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Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects

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  • Shoya Ishimaru

Abstract

This study proposes an econometric framework to interpret and empirically decompose the difference between IV and OLS estimates given by a linear regression model when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference in weights on the treatment levels, and the difference in identified marginal effects that arises from endogeneity bias. Applications of this framework to return-to-schooling estimates demonstrate the empirical relevance of this distinction in properly interpreting the IV-OLS gap.

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  • Shoya Ishimaru, 2021. "Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects," Papers 2101.04346, arXiv.org, revised Jun 2022.
    • Handle: RePEc:arx:papers:2101.04346
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    2. Tymon Sloczynski, 2021. "When Should We (Not) Interpret Linear IV Estimands as LATE?," CESifo Working Paper Series 9064, CESifo.
    3. Shoya Ishimaru, 2021. "What Do We Get from Two-Way Fixed Effects Regressions? Implications from Numerical Equivalence," Papers 2103.12374, arXiv.org, revised Oct 2024.
    4. Galofré-Vilà, Gregori, 2023. "Spoils of War: The Political Legacy of the German hyperinflation," Explorations in Economic History, Elsevier, vol. 88(C).

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