The gender gap in educational attainment in India: How much can be explained? by Geeta Gandhi Kingdon Department of Economics University of Oxford August, 2001 Abstract Differential treatment of sons and daughters by parents is a potential explanation of the gender gap in education in developing countries. This study empirically tests this explanation for India using household survey data collected in urban Uttar Pradesh in 1995. We estimate educational enrolment functions and selectivity-corrected educational attainment functions, conditional on enrolment. The gender difference in educational attainment is decomposed into the part that is explained by men and women’s differential characteristics and the part that is not so explained (the conventional ‘discrimination’ component). The analysis suggests that girls face significantly different treatment in the intra-household allocation of education – there is a large unexplained component in the gender gap in schooling attainment. A detailed decomposition exercise attempts to discover the individual factors most responsible for the differential treatment. Keywords: Gender, educational attainment functions, Oaxaca decomposition, India JEL Classification: I21, J16, J23, J31, J71 Correspondence: Department of Economics, University of Oxford, Oxford OX1 3UQ email: [email protected]Acknowledgements: The research was supported by a Wellcome Trust grant No. 053660 and the data collection was funded by a McNamara fellowship of the World Bank awarded to the author in 1995. The paper has benefited from the comments of Jean Drèze and Haris Gazdar. The usual disclaimers apply.
Transcript
The gender gap in educational attainment in India: How much can be explained?
by
Geeta Gandhi Kingdon
Department of Economics
University of Oxford
August, 2001
Abstract
Differential treatment of sons and daughters by parents is a potential explanation of the gender gap in education in developing countries. This study empirically tests this explanation for India using household survey data collected in urban Uttar Pradesh in 1995. We estimate educational enrolment functions and selectivity-corrected educational attainment functions, conditional on enrolment. The gender difference in educational attainment is decomposed into the part that is explained by men and women’s differential characteristics and the part that is not so explained (the conventional ‘discrimination’ component). The analysis suggests that girls face significantly different treatment in the intra-household allocation of education – there is a large unexplained component in the gender gap in schooling attainment. A detailed decomposition exercise attempts to discover the individual factors most responsible for the differential treatment.
Keywords: Gender, educational attainment functions, Oaxaca decomposition, India JEL Classification: I21, J16, J23, J31, J71 Correspondence: Department of Economics, University of Oxford, Oxford OX1 3UQ email: [email protected] Acknowledgements: The research was supported by a Wellcome Trust grant No. 053660 and the data collection was funded by a McNamara fellowship of the World Bank awarded to the author in 1995. The paper has benefited from the comments of Jean Drèze and Haris Gazdar. The usual disclaimers apply.
1
1. Introduction
Recent research suggests that female schooling is more important than male schooling
for social outcomes such as fertili ty, child health, and infant mortality1. The literature also
suggests that the economic gains from women’s education are generally at least as high as
those from men’s education [Schultz 1993]. Thus, women’s educational backwardness is of
concern not only because it is inequitable but also because it is socially and economically
inefficient.
In the economics of education literature, there are two frequently cited explanations of
the gender gap in education. Firstly, that the gap is due to labour market discrimination
against women: if the labour market rewards women’s education less well than men’s (i.e. the
rate of return to women’s schooling is lower than to men’s), then girls will face poorer
economic incentives to invest in schooling than boys. A second major explanation for the gap
is that parents treat sons and daughters differentially. This differential treatment may arise
either because of son-preference, which causes parents to give a greater weight to the welfare
of sons, or it may arise because parents value only that part of the return to a child’s schooling
which accrues to them personally - and the returns to a daughter’s education are reaped largely
by her in-laws’ family. This is compounded by the fact that societal norms in some countries
require parents to accumulate a dowry for daughters but not for sons. Thus, girls may lose
out in the intra-household allocation of education because of a potentially strong asymmetry in
parental incentives to educate sons and daughters.
This paper examines the extent to which differential treatment of sons and daughters in
education can be explained in India, a country that suffers from a well-documented high level
of gender-inequality in education, as well as in a number of other welfare measures, such as girl
and boy survival chances, longevity, and anthropometric status [Shariff, 1999]. India, with a
Gender Development Index (GDI) of 0.410 ranks 103 among the 137 countries for which the
GDI has been constructed2 [UNDP 1996]. While gender inequality in education in rural India
2
has received the most attention, even in urban India, this gap is significant. For example, in the
urban data for the present study, women had significantly fewer years of education than men at
the 1 per cent level (see Table 1).
The differential treatment explanation is tested by examining the intra-household
allocation of educational enrolment and of years of schooling. While many studies in India
document the gender gap in school enrolment and educational attainment, only one recent
study [Pal, 2001] examines whether the gender gap persists when other household factors are
held constant in a multivariate framework3. Whether there is gender differentiated treatment
in the intra-household allocation of education in India is not a trivial question: although the
observed gender disparity in educational outcomes (as well as in other welfare measures such
as mortality) in India would suggest that there is likely to be strong parental discrimination
against girls, recent econometric studies of the intra-household allocation of educational
expenditure (as well as of consumption expenditure generally) have failed to unequivocally
confirm such discrimination [Subramanian and Deaton 1990; Subramanian 1995].
Section 2 describes the data and the methodology. Enrolment choice is modelled in
section 3 while section 4 examines the determinants of educational attainment for men and
women. Section 5 applies the Blinder-Oaxaca method to measure the extent of sex
discrimination in the intra-family allocation of education. The final section concludes.
2. Data and method
The data for this study came from a purpose-designed stratified sample survey of 1000
households in 1995 in the Urban Agglomeration of Lucknow district, Uttar Pradesh. The
sampling procedure and details of survey instruments and implementation are given in Kingdon
[1998b]. The household questionnaire based on the pattern of the World Bank’s LSMS
studies obtained information not only on personal characteristics and parental background but
3
also on detailed aspects of household members’ education, time allocation, and labour market
activities, if any. The survey yielded data on 4560 individuals aged 6 years old and over.
Educational enrolment
While modelli ng school enrolment choice is an interesting exercise in its own right, it is
also needed for the consistent estimation of educational attainment functions for reasons
detailed below. Individuals base their decision to participate in schooling upon their evaluation
of the net benefits of schooling, say N= (B - C) where B is total benefit and C is total cost.
Individuals will only enter school if the benefit (B) exceeds the cost (C). Thus, enrolled
individuals are those for whom B>C. For non-enrolled persons, B<=C.
Let I* be the net benefit of enrolli ng in school. That is,
I* = B - C (1)
I* is a function of a set of variables W which affect either the benefit of schooling or the cost of
schooling or both. This can be expressed as
I Wi i i* = +γ ε (2)
where γ is a vector of coefficients and ε a stochastic disturbance term. As I* is unobserved, we
define an indicator variable I such that I=1 when an individual is observed to be enrolled, and
I=0 when an individual is not enrolled. Thus, individuals are faced with a dichotomous choice:
Ii =1 if I Wi i i* > ⇒ + >0 0γ ε
Ii = 0 if I Wi i i* ≤ ⇒ + ≤0 0γ ε (3)
Thus, the sample selection rule (SSR) for school enrolment is that
I*>0
⇒ γ εWi i+ > 0
⇒ ε γi iW> − (4)
4
If it is assumed that ε is normally distributed with zero mean and unit variance, then the choice
between enrolment or not can be written as a probit model4 where the probability of enrolment
is given by
pr I pr I pr Wi i i i( ) ( ) ( )*= = > = + >1 0 0γ ε
= > −pr Wi i( )ε γ
= −Φ( )γWi (5)
where Φ(.) is the normal distribution function. This probability can be estimated using
maximum likelihood methods [Greene 1993]. Since the choice under consideration is
dichotomous - enrolment or not - a binary formulation of the probit is used.
