74 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
enrollment rate for boys in this age group does not mean that most boys let alone girls in Conakry progress to
upper secondary school: all but 4 of enrolled boys aged 16–17 and a similar proportion of enrolled girls are still
attending either lower secondary school grades 7–10 or primary school, a reflection of late starting age or high
grade repetition.
29
Table 3 shows years of schooling or grade attainment of children 10–18 by sex and education level of the
mother and father. We should point out that average schooling levels of parents and especially of mothers is
very low: about 75 of the mothers in the sample and 65 of the fathers have less than a primary education,
and almost 70 of mothers and 60 of fathers have no schooling at all. In looking at these descriptive results,
it should be kept in mind that most of these children 80 of boys and 61 of girls are currently attending
school and thus will likely have an ultimate grade greater
Table 3 Boys and girls aged 10–18: mean years of schooling by sex and
level of mother’s and father’s education Boys
Girls Mother’s education
a
Less than primary 4.37
3.28 Completed primary
5.14 4.66
Completed secondary or higher 5.54
5.47 Father’s education
b
Less than primary 4.31
3.05 Completed primary
4.81 4.48
Completed secondary or higher 5.55
5.54
a
On sample with mother schooling data.
b
On sample with father schooling data.
29
Enrollments for children who do not live with a parent show a similar pattern of gender differences but are substan-
tially lower for both boys and girls. Of course, some older chil- dren in this group have finished their schooling and left home,
but younger children not living with a parent i.e. fostered children also have lower enrollments. Multivariate analysis on
the samples of all boys and all girls under 15 results available from the authors indicates an independent negative effect of
being fostered on the probability of enrollment controlling for household income and other factors. This does not prove, how-
ever, that the practice of fostering is detrimental to child school- ing; in fact, anthropological evidence suggests that fostering is
often used by parents to secure an education for their children see Ainsworth, 1992 and references therein. As noted, living
without a parent is correlated with having been born outside of Conakry, which for most such children would mean a rural area
where schooling opportunities are relatively limited. Fostered children’s access to schooling in Conakry may thus be superior
to what it would have been in the place of origin, even if it is inferior to that of native born children or those living with
a parent.
than their current grade. Because of this, the association of parental education and completed schooling will be
understated by the cross tabulations in the table, which shows the relation of parents’ education to current
grade.
30
This is a sample censoring problem and is explicitly addressed in the ordered probit model of grade
attainment discussed in the previous section. Even with censoring, the table indicates a substantial positive
impact of parental education on children’s schooling, especially for girls. Girls whose mothers have less than
a completed primary education have, on average, 3.3 years of school compared with about 4.4 years for
boys, but girls with mothers who have a completed sec- ondary education have about 5.5 years of schooling,
similar to boys. For father’s education the pattern is simi- lar. These figures thus suggest that improvements in the
education of parents are associated with greater increases in the schooling of daughters than of sons, an issue
addressed in the estimations discussed below.
5. Empirical results
5.1. Years of schooling Table 4 reports parameter estimates from ordered pro-
bit models of years of schooling for boys and girls aged 10–18. For each case we report results from specifi-
cations excluding and including the numbers of children by age and sex in the household. As we noted, household
composition is expected to influence the demand for schooling by altering the marginal costs of children’s
especially girls’ time. However, while such variables are often included when analyzing schooling behavior,
it is possible that household structure, in particular the number of children, is jointly determined with schooling
investments. Such endogeneity is inherent in the quan- tity–quality model, in which parents jointly determine
the number of children and their “quality” subject to the income constraint, a vector of prices and other exogen-
ous factors. This implies that estimates of the effects of siblings, and potentially other variables, on schooling
investments will be biased. Naturally, this is less of an issue where parity can be considered largely exogenous.
In our sample, contraceptive use is very low and actual
30
With a positive impact of parental schooling, children of more educated parents are likely to continue in school longer,
hence have their ultimate schooling underestimated relative to children of less educated parents.
