EconBase
← All papers

Decomposing Inequalities using Machine Learning and Overcoming Common Support Issues

Emmanuel Flachaire, Bertille Picard

arXiv 17 Nov 2025 · Econometrics

arXiv:2511.13433 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The Kitagawa-Oaxaca-Blinder decomposition splits the difference in means between two groups into an explained part, due to observable factors, and an unexplained part. In this paper, we reformulate this framework using potential outcomes, highlighting the critical role of the reference outcome. To address limitations like common support and model misspecification, we extend Neumark's (1988) weighted reference approach with a doubly robust estimator. Using Neyman orthogonality and double machine learning, our method avoids trimming and extrapolation. This improves flexibility and robustness, as illustrated by two empirical applications. Nevertheless, we also highlight that the decomposition based on the Neumark reference outcome is particularly sensitive to the inclusion of irrelevant explanatory variables.

Citation extraction

30
references
70
in-text mentions
30
distinct cited
0
self-citations
15,402
main-text words

appendix boundary found by appendix_command · 78% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Neumark, David (1988) Employers' discriminatory behavior and the estimation of wage discrimination1.000114100%
2Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters1.00094100%
3Strittmatter, Anthony and Wunsch, Conny (2021) The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?0.9285380%
4N. Fortin and T. Lemieux and S. Firpo Decomposition Methods in Economics0.87452100%
5M. Busso and J. DiNardo and J. McCrary (2014) New evidence on the finite sample properties of propensity score reweighting and matching estimators0.7374350%
6Ben Jann (2008) The Blinder-Oaxaca decomposition for linear regression models0.7374275%
7Blinder, Alan S (1973) Wage discrimination: reduced form and structural estimates0.64422100%
8Oaxaca, Ronald L (1973) Male-female wage differentials in urban labor markets0.64422100%
9Rubin, D.B Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies0.64422100%
10Freedman, D. A (1981) Bootstrapping regression models0.58531100%

Showing the top 10 of 30 scored citations.