Rigging the Scores: Corruption through Scoring Rule Manipulation in Public Procurement Auctions
Public procurement is highly susceptible to corruption, especially in developing countries. Although open auctions are widely adopted to curb it, I document that corruption remains prevalent even within this procurement format. Procurement officers can collaborate with firms to manipulate scoring rules, ensuring predetermined winners, while corrupt firms submit non-competitive bids to meet minimum bidder requirements. Using extensive data from Chinese public procurement auctions, I introduce model-driven statistical tools to detect such corruption, identifying a corruption rate of 65%. A procurement expert audit survey confirms the tools' reliability, with a 91% probability that experts recognize suspicious scoring rules when flagged. Firm-level analysis reveals that local, state-owned, and less productive firms are favored in corrupt auctions. Lastly, I explore policy implications. Analysis of the anti-corruption campaign suggests that general investigations may be insufficient to address deeply ingrained corrupt practices. Using counterfactuals based on an estimated structural model, I find that implementing anonymous call-for-tender evaluations could improve social welfare by 10% by eliminating suspicious rules and encouraging broader participation.
Summary: The complaint system provides a means for participants to report potential corruption and collusion in public procurement auctions. However, the frequency of such complaint cases is typically low. In this study, I gathered a dataset of complaints from China's public procurement system and applied the Regression Discontinuity Design (RDD) to analyze close-game auctions. My findings indicate that bidders who won by a small margin tended to submit bids that were, on average, 5% higher than those of the losing bidders, who were consequently more inclined to lodge complaints. Notably, I found that at least 20% of the auctions in the complaint dataset were corrupt. When extending this methodology to the entire public procurement auction dataset, it appeared that 9% of the auctions in close-game scenarios were corrupt. The observed discontinuity disappeared following anti-corruption investigations, thereby validating the methodology. Additionally, I developed a model to investigate the decision-making process behind the lodging of complaints, with a specific focus on those bidders who lost by a narrow margin. In a counterfactual analysis, I noted that the 'garbling game' associated with investigations could potentially lead to an increase in complaints.
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