75 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
Table 4 Random effects ordered probit models of years of schooling for males and females aged 10–18 asymptotic t-statistics in parentheses
1 2
3 4
Variables Females
Males Intercept
2 5.064
2 5.855
2 1.004
2 1.572
3.156 3.453
0.581 0.839
Age 12–13 2
0.119 2
0.152 0.238
0.236 0.613
0.752 1.129
1.092 Age 14–15
0.082 0.048
0.289 0.245
0.436 0.248
1.375 1.114
Age 16–17 2
0.164 2
0.217 0.399
0.345 0.817
1.034 1.922
1.575 Age 18–19
0.032 2
0.048 0.320
0.292 0.130
0.193 1.231
1.086 Mother’s schooling
0.088 0.092
0.032 0.025
3.905 3.931
1.262 0.931
Father’s schooling 0.081
0.082 0.075
0.078 4.260
4.268 3.593
3.632 Mother missing
2 0.198
2 0.491
0.001 0.131
0.828 1.865
0.007 0.536
Father missing 2
0.105 2
0.177 0.028
0.070 0.588
0.940 0.134
0.299 Log expenditure per
0.515 0.606
0.200 0.235
adult 3.414
3.774 1.255
1.361 Siblings , 5
– 2
0.364 –
2 0.107
3.813 0.940
Brothers 5–12 –
2 0.044
– 0.096
0.463 0.927
Sisters 5–12 –
0.163 –
0.250 1.699
2.137 Brothers 13–20
– 0.158
– 0.100
1.609 0.835
Sisters 13–20 –
0.299 –
2 0.059
2.448 0.528
Other children , 5 –
0.075 –
2 0.113
0.946 1.571
Other children 5–12 –
0.031 –
2 0.076
0.493 1.170
Other boys 13–20 –
0.050 –
0.054 0.572
0.590 Other girls 13–20
– 0.074
– 0.188
0.760 1.753
Continued.
and desired numbers of children are high.
31
Still, women or households may indirectly exercise control over fer-
tility, for example through the time of the mother devoted to schooling, which affects the age of marriage
and thus possibly also parity.
31
A detailed interview on fertility history, including ques- tions about contraceptive practices, was given to a randomly
selected woman from each household as part of the survey. Less than 5 of the women in this subsample who were of childbear-
ing age reported using any form of contraception. The number of children desired, while smaller for women under 35 than
In the absence of any plausible instruments to control for the endogeneity of children, we deal with the poten-
tial endogeneity problem simply by estimating the mod- els both with and without the numbers of children i.e.
household members under 21 years old.
32
The latter will
over 35, was nonetheless between 6.0 and 6.5 for the former. A complete discussion of the results for the fertility survey can
be found in Desai, Sahn and del Ninno 1992.
32
The number of adults in certain age or sex categories may also be endogenous. It is possible, for example, that parents
deciding to extend a girl’s schooling will have adult female
76 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
Table 4 Continued.
1 2
3 4
Variables Females
Males Men 21–64
0.074 0.054
0.128 0.127
1.361 0.964
1.855 1.749
Women 21–64 0.120
0.040 0.027
0.085 1.884
0.522 0.411
0.985 Men 64
0.455 0.369
0.049 0.010
1.712 1.390
0.0191 0.039
Women 64 2
0.038 2
0.121 0.0130
0.239 0.158
0.518 0.471
0.796 Fulani
2 0.143
2 0.129
2 0.149
2 0.090
0.776 0.717
0.835 0.487
Malinke 0.263
0.077 0.155
0.116 1.205
0.340 0.675
0.491 Other ethnic group
2 0.869
2 0.921
0.147 0.201
2.654 2.738
0.410 0.546
r 0.491
0.465 0.452
0.441 6.231
5.607 4.692
4.320 No. of observations
766 899
Notes: Estimates for threshold parameters not shown. Significant at 10 level; significant at 5 level; significant at 1 level.
be the appropriate reduced-form schooling demand model as long as the omitted variables are correctly
assumed to be endogenous and all relevant exogenous variables are included.
