Income Inequality and Economic Freedom Revisited: Are Freedom and Equality Conflicting Values? Evidence from the twenty-first Century

Abstract

This empirical research addressed the short – and long-run relationship between economic freedom (and its subcomponents) and income inequality using a panel of 102 countries between 2000 and 2018. The results of employing an autoregressive distributed lag model showed that economic freedom has a detrimental impact on income inequality measured by any of the main inequality indicators. However, the results point to a relatively inelastic relationship. Additionally, the study explored the interactions between the subcomponents of economic freedom and income inequality, again pointing to a rigid relationship. While the size of the government and legal property rights increase income inequality, deregulation exerts the opposite effect. This paper closes with future guidelines for research.

KEYWORDS:

• Income inequality
• economic freedom
• gini index
• panel data
• political economics

Jel classifications:

• D63
• F62
• H11
• O15
• P16

Disclosure Statement

No potential conflict of interest was reported by the authors.

Availability of Data and Materials

Data is available on request from the corresponding author.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 See, amongst others, Jenkins (1991) and Silber (2012).

2 See, amongst others, Berggren (2003); Krieger and Meierrieks (2016); Gwartney et al. (1996); Feldmann (2017); Bengoa and Sanchez-Robles (2003); Graeff and Mehlkop (2003); Gehring (2013); Dreher et al. (2012).

3 Once, until 2000, the EFW index was available only in a 5-year time period.

4 Full methodology, data collection and indicators review can be accessed in Gwartney et al. (2020, pp. 213–225).

5 See Best (2008) for the mathematical error correction re-parametrization.

6 This rule of thumb is explicitly analyzed in O’brien (2007). The author concludes that even with VIF values exceeding the rule of thumb of 10 (and mean VIF of 4), one can confidently derive conclusions, since the model does not suffer from multicollinearity. Although Model (1) presents a mean VIF of 4.25, one still follows the conclusions of O’brien (2007) since VIF values are inferior to 10.

7 Serial correlation (or autocorrelation) occurs when observations of the error term are correlated with each other. Note that ${\epsilon }_{it}=\rho \ast {\epsilon }_{it-1}+{\mu }_{it}where-1<\rho <1$. If $\rho \ne 0$ there is presence of first-order serial correlation.

8 As they are expressed in logarithmic differences.

Additional information

Funding

CeBER R&D unit funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., project UIDB/05037/2020.

Notes on contributors

Daniel Machado

Daniel Machado is a PhD candidate in Economics at University of Coimbra, Faculty of Economics.

José Alberto Fuinhas

José Alberto Fuinhas is an Associate Professor, with habilitation, at the Faculty of Economics of University of Coimbra (Portugal). His main research interests are in energy economics and monetary and financial economics.