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Why Governments Should Invest More To Educate Girls



Women and men often receive the same percentage increase in their wage rates with advances in schooling. Because these returns decline with more schooling, the marginal returns for women will tend to exceed those for men, especially in countries where women are much less educated. The health and schooling of children are more closely related to their mother's education than father's. More educated women work more hours in the market labor force, broadening the tax base and thereby potentially reducing tax distortions. These three conditions, it is argued, justify the disproportionate allocation of public expenditures toward women's education.
P.O. Box 208269
New Haven, CT 06520-8269
T. Paul Schultz
Yale University
September 2001
Note: Center Discussion Papers are preliminary materials circulated to stimulate discussions and
critical comments.
I have benefitted from the comments of Lawrence Chickering on a previous draft of this
paper and the suggestions of the editor and two anonymous referees of the Journal of
World Development. I am grateful for the support of the Rockefeller Foundation, whose
grant for research and training on the family in low income countries contributed to many
of the ideas summarized here. I am responsible for any errors that remain.
This paper can be downloaded without charge from the Social Science Research Network
electronic library at:
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Women and men often receive the same percentage increase in their wage rates with advances
in schooling. Because these returns decline with more schooling, the marginal returns for women will
tend to exceed those for men, especially in countries where women are much less educated. The
health and schooling of children are more closely related to their mother’s education than father’s.
More educated women work more hours in the market labor force, broadening the tax base and
thereby potentially reducing tax distortions. These three conditions, it is argued, justify the
disproportionate allocation of public expenditures toward women’s education.
Keywords: Gender, Returns, Education, Development, Externalities, Taxes
JEL Codes: I21, I22, J16, J31
Evidence from a growing number of countries in all regions of the world demonstrates that
increasing investments in women's human capital, especially education, should be a priority for
countries seeking to increase both economic growth and human welfare. The case for directing
educational investment to women is stronger, the greater the initial disparity in investments between
women and men. Although gender equity is one possible reason for supporting a reallocation of
public educational resources to favor females, the arguments advanced in this paper are based only
on economic efficiency – or, in other words, maximizing social output -- which can also justify
governments investing more in women than in men.
Enrollment in school represents the largest component of the investment in human capital in
most societies, and arguably the component over which public policy has the most immediate control
through its administration of public schools and regulatory capacity. This paper summarizes the
mounting empirical evidence from around the world that the social returns to the years of schooling
of females are greater than the return to males. The evidence comes primarily from representative
household surveys and censuses. Given the diversity of cultures, differences in production techniques
employed at different stages of economic development, different resources available to complement
the labors of men and women, and marked differences in skill specializations that women and men
pursue in different parts of the world, there will inevitably be some exceptions to these predominant
patterns and empirical regularities (Boserup, 1970; King and Hill, 1993; Schultz, 1995b; Behrman,
1997). But there are few instances in international quantitative social science research where the
application of common statistical methods has yielded more consistent findings than in the area of
gender returns to schooling. Therefore, most of my conclusions seem warranted for most settings
in the world, with, of course, differences in degree.
This evidence may explain why regions of the world which have achieved the most economic
and social progress over the past several decades are those -- among other things -- that have most
successfully promoted equal educational achievements for men and women. East Asia, Southeast
Asia, and Latin America are examples of regions in which significant progress has been made.
Conversely, regions that have lagged behind in their growth -- notably South and West Asia, the
Middle East and North Africa, and Sub-Saharan Africa -- have lagged badly in their relative
investments in women’s schooling, thus limiting women's contributions to economic and social
Although general conclusions about the impacts of social investments in men versus women
are consistent and reliable in most parts of the world, economic, social, and political conditions do
vary in particular countries and sub-populations. Therefore, strategies for responding and designing
efficient social policies to redistribute education by gender must be developed through research in
particular settings. Coordinated and focused country-specific programs of research are needed to
evaluate policy options within the institutional and cultural constraints of each country. While this
paper reviews the reasoning and research behind the policy initiative proposed here -- laying out their
qualifications, limitations, and statistical assumptions -- much new applied research will be needed
to chart the most promising policy options. Section 2 examines the evidence of the private wage
returns to schooling for women and men, and the general problems of assessing the productivity of
male and female workers with different amounts of education. Section 3 considers social externalities
or benefits from schooling that are not captured by the private individual or family, and asks how
these differ for male and female schooling. Section 4 explores briefly some of the public finance
implications of reallocating human capital from men to women. Section 5 reviews some of the
institutional options which could accomplish this reallocation of resources, and section 6 concludes.
The gap between men's and women's years of completed schooling is a rough but informative
indicator of the gender difference in many forms of human capital.1 The literature on human capital
returns was first built on evidence of wage differences among males in the US 1940 Census cross-
tabulated by their schooling and age (Becker, 1964). This first step of empirically implementing the
calculation of a lifetime private rate of return to schooling avoided the ambiguities posed by women
and the problems of inferring labor productivity for persons outside of the wage labor force. In most
of the poorest populations of the world women rarely work for a wage. Thus, the foremost problem
in constructing a satisfactory measure of the productivity of women with different amounts of
schooling is to be able to explain which women decide to work outside of their family for a wage
(Heckman, 1980). Only with such an explanation in hand, is it then possible to correct estimates of
the wage function (which implies a return on schooling) for the potential sample-selection bias due
to the researcher only having data on the productivity of wage earners.
Fortunately, the movement of women into the labor force over the last fifty years has been
the most significant development in labor economics of high-income countries. It has therefore been
subjected to much analysis. The three variables emphasized in models of the determinants of female
labor force participation are (1) the woman's own market wage opportunities (often proxied by her
schooling and age), (2) her sources of nonearned income that reduce her dependence on her own
market earnings and thus her market labor supply, and (3) the wage opportunities of her husband or
extended family. Since the woman's own wage is only observed if she works for a wage, it is the
censored variable we want to correct for sample selection bias. Also, many women do not have a
husband or do not reside with other working family members, although they may have access to a
family support network. These family composition variables, along with her fertility, should be
treated as jointly determined with her allocation of time over her lifetime, and thus they are not strictly
independent of her labor supply, and cannot serve as an exogenous basis for predicting whether she
participates in the labor force. In other words, if she has more children in the home, she tends to have
paid a price in terms of her experience and productivity in the market labor force, and she is also less
likely to participate in market work, other things being equal. But unless fertility is due to random
arrivals of twins, for example, it cannot be used to infer the causal "effect" of fertility or the presence
of a young child on her time allocation. That leaves variables representing the woman's claims on
nonearned income, inherited assets, dowries, or social capital as the most likely source of information
to predict her probability of working in a wage job. This empirical approach to identifying a sample
selection model for women wage earners assumes that these nonearned income claims of the woman
do not affect the wage rate she could expect to receive in the market labor force. The greater her
nonearned income resources, the less likely she is to be in the wage labor force (Smith, 1980; Schultz,
1995a). Although this nonearned income variable may be difficult to assess in some settings and
represents a small fraction of a person's lifetime wealth, it provides, in many studies, a significant
predictor for which women (and men) participate in the wage labor force, and allows one to
implement a statistical technique for dealing with the potential sample-selection bias encountered in
analyzing wage functions for women (and men) (Heckman, 1980).
The wage determining function of women is specified in the same form as proposed by Mincer
(1974) for men, except that in the case of women the variable representing years of post-schooling
experience does not approximate with the same precision as for men the accumulation of labor market
experience that is expected to affect current productivity in wage employment. This is because
women may be less permanently attached to the labor force than men and spend more of these years
after schooling ends engaged in home production and child care activities which may not increase
proportionately their productivity in the wage labor force. Differences in the parameters of the wage
function for women and for men should, therefore, be approached with caution, and not presumed
to reflect labor market discrimination, for they may be measuring different things (e.g., Birdsall and
Sabot, 1993). In this case at hand, the postschooling experience variable measures the underlying
concept of wage earning skills with greater measurement error for women than for men, imparting
a downward bias to its coefficient in women's compared to men's wage function estimates.2
For representative samples, the logarithm of the hourly wage rates has been analyzed in many
countries in association with the schooling and postschooling experience of wage earners. An
empirical description of wage structures in countries in all regions of the world has emerged from
which several generalizations can be drawn. When the log wage is regressed on years of schooling,
the estimated coefficient on schooling indicates the percentage change in wages received for attending
an additional year of school. This schooling coefficient has the additional interpretation of a private
internal rate of return on the family's investment in that individual's schooling, if the opportunity cost
of the time of the student while she is attending school for that extra year approximates the private
family cost of going to school, and other simplifying assumptions are maintained (Mincer, 1974).
This proportionate increase in wages associated with an additional year of schooling tends to be about
the same magnitude for women and men, whether or not one performs the justified correction for
sample-selection bias discussed in the previous paragraph. If there is a systematic difference between
these estimates of the private return on schooling for men and women, it tends to favor women more
often than men, particularly in populations where women have in the past received substantially less
education than men (King and Hill, 1993; Schultz, 1988, 1995a; Duraisamy, 2000). Even when
private internal rates of return to schooling are higher for women than for men, the overall level of
wages tend to be lower for women than men. In other words, the absolute magnitude of both the
opportunity cost of not working to attend school, and the wage gains associated with completing an
added year of school tend to be smaller for women than men, but the ratio of the wage gain to the
opportunity cost of schooling is roughly similar for men and women at each specific level of
schooling, e.g. primary, secondary, university.
There has been a long debate on how to get beneath this partial correlation between years of
schooling and log wages to disentangle the true causal effect that should inform public policy and
would represent the labor productivity effect that society could expect when it increases the
schooling of representative members of the population (Griliches, 1977). The most widespread worry
is that other factors affecting labor productivity are omitted from the analysis when estimating the
effect of human capital on wage rates, and these omitted factors may themselves be correlated with
the observed measure of human capital, i.e. years of schooling. The most frequently mentioned
omitted variable is the "ability" of the individual which is expected to raise productivity and to be
positively related to schooling. The omission of ability from the wage function leads in this case to
an upward bias in the estimates of the return to schooling. An analogous argument is made that
family wealth may permit richer parents to borrow at lower interest rates to invest in their children's
schooling, and thus poorer families face a constraint on their credit which leads them to invest less
in their children's schooling than the rich (Becker, 1967; Jacoby, 1994; NaRanong, 1998). Family
wealth could also merely increase the demand for children's human capital for consumption purposes,
and this plausible hypothesis would also encourage the same tendency for relative "over investment"
by richer families in the schooling of their children compared to those of the poor. It is less clear than
in the case of omitted "ability," what the direction of the bias introduced by the omission of parental
wealth. If imperfect labor markets allow wealthy families to obtain for their children jobs for which
they are paid wages in excess of their marginal product, this omission of family wealth might bias
upward estimates of the wage returns to schooling, or conversely, the “over-investment” of wealthy
families in less promising students could introduce a downward bias.
