Uncovering the Dynamics of the Wealth Distribution

TL;DR

This paper introduces a new stochastic model to decompose and analyze the evolution of wealth distribution, identifying key drivers of wealth inequality and evaluating the effects of wealth taxation using historical US data since 1962.

Contribution

It develops a nonparametrically identified decomposition of wealth dynamics that can be estimated from cross-sectional data and applies it to analyze US wealth inequality and tax policy effects.

Findings

Top 1% wealth share increased due to savings, returns, and income inequality.

The estimated optimal wealth tax rate is around 12%.

Tax revenue is lower in dynamic models than static ones.

Abstract

I introduce a new way of decomposing the evolution of the wealth distribution using a simple continuous time stochastic model, which separates the effects of mobility, savings, labor income, rates of return, demography, inheritance, and assortative mating. Based on two results from stochastic calculus, I show that this decomposition is nonparametrically identified and can be estimated based solely on repeated cross-sections of the data. I estimate it in the United States since 1962 using historical data on income, wealth, and demography. I find that the main drivers of the rise of the top 1% wealth share since the 1980s have been, in decreasing level of importance, higher savings at the top, higher rates of return on wealth (essentially in the form of capital gains), and higher labor income inequality. I then use the model to study the effects of wealth taxation. I derive simple formulas for how the tax base reacts to the net-of-tax rate in the long run, which nest insights from several existing models, and can be calibrated using estimable elasticities. In the benchmark calibration, the revenue-maximizing wealth tax rate at the top is high (around 12%), but the revenue collected from the tax is much lower than in the static case.