JOURNAL ARTICLE

Public corruption and the allocation of government contracts.

  • Published In: Review of Financial Economics, 2023, v. 41, n. 1. P. 3 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zuobao Wei; Yicheng Zhu 3 of 3

Abstract

Public corruption in the government procurement process is rampant and its cost is huge, even among developed countries. Some scholars estimate that about 20%-30% of the values of government projects are lost due to public corruption. In this paper, we examine how public corruption impacts the allocation of U.S. federal contracts. Using the U.S. Department of Justice corruption convictions data and the federal contract data from 2000 to 2018, we find that firms located in more corrupt states receive more federal contract dollars, more important contracts in terms of their contributions to firm revenues, and contracts with higher visibility among federal contractors. We construct an influence/favoritism index that takes into account defense contracts, cost- plus contracts, and multi- year contracts, and document that the index is positively related to corruption levels. These results hold after we conduct several robustness tests, including 2SLS regressions, propensity- score matching analysis, and using alternative corruption measures. Our empirical findings are consistent with the hypothesis that corruption plays an important role in how federal contracts are allocated. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Review of Financial Economics. 2023/01, Vol. 41, Issue 1, p3
  • Document Type:Article
  • Subject Area:Military History and Science
  • Publication Date:2023
  • ISSN:1058-3300
  • DOI:10.1002/rfe.1157
  • Accession Number:161381576
  • Copyright Statement:Copyright of Review of Financial Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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