IDEAS home Printed from https://ideas.repec.org/p/use/tkiwps/2110.html
   My bibliography  Save this paper

Identifying Politically Connected Firms: A Machine Learning Approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://dspace.library.uu.nl/bitstream/handle/1874/419999/LEG_USE_WP_21_10.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Titl, Vitezslav & De Witte, Kristof & Geys, Benny, 2021. "Political donations, public procurement and government efficiency," World Development, Elsevier, vol. 148(C).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. 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.
    14. Titl, Vitezslav & Geys, Benny, 2019. "Political donations and the allocation of public procurement contracts," European Economic Review, Elsevier, vol. 111(C), pages 443-458.
    15. 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.
    16. 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).
    17. 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.
    18. 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.
    19. Mara Faccio, 2006. "Politically Connected Firms," American Economic Review, American Economic Association, vol. 96(1), pages 369-386, March.
    20. Wheeler, Andrew Palmer & Steenbeek, Wouter, 2020. "Mapping the risk terrain for crime using machine learning," SocArXiv xc538, Center for Open Science.
    21. 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.
    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. 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.

    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. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    2. Colonnelli, Emanuele & Lagaras, Spyridon & Ponticelli, Jacopo & Prem, Mounu & Tsoutsoura, Margarita, 2022. "Revealing corruption: Firm and worker level evidence from Brazil," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1097-1119.
    3. Yuping Deng & Yanrui Wu & Helian Xu, 2020. "Political Connections and Firm Pollution Behaviour: An Empirical Study," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(4), pages 867-898, April.
    4. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Barraza, Santiago & Rossi, Martín A & Ruzzier, Christian A, 2022. "Sleeping with the enemy: The perils of having the government on(the)board," Journal of Comparative Economics, Elsevier, vol. 50(3), pages 641-651.
    6. Yu-Hong Ai & Di-Yun Peng & Huan-Huan Xiong, 2021. "Impact of Environmental Regulation Intensity on Green Technology Innovation: From the Perspective of Political and Business Connections," Sustainability, MDPI, vol. 13(9), pages 1-23, April.
    7. Michelson, Noam, 2023. "The revolving door of former civil servants and firm value: A comprehensive approach," European Journal of Political Economy, Elsevier, vol. 79(C).
    8. Xie, Rui & Zhang, Jiahuan & Tang, Chuan, 2023. "Political connection and water pollution: New evidence from Chinese listed firms," Resource and Energy Economics, Elsevier, vol. 74(C).
    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. Deng, Yuping & Wu, Yanrui & Xu, Helian, 2019. "Political connections and firm pollution behaviour: An empirical study," BOFIT Discussion Papers 4/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    11. Yuping Deng & Yanrui Wu & Helian Xu, 2020. "Political Connections and Firm Pollution Behaviour: An Empirical Study," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(4), pages 867-898, April.
    12. Ashton Hawk & Debtanu Lahiri & Gonçalo Pacheco‐de‐Almeida, 2023. "The half‐life of political capital: An examination of the temporal effects of board political connections," Strategic Management Journal, Wiley Blackwell, vol. 44(13), pages 3252-3288, December.
    13. David Schoenherr, 2019. "Political Connections and Allocative Distortions," Journal of Finance, American Finance Association, vol. 74(2), pages 543-586, April.
    14. Amore, Mario Daniele & Bennedsen, Morten, 2013. "The value of local political connections in a low-corruption environment," Journal of Financial Economics, Elsevier, vol. 110(2), pages 387-402.
    15. Wong, Wai-Yan & Hooy, Chee-Wooi, 2018. "Do types of political connection affect firm performance differently?," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 297-317.
    16. Lei, Yu-Hsiang, 2021. "Quid pro quo? Government-firm relationships in China," Journal of Public Economics, Elsevier, vol. 199(C).
    17. repec:zbw:bofitp:2019_004 is not listed on IDEAS
    18. Park, SeHyun, 2023. "Profitability of politically corrupt firms: Evidence from Romania," Emerging Markets Review, Elsevier, vol. 54(C).
    19. González, Felipe & Prem, Mounu, 2018. "The value of political capital: Dictatorship collaborators as business elites," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 217-230.
    20. Miroslav Palanský, 2021. "The value of political connections in the post-transition period: evidence from Czechia," Public Choice, Springer, vol. 188(1), pages 121-154, July.
    21. Moon, Terry & Schoenherr, David, 2022. "The rise of a network: Spillover of political patronage and cronyism to the private sector," Journal of Financial Economics, Elsevier, vol. 145(3), pages 970-1005.

    More about this item

    Keywords

    Political Connections; Corruption; Prediction; Machine Learning;
    All these keywords.

    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:use:tkiwps:2110. 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: Marina Muilwijk (email available below). General contact details of provider: https://edirc.repec.org/data/eiruunl.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.