Income Inequality in Indian States

The motivation of this paper is to report and compare income inequality measures across the states in India. There is no previous published research on income inequality across states. The research on economic inequality in India has mostly focused on inequality in consumption expenditures as collected in the National Sample Surveys (Chancel and Pikkety 2019; Vakulabharanam 2010; Subramanian and Jayaraj 2013; Chamarbagwala 2010; Denninger and Squire 1996; Ghosh nd; Chakravorty et al 2016; Frazer 2006; Pal and Ghosh 2007; Mazumdar et al 2017; Cornia and Court 2001; Cornia 2003; Chauhan et al 2015; Jha 2000; Heshmati 2004; Mishra and Parikh 1997). Even the most comprehensive sources on international income equality across countries include Gini coefficient measures for India, based on consumption expenditure and not income (UNU-WIDER 2021; WID.world 2021). This is because income data has been lacking for India to estimate the income inequality measures. While there has been research on convergence of gross state domestic products (GSDPs) in India (Das et al 2015), there has been no research looking at differences in within-state income inequality across states, and the trends in within-state income inequality across time. Therefore, this paper fills an important gap in our understanding of income inequality in India.

Issues on income inequality have received much attention in the recent past. Pioneering research on top incomes has pointed to increasing income inequality in the past few decades since the 1980s (Chancel and Piketty 2019; Piketty and Saez 2013; Atkinson et al 2011; Chancel and Piketty 2021). However, top incomes reflect only a limited view of income inequality; therefore, other measures like the Gini coefficient are also popular in contemporary research (Vakulabharanam 2010; Jaikumar and Sarin 2015; Subramanian and Jayaraj 2013; Bhaumik and Chakrabarty 2006; Ang 2010; Denninger and Squire 1996; Pavcnik 2011; Chakravorty et al 2016; Ranganathan et al 2016; Rawal and Swaminathan 2012; Pickett and Wilkinson 2015; Frazer 2006; Jha 2000; Mishra and Parikh 1997). In addition, data on incomes at household or individual level is also difficult to come by, especially in developing countries. Therefore, sometimes consumption expenditure is proxied for income (UNU-WIDER 2021); this is the case for India. However, income data has become available through national surveys (Desai et al 2005, 2012). This paper takes advantage of the data for households, obtained from national surveys, to estimate the Gini coefficient and other income inequality measures for Indian states. The Gini coefficient, with its intuitive understanding related to the Lorenz curve, is a popular measure of income inequality. However, it does not meet many of the desirable criteria that a measure of inequality should (Cowell 1995). This paper, therefore, reports measures of inequality from generalised entropy (GE) and Atkinson group of measures in addition to the Gini coefficient. These additional measures are theoretically appealing as they are derived from intuitively appealing axioms based in economic theory. These measures, taken together, capture different aspects of income inequality and give more or less weight to inequality at different parts of the income distribution. For example, GE(a) measure with low values of parameter a, gives more emphasis to inequality in the lower part of the distribution, so does Atkinson’s measure with low inequality aversion parameter.