Social capital predicts corruption risk in towns

Author:

Wachs Johannes1ORCID,Yasseri Taha23ORCID,Lengyel Balázs45,Kertész János16

Affiliation:

1. Department of Network and Data Science, Central European University, Budapest 1051, Hungary

2. Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford OX1 3JS, UK

3. Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK

4. Agglomeration and Social Networks Lendület Research Group, Hungarian Academy of Sciences, Budapest 1097, Hungary

5. International Business School Budapest, Budapest 1037, Hungary

6. Institute of Physics, Budapest University of Technology and Economics, Budapest 1111, Hungary

Abstract

Corruption is a social plague: gains accrue to small groups, while its costs are borne by everyone. Significant variation in its level between and within countries suggests a relationship between social structure and the prevalence of corruption, yet, large-scale empirical studies thereof have been missing due to lack of data. In this paper, we relate the structural characteristics of social capital of settlements with corruption in their local governments. Using datasets from Hungary, we quantify corruption risk by suppressed competition and lack of transparency in the settlement’s awarded public contracts. We characterize social capital using social network data from a popular online platform. Controlling for social, economic and political factors, we find that settlements with fragmented social networks, indicating an excess of bonding social capital has higher corruption risk, and settlements with more diverse external connectivity, suggesting a surplus of bridging social capital is less exposed to corruption. We interpret fragmentation as fostering in-group favouritism and conformity, which increase corruption, while diversity facilitates impartiality in public life and stifles corruption.

Funder

Hungarian Scientific Research Fund

Engineering and Physical Sciences Research Council

Publisher

The Royal Society

Subject

Multidisciplinary

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