metadata: authors: 0: author: Gill,Indermit S.
count: Peru
docna: 0: docna: Does the structure of production affect
            demand for schooling in Peru?
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Labor Markets,Educational Sciences,Gender and Development,Food Security
teratopic: Macroeconomics and Economic Growth,Health, Nutrition and Population,Environment,Education
abstracts: cdata!: The author uses the regional structure
            of the economy, proxied by the shares of services and
            industry in regional gross domestic product (GDP), as an
            indicator of the demand for educated workers.  By examining
            whether the level of schooling as a function of shares of
            services and industry differs for men and women, a gender
            bias can be detected in the demand for schooling. The author
            estimates schooling demand functions for males and females
            using data for Peru in the 1980s. His primary findings are :
            1) as services and industry increase their shares of GDP,
            relative to the share of agriculture, the demand for
            schooling of both males and females increases, 2) as the
            share of services in GDP increases compared to agriculture,
            the demand for schooling by women increases more than the
            demand for schooling by men, 3) and increase in the share of
            industry relative to agriculture is associated with an
            increase in the demand for schooling of men rather than of
            women, 4) a decrease in the supply price of schooling
            increases the level of schooling attained by both sexes, but
            the gain is larger for women, and 5) increases in wealth are
            associated with increases in the demand of both sexes for schooling.
geo_reg_key: 119228,517191
repnme: repnme: Does the structure of production affect
            demand for schooling in Peru?
display_title: Does the structure of production
            affect demand for schooling in Peru
listing_relative_url: /research/1990/08/700201/structure-production-affect-demand-schooling-peru
topicv3: Agriculture and Food Security m1326205 435,Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Health, Nutrition and Population,Environment,Education
origu: Policy, Research & Ect Affairs
no_of_pages: 60
author_keys: 0: author_key: 92804
model_extractions: <!-- Start of picture text --> Plcy, Research, and External Affairs|<br>Women In Development _J<br>WPS 468<br><!-- End of picture text -->

This paper -a product of the Women in Development Division, Population and Human Resources Department -is part of a larger effort in PRE to d.termrine if and how women's productivity (and thus family welfare) are improved when women are given more access to education, training, credit,'health care, and otherpublic resources. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please conract Maria Abundo, room S9-123, extension 36820 (55 pages, including tables).

Analyses of gender differences in investments in human capital typically emphasize farnily resources as the determining factor. These studies usually find that investments in male offspring are greater, that these differences narrow as the level of household wealth increases, and that equity is also affected by the composition of household wealth (proxied by the amount the mother earns and/or her educational level).

Gill addresses one drawback of these analyses: they do not explicitly consider the factors **that** determine the _demand_ for schooling and health - other than tastes - and why this differs for men and women. Gill uses the regional structure of the economy, proxied by the shares of services and industry in regional gross domestic product (GDP), as an indicator of the demand for educated workers. By examining whetherie level of schooling as a function of shares of services and industry differs for men and women, he looks for gender bias in the demand for schooling. Gill estimates schooling demand functions for males and females using household data from the Peruvian Living Standards Survey, and provincial data from the Peruvian census.

# Gill's primary findings are:

As services and industry increase as a share of GDP, relative to agriculture's share, the demand for schooling increases for both boys and girls. (Both industry and services reward education more than agriculture does. Parents form expectations about the sector their children are likely to work in as adults and choose levels; of schooling accordingly.)

* As services' share in GDP increases compared to agriculture (holding industry's share constant), girls' demand for schooling increases more than boys' demand for schooling.

An increase in industry's share in GDP relative to agriculture (holding services' share constant) is more closely associated with an increase in the demand for schooling of boys than of girls.

* A decrease in the supply price of schooling increases the level of schooling attained by both sexes, but the gain is larger for women.

* Increases in wealtd,. all else being eqZal, are associated with increases in both sexes' dtnand for schooling.

What are the policy implications of these findings? Some ways to increase educational levels, especially those of women, include (on the supply side) lowering the supply price of - schooling improving access to secondary - scniooling, for example and (on the demand side) expanding the services sector. The demand- side prescription contradicts the World Bank and IMF policy advice that developing countries foster the growth of tradables to service their extemal debt.

**The PRE Working Paper Scries disseminates tChe indings oe work under way** in tLe **Bank's Policy. Research, and Extemal Affairs Complex .An objective of the scrics is to get thlsefindings out quickly, cven if presentatiois are lesstlhan fully polished. 'Mc findings, interpretations, and conclusions** in **these papers do not necessarily represent official Bank policy.**

)roduc&d by **tlic PRE** Dissemination **Centcr**
# The Structure of Production as a Determinant of the Demand for Human Capital in Peru

# by Indermit Gill

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|The Theoretical<br>Framework|6|
|3.|Some Theoretical<br>Extensions|12|
||Experience as a Factor of Production|13|
||Forming of Expectations|14|
|4.|Household Level Empirical Evidence|17|
||Definition<br>of Dependent Variable:<br>Schooling Shortfalls|17|
||Definitions<br>of Independent Variables|19|
||Ree-its of the Schooling Regressions|23|
|5.|Province Level Empirical Evidence|35|
||Definitions<br>of Dependent Variable:<br>Illiteracy Rates|35|
||Definitions<br>of Independent Variables|36|
||Description<br>of the Data|38|
||Results of the Schooling Regressions|41|
|6.|Conclusions<br>and Policy Implications|51|
|Ref|erences|53|
|App|endix|55|
Analyses of gender differences in investments in human capital typically emphasize family resources as the determining factor. (See Schultz and Rosenzweig (1982), Gertler and Alderman (1989) for investment mn health, and King and Bellew (1989) for investment in schooling.) These studies approach the problem as one of investment by parents in the human capital of male and female children. These studies usually find that investments in male offspring are greater, that these differences narrow as the _level_ of household wealth increases, and that the _composition_ of household wealth (proxied by either the amount earned by the mother and/or her education level) affects equity as well.

There are two major drawbacks in these analyses. First, the empirical segments confound the effects (a) of a gender bias inherent in the utility function of parents, (b) of gender differences in market returns to human capital, and (c) of gender differences in appropriability of returns to investments by parents in children. This paper does not address this issue. Second, these analyses contaln no explicit consideration of the factors determining the _demand_ for schooling and health, other than tastes, and why this differs for males and females. It is the second shortcoming Mr Gill is an assistant professor at the State University of New York at Buffalo and is a consultant to the World Bank.

The author is grateful to Barbara Herz, Emmanuel Jimenez, Shahid Khandker, Thomas Mroz, John Newman, Pats Pattabiraman, Maurice Schiff, T. Paul Schultz and Jacques van der Gaag for helpful discussions, and to Nsrcia Schafgans and Ricardo Lago for help with the data.

1
that this paper seeks to rectify.1 In this paper I use the regional structure of the economy, proxied by the shares of services and industry in regional gross domestic product (GDP), as an indicator of the demand for educated workers. By examining whether the level of schooling as a function of shares of services and industry differs for men and women, we can detect gende bias in the demand for schooling.

Based on the theoretical framework developed in Gill and Khandker (1990), I estimate schooling demand functions for males and females using data for Peru in the 1980s. A separate estimate covers households and provinces (called "departments' in Peru). Household data are from the Peruvian Living Standards Survey (PLSS); information on the provinces is based on census data. Findings confirm the results obtained in Gill and Khandker using country-level data for about 100 countries in 1965 and 1987. The primary findings are:

- As services and industry increase their shares of GDP, relative to the share of agriculture, the demand for schooling of both males and females increases.

- **O** As the share of services in GDP increases compared to agriculture (holding the share of industry constant), the demand for schooling by women increases more than the demand for schooling by men.

IIt should be mentioned here that the absence of explicit consideration of demand-side factors in the market for labor is a weakness of much of neoclassical labor economics.

2
**o** An increase in the share of industry relative<sup>to agriculture</sup><sup>**(.**</sup> ding the share of services constant), is more<sup>closely associated with an</sup> increase in the demand for schooling<sup>of men than of women.</sup>

**o** A decrease in the supply price of schooling<sup>increases the level of</sup> schooling attained by both sexes, but<sup>the gain is larger for women.</sup> **o** Increases in wealth, _ceteris paribus,_ are associated with increases in the demand of both sexes for schooling.

The plan of the paper is as follows: Sectior. z introduces the basic theory. A representative family is<sup>assumed that has an adult couple</sup> and one female and one male child. The<sup>issue of fertility is thus entirely</sup> sidestepped. Parents are assumed to be the<sup>decision-mnakers</sup> regarding investments in human cap .tal of children.<sup>They do so because the attained</sup> utility of their chil..cen matters to them. Attained utility depends<sup>upon the</sup> income of children as adults and income<sup>in turn depends upon the human</sup> capital was invested in them by their parents.<sup>The demand for schooling of</sup> children is focussed upon. Schooling is demanded<sup>differentially in different</sup> sectors of the economy: industry and services<sup>reward education more than</sup> agriculture.2 Parents form expectations<sup>about the sector of the economy</sup> 2Schultz (1975) argues that this demand for education reflects the higher rates of change in industry.<sup>Mincer and Higuchi (1988) and Gill</sup> (1989) find that sectoral rates of<sup>technical change in the U.S. economy</sup> between 1960-1985, were related positively<sup>to the rates of return to</sup> education. Welch (1970) found similar relationships<sup>in U.S. agriculture.</sup> These results imply that the rates of<sup>return to schooling, and therefore the</sup> demand for educated workers, would<sup>be highest in industry (especially</sup> manufacturing), lower in trade and services,<sup>and lowest in agriculture.</sup> 3
that their children are likely to work in as adults, and choose levels of schooling for each child accordingly.

In the basic model, parents use their own time allocation as a proxy for the time-allocation patterns that their children will choose. In section 3 this last assumption is amended. Parents forn Apectations of time-use of children as adults based on both their own work experience and the general pattern in the region of residence, as well as the probability of migration to oth.'ar regions. Section 3 also discusses the implications of adding sector-specific work experience (job training) as an additional component of human capital.