Educational attainment
It is desired to fit educational attainment (years of education, or EDYRS) functions
separately for men and women in an unbiased fashion. This raises the issue of the censoring of
the dependent variable, EDYRS, at zero. For those who never enrolled, the value of EDYRS is
equal to zero. When the dependent variable is censored at zero for a signi ficant fraction of the
observations, conventional regression methods fail to account for the qualitative difference
between limit (zero) observations - that is, never-enrolled persons for whom EDYRS=0 - and
non-limit observations, that is, persons who enrolled and for whom EDYRS>0. The method
that uses only non-limit observations suffers from sample selection bias, since persons for
whom EDYRS takes positive values may not be a random draw from the population, but a self-
selected (or hierarchially selected) group. This is plausible if more highly ambitious or
motivated children are more likely to be enrolled in school than children with lower levels of
these unobserved qualities. With self-selected samples, the mean value of the error term in the
educational attainment equation may not equal zero, violating a basic assumption of the
classical OLS model. More seriously, the error term may be correlated with the included
variables, leading to biased estimates.
5
The tobit procedure has been used in the literature to model censored dependent
variables but it is a restrictive solution, modelli ng simultaneously the decision to receive any
education at all (choice of enrolment in school) and the decision to acquire extra years of
education. A more satisfactory approach is to treat the decision to enrol as essentially separate
from the decision to attain further years of schooling. The Heckman procedure, which unifies
least squares regression and discrete choice models, has now come to dominate the applied
literature where censored or truncated variables are to be modelled. We adopt the Heckman
procedure to model the determinants of the censored variable EDYRS and will estimate the
selectivity term lambda from the enrolment choice model of the next section.
For the purposes of analysing the determinants of educational attainment, one must
have a sample of persons who have completed education. We limit the sample to adults aged
23 and over who have completed education. Only 3.2 per cent of men and 2.0 per cent of
women aged >=23 years old were excluded as they were still enrolled in education. In other
words, most persons had completed their desired (or possible) spells of education by age 22
years.
The survey asked respondents information about the time when they were children of
age 14. It is clear that retrospective variables such as individual’s health as a child (ACHEAL),
assets-owned/ parental wealth (PAWEAL), and whether either parent read daily newspaper
(PANEWS) are likely to be measured with a progressively greater degree of error as age
increases because memory recall deteriorates over time. Given this data limitation, we use an
upper age-limit of 45 years old, that is, use the sub-sample of persons aged 23-45 years for our
analysis and discussion.
The data suggest that an important cause of females' educational disadvantage vis-a-vis
males in the sample district is their much inferior enrolment rate than males. Indeed, as table 1
shows, the disadvantage of females in school enrolment rate is much greater than their
disadvantage in years of education attained (conditional on enrolment), although the latter is
6
still statistically significant. While the average EDYRS of ever-enrolled women is 5.5 per cent
lower than that of males, enrolment of females is 16.6 per cent lower than that of males.
Consequently, we focus not only on the determinants of educational attainment but also
importantly on the determinants of enrolment. Note also in table 1 that the raw gender
differences in both enrolment and EDYRS have narrowed over time.
3. Enrolment choice
Since the enrolment outcome is a binary one, I use the discrete choice probit
framework. A pooled model with both male and female observations and a dummy for gender
shows that sex is a highly important variable in explaining enrolment, with a very large
coefficient and a t-value of 14.5 (Appendix 1). This model shows that even after controlling
for parental background, religion, and caste, girls lose out in the intra-household allocation of
schooling. Given that enrolment varies much by gender, and our interest in gender-based
comparisons, it was appropriate to estimate the discrete choice model of enrolment separately
for males and females rather than imposing the restriction that all coefficients were the same
for males and females in the enrolment function. The definitions of the variables are given in
table 2 and their means and standard deviations in tables 3a and 3b. Table 4 sets out the
estimated enrolment probit.
7
Discussion of results
The estimated parameters of the AGE variables suggest that there has been an
improvement in the enrolment rates of females over time but not in the enrolment rates of
males. The enrolment rate of women aged 31-38 is significantly higher (at the 10 per cent
level) than the enrolment rate of women aged 39-45, which is the base category for age. Age
23-30 is nearly significant at the 10 per cent level too, its lower coefficient perhaps reflecting
the fact that some (enrolled) women in this age range have not yet completed education and so
are excluded from our sub-sample. The negative sign on the age variables in the male equation
is surprising, though the effect is not statistically different from zero at conventional levels of
significance.
Being MUSLIM exerts a powerful negative influence on the probabili ty of enrolment in
both the male and the female models, as does belonging to the low and backward castes
(LOWCASTE). This may imply that even after controlli ng for parental education and wealth,
religious and caste factors are important taste shifters in the demand for education. However, a
more plausible explanation is that the well documented lower returns to education of lowcastes
due to wage and job discrimination lowers their motivation to acquire any schooling5.
Family's economic status when respondent was a child (PAWEAL) is an important
determinant of enrolment for both boys and girls and its effect is concave: higher parental
wealth increases the probabili ty of enrolment, but at a decreasing rate. Thus, even with the
availabili ty of very low-fee primary schooling in India6, enrolment is importantly related with
wealth. Persons from poor backgrounds may have a lower probabili ty of enrolli ng in school
because of the high opportunity cost of enrolli ng - if children are working to supplement family
income – and/or because of their inabili ty to afford other non-fee schooling expenses.
Household survey data on educational spending show that even so-called ‘ fee-free’ schooling
has substantial costs in India. For instance, the PROBE report [Probe Team, 1999, p16] found
that in rural north India in 1995-96, parents spent about Rupees 318 per year on each child
8
who attends government (i.e. tuition-free) school, so that a typical agricultural labourer with 3
such children would have to work for about 40 days in the year just to send them to primary
school.
The parental education variables (MAEDYRS, PAEDYRS, PAEDYRSQ) are all strongly
significant in the female regression but not so in the male enrolment equation, where only
MAEDYRS is significant at the 10 per cent level. The results suggest that while for males to be
enrolled in school, it is not so important that their parents be well-educated, for females,
parental schooling is highly important to their access to education. At the mean of PAEDYRS,
the effect of PAEDYRS is lower than the effect of MAEDYRS in both the male and female
regressions, suggesting that mother’s education matters more to child schooling than does
father’s education.
Whereas bad health as child (ACHEAL) is a significant deterrent to enrolment for boys,
it is apparently no so for girls. There is no mechanism to explain this gender asymmetry except
inferring that parents are more responsive to sons' ailments than to daughters' , something
which is not implausible in the context of India, given the relative neglect of female children' s
ill nesses in India which contributes to its well -known very male sex ratio.
Finally, ‘mother ever worked’ in the labour market (MAWORKED) exerts a negative
influence on enrolment probabili ties in both male and female sub-samples, though its effect is
statistically different from zero at the 10 per cent level only in the female equation. There are
two possible interpretations of this: One suggests that the educational ' cost' of a working
mother falls disproportionately on daughters. For example, if mother working implies that
household tasks must be delegated to children, they are given to girls more often than to boys
if parents care less about girls' schooling. Although we do not have data on time spent in
domestic activities by the respondents when they were children, we do have this data available
for those who are children presently in our dataset (aged 6 to 14 years old). Table 5 shows that
in this sub-sample, in households where mother works, girls' time on domestic chores increases
9
by 50 per cent but boys' only by 17 per cent. This suggests that the inferior enrolment rate of
girls may partly be explained by the extra household work that falls disproportionately on them
when mother is working.
If true, this means that women’s labour force participation is bad for girls’ schooling
and it has the unfortunate implication that policies to enhance women's labour force
participation will adversely affect girls' education. However, the effect may not be so
deleterious as that suggested by the coefficient on MAEDYRS: we observe that in the past 25
years or so, both female labour force participation and female education have been rising in
India, indicating that the negative effect of a working mother on girls' education is more than
outweighed by other factors, and suggesting grounds for longer-term optimism.
There is also a second possible interpretation of the effect of MAWORKED. The
variable MAWORKED does not refer specifically to mother's working when the individual was
a child of age 5 or 6 (the age at which school enrolment typically takes place). Thus, it is
possible that it reflects the respondent’s economic background, with those whose mothers
worked being the ones from poor families. This view appears to be supported by Murthi,
Guio, and Drèze [1997] who state that “female labour force participation in India is often a
reflection of economic hardship” . If this is the case then it is not so much the educational cost
of a working mother but, rather, the educational cost of poverty that falls disproportionately on
daughters.