This commonplace statistical problem of omitted-variable bias is compounded by an errors-in-
measurement bias that arises if the human capital stock variable, i.e. education, is itself not reported
accurately or measured precisely. Griliches (1977), among others, has illustrated how efforts to
"control for" omitted-variable bias which might be expected to otherwise overstate the wage returns
to human capital will also augment the errors-in-measurement bias that attenuates the estimates of
the wage returns to the poorly measured human capital input. The net effect of these generally
offsetting sources of bias is not obvious on a priori grounds. A proposed solution to this dilemma
in econometrics is to specify a suitable instrumental variable that is correlated with schooling, but is
not likely to be related to the worrisome omitted variables.3 For example, a locality-specific price for
an input to produce the form of human capital, such as a monetary price of school tuition or time
price of attendance is often approximated by the distance of the child's residence from the closest
school. It would be desirable if this local price or program variation across the sample that is used
to predict schooling was closely related to the policy instrument that society would be inclined to
manipulate to change the demand for schooling. In other words, if the wage returns to schooling
exceeded or fell short of some equilibrium return, the natural policy variable would be to build (or
close) more neighborhood schools? It is also critical that this locality "price of schooling" not be
correlated with omitted determinants of the demand for schooling. In contemporary program
evaluation studies, estimates of the returns to schooling may be based on variation in school
attainment associated with an otherwise random policy variable should approximate the school
returns for those segments of the population who are most likely to be influenced in their school
decision by the program changes. Using this source of policy variation as the instrumental variable
allows the researcher to interpret the estimated return as not the average returns for an entire
population but the marginal returns for those treated and most likely to respond to the treatment by
changing their schooling decisions.
A series of studies of returns to education in the United States using this instrumental variable
methodology has yielded estimates which are similar to those obtained by ordinary regression (least
squares), or sometimes as much as 10 to 20 percent higher. One might conclude that both sources
of parameter bias are relatively unimportant or they happened to cancel each other in standard
statistical fits of wages to schooling. Another possibility is that school returns differ at the margin
for various segments of the population, and this heterogeneity in wage response to the treatment
provided by schooling accounts for why different instrumental variables imply different estimates of
returns; in other words the different instruments affect the schooling of different groups whose
returns actually differ from the average (Card, 1999). There are fewer investigations in low-income
countries using instrumental variables to predict schooling levels and wage functions for women and
men. Parallel investigations of World Bank Living Standard Measurement Surveys from Ghana and
Côte d'Ivoire from the end of the 1980s, for example, did not find the instrumental variable estimates
of schooling returns were significantly different from those reported by standard regressions (ordinary
least squares), whereas wage returns to health, proxied by height and weight-to-height-squared,
tended to increase substantially when estimated by instrumental variables, suggesting that
heterogeneity and measurement error are more serious sources of bias in the case of health than they
are for schooling (Schultz, 1995b).
These problems of estimation bias are potentially as serious for the study of male or female
wage returns to schooling, and few indications have yet emerged that they operate to a different
degree for men and women. As noted earlier, there is some suggestive evidence that correcting for
sample-selection bias does increase schooling returns for women more than it does for men, but
further research will be needed to confirm the generality of this empirical regularity (Schultz, 1995a).
In conclusion to this section, it should be noted that there is an alternative to estimating wage
functions for men and women and comparing their returns to schooling. It involves estimating
production functions or cost functions, and derive from these estimates the marginal products of male
and female labor inputs with more and less schooling. I do not know of production functions that
have sought to extract both the marginal product of male and female labor, where labor inputs are
disaggregated by levels of school attainment. It has proven difficult to disaggregate labor by gender
when estimating production functions, perhaps because the labor input allocations are in fact
endogenous, and likely to be related to unobserved endowments of the workers or other omitted
production input variables (e.g. Huffman, 1976; Quisumbing, 1996; Schultz, 1998; Fafchamps and
Quisumbing, 1999). To perform the further disaggregation of labor inputs by gender, age, and by
schooling, may not yield precisely defined production function estimates, and thus is not yet a source
of insight into male and female returns on schooling which are comparable to those widely derived
from wage functions.
From the earliest investigations of the market returns to schooling it was taken on faith that
rates of return to additional years of schooling would have a tendency to decline at more advanced
levels of schooling. Individuals were assumed to first acquire the schooling skills that were most
highly rewarded in the labor market, and continue to invest in more skills until returns fall to the cost
of borrowing further capital (Becker, 1964; 1981). Psacharopoulos and Woodhall (1985) note that
the highest returns to schooling in the low income world occur at the primary school level, where
most of the world’s population reside, and that returns tend to decline at secondary and higher
educational levels, particularly when social returns include public school expenditures. This general
pattern of diminishing returns to schooling justifies expanding first basic education in low-income
countries, before making large investments in more costly higher education.4 If women tend to be
concentrated at lower levels of education than men, and the returns are generally higher at these
lower levels of schooling, then closing the gender gap in years of schooling will purchase higher
returns than raising the overall distribution of schooling that leaves the existing differentials between
men and women unchanged.5
A standard reason to expend public resources on an activity is that the individuals who
determine how much of that activity to demand (produce) do not take into account some social
benefits and costs associated with the activity, because they do not privately capture them or pay for
them, respectively. Social benefits and costs of schooling that are not borne privately by students or
their families have been discussed in the initial conceptualizations of human capital by Schultz (1961)
and Becker (1964). But they have not often been quantified so as to inform calculations of the social
returns to schooling. Studies have generally quantified only the public costs of education. Factoring
these additional costs into the private wage return calculation, of course, reduces the calculated social
returns to schooling, most dramatically for tertiary levels of schooling, where the public costs tend
to be many times larger than the public costs of primary or even secondary schooling per year per
student (Psacharopoulos and Woodhall, 1985). But discussions of social benefits of education remain
abstract (e.g. they enhance the operation of democracy) and not monetized in a form that they can
be incorporated into the economic calculations of social returns. At the macro economic level,
schooling has been the most powerful "nontraditional" input discovered to explain the puzzle of
modern economic growth (Denison, 1962; Schultz, 1961; Kuznets, 1966; Jorgenson, 1995). Some
cross-country regressions explaining aggregate growth with economic inputs and institutions do not
always find the anticipated partial correlation with measured changes in schooling (e.g., Benhabib and
Spiegel, 1994). But Krueger and Lindahl (1998) have argued that aggregate measures of adult
schooling are dominated by long run trends, and short run changes over time in these measures are
mostly measurement error, and not surprisingly, uncorrelated with growth rates.
Although there are few widely accepted empirical estimates of the macro economic
externalities of schooling on economic growth, there is microeconomic evidence of intergenerational
externalities in the production of human capital.6 The most salient examples are a number of home
production processes coordinated by the family that are affected by the schooling of its members, and
for which society often assigns a special value, or a social value in excess of the private benefits which
individuals in these families capture. Most of these exceptions relate to the formation of human
capital in children, or investments in the productivity of future generations. It is not obvious that
societies should always be inclined to encourage investments in future generations, for to sacrifice
current consumption for future generations, whose income might be greater than those currently
living, is not necessarily desirable. But most societies appear to view such human capital investments
in children as an activity it is willing to subsidize. Consequently, if the schooling of parents
contributes to their children attaining more education, the parent schooling also warrants a subsidy
due to its externalities.
The conclusion of many empirical studies of child development is that increased schooling of
the mother is associated with larger improvements in child quality outcomes than is the increased
schooling of the father. This has been studied with birth outcomes (e.g. birth weight), child survival,
good nutrition, earlier entry into school, increased school enrollment adjusted for age, and more years
of schooling completed on reaching adulthood.7
There is a substantial empirical literature suggesting that adding to a mother's schooling will
have a larger beneficial effect on a child's health, schooling, and adult productivity than would adding
to a father's schooling by the same amount. This finding is consistent with recent studies grounded
in the bargaining models of family resource allocation which report increments to the nonearned
income of mothers (that empowers them) have a larger beneficial effect on the consumption and
human capital of children than a similar increase in the nonearned income of fathers (see reviews in
Strauss and Beegle, 1996; Thomas, 1990, 1994; Quisumbing, 1995; Haddad, et al., 1997; Schultz,
1998; Alderman and King, 1998).
In assessing this interdisciplinary literature it is important that the schooling and resources
controlled by women are appropriately evaluated, and that confounding factors are suitably
controlled. Some early studies relied on the labor market earnings or total income of women to
measure women's control of economic resources (Kennedy and Cogill, 1986; Blumberg, 1988).
These measures of "women's bargaining resources" are less than satisfactory because they are affected
by the women's market labor supply decisions, and time allocation could also be affected by her
fertility and correlated with her compensatory child expenditure patterns. For example, using our
previous results, women with more inherited wealth and nonearned income may allocate less of their
time to working in the wage labor market and thus have less earnings, but allocate more time to child
care and coordination of home production. This should not be interpreted to indicate that these
women had less economic control of resources in the family.
In both the unified family model and bargaining family models the productive value of the
husband's and wife's time are expected to modify consumption and investment patterns, because the
value of the time of family members enters into the opportunity costs of many consumption
commodities and investment activities, and thereby modifies the entire structure of family demands
(Becker, 1981). Augmenting a mother's schooling could increase her capacity to produce child
human capital by a larger amount than does the father's schooling increase his corresponding capacity.
He also may spend less time than she does in child care. Thus, if the mother's schooling produces
more favorable child outcomes than does the father's schooling, that is evidence of a favorable social
externality associated with public investments in female schooling, but it is not by itself evidence that
women have different preferences for child human capital, or that the unified model of family behavior
must be rejected in favor of the bargaining model of the family that can accommodate a world where
men and women pursue different objectives with their own separable resources.
A better approach to distinguish between the unified family model and forms of the family
bargaining model involves testing whether the personal distribution of nonearned income in the family
affects the allocation of household resources to child consumption and human capital investments.
Perhaps the most readily interpreted evidence of this form is when an individual's own nonearned
income is associated with a greater increase in child height, weight-for-height, and calorie intake,
holding constant for the family's total nonearned income and the shadow value of the time (i.e., wage
rates) of both spouses. This empirical regularity strongly suggests that the pooling of family
resources is less than perfect. When women control more nonearned income, indicators of child
development improve by a greater amount than when men control these resources, holding constant
the total budget constraint for the family.
The next analytical problem in relating the schooling of mothers and fathers to child
development is caused by the modification of family composition with changes in the schooling of the
parents. Family living arrangements express the parents' preferences for patterns of consumption and
investment. Marriage, separation, divorce, and childbearing are all, to some extent, choices made by
adults to improve their expected welfare. How is one to deal with the self selection of those women
who are living with a spouse, or living on their own, or living with another relative? How is one to
treat the potential earnings or nonearned income of a man resident in her household, if he is not
currently married to her? All these ambiguities in what constitutes the appropriate evaluation of the
child development externalities of mother's and father's schooling should caution us from drawing
definitive conclusions from the existing empirical evidence, because most of this evidence is estimated
from only husband-wife coresidential units. I would conjecture that the conclusions noted earlier will
not be reversed, if we learn how to control more adequately for the joint determination of family
composition and child development. But the challenge to “endogenize” the family’s composition
within our models of household production needs further research.
