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Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization

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  • Achim Ahrens
  • Alessandra Stampi-Bombelli
  • Selina Kurer
  • Dominik Hangartner

Abstract

Research underscores the role of naturalization in enhancing immigrants' socio-economic integration, yet application rates remain low. We estimate a policy rule for a letter-based information campaign encouraging newly eligible immigrants in Zurich, Switzerland, to naturalize. The policy rule assigns one out of three treatment letters to each individual, based on their observed characteristics. We field the policy rule to one-half of 1,717 immigrants, while sending random treatment letters to the other half. Despite only moderate treatment effect heterogeneity, the policy tree yields a larger, albeit insignificant, increase in application rates compared to assigning the same letter to everyone.

Suggested Citation

  • Achim Ahrens & Alessandra Stampi-Bombelli & Selina Kurer & Dominik Hangartner, 2023. "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization," Papers 2305.00545, arXiv.org, revised Feb 2024.
  • Handle: RePEc:arx:papers:2305.00545
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    1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.
    2. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    3. Christopher R. Knittel & Samuel Stolper, 2021. "Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 440-444, May.
    4. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    6. Lechner, Michael & Smith, Jeffrey, 2007. "What is the value added by caseworkers?," Labour Economics, Elsevier, vol. 14(2), pages 135-151, April.
    7. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    8. Saurabh Bhargava & Dayanand Manoli, 2015. "Psychological Frictions and the Incomplete Take-Up of Social Benefits: Evidence from an IRS Field Experiment," American Economic Review, American Economic Association, vol. 105(11), pages 3489-3529, November.
    9. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
    10. Amy Finkelstein & Matthew J Notowidigdo, 2019. "Take-Up and Targeting: Experimental Evidence from SNAP," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1505-1556.
    11. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
    12. Keisuke Hirano & Jack R. Porter, 2009. "Asymptotics for Statistical Treatment Rules," Econometrica, Econometric Society, vol. 77(5), pages 1683-1701, September.
    13. Goldin, Jacob & Homonoff, Tatiana & Javaid, Rizwan & Schafer, Brenda, 2022. "Tax filing and take-up: Experimental evidence on tax preparation outreach and benefit claiming," Journal of Public Economics, Elsevier, vol. 206(C).
    14. Frolich, Markus, 2008. "Statistical Treatment Choice: An Application to Active Labor Market Programs," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 547-558, June.
    15. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    16. Hainmueller, Jens & Hangartner, Dominik, 2013. "Who Gets a Swiss Passport? A Natural Experiment in Immigrant Discrimination," American Political Science Review, Cambridge University Press, vol. 107(1), pages 159-187, February.
    17. Christina Gathmann & Nicolas Keller, 2018. "Access to Citizenship and the Economic Assimilation of Immigrants," Economic Journal, Royal Economic Society, vol. 128(616), pages 3141-3181, December.
    18. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    19. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    20. Manski, Charles F., 2007. "Minimax-regret treatment choice with missing outcome data," Journal of Econometrics, Elsevier, vol. 139(1), pages 105-115, July.
    21. Felfe, Christina & Kocher, Martin G. & Rainer, Helmut & Saurer, Judith & Siedler, Thomas, 2021. "More opportunity, more cooperation? The behavioral effects of birthright citizenship on immigrant youth," Journal of Public Economics, Elsevier, vol. 200(C).
    22. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    23. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
    24. Dancygier,Rafaela M., 2010. "Immigration and Conflict in Europe," Cambridge Books, Cambridge University Press, number 9780521150231.
    25. Dancygier,Rafaela M., 2010. "Immigration and Conflict in Europe," Cambridge Books, Cambridge University Press, number 9780521199070.
    26. Hainmueller, Jens & Hangartner, Dominik & Ward, Dalston, 2019. "The Effect of Citizenship on the Long-Term Earnings of Marginalized Immigrants: Quasi-Experimental Evidence from Switzerland," SocArXiv 24qas, Center for Open Science.
    27. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    28. Keller, Nicolas & Gathmann, Christina & Monscheuer, Ole, 2015. "Citizenship and the Social Integration of Immigrants: Evidence from Germany's Immigration Reforms," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113184, Verein für Socialpolitik / German Economic Association.
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