Missing the Wealthy in the HFCS: Micro Problems with Macro Implications. Waltl, S. R. & Chakraborty, R. The Journal of Economic Inequality, 20(1):169–203, March, 2022. ![Missing the Wealthy in the HFCS: Micro Problems with Macro Implications [link]](https://bibbase.org/img/filetypes/link.svg "link")
Link doi abstract bibtex Wealth aggregates implied by the Household Finance and Consumption Survey (HFCS) usually yield much lower amounts than macroeconomic statistics reported in the National Accounts. An important source of this gap may be the under-representation of the wealthiest households in the HFCS. This article therefore combines a semi-parametric Pareto model estimated from top survey data and observations from rich lists with a non-parametric stratification approach to quantify the impact of the missing wealthy households on component-specific micro-macro gaps. We find that unadjusted micro data substantially underestimates wealth inequality. The largest effects are documented for equity. For other components, the missing wealthy explain less than ten percentage points of the micro-macro gap. We find that differences in oversampling strategies limit the cross-country comparability of unadjusted survey-implied wealth distributions and that our top tail adjustment leads to measures that are internationally better comparable.
@article{WaltlChakraborty2022,
title = {Missing the Wealthy in the {{HFCS}}: Micro Problems with Macro Implications},
author = {Waltl, Sofie R. and Chakraborty, Robin},
year = {2022},
month = mar,
journal = {The Journal of Economic Inequality},
volume = {20},
number = {1},
pages = {169--203},
doi = {10.1007/s10888-021-09519-1},
url = {https://doi.org/10.1007/s10888-021-09519-1},
abstract = {Wealth aggregates implied by the Household Finance and Consumption Survey (HFCS) usually yield much lower amounts than macroeconomic statistics reported in the National Accounts. An important source of this gap may be the under-representation of the wealthiest households in the HFCS. This article therefore combines a semi-parametric Pareto model estimated from top survey data and observations from rich lists with a non-parametric stratification approach to quantify the impact of the missing wealthy households on component-specific micro-macro gaps. We find that unadjusted micro data substantially underestimates wealth inequality. The largest effects are documented for equity. For other components, the missing wealthy explain less than ten percentage points of the micro-macro gap. We find that differences in oversampling strategies limit the cross-country comparability of unadjusted survey-implied wealth distributions and that our top tail adjustment leads to measures that are internationally better comparable.},
keywords = {Methods of Estimation of Wealth Inequality}
}
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