1. 1
Is Gender Equality in Education An Impetus to Economic
Growth? - A Cross Country Analysis
EC428: Development and Growth
Word count: 5872
Candidate Number: 51994
Abstract
Gender bias in education and economic growth are intertwined. Using panel regressions
for 127 countries over 11 years from 2000-2010, this essay explores whether increasing
the ratio of girls to boys enrolled in various levels of education, contributes positively
towards growth. The results support the view of a positive association between reducing
the gender gap and economic growth.
Keywords
Economic growth, Gender Inequality, Cross-country regressions
2. 2
1 Introduction
“If you educate a man you simply educate an individual, but if you educate a woman, you
educate a whole nation.” - James Emman Kwegyir Aggrey, Ghanaian Educator.
Gender inequality in education is a stark reality in our world today. This inequality is
more prevalent in developing countries. The numbers speak for themselves; in
Afghanistan the ratio of girls to boys in primary school, in 2012, was as low as 72. This
number worsens to 55 when looking at secondary school enrolment. The same ratios in a
developed country like France are 100 and 101 respectively (World Development
Indicators, The World Bank 2012). This implies that parents in countries with such a bias
invest very little, or nothing at all, in girls’ education. Why might this be? The reasons can
be categorized into the following:
a) Social Preferences and Norms
Owing to tradition, culture and social norms, parents simply do not believe in sending
their daughters to school. In such societies, girls are associated with household chores
like cooking, washing, and taking care of their younger siblings and parents prefer to
educate boys, as they are considered to be the future earners of the family.
b) Poverty
Though governments around the globe have subsidized education, it is not entirely free.
Transportation costs, costs of stationary and cost of uniforms etc. are still borne by
parents. Extreme circumstances faced by poverty stricken families often force parents to
take decisions at the expense of girls.
3. 3
c) Returns from girls’ education
When male and female labour is not perfectly substitutable, parents consider the returns
from their daughters’ schooling to be less than the returns from their sons’ education. In
such circumstances, not educating girls may be economically efficient (Gertler and
Alderman 1989). Another reason why parents prefer to educate their sons is because
sons are expected to take care of the family when the parents grow old. The same cannot
be expected of a girl, because after marriage she resides with her husband and in-laws
and can no longer contribute substantially to her parents.
Educating women has immense benefits. A well-educated mother can take better care of
her children, in terms of their health, nutrition and education. She thus raises a healthier
family. She becomes more productive in her home and workplace. Keeping the levels of
males constant, the addition of more able females in the workforce serves to increase
productivity of human capital in a country, which directly translates into higher growth
(Klasen 2000). Educated women tend to have greater bargaining power while making
decisions such as how many children to have. Low fertility rates reduces the dependency
ratio in the economy which means workers now need to distribute their earnings among
less people, thus raising the income per capita.
Advocacy of gender equality has been made on two grounds. One is intrinsic and the
other is instrumental (Klasen and Lamanna 2009; Klasen 2000). The former sees gender
equality as a basic right and as a means to promote well-being and justice. Under this
argument, gender equality needs no justification; it is essential in itself. However, without
undermining the importance of this reason, the focus of this paper is on the latter, which
sights the economic advantages of gender equality, particularly equality in education.
Estimates suggest that no country has observed both a ratio of girls to boys in primary
4. 4
education that is lower than 90 and GDP per capita exceeding $10000 (Ward, Lee,
Baptist and Jackson 2010). Using a panel of 127 countries over the period 2000-2010,
this essay documents the positive impact gender equality, as measured by the ratio of
girls to boys in primary education, secondary education and both in primary and
secondary education, can have on economic growth. The intention of this study is to
explore the direction of this relationship and not to establish causal relation.
Figure 1
Source: STATA 12. Estimates are from the World Bank’s World Development Indicators.
The x-axis plots the ratio of girls to boys in primary and secondary school.
The figure above plots the relationship between GDP per capita growth and the ratio of
girls to boys in primary and secondary education for 78 developing countries in the year
2010. The line plot is clearly upward sloping, indicating a positive relation between the
two. My empirical results also point in the same direction. A more formal treatment of
this phenomenon will be discussed later in the essay.
5. 5
The next section gives a brief literature review and explores the channels via which
gender gap in education impacts growth. Section 3 lists the data sources and variables
used in this study. Section 4 explains the empirical strategy used. Section 5 analyses the
results. Section 6 concludes.
2 Literature Review
How Can Gender Gap In Education Impact Growth?