Section 4 uses household da. a from the PLSS to test the implications of the theoretical framework developed in sections 2 and 3. The advantages of using household data are that the schooling attainment of children is directly observed, and the effects of intrahousehold factors on the demand for schooling of boys and girls can be accounted for by including household information such as the education and occupation of parents.

In section 5, I test the implications of the theory using provincial data for 25 departments in Peru in the 1980s. Illiteracy rates are used as a proxy for investment in schooling. I conduct tests to ensure that department illiteracy rates are satisfactory measures of department schooling attainment. The findings confirm the main implications of the theory, and add to the evidence from household analysis.

Section 6 discusses the policy implications of the study. The policy implications are of two types: Supply-related and demand-related.

4
Supply-relaced policy preserLptions **are** those targeted towards<sup>lowering</sup> the supply price of schooling, such **as** improving<sup>the access to secondary</sup> schoolLng. Dewarw-related polLcLes aim at increasing the demand for education: The main policy recommendation entails the expansion of the servlces sector. This contradicts policy advice<sup>given by the World Bank and</sup> the IMF that developing countries foster the<sup>growth of tradables to servLce</sup> their external debt. Another policy implicatlon<sup>emphasizes the importance</sup> of Lnformation about the rates of raturn to schooling<sup>ln the market and the</sup> home sector.

5
# 2. THE THEORETICAL FRAMEWORK

Human capital is assumed to consist of two components: schooling and health.<sup>3</sup> Parents value only their own .onsumptior and the attainable utility -- that is, the full income -- of their children as adults. ASF that a couple has only one female and one male child.' The parents' utility function is where C is the quantity of a general consumption good consumed by the parents, and Rfand R are the "full" incomes of the girl and _b,;_ respectively when they are adults.

R depends on the human capital of children. Human capita. has sev'eral observable components, e.g., schooling, health and job t_aLning. I assume here that schooling (S) is the only form of human capital. The terms human capital and schooling will be used interchangeably unless otherwise indicated. Thus the returns to human capital functions for the girl and the boy are Rf - Rf(Sf) R R (S ) (2a) (2b) 3The theoretical framework for this paper departs considerably from previous theoretical attempts to analyze gender differences in investments in human capital, such as Gertler and Ald rman's (1989) analysis of investments in health.

'The issue of fertility is thus sidestepped. See Becker and Tomes (1976) for a theoretical discussion of fertility and quality-quantity tradeoffs, and Schafgans (1990) for an empirical treatment using Peruvian data.

6
The budget constraint of the household is Y - C **+** Ps(St+S) (3) where PF represents the price of schooling, and the price of the consumrtion good has been normalized to equal 1.

There are two main sectors of employment: home and the market. The rates of return to human capital are sector-specific. Thus overall returns to schooling depend upon the extent to which time is allotted between the household and the market (all nonhousehold) activities.

In this paper market activities are subdivided into agriculture, services and industry, identified as follows o - Household

- 1 - Agriculture

- 2 - Services

- 3 - Industry

Total returns to human capital are a time-weighted sum of returns in each sector. I assume Cobb-Douglas return functions where tfi and t i is the fraction of time devoted devoted to activity i by female and male childcen respectively _when they are adults, 5_ and SAlternatively, ti can be interpreted as the _probability_ of the child being employed in sector i as an adult.

7
S <!-- Start of picture text --> 3 3<br>-<br>E tts tot ,<br><!-- End of picture text -->

# The following assumptions are made:

- (i) tfo x to : Everybody gets married and has children, and<sup>women spend</sup> more time at hom-., than men. This could be because bearing<sup>and rearing</sup> children is more demanding of women's time.<sup>(See, for related</sup>

- examples, Becker 1985). This raises the issue<sup>ofendogeneity, since</sup> women may _choose_ not to have children. In that<sup>case, from the</sup> viewpoirt of this theory, the difference between<sup>men and women</sup> disappears. Alternatively, it can be explained<sup>as a cultural or</sup> institutional constraint. In any case, because<sup>of time constraint</sup>

- (5), women generally have less time for market<sup>activities than men.</sup>

(Li) p < I s 6 : The rates of return to schooling<sup>are high in the</sup> industrial and service sectors and low in the agricultural<sup>sector.6</sup> However, these rates of return do not depend<sup>upon the sex of the</sup> workers: There is no sex discrimination in<sup>the marketplace.</sup> 7 Slnce the productivity of schooling at home,<sup>a,is not easily or</sup> SAlthough I do not have measures of the rates<sup>of return to schooling</sup> in agriculture compared to the other two sectors,<sup>there is strong evidence</sup> for Peru that supports this assumption. Schafgans<sup>(1990) finds that labor</sup> force participation of both men and women in agriculture<sup>declines with</sup> education, and participation in the nonagricultural wage sector (sectors 2 and 3 in this paper) increases as the education<sup>of workers increases.</sup> 7The point is not .hat there is _In fact_ no<sup>discrimination against</sup> women. The rationale for this assumption is simply<sup>that discrimination</sup> (either at home or in the market) is a very<sup>di. ficult concept to quantify.</sup> Since the focus of this paper is empirical,<sup>I abstract from assertions that</sup> are unverifiable either in principle or in practice.

8
directly observable, nothing is assumed about the magiicude of a relative to P, 7, and 6.

It is important to remember that t 1 and t, are not choice variables for the parents. These are time-allocation decisions by children when they become adults. The only choice variables in the current framework are S sand C. Parents may , Impute the values of t and t from their own experiences and expectations of market conditions when thair children will work. Tlis point will be discussed later.

Parents maximize their (one-period) utility function given in equation (1) subject to the budget constraint (3). The first order conditions for maximization are : u *t a SC I+ ( t S 1 t -i St 1+ t S6 1-p AP 0 (6a) BR [ _£-1_ ( t a j 1 t3 7 S<sup>1 1</sup> + e~ Ss )31 - AP - 0 (6a) 813 -A - 0 (6c) aC aR~~~~~~~~~~~~~~~~~~~~~~~~~~~~f 8This assumes that there is no bargaining about transfers from the children to the parents. Suppose instead that contracts bind children to support their parents when they are old. Theh parents may in fact decide t and t~ as well.

Niotice that there is a self-fulfilling naturet the parents'decision. If parents choose the schooling levels under the assumption of a set of t and t , and if they are correct about the rates of return to schooling in each sector, children cannot do better than allocate their time exactly as their parents expected.

9
Y- C - P(sf+ S ) - O (6d) We can solve for the demand functions for S , S and C. The schooling demand functions will be of the form where t , i - 0,1,2,3 are combinations of t and **t1** for each i. Note that since both parents have the same utility function, only aggregate t enter the schooling demand functions. These can be written as Equations %3a) and (8b) incorporate the additional constraint faced by women, **tfo** to (that they must spend at least a fixed fraction **t** of their time at home), in the functional form, S f(.). Since females are expected to marry and become mothers, the functional form of schooling demand will differ from that of males. Other than this restriction, however, there is no difference between boys and girls.

> The signs of _as;!_ 8Ps and _aS/ aY_ (and _dS_ / _aPs_ and _aS_ / _aY_ ) are predicted by standard consumer theory as being negative and positive respectively, if quality of children is a normal good. Assuming that parents value the happiness of male and female children equally, the theory

10 Following Becker (1981), parents are altruistic toward their children.

10
predicts that these coefficients will be roughly equal in magnitude if the curvature in the utility function is small. That is, if the second 2 2 2 2 derivatives, a s/ P82 and a S*/ ay2 (and 8 S*/ 8p%2 and 8 S*/ dY) are close to zero. If these coefficients are different for males and females, and since we know that mean levels of S f are small compared to S , this is indicative of curvature in the utility function.

However, the theory as it stands contains no predictions regarding the magnitudes of aS/ _at_ , OSf at2 and S/ 8t3 relative to 8S*/ _at,_ 3 m~~~~Z _cSi_ at2 and _AS*/_ at3 respectively. More structure is needed to determine whether, for example, an increase in the time spent by parents in sectoLs 2 and 3 (relative to sector 0 and/or 1) increases or decreases the demand for children's education and whether these magnitudes of response are different for females than for males.

11
# 3. SOME THEORETICAL EXTENSIONS

The theory predicts that within a region, as a sector with a relatively high rate of return to education increases its share in total employment, demand for the education of children in that region will rise.1 The limitations of the framework are:

- (1) The theory contains no implications for the gender composition of this increase in demand for schooling: It assumes that if an educationintensive sector (say, industry) increases in importance, the demand for schooling of boys and girls will rise symmetrically. The theory is now extended to allow for systematic differences across sex to such shifts in the demand curve for schooling.

- (2) The theory assumes that the _local_ structure of the economy (tI, t 2 , and _t_ of the parents) determines the demand for education. This raises the question of how parents form expectations regarding their children's future. Issues such as the likelihood and ease of migration may be significant, and these factors may not be gender-neutral. Also, since the rate of return to schooling in the home sector, a, is not observed directly (that is, in terms of wages), perceptions regarding a may be related to household attribute **s** uch as the education of the mother.

l<sup>1</sup> If the industrial sector increases in importance, relative to services and/or agriculture, we expect schooling to rise. Similarly, if the service sector's share rises _relative to agriculture, with no change In the share of the Industrial sector,_ then the demand for education will unambiguously increase. However, if the share of industry declines at the same time that the share of services rises, the effect on aggregate schooling demand is ambiguous.

12
Experience as a Factor of Production The only distinction between men and women is that, in general 3 3 i1 tz A-1 x 9 since tto to > tM0 ; women must<sup>spend more time at home than men. Suppose</sup> now that experience in sector 3 (time allocated to sector 3 activities) adds to the returns to schooling in that sector, but the other sectors' rates of return to schooling are not dependent on the time spent. That is _6_ - 8 (t3), 6S > 0 (10) So the returns to schooling functions (equations (4a) and (4b) ) are rewritten as Combined with restriction (9), equations (Ila) and (llb) imply that males will allocate more time to the industry sector than _equally schooled_ females because, holding educational attainment constant, women spend less time in market activities than males. This implies that men have a comparative advantage in working<sup>in marketsectors</sup> where the returns to 12 schooling increase with time spent.

