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Identifying Politically Connected Firms: A Machine Learning Approach

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  • Vitezslav Titl
  • Fritz Schiltz

Abstract

This article introduces machine learning techniques to identify politically connected firms. By assembling information from publicly available sources and the Orbis company database, we constructed a novel firm population dataset from Czechia in which various forms of political connections can be determined. The data about firms’ connections are unique and comprehensive. They include political donations by the firm, having members of managerial boards who donated to a political party, and having members of boards who ran for political office. The results indicate that over 85% of firms with political connections can be accurately identified by the proposed algorithms. The model obtains this high accuracy by using only firm-level financial and industry indicators that are widely available in most countries. We propose that machine learning algorithms should be used by public institutions to identify politically connected firms with potentially large conflicts of interests, and we provide easy to implement R code to replicate our results.

Suggested Citation

  • Vitezslav Titl & Fritz Schiltz, 2021. "Identifying Politically Connected Firms: A Machine Learning Approach," Working Papers 2110, Utrecht School of Economics.
    • Handle: RePEc:use:tkiwps:2110
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    References listed on IDEAS

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    1. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    2. Raymond Fisman & Yongxiang Wang, 2015. "The Mortality Cost of Political Connections," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1346-1382.
    3. Titl, Vitezslav & De Witte, Kristof & Geys, Benny, 2021. "Political donations, public procurement and government efficiency," World Development, Elsevier, vol. 148(C).
    4. Albert Meijer & Martijn Wessels, 2019. "Predictive Policing: Review of Benefits and Drawbacks," International Journal of Public Administration, Taylor & Francis Journals, vol. 42(12), pages 1031-1039, September.
    5. Federico Cingano & Paolo Pinotti, 2013. "Politicians At Work: The Private Returns And Social Costs Of Political Connections," Journal of the European Economic Association, European Economic Association, vol. 11(2), pages 433-465, April.
    6. Giovanni Mastrobuoni, 2020. "Crime is Terribly Revealing: Information Technology and Police Productivity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2727-2753.
    7. 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.
    8. KONDO Satoshi & MIYAKAWA Daisuke & SHIRAKI Kengo & SUGA Miki & USUKI Teppei, 2019. "Using Machine Learning to Detect and Forecast Accounting Fraud," Discussion papers 19103, Research Institute of Economy, Trade and Industry (RIETI).
    9. Titl, Vitezslav & Geys, Benny, 2019. "Political donations and the allocation of public procurement contracts," European Economic Review, Elsevier, vol. 111(C), pages 443-458.
    10. USUKI Teppei & KONDO Satoshi & SHIRAKI Kengo & SUGA Miki & MIYAKAWA Daisuke, 2019. "Using Machine Learning to Detect and Predict Corporate Accounting Fraud (Japanese)," Discussion Papers (Japanese) 19039, Research Institute of Economy, Trade and Industry (RIETI).
    11. Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.
    12. Mara Faccio, 2006. "Politically Connected Firms," American Economic Review, American Economic Association, vol. 96(1), pages 369-386, March.
    13. Asim Ijaz Khwaja & Atif Mian, 2005. "Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(4), pages 1371-1411.
    14. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    15. Claessens, Stijn & Feijen, Erik & Laeven, Luc, 2008. "Political connections and preferential access to finance: The role of campaign contributions," Journal of Financial Economics, Elsevier, vol. 88(3), pages 554-580, June.
    16. Mara Faccio, 2010. "Differences between Politically Connected and Nonconnected Firms: A Cross‐Country Analysis," Financial Management, Financial Management Association International, vol. 39(3), pages 905-928, September.
    17. Sandip Sukhtankar, 2012. "Sweetening the Deal? Political Connections and Sugar Mills in India," American Economic Journal: Applied Economics, American Economic Association, vol. 4(3), pages 43-63, July.
    18. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    19. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    20. G. O. Mohler & M. B. Short & Sean Malinowski & Mark Johnson & G. E. Tita & Andrea L. Bertozzi & P. J. Brantingham, 2015. "Randomized Controlled Field Trials of Predictive Policing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1399-1411, December.
    21. Wheeler, Andrew Palmer & Steenbeek, Wouter, 2020. "Mapping the risk terrain for crime using machine learning," SocArXiv xc538, Center for Open Science.
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    Cited by:

    1. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    2. Coco, Giuseppe & Monturano, Gianluca & Resce, Giuliano, 2025. "Predicting Delays in Cohesion Infrastructure Projects," Economics & Statistics Discussion Papers esdp25099, University of Molise, Department of Economics.

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    Keywords

    Political Connections; Corruption; Prediction; Machine Learning;
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