Most empirical studies of the effect of parent schooling on child development are flawed for
the purposes of this paper, because they include control variables that are likely to be affected
themselves by parent schooling. For example, a common practice is to control for family income,
husband and wife earnings, or fertility in assessing the effect of parent schooling on child
development. But if these control variables are thought to affect child human capital, and also are
jointly determined by the mother's or father's schooling, what can be learned from existing data? It
is certainly no longer a "total" effect of schooling on the child outcome, nor is it an acceptable
estimate of a "net" effect. If the intervening variable, such as family market income, is positively
affected by the father's schooling, then it might be expected that some of the beneficial effect of
father's schooling would be captured by family income and the "net" effect of father's schooling
controlling for family income would be algebraically smaller than the total effect (not conditioned on
family income). If as seems more likely, family income is itself a family choice variable that
incorporates husband and wife labor supply decisions and joint specialization and reflects the
preferences of both father and mother, the direction of the (simultaneous equation) bias is not clear
(Becker, 1981; Schultz, 1981). Nonearned income, land, inherited assets may potentially serve as
controls for nonhuman wealth of the family, if they are not affected themselves by the schooling of
the parents. These nonhuman capital variables can then be used as instrumental variables to estimate
the effect of lifetime family income levels, approximated by variables such as total family expenditures
per adult. As with family composition variables discussed earlier, most direct controls for family
incomes, parent earnings, or fertility make estimates of effects on child human capital development
difficult to interpret as an indication of the total effects of mother's and father's schooling.
This interpretation of the empirical record needs much more nuanced study. One strategy
postulates the roles of unobservable variables, such as preferences for child schooling which differ
for men and women. Suppose men who prefer to have fewer children and better educated children
seek wives who are better educated and thus more productive in producing human capital in their
children. These (unobserved) preferences of men for lower fertility and higher "quality" children
would lead them to make the necessary sacrifices in other areas (i.e. reduce their other consumption)
to marry better educated women. Or more specifically, it would lead them to marry better educated
women than they would be expected to marry, on average, in the normal functioning of the marriage
market without such heterogeneous preferences. In this case, it becomes ambiguous whether the
lower fertility and increased child schooling associated with a mother's schooling is a causal effect of
the enhanced home productivity of a woman's schooling, the preferences of women for higher quality
children, or an incidental outcome of the marriage matching process, which involves men's and
women's preferences.
In rural Bangladesh and India empirical evidence has been assembled, conditional on a
structural model, which suggests part of the correlation between women's schooling and their
children's schooling is due to the marriage matching process, and consequently can be attributed to
men's preferences rather than to women's differential productivity in schooling their children (Foster,
1996; Behrman et al. 1997). The Indian study first notes that women's schooling does not contribute
to increased agriculture productivity, whereas men's schooling is strongly linked to the adoption of
new agricultural technologies since the 1960s and consequently to increases in rural incomes (Foster
and Rosenzweig, 1995). Women's and men's schooling may also not earn much of a private return
in the daily rural wage labor market in India. A remaining possible economic reason for sending girls
to school in increasing numbers by rural Indian and Bangladeshi families is that the better educated
women are able to increase the schooling (and health) of their children. Men who want better
educated (healthier) children are thus motivated to marry a better educated women with increased
capacity to produce child human capital. An improved understanding of the joint determination of
the marriage market and these home child human capital production processes could affect the
magnitude of estimates of the technological productivity of female education on child human capital,
and plausibly reduce them in circumstances where women's schooling is privately valued by men
mainly for its productive effects on childrearing.
Another dimension of the marriage market, the quality of match between partners, could have
additional implications for private and social welfare. In this case there is also very little theoretical
or empirical research to build on, and the implications are thus speculative. It is necessary to make
a number of simplifying assumptions to illustrate the nature of the problem, although they can, in
some cases, be relaxed later. Suppose that an individual benefits not only from the increased
production possibilities that a more educated spouse brings to a marriage, as assumed in standard
economic models of marriage (Becker, 1981), but also is rewarded by a positive consumption
complementarity between the husband's and wife's schooling. For simplicity this matching benefit
from the interaction of husband's and wife's schooling might be assumed loglinear as are the schooling
effects in the wage equation. Suppose further that the marriage market matched the most schooled
man with the most schooled woman, and so on down through the schooling-ranked men and women,
so that the rank correlation between the spouses education is perfect, i.e., rho=1.0. Then, if the years
of schooling were distributed similarly for men and women, the summed welfare of the matched
couples would be greatest given any total stock of schooling available to the population, when the
average gender gap in schooling was zero. This result depends on market returns to schooling for
men and women being the same, a pattern widely observed and noted in section 2. Of course, the
match correlation of schooling of husband and wife is not 1.0, as assumed, but perhaps between .4
and .6 (Kremer, 1997). Nonetheless, there is a tendency for the gender gap in schooling to diminish
with economic development in this century, and perhaps for the correlation between the schooling
of husband and wife to increase.8
The final potential externality of schooling relates to fertility, which is widely found to be
inversely related to women's schooling (Schultz, 1973, 1981; Cochrane, 1979). If family planning
programs are currently subsidized by the state because a reduction in fertility is thought to bring a
social benefit, then increasing the schooling of girls should be subsidized for it is clearly associated,
in a decade or less, with diminished fertility. All societies do not support family planning because they
desire to reduce fertility; some endorse these programs to improve women's lifetime opportunities
and strengthen their reproductive rights. There are also a handful of instances in Africa where the
first few years of female education seem to have little effect on a woman's fertility, perhaps because
of the low quality of available primary education, or the counterbalancing effect of education on
improved reproductive health and reduced sexually transmitted diseases that contribute to
subfecundity and thus prevent some women from having the number of births they want. On balance,
the evidence suggests that increments to the schooling of men, holding constant the educational
attainment of women, are associated in low-income countries with increases in fertility, although this
pronatal effect of male education seems to diminish as the country develops (Schultz, 1973, 1994).
The social costs of high fertility and rapid population growth are difficult to scientifically quantify
(National Research Council, 1986), but many countries have concluded that their society stands to
gain in the long run by slowing rapid population growth, and this conclusion would justify assigning
a higher priority to women's education than to men's.
To conclude this section, if the private market wage returns are of comparable magnitudes
for men and women, but the social externalities associated with reduced child mortality, increased
child anthropometric capacities, increased child school enrollments, and decreased fertility are all
linked more positively to women's schooling than they are to men's schooling, and these outcomes
are valued by society, it is efficient for society to invest more in the schooling of women than of men.
Whether these social externality benefits associated with women’s schooling vary by the level of her
schooling has not been systematically explored across countries and levels of development. However,
one investigation of contemporary rural India found that mother’s literacy and some primary
schooling had a larger effect on the child’s school work and attainment than did her post-primary
schooling, suggesting higher social returns for the most basic levels of female schooling (Behrman
, et al. 1997). A deeper understanding of the marriage market may sharpen our insights into these
connections and how to manipulate them efficiently, but is unlikely to reverse these basic findings.
The magnitude of the subsidy that would be socially optimal will depend on the value society assigns
to slowing population growth and formation of more human capital among its youth. Where female
school enrollments are markedly lower than male, there is a prima facie case for greater subsidies for
female education. The only reason to revise this rule of thumb is if market wage returns for female
schooling fall substantially below those of male schooling, presumably due to an overproduction of
women's human capital given the social institutions prevailing in the labor market and the derived
demands for various types of labor in the economy. I have not found a compelling empirical study
that reports evidence of such an “overproduction” of women's schooling.
Individuals are expected to weigh taxes as they do wages and prices in allocating their time
and determining the composition of their consumption and investments, to the extent that taxes differ
among productive activities, outlays, and persons. Because governments must realistically obtain their
revenues from taxes on readily monitored activities, such as work in the market that produces
earnings, most taxes discourage, although differentially, engaging in market production activities and
thereby impose a dead-weight efficiency loss on society. There are two ways that this loss due to
taxes can be affected by the gender gap in schooling. First, by increasing the share of social activities
that are taxed, which allows the government to lower the overall tax rate. Second, the tax rate can
be raised on labor for which the supply is more inelastic or unresponsive to the tax, in order to reduce
the tax rate on activities which exhibit elastic responses to the tax rate and hence are more distorted
by the tax. Differences between the market labor supply elasticity of men and women could,
therefore, influence the efficient design of a tax system for individuals and families and thereby modify
social priorities for subsidizing the schooling of women versus men (Boskin and Sheshinsky, 1983;
Apps and Rees, 1988).
Some demographic groups in the population tend to increase, on average, their supply of labor
to taxable market activities as they become better educated, as do married women, whereas other
groups are less responsive, as with adult men. This empirical regularity is presumably because the
elasticity of women’s market labor supply with respect to their own wage ( and education) tends to
be algebraically greater than it is for men (Schultz, 1981; Killingsworth, 1983). This empirical
regularity may be partly understood in terms of men generally working full time in the market, and
they are thus unable to increase greatly their market labor supply when their education and wages
rise. In contrast, women have until the 20th Century allocated most of their time to work focused in
their home, which is often readily combined with child care responsibilities, and thus women have
been observed to increase their market labor supply when their educational levels are higher or rising
(Schultz, 1990; Fogel, 1999).
Moreover, estimates of family labor supply which allow for the simultaneous determination
of a couple’s labor supply find that the cross-effect of the husband’s wage ( or schooling ) tends to
reduce his wife’s market labor supply, whereas the effect of the wife’s wage (schooling) on her
husbands labor supply is not substantial or statistically significant (Killingsworth, 1983; Schultz,
1981). Consequently, the female schooling effect is to directly increase women’s own labor supply
and market earnings tax base, while the cross effect of male schooling on her labor supply is negative,
reinforcing the previous conclusion that the market earnings tax base would expand more rapidly in
a society given its average education level, if the schooling of women were able to catch up to that
of men.
If school administrators could accept more girls rather than boys at the margin to enroll in
school, this reallocation of education by gender would thereby contribute to increase the share of
adult time allocated to market work, and thus to broadening the tax base. This increment in the
taxable share of social output allows the government, in principle, to lower the overall tax rate and
thereby reduce the dead-weight loss associated with raising any specified amount of revenue.
A second objective in the optimal design of taxes is to set rates on different factors of
production to tax more heavily the inelastically supplied resources, such as Henry George’s tax on
land, in order to reduce the overall deadweight losses from a tax regime. The greater elasticity of
women’s labor supply compared with that of men’s would, according to this second objective of
public finance, encourage governments to tax more heavily the inelastically supplied source of labor
--- that provided by adult men --- and thereby be able to reduce the tax rate on women’s market labor
supply. This less distorted structure of differential taxes on the market earnings of women and men
is ironically the opposite of the structure adopted in some societies. In the United States, for
example, married women pay the progressively higher tax rate based on her husband’s earnings when
she enters the labor force as a “secondary worker,” perhaps to encourage married women to
specialize their production within the home, rather than in the labor market (e.g., McCaffery, 1997).