For example, assume there are two levels of education: high and laAlternatively, the comparative advantage of women in services may simply be due to the fact that<sup>servicee</sup> provide more opportunities to work close to home than does industry (Smith and Stelcner 1990).

13
low. Industry and services use only workers with high levels of education, while agriculture uses workers with low levels of education (regardless of sex). Workers with high levels of schooling will work in the other two sectors. Assume that experience is rewarded in industry and not in services (a simplification). More schooled workers with higher amounts of allocable market time (males) will be employed in industry. More schooled females will work in the services sector, where the returns to schooling do not depend on experience.

One consequence is that an increase in the industrial sector's share of GDP will raise the demand for education by both males and females, but more for males. Conversely, a rise in the service sector's share in total employment will increase the demand for schooling by both male and females, but more for females. These are testable implications of the theory.

# Forming of Expectations

The model assumes that parents, in deciding how to educate their offspring, base the decision on the amount of time the parents spend on four areas: household activities, agriculture, services, and industry, and the rates of return to schooling in each of these sectors. For example, they expect daughters to allocate time the same way as the mother, and sons to follow their father's patterns. They further expect that the relative rates of return to schooling in each sector will remain unchanged. In the context of Peru in the 1980s this assumption is likely to be incorrect, since a 14
large flow of migrants moves between rural and urban areas.

This sub-section modifiies the theory to allow for migration. Parents have some information about potential work opportunities for their children (other than their own occupations) and incorporate this information into the decision on their children's education. So, for example, parents may use the _average_ levels of t and t i in the region or country to determine the probability of employment of female and male children in sector i. Parents may use t1 (averaged over both sexes) in deciding how much to educate their children. This may entail migration by children when they are adults; this migration may or may not be gender-neutral.

Vector Z is added as an argument in the two schooling equations where Z represents both linfrastructural" variables (Z1) that represent the ability of adults to migrate, and household-specific variables (Z 2 ) that represent the ability of the parents to decode information regarding potential opportunities for children. Z includes both factors that determine the degree to which employment in each sector is perceived as possible (tfi - and tm, for i 0,1,2,3) and factors that determine the accuracy with which - parents observe the true rates of return to schooling (a, fi, and 6).

In the empirical sections, both equations (8) and (12) will be estimated to see whether or not these variables Z have independent and 15
gender-variant effects on investment in schooling. These equations were estimated both by ordinary least squares (OLS) and two-stage least squares (TSLS) techniques to account for possible endogeneity of household income. The results were very similar. In this paper only the TSLS results are reported.

16
# 4. HOUSEHOLD LEVEL EMPIRICAL EVIDENCE

Analysis at the household level has several advantages. First, since the theoretical model is one of household decisions, empirical testing should be done at the household level. Second, it is possible to quantify the effect of intrahousehold distribution of potential or actual earnings (for instance, the education or earnings of the mother compared to the father's) on investment in the human capital of children. Third, the measure of investment is more reliable for the households than the illiteracy rates used in the next section, and the primary and secondary school enrollment rates used in Gill and Khandker (1990).

The sample should ideally consist of households in the PLSS that have at least one male and one female child of school age. But when the sample was restricted to such households, the number of observations dropped significantly.

# Definition of Dependent Variable: Schooling Shortfalls

The problems in comparing schooling levels of boys and girls are:

- Each boy and girl is likely to have different levels of access to schooling, such as household income, distance from school, quality of schools available, and so on.

- O The boy and girl being compared are likely to differ in age, and hence on this account alone would differ in years of completed

schooling. Thus we need to use a measure of educational attainment adjusted for age.

- O A related complication is that time or cohort effects will be confused

17
with the true schooling differentials. Suppose, for example, that the government makes primary school attendance compulsory for all children between 5 to 10 years of age in year t. Then comparing the adjusted schooling attainment of a girl who is 10 years old in year t+5 with a boy who is 15 in the same year is likely to understate<sup>the actual</sup> difference in the schooling of girls. Conversely, comparing<sup>a boy who</sup> is 10 in year t+5 with a girl who is 15 in the same year will<sup>overstate</sup> this difference. It is important to weed out these cohort-specific effects.

To resolve the first problem, we have included family characteristics in the regression. Regarding the second problem,<sup>the</sup> comparison is not between attained schooling but _shortfalls_ in schooling attainment of boys and girls. This shortfall, for a child J, is defined<sup>as</sup> (AgeJ - 5) - (Schooling in Years) - (Schooling Shortfall) , for j - f,m. That is, the shortfall is equal to potential schooling (age-5)<sup>minus actual</sup> 13 schooling (alternatively, the highest grade completed). The nice thing about this measure is that it is a familiar one: it is identical<sup>to Mincer's</sup> (1974) definition of potential work experience for adults. The<sup>only</sup> 13Within the household, it is also possible to compare the quality of schools attended by male children versus the quality of schools<sup>attended</sup> by female children. For example, in the PLSS, it is possible to determine whe-her the child attended a private or a public school. Indices of quality of schooling of and school-related expenditure on male and female offspring can be incorporatod to make the schooling differential variable better approximate differential investment in schooling rather than differential schooling attainment.

18
difference is in its application. We deal with the third problem by estimating cohort-specific schooling equations, estimating separately for each of five age groups: 6-15 years, 16-25 years, 26-35 years, 16-45 years and 46-65 years.14

# Definitions of Independent Variables

- Y: _Household Income_ is proxied by using total (food and nontood) expenditures in the household. 'The advantages of using expenditures rather than income are that: (a) expenditures are less subject to errors in reporting, since they are reported by component. Each component (food, clothing, and so on) is less likely to be

- systematically under- or over-reported; and (b) total expenditures are a better proxy for permanent income, which is generally the relevant budget constraint.

The disadvantages are that: (a) since expenditures on schooling are a component of the total, the issue of endogeneity of a right-hand sidf variable becomes a problem; and (b) schooling levels and 14Ideally, to purge cohort effects from the comparison of male and female schooling levels, the following procedure should be adopted. The _average_ schooling shortfall for male children in each cohort group is first calculated. Then the difference of the schooling shortfall of each child i, male and female, from the average schooling shortfall of male children in the cohort that child i belongs to, is defined as M (t) (Schooling Shortfall) - JE (Male Schooling Shortfall)k / M(t) - _Si_

- j - m, f; and k 1, 2 ...... M(t)

where j denotes the male children in the cohort, t, that i belongs to, and M(t) is the number of males in cohort t.

19
household income may be jointly determined<sup>by other variables, and</sup> this results in simultaneous equations bias.<sup>(See Schultz 1989 for a</sup> discussion of this problem for fertility decisions.) To overcome these problems, the estimation isi done in two stages. The first stage consists of estimating household<sup>expenditures per</sup> adult from the following regression Expenditures/Adult - f + C Age + 4 **Age2** + C Schooling O 1 2 3 + C Schooling + C Training + C Public School? 4 ~~~~5 6 + C Landholding + (*Unearned Income Age, Schooling and Training are the age, education, and training<sup>of</sup> the head of the household and the spouse, Public School?<sup>is a binary</sup> variable that a0ks whether the head and spouse attended<sup>a private or a</sup> public school, Rural is a region dummy which equals<sup>1 if the region of</sup> residence is rural, and 0 otherwise, Landholding is<sup>the total area of</sup> land sown or rented out by the household, and Unearned<sup>Income is the</sup> sum of all income other than wage and salaries. The fitted<sup>values used</sup> are from a regression that excludes Landholding, because the sample was reduced to one-third of its size when landholding<sup>was included.</sup> The full regression results are in Appendix I.

t: The structure of the economy, or the time spent in<sup>each of the three</sup> market sectors, **t1,** t2, and t3, is proxied by their<sup>_Share In_</sup> _Departmental GDP._ 20
- ti - Share of agriculture (farming, fishing and frestry),

- ta Share ef services (personal and business services, health care,

hotels, tourism, and so on),

- 3 Share of industry (manufacturing, mining, construction, and so

on), averaged over 1979-85. Ideally, I would also have experimented with the share of each sector in department _employment,_ but data were not available. It may be more appropriate to use sector shares in income, since it is the earning power<sup>of children</sup> that parents are concerned about, not hours worked. Using<sup>sectoral</sup> shares in departmental GDP as proxies for ti assumes that within<sup>each sector,</sup> the labor intensity of production does not change as sectoral output changes.

p- Changes in P5 are proxied by a _rural-urban dutwi_ variable. I assume that the price of schooling is lower .n urban than in rural<sup>regions.</sup> This could be because the average distance to school in rural regions is greater than that in urban regions.<sup>Thuswithin each department,</sup> the parameter P8 varies with region of residence.

Z2 The estimation in this section uses household data from the PLSS for subset Z2 variables, as follows: is 15Alternatively, it could be because rural areas are generally agricultural, and urban areas are more industrial. If children's labor is more valuable in agrarian economies, then the price of schooling would include the higher opportunity cost in rural areas. This effect<sup>confounds</sup> the structure of the economy with the rural-urban decomposition, and is ignored here.

21
- _(1) Hlghest level of education completed by Mother_

- _(2) Highest level of education completed by Father_

_(3) Mother's Longest Occupation_

- _(4) Father's Longest Occupation_

The level of education of the paz.nts is measured as follows:

-1 - Never attended, 0 - None, 1 - Initial, 2 - Primary, 3 - - - Regular secondary, 4 Technical secondary, 5 Postsecondary - Non-university, 6 University.

The occupation of the parents is measured as follows: 1 - Did not work, 2 - Missing and not elsewhere classified, 3 - Agriculture, Fishing and Forestry, 4 - Sales vendors, 5 - Service workers, 6 - Production & Transportation, 7 - Clerical, 8 - Professional and Government.

