Identifying Politically Connected Firms: A Machine Learning Approach*
利用机器学习技术，基于捷克企业公开数据和Orbis数据库，构建了包含多种政治关联形式的企业数据集，发现仅用财务和行业指标即可准确识别超过85%的政治关联企业，对监管机构识别利益冲突有实用价值。
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.