Rank Correction: A New Approach to Differential Non-Response in Wealth Survey Data

This paper develops a new approach for dealing with the under-reporting of wealth in household survey data (differential nonresponse). The current practice among researchers relying on household wealth survey data is one out of three approaches. First, simply ignore the problem. Second, fit a Pareto distribution to the tail of the survey data and use that distribution. Third, add rich list data to the sample and fit a Pareto distribution to the combined data (Vermeulen, 2018). We propose a fourth approach - the rank correction approach - which improves over the first two and does not require information drawn from publicly available rich lists. We show by means of Monte Carlo simulations that this rank correction approach substantially reduces nonresponse bias in the Pareto tail estimates. Applying the procedure to wealth survey data (HFCS, SCF, WAS) yields substantial increases in aggregate wealth and top wealth shares, which are closely in line with wealth summary statistics from other sources such as the World Inequality Database. As such the rank correction approach can serve as a complement and robustness check to Vermeulen's (2018) rich list approach and as an attractive alternative to the second approach in situations where rich list data is not available or of poor quality.