3 Drawing on the recent literature on the formal measurement of inequality of opportunity – as distinct both from income or wealth inequality and from economic mobility – this paper seeks to address that question directly. Is it possible that inequality – like cholesterol – comes in many varieties, and that some are worse for the health and dynamism of an economy than others? In particular, is it possible that the two broad categories of sources of inequality suggested by Roemer (1998) – opportunities and efforts – have opposite effects on economic performance? If so, one reason for the ambiguity in past empirical studies of the relationship between inequality and growth might have been the failure to distinguish between the two types of inequality.

Unfortunately, measures of inequality of opportunity were not readily available for a large number of countries, in the way that income inequality measures were in the Deininger and Squire (1996) dataset, or the World Income Inequality Database of WIDER. We therefore constructed original measures of inequality of opportunity from unit-record data from 118 income or expenditure household surveys (IES) for 42 countries, and 134 Demographic and Health Surveys (DHS) for 42 countries. These indices were combined with information on the other explanatory variables used by Forbes (2000), which are illustrative of the set of regressors typically used in the literature. Although we use the same Difference GMM specification as Forbes (2000) for comparison purposes, we also draw on more recent developments in the estimation of Generalized Method of Moments models, including a number of System GMM specifications which are designed to alleviate the weak instruments problem that plagues Difference GMM with highly persistent data.

A preview of our results is as follows. In neither of the two country samples – one using the income or expenditure surveys and the other using the DHS – do we find any support for the finding in Forbes (2000) and Li and Zou (1998) of a positive coefficient on income inequality. Instead the coefficient on income inequality is negative in most of our specifications (including Difference GMM) and often significantly so, raising questions about the claim that the negative signs in earlier, OLS specifications were entirely due to time-invariant omitted variables.

However, we do not find support on these data for the hypothesis that decomposing overall income inequality into a component associated with inequality of opportunity and a residual component (notionally related to inequality arising from effort differences) would help resolve the inconclusiveness of empirical estimates of the relationship between inequality and growth. In the income or expenditure survey sample, it is the residual inequality component (driven by efforts and omitted circumstances) that maintains a statistically significant negative coefficient in most specifications, with the inequality of opportunity component typically insignificant. In the DHS sample the coefficient on inequality of opportunity is generally negative, but it is only significant (at the 10% level) in one of the four preferred specifications.

The paper is organized as follows. The next section briefly reviews the literature on the relationship between inequality and growth, with a focus on the main empirical papers. Section 3 introduces the concept and measurement of inequality of opportunities. Section 4 describes the econometric specification and the data used in the analysis. Section 5 describes the estimation procedures and presents the results. Section 6 concludes.