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Revisiting Returns to Education in Thailand: Structural Causal Model Framework

Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, 239 Huay Keaw Rd., Muang, Chiang Mai, 50200, Thailand

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Supanika Leurcharusmee

https://orcid.org/0000-0001-9349-000X

Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, 239 Huay Keaw Rd., Muang, Chiang Mai, 50200, Thailand

E-mail Address: supanika.l@cmu.ac.th

Corresponding author.

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https://doi.org/10.1142/S0218488523400135Cited by:3 (Source: Crossref)

This article is part of the issue:

Special Issue on Uncertainty in Economics and Finance

Abstract

This paper revisits the causal relationship between education and earnings, or the returns to education, using Thailand’s socio-economic household survey data. We show that, with the minimum modeling assumption, the causal effect of education on earnings can be nonparametrically identified only when there is no unobserved ability affecting both education and earning, and when the selection mechanisms are conditionally independent to earnings conditioning on potential experience and education. The causal effects of education on earnings are estimated using a nonparametric approach with selection bias adjustment and parametric approaches with selection bias and ability bias adjustments. The results show different estimates for returns to education across model specifications, indicating the importance of the bias adjustments and parametric assumptions.

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References

1. A. Ahrens, C. B. Hansen and M. Schaffer , PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference, Statistical Software Components Boston College Department of Economics (2019). Google Scholar
2. J. D. Angrist , Conditional independence in sample selection models, Economic Letters 54 (1996) 103–112. Crossref, Web of Science, Google Scholar
3. E. Bareinboim, J. Tian and J. Pearl, Recovering from Selection Bias in Causal and Statistical Inference, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014). Google Scholar
4. E. Bareinboim and J. Tian , Recovering Causal Effects from Selection Bias, in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, eds. S. Koenig and B. Bonet , Association for the Advancement of Artificial Intelligence (Palo Alto, CA: AAAI Press, 2015). Crossref, Google Scholar
5. G. S. Becker , Human capital: A theoretical and empirical analysis, with special reference to education (University of Chicago Press, 1964). Google Scholar
6. G. S. Becker and B. R. Chiswick , Education and the Distribution of Earnings, The American Economic Review 56(1/2) (1966) 358–369. Web of Science, Google Scholar
7. C. Belzil and J. Hansen , Unobserved ability and the return to schooling, Econometrica 70(5) (2002) 2075–2091. Crossref, Web of Science, Google Scholar
8. D. Card , Earnings, Schooling, and Ability Revisited (No.4832), National Bureau of Economic Research, Inc (1994). Google Scholar
9. D. Card , The causal effect of education on earnings, Handbook of Labor Economics 3 (1999) 1801–1863. Crossref, Google Scholar
10. D. Card , Estimating the return to schooling: Progress on some persistent econometric problems, Econometrica 69 (2001) 1127–1160. Crossref, Web of Science, Google Scholar
11. P. Carneiro, J. J. Heckman and E. J. Vytlacil , Estimating marginal returns to education, American Economic Review 101(6) (2011) 2754–2781. Crossref, Web of Science, Google Scholar
12. B. Chen and J. Pearl, Graphical tools for linear structural equation modeling, Technical Report R-432, Department of Computer Science, University of California, Los Angeles, CA. (2014). Google Scholar
13. D. R. Cox , Planning of Experiments (New York: Wiley, 1958). Google Scholar
14. M. Dickson , The causal effect of education on wages revisited, Oxford Bulletin of Economics and Statistics 75(4) (2013) 477–498. Crossref, Web of Science, Google Scholar
15. R. A. Fisher , The Design of Experiments (London: Oliver and Boyd, 1935). Google Scholar
16. R. Frisch , A dynamic approach to economic theory: Lectures by Ragnar Frisch at Yale University (Department of Economics, Oslo, 1930) Google Scholar
17. R. Frisch, B. Olav and Q. Duo , A Dynamic Approach to Economic Theory: The Yale Lectures of Ragnar Frisch (Routledge, London, 2013). Crossref, Google Scholar
