The empirical evidence on the relationship between income inequality and economic growth is widely recognized and, now, there are rich databases for carry on panel-data type of analyses. However, time series studies for specific countries may be more attractive and yield revealing results. For this reason, we study hereafter the long-run relationship between economic growth and income inequality in the case of Mexico. To this end, a time series of data for the Gini coefficients from Solt (2011) is used over the period 1968–2010, within a cointegration exercise. Being related to a single country, our results are suffering less from problems of heterogeneity, endogeneity, and measurement errors, which are commonly encountered in cross-country growth regressions. We first investigate (and confirm) that the two series of per capita GDP and Gini index are cointegrated. Five different methodologies are implemented in our analysis, so that the robustness of cointegration results is guaranteed. We consistently also find that the relationship between those variables is negative. Moreover, results show the per capita GDP to be weakly exogenous. According to tests for Granger causality, unidirectional causality runs from per capita GDP to the Gini index.

Economic growth and income distribution in Mexico: A cointegration exercise

Carrera, Edgar J. Sánchez
2013

Abstract

The empirical evidence on the relationship between income inequality and economic growth is widely recognized and, now, there are rich databases for carry on panel-data type of analyses. However, time series studies for specific countries may be more attractive and yield revealing results. For this reason, we study hereafter the long-run relationship between economic growth and income inequality in the case of Mexico. To this end, a time series of data for the Gini coefficients from Solt (2011) is used over the period 1968–2010, within a cointegration exercise. Being related to a single country, our results are suffering less from problems of heterogeneity, endogeneity, and measurement errors, which are commonly encountered in cross-country growth regressions. We first investigate (and confirm) that the two series of per capita GDP and Gini index are cointegrated. Five different methodologies are implemented in our analysis, so that the robustness of cointegration results is guaranteed. We consistently also find that the relationship between those variables is negative. Moreover, results show the per capita GDP to be weakly exogenous. According to tests for Granger causality, unidirectional causality runs from per capita GDP to the Gini index.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11576/2657380
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