arXiv 18 Apr 2018 · Econometrics · 4 citations (OpenAlex)
arXiv:1804.06721 · PDF · DOI · OpenAlex · Extracted main text
Researchers increasingly leverage movement across multiple treatments to estimate causal effects. While these "mover regressions" are often motivated by a linear constant-effects model, it is not clear what they capture under weaker quasi-experimental assumptions. I show that binary treatment mover regressions recover a convex average of four difference-in-difference comparisons and are thus causally interpretable under a standard parallel trends assumption. Estimates from multiple-treatment models, however, need not be causal without stronger restrictions on the heterogeneity of treatment effects and time-varying shocks. I propose a class of two-step estimators to isolate and combine the large set of difference-in-difference quasi-experiments generated by a mover design, identifying mover average treatment effects under conditional-on-covariate parallel trends and effect homogeneity restrictions. I characterize the efficient estimators in this class and derive specification tests based on the model's overidentifying restrictions. Future drafts will apply the theory to the Finkelstein et al. (2016) movers design, analyzing the causal effects of geography on healthcare utilization.
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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 | Card, D., J. Heining, and P. Kline (2013) Workplace Heterogeneity and the Rise of West German Wage Inequality | 0.928 | 4 | 3 | 100% |
| 2 | Finkelstein, A., M. Gentzkow, and H. Williams (2016) Sources of Geographic Variation in Health Care: Evidence from Patient Migration | 0.874 | 7 | 2 | 100% |
| 3 | Abadie, A (2005) Semiparametric Difference-in-Differences Estimators | 0.811 | 4 | 2 | 100% |
| 4 | Chernozhukov, V., I. Fernandez-Val, J. Hahn, and W. Newey (2013) Average and Quantile Effects in Nonseparable Panel Models | 0.737 | 3 | 2 | 100% |
| 5 | Imai, K. and I. S. Kim (2016) When Should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data? Working Paper | 0.737 | 3 | 2 | 100% |
| 6 | Abowd, J. M., K. L. McKinney, and I. M. Schmutte (2015) Modeling Endogenous Mobility in Wage Determination, Working Paper | 0.644 | 2 | 2 | 100% |
| 7 | Angrist, J. and I. Fernandez-Val (2013) ExtrapoLATE-ing: External Validity and Overidentification in the LATE Framework | 0.644 | 2 | 2 | 100% |
| 8 | Angrist, J. and M. Rokkanen (2015) Wanna Get Away? Regression DIscontinuity Estimation of Exam School Effects Away from the Cutoff | 0.644 | 2 | 2 | 100% |
| 9 | Ashenfelter, O. and D. Card (1985) Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs | 0.644 | 2 | 2 | 100% |
| 10 | Bonhomme, S., T. Lamadon, and E. Manresa (2017) A Distributional Framework for Matched Employer Employee Data, Working Paper | 0.644 | 2 | 2 | 100% |
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