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Overidentification in Shift-Share Designs

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  • Jinyong Hahn
  • Guido Kuersteiner
  • Andres Santos
  • Wavid Willigrod

Abstract

This paper studies the testability of identifying restrictions commonly employed to assign a causal interpretation to two stage least squares (TSLS) estimators based on Bartik instruments. For homogeneous effects models applied to short panels, our analysis yields testable implications previously noted in the literature for the two major available identification strategies. We propose overidentification tests for these restrictions that remain valid in high dimensional regimes and are robust to heteroskedasticity and clustering. We further show that homogeneous effect models in short panels, and their corresponding overidentification tests, are of central importance by establishing that: (i) In heterogenous effects models, interpreting TSLS as a positively weighted average of treatment effects can impose implausible assumptions on the distribution of the data; and (ii) Alternative identifying strategies relying on long panels can prove uninformative in short panel applications. We highlight the empirical relevance of our results by examining the viability of Bartik instruments for identifying the effect of rising Chinese import competition on US local labor markets.

Suggested Citation

  • Jinyong Hahn & Guido Kuersteiner & Andres Santos & Wavid Willigrod, 2024. "Overidentification in Shift-Share Designs," Papers 2404.17049, arXiv.org.
  • Handle: RePEc:arx:papers:2404.17049
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    References listed on IDEAS

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    1. David H. Autor & David Dorn & Gordon H. Hanson, 2013. "The China Syndrome: Local Labor Market Effects of Import Competition in the United States," American Economic Review, American Economic Association, vol. 103(6), pages 2121-2168, October.
    2. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2022. "Quasi-Experimental Shift-Share Research Designs," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 181-213.
    3. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Aureo de Paula, 2019. "Inference on Causal and Structural Parameters using Many Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 1867-1900.
    4. Rodrigo Adão & Michal Kolesár & Eduardo Morales, 2019. "Shift-Share Designs: Theory and Inference," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 1949-2010.
    5. Rodrigo Ad~ao & Michal Koles'ar & Eduardo Morales, 2018. "Shift-Share Designs: Theory and Inference," Papers 1806.07928, arXiv.org, revised Aug 2019.
    6. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
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