Identifying Politically Connected Firms: A Machine Learning Approach

25 Pages Posted: 14 Jun 2021

See all articles by Deni Mazrekaj

Deni Mazrekaj

University of Oxford

Vítězslav Titl

Utrecht University - School of Economics

Fritz Schiltz

University of Leuven

Date Written: June 4, 2021

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 interests.

Keywords: Political Connections, Corruption, Prediction, Machine Learning

JEL Classification: D72,P16

Suggested Citation

Mazrekaj, Deni and Titl, Vítězslav and Schiltz, Fritz, Identifying Politically Connected Firms: A Machine Learning Approach (June 4, 2021). Available at SSRN: https://ssrn.com/abstract=3860029 or http://dx.doi.org/10.2139/ssrn.3860029

Deni Mazrekaj

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

Vítězslav Titl (Contact Author)

Utrecht University - School of Economics ( email )

Kriekenpitplein 21-22
Adam Smith Building
Utrecht, +31 30 253 7373 3584 EC
Netherlands

HOME PAGE: http://www.titl.name

Fritz Schiltz

University of Leuven ( email )

Celestijnenlaan 200F
B-3001
Leuven
Belgium

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