Abstract
We analyse the top tail of the wealth distribution in France, Germany, and Spain using the first and second waves of the Household Finance and Consumption Survey (HFCS). Since top wealth is likely to be under-represented in household surveys, we integrate big fortunes from rich lists, estimate a Pareto distribution, and impute the missing rich. In addition to the Forbes list, we rely on national rich lists since they represent a broader base of the big fortunes in those countries. As a result, the top 1% wealth share increases notably for the three selected countries after imputing the top wealth. We find that national rich lists can improve the estimation of the Pareto coefficient in particular when the list of national USD billionaires is short.
Highlights
Rising inequality in income and wealth is gaining increased attention in both public and academic debate
We find that national rich lists can provide a real value added, in countries where only few dollar billionaires make it onto the Forbes list, such as Spain
A potential explanation is the rather high number of USD billionaires who made it on the German Forbes list. These findings suggest that, in particular, when the Forbes list of national USD billionaires is short, national rich lists can improve the estimation of the Pareto coefficient, given that both lists are of similar quality
Summary
Rising inequality in income and wealth is gaining increased attention in both public and academic debate. We rely on an approach suggested by Vermeulen (2018), which combines household survey data with rich lists to jointly estimate a Pareto distribution for the top tail to adjust the top wealth distribution in France, Germany, and Spain. He finds that differential non-response seems to be rather high in some Eurozone countries, Germany This leads to an underestimation of the top wealth shares when the estimation is exclusively based on survey data without extreme tail observations. While the HFCS intends to oversample wealthy households to address potential non-observation bias, the selection criteria applied in the oversampling process differ across countries The results (provided in the Online Appendix), are relatively similar to those that rely on net wealth
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