The Journal of Economic Inequality最新文献

One of the basic questions that arise in measuring inequality in the distribution of a variable is how to define the inequality dominance relation (IDR) on the set of alternative distributions i.e. how to decide whether a particular distribution of the variable is to be considered to be no more unequal than another. Important advances in this line of research have been made in the case where the variable in question is cardinally measured. The case of ordinal variables, however, is a relatively unexplored area. This paper considers the case of ordered categorical variables. It adopts an approach to inequality ranking based on the notion of ‘status’ of the individuals and formulates a definition of the IDR by using a new a priori condition, Status Majorization, that one would intuitively expect this relation to satisfy. It is shown that the IDR, so defined, is compatible with the condition of Hammond Majorization. An empirical relation (i.e. a relation defined in terms of observed data) that implements the suggested definition is also obtained. An illustrative application is reported.

Based on data from the Eurosystem Household Finance and Consumption Survey (HFCS), we revisit the question of how differences in household characteristics can account for cross-country differences in wealth inequality. We first show that commonly used RIF-decompositions are typically tested positive for specification error due to the large differences in household characteristics between countries. We then present an alternative analysis for which we introduce a convenient graphical representation of the wealth distribution. Our results show that not only differences in wealth inequality but also differences in distributional shape can be largely accounted for by differences in homeownership across countries, but that, for some country comparisons, differences in household incomes also matter.

Standard growth incidence curves describe how growth episodes impact on the overall income distribution. However, measuring the pro-poorness of the growth process is complex due to measurement errors, and to the effect of shocks that may hit the percentiles of the income distribution in different ways. Therefore, standard growth incidence curves may misrepresent the true growth process and its distributive impact. Relying on a non-anonymous approach, we compare actual growth episodes at each percentile of the initial personalized distribution with counterfactual mobility profiles which rule out the presence of shocks. We consider Indonesia in 2000–2007 and 2007–2014, two growth spells in which there was substantial, significant upward mobility among the initially poorer, a sizeable part of which cannot be explained by unobserved individual endowments or standard socio-economic attributes. The difference between actual and expected growth is related, in the early 2000s, to the economy-wide transformations, which characterized the early years of the post-Suharto era. However, in the more recent years, it can be largely attributed to individual recovery from previous negative losses and high vulnerability and reactivity to shocks for the poor.

By using a unique dataset of more than 20,000 individuals with savings accounts from August 2019 to October 2020 in South Africa, this paper examines how the COVID crisis has affected savings behavior, and by affecting savings, impacted wealth inequality. We find that while COVID increased savings on average, the increase in average savings is due to a small group of higher income savers which substantially increased their savings, while a large group stopped saving.

Empirical findings suggest a positive correlation between inequality and social immobility, a phenomenon coined the Gatsby curve. This paper answers a simple question: When do Gatsby curves exist? We build a theoretical n-income environment in which parental investment and education improve the economic prospects of children. Gatsbian economies and Gatsby curves are formally defined, and we characterize the conditions under which they arise. A Gatsby curve arises when immobility (Trace of the transition matrix) and inequality (Gini coefficient) both change in the same direction following a change in an exogenous variable (an income level, the level of education).When an exogenous variable changes, the impact on inequality depends on a standard direct effect and on an indirect (composition) effect which accounts for the changes in the proportions of individuals with various incomes. We show that following an increase in some high income, immobility and inequality both increase if the indirect effect goes in the same direction as the direct effect. Thus, in such a case, the economy moves up a Gatsby curve. We also demonstrate that if the indirect effect goes in the same direction as the direct effect, and if education is a substitute to parental investment, then an increase in education leads to the economy moving down a Gatsby curve where it experiences both lower inequality and immobility. Finally, we show that an economy may go from being Gatsbian to non-Gatsbian, and vice versa.

Using the series of the top 1% income shares in 137 countries, I examine the relationship between top-end inequality and subsequent economic growth from the 1920s to the 2010s. These data enable a versatile exploration of various time horizons. To address concerns regarding chosen functional forms, I employ penalized spline methods to accommodate potential nonlinearities. Empirical findings suggest that the relationship between top-end inequality and subsequent growth is complex, contingent upon both the investigated time horizon and the level of economic development. I find some evidence for a positive link at medium levels of economic development, with this positive link being more pronounced in short- to medium-term associations. I also find that the positive medium-run association weakens as economic development advances. In advanced economies, a negative (or nonpositive) medium- to long-term relationship emerges between the top 1% income share and growth in many settings. Furthermore, I conclude that longer-run associations need to be investigated further.

The last few decades have seen transformative changes to the structure of employment, which have led to a deterioration in demand for middle-skill occupations, a process known as job polarisation. As demand for middle-skill workers shrinks, expectations about households’ income through their lifetime horizon must be adjusted. It is possible that these expectations loop back into the credit system and affect the lending behaviour of credit institutions or that they impact households’ self-assessment of their opportunities to borrow money. In this paper we study how the process of job polarisation affects credit demand and supply, studying its relationship with credit constraint and credit quality.

Abstract

We study the determinants of wage inequality and its fluctuations in six Central-Eastern European nations using European Union Statistics on Income and Living Conditions microdata from 2010 to 2019. Wage disparity in these countries changed in distinct ways. Inequality in Czechia and Romania is generally steady, has fallen consistently in Poland and Slovakia, and has increased in Bulgaria. Inequality has been steadily reducing in Hungary but has recently increased significantly. Therefore, this paper questions these countries' primary causes of wage inequality changes. In addition to providing a detailed description of inequality trends in these countries, we focus on examining the demographic and micro-level determinants alongside the minimum wage changes. We estimate these effects using RIF regression and RIF decompositions for various inequality measures. The changes in wage inequality in these countries were driven mainly by wage structure effects regardless of the increase or decrease in wage inequality. Changes in the returns to education and returns to permanent employment contracts are crucial in explaining decreased wage inequality. The increases in wage inequality in Hungary and Bulgaria are defined mainly by the changes in the estimated constants instead of micro-level determinants. The changes in the minimum wage explain most of the unknown factors in Bulgaria, and the spillover effects of the minimum wage may explain most of the unknown factors in Hungary. Our results can support the skill-biased technological change hypothesis in the case of Slovakia, Romania, Czechia, and Bulgaria.