Marc Henry, Romuald Meango, Ismael Mourifie
arXiv 18 May 2020 · Econometrics · publishedJournal of Econometrics (2023) · 2 citations (OpenAlex)
arXiv:2005.09095 · PDF · DOI · OpenAlex · Extracted main text
We derive sharp bounds on the non consumption utility component in an extended Roy model of sector selection. We interpret this non consumption utility component as a compensating wage differential. The bounds are derived under the assumption that potential utilities in each sector are (jointly) stochastically monotone with respect to an observed selection shifter. The research is motivated by the analysis of women's choice of university major, their under representation in mathematics intensive fields, and the impact of role models on choices and outcomes. To illustrate our methodology, we investigate the cost of STEM fields with data from a German graduate survey, and using the mother's education level and the proportion of women on the STEM faculty at the time of major choice as selection shifters.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | X. d'Haultf uille and A. Maurel (2013) Inference on an extended Roy model, with an application to schooling decisions in France | 1.000 | 10 | 3 | 100% |
| 2 | I. Mourifié, M. Henry, and R. Méango (2020) Sharp bounds and testability of a Roy model of STEM major choices | 1.000 | 7 | 3 | 100% |
| 3 | S. Kahn and D. Ginther (2017) Women and STEM | 0.811 | 4 | 2 | 100% |
| 4 | C. Manski and J. Pepper (2000) Monotone instrumental variables: with an application to the returns to schooling | 0.811 | 4 | 2 | 100% |
| 5 | V. Chernozhukov, S. Lee, and A. Rosen (2013) Inference on intersection bounds | 0.693 | 10 | 1 | 100% |
| 6 | P. Bayer, S. Khan, and C. Timmins (2011) Nonparametric identification and estimation in a Roy model with common nonpecuniary returns | 0.693 | 6 | 1 | 100% |
| 7 | Y.-C. Hsu, C.-A. Liu, and X. Shi (2019) Testing generalized regression monotonicity | 0.693 | 5 | 1 | 100% |
| 8 | D. Chetverikov (2019) Testing regression monotonicity in econometric models | 0.644 | 2 | 2 | 100% |
| 9 | F. Baillet, A. Franken, and A. Weber (2017) DZHW graduate panel 2009: Data and methods report on the graduate panel 2009 (1st and 2nd survey waves) | 0.644 | 2 | 2 | 100% |
| 10 | F. Saltiel (2021) Multidimensional skills and gender differences in STEM majors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 53 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters | 0.405 | 1 | 1 |