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Sharp bounds and testability of a Roy model of STEM major choices

Ismael Mourifie, Marc Henry, Romuald Meango

arXiv 26 Sep 2017 · Econometrics · publishedJournal of Political Economy (2020) · 5 citations (OpenAlex)

arXiv:1709.09284 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We analyze the empirical content of the Roy model, stripped down to its essential features, namely sector specific unobserved heterogeneity and self-selection on the basis of potential outcomes. We characterize sharp bounds on the joint distribution of potential outcomes and testable implications of the Roy self-selection model under an instrumental constraint on the joint distribution of potential outcomes we call stochastically monotone instrumental variable (SMIV). We show that testing the Roy model selection is equivalent to testing stochastic monotonicity of observed outcomes relative to the instrument. We apply our sharp bounds to the derivation of a measure of departure from Roy self-selection to identify values of observable characteristics that induce the most costly misallocation of talent and sector and are therefore prime targets for intervention. Special emphasis is put on the case of binary outcomes, which has received little attention in the literature to date. For richer sets of outcomes, we emphasize the distinction between pointwise sharp bounds and functional sharp bounds, and its importance, when constructing sharp bounds on functional features, such as inequality measures. We analyze a Roy model of college major choice in Canada and Germany within this framework, and we take a new look at the under-representation of women in STEM.

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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
1James Heckman and Bo Honoré (1990) The empirical content of the roy model1.00063100%
2Yu-Chin Hsu, Chu-Ahn Liu, and Xiaoxia Shi (2019) Testing generalized regression monotonicity1.00063100%
3Alfred Galichon and Marc Henry (2011) Set identification in models with multiple equilibria self1.00054100%
4Tim Bedford and Isaac Meilijson (1997) A characterization of marginal distributions of (possibly dependent) lifetime variables which right censor each other1.00053100%
5Victor Chernozhukov, Simon Lee, and Adam Rosen (2013) Inference on intersection bounds0.92844100%
6Richard Blundell, Amanda Gosling, Hidehiko Ichimura, and Costas Meghir (2007) Changes in the distribution of male and female wages accounting for employment composition using bounds0.87452100%
7Philipp Eisenhauer, James Heckman, and Edward Vytlacil (2015) The generalized roy model and the cost-benefit analysis of social programs0.87452100%
8James Heckman and Guilherme Sedlacek (1985) Heterogeneity, aggregation, and market wage functions: an empirical model of self-selection in the labor market0.84333100%
9James Heckman and Guilherme Sedlacek (1990) Self-selection and the distribution of hourly wages0.84333100%
10R Willis and Sherwin Rosen (1979) Education and self-selection0.81142100%

Showing the top 10 of 98 scored citations.

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Citing paperIntensityMentionsSections
1Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters0.92853
2Robust Likelihood Ratio Tests for Incomplete Economic Models0.92843
3Extending Economic Models with Testable Assumptions: Theory and Applications0.87452
4Discordant Relaxations of Misspecified Models0.64422
5Robust Identification in Randomized Experiments with Noncompliance0.51122
6Identifying the Effects of a Program Offer with an Application to Head Start0.40511
7Sharp Bounds for the Marginal Treatment Effect with Sample Selection0.40511
8Policy Transforms and Learning Optimal Policies I thank Jiaying Gu, Ismael Mourifie, Eduardo Souza-Rodrigues, Adam Rosen, Stanislav Volgushev and Yuanyuan Wan for their feedback and encouragement, and I am especially grateful to JoonHwan Cho for many hours of discussion that helped to improve this paper. A previous version of this paper appeared in my doctoral thesis at the University of Toronto. This research was supported by the Social Sciences and Humanities Research Council of Canada. All errors are my own0.40511
9Partial Identification and Inference for Conditional Distributions of Treatment Effects0.40511