Directory UMM :Data Elmu:jurnal:E:Economics of Education Review:Vol19.Issue1.Feb1999:

www.elsevier.com/locate/econedurev

Schooling of girls and boys in a West African country: the

effects of parental education, income, and household

structure

Peter Glick

*

_{, David E. Sahn}

Cornell University, 3M28 Van Rensselaer Hall, Ithaca, NY 14853, USA Received 4 June 1997; accepted 15 October 1998

Abstract

In this paper we investigate gender differences in the determinants of several schooling indicators—grade attainment, current enrollment, and withdrawal from school—in a poor urban environment in West Africa, using ordered and binary probit models incorporating household-level random effects. Increases in household income lead to greater investments in girls’ schooling but have no significant impact on schooling of boys. Improvements in father’s education raises the schooling of both sons and daughters (favoring the latter) but mother’s education has significant impact only on daught-ers’ schooling; these estimates are suggestive of differences in maternal and paternal preferences for schooling daughters relative to sons. Domestic responsibilities, represented for example by the number of very young siblings, impinge strongly on girls’ education but not on boys’. Policies such as subsidized childcare that reduce the opportunity cost of girls’ time in the home may therefore increase their ability to get an education. JEL 015, I211999 Elsevier Science Ltd. All rights reserved.

Keywords:Economic development; Human capital

1. Introduction

Low levels of human capital are widely considered to be a major impediment to economic growth and the elimination of poverty in sub-Saharan Africa. Recent studies of several African countries document the exist-ence of returns in the labor market to investments in edu-cation for both men and women.1_{Researchers and}

pol-* Corresponding author. Fax: 1 1-607-255-0178; e-mail: pjg4@cornell.edu

1_{Studies estimating earnings functions disaggregated by} gen-der and including appropriate controls for selection into the labor force or into particular sectors include those by Vijverberg (1993); Glick and Sahn (1997); and Appleton, Hoddinott, Krishnan and Max (1995). These studies generally find signifi-cant schooling impacts on both male and female earnings. It has been argued, however, that private returns to primary edu-cation in Africa have fallen in recent years (see Appleton et al., 1995; Knight, Sabot & Hovey, 1992).

0272-7757/99/$ - see front matter1999 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 2 - 7 7 5 7 ( 9 9 ) 0 0 0 2 9 - 1

icy-makers have increasingly recognized the benefits in particular to expanding girls’ access to schooling. Improvements in women’s education will help to elimin-ate gender inequalities in employment opportunities and earnings and will also have important non-market bene-fits such as better child nutrition and lower fertility (Strauss & Thomas, 1995). However, despite dramatic increases in both male and female enrollments in the first few decades after independence, girls’ schooling in African countries still lags behind that of boys at all lev-els and particularly at post-primary levlev-els (World Bank, 1988).

In Guinea, one of the poorest nations in Africa, pre-vious work has shown that the earnings of women and men increase with schooling in both self-employment and wage employment (Glick & Sahn, 1997). An ambitious education policy has been successful in raising primary enrollments over the last few years, reversing declines that occurred during the 1980s. Primary

enrollment rates remain below 40%, however—among the lowest in the world (World Bank, 1995). In addition, in spite of a commitment to improving girls’ access to schooling, the ratio of female to male primary students in 1993 was only 44%. This gender disparity in enrollments increases sharply with education level: girls represent only 25% of lower secondary students, 20% of upper secondary students, and just 6% of university students. Thus gender in Guinea is an important determinant both of attending school at all and of the level of schooling achieved.

In light of the benefits to investments in education, it is important to identify the factors underlying household decisions regarding the education of children, and especially decisions about girls’ schooling. The edu-cation of parents has been found in many studies to be one of the most important determinants of child school-ing. Of particular interest in the West African context, where incomplete pooling of household resources appears to be the norm and preferences of husbands and wives may diverge sharply,2 _{is whether maternal and} paternal schooling have equivalent effects on the cation of boys and girls. It might be expected that edu-cated women have both strong preferences for schooling their daughters (preferences which may not be shared by their spouses) and the ability to ensure that household resources are allocated for this purpose. If as a conse-quence of these factors a mother’s education has a greater impact on girls’ schooling than on boys’, there would be a further rationale for public investments in female schooling: the intergenerational effects of such investments will lead in the future to even greater reductions in the gender gap in schooling and ultimately, in earnings.

Boys and girls may also differ with respect to the ways in which household structure, in particular the presence of young children, impinges upon their ability to acquire an education by affecting the burden of household responsibilities. These responsibilities are likely to be imposed on girls more than boys. If this is the case, then policies (for example, subsidized childcare) that reduce the dependence of households on the domestic labor of girls may increase girls’ enrollments, thus also helping to close the gender gap in schooling.

Research on the household determinants of schooling is quite sparse for sub-Saharan Africa, owing in part to

2_{There is a sizable anthropological literature for Africa, and} West Africa specifically, indicating that men and women within households do not pool income or make expenditure decisions jointly. See, for example, Fapohunda (1988), Munachonga (1988), and Guyer and Peters (1987) and references therein. Complementing these studies is the econometric analysis of household expenditures in Coˆte d’Ivoire by Hoddinott and Had-dad (1995), who find that expenditure patterns differ depending on the share of total family income earned by women.

the shortage until recently of comprehensive household level data sets from the region.3 _{This study examines} schooling choices using household survey data from Conakry, the capital and largest urban area of Guinea. We focus on the impact of parental education, household structure, and income on the schooling of boys and girls. Several schooling outcomes are examined in the empiri-cal work in this paper: years of schooling or grade attain-ment; current enrollment status; and leaving school. We focus on multiple schooling indicators instead of a single one, such as current enrollment, for two reasons. First, each of the three illuminates a different aspect of school-ing choice and thus is of interest in its own right. Second, as described in detail below, each has both advantages and disadvantages, the latter largely reflecting limits in the available data. Since each approach is imperfect, checking for consistency of results with regard to key variables provides a useful informal test of the robust-ness of the findings.

The remainder of this paper is organized as follows. Section 2 outlines the conceptual framework underlying the empirical work and Section 3 discusses the econo-metric methodology. The dataset is described and some descriptive results are discussed in Section 4. The econo-metric results are presented in Section 5. The paper con-cludes in Section 6 with a discussion of policy impli-cations of the results.

2. Conceptual framework

Underlying the empirical analysis in this paper is a conceptual model of parental or household decision-making regarding investments in the education of boys and girls.4 _{We assume for the time being a “unitary”}

3_{Much of what has been done has used the World Bank’s} Living Standards Measurement Survey data from Ghana and Coˆte d’Ivoire (see, e.g. Tansel, 1997; Glewwe & Jacoby, 1994). An earlier analysis by Chernichovsky (1985) focused on chil-dren in rural Botswana.

4_{The model presented here, like the standard household} model, takes as the relevant decision-making unit a two-parent nuclear family. In West Africa, households tend to be larger than this, often including multiple generations as well as mul-tiple wives, and are linked in important ways to other house-holds in the extended family though flows of both resources and people, e.g. through child fostering (Lloyd & Gage-Bran-don, 1993; Ainsworth, 1992). For many purposes, the extended family consisting of a network of households may, in fact, be the relevant decision-making unit, which in the present context implies that characteristics of a child’s immediate family, such as parental backgrounds and the number and ages of siblings, should be of lesser importance for schooling outcomes. Direct formal empirical analysis of this issue would require detailed data on related households and their linkages (particularly rural–urban flows of resources and individuals), data which are rare or non-existent. However, the econometric results

model of the household such that preferences of the mother and father are identical, or that if they are not, the household nevertheless acts as if it were maximizing a single utility function (which would occur if the prefer-ences of only one of the parents counted). Parents are assumed to live for two periods. For a household con-sisting of a mother, father,mdaughters andnsons,5 par-ental preferences are assumed to be represented by a util-ity function

U5U(Ct,Ct11,Sd1,...,Sdm,Ss1,...,Ssn) (1) whereCtandCt+1denote household (parental) consump-tion in the first and second periods andSdiandSsjdenote the education of theith daughter andjth son. The second period consumption of the parents depends through remittances on their children’s income, denoted byYdit+1 andYsjt+1. Children’s income or wealth in the second per-iod depends in turn on the level of schooling attained in the first period as well as on child-specific variables Z such as sex and natural ability, e.g.Ydit+15Ydit+1(Sdi,Zdi) for theith daughter. Transfers from child to parent in the second period will vary by child characteristics through their effects both on income and on remittance propen-sities; in particular, for cultural reasons daughters may remit a smaller portion of their incomes than sons. Thus we have for second period parental consumption: Ct115Ct11(Yd1t11,...,Ydmt11,Ys1t11,...,Ysnt11; (2) Zd1,...,Zdm,Zs1,...,Zsn)

Parents are assumed to care about the wealth or income of their children because of the benefits to their own future consumption through remittances, but they may, of course, also be altruistic and care about their children’s future welfare. In this case children’s wealth would appear as separate arguments in the utility func-tion. In either case the nature of schooling as an invest-ment is clear: greater education expenditures, financed through reductions in current consumptionCtor though borrowing, result in higher levels of income for the chil-dren and consumption for the parents in the second per-iod.6_{The education of the children also enters into the}

presented below, and the fact that enrollments of fostered-in children are lower than those of children living with parents, suggest that even within extended families, the characteristics of the nuclear family retain substantial importance as school-ing determinants.

5_{The number of children is taken as predetermined rather} than as a choice variable, an assumption that raises concerns for the estimation. These are addressed below.

6_{For girls in particular there may also be significant} non-pecuniary returns to education investments not represented in this simplified model, such as improved child health, which par-ents may also value.

parental utility function Eq. (1) directly, since parents may also enjoy having educated children. The simplified framework above thus brings out the nature of education as both an investment good and a consumption good.7

Parents in the first period face a full income constraint:

F5V1Tmwm1Tfwf1

O

m

1

Tdiw*d1

O

n

1

Tsjw*s (3)

whereVis unearned income,Tm,Tf,Tdi, andTsjare the total times available to the mother, father, the ith daughter and thejth son; andwm,wf,w*dandw*sare their respective wage rates. The contribution of children’s time to household full income should be emphasized. Children in developing countries typically make pro-ductive contributions to household welfare through work in the home, for example by caring for younger children, or in the family farm or business. Where there is a mar-ket for child labor they can also work for others. If such a market exists,wdandwsare the market wage rates for girls’ and boys’ labor; otherwise they represent implicit prices of time determined endogenously by both the demand for household goods and services that use chil-dren’s labor and the home production technology (“*” is used to indicate that these are potentially endogenous variables).

Assuming for convenience that the time of children is divided between such work and schooling and that of parents between work and leisure, the full income con-straint can be expressed in terms of expenditures on leis-ure, goods, and schooling:

F5V1Lmwm1Lfwf1PcCt1

O

m

1

(PsSdi (4)

1w*

dTS_di)1

O

n

1

(PsSsj1w*sTS_sj)

whereLmandLfare the leisure of mother and father;Ps are direct costs per child of schooling; and Tdi and Tsj represent the time of the ith daughter and jth son, respectively, devoted to schooling. Regarding the com-position ofPs, direct schooling costs in Guinea do not generally include tuition since virtually all primary stu-dents and the great majority of stustu-dents in higher levels attend public schools, which are free.8 _{However, other}

7_{The latter aspect of schooling is emphasized in household} production models incorporating child quantity and quality (Becker & Lewis, 1973; Willis, 1973). In this framework, par-ents derive utility from both the number of children they have and their quality, an important dimension of which is their schooling, and both quantity and quality are choice variables.

8_{Private schooling was banned until 1984 and has yet to} make major inroads on general education: less than 5% of pri-mary school students are enrolled in private schools (Educational Development Center, 1994).

direct private costs such as books, uniforms, and trans-portation appear to be considerable: they were cited in interviews of parents and students as a major barrier to school attendance (World Bank, 1995). w*

dTSdi and

w*

sTS_sj, the hours of the daughter and son spent in school

or schoolwork multiplied by the price of their time, rep-resent the foregone contributions to household (or market) production of having the daughter and son attend school. Each term in brackets combines this opportunity cost with direct costs PsSdi orPsSsjand thus represents the total cost of schooling for each boy or girl.

Parents maximize utility subject to the full income constraint and the constraints relating earnings to school-ing and parental consumption to child earnschool-ings, resultschool-ing in reduced-form demand equations for boys’ and girls’ quantity of schooling (as well as for other goods and leisure) as functions of all prices and wages, vectors of individual factors Zand household and community

fac-tors H, and maternal and paternal educationSm andSf:

Sdi5Sdi(wm,wf,V,Pc,Ps,Sm,Sf,Zdi,...,Zdm,Zs1,...,Zsn,H) (5) Ssj5Ssj(wm,wf,V,Pc,Ps,Sm,Sf,Zdi,...,Zdm,Zs1,...,Zsn,H)

As indicated in the introduction, parents may have dif-ferent preference orderings, including difdif-ferent prefer-ences for schooling, in which case the assumption of a unique parental utility function such as that in Eq. (1) is invalid. A variety of theoretical models of the household that incorporate differing preferences of household mem-bers (e.g. husbands and wives, mothers and fathers) have been developed and are referred to as “collective” house-hold models. We briefly describe the approach here rather than present it formally because the reduced-form demand functions for schooling derived in such a frame-work are (for our dataset) identical to those just derived within the common preference framework. Collective models posit separate utility functions for the husband and wife. When preferences differ there must be some mechanism for determining how to allocate household resources; the most common assumption is that the household engages in a cooperative bargaining game leading to Nash equilibrium demands for commodities (McElroy & Horney, 1981). Each partner’s power in bar-gaining is a function of the income under his or her direct control and, ultimately, of the utility he or she would be able to achieve outside of the marriage—the “threat point”—since this represents the fallback position.9_The

9_{That is, with a stronger fallback position, a partner can} thre-aten more credibly to dissolve the partnership if allocations are not sufficiently in line with her or his preferences. More realisti-cally for smaller allocation decisions, the partner can threaten to retreat to a non-cooperative solution, where budgets are com-pletely separate, while maintaining the partnership (see Alder-man, Chiappori, Haddad, Hoddintot & Kanbur, 1995).

threat point, hence bargaining power, is a function of “extrahousehold environmental factors” (McElroy, 1990) that affect opportunities outside the partnership. These include unearned income accruing to the individual, sex-and education-specific wage sex-and employment rates, the legal framework (as regards, for example, child-support), and the partner’s possibilities for remarriage or financial support from relatives.

Reduced-form demand equations for schooling

derived from a bargaining framework would include such factors in addition to those already in Eq. (5). Test-ing the unitary household model against the more general collective model essentially involves testing for the sig-nificance of extrahousehold environmental variables that would not affect schooling under common preferences. A variant of this approach involves comparing the effects of unearned (that is, exogenous) income received by each spouse.10_{Unfortunately, our dataset lacks information on} (or variation in) these factors, so we cannot augment our reduced-form equations to conduct such a test. Still, the notion of non-unified preferences and bargaining over resources within the household has substantial appeal in the present context and will be helpful in interpreting some of the empirical results of this study.