The education levels of the mother and father are included to capture information-processing abilities of the household. Educated parents are likely to be better informed about the true rates of return to their children's education. The occupation of each parent is included to test whether parents base their expectations for their children on their own experiences, or wihether the crucial determinant is the general structure of production in the region of residence (as proxied by province t ratios).16 ± 1 6The education of the mother relative to that of the father, when their wages or incomes are not included, may also indicate the extent of female control of the household budget Y. Occupations of the mother and father are more likely to proxy share in _actual_ earned income of the household. Education, when occupation is included, better proxies _potential_ earned income. To the extent that the "bargaining position" of females depends on the income-earning capability of the mother, and not income actually earned, the mother's schooling will have an independent effect on 22
# Results of the Schooling Regressions

The general form of the estimated<sup>equations is</sup> Since the tL as measured add up<sup>to i, only two of the three shares can be</sup> included in a regression. The<sup>aim is to examine the relative increases in</sup> the demand for schooling as educati.on-intensive sectors grow in importance, and particularly whether industrial<sup>growth raises the demand for boys'</sup> education more than girls'. To<sup>hold the share of services constant while</sup> increasing the share of industry,<sup>we must include both the high education</sup> sectors in the regression, and<sup>omit the low education sector.</sup> Another aspect of the problem of<sup>multicollinearity</sup> is that the share of agriculture in GDP (the omitted<sup>class) and the degree of</sup> urbanization are highly (negatively) correlated. Since che share of agriculture is equal to 1 minus the<sup>sum of the shares of services and</sup> industry, this leads to high degree<sup>of multicollinearity</sup> in the above regressions. To overcome this problem,<sup>I use a four-way classification,</sup> with government services as the fourth category.<sup>The results below are</sup> computed with two classes, industry<sup>and nongovernment services, and two</sup> omitted classes, agriculture and government<sup>services. Since the share of</sup> gender equity in child investments. Studies have also found that the father's education has significant positive<sup>effects on the schooling of</sup> children. (See for example, Moock and<sup>Leslie 1986.) The education of both</sup> parents is hypothesized to have a<sup>positive effect on the schooling of both</sup> sons ans daughters.

23
government services is about .07, it is not a very important category quantitatively, but it helps to overcome the multicollinearity problem." The regressions estimated for females are: Sf 00 + 01Household-Income + 02Share-of-Industry + 03Share-of-Services + + Urbanization + 0 Father's Education + 48Mother's Education + 47Father's Occupation + 8Mother's Occupation + f (15a) and for males are: S - p0 + p Household Income + p2Share-of-Industry + 3<sup>Share-of-Services+p Urbanization</sup> + /5 Father's Education + jAMother's Education + p7Father's Occupation + p8Mother0s Occupation + _e_ (15b) The primary hypotheses to be tested are: HI. < 0, p1 < 0 : Schooling of the girl and the boy are normal goods.

H2. 46 < p1 : Since levels of schooling of girls are lower to begin with, this would imply that equity in human capital investments across sexes is a normal good.

H3. 02 < _0,_ p2 **<** 0 : Demand for schooling increases as the share of 17To test whether multicollinearity was severe, I used the singular value decomposition technique advocated by Belsley and others (1980). This test is essentially a measure of the sensitivity of coefficients to changes in the matrix of independent variables. This sensitivity is summarized as (square root of) the ratio of the largest eigenvalue of the X'X matrix to the smallest, and is called a "condition index." Condition indices of less than 30 are considered good, and those between 30 to 100 are considered to be indicative of moderate to strong multicollinearity (see Judge and others 1985). The largest condition index for the regressions in this paper was about 26.

24
industry increases at the expense<sup>of agriculture, holding the share of</sup> services constant.

- H4. 02 > 2 : Men have a cDmparative advantage<sup>in industry, since it</sup> rewards work experience as well<sup>as schooling. So when the share of</sup> industry rises holding the share of<sup>services constant, the demand for</sup> male schooling rises by more than<sup>the demand for female schooling.</sup>

- H5. 43 < _0, A3_ **<** O : Demand for<sup>schooling increases as the share of</sup> services rises at the expense of<sup>agriculture, holding the share of</sup> industry constant.

- H6. 43 < p3 : Women have a comparative advantage<sup>in services, since the</sup> rate of return to schooling in the<sup>services sector is independent of the</sup> time spent working in this sector.<sup>So when the share of services rises,</sup> holding the share of industry constant,<sup>the demand for female schooling</sup> rises by more than the demand for<sup>male schooling.</sup>

- H7. 04 < 0, p < 0 : A fall in<sup>the supply price of schooling associated</sup> with greater urbanization, holding<sup>the demand schedule for schooling</sup> constant, will raise schooling<sup>investments in both girls and boys.</sup>

- H8. 04'< p4 :The theory has no predictions<sup>about the</sup><sup>_relative_</sup> magnitudes of the responses of male and<sup>female schooling to changes in the supply</sup> price of schooling.

- Table 2 shows the results for<sup>the entire sample and separately for</sup>

- each of five age-groups: 6-15<sup>years, 16-25 years, 26-35 years, 36-45 years,</sup> and 46-65 years. Schooling decisions<sup>of those who are more than 35 years old</sup> were made when the structure of<sup>the province's economy was different. We</sup>

25
expect to find the strongest results for groups that are finishing school (16-25 years) and that have just finished school (26-35 years).

We expect to see that an increase in the share of services, holding the share of industry constant, will reduce the schooling shortfall of females by more than that of males. For industry, the effect on male schooling should be stronger than the effect on female schooling. The results indicate that for the sample as a whole, increases in the share of services are significantly and negatively correlated with schooling shortfall for females, but not for males. The share coefficient in the female regressions is greater in magnitude for the services sector than for industry. For males, on the other hand, it is the share of industry that is significant (at the 5 or 10 percent level). This is exactly what our theory predicted.

Results by age group show that coefficients for the shares of services and industry are insignificant for the age groups 6-15, 36-45, and 46-65 years. For the group 16-25 years old, the coefficient for the share of services is significant at the 1 or 5 percent level. For the group 26-35 years old, the share of industry and services show strong results.

Reassuringly, the correlation between the share of services and schooling seems to be stronger for females than for males. However, the magnitude is greater for the services sector for _both_ sexes -- a contradiction of the theory. For each of the age groups, the coefficient for the share of industry is never significant for females. However, for males in the two groups 26-35 and 36-45 years, the coefficient of the share of industry is 26
significantly negative at the 10 percent level of significance.

For the age-specific estimates, the degree of urbanization decreases the schooling shortfall of females, but not necessarily that of males. Surprisingly, urbanization has a strong unfavorable influence on the schooling shortfall for males in the pooled regressions. In the context of our model, where the rural dummy proxies the price of schooling, this implies that lowering the price of schooling increases the schooling of females more than males.

The education of both father and mother have significant positive effects on schooling. The education of the mother is more closely associated with the schooling of daughters, and the education of the father with the schooling of sons. The occupation of the mother never matters, while the occupation of the father seldom matters, with higher occupation implying a lower schooling shortfall for both female and male offspring. Household income has a strong beneficial effect on all groups except children aged 6-15 years. A surprising finding was that household income has a stronger effect on schooling of male children than female children. Thus while investment in education is a normal good, the data reject the view that gender equity is a normal good _when demand-side factors are included._ That is, increases in household income _per se_ will not lower the gender gap in schooling.

27
**Table 1**

MEANS AND STANDARD DEVIATIONS OF VARIABLES BY HOUSEHOLDS IN PERU: 1985-86

|**Variables**|**Mean**|**Standard**<br>Deviations|
|---|---|---|
|_Department-Level_<br>_RMS Variables:_|||
|Share of Industry in Dept.GDP<br>|0.36|0.14|
|Share of Agriculture in Dept.GDP|0.18|0.13|
|Share of Services etc.in Dept.GDP|0.39|0.13|
|Share of Govt. Services in Dept.GDP<br>|0.07|0.03|
|Degree of Urbanization|66.99|25.49|
|_Household-Level RHS Variables:_|||
|Predicted Household Income|787.26|385.13|
|Place of Residence (Dummy: 1-Rural, 0-Urban)<br>|0.43|0.49|
|Household Size|6.48|2.75|
|Total Landholding (Acres)|9.21|76.01|
|_Individual-Level_<br>_RHS Variables:_|||
|Father's Education Level<br>|1.48|1.76|
|Mother's Education Level|0.48|1.68|
|Father's Occupation|4.28|1.67|
|Mother's Occupation|2.98|1.63|
|_Individual-Level_<br>_LHS Variables:_|||
|Female: Education in Years|4.59|4.10|
|Male<br>: Education in Years<br>|5.49|4.13|
|Female: Highest Level of Education Completed|1.94|1.69|
|Male<br>: Highest Level of Education Completed|2.43|1.50|
|Female: Age in Years<br> <br>|26.70|16.04|
|Male<br>: Age in Years|26.12|16.13|
|Female Schooling Shortfall: 6-15 years|2.76|1.92|
|Male Schooling Shortfall: 6-15 years<br>|2.57|1.73|
|Female Schooling Shortfall: 16-25 years|7.89|4.35|
|Male Schooling Shortfall: 16-25 years|7.34|3.75|
|Female Schooling Shortfall: 26-35 years|18.76|5.84|
|Male Schooling Shortfall: 26-35 years|16.89|5.28|
|Female Schooling Shortfall: 36-45 years|31.01|5.67|
|Male Schooling Shortfall: 36-45 years<br>|29.02|5.76|
|Female Schooling Shortfall: 46-65 years|46.33|7.03|
|Male Schooling Shortfall: 46-65 years|44.54|7.13|