33
Of course, the relation of family size and structure to children’s schooling is itself of con-
relatives come to live in the household to assist in housework. Estimation of models excluding all household structure covari-
ates for current enrollment and school departures as well as years of schooling revealed no substantive changes in the esti-
mates for parental education while reducing the effects of household per adult expenditures. The latter is not surprising
given the correlation of such a measure of household income with household size and structure. However, the pattern by gen-
der in the estimates for expenditures and their significance lev- els discussed below did not change.
33
These conditions for obtaining the correct reduced form need to be emphasized. If the household or fertility variables
in fact are exogenous determinants of schooling, their exclusion implies a misspecified reduced form, which will suffer from
omitted variable bias if the included covariates are correlated with the excluded fertility variables. If fertility is correctly
assumed to be endogenous to schooling decisions, these vari- ables do not belong in a reduced form, but strictly speaking,
one should include all exogenous determinants of fertility and typically only some of these e.g. a woman’s education are
observed. If unobserved exogenous determinants of the price of child quantity are correlated with the included exogenous
variables, the estimates on the latter will suffer from omitted variable bias Browning, 1992. There is probably little vari-
ation in omitted determinants of fertility within Conakry, which would suggest that there is no such bias, but this may not be
the case throughout Guinea. Mothers of children in the sample
siderable interest. For the results of the models including these variables, it may be possible at least to assess the
direction of the simultaneity biases that might occur, as we discuss below.
We first consider the estimates in Table 4 for parental education and log expenditures per adult, which is
included as a measure of household permanent income. More years of schooling of both mothers and fathers lead
to higher grade attainment of girls columns 1 and 2; the estimates are similar for the models with and without
child variables. Better schooling of fathers also raises the grade attainment of boys columns 3 and 4, but school-
ing of mothers does not: the mother’s coefficients in the boys’ ordered probits are only about a third as large as
those for father’s schooling and do not approach statisti- cal significance in either specification. Similarly, house-
hold permanent income, proxied by the log of per adult expenditures, has a positive and highly significant effect
on grade attainment of girls in both models, but does not affect boys’ schooling.
34
The coefficients on expendi-
who migrated to Conakry from different regions of the country after the start of their childbearing years may indeed have been
exposed to differing unobserved determinants of fertility that are correlated with the included regressors.
34
Household expenditures may not be exogenous to school- ing choices. In the present context simultaneity bias will not
arise from parents keeping children out of school to work which would make school status a determinant of family
income since reported participation in family enterprises and wage work is very low for males and females under 18 in Con-
akry. It may occur, however, if parents are heterogeneous with
77 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
Table 5 Effects of changes in parental education and household per adult expenditures on predicted probabilities of primary and lower second-
ary schooling of girls and boys
a
1 2
3 4
5 Change in independent variable
Increase in per adult household expenditures Level of schooling
Increase in mother’s Increase in father’s
10
d
50
d
100
d
schooling from 0 to 6 years
b
schooling from 0 to 6 years
c
Girls Primary
0.194 0.178
0.021 0.084
0.136 Lower secondary
0.207 0.182
0.022 0.096
0.165 Boys
Primary 0.029
0.097 0.004
0.018 0.030
Lower secondary 0.054
0.175 0.008
0.035 0.059
a
Calculated from ordered probit model results in Table 4 see text.
b
Shows the change in the probability of completing the indicated schooling level resulting from an increase in mother’s schooling from 0 to 6 years.
c
Shows the change in the probability of completing the indicated schooling level resulting from an increase in father’s schooling from 0 to 6 years.
d
Shows the change in the probability of completing the indicated schooling level resulting from an increase in household expenditures by the indicated amount.
tures for both girls and boys are somewhat larger by about 15 when the full set of household covariates is
included, but in either specification the pattern by gender is the same. These estimates thus suggest important dif-
ferences by gender in parental education and expenditure effects. However, the ordered probit coefficients do not
measure the actual impacts of these and other covariates on years of school, reflecting the non-linear structure of
the model. Instead, the actual impacts can be assessed through comparative statics calculations based on the
estimates and the data. Table 5 presents the results of such an exercise.