Thus, a redirection of human capital toward women should broaden the tax base and thereby
reduce tax distortions of consumption and production between market and nonmarket activities. In
addition, the market labor supply response associated with an increase in own schooling is more
positive for women than for men. This regularity may help explain the large increase in female market
labor supply in this century, first in the industrially advanced countries, and more recently throughout
most other parts of the world, at least in the nonagricultural sector of the economy (Schultz, 1981,
1990). One interpretation of this regularity in labor market behavior of women is that it is due to the
positive (uncompensated) wage effect caused by increasing the schooling and hence market
productivity of female workers. In the case of male labor supply, increasing schooling and
productivity is associated with little change in hours of labor supplied to the market labor force, and
in many countries there has been an actual contraction in male work hours (Schultz, 1981;
Killingsworth, 1983; Fogel, 1999). Moreover, estimates of family labor supply models suggest that
the cross-effect of the husband wage (schooling) on wife's labor supply tends to be negative and
substantial in magnitude, whereas the effect of the wife's wage (schooling) on husband labor supply
is not substantial nor statistically significant (Killingsworth, 1983). Consequently, the female
schooling effect on the women’s own market earnings tax base is positive, and the cross effect of
male schooling is negative, reinforcing the earlier conclusion that the market income tax base would
expand in most settings with a redirection of human capital formation or schooling from men to
The objective of increasing educational opportunities for women is probably as old as the
gender gap in schooling. Euripides may have even advanced some proposals for Greek Athens to
open their schools to women, as did Plato in his utopian Republic. The search for policy instruments
to accomplish this increase in women's education has a long social history. However, as with many
forms of social policy, rigorous evaluation of the success of various policy interventions are often
neglected. As with many praiseworthy goals, most policy reforms to advance the education of
women have resulted in legislation without mechanisms for enforcement, incentives to change
behavior, or delineation of indicators of success. Although there may be some successful policy
initiatives, most are probably not effective, and the program evaluation literature has made little
progress in sorting out which policy strategies are more effective or efficient. With their passage into
law and with sequestered appropriations, the public need for action is generally satisfied.
Nevertheless, a number of countries in different regions -- notably in East Asia and Latin America --
have achieved considerable success in promoting women’s education. The purpose of this section
is to collect a list of possible mechanisms that might advance women's schooling, to consider which
policies hold the greatest promise, to identify the information needed to monitor progress, and finally
to structure policy evaluation studies to refine the design of these initiatives (World Bank, 2001).
Differences in enrollments of boys and girls could arise because of either the decisions of
families or the operations of schools, or in other words, due to either private demands or public
supplies. Schooling can of course also be provided in the private sector, if public supplies are not
responsive to private demands. Conversely, gender discrimination in the operation of schools may
exist because there is widespread support for it in the community or in those segments of the
community that have the political power to modify educational institutions. This dichotomy between
private demands and public supplies may facilitate analysis of the determinants of schooling decisions,
first at the level of individuals and families, and then at the aggregate community level, where a more
complex social equilibrium framework may offer a fuller understanding of why some societies have
pursued such different priorities in public education, as say India, Sri Lanka, and Thailand.
Families are thought to weigh the costs and benefits of sending their children to school. In
some settings they decide it is more important for them to educate their boys than their girls. This
could be explained because the expected private rates of return, as discussed in section II, are larger
for boys than for girls over their children's lifetimes. Alternatively, the decision-making parents may
not be altruistically willing to view their children's lifetime gains as equivalent to their own, and they
will discount these expected productive gains of their children, unless the parents stand to personally
benefit from these gains. In some cultures, such as South Asia, sons are customarily responsible for
supporting their parents in old age and daughters are not. This would seem to suggest how cultural
arrangements of marriage and intergenerational support systems among kin could depress the
incentives for parents to invest in the schooling of their daughters compared with their sons. This
plausible hypothesis is widely accepted, but it neglects a role of the marriage market to assign a value
to the daughter's schooling. Parents should then be rewarded by the family of the husband of their
daughter for rearing a daughter who has more schooling, if indeed female schooling increases the
woman's lifetime productivity and contributes to the welfare of her husband's family.
However, if noneconomic cultural constraints or social norms preclude the wife from working
in productive activities, e.g. if she is confined by purdah to labor only within her family's household,
such cultural impediments to labor mobility might reduce the economic contribution of an educated
wife and curb parental investments in the schooling of girls. A cultural system that promotes such
an inefficient allocation of resources should be subject to market pressures to change. If this
explanation for low levels of female schooling is plausible, say in areas of South and West Asia, how
might public policy accelerate the cultural shift to allow labor markets to allocate more of women's
time to activities where her schooling enhances her productivity? If a woman's only option in the
rural labor force is to perform casual manual work by the day, the wage premium for schooling may
be limited. Women will need to engage in some farm management tasks which involves the allocation
of modern technological inputs for them to employ productively their schooling. Culture-specific
institutions may be designed to demonstrate how family welfare is enhanced by educating females and
allowing them access to managerial, nonagricultural, and extra-familial jobs. Perhaps farm extension
activities can directly assist in facilitating the off–farm employment and migration process for better
educated daughters?
The traditional approach to increase female enrollments has been to reduce the cost of
schooling to parents, by building schools closer to the population they serve, reducing tuition fees
specifically for females, providing girls with subsidies for their school uniforms or school feeding
programs, and extending fellowships for girls to attend boarding school where local secondary
schools are not available. Bangladesh has experimented since 1994 with fellowships for girls to
continue in secondary school. Some of these educational grants are treated as a bond which is
forfeited if the girl marries before the age of 18 (Arends-Kuenning and Amin, 2000). Mexico has
provided poverty alleviation grants to poor rural mothers to keep their children enrolled in school in
a program called "Progresa". The Mexican grants are roughly pegged at local child wage rates, but
are marginally higher for girls than boys, because the gender gap in enrollments in these poor Mexican
communities emerges at the secondary school level. Evaluation studies have found that in the
communities that were randomly selected to receive the initial phase of the Progresa educational
grants starting in 1998, the enrollment rates of girls increased by more than boys, especially for
children after finishing primary school and first entering the junior secondary school (Schultz, 2000).
More than two million Mexican households were participating in Progresa by the end of 1999, and
the new government of Fox plans to expand the scheme to involve poor families in urban areas as
Three states of Brazil have experimented with educational grants for mothers in poor
households who enroll all of their children between the ages of 7 and 14 in school. In 2001, the
Federal government of Brazil plans to expand this approach, Bolsa Escola, to the national level, and
coordinate it with two other poverty alleviation programs. One program provides cash transfers for
nutrition, while another expands a youth program to discourage children from working in hazardous
circumstances and to involve them in additional educational activities, or PETI (Sedlacek, 2001). At
the national level, Brazil’s enrollment rates are relatively low, but completed schooling in the past few
years is somewhat higher for girls than for boys. Therefore, the Bolsa Escola makes cash transfers
to poor mothers contingent on the enrollment of their children in compulsory primary school, but
does not explicitly favor girls.
There should also be administrative means to reduce gender inequalities in schooling within
families. For example, to be accepted at school an elder male child might be required to have his
(younger) sister(s) enrolled. Such quantitative restrictions, however, have problems: they neglect
differences between children in ability and motivation, and can place costly monitoring burdens on
schools. Communities could be rewarded when the female proportion of their graduating students
exceeds a threshold, but this could have the side effect of lowering the standards for a female
compared with a male graduate, and such quota targets could be misrepresented by teachers unless
strictly audited by central authorities.
Another strategy assumes that parents in some cultures do not want their daughters educated
with boys. In South and West Asia and North Africa the schooling of girls may be restricted by the
lack of sex segregated schools, particularly at the secondary level. Are girls schools, which avoid
mixing of the sexes after the primary level, more successful? Do female teachers succeed to a greater
degree in enrolling and advancing girls compared with male teachers? Do particular facilities or
qualitative features of schools contribute to raising female enrollment rates by a larger percentage
than male enrollment rates? There are few studies of such school quality or supply interventions
which are randomly allocated across communities and confirm that public expenditures on female
schools, female teachers, and female-oriented facilities contribute cost-effectively to increase the
educational attainment of women. But these are propositions that could be tested within educational
programs in Pakistan, Bangladesh, and some Middle Eastern countries. A word of caution is
nonetheless needed to indicate that any evaluation study of interventions must not only succeed in
introducing the intervention on a randomized basis, it must also collect representative surveys of the
local household population and link this information to the school with its measurement of student
standardized performance on tests along with information on teachers and classroom inputs. The
matched background population survey will determine which children enroll in school, as well as
which student do poorly and well within school. The population survey must measure the home
economic and social factors which affect private demands for schooling, such as the mother's and
father's schooling, nonearned income and asset of both parents, etc. For every dozen studies of
gender differences in student classroom performance, there is perhaps one that analyzes matched
information about the school system's inputs, and the characteristics of local families of both the
children who are enrolled and those who are not enrolled in school. Without analyzing these more
difficult to collect overlapping school and population samples, most policies designed to modify the
gender balance of schools cannot be evaluated.
In many international statistical studies of the wage structure, it has been found that the
increase in logarithms of wage rates associated with an additional year of a worker's schooling is of
about the same magnitude for women as it is for men. Corrections for many statistical and conceptual
problems that could make this wage comparison misleading, such as sample-selection bias, omitted-
variable bias, and measurement-error bias, have not been found to alter systematically this general
comparability of female and male wage returns to schooling. The current balance of evidence
indicates that these estimates of the private wage returns to schooling tend to be, if different,
somewhat higher for women than for men, holding constant the level of education being compared.
Since women tend to have less education than men, on average, and returns tend to be higher at lower
levels of schooling, the returns to schooling of the average girl are higher than the average boy. This
ranking in private returns is strengthened if the private direct costs of education are added to the
private opportunity costs, because boys often receive more family educational expenditures (e.g.,
Sipahimalani, 1999). Consequently, private returns to an additional year of schooling for the
representative female exceed those for the representative male, and social returns that factor in public
expenditures on schooling are even more favorable to a general increase in female relative to male
Social benefits or positive externalities related to investments in the human capital of children
in the form of child health, stature, and schooling are larger with an increment in the schooling of their
mother than their father. Fertility is also inversely related to female education in virtually all
populations and often fertility is directly related to male education in low income agricultural
societies. Consequently, when population growth is thought to impose social costs, female schooling
should be assigned a higher priority than male schooling, other things being equal.
