We estimate the causal real economic effects of a randomized anti-corruption crackdown on
local governments in Brazil using rich micro-data on corruption and firms. After anti …

Abstract According to United Nations, corruption is a systemic and adaptive phenomenon
that requires comprehensive and multidisciplinary approaches for its effective prevention …

Is it possible to predict malfeasance in public procurement? With the proliferation of e-
procurement systems in the public sector, anti-corruption agencies and watchdog …

The public health crisis caused by the COVID-19 pandemic, coupled with the subsequent
economic emergency and social turmoil, has pushed governments to substantially and …

We study how the disclosure of corrupt practices affects the growth of firms involved in illegal
interactions with the government using randomized audits of public procurement in Brazil …

Can machine learning support better governance? This study uses a tree-based gradient-
boosted classifier to predict corruption in Brazilian municipalities using budget data as …

Open data is a strategy used by governments to promote transparency and accountability in
public procurement processes. To reap the benefits of open data, exploring and analyzing …

Este documento explora tres grandes áreas de política pública que dejó la crisis del COVID-
19 en materia de transformación digital de los gobiernos. Un primer campo de acción …

Esta investigación busca evaluar el uso de algoritmos de aprendizaje de máquinas en la
detección temprana de actos de corrupción en la administración pública municipal de …

We investigate the relationship between successful revolutions and corruption using data on
revolutionary campaigns since 1900 and corruption measures retrieved from the Varieties of …