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Education policy reform, local average treatment effect and returns to schooling from instrumental variables in the Philippines

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  • Chris Sakellariou

Abstract

A nationally representative dataset from the Philippines is used to derive returns to schooling estimates from instrumental variables, utilizing a supply-side intervention in the education market capable of generating significant changes in schooling. These estimates apply to a subgroup of, mainly, liquidity constrained individuals, in the spirit of the Local Average Treatment Effect (LATE) literature. Returns to schooling estimates which apply to a subgroup of individuals affected by policy intervention may be more interesting from a policy perspective than the return to the 'average' individual. The findings are consistent with other recent evidence suggesting that the causal effect of education, at least for certain subgroups of individuals, is as big or bigger than what is suggested by OLS estimates.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 38 (2006)
Issue (Month): 4 ()
Pages: 473-481

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Handle: RePEc:taf:applec:v:38:y:2006:i:4:p:473-481

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References

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  1. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2004. "Evaluating the impact of education on earnings in the UK: models, methods and results from the NCDS," LSE Research Online Documents on Economics 19451, London School of Economics and Political Science, LSE Library.
  2. Arjun Bedi & Noel Gaston, 1997. "Returns to endogenous education: the case of Honduras," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 519-528.
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  4. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  5. Kevin Denny & Colm Harmon, 2000. "Education policy reform and the return to schooling from instrumental variables," IFS Working Papers W00/06, Institute for Fiscal Studies.
  6. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
  7. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  8. Maluccio, John A., 1998. "Endogeneity of schooling in the wage function," FCND discussion papers 54, International Food Policy Research Institute (IFPRI).
  9. Chris Sakellariou, 2004. "The use of quantile regressions in estimating gender wage differentials: a case study of the Philippines," Applied Economics, Taylor & Francis Journals, vol. 36(9), pages 1001-1007.
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  11. Ichino, Andrea & Winter-Ebmer, Rudolf, 1998. "Lower and Upper Bounds of Returns to Schooling: An Exercise in IV estimation with Different Instruments," CEPR Discussion Papers 2007, C.E.P.R. Discussion Papers.
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Cited by:
  1. Chuang, Yih-chyi & Lai, Wei-wen, 2010. "Heterogeneity, comparative advantage, and return to education: The case of Taiwan," Economics of Education Review, Elsevier, vol. 29(5), pages 804-812, October.
  2. Aashish Mehta & Hector Villarreal, 2008. "Why do diplomas pay? An expanded Mincerian framework applied to Mexico," Applied Economics, Taylor & Francis Journals, vol. 40(24), pages 3127-3144.
  3. Patrinos, Harry Anthony, 2008. "Quality of schooling, returns to schooling and the 1981 vouchers reform in Chile," Policy Research Working Paper Series 4617, The World Bank.

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