Combining the larger private wage returns and the beneficial social externalities associated
with female schooling, there is a strong economic efficiency case to reduce the gender gap in
schooling, particularly where child survival is relatively low and fertility is relatively high. From a
public finance perspective, the increased schooling of women can be expected to increase the
participation of women in the market labor force (and not reduce that of men) and thereby broaden
the society's tax base. The effects of taxes on the distortion of the allocation of time and resources
between market and nonmarket production can thus be reduced, given the public sectors revenue
The economic efficiency case for redirecting social investments toward the education of
women is strong, but the mechanisms that can accomplish this objective have not been rigorously
studied. They involve for the most part understanding more precisely how the family will respond
to different inducements. Will subsidies for girls’ education repay the public sector and shift the
gender balance of enrollment rates in families, or is the family demand for male relative to female
schooling price inelastic? If women are largely restricted from working outside of their family and
reaping many of the productive advantages that come from their schooling, how does a society
intervene and design a culturally acceptable program to change this pattern of lifetime allocation of
women's time? One strategy may be to encourage rural industries that employ locally more educated
women, as occurred in Taiwan and China, and to some degree in Korea and Thailand, and may now
be occurring in Bangladesh. A few decades ago these factories were viewed by some observers as
exploiting rural women by paying them excessively low wages. Another assessment of this situation
may be in order. How effective is such rural industrialization in increasing women's employment in
the wage labor force in South and West Asia and the Middle East? How do rural employment
opportunities in non-agriculture influence the gender gap in schooling? Can such a pattern of
development be sustained in sub-Saharan Africa? Will this pattern of development in rural areas have
the expected effect on the investment of rural families in female schooling and will it also accelerate
the rural-urban migration of these better-educated women?
In conclusion, it should be emphasized that macro indicators of development confirm the
conclusions drawn here from the micro economic studies of individuals and families. Countries that
have equalized their educational achievements for men and women in the last several decades have
on the average grown faster. Except for the indigenous populations in which a substantial disparity
persists between the schooling of boys and girls, Latin America has provided nearly as many years
of schooling to females as to males (if not always of the same quality), and the growth record of this
continent until the 1980s debt crisis was impressive. East Asia has increased the schooling of women
much faster than that of men, closing a historically pronounced gender gap in these patriarchal
societies in a few short decades. Southeast Asia draws on Malay cultural roots that were less gender
biased, and sometimes even matriarchal, and the schooling of women increased in this region more
rapidly than that for men, but the initial gaps were often smaller than in East Asia. Despite recent
financial crises in the region, the economic growth record remains one of great success. South and
West Asia has achieved less uniform and lower average growth. This region is notable for investing
relatively less in basic education and much less in women relative in men, possibly accounting for their
subpar growth performance until the 1990s, despite high investment rates in nonhuman capital. Sub-
Saharan Africa has had the worst growth record, the most political turmoil, highest rate of population
growth, lowest domestic investment rates, and has attracted the least foreign investment. Africa,
with the exception of South Africa, provided schooling mainly to males, although women were
heavily engaged in the subsistence and market economies, and should therefore have had as much to
gain from schooling as did men. Why African women received such a small share of schooling
resources is a puzzle which has not been accounted for by analysis. This traditional disparity is
changing in Africa as young women are catching up to men in terms of schooling, and even
surpassing them in such countries as Kenya. This paper has focused on the microeconomic evidence
from household surveys and censuses of the private productive returns and social externalities of
human capital and schooling by gender. Merging school administrative and household survey
information on the school and family inputs, enrollments, and test scores should provide a firmer basis
for evaluating national policy options to equalize educational opportunities between females and
males, and also between the poor and rich families, and rural and urban areas. The improvements in
time series on educational attainment and earnings of the adult workforce by age and sex should
provide countries with a reliable monitoring mechanism to assess private returns to schooling. At an
aggregate level such merged administrative/survey data may also improve cross-country analyses of
the contributions of education and health to modern economic growth, which are currently limited
by poor data and ad hoc frameworks that lead to fragile and implausible growth regressions (Krueger
and Lindahl, 1998). Eventually, inter-country differences in economic growth may shed light on the
determinants of and consequences of the gender gap in schooling and even help to quantify the value
of the social externalities associated with female schooling, which remains an important, if
controversial, element of the microeconomic case surveyed here, which justifies increased public
subsidies for female schooling in many parts of the world.
1. The gender gap in schooling tends to mirror a host of other, more difficult to measure gender
differences in human capital, such as (1) early childhood nutrition and health care (e.g. often
proxied by reduced adult height, called stunting), (2) nutritional status determined by nutrient
intakes relative to energy demands of work, as modified by protective health care (e.g. often
proxied at low income levels by weight-for-height or BMI, called wasting), (3) different types
of years of schooling for which the market returns differ (e.g. training to be teachers or nurses
versus engineers and doctors, and other indicators of quality or resource intensity of that
training), and (4) on-the-job training opportunities (often associated with sex segregation of
jobs and promotional ladders), etc.
2. On the other hand, if postschooling experience of a woman is measured by her realized years
of experience working in the labor force, then this more precisely measured experience variable
is also a choice variable of the adult woman, which is likely to be “endogenous” to the wage
function (i.e., correlated with the wage error), because it is jointly determined with lifetime
specializations between home and market production and hence realized market wages. An
analogous problem arises when studying the determinants of men’s wages, when researchers
want to estimate the productive returns to job tenure or seniority on the job (Altonji and
Shakotko, 1987)
3. Another approach is to estimate wage returns to schooling using only the variation between
individuals who share the same omitted variables, when these unobserved variables might
otherwise bias the resulting cross sectional estimates. For example, between siblings the
relationship between education and wages may not be modified by shared variables representing
their parents’ wealth and some common family genetic endowments, and between fraternal and
identical twins, which share even more aspects of their early childhood environment and genetic
predispositions (Griliches, 1977; Solon, 1999).
4. There are aggregate growth theories that assume an externality due to human capital formation,
but I am familiar with only a few empirical analyses of modern growth performance of national
(or regional) economies that find evidence of externalities, or in other words that find income
growth effects of schooling at the aggregate level exceed systematically the income growth
which is privately realized by individuals in the form of wage differences of workers according
to their schooling.
5. The literature on these issues is enormous and full of complexities that cannot be adequately
examined in the scope of this paper. The evidence of mother’s education lowering her child
mortality was widely accepted after the Latin American Census samples of the 1960s and 1970s
were cross tabulated and World Fertility Surveys became available for a widening sample of
low-income countries in the 1980s (e.g. Behm, 1976, 1980; Caldwell, 1979; Schultz, 1980;
Cochrane et al., 1980; Rosenzweig and Schultz, 1982a, 1982b; Farah and Preston, 1982;
Mensch et al., 1985; Barrera, 1990; Thomas et al., 1990). The studies of anthropometric
indicators (i.e., height and weight) of child health began somewhat later, but also clearly
indicated that better education of the mother was correlated with better height and BMI
indicators for her children (summarized in Behrman and Deolalikar, 1988, 1989; Behrman and
Wolfe, 1984, 1989; Strauss and Thomas, 1995, 1998). Schooling of children is commonly
related positively to maternal education (e.g. Behrman, 1997; Rosenzweig and Evenson, 1977;
Chernichovsky, 1985; King et al., 1986; Duraisamy, 1988; Duraisamy and Malathy, 1991;
Holmes, 1997; Malathy, 1993; Jacoby, 1994; Rosenzweig and Wolpin, 1994; Glewwe and
Jacoby, 1994, 1995; Lloyd and Blanc, 1995; Haveman and Wolfe, 1995; Lavy, 1996; Subbarao
and Ramey, 1995; Tansel, 1997; Holmes, 1997; Behrman et al., 1997; NaRanong, 1998;
Sipahimalani, 1999). Going beyond education, studies differ in how they measure women's
control over resources, employing first labor market productivity and then wealth and non-
earned income (Kennedy and Cogill, 1986; Senauer et al., 1986; Engel, 1988; Blumberg, 1988;
Kennedy and Peters, 1992; Haddad and Hoddinott, 1994; Thomas, 1990, 1994; Thomas and
Chen, 1994; Hoddinott and Haddad, 1995). The studies also control in different ways for the
endowments of the husband, family income, and family composition. As argued in this paper,
there are serious analytical problems with most methods for dealing with family composition,
and consequently there is continuing search for better methods to model explicitly marriage
matching and marital status (e.g. Boulier and Rosenzweig, 1984; Schultz, 1994; Foster, 1996;
Behrman et al., 1995, 1997).
As in most empirical generalizations, there are exceptions where the positive partial
correlation of the father’s schooling with the child’s schooling is higher than that of the
mother’s schooling, often in populations where there is more variation in father’s than mother’s
education, due to the majority of mothers having little or no schooling, as in a study of Pakistan
or sub-Saharan Africa (King, et al. 1986). Other studies have excluded families without both
a father and mother in residence, which can reduce sample size substantially and alter the
estimated effects of mother’s and father’s schooling on child development indicators (e.g. Lam,
6. For example, in Taiwan the difference between the average years of schooling completed of
men and women born between 1917 and 1921 was 4.2 years according to the 1976 Family
Income and Expenditure Survey. By 1995, men and women born between 1966 and 1977
reported a gender gap in schooling of .23 years. By age thirty virtually all women in Taiwan
were married, and the correlation of schooling of wives age 30-34 and their husbands was about
.4 in 1976 and this correlation had increased to nearly .6 by 1995. (Schultz, 1998).
Measurement of the match correlation is complicated when, as in most modern societies,
virtually all women are not currently married. Then it is necessary to again correct the estimate
of the match correlation for the selection of the sample of currently married couples.
7. Although this empirical generalization may still be valid for most countries, there are now
documented exceptions where virtually all members of young birth cohorts have completed
primary schooling, and a shortage of secondary educated workers has emerged. Wage returns
at this intermediate level of schooling are then likely to exceed the returns earned at the primary
level (Schultz, 1988). See the case cited of Egypt in Birdsall and O’Connell (1999).
8. Assessing the effect of health status on worker productivity poses a parallel issue that increases
in the intake of nutrients or anthropometric proxies for the stock of health tend to have a larger
effect on worker productivity at lower levels (Strauss, 1986; Strauss and Thomas, 1995, 1998).
Evidence on gender differences in health status are more fragmentary. Certainly in population
of South and West Asia where the gender gap in schooling is large, we might expect the gender
gap in health to also be relatively large. The low ratio of female to male child survival (after the
first month of life) in India is now well studied and coherently linked to women's low
productivity and high dowries. In this case, one would expect a given increment in health status
might yield a greater market productivity return for women than for men, although I know of
no analysis confirming this pattern, perhaps because of the limitations on women working in
manual labor outside of their families.
Alderman, H. and E.M. King, (1998), “Gender Difference in Parental Investment in Education,”
Structural Change and Economic Dynamics, 9:453-468.
Altonji, J.G. and R.A. Shakotko, (1987), “Do Wages Rise with Job Seniority,” Reviews of
Economic Studies, 54:437-459.
