Identifying Politically Connected Firms: A Machine Learning Approach*

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11220%2F24%3A10471998" target="_blank" >RIV/00216208:11220/24:10471998 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=vA009a1SWs" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=vA009a1SWs</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/obes.12586" target="_blank" >10.1111/obes.12586</a>

Result language

angličtina

Original language name

Identifying Politically Connected Firms: A Machine Learning Approach*

Original language description

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&apos; 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50501 - Law

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Oxford Bulletin of Economics and Statistics

ISSN

0305-9049

e-ISSN

1468-0084

Volume of the periodical

86

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

137-155

UT code for WoS article

001119591400001

EID of the result in the Scopus database

2-s2.0-85178240645