As elucidated by Stephen Klasen (2000, 2002), there are several pathways through which
gender gap in education can impact growth. The first channel is called the ‘Selection-
Distortion Effect’. If it is assumed that both the genders have a homogenous dispersal of
ability, then gender bias in education would mean that less able males are being awarded
the opportunity to get educated as opposed to more able females. This has two
consequences. One is that the average productivity of human capital in such a country
will be much lower when compared to a country where such a bias doesn’t exist. Low
productivity translates into low growth. The second consequence is a dampening effect
on investments because a country with low human capital productivity will yield low
returns on investments.
The second channel explores how gender equality affects growth via the ‘externality’
channel. Promoting female education now (and thus lowering gender inequality) will
have positive spillovers on the quality of human capital in the future. Having realized the
importance of education themselves, educated mothers would make sure their children
get educated at least as much as she did. Slotsky (2006) reports that women allocate a
greater portion of family income towards the well-being of children than men. They are
6. 6
more risk averse and tend to have a higher propensity to save and invest judiciously.
Since women are more sensitive to women’s problems, their political empowerment can
possibly lead to a larger number of social programs benefiting other women, children
and disadvantaged sections of society.
A family where both the parents are well educated can strive to provide each other and
their children support and motivation throughout their schooling. This can lead to a
decrease in drop out rates. In conjunction to the previous argument, the returns on
physical capital will also increase with the increasing productivity of workers, spurring
investments in a country.
The third channel is called the ‘fertility channel’. If it is believed the returns to females in
the labour market increase with the level of her educational attainment, then it not only
increases her bargaining power in the family, but also negatively affects fertility. Since
women allocate more time to raising children than men, having more children would
mean the family forgoes the income it could earn had the mother not be bearing extra
children. Low fertility leads to lower population growth and higher income per capita in
the country. However this effect cannot last forever because eventually low fertility will
translate into a higher dependency ratio leading to lower GDP per capita.
As seen in Figures 2-4, there seems to be a negative correlation between a decrease in
inequality and fertility rates. These figures plot the various measures of inequality used in
this study and fertility rates for all countries in 2010. There seems to be a strong
downward sloping relationship between the ratio in secondary education and fertility
rates (Figure 2), suggesting that as more and more women are educated, so much so that
the ratio exceeds 100, the fertility rate can be as low as 0. To test this hypothesis, a fixed
effects regression will be estimated.
7. 7
Figure 2
Source: STATA 12. Estimates are from the World Bank’s World Development Indicators.
The x-axis plots the ratio of girls to boys in secondary school.
Figure 3
Source: STATA 12. Estimates are from the World Bank’s World Development Indicators.
The x-axis plots the ratio of girls to boys in primary school.
8. 8
Figure 4
Source: STATA 12. Estimates are from the World Bank’s World Development Indicators.
The x-axis plots the ratio of girls to boys in primary and secondary school.
Empirical Evidence
Growth models have for long attracted many in the field of economics. The classic
economic growth model proposed by Robert Solow in 1956 showed that savings and
population growth rate explained why some countries are rich and others poor. This
inspired the pioneering work by Mankiw, Romer and Weil (1992), who extended the
Solow model to incorporate both physical and human capital. Their results lead them to
conclude that human capital cannot be ignored when analyzing the sources of growth.
The prominence of endogenous growth theory sparked the interest of many, who went
on to include human capital disaggregated by gender. This was proxied by differences
between girls and boys in educational achievements (Kabeer and Natali, 2013) to
establish a link between education and economic growth.
9. 9
Barro and Lee (1994) were among the first to carry out cross-country regressions in this
topic. They used a panel data set for 85 countries between 1965-1975 and 95 countries
between 1975-1985 to examine the sources of growth. Among the regressors were male
and female secondary school rates. The results indicate that increasing secondary
schooling of males by one year increases the growth rate by 1.34 percentage points per
year. However, a controversial result was the negative relationship between initial level of
female secondary attainment and growth. A reason the authors give for this ‘puzzling’
finding is that a large dispersion of male and female secondary attainment signifies
backwardness, so a very low female secondary attainment implies more backwardness
and more potential for growth due to the convergence effect. Several more studies
seemed to verify Barro and Lee’s claim. Barro and Sala-i-Martin (1995) extended the
Barro and Lee model to include higher and secondary education for both genders, thus
having four distinct variables for education. Their empirical analysis lent support to the
negative correlation. Providing further support to Barro and Lee’s results was Perotti
(1996) who also found that male education was positively linked to growth, whereas
female education is negatively related. The only study to prove otherwise was Caselli et al
(1996) who tested various cross country regressions using generalized method of
moments estimator that overcomes the issues of correlated individual effects and
endogenous regressors. They re-estimate the Barro and Lee regression and obtain
positive and significant coefficient on the female education variable.