The direction of the effects of a number of the vari-ables in the schooling demand function can be predicted from theory.11_{In particular, factors that raise perceived} returns or lower the costs of education will raise invest-ments in education. The schooling of the parents (Smand Sf) is one such factor and is expected to be positively associated with children’s schooling. Educated parents are more able to assist in their children’s learning, raising the returns relative to less educated parents, and are also

10_{The unitary model assumes that income is completely} pooled so the source of income should not matter for allo-cations; hence common preferences are rejected if the effects of husband’s and wife’s unearned income differ. The equality of male and female income effects has been rejected for out-comes such as own labor supply (Schultz, 1990), child health (Thomas, 1990), and household expenditure shares on health, education and housing (Thomas, 1993). Rao and Greene (1991) find that a woman’s bargaining power, proxied by female employment rates and sex ratios (reflecting re-marriage possibilities) significantly affects fertility decisions. Handa (1996) uses female headship, treated as endogenous, as a proxy for maternal bargaining power within the household in school-ing demand equations and finds a positive effect of this variable on schooling. However, some of the exclusion restrictions imposed for identification of the structural schooling equations are questionable; it is assumed, for example, that age and edu-cation of the head has no direct impact on child schooling.

11_{Our discussion of these effects is informal. For rigorous} theoretical models of human capital accumulation treating the schooling demand effects of some of the factors we consider, see, for example, Barros and Lam (1992) and Behrman, Pollak and Taubman (1995).

more likely to recognize the benefits of schooling. Posi-tive parental schooling impacts are also expected from a schooling as a consumption good perspective, since bet-ter-educated parents are likely to enjoy educated children more than less-educated parents; thus mother and father education will act as taste-shifters in the schooling demand functions.

Like parents’ education, household income will, under plausible assumptions for developing countries, posi-tively influence the demand for children’s education. Poor households may be unable to afford the direct or indirect costs of schooling and be constrained in their ability to borrow to cover the costs. Since wealthier households are likely to be able to pay for schooling out of current income or savings (and have easier access to credit), children from such households are expected to be more likely to enroll and to stay in school longer. Income will also have a positive effect on schooling if education is a “normal” consumption good.12

Gender differences in schooling—namely, greater schooling for boys—can come about because the returns to educating boys are greater than for girls or because the costs are lower, or because parents simply prefer edu-cating sons.13_{With regard to returns, if women are} dis-criminated against in the labor market in terms of access to employment or in earnings, the monetary benefits to

12_{Another potentially important determinant of the demand} for schooling is school (or teacher) quality, which affects the labor market productivity benefits of schooling and possibly, through impacts on grade repetition, the costs of attaining a given grade level. Data on school quality are not available for this study; even if they were, it is uncertain whether sufficient cross-sectional variation would exist within the region surveyed (urban and peri-urban Conakry) to enable estimation of the effects of changes in quality on schooling demand. The same considerations apply to direct costs of schooling (books, uni-forms, etc.). Thus we focus on the socio-economic character-istics of households.

13_{More precisely, if direct impacts on parental utility of child} schooling are ignored, so that parents care only to maximize their consumption, the first order conditions of the model imply that the relative marginal effects of female and male education on parents’ second period consumption will equal the relative marginal costs of educating girls and boys (the ratio of opport-unity costs if direct costs are zero), i.e.

∂Ct11/∂Sd

∂Ct11/∂Ss

5w

* d w* s

With diminishing marginal second period consumption returns to schooling, a reduction in the marginal benefit to schooling girls relative to boys (the ratio to the left of the equality) implies a lower optimal level of investment in girls’ education. Similarly, an increase in the relative opportunity cost for girls implies a reduction in girls’ schooling relative to that of boys.

investing in their education will be lower than for boys.14 As noted, there may be substantial returns to female schooling in non-market production, but parents may not be aware of these non-pecuniary benefits or may value them less than monetary ones. Even if educated girls go on to work and receive earnings on a par with men, income remittances to parents from married adult daugh-ters, who join their spouses’ families, may be lower than from adult sons. Finally, the returns to parents from edu-cating girls could be low because the quality of the schooling that girls receive is poor, reflecting school and teacher attitudes or interruptions in attendance or school-work resulting from girls’ household obligations.

The last factor mentioned points to the possibility that the opportunity cost of educating girls is higher than for boys. Girls in developing countries are typically called on to perform more household work than boys, reflecting cultural or social attitudes toward the proper economic roles of women and girls. Given these attitudes, the mar-ginal cost of girls’ time will be higher than boys’ (w*

d>w*s) and consequently the demand for their school-ing will be lower.15,16 _{For the same reasons, it would}

14_{More precisely, the incremental effects of schooling on} expected earnings, determined by employment entry prob-abilities and wages, must be lower for women for this to be the case. Previous work on Conakry using this dataset (Glick & Sahn, 1997) was unable to reject the hypothesis that the returns to schooling (in either wage employment or self-employment) were the same for men and women, but did find a non-linear effect of schooling on women’s employment probabilities: rela-tive to no education, the likelihood of working fell with primary education and rose with secondary schooling and college (levels which very few women in Guinea have attained). There appear to be relatively few opportunities outside of small-scale self-employment for women with just a primary education.

15_{This may not be the case if there is also a strong demand} for boys’ labor on the family farm or in a family enterprise, but neither of these possibilities is very relevant in the current context. Agriculture is not a major activity in the urban and peri-urban setting of this study, and participation of adolescent boys (and girls) in household enterprises (and for that matter, in wage employment) appears to be quite low (Glick & Sahn, 1997).

16_{The distinction between “cultural attitudes” that shape} con-straints and “household preferences” regarding the sexual division of labor in the household is not a sharp one, since the latter are shaped by the former. Consequently, the distinction maintained by economists between preferences and exogenous constraints is also blurred: what appears to be a manifestation of household constraints (differential opportunity costs of boys and girls) may instead ultimately reflect preferences about the allocation of time within the household. There is, however, one situation in which opportunity costs will appear to be higher for girls for reasons having nothing to do with household prefer-ences. Lower benefits (especially in employment probabilities) to female schooling may induce even parents who have no gen-der bias to have their daughters specialize early in household

also be expected that certain changes in household struc-ture will affect girls’ schooling more strongly than boys’. An increase (assumed exogenous) in the number of very young children, for example, may raise the demand for the labor of girls in childcare in the home. Given the time constraint, this will reduce their schooling relative to that of boys. By the same logic, additional older sib-lings or adult women may reduce the opportunity cost of a girl’s time by providing substitutes for household work or through economies of scale in household pro-duction, thereby raising the likelihood of enrollment or the average level of schooling among girls in the house-hold.17_{In the empirical work, we do not attempt to} esti-mate endogenous shadow prices of time for girls and boys. Instead, the reduced-form schooling demand func-tions include family composition and other household factors (represented by H in Eq. (5)) that are expected to influence, possibly differentially by gender, the value of children’s time.

Would the collective household model with individual preferences generate any different expectations about the impacts of the factors discussed above on girls’ and boys’ schooling? The model predicts that factors that raise the bargaining power of the wife should increase allocations to goods she prefers. The mother’s education stands out as one such factor for which the dataset pro-vides information. Women with more schooling are able to earn more, improving their fallback position and (if they are actually working) the level of income under their direct control. Thus, if women value the schooling of their children more than men do, maternal schooling will have a stronger impact than paternal schooling on children’s education. Further, mothers may prefer to allo-cate resources (including for human capital) to daughters while fathers prefer sons, as suggested by evidence for

work, in preparation for their future likely roles as homemakers. As a result of this initial specialization and investment, girl’s home productivity will be higher than boys in later periods, hence their opportunity costs will be higher. This is akin to the “efficient specialization” hypothesis of Becker (1981). Thus the gender difference in the price of time is a manifestation of lower returns to schooling for girls, not parental preferences.

