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A panel vector AutoRegression analysis of income inequality dynamics in each of the 50 states of USA

A panel vector AutoRegression analysis of income inequality dynamics in each of the 50 states of USA PurposeThe purpose of this paper is to investigate the income inequality dynamics in each of the 50 states of USA over the period 1981-2011.Design/methodology/approachThe paper estimates an augmented Kuznets curve panel Vector AutoRegression in per capita income, economic freedom, educational attainment, unemployment, and population ageing along with evaluating generalized impulse responses functions (GIRF) and generalized forecast-error variance decompositions (GFEVD).FindingsAll the variables are integrated of order one and are panel cointegrated. Kuznets’ hypothesized inverted U-shaped relationship between inequality and growth is not supported by the data. Unemployment and population ageing have statistically significant positive effects on inequality in the long-run; education has statistically significant negative impact; economic freedom has statistically insignificant positive effect. Long-run bidirectional causality exists among the variables. GFEVD show that excluding income inequality itself, variation in income inequality is more influenced by perturbations in per capita income, educational attainment, and unemployment. GIRF corroborate the results of the GFEVD.Originality/valueThis paper fulfills an identified need to study the causal relationship between inequality and its determining factors without assuming the a priori exogeneity or endogeneity of the underlying variables. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Social Economics Emerald Publishing

A panel vector AutoRegression analysis of income inequality dynamics in each of the 50 states of USA

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0306-8293
DOI
10.1108/IJSE-06-2015-0154
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to investigate the income inequality dynamics in each of the 50 states of USA over the period 1981-2011.Design/methodology/approachThe paper estimates an augmented Kuznets curve panel Vector AutoRegression in per capita income, economic freedom, educational attainment, unemployment, and population ageing along with evaluating generalized impulse responses functions (GIRF) and generalized forecast-error variance decompositions (GFEVD).FindingsAll the variables are integrated of order one and are panel cointegrated. Kuznets’ hypothesized inverted U-shaped relationship between inequality and growth is not supported by the data. Unemployment and population ageing have statistically significant positive effects on inequality in the long-run; education has statistically significant negative impact; economic freedom has statistically insignificant positive effect. Long-run bidirectional causality exists among the variables. GFEVD show that excluding income inequality itself, variation in income inequality is more influenced by perturbations in per capita income, educational attainment, and unemployment. GIRF corroborate the results of the GFEVD.Originality/valueThis paper fulfills an identified need to study the causal relationship between inequality and its determining factors without assuming the a priori exogeneity or endogeneity of the underlying variables.

Journal

International Journal of Social EconomicsEmerald Publishing

Published: Jun 12, 2017

References