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Author: Ishimaru, Shoya
Resulting in 2 citations.
1. Ishimaru, Shoya
Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects
Review of Economics and Statistics published online (25 January 2022): DOI: 10.1162/rest_a_01169.
Also: https://direct.mit.edu/rest/article/doi/10.1162/rest_a_01169/109261/
Cohort(s): NLSY79
Publisher: MIT Press
Keyword(s): Educational Attainment; Educational Returns; Modeling, Instrumental Variables; Modeling, OLS; Statistical Analysis; Wages

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.
Bibliography Citation
Ishimaru, Shoya. "Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects." Review of Economics and Statistics published online (25 January 2022): DOI: 10.1162/rest_a_01169.
2. Ishimaru, Shoya
Essays on Education and Labor Market
Ph.D. Dissertation, Department of Economics, The University of Wisconsin - Madison, 2020
Cohort(s): NLSY97
Publisher: ProQuest Dissertations & Theses (PQDT)
Keyword(s): College Education; Educational Outcomes; Geocoded Data; Labor Market Outcomes; Local Area Unemployment

Permission to reprint the abstract has not been received from the publisher.

The first chapter examines the importance of college and labor market options associated with childhood location in shaping educational and labor market outcomes experienced by a person later in life. I estimate a dynamic model that considers post-high school choices of whether and where to attend college and where to work, subject to home preferences, mobility costs, and spatial search frictions. The estimated model suggests that spatial gaps in local college and labor market options in the United States give rise to a 6 percentage point gap in the college attendance rate and an 11% gap in the wage rate at 10 years of experience between the 90th and 10th percentiles of across-county variation in each outcome.
Bibliography Citation
Ishimaru, Shoya. Essays on Education and Labor Market. Ph.D. Dissertation, Department of Economics, The University of Wisconsin - Madison, 2020.