17_{Note that this conflicts with a major implication of the} quantity–quality model described in note 7: that child quantity and quality (average schooling) should be inversely related. An inverse relationship exists because having more children raises the cost of providing a given amount of education resources to each child, and conversely, a higher level of quality raises the implicit price of child quantity. However, in a context where child work is important, so that the cost of schooling includes the opportunity cost of foregone labor in the home or family farm/enterprise, the relation of the number of children to aver-age educational investment is less obvious. As noted in the text, increases in the number of children in particular age or sex categories can lower the marginal value of an individual child’s time, encouraging school investments.

child height in the US, Ghana and Brazil presented in Thomas (1992). Then increases in mother’s schooling would have a larger beneficial effect on daughters’ edu-cation than on sons’, and father’s schooling would favor sons’ education. The former is particularly plausible because the mother’s bargaining power and her prefer-ences for daughters’ schooling are both likely to rise with her own education.

Note, however, that these relationships of maternal and child schooling are also compatible with unified household preferences. For example, a larger maternal education impact on girls’ education than boys’ may reflect maternal preferences for schooling girls in a bar-gaining framework or that households in which the mother has an education also have strongcommon pref-erences for girls’ schooling. The latter will arise through marital sorting if men who choose educated wives also want educated daughters (or if educated women choose spouses who also have a preference for educating daughters), resulting in heterogeneity in preferences between households rather than within them. The prob-lem, as stated above, is that the dataset does not provide a measure of individual bargaining positions that would not also be a determinant of schooling under common household preferences.18

3. Empirical approaches 3.1. Years of schooling

The first schooling outcome we investigate, years of schooling or grade attainment, is an indicator of the cumulative investment in an individual’s education. We use an ordered probit model to estimate the determinants of years of schooling. This approach allows us to incor-porate several features of the data that simpler alterna-tives, such as ordinary least squares, cannot. For example, OLS assumes a continuous distribution for the dependent variable, grade level. Grade attainment, how-ever, represents the outcome of a series of ordered dis-crete choices—whether to go on to the next grade or withdraw from school. Also, the distribution of years of schooling is usually not normal around the mean. Typi-cally it is bimodal or even trimodal, with peaks

rep-18_{Thomas (1992) uses as proxies for bargaining power the} unearned income of each parent as well as a dichotomous vari-able for whether the mother is more educated than the father, arguing that the latter indicator will be associated with greater bargaining power of the mother relative to the father. This is plausible, but in terms of distinguishing common from individ-ual preferences such an indicator may not help much: choosing a wife who is well educated or even better educated may reflect the husband’s preference for educated women, hence for girl’s schooling.

Fig. 1. Males and females aged 10–18: distribution of years of schooling.

resenting completion of specific levels, e.g. secondary school. In our sample, as shown in Fig. 1, the distribution of years of school is not punctuated by sharp spikes but nevertheless bears little resemblance to a normal distri-bution. For girls especially many observations cluster at zero (no schooling); for both boys and girls there are sharp drops following 6th grade (completed primary) and 10th grade (completed lower secondary).

A third feature of the data is sample censoring. For children who are still enrolled, the current grade level does not necessarily represent their final grade attain-ment; all that is known is that they will ultimately have completed at least their last grade. The data on schooling for these observations, in other words, are right-cen-sored. Not taking this censoring into account—that is, considering the education of such individuals to be ident-ical to those who have ended their schooling at that grade—will result in biased estimates of the effects of the regressors on true (potential) grade attainment.19

The ordered probit model treats grade attainment as the outcome of ordered discrete choices and does not assume a normal distribution for the dependent variable. The model can also be extended to allow for right-cen-soring of years of schooling. Although completed

19_{The censoring problem can be eliminated by restricting the} sample to cohorts old enough to have completed their schooling by the time of the survey, but this restricts the focus to an older group in a context where the determinants of schooling may have changed over time. In any case, as described in the next section, data on parental backgrounds as well as relevant house-hold characteristics are unavailable for adults not still residing with their parents.

schooling for children who are still in school is not known, it is known that the final grade attained will be at least as high as the last grade. This information is incorporated into the likelihood function of the model, making it possible to calculate unbiased estimates of pro-jected completed schooling of an individual of given characteristics.20

A further consideration for the estimation is that the residual terms are unlikely to be independent, reflecting the fact that more than one child from the same house-hold may appear in the estimating sample. Children in the same household are likely to share unmeasured (by the researcher) traits that make them more or less able to perform well in school, affecting their demand for schooling in a similar way. Alternatively, households may be heterogeneous with respect to unobserved prefer-ences for education. In either case, the error terms for children from the same household will be correlated through a common family-level component, and if ignored this lack of independence may result in substan-tially underestimated standard errors. We allow for such correlations in the model through a random effects or variance component structure for the residuals; that is, the errors of the index functions for schooling in the ordered probit formulation are assumed to consist of a common household heterogeneity component and an idiosyncratic individual error. The estimation procedure used is that suggested by Butler and Moffit (1982) for the random effects binary probit model. Details of the

20_{The first use of the ordered probit model for the analysis} of cumulative schooling appears to be Lillard and King (1984).

likelihood function and estimation of the random effects ordered probit model with censoring are presented in Appendix A.

We estimate separate ordered probit models of years of schooling for boys and girls aged 10–18. The reason for choosing age 10 as the lower end of the range is that delayed primary enrollment appears to be common in Conakry, so that some children who are, say, 8 or 9 and who have never been enrolled may yet enter. For these children it would clearly be inappropriate to assign a value of zero for grade attainment. By including only children 10 and over, we can be reasonably sure that those who have not yet enrolled will never enroll and can be assigned zero years of schooling. The choice of age 18 as the upper end of the range is dictated in part by the desire to minimize selectivity problems caused by older children being absent from the household, an issue we discuss in detail below.

3.2. Current enrollment status

Next we estimate the determinants of school

enrollment status at the time of the survey. For assessing the effects of factors that are time-varying, analyzing the current enrollment decision has advantages over the analysis of cumulative schooling just outlined, since it allows us to relate current school choices to contempor-aneous aspects of the household such as income and household structure. The latter includes the presence of young siblings, whose arrival will likely alter the allo-cation of time among schooling, household work, and other uses.21_{On the other hand, a disadvantage of the} current enrollment approach is that (unlike the ordered probit model of grade attainment) it implicitly assumes that the current enrollment decision is independent of the choices made in previous years; that is, it ignores the cumulative nature of schooling decisions. This is gener-ally not a very plausible assumption: with the exception of younger children just beginning their schooling, a child is more likely to be in school today if he or she has been previously enrolled than if she has never attended

21_{If parents determine both the number and spacing of} chil-dren and future investments in their schooling at the start of childbearing or marriage, events such as a recent birth are anticipated and reflected in the cumulative schooling of older siblings. It would then suffice only to look at the ordered probit models of grade attainment (but excluding sibling covariates from the equation, since they would clearly be endogenous). However, this assumes a very high level of planning on the part of parents as well as an absence of uncertainty, both of which are unlikely in a poorly educated population with limited access to contraceptives. The issue of the endogeneity of children is considered further below.

school.22 _{Current enrollment decisions are estimated} using a binary probit, with the dependent variable taking the value of 1 if the child is currently attending school and zero otherwise. As with the ordered probits, we allow for intrahousehold correlations in the disturbances. The model we estimate is therefore the random effects binary probit model of Butler and Moffit (1982). 3.3. Leaving school