28
Table 2 HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, **1985-86**

|Independent<br>Equation 1<br>Equation 2|Equation 3|
|---|---|
|Variables<br>Female<br>Male<br>Female<br>Male|Female<br>Male|
|All Age Groups||
|Constant<br>-5.043 -3.841<br>-5.342 -3.665|-5.031 -3.899|
|(0.64) (0.62)<br>(0.62) (0.62)|(0.64) (0.64)|
|Industry share<br>-1.640 -1.310<br>-1.002 -1.166|-0.943 -1.172|
|(0.68) (0.67)<br>(0.67) (0.67)|(0.67) (0.67)|
|Services share<br>-3.215 -1.281<br>-2.251 -1.112|-2.165 -1.216|
|(0.83) (0.82)<br>(0.81) (0.81)|(0.82) (0.82)|
|Rural dummy<br>0.836<br>-0.527<br>0.233<br>-0.678|0.197<br>-0.715|
|(0.20) (0.20)<br>(0.20) (0.21)|(0.21) (0.21)|
|Household income -0.007 -0.010<br>-0.006 -0.010|-0.006 -0.010|
|(0.00)<br>(0.00)<br>(0.00)<br>(0.00)|(0.00)<br>(0.00)|
|Father's education<br>-0.419 -0.304|-0.388 -0.302|
|(0.06) (0.06)|(0.06) (0.06)|
|Mother's education<br>-0.574<br>0.007|-0.556 -0.005|
|(0.06) (0.06)|(0.06) (0.06)|
|Father's occupation|-0.129<br>0.022<br>(0.06) (0.06)|
|Mother's occupation|-0.015<br>0.085<br>(0.05) (0.05)|
|Age group<br>11.251 11.181<br>11.091 11.116|11.096 11.120|
|(0.06) (0.06)<br>(0.07) (0.07)|(0.07) (0.07)|
|2<br>Adjusted R<br>0.885<br>0.899<br>0.892<br>0.900|0.892<br>0.900|
|Sample size<br>4281<br>4024<br>4281<br>4024<br>4|281<br>4024|

Note: Dependent variable is schooling shortfall - age - schooling - 5 Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

29
Table 2 (Continued)

# HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, **1985-86**

|Independent<br>Equa|tion 1|Equation 2|Equat|ion 3|
|---|---|---|---|---|
|Variable.-<br>Female|<br>Male|Female<br>Male|Female|<br>Male|
|P|opulation A|ged 6 to 15 years|||
|Constant<br>0.765|<br>2.768|1.448<br>3.378|1.595|3.209|
|(1.40)|(1.41)|(1.38) (1.00)|(1.39)|(1.03)|
|Industry share<br>2.474|<br>1.201|2.047<br>1.152|2.518|<br>1.106|
|(1.58)|(1.31)|(1.52) (1.24)|(1.56)|(1.24)|
|Services share<br>1.680|<br>-0.297|3.101<br>0.179|3.827|<br>0.071|
|(2.00)|(1.65)|(1.92) (1.57)|(2.05)|(1.60)|
|Rural**dummy**<br>1.928|<br>0.168|1.383<br>0.142|1.424|<br>0.125|
|(0.49)|(0.40)|(0.49) (0.38)|(0.49)|(0.38)|
|Household income 0.001|<br>-0.000|0.000<br>0.000|0.000|<br>-0.000|
|(0.00)|(0.00)|(0.00)<br>(0.00)|(0.00)|(0.00)|
|Father's education||-0.042 -0.309|-0.010|-0.335|
|||(0.14) (0.12)|(0.14)|(0.13)|
|Mother's education||-0.465 -0.163|-0.433|-0.193|
|||(0.12) (0.10)|(0.13)|(0.11)|
|Father's occupation|||-0.129|<br>0.027|
||||(0.11)|(0.11)|
|Mother's occupation|||-0.056|0.109|
||||(0.10)|(0.09)|
|Adjusted R<sup>2</sup><br>0.092|<br>-0.009|0 t91<br>0.102|0.189|0.101|
|Sample size<br>156|170|156<br>170|156|170|

Note: Dependent variable is schooling shortfall - age - schooling - 5 Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

30
Table 2 (Continued)

# HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, **1985-86**

|Independent<br>Equat|ion 1|Equation 2|Equat|ion 3|
|---|---|---|---|---|
|Variables<br>Female|Male|Female<br>Male|Female|<br>Male|
|Po|pulation Age|d 16 to 25 years|||
|Constant<br>14.305|16.296**|14.038<br>14.353|14.689|<br>14.039|
|(1.13)|(1.26)|(1.08)<br>(1.24)|(1.11)|<br>(1.31)|
|Industry share<br>-1.173|-0.866|-0.912<br>-0.355|-0.813|<br>-0.388|
|(1.28)|(1.57)|(1.21)<br>(1.52)|(1.21)|<br>(1.52)|
|Services share<br>-5.117|-4.939|-4.479<br>-4.084|-4.106|<br>-4.243|
|(1.61)|(1.70)|(1.52)<br>(1.66)|(1.53)|<br>(1.66)|
|Rural dummy<br>2.333|1.927|1.828<br>1.383|1.685|1.336|
|(0.41)|(0.45)|(0.39)<br>(0.45)|(0.39)|(0.45)|
|Household income -0.003|-0.004|-0.002<br>-0.004|-0.001|-0.003|
|(0.00)|(0.00)|(0.00)<br>(0.00)|(0.00)|(0.00)|
|Father's education||-0.404<br>-0.416|-0.340|-0.420|
|||(0.11)<br>(0.12)|(0.11)|(0.13)|
|Mother's education||-0.616<br>-0.282|-0.573|-0.304|
|||(0.10)<br>(0.12)|(0.11)|(0.12)|
|Father's occupation|||-0.328|0.031|
||||(0.11)|(0.11)|
|Mother's occupation|||0.123|0.117|
||||(0.08)|(0.09)|
|2<br>Adjusted R<br>0.286|0.359|0.366<br>0.399|0.374|0.399|
|Sample size<br>645|445<br>|645<br>445<br>|645|445|

Note: Dependent variable is schooling shortfall = age - schooling - 5

* **** Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

31
Table 2 (Continued) HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, 1985-86

|Independent<br>Equat|ion 1<br>Equation 2|Equation3|
|---|---|---|
|Variables<br>Female|Male<br>Female<br>Male|Female<br>Male|
|Po|pulation Aged26 to 35 years||
|Constant<br>26.685|26.601<br>25.9919 26.117|26.662 26.230|
|(0.99)|(0.93)<br>(0.95) (0.92)|(1.00) (0.97)|
|Industry share<br>-1.384|-1.733<br>-0.542 -1.590|-0.470 -1.384|
|(1.12)|(1.01)<br>(1.07) (1.00)|(1.07) (1.00)|
|Services share<br>-3.845|-2.639<br>-3.233 -2.170|-3.311 -2.124|
|(1.38)|(1.32)<br>(1.30) (1.30)|(1.31) (1.31)|
|Rural dummy<br>1.862|0.525<br>1.130<br>0.305|1.029<br>0.226|
|(0.35)|(0.33)<br>(0.34) (0.33)|(0.34) (0.34)|
|Household income -0.006|-0.008<br>-0.004 -0.007|-0.004 -0.007|
|(0.00)|(0.00)<br>(0.00)<br>(0.00)|(0.00)<br>(0.00)|
|Father's education|-0.538 -0.303|-0.471 -0.272|
||(0.10) (0.09)|(0.10) (0.09)|
|Mother's education|-0.606 -0.262|-0.576 -0.261|
||(0.10)<br>(0.09)|(0.10)<br>(0.09)|
|Father's occupation||-0.245- -0.121|
|||(0.10)<br>(0.09)|
|Mother's occupation||0.031<br>0.093|
|||(0.07) (0.07)|
|2<br>Adjusted R<br>0.404|0.500<br>0.462<br>0.515|0.464<br>0.515|
|Sample size<br>1148<br>|957<br>1148<br>957<br>1|148<br>957|

Note: Dependent variable is schooling shortfall - age - schooling - 5 Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

32
Table 2 (Continued)

# HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, 1985-86

|Independent<br>Equat|ion 1<br>Equa|tion 2<br>Equation 3|
|---|---|---|
|Variables<br>Female|<br>Male<br>Female|<br>Male<br>Female<br>Male|
|-Po|pulation Aged 36 to|45 years|
|Constant<br>38.049<br>(0.97)|39.942<br>36.471<br>(0.84)<br>(0.93)|39.570<br>36.795 39.437<br>(0.85)<br>(0.97) (0.89)|
|Industry share<br>-0.730<br>(1.15)|-1.850<br>0.073<br>(0.94)<br>(1.10)|<br>-1.404<br>0.074<br>-1.448<br>(0.94)<br>(1.10) (0.93)|
|Services share<br>-0.005<br>(1.36)|-1.650<br>1.033<br>(1.18)<br>(1.29)|<br>-1.360<br>1.136<br>-1.407<br>(1.17)<br>(1.30) (1.18)|
|Rural dummy<br>0.930<br>(0.35)<br>Household income -0.009<br>(0.00)|<br>-0.547<br>0.346<br>(0.30)<br>(0.34)<br>-0.011<br>-0.006<br>(0.00)<br>(0.00)|<br>-0.738<br>0.272<br>-0.734<br>(0.31)<br>(0.34) (0.31)<br>-0.010<br>-0.006 -0.010<br>(0.00)<br>(0.00)<br>(0.00)|
|Father's education|-0.382<br>(0.09)|-0.294<br>-0.339 -0.303<br>(0.09)<br>(0.09)<br>(0.'09)|
|Mother's education|-0.778<br>(0.10)|-0.095<br>-0.756 -0.102<br>(0.09)<br>(0.10)<br>(0.09)|
|Father's occupation||-0.174<br>0.044<br>(0.10)<br>(0.09)|
|Mother's occupation||0.088<br>0.010<br>(0.08) (0.07)|
|Adjusted R<sup>2</sup><br>0.435<br>Sample size<br>1064<br>1|0.610<br>0.493<br>030<br>1064|<br>0.617<br>0.494<br>0.616<br>1030<br>1064<br>1030|
|Note: Dependent variable i<br>Significant at 10 perce<br>Standard errors in paren|s schooling shortf<br>nt,<br>at 5 percent<br>theses.|all- age- schooling- 5<br>level,<br>at 1 percent level.|

