Wealth Survey Calibration Using Income Tax Data

Working Paper 2023-659

Abstract

Wealth surveys tend to underestimate wealth concentration at the top due to the missing rich problem. I propose a new way of improving the credibility of wealth surveys by making them consistent with tabulated income tax data. This is possible with the harmonized triannual Household Finance and Consumption Survey (HFCS), which collects data on both income and wealth. I achieve consis- tency by calibrating survey weights using the income part of HFCS. I apply the calibration method of Blanchet, Flores, and Morgan (J Econ Inequal 20(1):119- 150, 2022) in a new context and propose a new, intuitive way to determine the merging point where the calibration starts. I then use the calibrated weights with HFCS wealth values. Tested on Austria, calibration aligns the survey totals closer to the National Accounts, with wealth inequality increasing in the second and third survey waves. I also find a strong downward bias in the Austrian HFCS income distribution. Following the calibration, I test other top tail adjustments: replacing the survey top tail with a Pareto distribution and combining the data with a magazine rich list.

Authors: Daniel Kolar.