IDEAS home Printed from https://ideas.repec.org/p/bis/biswps/1181.html
   My bibliography  Save this paper

Synthetic controls with machine learning: application on the effect of labour deregulation on worker productivity in Brazil

Author

Listed:
  • Douglas Kiarelly Godoy de Araujo

Abstract

Synthetic control methods are a data-driven way to calculate counterfactuals from control individuals for the estimation of treatment effects in many settings of empirical importance. In canonical implementations, this weighting is linear and the key methodological steps of donor pool selection and covariate comparison between the treated entity and its synthetic control depend on some degree of subjective judgment. Thus current methods may not perform best in settings with large datasets or when the best synthetic control is obtained by a nonlinear combination of donor pool individuals. This paper proposes "machine controls", synthetic controls based on automated donor pool selection through clustering algorithms, supervised learning for flexible non-linear weighting of control entities and manifold learning to confirm numerically whether the synthetic control indeed resembles the target unit. The machine controls method is demonstrated with the effect of the 2017 labour deregulation on worker productivity in Brazil. Contrary to policymaker expectations at the time of enactment of the reform, there is no discernible effect on worker productivity. This result points to the deep challenges in increasing the level of productivity, and with it, economic welfare.

Suggested Citation

  • Douglas Kiarelly Godoy de Araujo, 2024. "Synthetic controls with machine learning: application on the effect of labour deregulation on worker productivity in Brazil," BIS Working Papers 1181, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:1181
    as

    Download full text from publisher

    File URL: https://www.bis.org/publ/work1181.pdf
    File Function: Full PDF document
    Download Restriction: no

    File URL: https://www.bis.org/publ/work1181.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    2. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    3. Maty Konte & Wilfried A Kouamé & Emmanuel B Mensah, 2022. "Corrigendum to: Structural Reforms and Labor Productivity Growth in Developing Countries: Intra or Inter-Reallocation Channel?," The World Bank Economic Review, World Bank, vol. 36(3), pages 800-800.
    4. Maty Konte & Wilfried A Kouamé & Emmanuel B Mensah, 2022. "Structural Reforms and Labor Productivity Growth in Developing Countries: Intra or Inter-Reallocation Channel?," The World Bank Economic Review, World Bank, vol. 36(3), pages 646-669.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    6. Alberto Abadie & Jérémy L’Hour, 2021. "A Penalized Synthetic Control Estimator for Disaggregated Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1817-1834, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emiliano Toni & Pablo Paniagua & Patricio 'Ordenes, 2024. "Policy Changes and Growth Slowdown: Assessing the Lost Decade of the Latin American Miracle," Papers 2407.02003, arXiv.org.

    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. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    2. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    3. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    4. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    5. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    6. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    7. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    8. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    9. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    10. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    11. Eli Ben‐Michael & Avi Feller & Jesse Rothstein, 2022. "Synthetic controls with staggered adoption," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 351-381, April.
    12. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    13. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    14. Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick, 2021. "A Design-Based Perspective on Synthetic Control Methods," Papers 2101.09398, arXiv.org, revised Jul 2023.
    15. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    16. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2023. "Same Root Different Leaves: Time Series and Cross‐Sectional Methods in Panel Data," Econometrica, Econometric Society, vol. 91(6), pages 2125-2154, November.
    17. Justin Wiltshire, 2021. "allsynth: Synthetic control bias-corrections utilities for Stata," 2021 Stata Conference 15, Stata Users Group.
    18. Lionel Fontagn'e & Francesca Micocci & Armando Rungi, 2024. "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Papers 2407.07652, arXiv.org, revised Jul 2024.
    19. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    20. repec:ags:aaea22:335586 is not listed on IDEAS
    21. Verhagen, Mark D., 2023. "Using machine learning to monitor the equity of large-scale policy interventions: The Dutch decentralisation of the Social Domain," SocArXiv qzm7y, Center for Open Science.

    More about this item

    Keywords

    causal inference; synthetic controls; machine learning; labour reforms; productivity;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J50 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - General
    • J83 - Labor and Demographic Economics - - Labor Standards - - - Workers' Rights
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:bis:biswps:1181. 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: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.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.