Apps, P.F. and R. Rees (1988) "Taxation and the Household," Journal of Public Economics
Arends-Kuenning, M. and S. Amin (2000), “The Effects of Schooling Incentive Programs on
Household Resource Allocation,” Population Council Policy Research Division Working Paper
No. 133, New York.
Barrera, A. (1990) "The Role of Maternal Schooling and its Interaction with Public Health Programs
in Child Health Production," Journal of Development Economics 32:69-91.
Becker, G.S. (1964) Human Capital, New York, NY: Columbia University Press.
Becker, G.S. (1967) "Human Capital and the Personal Distribution of Income," Woytinsky Lecture,
University of Michigan, Ann Arbor, MI.
Becker, G.S. (1981) A Treatise on the Family, Cambridge, MA: Harvard University Press.
Behm, H. (1976) La Mortalidad en los Primeros Anos de Vida en los Paises de America Latina, San
Jose, Costa Rica: CELADE.
Behm, H. (1980) "Socioeconomic Determinants of Mortality in Latin America," in Socioeconomic
Determinants and Consequences of Mortality, Geneva: WHO.
Behrman, J.R. (1997) "Mother's Schooling and Child Education," processed, University of
Pennsylvania, Philadelphia.
Behrman, J.R. and A. Deolalikar (1988) "Health and Nutrition," in H. Chenery and T.N. Srinivasan,
eds., Handbook on Economic Development Vol. 1, Chapter 15, Amsterdam: North Holland
Publishing Company.
Behrman, J.R. and A. Deolalikar (1989) "Seasonal Demands for Nutrient Intakes and Health Status
in Rural South India," in D.E. Sahn, ed., Causes and Implications of Seasonal Variability in
Household Food Security, Baltimore, MD: Johns Hopkins University Press.
Behrman, J.R. and B.L. Wolfe (1984) "More Evidence on Nutrition Demand: Income Seems
Overrated and Women's Schooling Underemphasized," Journal of Development Economics
Behrman, J.R. and B.L. Wolfe (1989) "Does More Schooling Make Women Better Nourished and
Healthier?" Journal of Human Resources 24:644-663.
Behrman, J.R., N. Birdsall and A. Deolalikar (1995) "Marriage Markets, Labor Markets and
Unobserved Human Capital," Economic Development and Cultural Change 43:585-601.
Behrman, J.R., A. Foster and M.R. Rosenzweig (1997) "Women's Schooling, Home Teaching, and
Economic Growth," Journal of Political Economy, 107(4):632-714.
Benhabib, J. and M.M. Spiegel (1994), “The Role of Human Capital in Economic
Development,” Journal of Monetary Economics, 34:143-174.
Birdsall, N. and C. Graham (eds.) (2000), New Markets, New Opportunities?, Washington DC:
Carnegie Endowment for International Peace and Brookings Institution.
Birdsall, N. and L. O’Connell (1999), “Putting Education to Work in Egypt,” Carnegie
Endowment for International Peace, Global Policy Program Working Paper No. 5, Washington,
Birdsall, N. and R. Sabot (eds) (1993) Labor Market Discrimination in Developing Countries,
Washington, DC: The World Bank.
Blumberg, R.L. (1988) "Income under Female Versus Male Control," Journal of Family Issues 9:51-
Boserup, E. (1970), Women’s Role in Economic Development, London, St. Martin’s Press.
Boskin, M.J. and E. Sheshinski (1983) "Optimal Tax Treatment for the Family," Journal of Public
Economics 20:281-297.
Boulier, B.L. and M.R. Rosenzweig (1984) "Schooling, Search, and Spouse Selection," Journal of
Political Economy 94:712-732.
Caldwell, J.C. (1979) "Education as a Factor in Mortality Decline," Population Studies 33:395-413.
Card, D., (1999), "The Causal Effect of Education on Earnings," Vol 3A, Chapter30, Handbook of
Labor Economics, (eds) O. Ashenfelter and D. Card, Amsterdam: North Holland Publishing
Chernichovsky, D. (1985) "Socioeconomic and Demographic Aspects of School Enrollment and
Attendance in Rural Botswana," Economic Development and Cultural Change 33:319-332.
Cochrane, S.H. (1979) Fertility and Education, Baltimore, MD: Johns Hopkins University Press.
Cochrane, S.H., J. Leslie and D.J. O'Hara (1980) "The Effects of Education on Health," The World
Bank Staff Working Paper 405, Washington, DC.
Denison E.F. (1962) The Sources of Economic Growth in the United Stated and the Alternatives
before us, Committee for Economic Development, Supplementary Paper No. 13, New York
Duraisamy, P. (1988) "An Econometric Analysis of Fertility, Child Schooling and Labour Force
Participation of Women in Rural Indian Households," Journal of Quantitative Economics 4:293-
Duraisamy, P., (2000), “Changes in Returns to Education in India, 1983-1994: By Gender, Age-
Cohort, and Location,” Economic Growth Center Paper No. 815, Yale University, New Haven,
CT (forthcoming in Economics of Education Review).
Duraisamy, P. and R. Malathy (1991) "Impact of Public Programs on Fertility and Gender
Specific Investment in Human Capital in Children in Rural India," in Research in Population
Economics, Vol. 7, Greenwich, CT: JAI Press.
Engel, P.L. (1988) "Intrahousehold Allocation of Resources: Perspective from Psychology," in B.L.
Rogers and N.P. Schlossen, eds., Intrahousehold Resource Allocation, Tokyo: United Nations
University Press.
Fafchamps, M. and A.R. Quisumbing (1999) "Human Capital, Productivity, and Labor Allocation in
Rural Pakistan," Journal of Human Resources 34(2):369-406.
Farah, A.A. and S.H. Preston (1982) "Child Mortality Differentials in Sudan," Population and
Development Review 8:365-383.
Fogel, R.W., (1999), “Catching Up with the Economy,” American Economic Review, 89(1):1-21.
Foster, A. (1996) "Analysis of Household Behavior When Households Choose Their Members,"
processed, University of Pennsylvania.
Foster, A, and M, Rosenzweig (1995) "Learning by Doing and Learning from Other: Human Cognital
and Technical Change in Agriculture," Journal of Political Economy, 103(6):1176-1209.
Glewwe, P. and H. Jacoby (1994) "Student Achievement and Schooling Choice in Low Income
Countries: Evidence from Ghana," Journal of Human Resources 29:842-864.
Glewwe, P. and H. Jacoby (1995) "An Economic Analysis of Delayed Primary School Enrollment and
Childhood Malnutrition in a Low Income Country," Review of Economics and Statistics 77:156-
Griliches, Z. (1977) "Estimating the Returns to Schooling," Econometrica 45:1-22.
Haddad, L. and J. Hoddinott (1994) "Women's Income and Boy-Girl Anthropometric Status in Côte
d'Ivoire," World Development 22:545-553.
Haddad, L., J. Hoddinott and H. Alderman (1997) IntraHousehold Resource Allocation in Developing
Countries, Baltimore, MD: Johns Hopkins University Press.
Haveman, R.H. and B.L. Wolfe (1995) "The Determinants of Children's Attainments," Journal of
Economic Literature 33:1829-1878.
Heckman, J.J. (1980) "Sample Selection Bias as a Specification Error" in Female Labor Supply, (ed)
J.P. Smith, Princeton University Press: Princeton, NJ.
Hoddinott, J. and L. Haddad (1995) "Does Female Income Share Influence Household Expenditure,"
Oxford Bulletin of Economics and Statistics 57:77-96.
Holmes, J. (1997) "Measuring the Determinants of School Completion in Pakistan: Analysis of
Censoring and Selection," Economic Growth Center Discussion Paper No. 794, Yale University,
New Haven, CT.
Huffman, W (1976) "The Value of the Productive Time of Farm Wives," American Journal of
Agricultural Economics 58:836-841.
Jacoby, H. (1994) "Borrowing Constraints and Progress Through School: Evidence from Peru,"
Review of Economics and Statistics 76:145-158.
Jorgenson, D.W. (1995) Productivity, Vol I. Postwar U.S. Economic Growth, Cambridge MA: MIT
Kennedy, E.T. and B. Cogill (1986) "Income and Nutritional Effects of the Commercialization of
Agriculture: The Case of Kenya," Processed, International Food Policy Research Institute,
Washington, DC.
Kennedy, E. and P. Peters (1992) "Household Food Security and Child Nutrition," World
Development 20:1077-1085.
Killingsworth, M. (1983) Labor Supply, Cambridge: Cambridge University Press.
King, E.M. and M.A. Hill (eds), (1993) Women's Education in Developing Countries, World Bank,
Baltimore MD: John Hopkins University, Press.
King, E.M., J.R. Peterson, S.M. Adioetomo, L.J. Domingo and S.H. Syed (1986) Change in the
Status of Women Across Generations in Asia, Santa Monica, CA: The Rand Corporation.
Kremer, M. (1997) "How Much Does Sorting Increase Inequality," Quarterly Journal of Economics
Krueger, A.B. and M. Lindahl (1998), “Education for Growth in Sweden and the World,”
Princeton NJ:Princeton University.
Kuznets, S. (1966) Modern Economic Growth: Rate, Structure, and Spread, New Haven CT, Yale
University, Press
Lam, D., (2000), “Generating Extreme Inequality,” Population Studies Center, University of
Michigan, Ann Arbor, MI.
Lavy, V. (1996) "School Supply Constraints and Children's Educational Outcomes in Rural Ghana,"
Journal of Development Economics 51:291-314.
Lloyd, C.B. and A.K. Blanc (1995) "Children's Schooling in SubSaharan Africa: Role of Fathers,
Mothers, and Others," processed, Population Council, New York, NY.
McCaffery, E.J. (1997), Taxing Women, University of Chicago Press, Chicago, IL.
Malathy, R. (1993) "Women's Choice of Work and Fertility in Urban Tamil Nadu, India," Economic
Growth Center Discussion Paper No. 695, Yale University, New Haven, CT.
Mensch, B., H. Lentzner and S. Preston (1985) Socioeconomic Differentials in Child Mortality in
Developing Countries, New York, NY: United Nations.
Mincer, J. (1974) Schooling Experience and Earnings, New York, NY: Columbia University Press.
NaRanong, V. (1998) "Gender, Credit Constraints, and Education in Rural Thailand," Economic
Growth Center Discussion Paper No. 792, Yale University, New Haven, CT.
National Research Council (1986) Population Growth and Economic Development: Policy Questions,
Washington, DC: National Academy Press.
Psacharopoulos, G. and M. Woodhall (1985) Education for Development, New York, NY: Oxford
University Press.
Quisumbing, A.R. (1995) "The Extended Family and Intrahousehold Allocation," Food Consumption
and Nutrition Division, Dis. Paper 3, International Food Policy Research Institute, Washington,
Quisumbing, A.R. (1996) "Male-Female Differences in Agricultural Productivity," World
Development 24:1579-1595.