Many have scrutinized Barro and Lee’s results. Some pointed out specification errors and
large standard errors indicating multicollinearity. Since male and female schooling are
highly correlated to one another, this makes it difficult to assess their individual effects.
Perhaps the most plausible explanation was given by Stokey (1994) and later empirically
tested by Lorgelly and Owen (1999). Stokey claimed that female coefficient was
10. 10
capturing effects of certain regions. One such region included Hong Kong, Singapore,
Taiwan and Korea, which are famously called the East Asian Tigers. These countries
despite having low levels of female education and very high levels of inequality in
education experienced high growth rates. This, therefore, highlighted the importance of
using regional dummies in such regressions (Kabeer and Natali, 2013).
Hill and King (1995) found a positive correlation between growth and gender bias in
education. A major difference between their study and previous studies is that, instead of
using the growth of GDP, they use levels of GDP. To proxy for inequality1
the authors
used the proportion of female to male enrolments in either primary or secondary
education, depending on which one is the largest. The present essay differs from others
in that it includes the ratio for both primary and secondary school enrolments. Their
results suggest that a high ratio of female to males is linked to high growth rates. They
also find that inequality is inversely correlated with life expectancy and directly correlated
to infant mortality and fertility rates. This fact points out the indirect effect inequality can
have on growth.
Dollar and Gatti (1999) attempt to find answers to three questions. First, if low
investment in female education is simply efficient for developing countries. Second,
whether gender inequality reflects varying social preferences about the roles each gender
should play. Third, identifying market failures that may be the cause of such inequality
and if this market failure declines over time as countries progress. Using a panel data set
of 127 countries over five year periods from 1975 to 1990, the results indicate that the
coefficient on male secondary achievement is negative and that on female secondary
1
‘Economic Growth’ and ‘Growth’ will henceforth be used synonymously; so will
‘Gender Inequality in Education’ and ‘Inequality’.
11. 11
education achievement is positive. This is in contrast to Barro and Lee’s (1994) findings.
Therefore, their analysis leads them to conclude that not investing in girls’ education is
not the economically efficient outcome and that market failures do hamper growth in
developing countries. They also suggest that the impact of inequality varies with the stage
of development, In an agrarian economy, the returns from educating one member of the
family are greater than the returns from having a second member that is literate. In such
an economy, preference for having boys educated is only a minor distortion. But, as a
country develops and becomes more and more industrialized, people transition from
being agricultural labourers to wage labourers. In this case, discriminating against girls
would have large consequences for growth, as valuable human capital that could
potentially earn great returns is not being invested in.
Further investigation by Klasen (1999,2000 and 2002) revealed similar results. Examining
109 countries from 1960-1992, he runs both cross section and panel regressions with
ten- year intervals as one observation and includes regional dummies. The study uses
four different measure of inequality; initial level of education in 1960, female-male ratio
of total years of schooling in 1960, the annual absolute growth in total years of schooling
during 1960-1990 and the female-male ratio of the growth in years of schooling between
1960-1990. What’s unique in this paper is the author’s ability to estimate different
bounds of the impact of inequality on growth. When male education is used to measure
the average human capital then the upper bound estimate is calculated. This is because
the specification assumes that inequality can be decreased without affecting levels of
male education. However, if this assumption does not hold and increasing female
education leads to a proportionate decrease in male education, a lower bound estimate is
achieved. The paper not only considers the direct effects of inequality on growth, but
also its indirect effects through population growth (thereby the labour force growth) and
12. 12
investments. His finding is that initial levels of female-male ratio of schooling
achievements as well as female-male ratio of increase in the level of schooling have
positive coefficients that are also significant. He posits if the female-male ratio of growth
in schooling grew from 0.5 to 1.0 then annual growth rate would go up by 0.4%. His
main conclusion is that between 0.4-0.9% of the differences in growth rates between
East Asia and Sub Saharan Africa, South Asia, and the Middle East exist due to sizeable
gender gaps in education in the latter three regions. By limiting the sample to only
African Countries, it appears that the impact of this inequality is far more when
compared to the overall sample regression, indicating that gender bias in education
matters more in Africa than elsewhere. Additionally, it is shown that high inequality leads
to high fertility and child mortality rates. Klasen collaborates with Lamanna in 2009
(Klasen and Lamanna 2009) to test the above predictions with a sample till 2000 and
finds similar results.
Another paper by Esteve-Volart (2000) uses data for 87 countries from 1965-1989 to
assess the correlation between the female to male ratio is primary school in 1965 and the
per capita growth of real GDP. Like previous studies her overall measure of human
capital was secondary level schooling. The results are consistent with the fact that
increasing overall education, as measured by secondary schooling, and decreasing the
gender gap in primary education contributes positively to growth. Increasing male
educating does not necessarily hamper growth, because it does add to the stock of
human capital, but if this increase is not accompanied with an equal rise in female
education, gender inequality would rise and in turn have a dampening effect on growth.
