Markus P. A. Schneider

Is inequality deadly and for whom? A Bayesian Model Averaging analysis

Abstract The persistent correlations between income and mortality, and inequality and mortality in the US are well established. It has been demonstrated with various empirical models at different levels of analysis. However, there is no consensus on the relevant confounding factors or even the functional specification of the income–inequality–mortality relationship across the literature. We interpret this as significant model uncertainty when it comes to the correct specification of the implied econometric model and provide a systematic approach to address it. Using BMA, we conduct a large-scale analysis considering millions of models to determine a model-averaged inequality effect explaining cross-sectional variations in mortality at the county level. The results also suggest a best set of confounding factors and emphasize the importance of controlling for unobserved State-specific factors. Furthermore, we uncover a robust nonlinear income–inequality–mortality relationship that challenges typical assumptions in the literature.

Revisiting the thermal and superthermal two-class distribution of incomes

This paper offers a two-pronged critique of the empirical investigation of the income distribution performed by physicists over the past decade. Their finding rely on the graphical analysis of the observed distribution of normalized incomes. Two central observations lead to the conclusion that the majority of incomes are exponentially distributed, but neither each individual piece of evidence nor their concurrent observation robustly proves that the thermal and superthermal mixture fits the observed distribution of incomes better than reasonable alternatives. A formal analysis using popular measures of fit shows that while an exponential distribution with a power-law tail provides a better fit of the IRS income data than the log-normal distribution (often assumed by economists), the thermal and superthermal mixture’s fit can be improved upon further by adding a log-normal component. The economic implications of the thermal and superthermal distribution of incomes, and the expanded mixture are explored in the paper.

Redistribution in the Age of Austerity: Evidence from Europe, 2006-13

We examine the relationship between changes in a countrys public sector fiscal position and inequality at the top and bottom of the income distribution during the age of austerity (2006-13). We use a parametric Lorenz curve model and Gini-like indices of inequality as our measures to assess distributional changes. Based on the EUs Statistics on Income and Living Conditions SLIC and International Monetary Fund data for 12 European countries, we find that more severe adjustments to the cyclically adjusted primary balance (i.e., more austerity) are associated with a more unequal distribution of income driven by rising inequality at the top. The data also weakly suggest a decrease in inequality at the bottom. The distributional impact of austerity measures reflects the reliance on regressive policies, and likely produces increased incentives for rent seeking while reducing incentives for workers to increase productivity.

A tale of two Ginis in the US, 1921–2012

Following a methodology proposed by Jantzen and Volpert (2012), we use IRS Adjusted Gross Income (AGI) data for the United States (1921-2012) to estimate two Gini-like indices representing inequality at the bottom and the top of the income distribution. We also calculate the overall Gini index as a function of the parameters underlying the two indices. Our findings can be summarized as follows. First, we find that the increase in the Gini index from the mid 1940s to the late 1970s seems to be mostly explained by an increase in inequality at the bottom of the income distribution, which more than offsets the decrease in inequality at the top. The implication is that middle incomes gained relative to high incomes, but especially relative to low incomes. Conversely, it is rising inequality at the top that appears to drive the rise in the Gini index since 1981. Second, inequality at the top of the income distribution follows a U-shaped trajectory over time, similar to the pattern of the share of top incomes documented by Piketty and Saez (2003, 2006) and Atkinson, Piketty, and Saez (2011). Third, the welfare effects of the different forces behind an increasing Gini index can be evaluated in light of the Lorenz dominance criterion proposed by Atkinson (1970): both top-driven and bottom-driven increases in the index appear not to imply strict Lorenz dominance by previous income distributions, and therefore are not associated with lower welfare in an absolute sense. In a relative sense, however, once average growth rates over the two periods are taken into account, the top-driven increase in inequality since 1981 appears to have been welfare reducing.

Angus Deaton's Nobel Prize for Confronting Theory with Facts

ABSTRACT Angus Deaton, recipient of the 2015 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, is broadly recognized as an outstanding applied economist who is well deserving of the prize. He has made a myriad of important contributions—alone and with renowned co-authors—across many fields. In this review of his work, I argue that it is his methodological consistency and focus on the two-way connection between empirics and theory, together with his attention to detail of how things are measured, that support his recognition by his peers. Informed by a career of doing careful econometric work using household surveys—especially on consumption, development, well-being and health—in his popular writing, Deaton chooses to focus on the importance of institutions, history and other disciplines.

What Gradient? A BMA Approach to Untangling Income, Inequality, and Mortality

In this update to an earlier analysis using BMA, I consider both a wider variety and some recoded plausible controls that might affect the observed relationship between income and mortality, and inequality and mortality. I also suggest that the much of the empirical investigation of inequality and health outcome (including mortality) rests on the incorrect proposition that income has a direct causal link to health, whereas its influence is likely to only ever be indirect. Consistent with this speculation, I find that when the right controls are selected from a sufficient number and variety of candidates, the relationship between income and mortality disappears, as does the relationship between inequality and mortality. This is a novel finding with cautionary implications for public health policy in the US, where a known bias is to assume that income growth will solve local health problems.

Revisiting Inequality as a Determinant of Mortality: A Bayesian Model Averaging Approach

The persistent correlations between income and mortality, and inequality and mortality in the US is well established. It has been demonstrated with various empirical models at different levels of analysis. However, there is no consensus on the relevant confounding factors or even the functional specification of the income-inequality-health relationship across the literature. We interpret this as significant model uncertainty when it comes to the correct specification of the implied econometric model and provide a systematic approach to address it. Using BMA, we conduct a meta-analysis to determine the best model for explaining cross-sectional variations in mortality at the county level. The results provide a best set of confounding factors and emphasize the importance of controlling for unobserved State-specific factors. Furthermore, we uncover a robust nonlinear income-inequality-mortality relationship that challenges typical assumptions in the literature.