Rosenzweig, M.R. and R.E. Evenson (1977) "Fertility, Schooling and the Economic Contribution of
Children in Rural India," Econometrica 45(5):1065-1080.
Rosenzweig, M.R. and T.P. Schultz (1982a) "Child Mortality and Fertility in Colombia," Health Policy
and Education 2:305-348.
Rosenzweig, M.R. and T.P. Schultz (1982b) "Market Opportunities, Genetic Endowments and the
Intrafamily Distribution of Resources," American Economic Review 72:803-815.
Rosenzweig, M.R. and K.J. Wolpin (1994) "Are There Increasing Returns to Intergenerational
Production of Human Capital," Journal of Human Resources 29:670-693.
Schultz, T.P. (1973) "A Preliminary Survey of Economic Analysis of Fertility," American Economic
Review, 63:(2):77-78.
Schultz, T.P. (1980) "Interpretation of Relations among Mortality, Economics of the Household, and
the Health Environment," in Socioeconomic Determinants and Consequences of Mortality
Differentials, Geneva: WHO.
Schultz, T.P. (1981) Economics of Population, Reading, MA: Addison-Wesley.
Schultz, T.P. (1988) "Educational Investment and Returns," in H. Chenery and T.N. Srinivasan, eds.,
Handbook of Development Economics, Vol. 1, Chapter 13, Amsterdam: North Holland.
Schultz, T.P. (1990) "Women's Changing Participation in the Labor Force," Economic Development
and Cultural Change 38:451-488.
Schultz, T.P. (1994) "Marital Status and Fertility in the United States," Journal of Human Resources
Schultz, T.P., ed. (1995a) Investment in Women's Human Capital, Chicago, IL: University of Chicago
Schultz, T.P. (1995b) "Human Capital and Development," in Agricultural Competitiveness, 22nd
International Conference of Agricultural Economics, (eds) G.H. Peters et al., Aldershot,
England: Dartmouth Pub. Co.
Schultz, T.P. (1996) "The Demand for Children in Low Income Countries," Vol. 1, Chapter 13,
Handbook of Population and Family Economics, (eds.) M.R. Rosenzweig and O. Stark,
Amsterdam: North Holland Pub. Co.
Schultz, T.P. (1998) "Women’s Roles in the Agricultural Household, Bargaining and Human Capital
Investments," Agricultural and Resource Economic Handbook, (ed), B. Gardner and G. Rausser,
Amsterdam: North Holland Publishing Co. (forthcoming).`
Schultz, T.P., (2000), “Progresa’s Impact on School Enrollments from 1997/98 to 1998/99,”
International Food Policy Research Institute, Washington, DC.
Schultz, T.W. (1961) "Investments in Human Capital," American Economic Review, 51(1):1-17.
Sedlacek, G. (2001), “Three-in-One Social Policy: Insurance, Assistance, and Investment through
Latin America’s Conditional Targeted Transfers,” Inter-American Development Bank,
Washington, DC.
Senauer, B., D. Sahn and H. Alderman (1986) "The Effects of the Value of Time on Food
Consumption Patterns in Developing Countries: Evidence from Sri Lanka," American Journal
of Agricultural Economics 68:920-927.
Sipahimalani, V. (1999) "Education in Rural India," unpublished Ph.D. dissertation, Yale University,
New Haven, CT.
Smith, J.P. (ed.), (1980) Female Labor Supply, Princeton NJ, Princeton University Press.
Solon, G., (1999), “Intergenerational Mobility in the Labor Market,” in D. Ashenfelter and D.
Card (eds)Handbook of Labor Economics, Vol. 3, Chapter 29, Elsevier Science, Amsterdam.
Strauss, J. (1986) "Does Better Nutrition Raise Farm Productivity?", Journal of Political Economy
Strauss, J. and K. Beegle (1996) "Intrahousehold Allocations," MSU International Development
Working Paper No. 62, Michigan State University, East Lansing, MI.
Strauss, J. and D. Thomas (1995) "Human Resources: Empirical Modeling of Household and Family
Decisions," in J. Behrman and T.N. Srinivasan, eds., Handbook in Development Economics,
Vol. 3A, Chapter 34, Amsterdam: Elsevier.
Strauss, J. and D. Thomas (1998) "Health, Nutrition and Economic Development," Journal of
Economic Literature 36(2):766-817.
Subbarao, K. and L. Ramey (1995) "Social Gains from Female Education," Economic Development
and Cultural Change 44(1):105-128.
Tansel, A. (1997) "Schooling Attainment, Parental Education, and Gender in Côte d'Ivoire and
Ghana," Economic Development and Cultural Change 45:825-856.
Thomas, D. (1990) "Intra Household Resource Allocation: An Inferential Approach," Journal of
Human Resources 25:635-664.
Thomas, D. (1994) "Like Father, Like Son; Like Mother, Like Daughter," Journal of Human
Resources 29:950-989.
Thomas, D. and C.L. Chen (1994) "Income Shares and Shares of Income: Empirical Tests of Models
of Household Resource Allocation," Working Paper 94-08, Santa Monica, CA: The Rand
Thomas, D., J. Strauss, and M.H. Henriques (1990) "Child Survival, Height for Age and Household
Characteristics," Journal of Development Economics 33:197-234.
World Bank Staff, (2001), Engendering Development Through Gender Equality in Rights,
Resources, and Voice, World Bank and Oxford University Press, New York.
... Having more educational qualification is more beneficiary for women than men (Peet et al., 2015;Patrinos and Montenegro, 2014). An extra year spent for girls" primary education can increase wage by 10 percent, 15-25 percent for each secondary year, and about 17 percent for tertiary education year (Schultz, 2002;Patrinos and Montenegro, 2014;Sperling et al., 2016). ...
... An extra year spent for girls" primary school can increase wage by 10 percent, and 15-25 percent for each secondary year, and about 17 percent for tertiary education (Schultz, 2002;Patrinos and Montenegro, 2014;Sperling et al., 2016). In OECD countries, over the past 50 years, more educational qualification accounts for about 50 percent economic growth (OECD, 2012). ...
Ethnicity and womanhood lack various privileges in mainstream and male-dominated society. The study firstly examines the cultural capital as well as empowerment of Chakma and Bengali women and then seeks the impacts of cultural capital on their empowerment. A sample of 216 respondents has been selected using purposive sampling and has been interviewed with predefined semi-structured questionnaire in the hill area of Rangamati Sadar, Juraichari and Barkal Upazila. Besides 216 surveys, 5 key informant's interviews and 6 case studies have been conducted. The survey data have been analyzed with SPSS software using both descriptive and bivariate statistics. Likewise, the case study and key informant's interview have been analyzed with NVivo software. Descriptive statistics has been applied to examine the data collected on socio-demographic characteristics, cultural capital indicators and forms of empowerment. For bivariate analysis, T-test, chi-square test and simple linear regression have been used to test the relationship and effect size of the variables. The result of the study divulges that the Chakma women have more cultural capital regarding embodied and institutionalized. On the contrary, the Bengali women have more objectified cultural capital. In terms of empowerment e.g. economic, social, political and physical, Chakma women have more empowerment compared to the Bengali. The T-test value and mean difference also indicate the unequal cultural capital of Chakma and Bengali women. The chi-square analysis explores that each variable of embodied, objectified and institutionalized cultural capital is significantly associated with the empowerment. Similarly, the simple linear regression shows that the cultural capital including embodied, objectified and institutionalized has significant effects on empowerment. The Chakma and Bengali women who had more cultural capital also had more empowerment. In fine, it is obvious that the Chakma women have more cultural capital and empowerment than the Bengali. The findings supported and reproduced the theory of Cultural Reproduction, Gendered Cultural Capital, Cultural Feminism, Indigenous Feminism and Critical Race.
... Economic activity, notably efficiency and productivity, is significantly influenced by gender equality or a gender-balanced division of labour (Bertay et al., 2020;Kennedy et al., 2017). Several previous studies have discovered that higher levels of female educational attainment have a positive influence on the economy (Schultz, 2002). Nowadays, there are lots of women working in a men monopoly industry where the number of women leaders worldwide also increase from year to year (Hamdan & Saraih, 2021). ...
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The goal of this study is to build to investigate the elements that influence gender equality in Karachi Societies. Karachi is one of Pakistan's largest cities, a developing country in South East Asia. When it comes to gender, the country, like many other developing countries, has a substantial inequality in resources and rights. Women in developing countries are influenced by a variety of socio-cultural practices and racial factors, including a diverse range of spiritual traditions, a complicated legal system, and complex economic and political quandaries. In this study, the non-probabilistic convenience sampling strategy was used for the reason of addressing incomplete population of the target audience during the COVID-19 lockout. Structural equation modelling (SEM) was carried out in this study with the help of smart PLS software to establish a causal link between the predictor, mediating, moderating, and criterion variables. Keeping a watch on the data, it becomes clear that the independent variables separately have a weak to moderating impact towards the variable there pertaining to be at the mediator factor are the dependent variable. The mediating variables have a relatively stronger contribution to gender equality. The beta value of each of use is 0.36, indicating a weak to moderate contribution, whereas the beta value of distinction is 0.59, indicating a moderate to higher contribution to gender equality. The average contribution value of all independent variables is greater than 0.20, with some values reaching up to 0.30, indicating a moderate to weak contribution to gender equality. Many studies have been conducted to investigate the concerns and causes related to women's challenges; however, lack of causal (explanatory) research has been conducted on the subject. Thus, this research provides fruitful knowledge to the existing literature on the mediation and moderation analyses towards gender equality among societies in Pakistan.
... Education and societal development are inextricably linked (Nafukho et al., 2004;Rena, 2008;Schultz, 2002;Tarabini & Jacovkis, 2012). Indeed, research suggests that higher education provides a significant contribution to society's improvement (Kimenyi, 2011;Özdoğan Özbal, 2021;Teal, 2011). ...
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This mixed methods research study explored how emergency remote teaching and learning, enacted in March 2020 due to the COVID-19 pandemic, impacted university students. We asked: What, if any, challenges did students face that hindered their ability to learn successfully online? In Spring 2021, a Qualtrics survey was emailed to 1,481 undergraduate and graduate students at a university in New York City, and students were asked to forward the survey to friends in and beyond the university. A total of 254 (17.2%) responded; 59.6% were graduate students and 69.9% were full-time students. The survey had closed- and open-ended items. The open-ended item analyzed in the current study asked students to identify any online learning challenges. Responses were analyzed via a fully integrated, four-phase mixed methods analysis: constant comparison; a crossover mixed analysis by two coders that yielded an intensity-based, inter-respondent matrix of themes; descriptive-based quantitizing; and inferential-based quantitizing via a canonical correlation analysis and Fisher’s Exact Tests. The most prevalent findings were No Challenges (34.26%), Living Environment (25.93%), Technology-Related (20.37%), and Overall Learning Experiences (19.44%). Students experiencing No Challenges were statistically significantly more likely to report that they had full access to their own computer (p = .006) and reliable Internet connection (p = .001). For Living Environment, students noted major life issues that created challenges: sickness, crowded spaces, and multiple jobs, including serving as a caregiver. Technology-Related findings revealed student dissatisfaction with the online learning experience. For Overall Learning Experiences, students described their need for interactive learning experiences and responsive professors. A key conclusion is that higher education institutions should create more meaningful student learning experiences, develop student-centered pedagogies, and provide greater equity in university learning communities.