The table shows, first, the effects of mother’s and father’s primary education on the probabilities of daught-
ers and sons completing primary school i.e. on prob [years of schooling 6] and lower secondary school
respect to preferences such that those with a propensity to invest more in their children’s education work more and earn more
income, implying an upward bias to the estimated effects of expenditure or income. Experiments with predicted expendi-
tures, using as instruments household assets and non-labor income, in general yielded very poor results: the coefficients on
expenditures were not at all robust to changes in the specifi- cations of the predicting and schooling equations and were
occasionally wrongly negatively and even significantly wrongly, signed. Thus we rely on reported values of household
expenditures in spite of the potential for endogeneity.
prob [years of schooling 10].
35
The change in the probability is calculated as the difference in the prob-
abilities of completing a given level when the parent has 6 years of schooling and when he or she has zero years
of schooling, evaluating all other variables at the sample means for girls or boys. Since average levels of school-
ing of parents in the sample are low, the no-schooling to primary focus is appropriate. Also shown are the
effects on primary and lower secondary completion of increases in per adult expenditures of 10, 50 and 100
above mean expenditures. We use the estimates from the models including the full set of household composition
variables columns 2 and 4 in Table 4 to calculate the comparative statics, but the results for both parental edu-
cation and income were robust to changing the basis of the calculations from the full to the reduced excluding
children covariates model estimates.
As shown in the first column, mother’s primary schooling raises the probability that a girl will achieve
at least a primary education by about 20 percentage points over the no maternal schooling base case, with a
similar impact on lower secondary schooling. Note that these are absolute changes in the predicted probabilities
e.g. in the case of primary completion, from 0.58 when the mother has no education to 0.77 when the mother
35
Students in Guinea attend 4 years of college or lower sec- ondary schooling grades 7–10 and 3 years of lycee or upper
secondary schooling grades 11–13.
78 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
has a primary education.
36
These effects on girls are much larger than the calculated maternal schooling
impact on sons’ primary and secondary attainment prob- abilities just 0.03 and 0.05 percentage points, respect-
ively; recall as well that the parameter estimates in the boys’ ordered probit model were not significant.
Column 2 of Table 5 shows the effects of father’s schooling. For primary school completion, paternal
schooling impacts, like maternal schooling effects, are higher for daughters than sons, though the difference in
the daughter and son effects is much less dramatic than for maternal schooling. For lower secondary completion
paternal schooling effects are similar for daughters and sons. Comparing maternal and paternal impacts reading
across columns 1 and 2, in the case of girls, the effects of father’s primary education on primary and lower sec-
ondary completion are similar to the effects of mother’s education, but for boys father’s primary education has
impacts on primary and secondary completion that are more than three times greater than those of mother’s pri-
mary education.
What stands out most strongly from these simulations is that improvements in maternal schooling have much
greater impacts on the education of daughters than of sons. Paternal schooling overall also seems to favor
daughters’ education over sons’, but to a lesser degree. Thus the relative benefit to girls, defined as the differ-
ence in the improvements in girls’ and boys’ schooling, is greater from mother’s education than from father’s
education. One explanation for this, noted above, is rooted in a bargaining model of resource allocation
within the household: educated mothers have strong pref- erences for educated daughters, and being educated con-
fers the power to direct household resources toward daughters’ human capital development. Also as noted,
however, the findings are consistent with unified house- hold preferences under interhousehold heterogeneity in
preferences for schooling daughters.
37
The data do not allow us to distinguish these explanations empirically,
36
The percentage, i.e. proportional, increase in probability in the case of primary completion is 0.32 5 0.190.58.