10
4. Educational attainment
We turn next to the educational attainment equations. Years of education (EDYRS) is
modelled separately for men and women as pooling was strongly rejected by a chow test
(F=38.75 and F-critical=1.67). The pooled model of EDYRS (Appendix 2) shows that the
coefficient on the gender dummy is significant at the 1 per cent level, suggesting that girls have
significantly lower allocations than boys even after standardising for household characteristics.
Consistent with the discussion in the methodology section, it is desired to estimate a
model of educational attainment correcting for possible bias due to endogenous selection of
individuals into the sample of persons with positive years of schooling. However, as is often
the case in much of the applied literature, we have no plausible variables that belong in the first
stage ‘ever enrolled’ selection equation and that do not belong in the second stage ‘schooling
attainment’ equation.
Given the lack of credible exclusion restrictions, we followed two alternative
approaches to achieve identification of the selectivity term, lambda, though neither is ideal.
Firstly, identification through functional form and, secondly, using variables that are actually
insignificant in an OLS attainment equation (for each gender separately) but significant in the
enrolment equation. In the latter case, there is no a priori theoretical justification but rather an
empirical justification for the chosen exclusion restrictions. We estimate these two models
and compare them with the simple OLS equation of schooling attainment. The selectivity
corrected equations of years of education, conditional on enrolment, are presented in Table 6,
using both methods of identification of lambda. Both show that selectivity into schooling is
unimportant for women but is important for men, with a significant negative coefficient. The
negative sign is surprising as we had expected that those who are not likely to enrol but did
enrol (i.e. non-participant types) must be those with higher ambition and/or motivation -
qualities which will be positively related to EDYRS. There are two ways to explain this rather
counter-intuitive result. One is that ambition and motivation are not sufficient to sustain
11
disadvantaged people in education, i.e. well motivated males who live in poverty are forced to
relinquish education while the well-off males with both low and high motivation continue in
schooling. A second, more plausible explanation is that in the absence of a plausible
identification strategy, one cannot trust the sign and size of the coefficient on lambda.
Table 7 presents the OLS equation of educational attainment. It is clear from a
comparison of the coefficients in Tables 6 and 7 that the exclusion of lambda makes little
difference to the estimated parameters of the attainment function. Even a casual inspection
confirms little change in the estimated coefficients. In virtually no case has the coefficient
moved by more than 1 standard error between the selectivity-corrected and OLS
specifications. A series of wald tests confirm that not a single coefficient difference between
Tables 6 and 7 is statistically significant at even the 10 per cent significance level7. Given the
imperfect selectivity correction strategy and, more importantly, given that correction for
selectivity (such as was possible) does not significantly alter the estimated parameters of the
EDYRS model, we use the simple OLS equation of EDYRS in Table 7 as the preferred
specification in the rest of the paper, though we will compare the effect of using OLS versus
selectivity corrected specifications on the decomposition exercise in the next section.
The explanatory power of the female EDYRS model in Table 7 is substantially higher
than that of the male model. This suggests that for women, the combined role of personal
endowments, measured household characteristics, and variables representing parental 'taste for
education' is relatively more important in determining achievement than for males.
The base category for the AGE variables is Age 39-45 years. The negative coefficients
on the variable Age 23-30 in both male and female regressions appears to be the result of
sample selection since 8 per cent of males and 5 per cent of females in that age-group have not
finished their education and so are excluded from our analysis, which takes into account only
those 23-45 year olds who have completed their education. Thus, the age cohort of 23-30
12
year olds excludes persons with high values of EDYRS but includes those who stopped
studying early and have low values of EDYRS.
The coefficient on Age 31-38 shows a weak improvement in EDYRS for females over
time but, surprisingly, there is a weak deterioration in EDYRS for men and this effect is
significant at the 10 per cent level. We have not been able to find any plausible explanation for
this finding except the possibili ty that widespread graduate unemployment in India [Blaug,
Layard, and Woodhall 1969] and the increase in vocational courses has indeed lessened the
average years of general education acquired by males. Another possible explanation may be if
universities have become more selective in their student intake over time and reversed their
former 'open-door' admissions policy, thereby adversely affecting the access to higher
education among younger cohorts8.
Being MUSLIM has a strong negative effect on years of education acquired by men.
This phenomenon may reflect both lower taste for education among, and employer
discrimination against, Muslims in India. Muzammil [1994, p6] states that the perpetuation of
ancestral manual occupations in most Muslim families implies that little effort is made by
Muslims for the better education of their children (lower taste argument). He also points out
(p8) that Muslims are the subject of employment discrimination in both the government sector
and particularly severely, the private sector (discrimination argument). Such discrimination is
likely to lower the expected rate of return to education for Muslims and cause them to desire
fewer years of schooling.
The fact that Muslim females are not significantly behind non-Muslim females in
educational attainment (conditional on enrolment) suggests that most of the educational
discrimination against girls by Muslim parents occurs at the stage of enrolment (see enrolment
function in table 4); those Muslim girls whose parents are liberal enough to enrol them in
schooling have educational aspirations that are not significantly below those of their non-
Muslim counterparts.
13
Conditional on enrolment, low and backward caste (LOWCASTE) men attain the same
years of education as high caste men, ceteris paribus. However, low caste women lag behind
high caste women. This appears to suggest that while lowcaste men take advantage of the
'reservation' measures the government has put in place for improving the representation of low
castes at different levels of education (particularly higher education), low caste women do not.
Another explanation is that mandatory 'reservation' for low caste persons in highly-paid public
sector jobs since independence has given lowcaste men an economic incentive to enhance their
education but that for lowcaste women, this incentive has not been sufficient to induce them to
discard their traditional educational conservatism.
The effect of LOWCASTE on educational attainment for the two genders is consistent
with the finding in Kingdon [1998a] that there is greater wage discrimination against low
castes in the female labour market than in the male.
Parental wealth is a highly important determinant of educational attainment for both
sexes though the effect is weaker for males, both quantitatively and qualitatively. Different
measures of the home educational environment (BKHOME and PANEWS) are significant for
grade attainment of men and women.
From the point of view of analysing the intra-household allocation of education,
perhaps the most interesting result of the analysis is the effect of the parental education
variables. Mother's education is very important to girls' schooling but not to boys'; Father's
education is important to both boys' and girls’ schooling. This result has policy implications
for reducing gender inequality in education and we discuss this issue later.
Another parental variable which has a large impact on girls' education but not on boys'
is parents' opinion about the importance of girls' education (EDEQUAL)9. Girls whose parents
believed in gender equality in education attained very significantly more education than other
girls. This is similar to the findings in Drèze and Kingdon [2001] where a similar attitudinal
variable was employed. Interestingly, this variable has no effect on educational attainment for
14
boys in Table 6, unlike in Drèze and Kingdon’s paper where even sons’ education benefited
from having parents who believed in gender equali ty in education.
Father worked in a white collar occupation (PAWHITE) is of consequence to children' s
educational attainment in both male and female samples. However, surprisingly, a working
mother (MAWORKED) exerts a strongly negative influence only on boys' EDYRS. This is in
contrast to the effect of MAWORKED in the enrolment function where a "working mother"
significantly lowered the probabili ty of enrolment for girls (t=1.70) but not for boys. In other
words, most of the negative influence of "mother worked" on girls' education occurs at the
stage of enrolment and on boys' education at the post-enrolment stage.
The age at marriage variables are highly important in explaining years of education
acquired for both sexes, though the effect is particularly powerful for females, in both
quantitative and qualitative terms. The base category is ‘married at age 21 or above’. Those
who married very young (at <=17 years old) had, on average, about 2.3 years less education
than those who married late. Persons who married between ages 18 and 20 years old likewise
had significantly less education than those who married after 2010.
Observe that measures of abili ty (SRAVEN categorised into low, medium, and high
abili ty) are very significant determinants of EDYRS for males as well as females, although their
quantitative effect is bigger in the male equation. The base category for the abili ty variables is
the low abili ty category (ABILLOW, or SRAVEN score of <=10). Medium abili ty and high
abili ty individuals have decidedly higher education than individuals with low measured abili ty,
as might be expected a priori. After ' age at marriage' , abili ty is the most important determinant
of EDYRS in the female equation; in the male equation, it is the most important, with high
abili ty men gaining, on average, nearly 3 years' more schooling than low abili ty men, ceteris
paribus11.