33
Table 2 (Concluded) HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, **1985-86**

|Independent<br>Equation 1<br>Equation 2|Equation 3|
|---|---|
|Variabies<br>Female<br>Male<br>Female<br>Male|Female<br>Male|
|Population Aged 46 to 65 years||
|Constant<br>54.175<br>54.047<br>52.687<br>54.174|52.731<br>53.386|
|(1.29)<br>(1.14)<br>(1.28)<br>(1.15)|(1.36)<br>(1.22)|
|Ineustry share<br>-3.200<br>-1.246<br>-2.321<br>-1.227|-2.353<br>-1.282|
|(1.56)<br>(1.36)<br>(1.55)<br>(1.36)|(1.55)<br>(1.36)|
|Services share<br>-4.428<br>1.215<br>-2.814<br>1.153|-2.696<br>0.910|
|(1.91)<br>(1.66)<br>(1.89)<br>(1.66)|(1.90)<br>(1.67)|
|Rural dummy<br>0.093<br>-0.947<br>-0.498<br>-0.993|-0.430<br>-0.996|
|(0.45)<br>(0.40)<br>(0.45)<br>(0.40)|(0.46)<br>(0.40)|
|Household income -0.008<br>-0.013<br>-0.006<br>-0.012|-0.006<br>-0.013|
|(0.00)<br>(0.00)<br>(0.00)<br>(0.00)|(0.00)<br>(0.00)|
|Father's education<br>-0.307<br>-0.233|-0.331<br>-0.257|
|(0.13)<br>(0.11)|(0.13)<br>(0.12)|
|Mother's education<br>-0.636<br>0.176|-0.647<br>0.151|
|(0.16)<br>(0.13)|(0.16)<br>(0.13)|
|Father's occupation|0.091<br>0.191|
||(0.14)<br>(0.13)|
|Mother's occupation|-0.146<br>0.127|
||(0.11)<br>(0.10)|
|Adjusted R<br>0.201<br>0.381<br>0.227<br>0.382|0.227<br>0.383|
|Sample size<br>1264<br>1418<br>1264<br>1418<br>1|264<br>1418|

Note: Dependent variable is schooling shortfall = age - schooling - 5 Significant at 10 percent, at 5 percent level, at 1 percent level. Standard Errors in parentheses.

34
# 5. PROVINCE LEVEL EMPIRICAL EVIDENCE

# Evidence from provincial data confirms the findings of the

previous section. Because literacy and income data are based on the census, the results do not reflect any sampling bias in the PLSS. In the last section the focus was on household level variables (the subset Z2 ). In this section I experiment with various infrastructural parameters (Z 1), indicating the degree of development of transport and communications in each province or department.

Using data from the department level is tantamount to assuming that households within a department are identical with respect to the parameters they face: income (Y), the rates of return to schooling in the three sectors (a, 6, -, and 6), and the time-allocation parameters (t ). However, each department has two regions, rural and urban. As in the previous section I assume that the price of schooling (PS) is lower in urban than in rural regions.

# Definitions of Dependent Variable: Illiteracy Rates

Sr : The census data do not provide gender-specific schooling attainment or enrollment by age group for each department. Since illiteracy rates are available at the required level of disaggregation, I use cohortspecific illiteracy rates as a proxy for investment in schooling in the department. Clearly, literacy rates are a gross approximation for schooling levels. There are two defenses for this procedure: First, the study focuses on the _differences_ in human capital between males and females. Even though illiteracy rates do not reflect absolute 35
levels of human capital of each sex, they may provide reliable estimates of relative levels. Second, total illiteracy rates are highly negatively correlated with initial, secondary, postsecondary, and vocational enrollment ratios across departments (see Table 3). University level enrollmEnt ratios are weakly negatively correlated with illiteracy rates, but this is not surprising. Somewhat puzzling is the finding that primary school enrollment ratios are _poslelvely_ correlated with illiteracy rates. This is probably due to the fact that high actual primary school enrollment relative to the number that should be enrolled may indicate that students have fallen behind in the curriculum.

# Definitions of Independent Variables

- Y: Departmental _gross domestic product per caplta_ is used as the measure for household income.

- t: The structure of the economy, or the time spent in each of the three market sectors, tI, t2, and t3, is proxied by their _Share In departmental GDP._ These variables are defined as in the previous

section:

- ti - Share of Agriculture (farming, fishing and forestry),

- t2 - Share of Services (personal and business services, health care, hotels, tourism, and so on),

- t3 - Share of Industry (manufacturing, mining, construction, and so

on), 36
averaged over 1979-85.

P8: Changes in P8 are proxied by the _Degree of Urbanization_ (percentage of total department population living in urban areas) in 1985. The degree of urbanization is a continuous variable. Therefore Ps (0 s Ps5 100) is a continuous variable. I also experimented with a rural-urban dummy variable (P. - p,v, where p and u are the price of schooling a child in rural and in urban areas respectively). This is done by estimating male and female schooling regressions separately for rural and urban schooling levels across departments. However, income per capita, Y, and the potential work opportunities by sector, ti, are still department-level: department GDP figures are not available separately for each region. Thus it is assumed that ti do noc vary across regions within a department, but Ps does. This assumes t-hat, within a department, it is costless for _an_ adult to migrate from rural to urban urban regions (or vice versa), but the cost of sending a _child_ to school from rural to urban regions is very high.

Z : The vector of department-level infrastructural variables Z include _Roads per Square Kilometer,_ and _Post Offices per Square Kilometer._ I also experimented with _Telegraph Offices per Square Kilometer, Telephone Lines per Capita,_ and some other variables. Since household specific variables Z2 are only available in a household-level survey, this section only employs department-level infrastructural variables (subset Z ) in the schooling regressions.

37
# Description of the Data

Table 4 shows the means and standard deviations of the dependent and all the independent variables used in the illiteracy regressions. Industry's share is the largest, but is also relatively more variable across departments than the share of services (as evidenced by values of the coefficients of variation of 0.54 and 0.36 respectively). The share of agriculture in GDP (CV- 0.61) is also highly variable across sectors. It seems that the share of industry increases most rapidly at the expense of the share of agriculture.

Average illiteracy rates for all ages and for both sexes are higher in rural areas. The aggregate gender differential (female illiteracy divided by male illiteracy) is lower for rural (2.52) than for urban areas (3.54). Given the values of the standard deviations, this difference appears to be statistically significant. A plausible (demand-side) explanation of this finding is that agricultural activities are less education-intensive for women _and_ men. That is, the predominance of a secter with low education intensity in a region will result in low demand for education in that region, but, under reasonable assumptions, in higher equity across sexes in investment in schooling. Since the supply price of education is probably lower in urban areas, and if the lower price prompts a greater response from girls than boys, the ratio between male and female illiteracy rates should be _lower_ in urban areas. This points out a weakness of studies that focus only on shifts in supply of schooling, and highlights the importance of studying the determinants of shifts in the schooling demand curve.

38
Table 3

# CORRELATION BETWEEN ENROLLMENT RATIOS AND ILLITERACY RATES: PERU, 1985-86

|ENROLLMENT<br>RATIO|TOTAL||CO|HORT GRO|UP (IN YE|ARS)||
|---|---|---|---|---|---|---|---|
|(by school<br>g-oup)||15-19|20-24|25-29|30-34|35-39|40+|
|Initial|-.596|-.538|-.552|<br>-.1'0|-.597|-. 616|-. 587|
|Primary|.694|.694|<br>.691|<br>.327|.691|.711|<br>.691|
|Secondary|-.815|-.881|-.847|<br>-.639|-.829|-.851|-.812|
|Postsecondary|-.669|-.696|-.672|<br>-.516|-.682|-.714|-.658|
|University|-.402|-.444|-.439|<br>-. 332|-.394|-.406|-.412|
|Other|-.725|-.663|-.689|<br>-.483|-.731|-.751|-.736|

- _Notes:_ l.All correlations except those marked by an asterisk are significant at the 5 percent level. All correlations are significant at the 10 percent level. The test used to determine statistical significance of the correlation is the t-statistic

t - [(n-2r)/(1-r )] . n is the number of observations, r is the computed correlation coefficient, and (n-2) is the number of degrees of freedom.

- 2.The number of observations is 23. Lima and Callao, and Loreto and Ucayali are aggregated to maintain conformity with available GDP accounts.

- 3.Enrollment Ratios are calculated as ratios of total enrollment in the department to total population. For an idea of how much agedistributions differ across department, see column 5 of Table in the Appendix.

- _Sources:_ For illiteracy rates, enrollment and population, _Peru: Compendio EstadlstLco, 1987,_ Systema Estadistico Nacional, Instituto Nacional de Estadistica.

