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

Shoya Ishimaru

arXiv 12 Jan 2021 · Econometrics · publishedThe Review of Economics and Statistics (2022) · 5 citations (OpenAlex)

arXiv:2101.04346 · PDF · DOI · OpenAlex · Extracted main text

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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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Carneiro, Pedro, Heckman, James J, Vytlacil, Edward J (2011) Estimating marginal returns to education0.9285380%
2Oreopoulos, Philip (2006) Estimating average and local average treatment effects of education when compulsory schooling laws really matter0.90912475%
3Cameron, Stephen V, Taber, Christopher (2004) Estimation of educational borrowing constraints using returns to schooling0.87472100%
4Imbens, Guido W, Angrist, Joshua D (1994) Identification and estimation of local average treatment effects0.8746467%
5Yitzhaki, Shlomo (1996) On using linear regressions in welfare economics0.8746467%
6Acemoglu, Daron, Angrist, Joshua (2000) How large are human-capital externalities? Evidence from compulsory schooling laws0.81115453%
7Angrist, Joshua D, Krueger, Alan B (1999) Empirical strategies in labor economics0.81142100%
8Mogstad, Magne, Wiswall, Matthew (2010) Linearity in instrumental variables estimation: Problems and solutions0.81142100%
9Angrist, Joshua D, Imbens, Guido W (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity0.7948450%
10Lochner, Lance, Moretti, Enrico (2015) Estimating and testing models with many treatment levels and limited instruments0.78223548%

Showing the top 10 of 61 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Difference in Differences with Time-Varying Covariates0.64422
2Linear Regression in a Nonlinear World0.51121
3When Should We (Not) Interpret Linear IV Estimands as LATE?0.40511
4How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on 67 Replicated Studies0.40511
5Difference-in-Differences when Parallel Trends Holds Conditional on Covariates0.40511
6Causal Inference for Aggregated Treatment0.40511
7What Do We Get from Two-Way Fixed Effects Regressions? Implications from Numerical Equivalence0.00011