Our third approach is somewhat novel and focuses on a transition in schooling status: leaving school in the past 5 years. The ordered probit model of grade attainment implicitly also models the decision of when (i.e. after how many years of school) to withdraw. Here, however, we focus on the school departure decision within a spe-cific historical period. Since this period is recent (the last 5 years), our right-hand-side variables should fairly closely capture the circumstances relevant to the decision. The survey does not actually record the time since leaving school, so we impute it from the data using information on the ages of students currently in each grade. Years since leaving school is estimated as the individual’s current age minus the sex-specific median age of students currently enrolled in the last grade that the individual attended. Thus our dependent variable for withdrawing in the past 5 years, WITHDRAW, equals 1 if, for an individual who is no longer enrolled and who reportssyears of schooling, agei2medians,5, where ageiis the child’s age and mediansis the median age for grade s. It equals zero for individuals who are still enrolled. Individuals no longer in school but who com-pleted upper secondary school during the period are also assigned WITHDRAW50, while those who were never enrolled are not included in the sample.23_{Inferring recent}

22_{This may reflect state dependence: attendance in periodt} makes attendance int11 more likely because (among other possible reasons) the cumulative acquisition of skills is more efficient with continuous schooling than with off-and-on enrollment. An alternative explanation involves individual or household heterogeneity: children with a propensity for learning (or who have parents with a preference for schooling) will be more likely to be enrolled each year, regardless of the direct effect of attendance in one year on attendance in the next. These concepts are discussed in the context of female labor force par-ticipation in Heckman (1981).

23_{Note that withdrawing from school is not the same as} drop-ping out of school as that term is usually defined. Dropdrop-ping out usually refers to departures from school prior to completing a certain level, e.g. primary. Our definition is broader and focuses on stopping one’s schooling in a specific period as a function of contemporaneous household factors or events. Thus a child who completed primary school during the period and declined to continue to secondary is said to have left school (though secondary graduates who do not go to university are not counted as school leavers). We thank an anonymous reviewer for stress-ing the importance of this distinction to us.

school exit decisions in this way allows in a limited way for a dynamic analysis of schooling transitions using cross-section data. For example, a key demographic vari-able on the right-hand side is the number of siblings under 5, i.e. the number of brothers and sisters born (and surviving and remaining in the household) during the last 5 years. The analysis thus shows how school status is affected by “fertility shocks”, that is, additions to the family of young children (treated as exogenous) during the period.

The determinants of leaving school are estimated using random effects probit on the samples of boys and girls aged 15–18 who, using the method just described, are inferred to have been enrolled at the start of the per-iod. Note that because of the way the dependent variable is constructed, we are effectively analyzing behavior since age 10 for children currently 15, since 11 for those now 16, and so on. Since leaving school earlier than age 10 is rare, restricting the sample to children 15 and older is appropriate.24

4. Data and descriptive statistics

The data used in this study are taken from a survey of 1725 households conducted in Conakry in 1990. The survey contains detailed information on a wide range of socioeconomic factors such as education, labor force activity and earnings, assets, and health. Since the house-hold survey does not contain information on the charac-teristics of schools, the focus in this study is on the effects of household and individual factors on schooling. Our sample for analysis consists of boys and girls aged 10–18 living with at least one parent. Means and stan-dard deviations of the explanatory variables for boys and girls are given in Table 1.

We restrict the sample to children residing with a par-ent for two reasons. First, one of our primary concerns is the impact of parental education on child schooling,

24_{Since the sample consists of individuals inferred to have} been in school 5 years earlier, the results must be regarded as being conditional on prior enrollment. Two other selection-related concerns should be noted. First, those who have recently left school and have also left the household are not recorded in the survey. Second, years since leaving school will, in general, be underestimated for children who started school late or repeated more grades than the typical child in their last grade, since these children would have been attending their last grade at a higher age than the median for the grade; the reverse is true for early starters and those who repeated less than the average. Overestimation of years since departure will cause some in the second group to be incorrectly dropped from the sample as non-recent (i.e. not within the last 5 years) school leavers, which might impart a bias to the estimates on the sample excluding them.

and the survey does not record the educational attain-ment of parents who do not live with their children (or of children who do not live with their parents). Second, for older children who no longer live with their parents, the characteristics of their present household, such as income and demographic composition, are not likely to be the relevant ones for determining the education out-comes of interest and may, in fact, be the outout-comes of prior schooling decisions: consider, for example, the case of a young man quitting school, going to work, and start-ing his own family. Given the cumulative nature of schooling decisions, the circumstances of the household in which the child was raised are more relevant, so for this reason as well we focus on children still living in the household of the parent or parents.

Although the choice of sample is necessary for these reasons, it involves a significant sample reduction. A siz-able percentage of children do not live with either parent and this ratio rises with age. For example, among all children in the sample age 13–18, 31% of girls and 44% of boys are living away from their parents. The data thus suggest that child fostering is an important phenomenon in Guinea, as elsewhere in West Africa (Ainsworth, 1992). In addition to fostering, older teenage children may move out of the households in which they were raised in order to marry, work, or attend school; early marriage of girls in particular is evident from our data. This raises the issue of selection bias in our estimates of the determinants of schooling, since households in which children are living with their parents may differ from other households in terms of unmeasured preferences or propensities for school (or children who leave may be different from children in the same household who stay).25_{The Conakry survey does not record the numbers}

25_{The potential biases involved in restricting estimation of} schooling functions to the sample of children living with a par-ent can be illustrated with the following elaboration of the Heckman selectivity model (Heckman, 1979; see also Pitt (1997) for a more detailed exposition of a model similar to the following). Assume that equations for the level of schooling (S) and selection into the sample of children observed to be living at home (H, an index function such thatH> 0 implies selection into the sample) take the following linear forms:

Si5xib11ub21ei5xib11ei Hi5wig11ug21ui5wig11mi

wherexi and wi are vectors of exogenous variables, u is an unobserved (to the researcher) heterogeneity factor representing household (parental) preferences for schooling (we focus on household rather than individual heterogeneity in this example), andeianduiare independent, normally distributed random error terms. Althoughei andui are independent,ei andmi are not, because of the presence of u in both terms: thus we have corr(ei,mi)5b2g2s2u. To find the effect of a change in an exogenous variablexkappearing in both equations on schooling

Table 1

Boys and girls ages 10–18: variable means and standard deviations

Girls Boys

Variable Mean Standard Mean Standard

deviation deviation

Years of schooling 3.714 2.933 4.595 2.708

Currently in school (15yes) 0.608 0.488 0.798 0.402

WITHDRAW (51 if left school in the last 5 years)a _{0.317 0.467 0.175 0.379}

Age (years) 13.679 2.568 13.633 2.500

Mother’s years of schooling 2.574 4.465 2.086 3.970

Father’s years of schooling 3.149 5.119 3.287 4.982

Mother missing (15yes) 0.101 0.301 0.167 0.373

Father missing (15yes) 0.154 0.361 0.116 0.320

Log expenditure per adult 10.508 0.581 10.494 0.583

Siblings,5 0.701 0.804 0.600 0.762

Brothers 5–12 0.646 0.764 0.648 0.779

Sisters 5–12 0.521 0.677 0.475 0.649

Brothers 13–20 0.535 0.724 0.554 0.719

Sisters 13–20 0.466 0.657 0.448 0.689

Other children,5 0.850 1.169 0.948 1.247

Other children 5–12 1.192 1.472 1.316 1.504

Other boys 13–20 0.612 0.952 0.602 0.934

Other girls 13–20 0.569 0.927 0.680 1.022

Men 21–64 1.995 1.499 1.890 1.392

Women 21–64 2.201 1.362 2.169 1.347

Men > 64 0.102 0.303 0.114 0.317

Women > 64 0.089 0.319 0.085 0.290

Ethnicity (excluded5Soussou)

