arXiv 1 Nov 2023 · Econometrics · publishedEconometrics Journal (2025)
arXiv:2311.00439 · PDF · DOI · OpenAlex · Extracted main text
In the presence of sample selection, Lee's (2009) nonparametric bounds are a popular tool for estimating a treatment effect. However, the Lee bounds rely on the monotonicity assumption, whose empirical validity is sometimes unclear. Furthermore, the bounds are often regarded to be wide and less informative even under monotonicity. To address these issues, this study introduces a stochastic version of the monotonicity assumption alongside a nonparametric distributional shape constraint. The former enhances the robustness of the Lee bounds with respect to monotonicity, while the latter helps tighten these bounds. The obtained bounds do not rely on the exclusion restriction and can be root-$n$ consistently estimable, making them practically viable. The potential usefulness of the proposed methods is illustrated by their application on experimental data from the after-school instruction programme studied by Muralidharan, Singh, and Ganimian (2019).
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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 | Muralidharan, K., Singh, A., and Ganimian, A. J (2019) Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India | 0.976 | 14 | 3 | 93% |
| 2 | Lee, D. S (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 0.971 | 60 | 9 | 92% |
| 3 | Chernozhukov, V., Lee, S., and Rosen, A. M (2013) Intersection Bounds: Estimation and Inference | 0.941 | 6 | 3 | 83% |
| 4 | Imbens, G. W. and Manski, C. F (2004) Confidence Intervals for Partially Identified Parameters | 0.874 | 7 | 2 | 100% |
| 5 | Delius, A. and Sterck, O (2024) Cash Transfers and Micro-Enterprise Performance: Theory and Quasi-Experimental Evidence from Kenya | 0.737 | 3 | 2 | 100% |
| 6 | Horowitz, J. L. and Manski, C. F (1995) Identification and Robustness with Contaminated and Corrupted Data | 0.693 | 6 | 4 | 33% |
| 7 | Agranov, M. and Ortoleva, P (2017) Stochastic Choice and Preferences for Randomization | 0.644 | 2 | 2 | 100% |
| 8 | Barrow, L. and Rouse, C. E (2018) Financial Incentives and Educational Investment: The Impact of Performance-based Scholarships on Student Time Use | 0.644 | 2 | 2 | 100% |
| 9 | Behaghel, L., Crépon, B., Gurgand, M., and Le Barbanchon, T (2015) Please Call Again: Correcting Nonresponse Bias in Treatment Effect Models | 0.644 | 2 | 2 | 100% |
| 10 | Conley, T. G., Hansen, C. B., and Rossi, P. E (2012) Plausibly Exogenous | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 37 scored citations.