The variable REPEAT also represents aspects of abili ty: the less able are more likely to
fail and repeat classes. REPEAT exerts a potent negative effect on EDYRS in the male
15
equation and a more modest, though still weakly significant, influence on EDYRS in the female
equation.
The quality of primary school attended (SCQUAL) affects educational attainment
positively and significantly for both the sexes12. This suggests that after controlli ng for
parental education, occupation and wealth, and for social, religious, and health-related
influences, persons who drop-out of education prematurely are the ones who faced poor
quality primary schooling (i.e. attended poorly resourced and equipped schools). This implies
that the phenomenon of early dropping out, which represents large-scale wastage in Indian
education, is not only a demand-side problem: it is also importantly a supply side issue, with
low-quality schools faili ng to retain students in education. It suggests that the level of
dropping-out can be mitigated by upgrading the quality of primary schools.
5. How much of the gender gap in educational attainment can be explained?
Are males and females with comparable characteristics equally likely to enrol and to
attain similar years of schooling? What factors contribute most to the male-female gap in
schooling? To answer these questions, we decompose the gross gender difference in
enrolment probabili ty and in mean years of education into the component 'explained' by
differences in characteristics between the two groups, and the 'unexplained' component. The
unexplained component is conventionally regarded in the literature as the extent of sex
discrimination [Oaxaca, 1973; Blinder, 1973]. However, to the extent that lower allocations
of education to daughters reflect poorer economic returns to girls’ education, it may be argued
that such lower allocations are not discriminatory but, rather, a rational economic response by
parents. Thus, we prefer to use the term ‘differential treatment’ rather than discrimination. We
use the Blinder-Oaxaca technique for measuring differential treatment when two groups differ
in their characteristics and differ in the structure relating these characteristics to educational
attainment.
16
Assume that the mean years of schooling of females (f) is S f and that of males (m) is
Sm . Mean years of schooling is determined by
S b Xi i i=�
i = f,m
where X is the vector of the mean values of characteristics and �
b is the vector of estimated
coefficients of the educational attainment function.
The mean years of schooling of men, if they were educated according to women's
educational attainment function, would be the dot product �
b Xf m . The total gender difference
(T) in mean years of schooling can be divided into the part explained (E) by the different
personal characteristics of men and women and the part unexplained (D), reflecting differences
in the structure of the educational attainment function, that is, differences in �
b for the two
sexes.
T S Sm f= −
T b X b Xm m f f= −� �
{ } { }T X b b b X Xm m f f m f= − + −(� �
)�
( )
T = D + E
This can be referred to as standardising by male means. Similarly, the estimation of the years
of schooling of women if they face men's educational attainment function permits the
decomposition into D + E as follows:
T S Sm f= −
T b X b Xm m f f= −� �
{ } { }T X b b b X Xf m f m m f= − + −(� �
)�
( )
T = D + E
This can be referred to as standardising by female means. Since the decomposition may
be sensitive to the choice of index (standardising according to male means or female means),
ideally both decompositions should be carried out.
17
Following the procedure just outlined, Table 8 presents the decomposition of the gross
average gender difference in EDYRS into 2 components, i.e. the component that is due to
gender differences in inputs (different means of characteristics for males and females) and the
component that is due to the difference in coefficients in the male and female EDYRS
production functions. We do this for each variable separately to examine the contribution of
each individual variable to the gender difference in schooling. The figures in Table 8 are those
obtained by standardising with female means. The standardisation with male means gives quite
a similar decomposition.
Columns 2 and 3 of Table 8 show that there are significant differences in the mean
characteristics of men and women. Most of the parental and household traits generally favour
females: for example, ever-enrolled females have significantly higher values of parental
education, occupation and wealth and lower values of lowcaste than ever-enrolled males.
These differences in mean parental attributes probably reflect the sample selection effects
mentioned in the discussion of the enrolment function: While boys from uneducated and poor
backgrounds may enrol in school, girls only from relatively better-off and more educated
backgrounds will enrol. However, personal variables such as age-at-marriage and abili ty
favour males: a significantly smaller proportion of men than women are in the early-age-
marriage category and in the low-abili ty category, though a significantly higher proportion of
males than females repeat classes. Thus, while parental and household variables generally
favour females, personal characteristics generally favour males.
Collectively, their higher values of household background, parental, and school quality
variables imply a 0.508 years of EDYRS advantage for females, while the remaining (personal)
variables imply a 0.680 years of EDYRS disadvantage for females. Thus, taken as a whole,
girls’ inferior characteristics account for 0.172 years of their total EDYRS disadvantage vis-a-
vis boys. The remaining female disadvantage in educational attainment is explained by the
different production functions of EDYRS for men and women. Thus, for example, females’
18
school attainment responds less to ‘ father in white collar occupation’ and to ‘having high
abili ty’ (ABILHIGH), than does males’ schooling attainment. The coefficients on the age-at-
marriage variables also favour males, reinforcing their advantage in mean values of age-at-
marriage13.
In summary, the decomposition analysis suggests that, of the total gender difference in
EDYRS (0.684 years), only 0.172 years or about 25 per cent is explained by girls’ inferior
attributes. The remaining difference in EDYRS of 0.512 years (or about 75 per cent of the
difference) is due to the different production function of EDYRS faced by girls and boys. A
decomposition exercise using coefficients of the sample selectivity corrected attainment
equations yields quite similar results14.
Clearly, the large unexplained portion of the gender gap in schooling is due to
unobserved factors which may include parental attitudes (such as son preference) and/or lower
expected economic returns to girls schooling than boys.
In principle, it should be possible to examine the effect of labour market returns of men
and women on the amount of education boys and girls are allocated within a family. One way
would be to examine the effect of male and female employment rates (proxies for labour
market returns) on the educational attainment of males and females. This would be similar to
the strategy in Rosenzweig and Schultz [1982] where the authors investigate the effect of male
and female labour market participation rates on gender differences in resource allocation and
child survival. In practice, there are difficulties in doing so convincingly with the data in
hand15. However, I examine this issue in another paper using an alternative strategy, namely
to estimate gender-specific rates of return to education [Kingdon, 1998a] using the same
dataset as the one used here. This shows that women have significantly lower returns to
education than men. If the labour market changes only slowly, as appears to be the case in
India16, then today’s gender difference in labour market returns is likely to be similar to that in
the previous generation, and it would have shaped the expectations of returns to education for
19
our sample when their parents were making schooling decisions for them. This provides
support for the explanation that parental investment motives underlie the lower allocation of
schooling attainment to girls, though it does not rule out the existence of a son-preference
motive as well .
6. Conclusions
Both the gender difference in enrolment rate and the gender difference in average years
of education attained are statistically significant in the urban Indian sample used here, even
after controlli ng for a range of pertinent characteristics. The most important factors influencing
educational attainment of women are parental background, wealth, and opinions, individual
abili ty, age-at-marriage, and the quality of primary school attended. The analysis suggests that
75 per cent of the gender disparity in EDYRS is unexplained, only 25 per cent being accounted
for by girls’ inferior education-enhancing characteristics.
Both low and backward caste men and women and Muslim men and women have
lower enrolment than others, ceteris paribus. Moreover, low and backward caste women and
Muslim men have significantly lower educational attainment (conditional on enrolment) than
their high caste and non-Muslim counterparts, ceteris paribus. While different preferences may
explain part of this, labour market discrimination against these social groups also appears to be
responsible, at least to some extent.
The results show that the educational ‘cost’ of a working mother, or of poverty - where
a working mother signifies poverty - falls disproportionately on daughters. This probably
reflects parental beliefs about the gender division of labour: if a daughter is envisaged to be a
housewife for most of her adult life, her enrolment in school can be sacrificed (if necessitated
by mother’s working or by poverty) without the same compunction as sacrificing a son’s
schooling. Poverty bears particularly heavily against girls’ education: PAWEAL is highly
20
important in the female educational attainment equation but not so in the male educational
attainment function.
