Xiaolin Sun, Xueyan Zhao, D. S. Poskitt
arXiv 2 Oct 2024 · Econometrics
arXiv:2410.01159 · PDF · DOI · OpenAlex · Extracted main text
This paper addresses the sample selection model within the context of the gender gap problem, where even random treatment assignment is affected by selection bias. By offering a robust alternative free from distributional or specification assumptions, we bound the treatment effect under the sample selection model with an exclusion restriction, an assumption whose validity is tested in the literature. This exclusion restriction allows for further segmentation of the population into distinct types based on observed and unobserved characteristics. For each type, we derive the proportions and bound the gender gap accordingly. Notably, trends in type proportions and gender gap bounds reveal an increasing proportion of always-working individuals over time, alongside variations in bounds, including a general decline across time and consistently higher bounds for those in high-potential wage groups. Further analysis, considering additional assumptions, highlights persistent gender gaps for some types, while other types exhibit differing or inconclusive trends. This underscores the necessity of separating individuals by type to understand the heterogeneous nature of the gender gap.
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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 | Huber, M. and G. Mellace (2015) Sharp bounds on causal effects under sample selection | 1.000 | 10 | 3 | 100% |
| 2 | Chernozhukov, V., S. Lee, and A. M. Rosen (2013) Intersection bounds: Estimation and inference | 1.000 | 8 | 3 | 100% |
| 3 | Blanco, G., C. A. Flores, and A. Flores-Lagunes (2013) Bounds on average and quantile treatment effects of job corps training on wages | 1.000 | 7 | 3 | 100% |
| 4 | Maasoumi, E. and L. Wang (2019) The gender gap between earnings distributions | 0.950 | 28 | 4 | 86% |
| 5 | Lee, D. S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.909 | 24 | 6 | 75% |
| 6 | Blundell, R., A. Gosling, H. Ichimura, and C. Meghir (2007) Changes in the distribution of male and female wages accounting for employment composition using bounds | 0.874 | 6 | 2 | 100% |
| 7 | Zhang, J. L., D. B. Rubin, and F. Mealli (2008) Evaluating the effects of job training programs on wages through principal stratification | 0.874 | 6 | 2 | 100% |
| 8 | Bartalotti, O., D. Kédagni, and V. Possebom (2023) Identifying marginal treatment effects in the presence of sample selection | 0.874 | 5 | 2 | 100% |
| 9 | Fernández‐Val, I., A. Vuuren, F. Vella, and F. Peracchi (2023) Selection and the distribution of female real hourly wages in the united states | 0.874 | 5 | 2 | 100% |
| 10 | Heckman, J. J (1979) Sample selection bias as a specification error | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity | 0.511 | 2 | 1 |