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A distributional synthetic control method for policy evaluation

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  • Yi‐Ting Chen

Abstract

We extend the synthetic control method to evaluate the distributional effects of policy intervention in the possible presence of poor matching. The counterfactuals (or intervention effects) are identified by matching a vector of pre‐intervention quantile residuals of the treated unit and a convex combination of its potential‐control counterparts. The residuals are orthogonal to a set of observable common factors that control for the potentially poor matching. We also apply our method to a set of case studies that explore the distributional effects of state‐level minimum‐wage hikes in the USA.

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  • Yi‐Ting Chen, 2020. "A distributional synthetic control method for policy evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 505-525, August.
    • Handle: RePEc:wly:japmet:v:35:y:2020:i:5:p:505-525
    DOI: 10.1002/jae.2778
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    1. David Card, 1990. "The Impact of the Mariel Boatlift on the Miami Labor Market," ILR Review, Cornell University, ILR School, vol. 43(2), pages 245-257, January.
    2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    3. Sarah Bohn & Magnus Lofstrom & Steven Raphael, 2014. "Did the 2007 Legal Arizona Workers Act Reduce the State's Unauthorized Immigrant Population?," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 258-269, May.
    4. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    5. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    6. Andreas Billmeier & Tommaso Nannicini, 2013. "Assessing Economic Liberalization Episodes: A Synthetic Control Approach," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 983-1001, July.
    7. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    8. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    9. Lechner, Michael, 2011. "The Estimation of Causal Effects by Difference-in-Difference Methods," Foundations and Trends(R) in Econometrics, now publishers, vol. 4(3), pages 165-224, November.
    10. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    11. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2011. "Synth: An R Package for Synthetic Control Methods in Comparative Case Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i13).
    12. David S. Lee, 1999. "Wage Inequality in the United States During the 1980s: Rising Dispersion or Falling Minimum Wage?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 977-1023.
    13. Dukpa Kim & Tatsushi Oka, 2014. "Divorce Law Reforms And Divorce Rates In The Usa: An Interactive Fixed‐Effects Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 231-245, March.
    14. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    15. Cheng Hsiao & Qiankun Zhou, 2019. "Panel parametric, semiparametric, and nonparametric construction of counterfactuals," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 463-481, June.
    16. Arindrajit Dube & T. William Lester & Michael Reich, 2010. "Minimum Wage Effects Across State Borders: Estimates Using Contiguous Counties," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 945-964, November.
    17. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    18. Cheng Hsiao & H. Steve Ching & Shui Ki Wan, 2012. "A Panel Data Approach For Program Evaluation: Measuring The Benefits Of Political And Economic Integration Of Hong Kong With Mainland China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 705-740, August.
    19. David Card & Alan B. Krueger, 2016. "Myth and Measurement: The New Economics of the Minimum Wage Twentieth-Anniversary Edition," Economics Books, Princeton University Press, edition 2, number 10738.
    20. David Neumark & DMark Schweitzer & DaWilliam Wascher, 2004. "Minimum Wage Effects throughout the Wage Distribution," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
    21. Mora, Ricardo & Reggio, Iliana, 2012. "Treatment effect identification using alternative parallel assumptions," UC3M Working papers. Economics we1233, Universidad Carlos III de Madrid. Departamento de Economía.
    22. Jinyong Hahn & Ruoyao Shi, 2017. "Synthetic Control and Inference," Econometrics, MDPI, vol. 5(4), pages 1-12, November.
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    Cited by:

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    2. Maciej Berk{e}sewicz, 2023. "Survey calibration for causal inference: a simple method to balance covariate distributions," Papers 2310.11969, arXiv.org, revised Mar 2024.
    3. Shengli Dai & Weimin Zhang & Linshan Lan, 2022. "Quantitative Evaluation of China’s Ecological Protection Compensation Policy Based on PMC Index Model," IJERPH, MDPI, vol. 19(16), pages 1-24, August.
    4. Zhang, Cheng & Zhou, Xinxin & Zhou, Bo & Zhao, Ziwei, 2022. "Impacts of a mega sporting event on local carbon emissions: A case of the 2014 Nanjing Youth Olympics," China Economic Review, Elsevier, vol. 73(C).
    5. Kraft, Kornelius & Lammers, Alexander, 2021. "Bargaining Power and the Labor Share - a Structural Break Approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242342, Verein für Socialpolitik / German Economic Association.
    6. Ricardo Masini, 2022. "Distributional Counterfactual Analysis in High-Dimensional Setup," Papers 2202.11671, arXiv.org, revised Sep 2023.

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