Knowles et al. (2002) do things differently by assessing theory based specifications. They
augment the neoclassical growth model by adding female and male education as
13. 13
measures for gender disaggregated human capital, to find the long-run impact of
educational gaps on labour productivity. Using OLS estimation from 1960-1990, the
coefficient on female education is shown to be positive and statistically significant,
whereas the male counterpart is not significantly different from zero. Thus, high level of
labour productivity is associated with high female education. In response to Lorgelly and
Owen’s (1999) claim that Barro and Lee’s (1994) results were not robust to the omission
of the fast growing countries of East Asia, the authors carry out various sensitivity
analyses. Their results are robust to influential observations and instrumental variables
used to account for a possible simultaneity bias.
The studies mentioned above concentrate on the direct effects of reducing the gender
bias in education on growth. There are also a number of studies which emphasize the
indirect effects i.e. inequality may affect a variable which in turn impacts growth. Bloom
and Williamson (1998) hypothesize that the demographic transition (the shift from high
to low fertility and mortality rates) played a substantial role in the miraculous growth of
East Asia. About one third of the growth experienced by this region can be accounted
for by this ‘demographic gift’. Gender inequality enters the analysis because, improving
the gender gap in education leads to lower fertility rates among educated women. As a
result of this decreased fertility, the number of working age population rises whereas the
number of children fall. Thus an economy facing a growing working age population and
decreasing child dependency ratio will experience high levels of growth. But it is also
important to notice here that this effect cannot go on forever, because the dependency
ratio is bound to rise again, when the working age population gradually grows old.
14. 14
3 Variables and Data Sources
The data for this essay has been drawn from World Bank’s World Development
Indicators and Penn World Tables 7.1. The data set is in longitudinal form, which
involves 127 Countries over an 11-year period from 2000-2010. However, there were
some missing values, so I have 985 observations. The following variables are used in the
regression:
a) Growth of GDP per capita (Gdppcg)
Annual percentage growth rate of GDP per capita based on constant 2005 U.S Dollars.
(Source: WDI) Mean 2.60
b) Investment Share (Inv)
Investment Share of PPP converted GDP per capita (%). (Source: WDI) Mean 22.95
c) Population growth (Popgr)
Annual population growth (%). (Source: WDI) Mean 1.39
d) Gross Enrolment Ratio, Secondary (Grossen)
Total enrolment in secondary education expressed as a percentage of the population of
official secondary education age. (Source: WDI) Mean 77.53
e) Life Expectancy at birth (Lifeexp)
Total number of years a newborn infant would live if prevailing patterns of mortality at
the time of its birth were to stay same throughout its life. (Source: WDI) Mean 67.89
15. 15
f) Openness (Open)
The sum of exports and imports of goods and services measured as a share of gross
domestic product. (Source: Penn World Tables version 7.1) Mean 86.50
g) Ratio of girls to boys in primary and secondary education (Ratio)
Percentage of girls to boys enrolled at primary and secondary levels in public and private
schools. (Source: WDI) Mean 97.3
h) Ratio of girls to boys in primary school (RatioP)
Ratio of female to male primary enrollment is the percentage of girls to boys enrolled at
primary level in public and private schools. (Source: WDI) Mean 96
i) Ratio of female to male secondary enrolment (RatioS)
Ratio of female to male secondary enrolment is the percentage of girls to boys enrolled at
secondary level in public and private schools. (Source: WDI) Mean 97.4
j) Fertility rates
The number of children a woman is expected to give birth to if she were to live to the
end of her child bearing years. (Source: WDI) Mean 3.06
4 Empirical Strategy
My essay makes use of a panel data set instead of a cross section because human capital
is expected to contribute to growth and development in the long run. A cross sectional
16. 16
regression would not be able estimate the immediate impact of an increase in education
in that year as returns to education, as discussed above, are not instantaneous. Since
countries with high levels of education among males are likely to also have high levels
among females, adding the two variables as individual regressors will lead to
multicollinearity. Hence, similar to Chen (2004), I will use the ratio of girls to boys in
primary and secondary education as the main measure of inequality. The multicollinearity
problem is somewhat tackled because this ratio is very highly correlated to the entire
education stock of a country ( 𝜌 = 0.59). Although not perfectly uncorrelated, it is not
nearly as high as the correlation between total years of male and total years of female
education, when these two are taken as separate regressors (Brummet 2008). This essay
will also employ control variables that have been known to be highly significant in the
growth regression literature. After carrying out the hausman test, which rejected the
existence of random effects, the following fixed effects regression is estimated:
𝑔𝑑𝑝𝑝𝑐𝑔 = 𝛼 + 𝛽! 𝐼𝑛𝑣 + 𝛽! 𝑃𝑜𝑝𝑔𝑟 + 𝛽! 𝐺𝑟𝑜𝑠𝑠𝑒𝑛 + 𝛽! 𝐿𝑖𝑓𝑒𝑒𝑥𝑝 + 𝛽! 𝑂𝑝𝑒𝑛 + 𝛽! 𝑅𝑎𝑡𝑖𝑜 + 𝜀
(1)
My dependent variable is the growth rate of per capita GDP. Since human capital is
known to be influential in determining growth, in accordance with previous literature, I
use secondary school enrolment and life expectancy as measures of human capital
(Levine and Renelt 1992; Mankiw et al 1992). Investment share, population growth and
openness are regressors that are commonly included in cross-country growth regressions.