37
It is also possible that a stronger maternal education effect on daughters arises because mothers spend more time with
daughters, so improvements in mother’s schooling will raise the efficiency of learning and demand for schooling of girls more
than boys, with the opposite being the case for fathers and sons; this possibility is pointed out by Thomas 1992 in the context
of child health. This is consistent with common parental prefer- ences, but only if differing mother and father time allocations
are determined exogenously by cultural factors or technological efficiency considerations rather than preferences of mothers
to be with daughters and fathers to be with sons.
but the first interpretation is consistent with what is known about households in West Africa.
38
Comparison of the first two columns of Table 5 with the last three columns indicates that parental primary
education has impacts on children’s schooling, and especially daughter’s schooling, that are equivalent to
very significant increases in household income, proxied by per adult expenditures. Both mother’s and father’s
primary schooling have substantially larger impacts on girls’ primary and secondary completion probabilities
than a doubling of household income.
39
Some caution is necessary in making these comparisons since we are
inferring the effects of large changes in parental edu- cation and income from estimates that relate to marginal
changes in the independent variables. Consistent with the parameter estimates discussed above, additions to house-
hold expenditures have much larger calculated effects on girls than boys for each level of schooling attainment.
These differences are consistent with studies of other developing countries. DeTray 1988 and Gertler and
Glewwe 1992 report higher income elasticities for girls’ schooling in Malaysia and Peru, respectively,
while Schultz 1985 finds a similar pattern using cross- national data.
40
We next examine the effects of household structure on years of schooling. The extended ordered probit mod-
38
The linear schooling specification used in the models is potentially restrictive, so we also experimented with quadratics
in years of mother and father schooling as well as with dummy variables for parental schooling levels. These experiments con-
firmed the basic findings. In particular, in specifications using dummy variables for lower primary, upper primary, and second-
ary schooling of the parents, maternal schooling impacts for all levels were significant for girls’ grade attainment but none were
significant for boys’. Father’s primary and schooling dummies, in contrast, were significant for both boys and girls. Similar
experiments with the current enrollment probits specifications reviewed in the next section yielded the same conclusion.
39
The effect of mother’s schooling may, in fact, be underesti- mated. The simulations do not account for changes in other
factors that may be induced by changes in maternal education. In particular, more educated women tend to have fewer chil-
dren, which may permit higher investments in schooling for each.
40
Specifications using the value of household assets or assets per capita or per adult in place of expenditures per adult
as a measure of household resources yielded patterns in esti- mates for boys versus girls very similar to that seen for expendi-
tures: the size of the estimated effects was much greater for girls than boys, and often the effects on the latter were not
significant. To the extent that assets can be considered “more” exogenous than income or expenditures though this is not cer-
tain, given the likely dependence of assets on past labor income, this supports our conclusion that household resources
have a greater impact on the education of girls than on that of boys.
79 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
els in Table 4 columns 2 and 4 include a large number of children covariates. The manner in which we have
entered the numbers of children and young adults up to age 20 in the model is dictated by the complexity of
household structure in the Conakry sample. This com- plexity derives from the frequency of polygamy, which
means that many households in the survey include a male head with several wives, and the fact that households
often include individuals from the extended family. As a result, children living in a household may be related
to each other in one of several ways: as full siblings sharing both parents, as half-siblings having the same
father but different mothers, or as non-siblings. These distinctions are potentially important with regard to the
effects of the presence of other children on a child’s time allocation and schooling. Initial estimation of the grade
attainment, enrollment, and school withdrawal models indicated that the relevant distinction for schooling out-
comes is between full siblings and other children half- siblings and non-siblings. Based on these initial results,
and in the interests of avoiding having a very large num- ber of regressors, we present this specification in the
table, using “siblings” or “brothers” and “sisters” to denote full siblings and “other” to indicate all other chil-
dren.
