IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2204.09450.html
   My bibliography  Save this paper

Who Benefits from Political Connections in Brazilian Municipalities

Author

Listed:
  • Pedro Forquesato

Abstract

A main issue in improving public sector efficiency is to understand to what extent public appointments are based on worker capability, instead of being used to reward political supporters (patronage). I contribute to a recent literature documenting patronage in public sector employment by establishing what type of workers benefit the most from political connections. Under the (empirically supported) assumption that in close elections the result of the election is as good as random, I estimate a causal forest to identify heterogeneity in the conditional average treatment effect of being affiliated to the party of the winning mayor. Contrary to previous literature, for most positions we find positive selection on education, but a negative selection on (estimated) ability. Overall, unemployed workers or low tenure employees that are newly affiliated to the winning candidate's party benefit the most from political connections, suggesting that those are used for patronage.

Suggested Citation

  • Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
    • Handle: RePEc:arx:papers:2204.09450
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2204.09450
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Fernanda Brollo, & Forquesato, Pedro & Gozzi, Juan Carlos, 2017. "To the Victor Belongs the Spoils? Party Membership and Public Sector Employment in Brazil," The Warwick Economics Research Paper Series (TWERPS) 1144, University of Warwick, Department of Economics.
    4. Klenio Barbosa & Fernando V. Ferreira, 2019. "Occupy Government: Democracy and the Dynamics of Personnel Decisions and Public Sector Performance," NBER Working Papers 25501, National Bureau of Economic Research, Inc.
    5. Lakshmi Iyer & Anandi Mani, 2012. "Traveling Agents: Political Change and Bureaucratic Turnover in India," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 723-739, August.
    6. Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
    7. Rauch, James E. & Evans, Peter B., 2000. "Bureaucratic structure and bureaucratic performance in less developed countries," Journal of Public Economics, Elsevier, vol. 75(1), pages 49-71, January.
    8. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    9. Emanuele Colonnelli & Mounu Prem & Edoardo Teso, 2020. "Patronage and Selection in Public Sector Organizations," American Economic Review, American Economic Association, vol. 110(10), pages 3071-3099, October.
    10. Guo Xu, 2018. "The Costs of Patronage: Evidence from the British Empire," American Economic Review, American Economic Association, vol. 108(11), pages 3170-3198, November.
    11. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    12. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    13. Brassiolo, Pablo & Estrada, Ricardo & Fajardo, Gustavo, 2020. "My (running) mate, the mayor: Political ties and access to public sector jobs in Ecuador," Journal of Public Economics, Elsevier, vol. 191(C).
    14. Marc F. Bellemare & Casey J. Wichman, 2020. "Elasticities and the Inverse Hyperbolic Sine Transformation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(1), pages 50-61, February.
    15. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    16. Brassiolo, Pablo & Estrada, Ricardo & Fajardo, Gustavo, 2020. "My (Running) Mate, the Mayor: Political Ties and Access to Public Jobs in Ecuador," Research Department working papers 1521, CAF Development Bank Of Latinamerica.
    17. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    18. Jonathan M.V. Davis & Sara B. Heller, 2020. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 664-677, October.
    19. Jeffrey Weaver, 2021. "Jobs for Sale: Corruption and Misallocation in Hiring," American Economic Review, American Economic Association, vol. 111(10), pages 3093-3122, October.
    20. 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.
    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. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    2. Callen, Michael & Gulzar, Saad & Hasanain, Ali & Khan, Muhammad Yasir & Rezaee, Arman, 2023. "The political economy of public sector absence," Journal of Public Economics, Elsevier, vol. 218(C).
    3. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    4. Barbosa, Klenio & Ferreira, Fernando, 2023. "Occupy government: Democracy and the dynamics of personnel decisions and public finances," Journal of Public Economics, Elsevier, vol. 221(C).
    5. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    6. Brassiolo, Pablo & Estrada, Ricardo & Fajardo, Gustavo, 2020. "My (running) mate, the mayor: Political ties and access to public sector jobs in Ecuador," Journal of Public Economics, Elsevier, vol. 191(C).
    7. 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.
    8. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    9. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
    10. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    11. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
    12. Kayo Murakami & Hideki Shimada & Yoshiaki Ushifusa & Takanori Ida, 2022. "Heterogeneous Treatment Effects Of Nudge And Rebate: Causal Machine Learning In A Field Experiment On Electricity Conservation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1779-1803, November.
    13. Costanza Naguib, 2023. "Is the Impact of Opening the Borders Heterogeneous?," Diskussionsschriften dp2312, Universitaet Bern, Departement Volkswirtschaft.
    14. Jörg L. Spenkuch & Edoardo Teso & Guo Xu, 2023. "Ideology and Performance in Public Organizations," Econometrica, Econometric Society, vol. 91(4), pages 1171-1203, July.
    15. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
    16. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
    17. Newham, Melissa & Valente, Marica, 2024. "The cost of influence: How gifts to physicians shape prescriptions and drug costs," Journal of Health Economics, Elsevier, vol. 95(C).
    18. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    19. Paul S. Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2024.
    20. Zhexiao Lin & Fang Han, 2022. "On regression-adjusted imputation estimators of the average treatment effect," Papers 2212.05424, arXiv.org, revised Jan 2023.

    More about this item

    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:arx:papers:2204.09450. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.