Citizen Scrutiny and Government Efforts to Fight Corruption

点击翻译按钮获取中文摘要

Corruption in government partly accounts for the slow economic and social progress in many parts of the world. This research will use two projects to investigate how best to efficiently decrease corruption in government contracting. Governments have limited capacity to monitor all aspects of government activity, hence they rely on citizens volunteering information to fight corruption. However, citizens have their own interests and may not possess the same training and capabilities as government workers. The first project investigates how best to delegate the monitoring of public works projects to citizens, taking into consideration differences in their motivation and their ability to perform complex audit-related tasks. Only governments can investigate and sanction public officials based on information provided by citizen monitors. However, governments have limited capacity to analyze large volumes of information provided by citizen monitors. The second project therefore investigates how technology can be leveraged to process large volumes of incoming citizen reports and triage them. This project uses machine learning and artificial intelligence (AI) to detect patterns of corruption from the large number of reports and other types of information about corruption that citizens report. The results of this research project will not only provide inputs into policies to reduce corruption in government; it will also establish the US as the global leader in the fight against corruption.This research uses two projects to make major contributions to the development economics literature. First, it contributes to the growing empirical literature on the detection and measurement of corruption by developing machine learning and artificial intelligence (AI) tools and combining them with whistleblower reports to predict the presence of corruption, and study how anti-corruption efforts can be targeted. Second, the project contributes to the literature on tradeoffs between top-down and bottom-up monitoring of governments based on the study of a unique hybrid program—implemented by a central government that rely on citizen’s participation; it also investigate how best to design such a policy. Third, the study contributes to the literature on the use of machine learning in public policy, applying these tools to enhance anti-corruption efforts. Finally, the research contributes to the literature on the organization of the state by studying the allocation of scarce organizational resources to the fight corruption. The results of this research project will not only provide inputs into policies to reduce corruption in government; it will also establish the US as the global leader in the fight against corruption.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.