Lastly, the main measure of inequality is given by the ratio of females to males in both
primary and secondary education.
17. 17
As a robustness test, I will also re-estimate regression (1) using 2 alternate measures of
gender inequality. First, I will use the ratio of girls to boys in primary education to
measure inequality. Next, instead of primary education, I will examine the impact of an
increase in the ratio of girls to boys enrolled in secondary schools only.
A priori, we would expect Investment share, Gross Secondary Enrolment, and Openness
to have positive coefficients. Life expectancy is expected to have a negative coefficient
because, as the average lifespan of a person increases in a country, the dependency ratio
in that economy increases, a large burden lies on the working age population to take care
of their elders and as a consequence income in per capita terms will fall. The coefficient
of population growth also ought to be negative, as income is spread across more
individuals. If the claim of this essay is true, then it should be the case that the ratio of
girls to boys in primary and secondary education is positively correlated to growth. That
is to say, when more girls get educated, the growth rate increases through the channels
discussed above, namely, the selection distortion effect, the externality channel and the
fertility channel.
In order to empirically test the fertility channel, the following regression using fixed
effects estimation is carried out:
𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑡𝑦 = 𝛼 + 𝛽! 𝑃𝑜𝑝𝑔𝑟 + 𝛽! 𝑅𝑎𝑡𝑖𝑜 + 𝛽! 𝑔𝑑𝑝𝑝𝑐𝑔 + 𝛽! 𝐺𝑟𝑜𝑠𝑠𝑒𝑛 + 𝛽! 𝐿𝑖𝑓𝑒𝑒𝑥𝑝 + 𝜀 (2)
5 Results and Analysis
This section reports the results from the two regressions. Table 1 presents the fixed
effects coefficients and p-values. To allow for arbitrary autocorrelation between country
18. 18
variables over time, standard errors are clustered at the country level. The results are
indicative of the fact that gender inequality in education is inversely correlated with
economic growth. Said in other words, gender equality has a statistically significant
positive impact on economic growth. As expected, investment share and openness have
a positive impact on growth and is significant at less than 1 percent. Population growth
rate has a sizeable negative coefficient, which is also significant at less than 1 percent.
Life expectancy has a very small but significant negative coefficient. Gross enrolment in
secondary education has turned up to be negative, but is statistically insignificant. More
importantly, ratio of girls to boys in primary and secondary education is positively
correlated with growth and this result is significant at five percent.
Table 1
Source: STATA 12
R!
=0.13 F Test (P values)=0.000
When inequality is only observed in primary enrolment, the relation between the ratio
and growth, although positive, becomes insignificant. The significance of all the other
variables remains intact, except that the coefficient on life expectancy has increased from
Dependent
variable: gdppcg
Coefficient Robust Std.
Errors
P value
Constant 14.67 9.452785 0.123
Inv .27 0.0561436 0.000
Popgr -1.33 0.321745 0.000
Grossen -.01 0.021165 0.631
Lifeexp -.04 0.1515959 0.002
Open .05 0.0161555 0.003
Ratio .14 0.0629475 0.029
19. 19
-.04 to -0.57. The coefficients on the remaining variables are more or less same in
magnitude. The results can be seen in Table 2.
Table 2
Source: STATA 12
R!
=0.18 F Test (P values)=0.000
Table 3
Source: STATA 12
R!
=0.11 F Test (P values)=0.000
Dependent variable:
gdppcg
Coefficient Robust Std.
Errors
P value
Constant 21.22 9.119393 0.022
Inv -.030 0.0620375 0.001
Popgr -1.27 0.332999 0.000
Grossen -.003 0.0178655 0.834
Lifeexp -.57 0.1757043 0.000
Open .06 0.0139658 0.000
Ratio Primary .10 0.0932862 0.258
Dependent variable:
gdppcg
Coefficient Robust Std.
