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Corruption and the network structure of public contracting markets across government change
Title / Series / Name
Publication Volume
Publication Issue
Pages
Authors
Editors
Keywords
Corruption
Government change
Markets
Networks
Political turnover
Sociology and Political Science
Public Administration
SDG 16 - Peace, Justice and Strong Institutions
Government change
Markets
Networks
Political turnover
Sociology and Political Science
Public Administration
SDG 16 - Peace, Justice and Strong Institutions
URI
https://hdl.handle.net/20.500.14018/27098
Abstract
Corruption is thought to affect developed economies to a greater degree than developing countries. However, given our limited capacity to detect corruption, it may simply be harder to detect it in countries with stronger institutions. This article sets out to address this measurement challenge and to offer a tailored approach to one particular type of corruption: high-level corruption in government contracting. We describe a recently developed method to score procurement contracts for corruption risk. Then, using micro-level data from Hungary and the Czech Republic we analyze how corruption can distort public procurement markets, mapped as networks of buyers and suppliers. Proxying for corruption using a com-posite index of red flags derived from contract awards, we find that public sector buyers with high corruption risk have sparser network neighborhoods, meaning that they contract with fewer suppliers than expected. We interpret our results as evidence that corruption in procurement markets is fundamentally about the exclusion of non-favored firms. Political change has a significant effect on corrupt relationships: High corruption risk buyers with sparse neighborhoods rewire their contracting relationships roughly 20–40% more extensively than other buyers across years with government turnover. The article demonstrates how the political organization of corruption distorts market competition in OECD countries.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2020
Language
ISBN
Identifiers
10.17645/pag.v8i2.2707