Parents’ differential treatment of sons and daughters in education could be the result of
lower economic returns to girls’ education than to boys. Kingdon [1998a] and the studies
cited therein find this to be true for India. This finding is also supported by Behrman, Foster,
Rosenzweig and Vashishtha [2000] who report that in rural India in the early green revolution
period there were no direct economic returns to women’s schooling because women were not
involved in occupations where education was rewarded (though women’s schooling did have
benefits in terms of the home teaching of children). However, other possible explanations of
parents’ differential treatment of sons and daughters in education are that (i) differential
treatment partly reflects entrenched beliefs about the gender division of labour [Drèze and Sen
1995]; (ii) it reflects an asymmetry in parental incentives to educate girls and boys due to son
preference; (iii ) even if the economic returns to education were the same for boys and girls,
parents may value only that part of the return to a child’s education that accrues to them
personally (and the returns to a daughter’s education are often reaped by her in-laws); (iv) due
to the higher costs (opportunity costs and/or direct costs) of educating girls. These contrasts
in parental incentives have strong implications for public policy: parental motivation for male
education is high. For female education, however, it is important to address the conservatism
of social attitudes and parental inertia. It is also important to reduce wage and job sex-
discrimination in the labour market and boost women’s economic returns to education in order
to improve girls’ incentives to acquire schooling.
1 See, for example, evidence cited in King and Hill [1993], Subbarao and Raney [1995], and Drèze and Murthi [2001]. 2 The GDI attempts to capture achievement through the same set of basic capabiliti es included in the Human Development Index - li fe expectancy, educational attainment, and income -but adjusts the HDI for gender inequalit y. 3 I have come across only three studies that examine statisticall y the determinants of enrolment and schooling attainment for girls and boys in India [Duraisamy, 1992; Sipahimalani, 2001; Pal, 2001]. However, both Duraisamy and Sipahimalani papers fit enrolment and educational attainment functions separately for girls and boys (without decomposing the gender gaps), not a pooled model with a gender dummy and gender
21
interactions. Pal examines the gender gap in current enrolment (but not in educational at tainment) among 5-15 year olds in rural west Bengal. 4 Under alternative assumptions about the distribution of the error term in equation (2), the logit model can also be employed to predict probabiliti es of work force participation; however, we intend to use the probit model which is the discrete choice model most used in applications of the Heckman correction described in the next section. 5 Banerjee and Knight [1985], Chinnappan [1992], and Kingdon [1998a] all find that there is significant caste discrimination in the Indian labour market. However, using the present data, Kingdon [1998a] did not find statisticall y significant wage discrimination against Muslims, though the coeff icients on the MUSLIM dummies in her earnings functions were invariably negative. 6 Muzammil [1989, p 154-60] finds that in terms of 1970-71 constant prices, annual tuition fee in elementary education was meagre - Rs. 0.88 in 1950-51 and Rs. 1.48 in 1979-80. Tuition fees in primary schools was abolished for girls in 1967 and for boys in 1989. 7 For example, the 2χ value of the difference between the coeff icient on MUSLIM in tables 6 and 7 for men is
0.705. The criti cal value of 2χ (1) at the 5% (10%) level is 3.84 (2.71). Thus the null hypothesis that
selecols bb ˆˆ = cannot be rejected. This is not surprising since the change in the size of the coeff icient { 1.465 –
0.994=0.471) is only just over 1 standard error apart in the selectivity-corrected and OLS male equations. 8 This may not be a plausible explanation because the effect does not appear to apply to females. It may also not be plausible because those who fail to be selected for admission into a college or university do have the option of taking degree exams 'privately', that is, without being regular students in a college or university, though it is arguable that private exams are available only in certain subjects and not across all subjects. 9 The inclusion of attitudinal variables such as EDEQUAL is not standard in applied economic research, which typicall y includes only directly observable arguments. The main reason for being sceptical of attitudinal variables is because they may be jointly determined with the dependent variable (for example, what parents think about the importance of their daughters’ education compared with the education of their sons). However, the potential endogeniety of the parental attitude variable EDEQUAL was limited by asking a very general question, not directly related with the respondents’ circumstances: We asked what parents thought about the relative importance of education for males and females in general. Such attitudes may be determined earlier and by much wider considerations than peoples’ attitudes to their own sons’ and daughters’ education. 10 While 'age at marriage' variables may be endogenous to EDYRS, e.g. for some individuals, 'age at marriage' may be determined partly by EDYRS, evidence from the data collection suggests otherwise, at least for girls, many of whom reported 'I got married' as the reason for discontinuing education. However, this was rarely a reason given by boys for dropping out of education prematurely. Drèze and Sen [1995, p15] state that in India considerations involved in educational decisions are radicall y different for girls and boys: In the case of boys, economic reasons are strong. In the case of girls, they are more guided by (exogenous) marriage practices and by the gender division of labour. We were unable to deal with any potential endogeniety of the ‘age at marriage’ variables due to the lack of suitable instruments. Consequently, the results should be interpreted as correlational rather than causal, particularly in the male equation. Results without ‘age-at-marriage’ variables are given in Appendix 3 and they show that there is littl e change in the probit results. For example, between the ‘age-at-marriage’ inclusive and exclusive specifications, not a single coeff icient has changed by more than one standard error. So the endogeneity of these variables, if it exists, does not bias the other coeff icients in Table 7. 11 Scores on the Raven's test have been used as a measure of innate abilit y in a number of well -received empirical analyses of education [for example, Glewwe 1996; Boissiere, Knight, and Sabot 1985, inter alia]. Glewwe [1996] explains that while the Raven’s measure of abstract thinking ability may reflect other characteristics of the individual [see Raven et. al., 1984], it should also be well correlated with innate abilit y. 12 Kingdon [1996a,b] finds that schooling quality signif icantly affects another measure of educational success, namely student achievement. 13 Caution must be exercised in decomposing of the effect of ‘age-at-marriage’ on educational attainment. While we describe the lower mean ‘age-at-marriage’ of women then men as an inferior characteristic of women, in fact it is, in a sense, a manifestation of parental discrimination against girls. Thus, our calculation of the fraction of the gender gap in EDYRS that is discriminatory may be an underestimate. 14 Of the total gender difference in EDYRS (0.684), only 0.206 (or about 30%) is explained by girls’ inferior attributes. The remaining difference in EDYRS (0.478 or about 70%) is due to the different production function of EDYRS faced by girls and boys. Thus, compared to the decomposition using the OLS specification, the decomposition using the selectivity corrected specification yields a somewhat lower estimate of the unexplained component (70% instead of 75% of the total gender difference). Many studies note that sample
22
selection correction lowers the estimated amount of ‘discrimination’ by increasing the weight attached to the depreciation effect of female non-participation in the wage work force [for example, see Dolton and Makepeace 1986, p336; and Choudhury 1993, p337]. 15 Men and women’s expected labour market returns (as captured by male and female employment rates) are endogenous and must therefore be instrumented in the schooli ng attainment equation. Given that our d ata are from a single city and there was no community questionnaire, we have no good instruments for employment rates such as exogenous vill age or district labour market characteristics, unlike Rosenzweig and Schultz [ 1982]. 16 There is some support for thi s idea. Rosenzweig and Schultz assume that “ in a stable, slowly developing society such as in rural India, parents can reasonably expect that conditions which they face as adults will also condition in a similar way the behavior of their offspring” . While this may be thought to be true only for rural India, it seems true for urban India as well . For example, Kundu [ 1997, p. 442] reports that between 1977 and 1993, the labour force participation rate of rural and urban women remained reasonably stable at about 52-54% and 23-25% respectively.
1
References Banerjee, B and J. B. Knight, 1985, ‘Caste discrimination in the Indian labour market’ , Journal of
Development Economics, Vol. 17, pp.277-307. Behrman, J., A. Foster, M. Rosenzweig, and P. Vashishtha, 1999, ‘Women’s Schooling, Home Teaching, and
Economic Growth’ , Journal of Politi cal Economy, Vol. 7, No. 4, pp. 682-714. Blaug, M., R. Layard and M. Woodhall , 1969, The Causes of Graduate Unemployment in India, London:
Penguin Press. Blinder, Alan, 1973, ‘Wage Discrimination: Reduced Form and Structural Estimates’ , Journal of Human
Resources, Vol. 8, No. 4, Fall , pp. 436-55. Boissiere, Maurice, J. B. Knight, and R. H. Sabot, 1985, ‘Earnings, schooling, abili ty and cognitive skill s’ ,
American Economic Review, Vol. 75, pp.1016-1030. Chinnappan, G., 1992, ‘Significance of human capital approach to caste inequali ty’ , in Kothari, V. (ed.),
Issues in Human Capital Theory and Human Resource Development Policy , New Delhi: Himalaya Publishing House.
