This paper presents a review of the main available indicators to measure poverty and income inequality, examining their properties and suitability for different types of economic analyses, and providing real-world data to illustrate how they work. Although some of these metrics –such as the Gini coefficient– are most frequently used for this purpose, it is crucially important for researchers and policy-makers to take into account alternative methods that can offer complementary information in order to better understand these issues at all levels.
This paper presents an empirical research on how monetary policy can affect income distribution. After describing the channels through which monetary policy may have an impact on income distribution, we perform a panel analysis of 15 EU (European Union) countries covering the period 1995-2014. The results provide evidence of a significant positive relationship between real interest rates and income inequality measured as the Gini coefficient. However, this relationship only becomes significant in the medium term but not in the short term. Our findings call for greater attention by central bankers to the redistributive effects of monetary policy.
In this article, we seek to determine the main explanatory factors of individual preferences for redistribution in Spain. Methods. We use data from the World Values Survey capturing economic factors, political preferences, personal beliefs, and sociodemographic characteristics. Results. The results, obtained using both OLS and ordered logit regressions, reveal that factors regarding relative household income, personal beliefs, sociodemographic characteristics, and regional differences are the main determinants of the demand for redistribution. Conclusion. These results, coupled with long-standing trends that the Spanish society has been experiencing for decades, suggest that there may be an increase in the demand for redistribution in the coming years.
PurposeThis paper aims to study, by means of an empirical approach, how monetary policy might affect the distribution of individual income.Design/methodology/approachAfter describing the channels through which monetary policy could impinge on income distribution, the authors carry out a panel analysis of 62 countries that control their monetary policy for the period 1996–2015.FindingsUsing two possible proxy variables for monetary policy (the monetary aggregate M3 and the real interest rates), the results reveal a significant positive relationship between real interest rates and income inequality measured through the market Gini coefficient and polarization ratios. The findings suggest that central bankers should be more aware of the redistributive effects of monetary policy.Research limitations/implicationsIt should be mentioned the major challenge of data limitation in the empirical investigation on the relationship between monetary policies and inequalities.Practical implicationsThe empirical evidence presented in this paper supports the premise that central bankers should not ignore the unintended redistributive consequences of their actions. In this regard, it is worth noting that if, in addition to price stability, central banks are also responsible for financial stability; the rationale behind central bank independence needs to be reconsidered.Originality/valueAn outstanding feature of the paper is its sample size and the variety of countries included in the sample, which includes countries from all continents and with very different levels of economic development. Also, unlike papers based on forecasting modeling – e.g. Vector autoregression (VAR) or Structural vector autoregression (SVAR) models, the study follows an explanatory approach, including not only monetary variables, but also a series of regressors that may have a meaningful and significant impact on inequality, according to a wide literature.
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