There are quite dramatic differences in the effects of household composition on the schooling attainment of
girls and boys. Most notably, the number of siblings under 5 years of age has a strongly negative impact on
girls’ schooling but no effect on boys; the estimate for girls is significantly different from zero at the 0.001
level. The most plausible interpretation is that young sib- lings raise the demand for a girl’s time in childcare or
in other home activities the latter to permit the mother to devote more time to the younger children, making it
harder for her to continue her education. The fact that young siblings have no effect at all on boys’ grade attain-
ment lends support to this interpretation, since one would expect the burden of childcare or other housework to be
imposed primarily on girls. The number of sisters aged 13–20, in contrast, has a significantly positive impact on
girls’ grade attainment. This too points to the importance of household time allocation factors: as noted earlier,
having more girls in this age category should reduce the opportunity cost of an individual girl’s time through sub-
stitution or scale economies in household work. As with siblings under 5, the number of sisters aged 13–20 does
not have an impact on boys’ years of schooling. How- ever, there is a positive and significant effect of sisters
aged 5–12 on boys’ grade attainment. It is difficult to interpret this result, as few other household covariates—
indeed few other covariates overall—are significant for boys.
41
Unlike siblings, other children which as noted include half-siblings have few effects on educational attainment,
even for girls: for example, the number of other children in the household under 5, in sharp contrast to full sib-
lings under 5, does not reduce girls’ grade attainment.
42
This suggests that interdependencies in time and resource allocations and in patterns of support are
stronger among children who share a mother and a father than between children in the household who are not full
siblings or else have the same father but different mothers. Thus, for example, a teenage girl may have a
strong obligation to help her own mother care for her other children but much weaker ties to other women in
the household, including other wives of her father.
We have interpreted the estimates of sibling effects as reflecting variations in the opportunity cost of girls’ time.
However, the negative association of young siblings and girls’ school attainment but not the positive association
of older siblings and girls’ schooling is open to an alter- native interpretation based on the child quantity–quality
model, which predicts a negative association of the num- ber of children in the household and the average level of
child schooling. On the other hand, the quantity–quality framework would generally lead us to expect a negative
effect of young children on boys’ schooling as well as a negative, not positive, impact of older sibling categor-
ies on the schooling of both girls and boys. Thus an explanation based on differential opportunity costs by
gender is more convincing. However, since child edu- cation investments and family size may be jointly chosen
by parents, our estimates of the impacts of siblings on schooling may be biased. If parents with high schooling
preferences have smaller families, the results will over- state the negative impact of young children on the edu-
cation of their older siblings the absolute value of the parameter will be biased upward. Again, at first glance
this is not consistent with our finding of a negative
41
Chernichovsky 1985 finds for rural Botswana that the presence of an infant in the household lowers the probability
of enrollment and years of schooling while the number of chil- dren aged 7–14 increases enrollment and years of schooling.
He offers an explanation like ours for these findings, namely that these factors affect the value of time of school-age children.
He does not investigate whether these effects differ for boys and girls. However, in a very different setting, Pitt and
Rosenzweig 1990 find that teenage girls in Indonesia reduce their enrollment in school relative to that of boys in response
to the illness of an infant sibling, suggesting, as do our own findings, that housework obligations such as childcare impinge
more strongly on girls’ schooling.
42
A small percentage well under 5 of girls under 18 in the estimating sample have their own children. These children
were not included as regressors in the models, either separately or in the “other children” category. Although unplanned preg-
nancies have been identified as a cause of girls leaving school World Bank, 1995, in general it would not be wise to consider
childbearing to be exogenous to decisions regarding education. It turned out to matter very little for the results whether own
children were included or not.
80 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
younger sibling effect on girls but not on boys. However, other scenarios are possible which would yield such an
outcome even in the absence of direct time allocation effects of young children, so an upward in absolute
value bias in our estimate for girls remains a possi- bility.
43
Nevertheless, it seems unlikely that the very large differences in the siblings under 5 impacts for girls
and boys would be due solely to heterogeneity in tastes. Finally, the random effects specification provides esti-
mates of r, the intrahousehold correlation of errors in the equations for schooling in the ordered probit model
see Appendix A. These estimates are almost identical for girls and boys and are highly significant, indicating
that unmeasured household-level effects are important in the determination of schooling attainment.
