IDEAS home Printed from https://ideas.repec.org/a/bla/jeurec/v8y2010i4p913-945.html
   My bibliography  Save this article

How Important is Selection? Experimental VS. Non‐Experimental Measures of the Income Gains from Migration

Author

Listed:
  • David McKenzie
  • Steven Stillman
  • John Gibson

Abstract

How much do migrants stand to gain in income from moving across borders? Answering this question is complicated by non-random selection of migrants from the general population, which makes it hard to obtain an appropriate comparison group of non-migrants. New Zealand allows a quota of Tongans to immigrate each year with a random ballot used to choose among the excess number of applicants. A unique survey conducted by the authors 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 ballot, 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 ballot. 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 other than instrumental variables are found to overstate the gains from migration by 20-82%, with difference-in-differences and bias-adjusted matching estimators performing best among the alternatives to instrumental variables. (JEL: J61, F22, C21) (c) 2010 by the European Economic Association.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • David McKenzie & Steven Stillman & John Gibson, 2010. "How Important is Selection? Experimental VS. Non‐Experimental Measures of the Income Gains from Migration," Journal of the European Economic Association, European Economic Association, vol. 8(4), pages 913-945, June.
  • Handle: RePEc:bla:jeurec:v:8:y:2010:i:4:p:913-945
    DOI: j.1542-4774.2010.tb00544.x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1542-4774.2010.tb00544.x
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/j.1542-4774.2010.tb00544.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    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. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. 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.
    5. 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.
    6. 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.
    7. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    8. 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, President and Fellows of Harvard College, vol. 115(2), pages 651-694.
    9. 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.
    10. 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.
    11. 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.
    12. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    13. 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..
    14. 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.
    15. 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.
    16. Kaivan Munshi, 2003. "Networks in the Modern Economy: Mexican Migrants in the U. S. Labor Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 549-599.
    17. 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.
    18. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McKenzie, David & Gibson, John & Stillman, Steven, 2006. "How important is selection ? Experimental versus non-experimental measures of the income gains from migration," Policy Research Working Paper Series 3906, The World Bank.
    2. David McKenzie & John Gibson & Steven Stillman, 2006. "How Important is Selection? Experimental vs Non-experimental Measures of the Income Gains of Migration," Working Papers 06_02, Motu Economic and Public Policy Research.
    3. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    4. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
    5. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    6. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    7. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    8. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    9. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    10. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M., 2008. "Disentangling Treatment Effects of Active Labor Market Policies: The Role of Labor Force Status Sequences," Labour Economics, Elsevier, vol. 15(6), pages 1270-1295, December.
    11. Helena Holmlund & Olmo Silva, 2014. "Targeting Noncognitive Skills to Improve Cognitive Outcomes: Evidence from a Remedial Education Intervention," Journal of Human Capital, University of Chicago Press, vol. 8(2), pages 126-160.
    12. Seonho Shin, 2022. "Evaluating the Effect of the Matching Grant Program for Refugees: An Observational Study Using Matching, Weighting, and the Mantel-Haenszel Test," Journal of Labor Research, Springer, vol. 43(1), pages 103-133, March.
    13. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    14. Gueorgui Kambourov & Iourii Manovskii & Miana Plesca, 2020. "Occupational mobility and the returns to training," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(1), pages 174-211, February.
    15. Giuseppe PORRO & Stefano Maria IACUS, 2004. "Average treatment effect estimation via random recursive partitioning," Departmental Working Papers 2004-28, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    16. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    17. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    18. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    19. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    20. Kent Eliasson & Pär Hansson & Markus Lindvert, 2012. "Do firms learn by exporting or learn to export? Evidence from small and medium-sized enterprises," Small Business Economics, Springer, vol. 39(2), pages 453-472, September.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jeurec:v:8:y:2010:i:4:p:913-945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/eeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.