Errors
P
value
Constant 13.29 9.034323 0.044
Inv .24 0.0565902 0.000
Popgr -.15 0.3162007 0.000
Grossen -.01 0.0201241 0.605
Lifeexp -.34 0.1366227 0.014
Open .04 0.0146181 0.003
Ratio S .06 0.0335117 0.071
20. 20
Turning over to inequality in only secondary education, Table 3 reveals interesting
results. Unlike before, the ratio of girls to boys in secondary education is not only
positively related to GDP per capita growth, but is also significant at 10 percent. In
contrast to the regression that uses the ratio for both primary and secondary education,
the coefficient in this case is less than half of the earlier coefficient at 0.06, as against 0.14
(Table 1). For all other variables, the results are similar as in the previous two cases.
Table 4 enumerates the results from the fixed effects regression of fertility rates. All the
variables are statistically significant at less that 1 percent. GDP per capita growth and life
expectancy are inversely related. The important thing to notice is the significant negative
coefficient of ‘Ratio’. These results lend favorable evidence to the fact that as more girls
get educated, they have a larger say in matters like fertility. Since the opportunity cost of
bearing children will rise with the educational attainment of a woman, a family may
decide to have fewer children.
Table 4
Source: STATA 12
R!
=0.47 F Test (P values)=0.000
Dependent
variable:
Fertility Rates
Coefficient Robust Std.
Errors
P value
Constant 8.05 0.2603203 0.000
Gdppcg -.005 0.0011387 0.000
Popgr .11 0.0121936 0.000
Grossen -.003 0.0007137 0.000
Lifeexp -.04 0.0038216 0.000
Ratio -.02 0.0018466 0.000
21. 21
Caveats and Drawbacks
The cross-country regression presents an average impact for all the 127 countries
included in the sample, but due to the presence of varying unobservables across different
countries, its is highly improbable that average effects will be equal across all individual
countries. Moreover, missing observations and measurement errors problems always
plague growth regressions. I discuss three main drawbacks in detail (Bandiera and Natraj
2013).
a) Reverse Causality
An immediate concern of the above analysis as well as existing literature is the use of
cross-country regressions. Such a research design is limited in its capacity to establish
causality. To establish causality it must be that changes in gender inequality in education
are exogenous to economic growth. However this seems highly unlikely. The observed
variation in the ratios is almost certainly endogenous to growth as growth does dictate
how households make education decisions for their children. The general consensus
among economists is that growth has positive effects on gender inequality i.e. growth
promotes equality amongst the genders. Since economic growth relaxes the constraints
faced by families in poverty, these families become less vulnerable and no longer have to
make decisions at the margin of subsistence (Duflo 2012). Rose (1999) finds that in
India, poor households sacrifice the welfare of girls when they cannot feed everybody. If
the financial situation of such a household improves, due to increases in per capita
income, they are less likely to discriminate against girls. An equivalent argument can be
made for education. Constrained families are forced to educate only the elder male child
of the family because they cannot afford to send all their children to school, irrespective
of whether the younger children are boys or girls. Relaxation of these constraints on
22. 22
account of economic growth can facilitate parents in sending all their children to school,
thereby reducing the gender bias.
The sample of countries observed includes countries that are differing in various facets,
like income and stage of development. It is plausible for countries to have differences in
the amount of gender inequality because they are at different stages in the process of
development.
Dollar and Gatti (1999) are one of the few studies that test the possibility of reverse
causality. They estimate a simultaneous model of growth and inequality for many
countries at varied stages of development between 1975-1990. Initially when using the
full set of countries, the estimates turned out to be insignificant. Nevertheless, by limiting
the sample to countries which have 10.35% or higher rates of female secondary
enrolment, they found that for less developed countries, female and male education had
a very low and insignificant effect on GDP per capita growth. For more developed
countries the coefficient on male education had a weak negative effect, but the female
coefficient was strong and positive. The authors comprehend this as a convex
relationship between female secondary attainment and per capital income. They explain
the implication of this is that, as income rises to about $2000 per capita (PPP adjusted)
income, female educational rates are unlikely to catch up to their male counterparts. In
contrast, after surpassing the $2000 per capita income level, this trend seems to reverse
so that for the poorer countries there appears to be no relationship between female
attainment and growth but for the richer countries there is a significant positive relation.
Similarly Esteve-Volart (2000) also finds a convex relation between growth and
inequality. She proposes that as countries get richer, gender gaps in schooling narrow
down and this narrowing effect feeds back into the growth process and increase incomes
further.
