Identifying Politically Connected Firms: A Machine Learning Approach

Vítězslav Titl, Deni Mazrekaj and Fritz Schiltz

Oxford Bulletin of Economics and Statistics, 2024, vol. 86, issue 1, 137-155

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.

Date: 2024
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https://doi.org/10.1111/obes.12586

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Working Paper: Identifying Politically Connected Firms: A Machine Learning Approach (2021)
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