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How Important is Selection? Experimental vs Non-experimental Measures of Income Gains from Migration

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Author Info

  • David McKenzie

    ()
    (The World Bank)

  • John Gibson

    (University of Waikato)

  • Steven Stillman

    (Motu Economic and Public Policy Research)

Abstract

Measuring the gain in income from migration is complicated by non-random selection of migrants from the general population, making it hard to obtain an appropriate comparison group of non-migrants. This paper uses a migrant lottery to overcome this problem, providing an experimental measure of the income gains from migration. New Zealand allows a quota of Tongans to immigrate each year with a lottery used to choose amongst the excess number of applicants. A unique survey conducted by the authors in these two countries allows experimental estimates of the income gains from migration to be obtained by comparing the incomes of migrants to those who applied to migrate, but whose names were not drawn in the lottery, after allowing for the effect of non-compliance among some of those whose names were drawn. We also conducted a survey of individuals who did not apply for the lottery. Comparing this non-applicant group to the migrants enables assessment of the degree to which non-experimental methods can provide an unbiased estimate of the income gains from migration. We find evidence of migrants being positively selected in terms of both observed and unobserved skills. As a result, non-experimental methods are found to overstate the gains from migration, by 9 to 82 percent. A good instrumental variable works best, while difference-in-differences and bias-adjusted propensity-score matching also perform comparatively well.

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File URL: ftp://mngt.waikato.ac.nz/RePEc/wai/econwp/0603.pdf
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Bibliographic Info

Paper provided by University of Waikato, Department of Economics in its series Working Papers in Economics with number 06/03.

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Length: 47 pages
Date of creation: 31 Mar 2006
Date of revision:
Handle: RePEc:wai:econwp:06/03

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Keywords: migration; selection; natural experiment;

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References

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  1. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
  2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
  3. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  4. George J. Borjas, 1987. "Self-Selection and the Earnings of Immigrants," NBER Working Papers 2248, National Bureau of Economic Research, Inc.
  5. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
  6. Daniel Chiquiar & Gordon H. Hanson, 2002. "International Migration, Self-Selection, and the Distribution of Wages: Evidence from Mexico and the United States," NBER Working Papers 9242, National Bureau of Economic Research, Inc.
  7. Joop Hartog & Rainer Winkelmann, 2003. "Comparing migrants to non-migrants: The case of Dutch migration to New Zealand," Journal of Population Economics, Springer, vol. 16(4), pages 683-705, November.
  8. Ximena Clark & Timothy J. Hatton & Jeffrey G. Williamson, 2002. "Where Do U.S. Immigrants Come From, and Why?," NBER Working Papers 8998, National Bureau of Economic Research, Inc.
  9. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages C52-C83, 03.
  10. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
  11. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
  12. 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.
  13. Ashenfelter, Orley C, 1978. "Estimating the Effect of Training Programs on Earnings," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 47-57, February.
  14. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution And Dropout Bias In Social Experiments: A Study Of An Influential Social Experiment," The Quarterly Journal of Economics, MIT Press, vol. 115(2), pages 651-694, May.
  15. Chris Robinson & Nigel Tomes, 1982. "Self-Selection and Interprovincial Migration in Canada," Canadian Journal of Economics, Canadian Economics Association, vol. 15(3), pages 474-502, August.
  16. David Mckenzie & Hillel Rapoport, 2004. "Network Effects and the Dynamics of Migration and Inequality: Theory and Evidence from Mexico," Working Papers 2004-3, Department of Economics, Bar-Ilan University.
  17. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
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