Identifying Politically Connected Firms: A Machine Learning Approach

Vitezslav Titl; Deni Mazrekaj; Fritz Schiltz

doi:10.1111/obes.12586

Identifying Politically Connected Firms: A Machine Learning Approach

Vitezslav Titl^*
, Deni Mazrekaj^*
, Fritz Schiltz^*

^*Corresponding author for this work

KU Leuven

Research output: Contribution to journal › Article › Academic › peer-review

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. These findings suggest that machine learning algorithms could be used by public institutions to improve the identification of politically connected firms with potentially large conflicts of interest.

Original language	English
Pages (from-to)	137-155
Number of pages	19
Journal	Oxford Bulletin of Economics and Statistics
Volume	86
Issue number	1
Early online date	30 Nov 2023
DOIs	https://doi.org/10.1111/obes.12586
Publication status	Published - Feb 2024

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.

Funding

The firm accounting data for this study are protected by a confidentiality agreement and we are precluded from sharing the data with others. Interested readers can consult the corresponding author for information on how to obtain access to the data. The code for all figures and tables is available at https://doi.org/10.5281/zenodo.10113144. We would like to thank Climent Quintana-Domeque for his guidance and valuable suggestions, Benny Geys, Kristof De Witte, Giovanna D'Inverno, Mark Verhagen, Lamar Pierce, and Aniek Sies for their useful comments and suggestions and also Alice Navratilova for excellent research assistance. Deni Mazrekaj acknowledges funding by the Research Foundation Flanders (FWO) (grant number 1257721N) and by the European Research Council (grant number 681546). Vitezslav Titl acknowledges support from the Horizon Europe project ‘DemoTrans’ (grant 101059288). The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.

Funders	Funder number
HORIZON EUROPE Framework Programme	101059288
European Research Council	681546
Fonds Wetenschappelijk Onderzoek	1257721N

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Oxf Bull Econ Stat - 2023 - Titl - Identifying Politically Connected Firms A Machine Learning ApproachFinal published version, 1.33 MBLicence: CC BY-NC

Cite this

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Identifying Politically Connected Firms: A Machine Learning Approach

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