UC Berkeley

Public procurement, contributing to total GDP, not only impacts economic growth and development by shaping market dynamics and competition but also plays a key role in promoting fairness and mitigating corruption within public sector transactions. This dissertation dives into public procurement, focusing on the challenges of corruption, its detection, and its impact on businesses and the economy. In Chapter 1, I reveal the prevalence of corruption in scoring auctions in public procurement in China, design a model-based tool to detect this corruption, and discuss policies to reduce it. In Chapter 2, inspired by complaint data, I propose another method to detect corruption in close games, which complements the first chapter, and I also discuss how to better design a complaint system to curb corruption. In Chapter 3, I link public procurement data to universal firm-to-firm transaction data to study the direct and indirect effects of participating in the public procurement supply chain through the propagation of production networks. Together, these chapters provide a comprehensive understanding of public procurement's role in shaping economic outcomes and propose targeted strategies for reform.

My first chapter proposes a method to screen out scoring rule manipulation corruption in scoring auctions in public procurement and discusses the policies to reduce corruption and increase transparency. I start the chapter by documenting that corruption is widespread in scoring auctions. Procurement officers can collaborate with firms to manipulate scoring rules, favoring predetermined winners, while corrupt firms orchestrate non-competitive bids from others to meet minimum bidder requirements. Drawing from extensive data on public procurement auctions in China, I introduce a model-driven statistical tool to detect this specific form of corruption. The findings indicate a corruption rate of approximately 65%. A procurement expert evaluation audit study confirms the test's validity, revealing a 91% probability that experts identify suspicious scoring rules when the test signals potential corruption. I also link procurement data to comprehensive firm data to examine the distortions caused by corruption. I find that local and state-owned firms, as well as less productive ones, are more favored in corrupt auctions. Lastly, I explore policy implications from the anti-corruption campaign, as well as the counterfactuals by estimating a structural model, concluding that general corruption investigations may be insufficient to address deeply ingrained corrupt practices in the long run whereas implementing anonymous call-for-tender file evaluation could significantly improve social welfare.

My second chapter complements the first, which focuses on the unreasonably large score gaps between winners and losers. It examines the ex-post complaint dataset, proposes a method to detect corruption in close-game cases in scoring auctions, and discusses how to better design the complaint system. The complaint system enables reporting of potential corruption and collusion in public procurement auctions, offering insights not visible to outsiders and facilitating corruption detection. In this chapter, I have gathered a dataset of complaints from China's public procurement system. Based on the patterns observed in the ex-post complaints, where the price bids of winners are much higher than those of the complainants, I applied the Regression Discontinuity Design (RDD) to detect corruption. My findings indicate that, contrary to competitive cases where winners and losers are chosen at random, in complaints, winners tended to submit prices that were, on average, 5% higher than those of the losing complainants. This suggests 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 13% of the auctions in close-game scenarios were corrupt. To explore the low rate of complaints, 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, and conducted a counterfactual analysis. I found that protecting whistleblowers by concealing their names can increase the reporting rate, and that random auditing by the financial team does not crowd out the functionality of the complaint system.

In my third chapter, coauthored with Ming Li and Wei Lin, we study the direct and indirect effects of participating in the public procurement supply chain through the propagation of production networks, utilizing tax data that tracks firm-to-firm transactions in China. To quantify the effects of directly winning public procurement contracts on firms, we employ an event study design. Our estimates reveal that firms winning public procurement contracts experience increased purchasing activities and gain additional non-public procurement contracts in the following months without crowding out effects. Moreover, these spillover effects in the non-public sectors originate from competitive procurement projects, rather than potentially corrupt ones. We then use a model-based method to measure the total ratio of public procurement contracts to sales through both direct and indirect channels, using the complete firm production network. We find that although only 0.5% of firms directly participate in the public procurement supply chain, a greater number are involved indirectly. Using these total ratios, we explore the effect of public procurement on firm revenue through network propagation. Without considering the indirect channels through the production network, we would underestimate the role of public procurement demand.