39
Table 4

# HEANS AND STANDARD DEVIATIONS OF VARIABLES BY DEPARTMENT, 1981-82

|Variables|Me|ans and Stan|dard Devi|ations|
|---|---|---|---|---|
|||Mean|S.|D.|
|_RHS Variables:_|||||
|Per Capita GDP (Current Prices)||174.46|148.|65|
|Share of Industry in Dept.GDP||0.37|0.|20|
|Share of Agriculture in Dept.GDP||0.23|0.|14|
|Share of Setvices etc.in Dept.GDP||0.33|0.|12|
|Share of Govt. Services in Dept.GDP||0.07|0.|03|
|Degree of Urbanization||55.66|21.|76|
|Roads per 1000 Square Kilometers||785.06|2258.|45|
|Post Offices per 1000 Sq. Km.||2.82|1.|92|
||Ur|ban|Ru|ral|
||Mean|S.D.|Mean|S.D.|
|_LHSVariables:_|||||
|Female illiteracy rate: All ages|18.67|<br>10.84|46.22|16.76|
|Male illiteracy rate: All ages|5.44|3.12|19.43|9.09|
|Female/Male illiteracy ratio: All|3.54|0.87|2.52|0.51|
|Female illiteracy rate: 15-19 years|3.80|2.24|18.42|11.20|
|Male illiteracy rate: 15-19 years|1.62|0.74|6.96|3.55|
|Female/Male illiteracy ratio: 15-19|2.45|0.83|2.55|0.63|
|Female illiteracy rate: 20-24 years|5.85|3.99|26.19|14.42|
|Male illiteracy rate: 20-24 years|1.62|0.89|7.58|4.47|
|Female/Male illiteracy ratio: 20-24|3.81|1.47|3.93|1.44|
|Female illiteracy rate: 25-29 years|8.75|5.67|34.50|17.13|
|Male illiteracy rate: 15-19 years|1.95|1.09|10.25|5.93|
|Female/Male illiteracy ratio: 25-29|4.96|2.31|3.84|1.42|
|Female illiteracy rate: 30-34 years|13.88|9.04|44.94|18.56|
|Male illiteracy rate: 30-34 years|3.04|1.83|14.40|8.22|
|<br>Female/Male illiteracy ratio: 30-34|5.01|2.30|3.72|1.77|
|Female illiteracy rate: 35-39 years|22.08|12.77|55.89|17.56|
|<br>Male illiteracy rate: 35-39 years|4.52|2.54|19.83|9.93|
|Female/Male illiteracy ratio: 35-39|5.27|2.07|3.23|1.17|
|Female illiteracy rate: 40+ years|37.72|17.76|69.69|17.04|
|Male illiteracy rate: 40+ years|12.00|6.54|34.01|12.95|
|Female/Male illiteracy ratio: 40+|3.34|0.88|2.17|0.44|

_Notes:_ Means are Unweighted averages.

40
When we examlne the patterns across cohorts,<sup>we find a small</sup> dlfference for the younger age groups (15<sup>to 29 years) in this ratio across</sup> reglons, and a sLgnfiLcantly larger dlfference<sup>for people 30 years and</sup> older. One lnterpretatlon of thls flndlng is that over time, the gender dLfferences ln urban areas have narrowed more<sup>rapidly than in rural areas.</sup> AgaLn, a demand aide explanation seems plausible.<sup>The rapid growth of</sup> services ln urban areas can account for greater<sup>equity even if the supply</sup> price of schoolLng is constant over time, if women have<sup>a comparatlve</sup> advantage over men in worklng in services (see<sup>section 3)is</sup> It is difficult to rule out a supply-side explanation<sup>here. It may be that the</sup> supply price of schoollng has fallen relatively more<sup>in urban than in rural</sup> areas over time, and that female schooling has<sup>a higher price elasticity</sup> than male schooling, analogous to Gertler and<sup>Alderman's (1989) arguments</sup> for health.

Results of the Schooling RegressLons The general form of the estimated equations for<sup>females and males</sup> respectively ls S m 00 + Iker-Capita-GDP + *2Share-of-Industry + i3Share-of-Serviees + + Urbanization + ef (16a) gm - + PtlPr-Caplta-GDP + 2<sup>Share-of-Industry</sup> + P381ere-of-ServLces + p.UrbanLzatLon + e (16b) Note that men may still have an _absolute_ advantage in _both_ industry and services.

41
The first three slope coefficients in each equation measure demand shifts, while the last coefficient measures supply price effects. Since S stands for illiteracy rates, a negative coefficient implies a favorable effect on education levels. In this section, I refer to the absolute magnitudes of the coefficients when using the phrases "greater than" or "less than." Table 5 reports the results of the regressions. The first two rows report the results of the regressions (16a) and (16b). The results for the sample as a whole indicate no significant support for hypotheses Hl to H6 listed in section 4. The only significant variable is urbanization, although the signs of the other coefficients conform with the theory.

The insignificance of results using a sample of all ages is not surprising. Schooling decisions of age groups 35 years and above were made two decades ago. The structure of the department's economy and hence the demand for schooling is likely to have changed since then. It is more sensible to look at the relationship between the illliteracy of younger cohorts and department income, demand structure, and degree of urbanization. The relationships are likely to be strong for the youngest cohort group, and to diminish as the age of the cohort increases.

Results support this argument : for all but the oldest groups (35-39 years and 40 years and above) there is reasonably strong evidence that a rise in the share of the services sector, holding the share of industry constant, raises the schooling levels of females and males. As predicted by the theory, the coefficient for females is (2.5 to 5 times) 42
larger in magnitude, and intermittently significant at the 10 percent level of significance for a one-tailed test. The coefficient for industry's share is always greater in magnitude for females but never significant. This evidence, combined with the fact that the variance of the share of industry is in fact larger than that of the service sector, is evidence consistent with the view that a rise in the share of services in GDP leads to greater increases in the e'ucation of women than an equivalent increase in the share of industry. The coefficient for the degree of urbanization is always negative for both females and males, always greater in magnitude for females, and statistically significant at the 5 percent level for a one-tailed test.

Given the high degree of multicollinearity between the share of industry and the rate of urbanization, the high standard errors of the coefficients are not surprising. To increase the degrees of freedom (a common solution for multicollinearity), the female and male schooling equations are estimated separately for rural and urban regions, thus allowing the omission of the urbanization variable. This is roughly equivalent to treating the urbanization variable as a binary variable. It is a test of the alternative view that, within a department, it is costless for _an adult_ to migrate from rural to urban regions (or vice versa), but the cost of sending _a child_ for schooling from rural to urban regions is very high. The estimated equations for urban areas are Urban S. - 00 + l Per-Capita-GDP + 2Share-of-Industry 43
Urban S - _A0+_ j IPor-Capita-GDP + paShare-of-Industry **+** #3 Share-of-Services + f (178b) and for rural areas Rural St- **0** +0Per-Capita-GDP + **i** Share-of-Industry I #0 ~~~~~~~~~a **+** *3Share-of-Servicos + **e r(iS)** Rural S - p0 + " Per-Capita-GDP + **p** Share-of-Industry + p 3Share-of-ServLces + c (18b) The results for urban areas are rows 3 and 4; for rural areas, rows 5 and 6 in Table 5.

Regressions for groups aged 15-19, 20-24, and 25-29 years confirm the theory. The coefficients for the share of services in GDP are significant and larger in magnitude for females than for males, and larger in rural than in urban areas. The coefficients for the share of industry in GDP are generally insignificant for female schooling, and generally significant for urban male schooling. This seems to confirm the hypothesis that industry rewards schooling more than the main omitted class, agriculture, and that men have a comparative advantage in industry. Women, on the other hand, have a comparative advantage in services. The sign and magnitude of the coefficient for GDP indicate that female schooling increases by more than male schooling when income increases, and that the increases are larger for both females and males in rural areas.

Regressions that include variables proxylng Z1, roads per square kilometer, telephone lines per capita, and post offices per square 44
kilometer, were also estimated. The coefficients were insignificant, and the coefficients for per capita GDP, shares of services and industry, and urbanization were left largely unchanged.'9 The major limitation of this analysis is that the shares of each sector in a department may be jointly determined with education levels of men and women. For example, services may require more educated women than educated men, so provinces that have relatively more educated women will tend to have a larger share of services in GDP. The issue of causality between share of services and the demand for education of women is left unresolved. This is a crippling limitation for purposes of deciding policy.

I address this issue by estimating regressions by cohorts. Assuming that the sectors' shares in GDP are relatively stable across departments, the correlation between schooling and sectoral share should be stronger for younger cohorts if a higher share of services and industry _leads to_ a higher demand for education. On the other hand, if education intensive activities are concentrated in areas with high exogenous education levels, this would imply uniformly strong correlations between education levels and structure of the economy across _all_ age groups. The regressions discussed above indicate that the correlations are weak for older cohorts, implying support for the view that causality runs from economy structure to schooling levels, and not vice versa.

The results in this section are similar to those found in Gill and These results, which are not reported here, are available from the author.

45
Khandker (1990) for a sample of about 100 countries in 1965 and 1987. Migration is likely to be more frequent within a country than across national boundaries. The similarity of results by country and by province implies that the possibility of cross-department migration does not seem to be a significant factor in schooling decisions. This issue needs more examination, though, before any conclusive statement can be made on the effects of migration on the rates of return to human capital.

46
Table 5 CROSS-SECTION SCHOOLING REGRESSIONS: PERU, 1985-86

|Dependent||Ind|ependent V|ariables||
|---|---|---|---|---|---|
|Variable|CONSTANT|PER CAP.<br>GDP|INDUSTRY<br>SHARE|SERVICES DEGREE OF<br>SHARE<br>URBANIZN.|UNADJ.<br>R-SQR.|
|||All|Ages|||
|l.Female Total<br>Illiteracy Rate|73.713<br>|-0.019<br>(0.02)|9.196<br>(17.61)|-13.378<br>-0.647<br>(31.28)<br>(0.15)|.817|
|2.Male Total|33.479|-0.013|-0.584|-13.979<br>-0.239|.714|
|Illiteracy Rate||(0.01)|(10.42)|(18.49)<br>(0.09)||
|3.Female Urban|50.189|-0.040|-8.892|-60.113|.545|
|Illiteracy Rate||(0.02)|(13.72)|(17.42)||
|4.Male Urban|15.010|-0.012|-4.410|-16.739|.587|
|Illiteracy Rate||(0.00)|(3.76)|(4.77)||
|5.Female Rural|86.408|-0.049|-3.506|-80.723|.354|
|Illiteracy Rate||(0.03)|(25.73)|(32.67)||
|6.Nale Rural|42.407|-0.030|-4.380|-44.040|.362|
|Illiteracy Rate||(0.02)|(14.61)|(18.56)||

Note: Sample consists of 23 departments in Peru Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

47
# Table V (Continued)