Choudhury, Sharmila, 1993, ‘Reassessing the Male-Female Wage Differential: A Fixed Effects Approach’ ,
Southern Economic Journal, Vol. 60, No. 2, pp.327-40. Dolton, P. and G. Makepeace, 1986, ‘Sample Selection and Male-Female Earnings Differentials in the
Graduate Labour Market’ , Oxford Economic Papers, Vol. 38, No.2, pp.317-41. Drèze, Jean and Geeta Kingdon, 2001, ‘Schooling Participation in Rural India’ , Review of Development
Economics, Vol. 5, No. 2, pp.1-26. Drèze, Jean and M. Murthi, 2001, ‘Fertili ty, Education, and Development: Evidence from India’ , Population
and Development Review, Vol. 27, No.1, pp.33-63. Drèze, Jean and A. Sen, 1995, ‘Basic education as a poli tical issue’ , chapter in India: Economic Development
and Social Opportunity, Delhi: Oxford University Press. Duraisamy, P., 1992, ‘Gender, Intrafamily allocation of resources and child schooling in India’ , Discussion
Paper No. 667, Economic Growth Center, Yale University, January. Glewwe, Paul, 1996, ‘The Relevance of Standard Estimates of Rates of Return to Schooling for Education
Policy: A Critical Assessment’ , Journal of Development Economics, Vol. 51, No. 2, pp. 267-90. Greene, W.H., 1993, Econometric Analysis, 2nd Edition, New York: Macmill an. Heckman, James, 1979, ‘Sample selection bias as a specification error’ , Econometrica, Vol. 47, pp.153-161. King, E. and M. Hill , 1993, Women’s education in developing countries, Washington D.C.: John Hopkins
Press for the World Bank. Kingdon, Geeta Gandhi, 1996a, ‘The quali ty and eff iciency of private and public education: A case-study of
urban India’ , Oxford Bulletin of Economics and Statistics, Vol. 58, No. 1, pp.57-82. Kingdon, Geeta Gandhi, 1996b, ‘Student Achievement and Teacher Pay’ , Development Economics Discussion
Paper No. 74, STICERD, London School of Economics.
2
Kingdon, Geeta Gandhi, 1998a, ‘Does the Labour Market Explain Lower Female Schooling in India?’ ,
Journal of Development Studies, Vol. 35, No. 1, pp. 39-65. Kingdon, Geeta Gandhi, 1998b, Education of Females in India: Determinants and Economic Consequences,
McNamara Fellowships Off ice of the World Bank, Washington D.C. Kundu, Amitabh, 1997, ‘Trends and Pattern of Female Employment in India: A Case of Organised
Informalisation’ , Indian Journal of Labour Economics; Vol. 40, No. 3, pp. 439-51. Maddala, G. S., 1989, Introduction to Econometrics, New York: Macmill an. Murthi, M., A. Guio, and J. Drèze, 1997, ‘Mortali ty, fertili ty, and gender bias in India: A district level
analysis’ , Population and Development Review, Vol. 21, pp.745-782. Muzammil, Mohd., 1989, Financing of Education, New Delhi: Ashish Publishing House. Muzammil, Mohd., 1994, ‘Education and employment: A study of Muslims in UP (India)’ , paper presented at
the South Asia Visiting Scholar’s Programme, Queen Elizabeth House, University of Oxford. Oaxaca, R., 1973, ‘Male-female differentials in urban labour markets’ , International Economic Review, Vol.
3, pp.603-709. Pal, Sarmistha, 2001, ‘How Much of the Gender Differences in Child School Enrolment Can be Explained:
Further Evidence from India’ , mimeo, Cardiff Business School. Probe Team, 1999, Public Report on Basic Education in India, New Delhi: Oxford University Press. Raven, J.C., J.H. Court, and J. Raven, 1984, Manual for Raven’s Progressive Matrices and Vocabulary
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Schultz, T. P., 1993, ‘Returns to women' s education’ chapter 2 in King, E and M Hill (eds.) Women’s
education in developing countries, Washington D.C.: Johns Hopkins Press for the World Bank. Shariff , A., 1999, India: Human Development Report, New Delhi: Oxford University Press. Sipahimalani, Vandana, 2001, ‘Education in the Rural Indian Household: The Impact of Household and
School Characteristics on Gender Differences’ , mimeo, Yale University. Subbarao, K. and L. Raney, 1995, ‘Social Gains from Female Education: A Cross-National Study’ , Economic
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14, No. 4, pp.1-12. UNDP, 1996, Human Development Report, United Nations Development Programme, New York.
3
Table 1
Raw gender difference in enrolment and average years of education attained
Persons aged 23-45 Persons aged >=23*
Enrolment Years of education Enrolment Years of education
t-value for the difference 8.66 8.68 3.16 12.74 13.35 5.32
Note: * No upper age limit . This implies a sample of older persons. The sub-sample of persons aged 23-45 years old is the ‘younger’ sample. Non-enrolees are assigned zero years of education.
4
Table 2 Definitions of variables used in the enrolment and educational attainment functions
Variable Description
AGE 23-30 Dummy for persons between ages 23 and 30 years old
AGE 31-38 Dummy for persons between ages 31 and 38 years old
AGE 39-45 Dummy for persons between ages 39 and 45 years old
MUSLIM Religion Muslim? yes=1, no=0
LOWCASTE Belongs to the low or backward caste? yes=1, no=0
ACNSIB Number of siblings when a child
PAWEAL Index of parental wealth, based on assets owned by the respondent's family, when a child
PAWEALSQ Square of PAWEAL
BKHOME Index of number of books in parents' household, when a child. Takes values from 1 to 5,
with 1 representing less than 25 books and 5 representing more than 100 books.
PANEWS Whether either parent read a dail y newspaper when respondent was a child
MAEDYRS Mother's education in years
PAEDYRS Father's education in years
PAEDYRSQ Square of PAEDYRS
ACHEAL Index of respondent's health when a child. Takes values from 1 to 4, with 1 representing
very good health and 4 representing very bad health.
EDEQUAL Do parents think that girls' education is equally important as that of boys? yes=1, no=0
PAWHITE Father in a white collar occupation? yes=1, no=0
MAWORKED Did mother ever work in an income-generating activity? yes=1, no=0
NEVMARR Never married? yes=1, no=0
MARRAGE <=17 Age at marriage less than or equal to 17 years old
MARRAGE 18-20 Age at marriage between 18 and 20 years old, inclusive
MARRAGE >20 Age at marriage greater than 20 years old
SRAVEN* Score on the Raven's Progressive Matrices test of abilit y
ABILMISS Score on the Raven's test missing
ABILLOW Low abilit y, i.e. Raven's score less than or equal to 10
ABILMED Medium abilit y, i.e. Raven's score between 11 and 18, inclusive
ABILHIGH High abilit y, i.e. Raven's score greater than 18
REPEAT Ever repeated a class at school because of faili ng? yes=1, no=0
SCQUAL Index of qualit y of the primary school attended. Takes values between 0 and 10
depending on the number of resources that the respondent's primary school had.
Note: Variables ACNSIB, PAWEAL, BKHOME, and PANEWS are retrospective variables. That is, they represent information about the respondent when she/he was a child of 14 years old or younger. * Only sets A and C of the Raven's abilit y test were administered so that the minimum and maximum scores on the Raven's test in our data are 0 and 24 respectively.
5
Table 3a
Descriptive statistics for FEMALES aged 23-45 years old
SCQUAL - - 6.113 1.83 - - N 210 645 855 Note: The variables with superscript * are 0/1 variables, so that their means represent the proportion of ones in the sample. Thus, the mean of the variable 'AGE 23-30' of 0.324 in the non-enrolled sub-sample signifies that 32.4 per cent of all non-enrolled women in the 23-45 year age group fall within ages 23 and 30 years.
6
Table 3b
Descriptive statistics for MALES aged 23-45 years old
SCQUAL - - 5.898 1.80 - - LAMBDA - - 0.108 0.21 N 82 783 865 Note: The variables with superscript * are 0/1 variables, so that their means represent the proportion of ones in the sample.
