|||
|Yearly hours worked|Peru|1985|
-46-

# 4. Sector of Employment

Most of the country data sets, except data for the Dominican Republic (1989), provide information on the sector of employment for each individual. Each sample was split into "public" and "private" sub-samples using the following rule: PUBLIC Public sector employees.

- PRIVATE Private sector employees, self-employed individuals, employers and family workers.

Of course we had to adapt the above general rule depending on the availability of information in the respective country surveys:

- Argentina 1980, 1989

- PUBLIC Industry of natural gas and petroleum, manufacturing industry related to petroleum and minerals, electricity, gas and water supply industry, water and aerial transportation industry, communication industry, social services, public and defense administration, and drainage services.

- PRIVATE Manufacturing industry not related to petroleum refining, rubber, mineral, machinery and equipments, construction industry, commercial and services industries, ground transporation industry, finance and real estate, cleaning and laundry services.

- Bolivia 1986, 1989 PUBLIC Public administration and public employees or mixed.

PRIVATE Employees of private sector and cooperative.

- Brazil 1989 PUBLIC Social services and public administration.

# PRIVATE Self-employed.

# Mexico **1984**

- PUBLIC Industries of oil, electricity and natural gas, public administration, education and research.

- PRIVATE Industries of food processing, textile, furniture, printing, chemical industry, mineral, metal and machinery industry, manufacturing and construction industries, wholesales and retail, hotel industry, transportation industry, real estate and services.
- 47 -

# Peru 1985 PUBLIC Wage earners.

PRIVATE Self-employed.
-48-

## ANNE[X a BASIC **EARNINGs FUNCIONS**

# Argentina 1980

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.093|0.080|0.098|0.078|0.097|
|Experience (EX)|0.030|0.087|0.017|0.026|0.050|
|Experience-squared (EX<sup>2</sup>)|-0.0004|-0.0004|-0.0002|-0.0004|-0.0007|
|Log Hours (H) Worked|2.278|1.584|2.228|0.642|0.614|
|Constant Term|-3.191|-0.942|-3.096|3.231|3.056|
|R<sup>2</sup>|0.66|0.45|0.65|0.06|0.19w|
|N|6,156|2,745|3,411|2,030|-737|
|Means|||||||
|Earnings (Y)|735|1,234|333|1,207|1,309|
|S|7.9|8.1|7.8|7.9|9.5|
|EX|22|21|23|22|.21|
|H|31|43|22|47|40|
|Var (Ln Y)|3.621|2.554|2.673|1.543|1.024|

Source: _Encuesta Permanente de Hogares (EPH), CEPAL_ Notes: The earnings variable (Y) is in thousands of pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the _5%_ level or better.
-49 -

# ANNEx 3. BASIC EARNINGs **FUNeCoNs**

# Argentina 1989

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.103|0.107|0.112|0.010|0.089|
|Experience (EX)|0.045|0.052|0.039|0.046|0.048|
|Experience-squared (EX2)|-0.0006|-0.0007|-0.0006|-0.0007|-0.0007|
|Log Hours (H) Worked|0.613|0.355|0.590|0.38t|0.647|
|Constant Term|4.856|5.843|4.719|5.767|4.847|
|R<sup>2</sup>|0.35|0.32|0.40|0.26|0.35|
|N|4,760|2,965|1,795|2,463|1,097|
|Mens||||||
|Earnings (Y)<br>v|7,456|8,563|5,629|7,413|8,463.|
|S|9.1|8.7|9.8|8.7|11.0|
|EX|22|23|21|22|21|
|H|42|46|36|45|39|
|Var (Ln Y)|_0.656_|0.546|0.696|0.557|0.610|

Source: _Encuesta Permanente de Hogares (EPH), CEPAL._ Notes: The earnings variable (Y) is in australes per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
_-50 -_

# ANIx 3L BASc EARNINGS FuNcrIoNs

# BoUvia 1989

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.071|0.073|0.077|0.087|0.06i|
|Experience (EX)|0.040|0.046|0.028|0.053|0.032|
|Experience-squared (EX<sup>2</sup>)|-0.0005|-0.0006|-0.0004|-0.0007|-0.0003|
|Log Hours (H) Worked|0.514|0.301|0.594|0.234|0.726|
|Constant Term|3.278|3.726|2.994|3.656|2.919|
|R<sup>2</sup>|0.17|0.18|0.18|0.19|0.19|
|N|5,356|3,823|1,533|2,989|2,367|
|Mans||||||
|Earnings (Y)|364|402|270|361|369|
|S|10.1|9.5|11.5|8.7|11.7|
|EX|18|19|16|17|20|
|H|8.2|8.7|7.1|8.7|7.6|
|Var (Ln Y)|0.645|0.659|0.539|0.697|0.573|

Source: _Encuesta Integrada de Hogares (EIH), CEPAL._ Notes: The earnings variable (Y) is in bolivianos per month. H is the daily hours worked.

