Decomposition of the Gini coefficient using Stata
The Gini coefficient is widely used to measure inequality in the distribution of income, consumption, and other welfare proxies. Decomposing this measure can help you understand the determinants of inequality. In this presentation, I will use income data from Mexico to illustrate a user-written command, descogini, that implements the Gini decomposition proposed by Lerman and Yitzhaki (1985, Review of Economics and Statistics 67: 151–156). Using this command, the Gini coefficient for total income can be decomposed in three terms: how important the income source is with respect to total income; how equally or unequally distributed the income source is; and how the income source and the distribution of total income are correlated. In the presentation, I will also illustrate how to obtain the impact that a marginal change in a particular income source will have on total income inequality, as well as how to obtain bootstrap standard errors.
Decomposing Inequality and Obtaining Marginal Effects
This article describes a user-written command, descogini, that decomposes the Gini coefficient by income source and allows the calculation of the impact that a marginal change in a particular income source will have on inequality. descogini can be used with bootstrap to obtain standard errors and confidence intervals.
Did the metamorphosis from Export Processing Zones to Special Economic Zones improve the efficiency of trade enclaves in India?
In this paper, the efficiency of seven conventional Special Economic Zones in India is analyzed, by adopting a stochastic production frontier technique, for the period between 1986–1987 and 2007–2008.Results indicate that although the efficiency scores of these enclaves have improved over the years, specifically with the introduction of the SEZ policy (2000–2001), it is far below the threshold level as identified in the literature. Besides, one can also observe a discrepancy emerging between better export earning zones and efficient zones. On the determinants side, policy intervention and large geographical area, accompanied by clustering of units, are found to have impacted the efficiency scores of these enclaves positively.