We should note that an important determinant of grade attainment as well as current enrollment status and
school departure is performance on the examinations which a student must pass in order to go on to the next
level of school. We have no information on exam per- formance in the survey, but nationwide in Guinea girls
have lower passing rates than boys on end-of-cycle exams for primary, lower secondary, and upper second-
ary World Bank, 1995. Thus differences in test scores are potentially a factor behind gender differences in edu-
cational attainment and in our other schooling indicators. Note, however, that academic performance to a large
extent probably reflects parental assessments of the value of schooling for girls and boys and decisions regarding
the allocation of domestic work which result in differ- ences in the time and effort girls and boys devote to their
schooling. The household and individual level variables in our reduced-form specifications will implicitly capture
the effects of these processes.
44
5.2. Current enrollment Table 6 presents results from current enrollment ran-
dom effects probits for girls and boys using the same samples and explanatory variables as the ordered probit
43
In one such scenario, couples having “traditional” values prefer large families and are also disinclined to school daught-
ers, leading to a negative correlation of number of siblings and female schooling.
44
On the other hand, important supply factors, not recorded by the survey, may also be at work. In particular, classroom
and school environments in Guinea appear to be significantly less conducive to learning for girls than boys World Bank,
1995, negatively affecting their chances for promotion as well as their later productivity and earnings potential. Since con-
ditions in schools within Conakry are probably fairly homo- geneous, these unobserved supply constraints are unlikely to
vary much over the survey area except of course between girls and boys. Thus there are unlikely to be omitted variable biases
in our estimates of the effects of household factors using the separate samples of girls and boys though there may be interac-
models of years of school. As in the case of the ordered probits, the parameters of the binary probit model are
not equal to comparative static effects, that is, the change in the probability of being in school resulting from a
marginal change in an independent variable. We calcu- lated these marginal effects and their t-statistics, which
are functions of the parameters and the data, at the sam- ple means for girls and boys and present the results for
selected variables in the table. We report the marginal effects from the specifications including the children
covariates; however, the choice of specification made almost no difference for the estimates for parental
schooling and expenditures the full set of probit esti- mates and marginal effects for both specifications are
available on request from the authors. We also con- structed x
2
tests of equality of the marginal effects for boys and girls. For the full vectors of marginal effects
derived from the probit slope coefficients, equality of boys’ and girls’ effects is rejected at the 0.01 level in
both specifications.
45
The effects of key variables on current enrollment are quite similar to those for the years of schooling model.
46
Also, the estimates of r are again strongly significant for girls and boys. Mother’s years of schooling has a large
and highly significant impact on girls’ enrollment prob- abilities but no impact on boys’, while increases in
father’s schooling raises enrollment probabilities of both girls and boys. As before, the effect of father’s schooling
appears to be larger for girls the difference in the mar- ginal effects is marginally significant: p 5 0.11. Also
as before, however, the difference in the girls’ and boys’ effects is much less pronounced than for mother’s edu-
cation, for which the marginal effect is six times greater for girls than boys note that the boys’ marginal effect
is also insignificant. Thus the parental education impacts exhibit a pattern very similar to the ordered probit
results, with the relative benefit to girls’ schooling
tions of excluded supply factors and household factors such as income or parental education. However, it should be kept in
mind that the differences in mean schooling outcomes for girls and boys reflect not just household factors and parental prefer-
ences, but probably also gender differences in schooling environments as well as differences in labor market conditions
for men and women, as already noted.
45
The test statistic is d
m
2 d
f
V
m
1 V
f −
1
d
m
2 d
f
, where d
m
and d
f
are the relevant marginal effects not the probit para- meters themselves for boys and girls and V
m
and V
f
are the corresponding variance submatrices. The denominator is the
variance of d
m
2 d
f
; to simplify the computation of the vari- ance we have assumed rather restrictively that covd
m
,d
f
5 0. The statistic is distributed as chi-square j under the null, where
j is the number of restrictions.
