A decomposition method to evaluate the `paradox of progress' with evidence for Argentina

TL;DR

This paper introduces a novel decomposition method combining least-squares and quantile regressions to analyze the 'paradox of progress' in Argentina, revealing how education can increase income inequality due to wage structure convexity and heterogeneous returns.

Contribution

It develops a joint regression framework using functional derivatives to quantify the impact of wage structure and return heterogeneity on income inequality.

Findings

Education's role in income inequality in Argentina from 1992 to 2015.

Convexity of wage-education relationship influences inequality.

Heterogeneity in returns significantly contributes to the paradox.

Abstract

The `paradox of progress' is an empirical regularity that associates more education with larger income inequality. Two driving and competing factors behind this phenomenon are the convexity of the `Mincer equation' (that links wages and education) and the heterogeneity in its returns, as captured by quantile regressions. We propose a joint least-squares and quantile regression statistical framework to derive a decomposition in order to evaluate the relative contribution of each explanation. The estimators are based on the `functional derivative' approach. We apply the proposed decomposition strategy to the case of Argentina 1992 to 2015.