18. Z. Griliches , Estimating the returns to schooling: Some econometric problems, Econometrica: Journal of the Econometric Society (1977) 1–22. Crossref, Web of Science, Google Scholar
19. T. Haavelmo , The statistical implications of a system of simultaneous equations, Econometrica 11(1) (1943) 1–12. Crossref, Google Scholar
20. T. Haavelmo , The probability approach in econometrics, Econometrica 12(Supplement) (1944) iii–vi and 1–115. Google Scholar
21. J. J. Heckman , The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models, Annals of economic and social measurement 5(4) (1976) 475–492. Google Scholar
22. J. J. Heckman , Sample selection bias as a specification error, Econometrica: Journal of the econometric society (1979) 153–161. Crossref, Web of Science, Google Scholar
23. J. J. Heckman and G. Sedlacek , Heterogeneity, aggregation, and market wage functions: An empirical model of self-selection in the labor market, Journal of political Economy 93(6) (1985) 1077–1125. Crossref, Web of Science, Google Scholar
24. J. J. Heckman and R. Pinto , Causal analysis after Haavelmo, Econometric Theory 31(1) (2015) 115–151. Crossref, Web of Science, Google Scholar
25. J. J. Heckman, J. E. Humphries and G. Veramendi , Returns to education: The causal effects of education on earnings, health, and smoking, Journal of Political Economy 126(S1) (2018) S197–S246. Crossref, Web of Science, Google Scholar
26. J. J. Heckman and R. Pinto, Causality and Econometrics. Discussion paper series, No. 15081, IZA Institute of Labor Economics (2022). Google Scholar
27. P. W. Holland , Statistics and causal inference, Journal of the American Statistical Association 81(396) (1986) 945–960. Crossref, Web of Science, Google Scholar
28. J. A. John and N. R. Draper , An alternative family of transformations, Journal of the Royal Statistical Society: Series C (Applied Statistics) 29(2) (1980) 190–197. Google Scholar
29. A. Marshall , Principles of Economics (Ninth (Valorium) Edition ed.), (Cambridge University Press, London, 1961). Google Scholar
30. J. Mincer, Schooling, Experience, and Earnings. Human Behavior & Social Institutions No. 2 (1974). Google Scholar
31. M. Mogstad, A. Torgovitsky and C. R. Walters , The causal interpretation of two-stage least squares with multiple instrumental variables, American Economic Review 111(11) (2021) 3663–3698. Crossref, Web of Science, Google Scholar
32. J. Neyman , Statistical problems in agricultural experiments, Journal of the Royal Statistical Society II (Supplement) 2 (1923) 107–180. Google Scholar
33. NSO., The 2019 Household Socio-Economic Survey (National Statistical Office, Bangkok, 2019). Google Scholar
34. J. Pearl , Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Publishers Inc, San Francisco, 1988). Google Scholar
35. J. Pearl , A probabilistic calculus of actions, in Morgan Kaufmann, Uncertainty in Artificial Intelligence, R. L. de Mantaras & D. Poole (eds.), Vol. 10 (1994), pp. 454–462. Crossref, Google Scholar
36. J. Pearl , Causal diagrams for empirical research, Biometrika 82 (1995) 669–709. Crossref, Web of Science, Google Scholar
37. J. Pearl , The foundations of causal inference, Sociological Methodology 40 (2010) 75–149. Crossref, Web of Science, Google Scholar
38. J. Pearl , Causality: Models, Reasoning, and Inference (Cambridge University Press, Los Angeles, 2000). Google Scholar
39. J. Pearl , Trygve Haavelmo and the emergence of causal calculus, Econometric Theory 31 (2015) 152–179. Crossref, Web of Science, Google Scholar
40. J. Pearl and D. Mackenzie , The Book of Why (Penguin Books, London, 2019). Google Scholar
41. D. B. Rubin , Estimating causal effects of treatments in randomized and nonrandomized studies, Journal of Educational Psychology 66(5) (1974) 688–701. Crossref, Web of Science, Google Scholar
42. I. Shpitser and J. Pearl , Identification of conditional interventional distributions, in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, R. Dechter and T. Richardson , eds. (AUAI Press, Corvallis, OR, 2006), pp. 437–444. Google Scholar
43. K. Tangtipongkul , Rates of return to schooling in Thailand, Asian Development Review 32(2) (2015) 38–64. Link, Google Scholar
44. S. Tikka and J. Karvanen , Identifying Causal Effects with the R Package, Journal of Statistical Software 76(12) (2017) 1–30. Crossref, Web of Science, Google Scholar