23. 23
Easterly (1999) finds that income and gender inequality in education are negatively
correlated across countries. On the other hand, he finds that no correlation exists within
a given country. This means, that as a country becomes richer the gender gap does not
seem to diminish. Such varying accounts on the impact of growth on gender inequality
demonstrates that if a causal relation does exist between the two variables, in either way,
whether it is growth affecting inequality or vice versa, it is neither conclusive nor stable
across time and countries.
In order to circumvent this issue, the instrumental variable technique is used. A good IV
will be one that is correlated with gender inequality, and only reports that part of changes
in gender inequality that are not related to growth. Finding such a variable is extremely
difficult, especially because any macroeconomic variable used in place of gender
inequality will be related to growth. Dollar and Gatti (1999) use religion variables and
civil liberties as instruments for male and female education. But many studies including
Barro and McCleary (2003) and Cavalcanti et al. (2007) have highlighted that a
correlation between religion and growth exists, thus making the instruments invalid.
b) Omitted Variables
Another problem can be that that the positive correlation between the ratio of girls to
boys in both primary and secondary school and growth of GDP per capita is merely
reflecting the impact of variables that do not constitute the model. If this is true, then an
omitted variable bias means that the estimated coefficients of the ratios are overstated as
they pick up the effects of such omitted variables too.
Kremer and Miguel (2004) and Maluccio et al (2009)
establish that health improvements
affect both gender bias in education and economic growth. The papers carry out
randomized control trials to estimate the effect of an exogenous increase in the health
24. 24
status of children. The treatments in the two papers are deworming and nutritious food
supplements respectively. Kremer and Miguel (2004) find that the deworming program
that was carried out in Kenya increased primary school participation in treatment schools
by 7.5 percentage points and reduced school absenteeism by one quarter. They detect
significant spillover effects in the control group schools that also experienced a positive
effect on school participation for both boys and girls. The results in Maluccio et al (2009)
suggest that the treatment had a greater impact on schooling outcomes for girls as
compared to boys. To cite another example, Jayachandran and Lleras-Muney (2009)
evaluates the impact of a drop in maternal mortality in Sri Lanka between 1946 and 1953.
They find that 70% of the reduction in mortality increases female literacy by 2.5% and
female years of education by 4.0%
These studies, taken together, suggest that there exists a third variable like health
between gender inequality in education and growth. This variable can lead to variations
in gender inequality that can have direct or indirect consequences for growth.
To rectify the omitted variable problem, a regression must include all relevant variables.
Apart from the problems associated with identifying the entire set of such variables,
comes the issue of degrees of freedom. In panel data sets, each country is treated as a
separate observation, and adding more regressors will quickly expend the available
degrees of freedom.
c) External Validity
An implicit assumption present in the cross-country regression is that the relationship
between growth and gender equality in education in one country is tantamount to
another. Hence it predicts the relationship to be the same across all cross sectional units.
The one coefficient on ‘Ratio’ is expected to encompass the entire causal effect of
25. 25
improvement in the gender gap on growth for all countries and over the entire time
period. Such a universal parameter rarely exists. In other words, internal validity does not
necessarily indicate external validity. Caselli et al (1996) add country fixed effects in their
regression and analyse variations in gender inequality within countries instead of across
countries. Like Dollar and Gatti (1999), they find that the impact of female educational
attainment varies with the type of countries. For less developed countries the effect is
non-existent, but for more developed countries, the female coefficient of education has a
strong positive impact on growth. In short, evidence suggests that the magnitude of
impact may vary across countries and time.
6 Conclusion
Although promoting gender equality needs no justification and is an end in itself, this
essay examines the positive effects of not discriminating against women in education.
Using a panel data set of 127 countries from 2000-2010, this paper provides some
indicative evidence of the positive relationship between gender equality in education and
economic growth. When inequality is measured in only secondary school and in both
primary and secondary school, the correlation is significant. However, when analyzing
the ratio of girls to boys in primary education, the results become insignificant. The
results also reveal that a reduction in the gender gap or an increase in the ratio has
significant negative effects on fertility rates. One of the main channels via which growth
is affected is through greater accumulation of human capital. When this artificial
restriction to the pool of human capital is removed, so that more educated women can
enter the labour force, the human capital productivity of such an economy rises, which
contributes to its growth. Educating girls now, also has positive spillover effects for the
future stock of human capital, as better-educated mothers are more concerned about
26. 26
their children’s education and nutrition. Educated women are more likely to have a larger
say in family planning and may decide to have fewer children, due to the rising
opportunity cost of bearing more number of children. This leads to a decline in the
overall fertility rate in a country that contributes towards growth positively through the
‘demographic gift’.
