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How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration

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Listed:
  • McKenzie, David

    (World Bank)

  • Gibson, John

    (University of Waikato)

  • Stillman, Steven

    (Free University of Bozen/Bolzano)

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 noncompliance 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.

Suggested Citation

  • McKenzie, David & Gibson, John & Stillman, Steven, 2006. "How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration," IZA Discussion Papers 2087, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp2087
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    References listed on IDEAS

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    1. 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.
    2. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    3. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    4. 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.
    5. 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, Oxford University Press, vol. 115(2), pages 651-694.
    6. Daniel Chiquiar & Gordon H. Hanson, 2005. "International Migration, Self-Selection, and the Distribution of Wages: Evidence from Mexico and the United States," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 239-281, April.
    7. Mckenzie, David & Rapoport, Hillel, 2007. "Network effects and the dynamics of migration and inequality: Theory and evidence from Mexico," Journal of Development Economics, Elsevier, vol. 84(1), pages 1-24, September.
    8. Joshua 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.
    9. George J. Borjas, 2021. "Self-Selection and the Earnings of Immigrants," World Scientific Book Chapters, in: Foundational Essays in Immigration Economics, chapter 4, pages 69-91, World Scientific Publishing Co. Pte. Ltd..
    10. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    11. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Joop Hartog & Rainer Winkelmann, 2003. "Comparing migrants to non-migrants: The case of Dutch migration to New Zealand," Journal of Population Economics, Springer;European Society for Population Economics, vol. 16(4), pages 683-705, November.
    17. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    18. Kaivan Munshi, 2003. "Networks in the Modern Economy: Mexican Migrants in the U. S. Labor Market," The Quarterly Journal of Economics, Oxford University Press, vol. 118(2), pages 549-599.
    19. 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, January.
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    More about this item

    Keywords

    selection; natural experiment; migration;
    All these keywords.

    JEL classification:

    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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