High-Frequency Evidence on Corruption in 53 Countries: New Data from the MLP Project.

(With Jitender Swami and Erik Wibbels). Working paper.

Recommended citation: Corruption is associated with everything from poor governance outcomes and low-quality public services to poor economic growth and political instability. Corruption can have negative effects on civic space as it reduces political participation and generalized trust. However, corruption scandals have also motivated powerful protest movements and civic action in countries as diverse as Brazil, Guatemala and Moldova in recent years. Despite the centrality of corruption scandals to civic space dynamics in many countries, our capacity to understand when, why and where corruption elicits civic responses is sharply limited by constraints inherent in standard corruption data. Most such data, whether in the form of expert (e.g., Transparency International's Corruption Perceptions Index) or citizen (e.g., Afrobarometer) surveys, provide annual snapshots that preclude answering key questions such as: When do corruption scandals evoke protests, civic activism, legal changes and/or the collapse of governments? And what are the implications of corruption for the evolution of civic space more generally? In this project we introduce a new big data approach to measuring corruption that allows researchers and analysts to address these kinds of questions. Our corruption measure relies on the Machine Learning for Peace project's infrastructure, which has collected and classified over 90 millions articles published by international and local newspapers every day from 2012 until last month for nearly 60 countries. By measuring the share of monthly news reporting on corruption, we provide data on its salience. This measure does a good job of identifying corruption scandals and provides a tool for monitoring corruption in near real-time. To elucidate one potential use of the data, we analyze the relationship between corruption scandals and anti-corruption protests across 53 countries. We complement the cross-national evidence with case studies of Guatemala and Ghana, two cases with relatively high incidences of corruption, but where civic responses have varied a lot.