It is duly acknowledged that cross country regressions are only of limited interest because
the aggregate estimates they present are rife with problems, such as measurement errors,
omitted variables, reverse causality. These issues make cross-country estimates of limited
use for guiding policy. To transcend these problems micro level studies need to be
carried out.
28. 28
Ø Dollar, David, and Roberta Gatti. Gender inequality, income, and growth: are
good times good for women?. Vol. 1. Washington, DC: Development Research
Group, The World Bank, 1999.
Ø Easterly, W. (1999). Life during growth. Journal of economic growth, 4(3), 239-276.
Ø Esteve-Volart, B. (2000). Sex discrimination and growth. International Monetary
Fund.
Ø Esteve-Volart, B. (2004). Gender discrimination and growth: Theory and
evidence from India. Vol.
Ø Gertler, Paul and Harold Alderman (1989). “Family resources and Gender
Differences in Human Capital Investments,” paper presented at the conference
on “The Family, Gender Differences and Development,” Economic Growth
Center, Yale University
Ø John Ward, Bernice Lee, Simon Baptist and Helen Jackson, 2010. “Evidence for
Action: Gender Equality and Economic Growth”. The Royal Institute of
International Affairs. Chatham House and Vivid Economics.
https://www.chathamhouse.org/sites/files/chathamhouse/public/Research/En
ergy, %20Environment%20and%20Development/0910gender.pdf
Ø Kabeer, Naila, and Luisa Natali. "Gender Equality and Economic Growth: Is
there a Win‐Win?" IDS Working Papers 2013.417 (2013): 1-58.
Ø King, Elizabeth M., and M. Anne Hill, eds. Women's education in developing
countries: Barriers, benefits, and policies. World Bank Publications, 1997.
Ø Klasen, S., & Lamanna, F. (2009). The impact of gender inequality in education
and employment on economic growth: new evidence for a panel of countries.
Feminist economics, 15(3), 91-132.
Ø Klasen, Stephan. "Does gender inequality reduce growth and development?
Evidence from cross-country regressions." (2000).
Ø Knowles, S., Lorgelly, P. K., & Owen, P. D. (2002). Are educational gender gaps
a brake on economic development? Some cross‐country empirical
evidence. Oxford economic papers, 54(1), 118-149.
Ø Levine, R., & Renelt, D. (1992). A sensitivity analysis of cross-country growth
regressions. The American economic review, 942-963.
Ø Lorgelly, P. K., & Owen, P. D. (1999). The effect of female and male schooling
on economic growth in the Barro-Lee model. Empirical Economics, 24(3), 537-
557.
Ø Maluccio, J. A., Hoddinott, J., Behrman, J. R., Martorell, R., Quisumbing, A. R.,
& Stein, A. D. (2009). The impact of improving nutrition during early childhood
on education among Guatemalan adults*. The Economic Journal, 119(537), 734-763.
29. 29
Ø Mankiw, N. Gregory, David Romer, and David N. Weil. A contribution to the
empirics of economic growth. No. W3541. National Bureau of Economic
Research, 1990.
Ø Miguel, E., & Kremer, M. (2004). Worms: identifying impacts on education and
health in the presence of treatment externalities. Econometrica, 72(1), 159-217.
Ø Perotti, R. (1996) ‘Growth, Income Distribution, and Democracy: What the Data
say’, Journal of Economic Growth 1.2: 149-87
Ø Rose, E. (1999). Consumption smoothing and excess female mortality in rural
India. Review of Economics and statistics, 81(1), 41-49.
Ø Seema Jayachandran & Adriana Lleras-Muney, 2009. "Life Expectancy and
Human Capital Investments: Evidence from Maternal Mortality Declines-super-
," The Quarterly Journal of Economics, MIT Press, vol. 124(1), pages 349-397,
February
Ø Seguino, S. (2000). Gender inequality and economic growth: A cross-country
analysis. World Development, 28(7), 1211-1230.
Ø Sen, Amartya. 1990. “More than 100 Million Women Are Missing.” New York
Review of Books 37 (20).
Ø Solow, Robert M. "A contribution to the theory of economic growth." The
quarterly journal of economics (1956): 65-94.
Ø Stokey, N. L. (1994, June). Comments on Barro and Lee. In Carnegie-Rochester
Conference Series on Public Policy (Vol. 40, pp. 47-57). North-Holland.
Ø Stotsky, J. G. (2006). Gender and its relevance to macroeconomic policy: A
survey. IMF Working Papers, 1-68.
Ø World Development Indicators, The World Bank, Online Database available at
http://data.worldbank.org/indicator.