7
Table 4 Binary probit model of enrolment
Variables Females Males
coefficient t-value coefficient t-value
Intercept - 1.465 - 4.21 *** 1.102 2.57 ***
AGE 23-30 0.271 1.59 - 0.296 - 1.38
AGE 31-38 0.308 1.81 * - 0.278 - 1.30
MUSLIM - 0.786 - 4.40 *** - 1.469 - 5.84 ***
LOWCASTE - 0.329 - 2.10 ** - 0.758 - 3.56 ***
ACNSIB 0.042 1.21 0.036 0.86
PAWEAL 0.256 5.81 *** 0.282 5.47 ***
PAWEALSQ - 0.005 - 2.42 *** - 0.007 - 5.06 ***
PANEWS 0.089 0.41 - 0.207 - 0.68
MAEDYRS 0.126 2.36 *** 0.146 1.66 *
PAEDYRS 0.237 5.29 *** 0.128 1.63
PAEDYRSQ - 0.011 - 2.89 *** - 0.001 - 0.15
ACHEAL - 0.029 - 0.14 - 0.524 - 2.01 **
EDEQUAL 0.216 1.49 - 0.030 - 0.17
PAWHITE 0.377 1.48 0.065 0.16
MAWORKED - 0.393 - 1.70 * - 0.120 - 0.51
N 848 850
Log li kelihood - 213.72 - 149.39
Restricted log li kelihood - 471.29 - 265.17
McFadden's Pseudo R2 0.547 0.437
% 0s correctly predicted 77.3% 30.0%
% 1s correctly predicted 92.5% 98.3%
Note: McFadden's psuedo R2 is calculated as 1 - (Ln L/Ln L0 ), where Ln L is the maximum of the log li kelihood function and Ln L0 is the restricted log li kelihood, that is when the model is estimated with just the constant term [Maddala 1989]. The base category for age is the age-group 39-45 year olds. *** , ** , and * represent signif icance at the 1, 5, and 10 per cent levels respect ively, in all Tables.
Table 5 Time spent in domestic chores, hours per day
(children aged 6-14 years old)
Mother works N Hours per day spent on domestic chores By daughters By sons Yes 195 2.08 0.54 No 852 1.38 0.46 % difference for children with working mothers
50%
17%
1
Tab
le 6
Se
lect
ivit
y C
orre
cted
Edu
cati
onal
att
ainm
ent
func
tion
s, b
y se
x
Id
enti
fica
tion
of
lam
bda
by f
unct
iona
l for
m
Id
enti
fica
tion
of
lam
bda
base
d on
em
piri
cally
jus
tifi
able
ex
clus
ion
rest
rict
ions
Fem
ales
M
ales
F
emal
es
Mal
es
C
oeff
T
Coe
ff
T
C
oeff
T
Coe
ff
T
In
terc
ept
6.07
2 6.
73
***
9.
550
10.5
9 **
*
6.23
6 7.
12
***
9.
027
12.4
9 **
*
AG
E 2
3-30
-0
.403
-1
.49
-1
.604
-4
.89
***
-1.5
89
-4.8
9 **
*
AG
E 3
1-38
0.
330
1.24
-0.5
26
-1.7
7 *
-0
.522
-1
.77
* M
USL
IM
-0.3
27
-0.9
7
-1.0
07
-2.4
8 **
*
-0
.994
-2
.53
***
LO
WC
AST
E
-0.4
80
-1.7
7 *
0.00
4 0.
02
-0
.481
-1
.79
*
AC
NSI
B
-0.0
44
-0.8
8
-0.0
22
-0.3
9
-0.0
47
-0.9
3
-0.0
20
-0.3
5
PA
WE
AL
0.18
3 3.
23
***
0.
079
1.46
0.15
4 2.
76
***
0.
078
1.46
PA
WE
ALS
Q
-0.0
04
-2.8
4 **
*
-0.0
01
-0.8
6
-0.0
04
-2.5
2 **
*
-0.0
01
-0.8
6
BK
HO
ME
0.
085
0.64
0.30
3 1.
99
**
0.14
4 1.
08
0.
309
2.04
**
P
AN
EW
S 0.
714
2.72
**
*
-0.0
88
-0.2
8
0.68
0 2.
59
***
-0
.109
-0
.35
M
AE
DYR
S 0.
103
3.69
**
*
0.05
1 1.
45
0.
100
3.64
**
*
0.05
2 1.
52
P
AE
DYR
S 0.
139
4.22
**
*
0.12
9 3.
55
***
0.
138
4.18
**
*
0.13
0 3.
59
***
A
CH
EA
L -0
.727
-1
.73
* -0
.456
-0
.96
-0
.714
-1
.69
*
ED
EQ
UA
L 1.
029
4.23
**
*
-0.1
30
-0.4
6
1.04
1 4.
25
***
-0
.148
-0
.53
P
AW
HIT
E
0.57
1 2.
27
**
0.82
1 2.
48
***
0.
551
2.18
**
0.
794
2.44
**
*
MA
WO
RK
ED
-0
.156
-0
.40
-1
.155
-3
.06
***
-1.1
49
-3.1
1 **
*
NE
VM
AR
R
2.43
6 5.
41
***
1.
783
2.63
**
*
2.20
6 4.
95
***
1.
763
2.55
**
*
MA
RR
AG
E <
=17
-2
.404
-8
.09
***
-2
.311
-3
.74
***
-2
.431
-8
.16
***
-2
.316
-3
.64
***
M
AR
RA
GE
18-
20
-1.4
69
-5.6
9 **
*
-0.6
93
-2.1
7 **
-1
.523
-5
.86
***
-0
.696
-2
.10
**
AB
ILM
ISS
0.78
2 2.
73
***
0.
776
2.04
**
0.
781
2.70
**
*
0.81
9 2.
12
**
AB
ILM
ED
1.
139
4.53
**
*
1.42
3 3.
63
***
1.
142
4.51
**
*
1.44
0 3.
60
***
A
BIL
HIG
H
1.85
1 4.
61
***
2.
982
5.98
**
*
1.89
0 4.
68
***
3.
014
6.00
**
*
RE
PE
AT
-0.6
12
-1.9
8 *
-1.2
76
-4.9
7 **
*
-0.6
22
-2.0
2 **
-1
.278
-4
.89
***
SC
QU
AL
0.43
0 6.
24
***
0.
299
3.81
**
*
0.43
0 6.
22
***
0.
306
3.82
**
*
LAM
BD
A
-0.2
04
-0.4
2
-2.1
74
-2.6
3 **
*
-0.4
52
-0.9
8
-2.2
29
-2.8
7 **
*
R2
0.
6482
0.43
69
0.
6446
0.43
77
N
641
769
641
769
Mea
n o
f de
p va
r
11.6
1 12
.29
11.6
1 12
.29
Not
e:
The
rep
orte
d t-
valu
es a
re b
ased
on
sta
ndar
d er
rors
that
are
cor
rect
ed f
or t
he p
arti
cula
r fo
rm o
f he
tero
sked
asti
city
int
rodu
ced
whe
n t
he la
mbd
a te
rm (
inve
rse
of t
he M
ill’s
ra
tio)
is in
clud
ed in
the
estim
atio
n. T
his
is a
chie
ved
by u
sin
g th
e SE
LE
CT
com
man
d in
LIM
DE
P.
For
the
seco
nd s
et o
f es
timat
es, t
he e
xclu
sion
res
tric
tion
s ar
e as
foll
ows.
For
men
: LO
WC
AST
E a
nd A
CH
EA
L;
For w
omen
: A
GE
, AG
ES
Q, M
USL
IM, a
nd M
AW
OR
KE
D.
