Inequality and Economic Growth: Data Comparisons and Econometric Tests

Abstract

This paper discusses two issues in the relationship between inequality and economic growth: the data and the econometrics. We first review the inequality data set of Deininger and Squire, which, we argue, fails to provide adequate or accurate longitudinal and cross-country coverage. We then introduce our own measures of the inequality of manufacturing pay, based on the UNIDO Industrial Statistics. In our view, these provide indicators of inequality that are more stable, more reliable, and more comparable across countries than those of Deininger and Squire. Turning to the relationship between inequality and development, we diagnose several common econometric problems in the literature, including measurement error, omitted variable bias, serial correlation in longitudinal data, and the possible persistence of lagged dependent variables. By taking steps to account for these problems, we seek more reliable inferences concerning the relationship between inequality, national income and economic growth. We find evidence that generally supports Kuznets’ specification for industrializing countries: inequality tends to decline as per capita income increases. However, after 1981 two problems emerge. First, per capita GDP growth slows dramatically in most countries. Second, there is a worldwide trend toward rising inequality in our data, independent of GDP or its changes. The timing and geographic pattern of these increases suggest a link to the high real interest rates and global debt crisis of the period beginning in 1982.