[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119015-en":3,"doc-seo-119015-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119015,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Identifying Politically Connected Firms - A Machine Learning Approach","This article introduces machine learning methods to identify politically connected firms using information from publicly available sources and the Orbis company database. A novel firm population dataset is constructed for Czechia, enabling comprehensive measurement of political connections. Connections include political donations by the firm, board members who donated to political parties, and board members who ran for political office. Results show that over 85% of connected firms can be accurately identified, using only firm-level financial and industry indicators widely available across countries.","OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 86, 1 (2024) 0305-9049 doi: 10.1111/obes.12586  \nIdentifying Politically Connected Firms: A Machine Learning Approach *  \nVITEZSLAV TITL,†,‡,§ DENI MAZREKAJ§,¶,\\# and FRITZ SCHILTZ§  \n† Utrecht University School of Economics, Utrecht University, Kriekenpitplein 21 -22 Utrecht,  \n3584 EC, The Netherlands (e-mail: [v.titl@uu.nl](v.titl@uu.nl))  \n‡ Department of Economics, Faculty of Law, Charles University, Prague, Czechia  \n§ Leuven Economics of Education Research (LEER), KU Leuven, Naamsestraat 69 Leuven, 3000 ([e-mail: d.mazrekaj@uu.nl](e-mail: d.mazrekaj@uu.nl); [fritz.schiltz@kuleuven.be](fritz.schiltz@kuleuven.be))  \n¶ Department of Sociology, Utrecht University, Padualaan 14 Utrecht, 3584 CH, The Netherlands \\#Nuffield College, University of Oxford, New Road OX1 1NF, Oxford, UK  \nAbstract  \nThis 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.  \nI. Introduction  \nIn the heart of the second wave of the COVID-19 pandemic, on 26 November 2020, a controversial investigation was brought to light in a report published by the British  \nJEL Classification numbers: D72, D73, H83 .  \n*The firm accounting data for this study are protected by a confidentiality agreement and we are precluded from sharing the data with others. Interested readers can consult the corresponding author for information on how to obtain access to the data. The code for all figures and tables is available at [https://doi.org/10.5281/zenodo](https://doi.org/10.5281/zenodo).  \n10113144. We would like to thank Climent Quintana-Domeque for his guidance and valuable suggestions, Benny Geys, Kristof De Witte, Giovanna D’Inverno, Mark Verhagen, Lamar Pierce, and Aniek Sies for their useful comments and suggestions and also Alice Navratilova for excellent research assistance. Deni Mazrekaj acknowledges funding by the Research Foundation Flanders (FWO) (grant number 1257721N) and by the European Research Council (grant number 681546) . Vitezslav Titl acknowledges support from the Horizon Europe project ‘DemoTrans’ (grant 101059288) . The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.  \n137  \n© 2023 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n138 Bulletin  \nNational Audit Office (2020) . The spending watchdog found that more than half of the public pandemic contracts (£10.5 billion) related to personal protective equipment such as masks and protective gloves for healthcare workers, were awarded without a competitive tender. Nearly a third of these suppliers had links to politicians or senior officials and were referred to a ‘high priority’ channel, which was 10 times more likely to succeed in obtai","cbCairxmHPYsKrwr","https://ap.wps.com/l/cbCairxmHPYsKrwr","pdf",1398992,1,19,"English","en",105,"# Abstract\n# Introduction\n## Motivation and context\n## Data construction and method overview\n## Supervised machine learning approach","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"The study aims to identify politically connected firms by applying supervised machine learning techniques.\"},{\"question\":\"How are political connections measured in the dataset?\",\"answer\":\"Political connections are captured through multiple sources, including firm political donations, board members’ political party donations, and board members running for political office.\"},{\"question\":\"What features does the model use to achieve high identification accuracy?\",\"answer\":\"The model relies only on widely available firm-level financial and industry indicators, not specialized connection-specific variables.\"}]","Identifying Politically Connected Firms - A Machine Learning Approach | PDF",1785721917,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identifying-politically-connected-firms-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-politically-connected-firms-a-machine-learning-approach/119015/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"The study aims to identify politically connected firms by applying supervised machine learning techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are political connections measured in the dataset?",{"text":80,"@type":76},"Political connections are captured through multiple sources, including firm political donations, board members’ political party donations, and board members running for political office.",{"name":82,"@type":73,"acceptedAnswer":83},"What features does the model use to achieve high identification accuracy?",{"text":84,"@type":76},"The model relies only on widely available firm-level financial and industry indicators, not specialized connection-specific variables.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]