Gender Disparity: Its Manifestations, Causes and Implications

II. Data, Methodology and Coverage

Major gender-wise information across the districts utilized here pertaining to 1991 and 2001 are given in the Appendix on Data. Depending on the availability of district level head count ratio (HCR), inequality (Lorenz ratio) and purchasing power (Monthly Per Capita Consumer Expenditure- MPCE) with rural urban divisions from NSS 61^st Round (2004-05), we have selected six states of India for 2001. They are composed of 127 districts. The states are Assam, Chhatisgarh, Gujarat, Haryana, Kerala and Orissa. As Chhatisgarh was not existence in 1991, we had limit ourselves to rest five states which had in total 84 districts in 1991. District-wise information along with decadal shift and rural urban differences in sex ratio are reported in Table 1. Among the six states, Kerala, Haryana and Gujarat are relatively developed, whereas Assam, Chhatisgarh and Orissa are among the relatively backward states. More specifically, Kerala is the most developed state, and Orissa is the most backward according to NSS 61^st Round (Ghosh and Chatterjee, 2005; Ghosh and Gupta, 2008). At the state level, Kerala has lowest HCR in rural areas, 13.20%, whereas Orissa has the highest, 46.80% in 2004-05. In urban areas, Assam has the least HCR, 3.30%, and Orissa has the highest, 44.30%. In purchasing power, Kerala is at the top and Orissa at the bottom. Rural versus urban differences in purchasing power is the lowest in Kerala and highest in Assam. On the other hand, Gujarat has lowest inequality (Lorenz ratio being 0.304) and Kerala has the highest (0.40) in urban areas. Assam has the lowest (0.197) and Kerala has again the highest (0.341) in rural areas. Apart from these detailed district-wise data, we have also used state-wise male female wage disparity for the most vulnerable economic class in India namely the agricultural labourers.

As to measurement, there are various methods for deriving some concrete scores of gender disparity (male versus female)⁸ in terms of rural and urban areas and across the states as well as the districts (UNO, Bardhan and Klasen, 1999; Pal, Ghosh and Bharati, 2005; Ghosh et al. 2007-09).⁹ There is abounding evidence in the literature to show that in the developing world, whatever method one follows gender disparities appear to be intense and widespread. The same is true for Indian states too and more so in the districts. We intend to concentrate on linking the simplest form of gender inequality with other important socio-economic factors rather than elaborating on estimated differences among different methods. We have estimated gross gender inequality (GGI) with the help of a simple formula as mentioned below. As is well known, most of the existing measures of gender inequality can be reduced to the following simple formula under normal situation: