msc extended essay 2015

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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

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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.

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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

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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.

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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

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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.

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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.

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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.

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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

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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’.

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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

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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

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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.

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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

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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

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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.

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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

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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

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-.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


Table 3




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



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

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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


Dependent variable: Fertility Rates


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

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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

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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.

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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

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

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

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.

27

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