Quantifying Uncertainties in Estimates of Income and Wealth Inequality

TL;DR

This paper assesses the uncertainty in income and wealth inequality estimates in the US, highlighting how accounting for standard errors influences economic conclusions and policy implications.

Contribution

It introduces a comprehensive analysis of uncertainties in inequality estimates using two major data sets, improving understanding of their reliability and implications for economic research.

Findings

PUF estimates are more accurate for top 10 income shares.

SCF data is more reliable for wealth shares of top 10 to 0.5 percent.

Both data sets face challenges estimating the top 0.1 and 0.01 percent wealth shares.

Abstract

I measure the uncertainty affecting estimates of economic inequality in the US and investigate how accounting for properly estimated standard errors can affect the results of empirical and structural macroeconomic studies. In my analysis, I rely upon two data sets: the Survey of Consumer Finances (SCF), which is a triennial survey of household financial condition, and the Individual Tax Model Public Use File (PUF), an annual sample of individual income tax returns. While focusing on the six income and wealth shares of the top 10 to the top 0.01 percent between 1988 and 2018, my results suggest that ignoring uncertainties in estimated wealth and income shares can lead to erroneous conclusions about the current state of the economy and, therefore, lead to inaccurate predictions and ineffective policy recommendations. My analysis suggests that for the six top-decile income shares under consideration, the PUF estimates are considerably better than those constructed using the SCF; for wealth shares of the top 10 to the top 0.5 percent, the SCF estimates appear to be more reliable than the PUF estimates; finally, for the two most granular wealth shares, the top 0.1 and 0.01 percent, both data sets present non-trivial challenges that cannot be readily addressed.