Lower and upper bounds of returns to schooling: An exercise in IV estimation with different instruments |
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| Affiliation: | 1. European University Institute, I-50016 San Domenico di Fiesole, Firenze, Italy;2. University of Linz, A-4040 Linz, Austria;3. IGIER, Milan, Italy;4. CEPR, London, UK;5. WIFO, Vienna, Austria;1. Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota;2. Department of Urology, University of Minnesota, Minneapolis, Minnesota;3. Department of Biostatistics, University of Washington, Seattle, Washington;4. Department of Urology, University of Washington and Seattle Children’s Hospital, Seattle, Washington;5. Madigan Army Medical Center, Uniformed Services University of the Health Sciences, Tacoma, Washington;6. Boston University School of Medicine, Boston, Massachusetts;7. University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania;8. Department of Urology, Ohio State University, Columbus, Ohio;9. Cleveland Clinic, Cleveland, Ohio;10. Department of Urology, Duke University, Durham, North Carolina;11. Department of Urology, UC Irvine, Orange, California;12. Department of Urology and Oncology, Tulane University, New Orleans, Louisiana;13. Division of Urology, Southern Illinois University, Springfield, Illinois;14. Department of Urologic Sciences, University of British Colombia, Vancouver, British Columbia, Canada;1. School of Computer and Information, Anqing Normal University, Anqing 246133, Anhui, China;2. Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;1. Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China;2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China;3. Department of Mathematics, Hong Kong Baptist University, Hong Kong;1. College of Computation Science, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, PR China;2. Department of Mathematics, Jinan University, Guangzhou, 510632, PR China;3. Dept. of Math., National Tsing Hua University, Hsinchu 30013, Taiwan, ROC |
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| Abstract: | Several recent studies based on `exogenous' sources of variation in educational outcomes show IV estimates of returns to schooling that are substantially higher than the corresponding OLS estimates. Card (1995a, Earnings, schooling, and ability revisited. Research in Labor Economics 14, 23–48) suggests that these results are explained by the existence of heterogenity in individual returns and by the fact that these studies are based on instruments that influence only the educational decision of individuals with high marginal returns due to either liquidity constraints or to high ability. This conclusion is consistent with the local average treatment effect (LATE) interpretation of IV (Imbens and Angrist (1994, Identification and estimation of local average treatment effects. Econometrica 62, 467–475) according to which IV identifies only the average returns of those who comply with the assignment-to-treatment mechanism implied by the instrument. We show evidence for Germany suggesting that returns to schooling are heterogeneous, instruments matter and the LATE interpretation of IV makes sense. With an appropriate choice of instruments we also show how IV can be used to approximate the range of variations of returns to schooling in Germany. |
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