Gender Disparity: Its Manifestations, Causes and Implications

Spatial Distribution of Gender Inequalities: Districts as Eye Openers
In order to get a clearer picture of the 127 districts, we have presented eight scatter diagrams (Figure 1 through 8) relating to sex ratio, women education, work participation and economic development in two-dimensional plane for the year 2001. Figure 1 relates to infant sex ratio in rural and urban areas. The scatter plots are almost perfectly related in a positive way thereby implying that the districts with higher value of rural sex ratio do also have higher value in urban areas, and vice versa. On the other hand, infant sex ratio and purchasing power do not reveal any clear direction in rural areas, whereas sex ratio is completely independent of purchasing power (Figure 5) and HCR (Figure 6) in urban areas. As evident from Figure 2 in rural areas, districts with less than 20% poverty ratio contain wide variations in infant sex ratio ranging from the lowest value of 770 (Ambala district in the richer state of Haryana) and the highest value of 999 (Korba district of a poor tribal state of Chhatisgarh) in 2001. But HCR above 50% till 88% corresponds to above 1000 sex ratio reaching the maximum of 1017 in a tribal district called Dantewada of the same poor state, Chhatisgarh. Moreover, for HCR above 30%, higher poverty is associated with higher sex ratio. It is therefore obvious that the poorer regions have performed better in maintaining higher sex ratio thereby upholding the natural law.

In addition to these, Figure 3 presents another shocking feature that gross gender inequality (GGI) in rural areas is largely independent of infant sex ratio except very high values of the latter where there are some variations in GGI. On the whole, for an average value of GGI = 2, there are all possible combinations of sex ratios from 770 to 1017. Quite contrary to this, higher female literacy in rural areas is negatively related to GGI (Figure 4) thereby making female literacy an agenda for equality. Finally, GGIR appears to be independent of WPRF in rural areas (Figure 7), and urban infant sex ration is positively associated with WPRF (Figure 8). But in both cases, if the poorest district namely The Dang (situated in a richer state, Gujarat) is deleted, the association becomes a bit disperse. All the other two-dimensional coefficients have not shown any confirmed direction. Therefore, as revealed from the above discussion, the so-called better off districts display intense gender inequality except Kerala, while the worse off display a mixed picture. Finally, rural and urban gender disparities particularly in sex ratio are highly similar across the districts. In the absence of any systematic time series data, these findings have prompted us to undertake two types of OLS regression, one with 2001 cross-section data for 127 districts and the other with 1991 and 2001 pooled data set with one regional dummy and another time dummy separately for rural and urban areas. The results are statistically satisfactory, and in many cases are prima facie shocking to popular analysts.