133 S 36th St
Philadelphia, PA 19104
Institutional Affiliation: University of Pennsylvania
Information about this author at RePEc
NBER Working Papers and Publications
|April 2020||Sources of US Wealth Inequality: Past, Present, and Future|
in NBER Macroeconomics Annual 2020, volume 35, Martin Eichenbaum and Erik Hurst, editors
This paper employs a benchmark heterogeneous-agent macroeconomic model to examine a number of plausible drivers of the rise in wealth inequality in the U.S. over the last forty years. We find that the significant drop in tax progressivity starting in the late 1970s is the most important driver of the increase in wealth inequality since then. The sharp observed increases in earnings inequality and the falling labor share over the recent decades fall far short of accounting for the data. The model can also account for the dynamics of wealth inequality over the period---in particular the observed U-shape---and here the observed variations in asset returns are key. Returns on assets matter because portfolios of households differ systematically both across and within wealth groups, a feature in...
|December 2016||The Historical Evolution of the Wealth Distribution: A Quantitative-Theoretic Investigation|
with , : w23011
This paper employs the benchmark heterogeneous-agent model used in macroeconomics to examine drivers of the rise in wealth inequality in the U.S. over the last thirty years. Several plausible candidates are formulated, calibrated to data, and examined through the lens of the model. There is one main finding: by far the most important driver is the significant drop in tax progressivity that started in the late 1970s, intensified during the Reagan years, and then subsequently flattened out, with only a minor bounce back. The sharp observed increases in earnings inequality, the falling labor share over the recent decades, and potential mechanisms underlying changes in the gap between the interest rate and the growth rate (Piketty's r-g story) all fall far short of accounting for the data.