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on Contract Theory and Applications |
| By: | Leandro Arozamena (UTDT-CONICET); Juan José Ganuza (Universitat Pompeu Fabra); Federico Weinschelbaum (UTDT-CONICET) |
| Abstract: | A sponsor –e.g. a government agency– uses a procurement auction to select a supplier who will be in charge of the execution of a contract. That contract is incomplete: it may be renegotiated once the auction’s winner has been chosen. We examine a setting where one firm may bribe the agent in charge of monitoring contract execution so that the former is treated preferentially if renegotiation actually occurs. If a bribe is accepted, the corrupt firm will be more aggressive at the initial auction and thus win with a larger probability. We show that the equilibrium probability of corruption is larger when the initial contract is less complete, and when the corrupt firm’s cost is more likely to be similar to her rivals’. In addition, we examine how this influences the sponsor’s incentives when designing the initial contract. |
| Keywords: | Auctions, Cost overruns, Procurement, Renegotiation, Corruption |
| JEL: | C72 D44 D82 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:aoz:wpaper:334 |
| By: | Maryam Saeedi; Yikang Shen; Ali Shourideh |
| Abstract: | We examine the strategic interaction between an expert (principal) maximizing engagement and an agent seeking swift information. Our analysis reveals: When priors align, relative patience determines optimal disclosure -- impatient agents induce gradual revelation, while impatient principals cause delayed, abrupt revelation. When priors disagree, catering to the bias often emerges, with the principal initially providing signals aligned with the agent's bias. With private agent beliefs, we observe two phases: one engaging both agents, followed by catering to one type. Comparing personalized and non-personalized strategies, we find faster information revelation in the non-personalized case, but higher quality information in the personalized case. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19127 |
| By: | Maximilian Kasy; Jann Spiess |
| Abstract: | What is the purpose of pre-analysis plans, and how should they be designed? We model the interaction between an agent who analyzes data and a principal who makes a decision based on agent reports. The agent could be the manufacturer of a new drug, and the principal a regulator deciding whether the drug is approved. Or the agent could be a researcher submitting a research paper, and the principal an editor deciding whether it is published. The agent decides which statistics to report to the principal. The principal cannot verify whether the analyst reported selectively. Absent a pre-analysis message, if there are conflicts of interest, then many desirable decision rules cannot be implemented. Allowing the agent to send a message before seeing the data increases the set of decisions rules that can be implemented, and allows the principal to leverage agent expertise. The optimal mechanisms that we characterize require pre-analysis plans. Applying these results to hypothesis testing, we show that optimal rejection rules pre-register a valid test, and make worst-case assumptions about unreported statistics. Optimal tests can be found as a solution to a linear-programming problem. |
| Keywords: | pre-analysis plans, statistical decisions, implementability |
| JEL: | C18 D80 I23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11258 |