Assessing Vulnerabilities to Corruption in Public Procurement and Their Price Impact
Aly Abdou,
Olivier Basdevant,
Elizabeth David-Barrett and
Mihaly Fazekas
No 2022/094, IMF Working Papers from International Monetary Fund
Abstract:
Public procurement can be highly vulnerable to corruption. This paper outlines a methodology and results in assessing corruption risks in public procurement and their impact on relative prices, using large databases on government contracts and tenders. Our primary contribution is to analyze how price differential in public procurement contracts can be explained by corruption risk factor (aggregated in a synthetic corruption risk index). While there are intrinsic limitations to our study (price differentials can come from structural reasons, such as a limited number of potential suppliers) it still provides a guiding tool to assess where corruption risks would have the biggest budgetary impact. Such analysis helps inform mitigating policies owing to the granular data used.
Keywords: public procurement; corruption; corruption risks; procurement cost; price differential; corruption risk; price impact; procurement contract; data collection methodology; Global (search for similar items in EconPapers)
Pages: 31
Date: 2022-05-20
New Economics Papers: this item is included in nep-cta
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=518197 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2022/094
Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm
Access Statistics for this paper
More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().