Social status inequality and depression in Europe

It is theorized that income inequality is an indicator of status inequality and should therefore be associated with adverse health outcomes. In this article, we propose a novel way to capture status inequality more directly by measuring the distribution of self-perceived status in a society. We investigate whether status inequality in a society is associated with depression in the population. We show, first, that there is only a moderate association between subjective social status inequality and income inequality. Second, we provide evidence that depression is higher in countries with higher status inequality and that our novel measure of status inequality is more strongly associated with depression than the conventionally used income inequality measure. However, results are susceptible to influential country cases.

A New Logit-Based Gini Coefficient

The Gini coefficient is generally used to measure and summarize inequality over the entire income distribution function (IDF). Unfortunately, it is widely held that the Gini does not detect changes in the tails of the IDF particularly well. This paper introduces a new inequality measure that summarizes inequality well over the middle of the IDF and the tails simultaneously. We adopt an unconventional approach to measure inequality, as will be explained below, that better captures the level of inequality across the entire empirical distribution function, including in the extreme values at the tails.

The Role of Hours Changes for the Increase in German Earnings Inequality

Using data from the German Structure of Earnings Survey (GSES), this paper studies the role of changes in working hours for the increase in male and female earnings inequality between 2001 and 2010. We provide both classic decompositions of the variance of log earnings into the variances of hours, wage rates and their covariance, and decompositions based on reweighting the conditional hours distribution. Depending on the inequality measure considered, our results suggest that between 10 and 30% of the increase in male earnings inequality and 37 to 47% of the increase in female earnings inequality can be explained by changes in working hours. In addition, a large part of the inequality increase can be accounted for by changes in the composition of person and firm characteristics.

Anything Lorenz Curves Can Do, Top Shares Can Do: Assessing the TopBot Family of Inequality Measures

Newly precise evidence of the trajectory of top incomes in the United States and around the world relies on shares and ratios, prompting new inquiry into their properties as inequality measures. Current evidence suggests a mathematical link between top shares and the Gini coefficient and empirical links extending as well to the Atkinson measure. The work reported in this article strengthens that evidence, making several contributions: First, it formalizes the shares and ratios, showing that as monotonic transformations of each other, they are different manifestations of a single inequality measure, here called TopBot. Second, it presents two standard forms of TopBot, which satisfy the principle of normalization. Third, it presents a new link between top shares and the Gini coefficient, showing that properties and results associated with the Lorenz curve pertain as well to top shares. Fourth, it investigates TopBot in mathematically specified probability distributions, showing that TopBot is monotonically related to classical measures such as the Gini, Atkinson, and Theil measures and the coefficient of variation. Thus, TopBot appears to be a genuine inequality measure. Moreover, TopBot is further distinguished by its ease of calculation and ease of interpretation, making it an appealing People’s measure of inequality. This work also provides new insights, for example, that, given nonlinearities in the (monotonic) relations among inequality measures, Spearman correlations are more appropriate than Pearson correlations and that weakening of correlations signals differences and shifts in distributional form, themselves signals of income dynamics.

Comparisons Between Poorest and Richest to Measure Inequality

The observed increase in economic inequality, where the major concern is relative to the huge growth of the highest incomes, motivates to revisit classical measures of inequality and to offer new ways to synthesize the variability of the entire income distribution. The idea is to provide policy makers a way to contrast the economic position of the group of the poorer [Formula: see text] percent of the population and to compare their mean income to the one owned by the [Formula: see text] percent of the richest. The new measure is still a Lorenz-based one, but the significant focus is based here in equally sized and opposite parts of the population whose difference is so remarkable nowadays. We then highlight the specific information given by the new inequality measure and curve, by comparing it to the widely employed Lorenz curve and Gini index and the more recent Zenga approach, and provide an application to Italian data on household income, wealth, and consumption along the years 1980–2012. The effects of estimating inequality indices and curves from grouped data are also discussed.