The effects of nonlinear aggregation on the relationship between inequality and growth

It is well known from nonlinear aggregation theory that cross-sectional distributions play a central role in the determination of aggregate relations. This paper establishes a bridge between the aggregation and the inequality and growth literature by applying a log-linear aggregation method to a simple heterogeneous AK growth model. The aggregation effect is explicitly captured in the growth equation by the changes of the mean logarithmic deviation (MLD or Theil’s second measure) of the income, implying that increases in income inequality may be unambiguously associated with temporary increases in a country’s growth rate. Consequently, empirical studies of the long-run effects of income inequality may suffer from aggregation bias if the temporary effects of the MLD changes are not considered. As an empirical application, it is suggested that Brazil and China exhibited periods during which significant increases in growth rates were temporary and resulted, at least in part, from large increases in inequality