IV) The interaction of wealth and gender: gender differences in enrollment by wealth, and wealth differences by gender.
In order to investigate the interaction of wealth and gender and educational outcomes, the first four columns of Table 6 report the enrollment of 6 to 14 year olds disaggregated by wealth as well as by gender. The subsequent columns report the gender gap (ratio) by wealth group, and the wealth gap (ratio) by gender. (16) In order to ease the interpretation of this table, the left panels of Figure 2 plot the gap (ratio) among the poor against the gap (ratio) among the rich. Countries with points above the diagonal line are those where the gender gap (ratio) is larger among the poor than among the rich.
[Table 5 about here]
[Figure 2 about here]
The points in the top left hand panel of Figure 2 separate (perhaps not perfectly) into four main groups. The first is a group of countries where the female disadvantage is small, or negative, both for the rich and for the poor (that is less than about 9 percentage points). The second group is the group for which the female disadvantage is large for both the rich and for the poor. This group separates into the primarily Western African countries where it is slightly larger for the rich than for the poor (Benin, Burkina Faso, Cote dIvoire, Mali, Niger, and Senegal) and countries for which it is slightly smaller (Chad, Comoros, Togo, and Turkey). Next there is a group with low female disadvantage among the rich, but a reasonably large (greater than about 9 but less than about 15 percentage points) disadvantage among the poor (Mozambique, Guatemala, Uganda, and Cameroon). Last there is a group made up primarily of the North African and South Asian countries where the gender disadvantage is small among the rich but quite large among the poor (Egypt, Pakistan, India, Central African Republic, Nepal, Morocco). (17)
The somewhat different message conveyed by the lower left panel shows the relevance of using the differences versus the ratios approach to analyzing the gender disadvantage. By contrast to the absolute differences, the relationship between the male/female ratios among the rich and poor separates into three main groups. First, the group where the ratio is very close to one (less than 1.1) for both groups. Second, a group where the ratio is either small or moderate among the rich and moderate (between 1.1 and 1.5) among the poor (Bolivia, Cameroon, Comoros, Egypt, Guatemala, Mozambique, Nepal, Togo, Turkey, Uganda). Last is the group with a small or moderate ratio among the rich, but a large ratio for the poor (Benin, Burkina Faso, Central African Republic, Chad, Cote dIvoire, India, Mali, Morocco, Niger, Pakistan, Senegal).
Wealth differences in enrollment by gender
In contrast to the gender gaps by wealth, the right panels of Figure 2 show much more consistency between wealth gaps among males and females: in most countries the gap and the ratio are close to being equal for boys and girls. There is a group of countries however where the wealth gap is substantially larger among females than among males. The countries with the largest discrepancies (starting with the highest) are Pakistan (35 percentage points for boys and 64 percentage points for girls), Egypt (17 for boys and 39 for girls), and India (34 for boys and 55 for girls). Cameroon, Central African Republic, Mozambique, Morocco and Nepal are all close behind. In this case, the same set of countries is identified as having large discrepancies when using the ratios as the measure of disparity.
International correlates of the gender gap
In the descriptive exercise so far region appears to be a strong correlate of gender disparities. Figure 3 explores the relationship to four country level correlates in a series of bivariate scatterplots between the magnitude of the male-female gap and (the log of) GNP per capita in Purchasing Power Parity (PPP--which adjusts for differences in the cost of living across countries), income inequality as measured by the Gini index, income growth as measured by the GNP per capita growth rate, and public spending on primary education per student. All these variables are from the World Banks World Development Indicators (World Bank, 1999) and are averaged over the period since 1990 (Annex Figure 3 a shows the same figures for the male/female ratio).
[Figure 3 about here]
The story that emerges from these graphs is not one of a systematic relationship between the variables and the magnitude of the gender gap. The only correlate with a significant relationship at the ten percent level is a country s income inequality as measured by the Gini index (correlation coefficient equal to -.38, pvalue=.07, N=23). Other than the Gini index, income level is negatively but insignificantly related to the male female gap, and GNP per capita growth and public spending per student on primary education have close to zero and insignificant correlations. Of course this exploration is limited by its very narrow bivariate approach. As an indication though, the results do suggest that the few variables analyzed do not give a strong lead on this and more work needs to be done to explore the international correlates and determinants of gender gaps in education (for more discussion and a further exploration of this see Dollar and Gatti, 1999, Filmer, King, and Pritchett 1998).
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|Title Annotation:||The Structure of Social Disparities in Education: Gender and Wealth|
|Publication:||The Structure of Social Disparities in Education-Gender and Wealth|
|Date:||Nov 1, 1999|
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