46
However, as would be expected, enrollment probabilities of boys and girls decline with age. The results for the age con-
trols are omitted from the table to save space.
81 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
Table 6 Marginal effects of selected variables on probabilities of current enrollment and leaving school in the previous 5 years t-statistics
in parentheses Current enrollment ages 10–18
a
Leaving school ages 15–18
b
Variable Females
Males Females
Males Mother’s schooling
0.028 0.004
2 0.010
0.006 3.484
0.788 1.143
0.887 Father’s schooling
0.023 0.011
0.000 2
0.010 3.550
2.960 0.037
1.552 Log expenditure per adult
0.257 0.032
2 0.150
2 0.024
4.494 1.047
2.108 0.490
Siblings under 5 2
0.107 2
0.019 0.098
0.031 3.184
0.981 2.033
0.908 Other children under 5
0.010 2
0.021 0.035
0.019 0.355
1.589 0.878
0.901 Brothers 5–12
2 0.023
0.026 0.026
2 0.034
0.670 1.361
0.581 1.123
Sisters 5–12 0.066
0.039 2
0.104 2
0.028 1.851
1.803 2.097
0.867 Sisters 13–20
0.120 2
0.018 2
0.060 0.021
2.951 0.855
1.111 0.562
Brothers 13–20 0.047
0.012 2
0.016 0.031
1.362 0.563
0.322 0.916
Men 21–64 0.016
0.017 2
0.016 0.026
0.882 1.393
0.678 1.673
Women 21–64 0.025
0.019 2
0.039 2
0.033 0.945
1.226 1.269
1.490 Men over 64
0.108 2
0.018 2
0.160 0.038
1.242 0.405
1.302 0.554
Women over 64 2
0.063 0.021
2 0.246
2 0.090
0.724 0.359
1.366 1.111
r 0.404
0.486 –
0.545 4.177
4.651 1.894
No. of observations 766
899 208
288
Notes: Shows the change in probability from a marginal change in the variable. Calculated for variable k as fxb b
k
, where f is
the standard normal density function, x is the vector of data means, and b is the vector of estimated probit parameters adjusted by the scaling factor 1 1 r1 2 r
12
.
a
Calculated from estimates of random effects binary probits for current enrollment.
b
Calculated from estimates of binary probits for leaving school during the 5 years preceding the survey random effects specification for boys only—see main text.
Significant at 10 level; significant at 5 level; significant at 1 level.
greater for increases in maternal education than paternal education. Note that the absolute benefit to girls from
increases in mother’s education is actually fairly similar to that from father’s education: the marginal effects for
mother’s and father’s schooling in the girl’s enrollment probit are 0.28 and 0.23, respectively, and a Wald test
was unable to reject equality of the two effects p 5 0.71. Thus the greater relative benefit to girls from
mother’s schooling derives primarily from the very small estimated effect of maternal schooling in the boys
enrollment model.
Enrollment probabilities for girls, like girls’ years of schooling, are affected by household composition. Sib-
lings under 5 exert a negative effect while sisters aged 13–20 encourage girls’ school participation; both effects
are significant at the 1 level. Calculated at the average probability of enrollment, an additional sibling under 5
leads to a 17.5 reduction in the probability that a girl will be in school 2 0.107 over 0.61 mean enrollment
probability. In contrast, no impacts of young siblings or sisters aged 13–20 are found for boys. This suggests that
girls’ and boys’ current school status, like their ultimate grade attainment, differ in part because household obli-
gations impinge more strongly on girls. On the other hand, the number of sisters aged 5–12 has a marginally
significant positive effect on both girls’ and boys’
82 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
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
1 1 s
2 u
, where s
2 u
is the variance of the heterogeneity error component in the index function of the probit model. Hence r is between 0 and 1.
83 P. Glick, D.E. Sahn Economics of Education Review 19 2000 63–87
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 t 5
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
