Eskil Heinesen, Christian Hvid, Lars Kirkebøen, Edwin Leuven, Magne Mogstad
arXiv 1 Sep 2022 · Econometrics · publishedJournal of Labor Economics (2024) · 4 citations (OpenAlex)
arXiv:2209.00417 · PDF · DOI · OpenAlex · Extracted main text
We revisit the identification argument of Kirkeboen et al. (2016) who showed how one may combine instruments for multiple unordered treatments with information about individuals' ranking of these treatments to achieve identification while allowing for both observed and unobserved heterogeneity in treatment effects. We show that the key assumptions underlying their identification argument have testable implications. We also provide a new characterization of the bias that may arise if these assumptions are violated. Taken together, these results allow researchers not only to test the underlying assumptions, but also to argue whether the bias from violation of these assumptions are likely to be economically meaningful. Guided and motivated by these results, we estimate and compare the earnings payoffs to post-secondary fields of study in Norway and Denmark. In each country, we apply the identification argument of Kirkeboen et al. (2016) to data on individuals' ranking of fields of study and field-specific instruments from discontinuities in the admission systems. We empirically examine whether and why the payoffs to fields of study differ across the two countries. We find strong cross-country correlation in the payoffs to fields of study, especially after removing fields with violations of the assumptions underlying the identification argument.
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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 | Nibbering, D., Oosterveen, M., and Silva, P. L (2022) Clustered local average treatment effects: fields of study and academic student progress | 0.693 | 5 | 1 | 100% |
| 2 | Kirkeboen, L. J., Leuven, E., and Mogstad, M (2016) Field of study, earnings, and self-selection self | 0.683 | 23 | 1 | 96% |
| 3 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.585 | 3 | 1 | 100% |
| 4 | Altonji, J. G., Blom, E., and Meghir, C (2012) Heterogeneity in human capital investments: High school curriculum, college major, and careers | 0.405 | 1 | 1 | 100% |
| 5 | Altonji, J. G., Arcidiacono, P., and Maurel, A (2016) The analysis of field choice in college and graduate school: Determinants and wage effects | 0.405 | 1 | 1 | 100% |
| 6 | Duflo, E., Glennerster, R., and Kremer, M (2007) Using randomization in development economics research: A toolkit | 0.405 | 1 | 1 | 100% |
| 7 | Heckman, J. J., Urzúa, S. S., and Vytlacil, E. J (2006) Understanding instrumental variables in models with essential heterogeneity | 0.405 | 1 | 1 | 100% |
| 8 | Heckman, J. J. and Urzúa, S. S (2010) Comparing IV with structural models: What simple IV can and cannot identify | 0.405 | 1 | 1 | 100% |
| 9 | Kamat, V (2017) Identification with latent choice sets: The case of the head start impact study | 0.405 | 1 | 1 | 100% |
| 10 | Lee, S. and Salanié, B (2020) Filtered and unfiltered treatment effects with targeting instruments | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2SLS with Multiple Treatments | 0.843 | 4 | 3 |
| 2 | The Cascade Identity: 2SLS as a Policy Parameter in Capacity-Constrained Settings | 0.511 | 2 | 1 |
| 3 | Treatment Effects with Targeting Instruments | 0.405 | 1 | 1 |
| 4 | Sharp Testable Implications of Encouragement Designs | 0.405 | 1 | 1 |