Survey

The comparative role of determinants of household-level consumption expenditure inequalities (henceforth, inequalities) in rural India between two sub-periods, 1994–2005 and 2005–12 are examined, using three rounds of the National Sample Survey Consumer Expenditure Survey. The changes in the components of consumption expenditure and population characteristics are explored that explain inequalities during the two sub-periods, which represent distinct policy environments. We use both a priori and regression-based decomposition methods for the analysis. We find that there is a complete reversal of the role of education in explaining inequalities. It shifted from being an inequality-increasing factor during 1994–2005 to an inequality-equalising factor during 2005–12. This reversal is induced by decreasing consumption returns to education due to the depressed job market. The role of locational factors has increased in explaining the increase in inequalities over time. The non-food components induce an increase in the overall inequalities via an increased expenditure on durables. The within-group component contributes the most to the level of and change in inequalities.

Researchers have evaluated the quality of Sample Registration System data in the past and found errors in the estimated under-fi ve mortality rate. Discrepancies were observed in the age-specifi c death rates at ages 0–1 and infant mortality rate for India and state published by the Offi ce of the Registrar General of India in the SRS statistical report for 2018 and it was found that the published IMR and U5MR in the statistical report of many of the states are substantially different from the estimated IMR and U5MR in the life table constructed from the ASDRs published by the ORGI. Such errors are likely to incorrectly measure mortality gaps across population subgroups, for example, gender.

Despite efforts by various agencies to collect statistics on biotechnology, there is a paucity of reliable data leading to confounding estimates of the size of the industry. India needs to urgently evolve a universally acceptable definition and construct a framework for collecting data.

Most earlier studies exploring the cross-ountry variation in the degree of income inequality (measured by Gini ratio) are valuable but for the inappropriate data on inequality comprising estimates of Gini from incomparable data sets such as national and sub-ational surveys, per person and per household income distributions, and income and expenditure distributions. This paper explores the cross-ountry variation in income inequality based on relatively more comparable data and suggests that public policy aimed at reducing inequality and freedom may be the key variables that explain the variation in the Gini across countries. Prospects for launching national policy initiatives to reduce inequalities are rather dim since globalisation could be conveniently used to rationalise a relatively high level of income inequality.