The Gender Wealth Gap in Europe: Application of Machine Learning to Predict Individual‐level Wealth

Merike Kukk, Jaanika Meriküll and Tairi Room

Review of Income and Wealth, 2023, vol. 69, issue 2, 289-317

Abstract: This article provides comparative estimates of the gender wealth gaps for 22 European countries, employing data from the Household Finance and Consumption Survey. The data on wealth are collected at the household level, while individual‐level data are needed for the estimates of gender wealth gaps. We propose a novel approach using machine learning and model averaging methods to predict individual‐level wealth data for multi‐person households. Our results suggest that random forest performs best as the predicting tool for this exercise, outperforming elastic net and Bayesian model averaging. The estimated gender wealth gaps tend to be in favor of men, especially at the top of the wealth distribution. Men have 24 percent more wealth than women on average. We also find that a high home ownership rate is associated with a smaller country‐level gender wealth gap. Our estimates suggest that the individual‐level wealth inequality is on average 3 pp higher than the household‐level wealth inequality in multi‐member households.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/roiw.12596

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:revinw:v:69:y:2023:i:2:p:289-317

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0034-6586

Access Statistics for this article

Review of Income and Wealth is currently edited by Conchita D'Ambrosio and Robert J. Hill

More articles in Review of Income and Wealth from International Association for Research in Income and Wealth Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().