Inequality-Driven Growth: Unveiling Aggregation Effects in Growth Equations

Abstract

It is well known from nonlinear aggregation theory that 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, in agreement with the empirical findings of Forbes (AER, 2000). 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. The accelerated growth episodes observed in Brazil and China demonstrate that the increase in income inequality may have resulted in substantial temporary increases in the aggregate growth rates experienced by those countries.