Fulani 0.195 0.396 0.253 0.435

Malinke 0.193 0.395 0.191 0.394

Other ethnic 0.064 0.245 0.065 0.246

a_{For ages 15–18 and enrolled 5 years prior to survey (see text).}

conditional on being in the sample of children living at home, substitute for corr(ei,mi) in the standard expression for a regression conditional on sample selection (Heckman, 1979) and take the derivative with respect toxk:

∂E(Si|Hi51)

∂(xk) 5bk2gkAib2g2s2u

whereAi5l2i 1wig1li,liis the inverse Mills ratio evaluated atwig1and is positive, as isAias long aswig1> 0 (as we would expect for positive selection into the home sample). The second term on the right-hand side represents the bias in the estimate ofbk, the effect of the change inxkon schooling, arising from sample selection. The direction of bias will depend on the signs of g2 and b2, the effects of schooling preference on sample selection and schooling, respectively, as well as on the sign of

gk, the coefficient ofxkin the selection equation. Sinceb2is positive, for a variable that increases the probability of selection into the with-parent sample (gk> 0), the estimate ofbkwill be biased downward ifg2is also positive, that is, if parents who (conditional on observed factors) prefer educated children are also more likely to keep them at home.

of children of parents in sample households who do not reside at home, so we do not know the exact extent of fostering-out and marrying-out of the estimating sample. However, it is likely to be far less than implied by the high ratios just cited of children not living with parents to children living with parents. This is because most of the children in the former group come from non-Conakry (non-survey) households; that is, they are fostered into households in our sample from other, typically rural areas rather than from other households within Conakry. We infer this from the fact that the majority of children in the sample who do not live with parents are listed as recent migrants to Conakry (the opposite is true for chil-dren with parents). This is in agreement with evidence for the region that child fostering is more prevalent from rural to urban households than among households in urban areas (especially the same urban area) (Ainsworth, 1992). In addition, although early marriage for girls is not uncommon in Guinea (21% of girls aged 15–18 in the survey are married), most teenage girls in Conakry who are married are also migrants, and most of these

enrollments, and the number of non-sibling females aged 13–20 also appears to raise the enrollments of both boys and girls (not shown). Even here, however, the estimated impacts are greater for girls, by about 70% for sisters aged 5–12 and 77% for other girls aged 13–20, consist-ent with a greater responsiveness of girls’ school partici-pation to changes in household structure and responsi-bilities.

The effects of log per adult expenditures are once again much greater for girls than boys, for whom the marginal effect on enrollment is barely larger than its standard error.47_{One explanation for the larger income} effect for girls is that better off households can afford to hire help for childcare and other household work, reduc-ing girls’ domestic obligations (Appleton et al., 1995). Thus increases in household income will dispro-portionately benefit girls’ schooling because they relax the time constraints that girls face. The survey does not contain any specific information on expenditures on childcare or other hired domestic help. However, the hypothesis can be tested by interacting expenditures with siblings under 5 and sisters aged 13–20 in the female enrollment probits. The first interaction term should be positive and the second negative, that is, the absolute value of the impacts of these demographic factors should

47_{Calculated at the sample means for enrollment (0.61 for} girls and 0.80 for boys) and using the marginal effects from the extended model, the elasticity of the probability of enrollment with respect to expenditures is 0.42 for girls compared with only 0.04 for boys. The elasticity for girls is very large but appears to be of the same order of magnitude as the income elasticities implied by the estimates from Tansel (1997) of years of schooling in Ghana and Coˆte d’Ivoire. Tanzel does not find a consistent pattern of gender differences in income effects on boys’ and girls’ education in his two samples. However, in Ghana (but not Coˆte d’Ivoire) he finds, as we find here, that both father’s and mother’s education has stronger effects on girls than boys and that the difference in girl and boy impacts is much greater for mother’s schooling (though unlike in Conakry, father’s schooling still has larger absolute effects on girls).

48_{Alternatively, household income can affect girls’ schooling} through better performance on qualifying examinations, reflecting either opportunity cost factors (wealthier households hire household help, freeing girls’ time for schoolwork) or pref-erence factors (affluent parents value girls’ education more, so ensure that they study and do well on exams). As seen, we did not find (indirect) evidence of the former. Another possibility is that there is a relaxation of credit constraints with increased household income that enables wealthier parents to undertake less remunerative investments in girls’ schooling while poorer households can only afford to educate sons. This is not very compelling in the present context because the direct costs of schooling are low (virtually all enrolled children aged 10–18 attend free public schools). Also, opportunity costs in terms of foregone income (if not household production) appear to be low for girls in Conakry, few of whom are actually engaged in income-generating work.

decrease with income. Both interaction terms were insig-nificant, however, suggesting that the demand for girls’ schooling is more income elastic in the standard con-sumption good sense.48,49

5.3. Leaving school

Marginal effects from probit models of withdrawing from school in the 5 years prior to the survey are shown in the last two columns of Table 6. Again, we present the results for the specification including the children covariates. For girls, a function maximum could not be found for r in the acceptable parameter space (0,1); r

essentially was zero.50 _{This is probably due to the fact} that the smaller sample used here—individuals aged 15– 18 who attended school for some or all of the preceding 5 years—contains relatively few cases of multiple obser-vations from the same household. For girls, therefore, we present results of a simple binary probit for school departure while for boys the random effects estimates are shown.

Even with this rather different dependent variable and sample the story told by the estimates is consistent with our previous results. In particular, a greater number of siblings under 5 raises the likelihood of a girl having left

49_{Although as noted our descriptive analysis of the data} sug-gests that selectivity bias arising from the departure of older children from the household may not be a serious problem, we also ran the enrollment probits after dropping observations over 15 years of age (results available from the authors). Using these samples of children aged 10–15 led to no substantive differ-ences in the estimates, marginal effects, significance levels and boy–girl differences for expenditures, household composition (e.g. siblings under 5 and sisters aged 5–12 and 13–20), and maternal education. However, the marginal effect of father’s schooling on enrollment probabilities rises by about 50% for girls and falls by the same amount for boys when older children are dropped from the sample. This suggests that father’s school-ing may favor girls more strongly than indicated by the esti-mates for the full (ages 10–18) sample, with the difference in results for the two samples possibly indicating that older chil-dren living at home are self-selected according to schooling preferences (though if this were the case we might also expect changes in the effects of the other key regressors). On the other hand, the change in the paternal schooling estimates may reflect interactions with child age. Father’s schooling may simply have a larger impact on girl’s primary enrollment (hence on enrollments in the younger age group) than on secondary, so that a negative paternal schooling–daughter age interaction rather than selectivity lies behind the higher impact for younger girls. For boys, there is not much variation in enrollment among 10–15 year olds (most are enrolled), so it is not surprising that the influence of father’s schooling is felt more strongly for the sample including older boys.

50_{As described in Appendix A,r 5 s}2

u/(11s2u), wheres2u

is the variance of the heterogeneity error component in the index function of the probit model. Henceris between 0 and 1.

school in the last 5 years. This can be put in more dynamic terms (assuming the exogeneity of fertility covariates): the birth of a sibling over the period acts as a negative shock that induces girls to withdraw from school.51_{Although the number of sisters aged 13–20 has} no effect, having more sisters aged 5–12 reduces school leaving probabilities for girls. Girls in this age group may also act as substitutes in domestic work, allowing their older sisters to remain in school. Siblings have no impact on boys’ exits from school, in keeping with pre-vious results; essentially, none of the covariates in the boys’ probits had effects that were significant at conven-tional levels, in strong contrast to girls. Also consistent with earlier results is the difference in the girl and boy estimates for log household expenditures per adult. Higher expenditures reduces the probability that a girl will withdraw from school, while for boys the coefficient on expenditures is also negative but nowhere near sig-nificant in either specification.

