Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/323029 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
U.S.E. Working Papers Series No. 21-10
Verlag: 
Utrecht University, Utrecht University School of Economics (U.S.E.), Utrecht
Zusammenfassung: 
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.
Schlagwörter: 
Political Connections
Corruption
Prediction
Machine Learning
Dokumentart: 
Working Paper

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