Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We model criminal investigation as a principal-agent-monitor problem in which the agent can bribe the monitor to destroy evidence. Building on insights from Laffont and Martimort’s 1997 paper, we study whether the principal can profitably introduce asymmetric information between agent and monitor by randomizing the monitor’s incentives. We show that it can be the case, but the optimality of random incentives depends on unobserved preexisting patterns of private information. We provide a data-driven framework for policy evaluation requiring only unverified reports. A potential local policy change is an improvement if, everything else equal, it is associated with greater reports of crime.