# CROSS-SECTION SCHOOLING REGRESSIONS: PERU, 1985-86

|Dependent||In|dependent V|arlables|||
|---|---|---|---|---|---|---|
|Variable|CONSTANT|PER CAP.<br>_GDP_|INDUSTRY<br>SHARE|SERVICES D<br>SHARE<br>U|EGREE OF<br>RBANIZN.|UNADJ.<br>R-SQR.|
||Pop|ulation Ag|ed 15-19 Ye|ars|||
|l.Female Total<br>Illiteracy Rate|38.562<br>|-0.012<br>(0.01)|-7.420<br>(10.10)|-26.313<br>(17.94)|-0.237<br>(0.09)|.774|
|2.Male Total<br>Illiteracy Rate|11.364<br>|-0.004<br>(0.01)|.0.618<br>(4.04)|-5.171<br>(7.18)|-0.083<br>(0.03)|.658|
|3.Female Urban<br>Illiteracy Rate|10.945<br>|-0.009<br>(0.00)|-2.472<br>(2.65)|-13.242<br>(3.36)||.623|
|4.Male Urban<br>Illiteracy Rate|3.687<br>|-0.002<br>(0.00)|-0.947<br>(1.42)|-3.786<br>(1.80)||.280|
|5.Female Rural<br>Illiteracy Rate|54.960<br>|-0.040<br>(0.02)|-14.039<br>(15.80)|-69.955<br>(20.06)||.524|
|6.Male Rural<br>Illiteracy Rate|15.962<br>|-0.015<br>(0.01)|0.583<br>(6.02)|-18.415<br>(7.64)||.396|
||Pop|ulation Ag|ed 20-24 Ye|ars|||
|l.Female Total<br>Illiteracy Rate|55.657<br>|-0.013<br>(0.01)|-13.596<br>(12.65)|-37.260<br>(22.46)|-0.237<br>(0.11)|.816|
|2.Male Total<br>Illiteracy Rate|14.941<br>|-0.006<br>(0.01)|-2.693<br>(4.54)|-11.878<br>(8.07)|-0.073<br>(0.04)|.691|
|3.Female Urban<br>Illiteracy Rate|20.624<br>|-0.012<br>(0.01)|-9.157<br>(4.52)|-27.038<br>(5.74)||.655|
|4.Male Urban<br>Illiteracy Rate|4.951<br>|-0.002<br>(0.00)|-2.873<br>(1.14)|-5.568<br>(1.45)||.576|
|5.Female Rural<br>Illiteracy Rate|72.187<br>|-0.048<br>(0.02)|-19.606<br>(19.98)|-86.341<br>(25.36)||.511|
|6.Male Rural|18.686|-0.019|0.037|-21.900||.387|
|Illiteracy Rate||(0.01)|(7.58)|(9.63)|||

Note: Sample consists of 23 depertments in Peru Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

48
**T'olo** V (Continued)

CROSS-SECTION SCROOLING REOR8SSIONS: PERU, 1985-06

|Dependent<br>|Inde<br>|pendent<br>**Varlab**<br>**a_**<br>|||
|---|---|---|---|---|
|**Variable**|CONSTANT PER CAP.<br>GDP|INDUSTRY SERVICES D<br>SHARE<br>SHARE<br>U|EGREE OF<br>RBANIZN.|UNADJ.<br>R-SQR.|
||Population<br>Ag|ed 25-29Years|||
|l.Female<br>Total<br>Illiteracy<br>Rate|65.174 -0.017<br> <br>(0.02)|-7.644<br>-29.348<br>(15.14) (26.88)|-0.488<br>(0.13)|<br>.825|
|2.Male Total<br>Illiteracy<br>Rate|19.725 .0.010*<br> <br>(0.01)|.2.674 .15.610*<br>(5.77) (10.24)|-0.096*<br>(0.05)|.718|
|3.Feaale<br>Urban<br>Illlteracy<br>Rate|26.485 -0.021<br> <br>(0.01)|-7.588 -32.347<br>(6.93)<br>(8.79)||.585|
|4.Male Urban<br>Illiteracy<br>Rate|6.026 -0.004<br> <br>(0.00)|-2.351<br>-7.235<br>(1.08)<br>(1.37)||.734|
|5.Female<br>Rural<br>Illiteracy<br>Rate|85.822 -0.056<br> <br>(0.03)|-18.634 -97.214<br>(23.83) (30.26)||.483|
|6.Male Rural<br>Illiteracy<br>Rate|25.492 -0.027<br> <br>(0.01)|0.314<br>-29.957<br>(9.25) (11.74)||.452|
||PopulationAge|d30-34Years|||
|l.Fetale<br>Total<br>Illiteracy<br>Rate|84.684 -0.021<br> <br>(0.03)|-18.090 -62.375<br>(22.99) (40.83)|-0.451<br>(0.20)|.724|
|2.Male Total<br>Illiteracy<br>Rate|28.882 -0.011<br> <br>(0.01)|-6.516 -20.340<br>(8.03) (14.26)|-0.156<br>(0.07)|.738|
|3.Female<br>Urban<br>Illiteracy<br>Rate|40.319 -0.031<br> <br>(0.01)|-10.513 -48.771<br>(12.05) (15.30)||.509|
|4.Male Urban<br>Illiteracy<br>Rate|10.001 -0.005<br> <br>(0.00)|-5.422<br>-11.570<br>(1.78)<br>(2.25)||.738|
|5.Female<br>Rural<br>Illiteracy<br>Rate|93.902 -0.059<br> <br>(0.03)|-11.044 -94.348<br>(27.84)(35.35)||.397|
|6.Male Rural<br>Illiteracy<br>Rate|37.853 -0.033<br> <br>(0.01)|-6.396<br>-43.20 7<br>(12.29)<br>(15.60)||.470|

Note: Sample consists of 23 departments in Peru Signifieant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

49
# Table 5 (Continued)

# CROSS-SECTION SCHOOLING REGRESSIONS: PERU, 1985-86

|Dependent<br>||Ind<br>|ependent V<br>|ariables<br>|||
|---|---|---|---|---|---|---|
|Variable|CONSTANT|PER CAP.<br>GDP|INDUSTR<br>SHARE|SERVICES D<br>SHARE<br>UR|GREE OF<br>BANIZN.|UNADJ.<br>R-SQR.|
||Pop|ulation Age|d 35-39|Years|||
|l.Female Total<br>Illiteracy Rate|81.797|-0.015<br>(0.02)|17.634<br>(17.97)|4.990<br> <br>(31.91)|-0.845<br>(0.15)|.855|
|2.Male Total<br>Illiteracy Rate|36.829|-0.012<br>(0.01)|-,5.359<br>(10.29)|-19.729<br> <br>(18.28)|-0.235<br>(0.09)|744|
|3.Female Urban<br>Illiteracy Rate|55.197|-0.046<br>(0.02)|-8.302<br>(17.42)|-61.856<br> <br>(22.11)||.463|
|4.Male Urban<br>Illiteracy Rate|12.569|-0.010<br>(0.00)|-4.472<br>(2.71)|-13.281<br>(3.44)||.667|
|5.Female Rural<br>Illiteracy Rate|94.333|-0.057<br>(0.03)|1.534<br>(29.64)|-74.458<br> <br>(37.63)||.294|
|6.Male Rural<br>Illiteracy Rate|47.133|-0.038<br>(0.02)|-7.004<br>(15.87)|-49.919<br> <br>(20.15)||.416|
||Populati|on<br>Aged|40&More|Years|||
|l.Female Total<br>Illiteracy Rate|87.159|-0.018<br>(0.02)|41.269<br>(21.93)|18.442<br>(38.94)|-0.885<br>(0.19)|.792|
|2.Male Total<br>Illiteracy Rate|51.328|-0.017<br>(0.02)|8.004<br>(16.89)|-5.877<br>(30.00)|-0.442<br>(0.15)|.698|
|3.Female Urban<br>Illiteracy Rate|80.244|-0.068<br>(0.03)|2.073<br>(23.09)|-86.466<br>(29.32)||.487|
|4.Male Urban<br>Illiteracy Rate|27.812|-0.025<br>(0.01)|-4.198<br>(8.87)|-27.254<br>(11.27)||.459|
|5.Female Rural<br>Illiteracy Rate|94.272|-0.056<br>(0.03)|26.476<br>(30.11)|-56.789<br>(38.23)||.265|
|6.Male Rural<br>Illiteracy Rate|62.652|-0.044<br>(0.02)|-0.332<br>(21.75)|-54.795<br>(27.61)||.308|

Note: Sample consists of 23 departments in Peru Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

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# 6. CONCLUSIONS AND POLICY IMPLICATIONS

The main question addressed in<sup>this paper is: Do parents consider</sup> future labor activities when<sup>making schooling decisions for their childrel?</sup> To the extent that current<sup>demand for labor and remuneration reflect</sup> economic patteras in the future,<sup>the answer seems to be that they do.</sup> The main new policy implication<sup>that emerges from this paper and</sup> from Gill and Khandker (1990)<sup>is that the expansion of the service sector</sup> raises the levels of schooling<sup>of both men and women, but has a larger</sup> effect on women. That is, both<sup>increased human capital and equity between</sup> the sexes are associated with<sup>an increase in the service sector's share in</sup> CDP, at the expense of agriculture.<sup>This is probably an intermediate stage.</sup> As this structural transformation continues, the share of the industrial sector begins to play the role<sup>that services played at the earlier stage.</sup> This is in marked contrast to<sup>policy that emerges from well-known</sup> theories of economic growth,<sup>including Rostow (1960), Rosenstein-Rodan</sup> (1961), and others. It has been<sup>argued that in the process of growth,</sup> agriculture is the primary<sup>stage, industry the secondary stage, and services</sup> the tertiary stage. But two<sup>points should be kept in mind. First, the</sup> policies recommended here are<sup>not aimed at economic growth</sup><sup>_per se,_but</sup> _economlc growth with Increases in equity across gender._ Second, the nature of the services sector is very different<sup>in low-income countries.</sup> This policy also contradicts<sup>the World Bank's advice that</sup> developing countries need<sup>to increase the production of tradables. But at</sup> least as far as gender equity<sup>in earnings potential is concerned, it would</sup> 51
be better to encourage the expansion of sectors produclng nontradablos. Another implication of the findings ln this paper **is** that extending schooling facilities slgnificantly raises investment in schooling, and lowers the gender gap as well. The third implication is that lnformation about the rates of return tt schooling in home activities wll raise the scaicoling levels of females by more than those of sales.

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