Parental schooling does not have statistically signifi-cant effects on the school withdrawal decision for either boys or girls. This is not surprising given the restriction of the sample to individuals inferred to have been in school during the last 5 years; much of the impact of parental schooling would have come through selection into the sample. Still, the point estimates and marginal effects are suggestive of differences in maternal and paternal schooling effects for boys and girls, in line with our earlier results. For example, mother’s years of schooling is negatively associated with girls’ school withdrawal (t 5 1.14), while the estimate for father’s schooling is essentially zero. Note as well the negative estimate for father’s schooling in the boys’ probit (t5

1.55). Finally, consistent with the current enrollment pro-bit estimates, the number of non-sibling females aged 13–20 (not shown) is negatively associated with depar-tures from school for both girls and boys, though only marginally significantly for boys. Again, the absolute value of the marginal effect of this variable is greater for girls.

6. Conclusion

Low levels of school enrollments and low enrollment for girls in particular remain very serious policy concerns in Guinea and in Africa generally. In this study we have identified a number of household and individual factors that influence the schooling of boys and girls in Conakry,

51_{The effect of young siblings may be indirect or} uninten-tional rather than planned. Greater household responsibilities will reduce the time that can be devoted to schoolwork, reduc-ing the chances of passreduc-ing on to the next grade and resultreduc-ing in a higher likelihood of leaving school.

the main urban center of Guinea. We considered three schooling indicators: grade attainment, current enrollment, and leaving school.

Our analysis of these education outcomes provides a consistent picture of the importance of gender, parental education, and household income and composition as factors in schooling decisions. Education of the parents, as expected, is generally positively associated with child schooling, but these effects depend on the gender of the child, especially for mother’s education. Mother’s schooling has strong positive impacts on girls’ grade attainment and current enrollment status but has no effect on boys’ education, while father’s education has effects on the schooling of both girls and boys (though the effects on boys are smaller than for girls). This accords with expectations from a collective household model in which preferences of mothers and fathers differ and each partner’s power over resources is a function of his or her education. In such a situation, educated mothers might use their enhanced bargaining position to direct resources toward their daughters’ schooling. However, alternative explanations based on common household preferences cannot be ruled out. In particular, marital sorting on pref-erences for female schooling will produce couples in which the mother is educated and both parents desire that their daughters be educated.52

Increases in household permanent income, proxied by household expenditures per adult, have positive effects on grade attainment and current enrollment of girls and also reduce the probability that teenage girls will leave school. Like maternal schooling, however, expenditures do not have a significant impact on the schooling of boys. The importance of gender is equally in evidence when it comes to the effects of household structure. The presence of siblings under 5 has a strongly negative impact on girls’ grade attainment and current enrollment and also induces girls to leave school. No such impacts of siblings are found for boys, from which we infer that this and other household composition effects in the esti-mates for girls reflect variations in the opportunity cost of girls’ time in the home. Our findings accord with results of interviews with parents in rural areas of Guinea in which the need for girls to work in the home was cited as a major reason for not enrolling them in school (World Bank, 1995). What is of interest here is that even in an urban population, where the burden of domestic

52_{A finding that father’s education benefits sons’ education} more than daughters’, rather than merely favoring daughters’ schooling to a lesser degree than mother’s education, would make an even stronger case for the existence of differing par-ental preferences for girls’ versus boys’ schooling. It is the large difference in the relative benefits to girls’ schooling of maternal and paternal education that we consider suggestive of, though not conclusive evidence for, differences in mother and father preferences for the education of girls versus boys.

work should be less onerous, such work apparently still impinges upon girls’ ability to get an education.

Most of the interesting results of this study pertain to the determinants of investments in the schooling of girls. In fact, beyond the positive impacts of father’s school-ing, we uncovered few significant determinants of the schooling of boys in our Conakry sample (for whom as noted the enrollment rate is quite high), in sharp contrast to the results for girls. Consequently, our results point to a number of ways in which investment in girls’ edu-cation can be increased and the gender gap in schooling reduced, but they have less to say about raising overall (male and female) education investments. Therefore the following discussion focuses on the former.53,54

Our estimates suggest, first, that growth in household incomes will raise private schooling investments for girls faster than for boys; indeed, we found little effect of income on boys’ education. Therefore, policies that raise household incomes will, in general, increase gender equ-ity in schooling, though this will also depend on whether and how these policies change the relative opportunity costs of girls and boys and the relative labor market returns to female and male schooling. Such policies, of course, would also have direct benefits for overall house-hold welfare. On the other hand, in the short run, policies focused specifically on education may be more effective

53_{Our focus on gender gaps in education is not intended to} imply that raising enrollments of boys, or overall enrollments, is not also very important. Boys’ enrollments may be higher than girls’, but as noted in the introduction, enrollments of both in Guinea overall (if not in Conakry itself) remain very low by the standards of other developing countries.

54_{Implicit in a concern over gender inequalities in education} is the assumption that these inequalities are socially non-opti-mal, that is, there is under-investment in girls’ schooling (or in girls’ schooling relative to boys’). The case for this in principle is a strong one. As indicated earlier, parents may not invest in daughters’ education because the private benefits accruing to them are lower than for sons’ education (because they do not receive the same remittance income as from sons), or because they do not recognize or value highly non-market benefits of female schooling such as better child nutrition. The second possibility is a clear case of social, non-market returns exceeding private returns, hence a justification for public inter-vention. As for the first, low investments due to low remittances do not necessarily imply social under-investment if the level of remittances solely reflects educated daughters’ choices not to enter the labor market, but the situation is different if there is labor market discrimination (discussed below) or if for cultural reasons women who work do not remit to the same extent as sons. There is also a strong gender equity rational for equalizing schooling for girls and boys. Education improves women’s wel-fare by increasing the economic opportunities available to them (this can be the case even if there is labor market discrimination) and possibly also by increasing their bargaining power within the household. In general, parents are unlikely to value these benefits as highly as direct monetary ones.

in raising enrollments. Education interventions targeted specifically at girls would have immediate beneficial impacts on the gender schooling gap and these impacts would be compounded intergenerationally. This can be inferred from the fact that improvements in maternal schooling so strongly favor girls’ education: raising girls’ enrollments now is equivalent to raising maternal schooling, hence girls’ schooling relative to boys’, in the next generation. Thus there are intergenerational “increasing returns” for education equity to improve-ments in female schooling. Of course, given that school participation is low for boys as well as girls in Guinea (though the overall enrollment rate in the study area is relatively high), it would hardly be wise to suggest that all education efforts be directed at girls. Moreover, poli-cies to promote education generally instead of (or in addition to) targeting girls will also serve to reduce future gender gaps in schooling, if more slowly, because improvements in father’s schooling to a lesser extent also favor daughters’ education relative to sons’.55,56

As we have stressed, it is likely that the gender gap in schooling reflects, in part, the response of parents to the much different chances of success in the labor market for men and women. Although earnings functions esti-mates (Glick & Sahn, 1997) suggest that incremental effects of schooling on hourly earnings are statistically similar for men and women in Guinea, women are far less likely to enter the labor market, and in particular have very low probabilities of wage work. When adjusted for differences in entry probabilities, the

monet-55_{Based on our estimates one might go even further and say} that promoting enrollments overall or even favoringboysis to be recommended because this will have stronger overall (male and female) intergenerational benefits than will targeting girls. This is because, as demonstrated, father’s education effects on girls are only slightly lower (and not significantly lower) than mother’s effects on girls while father effects on boys are larger than mother effects on boys. However, given the substantial current gender inequality in schooling, there is a need for specifically targeting girls’ schooling while raising overall enrollments, that is, raising participation of girls and boys while shifting the balance toward girls.