All coefficients are statistically significant at the 5% level or better.
_-51_ -

# ANNEx 3.L **BASC** EARNIGS **FuNCIONS**

# Brazil 1979

|||Gen|der|Sec|tor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.114|0.118|0.089|n.a.|n.a.|
|Experience (EX)|0.071|OD70|0.053|n.a.|n.a.|
|Experience-squared(EX2)|-0.0011|-0.0011|-0.0009|n.a.|n.a.|
|Log Hours (H) Worked|-0.174|-0.270|-0.301|n.a.|n.a.|
|Constant Term|8.066|8.510|8.605|n.a.|n.a.|
|R<sup>2</sup>|0.34|0.37|0.28|n.a.|n.a.|
|N|6,655|5,529|1,126|n.a.|n.a.|
|Means||||||
|Earnings (Y)|10,244|10,943|6,813|n.a.|n.a.|
|S|5.3|5.2|_5.5_|n.a.|n.a.|
|EX|21|21|18|n.a.|n.a.|
|H|52|53|47|n.a.|n.a.|
|Var (LnY)|0.651|0.632|0.578|n.a.|n.a.|

Source: _Pesquisa Nacional por Amostra de Domicillos._ Notes: The earnings variable (Y) is in cruzeiros per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better. n.a. = Not available.
-52 **-**

# ANEX 3 **BASIC EARNIGS FUNWCIONS**

# **Brazil 1989**

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.147|0.154|0.142|0.150|0.114|
|Experience (EX)|0.064|0.073|0.051|0.038|0.054|
|Experience-squared (EX<sup>2</sup>)|-0.0009|-0.0010|-0.0008|-0.0005|-0.0008|
|Log Hours (H) Worked|0.724|0.310|0.595|0.936|0.829|
|Constant Term|1.954|3.567|2.314|1.454|1.915|
|R<sup>2</sup>|0.29|0.30|0.29|0.26|0.26|
|N|108,527|69,773|38,754|24,690|17,428|
|MeanS||||||
|Earnings (Y)|6,969|7,151|6,640|9,979|7,746|
|S|5.3|5.0|5.9|4.1|8.2|
|EX|21|22|20|28|20|
|H|43|46|39|43|36|
|Var (Ln Y)|1.631|1.474|1.680|2.051|1.608|

Source: _Pesquisa Nacional por Amostra de Domicillos, CEPAL._ Notes: The earnings variable (Y) is in nuevos cruzados per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
-53 -

# ANNx 3. **BASIC** EARNNGS FUNCTIONS

## **ChIle** 1989

|||Gen|der|Sec|tor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.120|0.121|0.132|0.114|0.112|
|Experience (EX)|0.042|0.048|0.029|0.041|0.029|
|Experience-squared<br>(EX<sup>2</sup>)|-0.0004|-0.0005|-0.0002|-0.0004|-0.0002|
|Log Hours (H) Worked|0.885|0.768|0.753|0.918|0.660|
|Constant Term|5.189|5.652|5.487|5.104|6.418|
|R<sup>2</sup>|0.40|0.39|0.45|0.37|0.29|
|N|37,679|26,823|10,856|33,794|3,885|
|Means||||||
|Earnings (Y)|40,275|43,878|31,373|38,232|58,044|
|S|8.5|8.2|9.4|8.1|12.3|
|EX|21|22|20|22|20|
|H|47|48|44|48|43|
|Var (Ln Y)|0.638|0.591|0.692|0.629|0.423|

Source: _Programa Integrado de Encuestas de Hogares (PIDEH) Encuesta Nacional del Empleo, IV Trimestre, 1989_ Notes: The earnings variable (Y) is in pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
-54 -

## AN 3. **BASIC** EARNNGS **FUNCTONS**

# Colombia 1980

|||Ge|nder|Sec|tor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.186|0.186|0.173|0.167|0.140|
|Experience (EX)|0.066|0.081|0.042|0.060|0.060|
|Experience-squared(EX<sup>2 </sup>)|-0.0008|-0.0010|-0.0005|-0.0008|-0.0009|
|Log Hours (H) Worked|0.584|0.395|0.577|0.690|0.434|
|Constant Term|4.020|4.668|4.299|4.000|4.665|
|**1R2**|0.34|0.33|0.35|0.35|0.10|
|N|11,373|7,017|4,356|8,136|3,237|
|Means||||||
|Eamings (Y)|8,401|10,124|5,626|10,374|3,442|
|S|6.9|7.0|6.8|7.7|5.0|
|EX|20|21|18|21|17|
|H|48|50|46|47|51|
|Var (Ln Y)|1.479|1.610|1.169|1.295|1.225|

Source: _Encuesta Nacional de Hogares, CEPAL._ Notes: The eamings variable (Y) is in pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the _5%_ level or better.
_-55 -_

# ANwx 3.1 BAsic EARNiNGS FuCTIONS

# Colombia 1989

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.140|0.145|0.129|0.137|0.119|
|Experience (EX)|0.047|0.059|0.035|0.046|0.035|
|Experience-squared (EX<sup>2</sup>)|-0.0005|-0.0006|-0.0004|_-. OOOS_|-0.0003|
|Log Hours (H) Worked|0.645|0.428|0.719|0.664|0.326|
|Constant Term|6.190|6.897|6.108|6.140|7.980|
|R<sup>2</sup>|0.33|0.32|0.36|0.30|0.38|
|N|27,021|16,272|10,749|23,974|3,047|
|MM||||||
|Earnings (Y)|53,643|60,592|43,124|50,279|80,116|
|S|8.2|8.1|8.3|7.8|11.2|
|EX|20|21|18|19|20|
|H|48|50|46|48|_45_|
|Var (Ln Y)|0.947|1.010|0.815|0.956|0.458|

Source: _Encuesta Nacional de Hogares, CEPAL._ Notes: The earnings variable (Y) is in pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.