1
Table 7 OLS Educational attainment functions, by sex
Variables Females Males
Coefficient t-value Coefficient t-value
Intercept 5.889 7.33 *** 9.015 10.17 ***
AGE 23-30 -0.388 -1.42 -1.694 -5.21 ***
AGE 31-38 0.348 1.30 -0.570 -1.93 *
MUSLIM -0.369 -1.13 -1.465 -4.02 ***
LOWCASTE -0.506 -1.88 * -0.249 -0.90
ACNSIB -0.041 -0.80 -0.007 -0.12
PAWEAL 0.192 3.58 *** 0.126 2.46 ***
PAWEALSQ -0.005 -3.12 *** -0.002 -1.78 *
BKHOME 0.076 0.56 0.269 1.77 *
PANEWS 0.724 2.72 *** -0.114 -0.37
MAEDYRS 0.103 3.62 *** 0.049 1.42
PAEDYRS 0.144 4.77 *** 0.164 4.85 ***
ACHEAL -0.728 -1.71 * -0.581 -1.24
EDEQUAL 1.037 4.20 *** -0.148 -0.53
PAWHITE 0.576 2.25 ** 0.773 2.36 ***
MAWORKED -0.176 -0.44 -1.263 -3.39 ***
NEVMARR 2.431 5.30 *** 1.783 2.56 ***
MARRAGE <=17 -2.399 -7.93 *** -2.258 -3.53 ***
MARRAGE 18-20 -1.465 -5.57 *** -0.748 -2.25 **
ABILMISS 0.790 2.71 *** 0.759 1.94 *
ABILMED 1.147 4.49 *** 1.454 3.61 ***
ABILHIGH 1.854 4.54 *** 2.922 5.78 ***
REPEAT -0.617 -1.96 ** -1.271 -4.83 ***
SCQUAL 0.431 6.15 *** 0.312 3.87 ***
R2
0.6487
0.4324
N 641 769
Mean of dependent variable
11.61 12.29
1
Tab
le 8
D
ecom
posi
tion
of
gend
er d
iffe
renc
es in
edu
cati
onal
att
ainm
ent,
us
ing
the
OL
S sp
ecif
icat
ion
of th
e at
tain
men
t fun
ctio
n
Mea
n ch
arac
teri
stic
s
t-te
st o
f th
e ge
nder
Coe
ffic
ient
s W
ald
(chi
-sq)
te
st o
f th
e ge
nder
D
iffe
renc
e in
ED
YR
S d
ue to
Var
iabl
es
Mal
es
Fem
ales
di
ffer
ence
in
char
acte
rist
ics
Mal
es
Fem
ales
di
ffer
ence
in
coe
ffic
ient
s C
oeff
icie
nts
(a)
Cha
ract
eris
tic
s (b)
Com
bine
d E
ffec
t (a
+b)
Inte
rcep
t
9.01
5 5.
889
***
3.12
5 0.
000
3.12
5 A
GE
23-
30
0.41
3 0.
409
-1
.694
-0
.388
**
* -0
.539
-
0.00
1 -
0.54
0 A
GE
31-
38
0.31
3 0.
340
-0
.570
0.
348
**
-0.2
87
- 0.
009
- 0.
296
MU
SLIM
0.
121
0.11
6
-1.4
65
-0.3
69
**
-0.1
33
- 0.
002
- 0.
135
LOW
CA
STE
0.
266
0.19
8 **
* -0
.249
-0
.506
0.06
8 -
0.03
4 0.
034
AC
NSI
B
4.99
0 5.
074
-0
.007
-0
.041
0.17
0 0.
003
0.17
4 P
AW
EA
L 9.
454
10.0
67
* 0.
126
0.19
2
-0.6
23
-0.1
18
-0.7
41
PA
WE
ALS
Q
144.
260
147.
770
-0
.002
-0
.005
0.32
7 0.
016
0.34
3 B
KH
OM
E
1.25
5 1.
270
0.
269
0.07
6
0.24
2 -
0.00
1 0.
241
PA
NE
WS
0.
514
0.59
5 **
* -0
.114
0.
724
**
-0.4
31
- 0.
058
- 0.
489
MA
ED
YRS
3.72
9 4.
317
**
0.04
9 0.
103
-0
.202
-
0.06
1 -
0.26
3 P
AE
DYR
S 7.
958
9.41
3 **
* 0.
164
0.14
4
0.15
6 -
0.21
0 -
0.05
4 A
CH
EA
L 1.
033
1.04
0
-0.5
81
-0.7
28
0.
152
0.00
5 0.
157
ED
EQ
UA
L 0.
686
0.67
0
-0.1
48
1.03
7 **
* -0
.812
0
.017
-
0.79
6 P
AW
HIT
E
0.38
8 0.
473
***
0.77
3 0.
576
0.
076
- 0.
049
0.02
8 M
AW
OR
KE
D
0.11
2 0.
076
**
-1.2
63
-0.1
76
**
-0.1
22
- 0.
006
- 0.
129
NE
VM
AR
R
0.19
0 0.
082
***
1.78
3 2.
431
-0
.123
0.
263
0.14
0 A
GE
MA
RR
<=
17
0.03
3 0.
254
***
-2.2
58
-2.3
99
0.
031
0.27
1 0.
302
AG
EM
AR
R18
-20
0.
162
0.31
8 **
* -0
.748
-1
.465
*
0.11
6 0.
228
0.34
4
AB
ILM
ISS
0.
485
0.24
0 **
* 0.
759
0.79
0
-0.0
15
0.19
4 0.
179
AB
ILM
ED
0.
305
0.36
0 **
1.
454
1.14
7
0.09
4 -
0.12
2 -
0.02
7 A
BIL
HIG
H
0.07
5 0.
057
2.
922
1.85
4
0.11
2 0.
025
0.13
6 R
EP
EA
T 0.
262
0.12
2 **
* -1
.271
-0
.617
-0.1
71
- 0.
086
- 0.
258
SCQ
UA
L 5.
898
6.11
3 **
0.
312
0.43
1
-0.6
99
- 0.
093
- 0.
792
Tota
l
0.51
2
0.17
2
0.68
4
2
1
Appendix 1
Pooled binary probit model of enrolment with gender dummy
Variable Coefficient t-ratio
Marginal effect
Intercept 0.2063 0.78 0.007
AGE 23-30 0.0582 0.45 0.002
AGE 31-38 0.0936 0.72 0.003
MUSLIM -1.0022 -7.10 *** -0.035
LOWCASTE -0.4577 -3.79 *** -0.016
ACNSIB 0.0327 1.25 0.001
PAWEAL 0.2631 9.04 *** 0.009
PAWEALSQ -0.0063 -6.80 *** -0.000
PANEWS -0.0105 -0.06 -0.000
MAEDYRS 0.1306 2.92 *** 0.005
PAEDYRS 0.1947 5.15 *** 0.007
PAEDYRSQ -0.0076 -2.23 ** -0.000
ACHEAL -0.2219 -1.35 -0.008
EDEQUAL 0.1165 1.07 0.004
PAWHITE 0.2881 1.35 0.010
MAWORKED -0.2943 -1.83 * -0.010
FEMALE -1.0797 -9.65 *** - 0.038
Log L
-373.61
Restricted Log L -771.46
Psuedo R2 0.5157
N 1698
2
Appendix 2
Pooled model of educational attainment with gender dummy
Variables Coefficient t-value
Intercept 8.3100 13.79 ***
AGE 23-30 -1.0783 -4.95 ***
AGE 31-38 -0.2033 -0.99
MUSLIM -0.9990 -3.96 ***
LOWCASTE -0.4009 -2.01 **
ACNSIB -0.0296 -0.76
PAWEAL 0.1390 3.70 ***
PAWEALSQ -0.0029 -2.97 ***
BKHOME 0.1749 1.68 *
PANEWS 0.2659 1.26
MAEDYRS 0.0752 3.29 ***
PAEDYRS 0.1419 6.11 ***
ACHEAL -0.6078 -1.86 *
EDEQUAL 0.3898 2.03 **
PAWHITE 0.6572 3.12 ***
MAWORKED -0.9020 -3.26 ***
NEVMARR -0.6225 -2.38 **
MARRAGE <=17 -2.8243 -10.26 ***
MARRAGE 18-20 -1.2445 -5.88 ***
ABILMISS 0.6676 2.79 ***
ABILMED 1.2971 5.62 ***
ABILHIGH 2.5067 7.68 ***
REPEAT -1.0707 -5.26 ***
SCQUAL 0.3588 6.53 ***
FEMALE -0.5572 -2.97 ***
R2
0.5157
N 1410
Mean of dependent variable
11.98
3
Appendix 3
Educational attainment function, without ‘age at marri age’ variables
