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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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    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.

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    Keywords

    Political Connections; Corruption; Prediction; Machine Learning;
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