Abstract
Researchers in demography, the labour market and health have observed that North Indian women face greater discrimination than women in other zones. This study examines whether a similar pattern is replicated with respect to completion of school education. We find that gender disparities are higher in northern states in rural areas. In urban areas, however, eastern states display greater disparities. This is also confirmed if we control for household traits, community characteristics and the regional context. However, when we decompose the differences in probability of completing school education across gender, the contribution of the control variables is found to be insignificant, relative to that of the coefficient effect (which is sometimes put forward as a measure of discrimination) in both rural and urban areas of Eastern India. The divergence in regional pattern of gender disparity from patterns observed for demographic and health indicators shows that gender discrimination is a complex multilayered phenomenon and the interaction between these layers may assume unexpected forms.
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Notes
In India, this is also referred to as completing Higher Secondary level of education.
This observation was explained by Dyson and Moore (1983) in terms of cultural practices: the prevalence of endogamous marriages in South India meant that women had more access to their kin, thereby increasing autonomy. Rahman and Rao (2004), however, fail to find empirical support for this explanation. Alternative explanations have been offered for this phenomenon. Bardhan (1974) and Miller (1981) have argued that the prevalence of wet rice cultivation in southern states has created a demand for women’s labour, increasing their participation in economic activities; this has empowered South Indian women. Rosenzweig and Schultz’s (1982) analysis also associated differences in child survival rates to differences in male and female labour force participation rates. Jeffrey (1993), on the other hand, linked lower levels of gender disparities in the south to higher levels of State investment in education and health. Murthi et al. (1995) and Das Gupta et al. (2004) also argued that public investment in these spheres in states like Kerala and Karnataka has promoted female agency and reduced gender differences in demographic outcomes.
Kundu and Rao (1986) showed that the original index proposed by Sopher failed to satisfy the axiom of additive monotonocity: if a constant is added to all observations in a non-negative series, ceteris paribus, the inequality index must report a decline.
The objective of taking the log is to reduce the leveling-off effect: states with high levels of attainments may show a lower level of disparity than states with low levels of attainments even though the gender gap is the same for both states.
As the dependent variable is binary (whether respondent has completed schooling or not) a logit model is used for this purpose.
In addition, to check robustness of the results, we also estimated logit models at the all-India level incorporating interaction dummies for gender and zone.
The 18–25 years group has been formed to maintain parity with subsequent econometric analysis.
An exceptionally high disparity level is observed in rural West Bengal (0.48). This is surprising, given the long period of rule by a coalition of Leftist parties and the impressive record of land reforms in the state. Cohort-wise analysis, however, reveals that gender disparities were extremely high after Independence (possibly because of the migration patterns after partition), and fell gradually since then, with a sharp fall in the 1980s, when the positive effect of land reforms would take place with a lag.
Christian missionaries in India have played an important role in spreading education, particularly in areas where there has been a high rate of conversion, as the Missions offered basic schooling to local children. This was very important as Christian missionaries tended to focus on backward areas with underdeveloped infrastructural facilities.
That is sex ratio for children aged 0–6 years.
Consistent results are obtained for [4]. The coefficients for interaction dummies only are reported:
CSR (Total) = 0.36 F*N + 0.39 F*E + 0.49 F*S
CSR (Rural) = 0.26 F*N + 0.32 F*E + 0.40 F*S
CSR (Urban) = 0.51 F*N + 0.48 F*E + 0.48 F*S
when all coefficients are significant at 1% level. ORs for other control variables are not reported to economize space. Complete results are given in Appendix Table 10.
Comparison of ORs between North and East interaction dummies reveal that ORs are lower for the North interaction dummy for India-Total and India-Rural samples. In the urban sample, however, the OR for the East interaction dummy is lower. This is consistent with the result reported in Table 3, that gender disparity is higher in the North than the East in India-Total and India-Rural, but lower in India-Urban.
In fact, the coefficient effect is found to be consistently greater than 100% in Eastern India. This implies that socio-economic characteristics explain less than nothing of the gender disparity. Another interpretation of the coefficient effect being greater than 100% is that the socio-economic context in Eastern India is actually more favourable for girls, and should have led to girls having higher probabilities than boys in completing school education. However, the coefficient effect is so strong that it reverses the situation, and results in an inferior outcome for girls.
This follows from by the insignificant t-value of TSPOP in the univariate regression of coefficient effect on TSPOP.
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Acknowledgments
The paper was presented at the Annual Conference on Growth and Development at the Indian Statistical Institute, Delhi on 16–18 December 2010. The author is grateful for comments from Geeta Gandhi Kingdon. In addition, discussions with Mousumi Dutta and Ankush Agarwal helped to shape the final outcome. Comments by the two anonymous reviewers greatly sharpened the thrust of the paper. The author alone remains responsible for remaining errors.
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Husain, Z. Gender disparities in completing school education in India: explaining geographical variations. J Pop Research 28, 325–352 (2011). https://doi.org/10.1007/s12546-011-9070-5
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DOI: https://doi.org/10.1007/s12546-011-9070-5