Convergence in Income Inequality: Further Evidence from the Club Clustering Methodology across States in the U.S

By Apergis, Nicholas; Christou, Christina et al. | International Advances in Economic Research, May 2018 | Go to article overview

Convergence in Income Inequality: Further Evidence from the Club Clustering Methodology across States in the U.S


Apergis, Nicholas, Christou, Christina, Gupta, Rangan, Miller, Stephen M., International Advances in Economic Research


Introduction

Dew-Becker and Gordon (2005) show that from 1966 to 2001, only the top 10% of the income distribution in the U.S. gained real income equal to the growth in labour productivity. Gordon (2009) also argues that abundant evidence documents that U.S. income inequality worsened since the 1970s.

Solow (1956) and Swan (1956) first proposed the convergence hypothesis as part of the neoclassical growth models. These models exemplify diminishing returns to factors of production, which predict that per capita income in poor countries will eventually converge to that in rich countries. The convergence hypothesis sparked enormous interest and led to an extensive literature testing convergence in average incomes both within and across countries.

Benabou (1996) noted that neoclassical growth models could imply convergence of the entire distribution of income, not just the mean. Inequality levels will fall in countries with high inequality and will rise in countries with low inequality. The idea of convergence clubs for income inequality reflects the conventional wisdom, as noted by Benabou (1996), that Latin American countries, on average, exhibit higher income inequality than European countries, who, in turn, exhibit, on average, higher inequality than East Asian countries. That is, do these different regions represent different convergence clubs for income inequality?

This paper contributes to the sparse literature on inequality convergence by empirically testing convergence across states in the U.S., using annual state-level data from 1916 to 2012 constructed by Frank (2014). This sample period encompasses a series of different periods that existing literature discusses: the Great Depression (1929-1944), the Great Compression (1945-1979), the Great Divergence (1980-present), the Great Moderation (1982-2007), and the Great Recession (2007-2009). Goldin and Margo (1992) identified the Great Compression as the time after the Great Depression, when income inequality fell dramatically compared to the Great Depression. Krugman (2007) described the period after the Great Compression as the Great Divergence, when income inequality grew. Piketty and Saez (2003) claim that the Great Compression ended in the 1970s and then income inequality worsened in the U.S. Thus, we anticipate that our analysis will document convergence in income inequality through the late 1970s and then divergence in the rest of the sample.

Our study of states across the U.S. provides a more homogeneous test for conducting convergence tests for income inequality than a panel of countries. The U.S. generally exhibits lower income inequality than Latin American countries, but higher income inequality than European countries. Does the U.S., however, also exhibit convergence clubs in income inequality? Our tests permit the testing for the existence of convergence clubs within the U.S.

The existing literature uses several alternative approaches to identify whether and when convergence occurs, with most analyses examining the convergence of per capita real gross domestic product (GDP) across countries. Initial empirical tests of the convergence hypothesis considered [beta]-convergence (Barro and Sala-i-Martin 1992; Mankiw et al. 1992; Quah 1996). Without additional control variables, the test considered absolute convergence, whereas with additional control variables, the test examined conditional convergence. Tests of [beta]-convergence generally estimate a log-linearized solution to a non-stochastic model with an additive error term. Alternatively, [sigma]-convergence (Friedman 1992; Quah 1993) argues that a group of countries/sectors/regions converge when the cross-section variance of the variable under consideration declines over time. However, as noted by Bliss (1999, 2000), the underlying assumption of an evolving data distribution introduces difficulties in the interpretation of the test distribution under the null. …

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