... Investing in education can take many forms, including the establishment and management of schools as a business venture and/or the acquisition of any particular type of education by individuals to enhance their employment prospects and income earning capacity. Schultz [4] suggested that by investing in education, governments, corporations, communities, NGOs, and individuals can help prepare young people for the challenges ahead. If children are really the leaders of tomorrow, then it is time we started investing in them. ...
... The literacy ratio and definition of a literate person are varied countries to country; however, as per standard UNESCO (2008) sustainable goals definition: "A literate a person who can read and write simple irrespective of the education level." In this connection, female education also significantly impacts female issues, i.e., birth control issues and female role in society to reduce the gender discrimination (Savas, 1978;Schultz, 2002). It has been studied that without upgrading the female enrollment in the institutions and providing equal opportunities to females, government cannot achieve its desired goals (Abu- Ghaida & Klasen, 2004). ...
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Social equity and family planning are core issues for Human development and health wellbeing of a country. This paper discusses the social equity issue regarding family planning and health-related decision-making among males and females in Pakistan. This study graphically demonstrates Pakistan Demographic Health Surveys of 2012-13 and 2017-18 aggregated analysis covered the time horizon 2007-2018 in family planning decision-making and Pakistan's birth control issues and discussed the descriptive data. Also, the Government of Pakistan's (GOP) policy initiates from 2007 to date have been discussed. By referencing this sociological inquiry, evidence shows that males' awareness, education, and decision-making for family planning have a higher impact on the Fertility rate than females' decision-making in Pakistan, which creates social equity gap. Moreover, this study recommend that male awareness and thinking be optimized and changed positively by involving practitioners, religious scholars, and education and awareness campaigns.
... Girls' education has its impacts beyond the girl herself, as the entire community and the country also get benefitted. Girls' education contributes to economic growth through an increase in productivity (Abu-Ghaida & Klassan, 2004;Birdsall et al., 1993), reduction in the wage gap, and increase in the tax base (Schultz, 2002). It also leads to several social benefits such as a reduction in the incidence of child marriage and infant and maternal mortality (Hill & King, 1995;Jensen, 2012), declines in population growth by having fewer children and using smart reproductive health practices (Sperling & Winthrop, 2016;Kim, 2016). ...
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Despite numerous established benefits of girls’ education, globally large numbers of girls are out-of-school (OOS). This poses challenges to achieving quality education (SDG 4) and gender equality (SDG 5) by 2030. In India, there are socioeconomic and spatial disparities also. The latest National Sample Survey (2017–18) data provides an opportunity to explore these issues. We used the unit-level data of 117,115 children (5–17 years). Our multivariate logistic regression analysis shows that the likelihood of OOS girls is at least 16% higher than that of boys. The probability declines at every stage of income quintile from ‘poorest’ to the ‘richest’. The likelihood in urban areas is almost 35% lower than the rural areas. Compared to the upper castes the probability is higher for the backward castes. Compared to Hindus, the likelihood is higher among Muslims but lower among Christian and Sikh children. Our three-layer cross-tabulation reveals that poor Scheduled-Tribes girls are the most vulnerable. The spatial plotting shows that the majority of the vulnerable regions belong to a few states viz. Rajasthan, Uttar Pradesh, Madhya Pradesh, Chhattisgarh, and Gujarat. Therefore, we argue for localized solutions for girls of diverse socioeconomic backgrounds in different regions. The relevance of this study also arises from the fact that there might be a further increase in the number of OOS girls due to the COVID-19 pandemic. ANOVA test suggests that there might be a shift of girls from private to government schools also, which calls for strengthening the public education system to prevent the problem from aggravating further.
... Empirical evidence suggests that the gender gap in education in developing countries considerably reduces the economic growth (Klasen, 2002;Knowles et al., 2002). Schultz (2002) conjectures that in developing economies, especially where women are less educated than men, disproportionately higher investment in the women's education is equitable as well as efficient. Increasing women's education contributes to reductions in mortality and fertility levels (Murthi et al., 1995). ...
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The literature on the relationship between social norms and female educational outcomes has ignored some important aspects. First, norms are unobservable; what we observe are practices that are manifestation of norms. Second, norms are not monolithic that can be measured using a single indicator. They are indicated by several indicators and each indicator is an imperfect measure of the underlying norm. Third, norms are dynamic and can be affected by various factors. This paper examines the relationship between social norms and female educational outcomes addressing these concerns. We estimate a MIMIC (Multiple-Indicator- andMultiple-Cause) model in structural equation framework using the Indian Human Development Survey (IHDS), 2011-12 data for different regions and social groups. We find that norms adversely affect educational attainment for females and the effect is stronger in rural region than urban. The effect is more pronounced among Brahmins in rural region, followed by OBCs, Forward Castes, Adivasis and Dalits, whereas it is significant only among OBCs and Dalits in urban region. While education of both parents has a positive impact on female education, the mother's education has a norm-breaking effect and father's education has a norm-binding effect.
... These attitudes are embedded in cultural norms and are influenced by marriage and kinship patterns which may lead parents to invest more emotional and financial resources in educating sons rather than daughters [7]. The centrality of preparing girls for marriage is pronounced in the north of India where parents have historically held lower aspirations for educating daughters rather than sons [5,[8][9][10][11]. ...
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We have observed cases of gendered impact of extended school closure during COVID-19. Past experience reveals to us that public health outbreaks have distinct gender dimensions and these crises underplay a role in widening inequalities. While COVID-19 is pri�marily affecting public health, spill over effects can be observed in education, stemming largely from extended school terminations. Out of the total population of students enrolled in education globally, UNESCO estimates that over 89% are currently out of school and this includes nearly 743 million girls. According to census estimates of India, the literacy rate has continued to climb to 73% in 2011; however, the gender gap has only narrowed slightly, with women still at literacy levels 16% below men (GOI 2011). Along with other marginalized populations, for girls, especially from minority groups, getting an education is already a struggle. Longer school closures could result in an increased risk of dropout for girls, particularly from lower income groups. Education is lifesaving for them and provides protection as well as instils hope for a brighter future. Therefore in this paper we focused to find out the major constraints faced by the school going girls particularly those who belongs to the marginalised section of the society during the COVID 19 pandemic. The paper is based on the selected case studies developed to support the observations regarding vulnerability of girl child during stressed situations to continue education in state of Uttar Pradesh in India. To get an overview of the problems, cases have been selected from different categories - students of primary level, secondary level and senior secondary level. Further, from each category student from rural, urban and the peri-urban areas of the state has been selected for the case studies. Keywords: Extended Interrupted Education; Gender Biased; Inequalities; Dropout
... New in our theory is a clear model on how women's empowerment, wealth, and educational outcomes are related. We propose that empowerment of women increases national levels of wealth; this is based on the idea that when women are better educated and when women have better opportunities to work, this leads to increased economic productivity and wealth [31,32]. Increases in wealth, in turn, result in fewer economic constraints on occupational choices (i.e., choosing an occupation largely for economic security) and enable greater freedom to make occupational choices based on personal interests. ...
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We investigated sex differences in 473,260 adolescents' aspirations to work in things-oriented (e.g., mechanic), people-oriented (e.g., nurse), and STEM (e.g., mathematician) careers across 80 countries and economic regions using the 2018 Programme for International Student Assessment (PISA). We analyzed student career aspirations in combination with student achievement in mathematics, reading, and science, as well as parental occupations and family wealth. In each country and region, more boys than girls aspired to a things-oriented or STEM occupation and more girls than boys to a people-oriented occupation. These sex differences were larger in countries with a higher level of women's empowerment. We explain this counter-intuitive finding through the indirect effect of wealth. Women's empowerment is associated with relatively high levels of national wealth and this wealth allows more students to aspire to occupations they are intrinsically interested in. Implications for better understanding the sources of sex differences in career aspirations and associated policy are discussed.
Reviews a number of issues regarding health and nutrition in developing countries and available studies on the determinants of health and nutrition and on their impact on productivity in developing countries. Considers first a theoretical framework and some issues pertaining to the empirical representation of health and nutrition. Then surveys existing studies on both health and nutrition determinants and on their productivity impact and concludes with some discussion of policy issues and directions for future research. -from Authors
The present study evaluates efficiency of a sample of engineering and polytechnic institutions in Kerala, a southern state of India, from the period 2003–2004 through 2005–2006. The efficiency is measured through a non-parametric approach, namely, data envelopment analysis (DEA). The study uses multistage DEA assuming input-oriented approach, and the efficiency scores of these institutions are computed and compared across various types of institutions. The study finds that diploma level polytechnic institutions are technically more efficient compared to engineering degree institutions, while the converse holds true for scale efficiency in Kerala. It is also found that all the large-sized engineering degree and most diploma institutions have exhausted scale economies and are operating under decreasing returns to scale. Further, the government institutions in the sample score over the other types of institutions in terms of technical efficiency and, in most cases, student enrolment as well, which suggests better utilisation of public resources.
Sudan presents an excellent opportunity for studying mortality conditions in poor countries. It is one of the 25 "least developed" countries by U.N. designation, most of whom have very little information on mortality and general health conditions. As the largest African country in area, Sudan is also a land of rich ecological contrast, stretching from desert areas in the North through savannah areas to dense equatorial jungle in the South. The northern portions are Arabic and Islamic, the southern portions black African. The 1955/56 census enumerated 597 tribes speaking some 115 languages. Aridity in the North and swamps in the South have retarded the development of these areas and fostered nomadism, population concentration is greatest in the middle belt and particularly along the Nile and its tributaries. This paper has since been published as: "Child Mortality Differentials in Sudan," by Abdul-Aziz Farah; Samuel H. Preston in, Population and Development Review, Vol. 8, No. 2. (Jun., 1982), pp. 365-383.
The article examines the determinants of children's school enrollment and completion of primary grade four--one of UNICEF's key indicators of social progress--in seven countries of sub-Saharan Africa, focusing on the role of parents and other household members in providing children with educational and residential support. While in most of these countries a substantial majority of 10-14-year-old children are currently enrolled in school, many fewer children by this age have attained a minimum of a fourth grade education, primarily due to late ages of entry into school and slow progress from grade to grade. The resources of a child's residential household--in particular the education of the household head and the household standard of living--are determining factors in explaining variations among children in these aspects of schooling. By contrast, a child's biological parents appear to play a less critical role, as demonstrated by comparing the educational record of orphans with that of children whose parents are still living. Furthermore, children living in female-headed households have better school outcomes than children living in male-headed households, when households with similar resources are compared.