Emmanuel Flachaire, Bertille Picard
arXiv 17 Nov 2025 · Econometrics
arXiv:2511.13433 · PDF · DOI · OpenAlex · Extracted main text
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.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Neumark, David (1988) Employers' discriminatory behavior and the estimation of wage discrimination | 1.000 | 11 | 4 | 100% |
| 2 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 9 | 4 | 100% |
| 3 | Strittmatter, Anthony and Wunsch, Conny (2021) The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter? | 0.928 | 5 | 3 | 80% |
| 4 | N. Fortin and T. Lemieux and S. Firpo Decomposition Methods in Economics | 0.874 | 5 | 2 | 100% |
| 5 | M. Busso and J. DiNardo and J. McCrary (2014) New evidence on the finite sample properties of propensity score reweighting and matching estimators | 0.737 | 4 | 3 | 50% |
| 6 | Ben Jann (2008) The Blinder-Oaxaca decomposition for linear regression models | 0.737 | 4 | 2 | 75% |
| 7 | Blinder, Alan S (1973) Wage discrimination: reduced form and structural estimates | 0.644 | 2 | 2 | 100% |
| 8 | Oaxaca, Ronald L (1973) Male-female wage differentials in urban labor markets | 0.644 | 2 | 2 | 100% |
| 9 | Rubin, D.B Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies | 0.644 | 2 | 2 | 100% |
| 10 | Freedman, D. A (1981) Bootstrapping regression models | 0.585 | 3 | 1 | 100% |
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