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This paper describes the correlations between inequality and the growth rates in cross-country data. Using nonparametric methods, we show that the growth rate is an inverted U-shaped function of net changes in inequality: changes in inequality (in any direction) are associated with reduced growth in the next period. The estimated relationship is robust to variations in control variables and estimation methods. This inverted U-curve is consistent with a simple political economy model but it could also reflect the nature of measurement errors, and, in general, efforts to interpret this evidence causally run into difficult identification problems. We show that this non-linearity is sufficient to explain why previous estimates of the relationship between the level of inequality and growth are so different from one another.
Keywords: inequality, growth, cross-country regressions
JEL classification: 011, 015
(ProQuest Information and Learning: ... denotes formulae omitted.)
1. Introduction
It is often that the most basic questions in economics turn out to be the hardest to answer and the most provocative answers end up being the bravest and the most suspect. Thus it is with the empirical literature on the effect of inequality on growth. Many have felt compelled to try to say something about this very important question, braving the lack of reliable data and the obvious problems with identification: Benabou (2000) lists 12 studies on this issue over the previous decade, based on cross-sectional ordinary least squares (OLS) analyses of cross-country data.
More recently, the literature received a substantial boost from the important work of Deininger and Squire (1996), who put together a much larger and more comprehensive cross-country data set on inequality than was hitherto available. Most importantly, their data set has a panel structure with several consecutive measures of income inequality for each country. This has made it possible to use somewhat more advanced techniques to investigate the effect of inequality on growth: Benhabib and Spiegel (1998), Forbes (2000), and Li and Zou (1998) all look at this relationship using fixed effects estimates, arguing that there are omitted country specific effects that bias the OLS estimates. In contrast, Barro (2000) uses a three-stage least squares (3SLS) estimator which treats the country-specific error terms as random, arguing that the differencing implicit in running fixed effects (or fixed...