56_{The government of Guinea has recently undertaken a} num-ber of steps to raise enrollments, both overall and of girls in particular. These include a program of school construction, especially in areas where there is no primary school, and an emphasis on the recruitment of female teachers for rural schools (World Bank, 1995). Targeting rural areas for school construc-tion will be particularly helpful to girls because being far from the nearest school impinges more heavily on girls’ schooling (World Bank, 1995). While the reluctance of parents to allow girls to travel long distances to school is certainly more relevant to rural areas, it may also be a factor for urban parents for post-primary schooling if the nearest facility is not close to home and the transportation infrastructure is poorly developed, as in Conakry.

ary returns to schooling, especially primary schooling, are substantially lower for females. Thus higher invest-ments in girls’ schooling may not be privately optimal— that is, optimal for parents, who may not value non-mon-etary benefits as highly as monnon-mon-etary ones. To the extent that gender discrimination in hiring and access to specific types of vocational training are responsible for women’s lower probabilities (controlling for education) of employment overall and formal employment in parti-cular, labor market policies that open up employment and training opportunities for educated women will be both efficiency increasing and gender-equity increasing; the former by removing distortions due to discrimination and the latter by raising women’s earnings and inducing parents to invest more in their daughters’ education.57 Also on the side of changing the benefits of education, improvements in the quality of the education that girls receive will raise the returns to investments in their schooling.

With regard to the (opportunity) costs of education, we have presented evidence that girls are constrained in their schooling in part by the demands placed on their time. To the extent that this derives ultimately from par-ental beliefs about girls’ roles in the household economy, removing the constraints will be a difficult task for pol-icy. In the long run, even in the absence of changes in parental attitudes, increased availability of market substi-tutes for domestic work (e.g. prepared foods) and changes in home technology (the use of electricity, refrigerators, etc.) could reduce households’ dependence on the labor of girls (Schultz, 1993). Our results show that this will have to involve an increase in incomes rather than merely urbanization, since domestic work obligations apparently remain a constraint on female schooling in an urban but very poor population where such substitutes presumably are available for those who can afford them. Government can directly address this constraint in several ways. Subsidized childcare (for example, in community daycare centers near women’s places of work) is often proposed as a way to reduce the time burden of working mothers, but it may also be a means of reducing the opportunity cost of girls’ time, permitting them to devote more time to getting an edu-cation. Another approach would be to offer less formal school alternatives for girls in the early morning or eve-ning (Bellow & King, 1995).58_{Parental attitudes about}

57_{For a detailed discussion of labor market conditions for} men and women and policy options, see Glick and Sahn (1997). 58_{Although such an approach tries to accommodate rather} than reduce the burden of housework on girls, programs of this type in other developing countries have been quite successful in raising girls’ participation. See Bellow and King (1995) for a survey and evaluation of these and other interventions to raise investments in girls’ schooling.

girls’ education and the allocation of their time may also be amenable to change, even where (as in Guinea) pecuniary returns to male and female schooling are unequal. In such contexts, promotional campaigns could raise parental awareness of non-market benefits of investing in female education, such as improved child survival and health.

Acknowledgements

We would like to thank Harold Alderman and three anonymous referees for their helpful comments.

Appendix A

Random effects ordered probit model of years of schooling

Assume for the time being that all individuals in the sample have completed their schooling, i.e. there are no censored observations, so that observed schooling equals completed schooling. Designate years of schooling for child j in household i as Sij; S can takes values of 0,1,2,...,max.59 _{An individual’s years of schooling is} determined by the value of a latent variableyij(an index of the “propensity” for schooling) that is defined by the following linear relationship:

yij5xijb 1 ui1uij (A1)

wherebis a vector of parameters andxijis a vector of independent variables. The error term has two compo-nents:ui, which is common to all children in the house-hold (the random effect) and captures heterogeneity in households’ preferences or propensities for schooling, and uij, an individual error term assumed to be inde-pendent ofuias well as uncorrelated across members of the household. Both error terms are assumed to be nor-mally distributed and for normalization unit variances are assumed for the uij. Thus we have ui|N(0,s2u), uij |

N(0,1). Estimation of the random effects model makes use of the fact that conditional on the common family effectuiand given the assumptions onuij, the schooling level probabilities for children within a family are inde-pendent. The conditional probabilities are derived as fol-lows. An individual will haveSyears of schooling if the value of yij falls between two threshold parameters or cut-off points corresponding to gradesSand S11:

59_{“max” in the general case will be the highest grade} attain-able. Since few individuals in our dataset in the sample used in the estimations have gone beyond the 10th grade, we com-bine all children with 10 years or more of school. “max” there-fore represents schooling of 10 or more years.

mS,yij,mS11 (A2) where themare the threshold parameters, which likeb

are to be estimated. For those with no schooling, we know only that the index variable falls below the lowest threshold, i.e.yij,m0; for those with the maximum level of schooling, we know that yij$mmax. Given normality ofuij, the probability conditional on the common family effect that the individual will be observed with Syears of completed schooling is

P(Sij)5 F(m02xijb 2 ui) forSij 50

P(Sij)5 F(ms2xijb 2 ui)2 F(ms212xijb (A3)

2ui) forSij P(1,2,...,max21)

P(Sij)512 F(mmax2xijb 2 ui) forSij5max whereFdenotes the cumulative density function of the standard normal distribution. These probabilities describe an ordered probit model with no censoring from uncompleted schooling at the time of the survey. To obtain unbiased estimates of the effects of the explana-tory variables on (and predictions of) the final grade attainment of an individual of characteristics xij, the model must be extended to account for censoring of the dependent variable for children still in school. We know that a currently enrolled student will ultimately attain at least his or her current gradeSij(we assume the current grade level is to be completed). The current grade thus forms a lower bound to eventual attainment and the underlying response variable is similarly bounded from below, i.e.yij$mS_ij. The probability of achieving at least the current grade is therefore

12 F(ms2xijb 2 ui) (A4)

The conditional likelihood for theith household con-sisting of nchildren is

Li(ui)5 P

n

j51

P(Sij) (A5)

The conditional probabilities depend on the household random effectui, which is unknown. The unconditional likelihood is the marginal likelihood obtained by inte-grating over all possible values of ui. Defining u˜_i5

ui/suand noting that the correlation of the disturbances

for children in the same household is r 5 s2

u/(s2u1 s2_u)5s2

u/(s2u11), the unconditional likelihood for the

household can be expressed as:

Li5

E

u˜

f(u˜i)Pn

j51

P(Sij) du˜i (A6)

whereP(Sij) equals (for individuals who have completed their schooling)

F(m02xijb 2 u˜i(r/12r)1/2) forSij 50

F(ms2xijb 2 u˜i(r/12r)1/2)2 F(ms212xijb (A7)

2u˜i(r/12r)1/2_{) forS}

ijP(1,2,...,max21)

F(mmax2xijb 2 u˜i(r/12r)1/2) for Sij 5max

An analogous transformation is made for censored observations. The product ofLifor all households is the likelihood function for the sample. Maximization of the likelihood function makes use of Hermite integration to evaluate the indefinite integral in Eq. A(6) (see Butler & Moffit, 1982). Consistent estimates and standard errors are obtained forb, the threshold parameters m (except for m0, which is set to zero for normalization), and r. The model was programmed and estimated in GAUSS.60 Finally, note that for the analysis of current enrollment and school withdrawal decisions, the basic setup is the same, with P(Sij) replaced by expressions for prob-abilities from a simple binary probit model as in Butler and Moffit.

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