Artificial Intelligence (AI) has the potential to significantly impact the income of individuals. Cross-country data shows that introduction of AI is inequality enhancing in developing and less developed countries. In this paper, we attempt to understand the reason for increase in wage...

Populist parties recently have shaken Western democracies, yet there is no consensus regarding the characteristics of populist voters. By using large-scale surveys from four European countries (France, Germany, Spain, and the U.K.), we investigate individual determinants of populist voting. Our...

This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude...

Assets in the fossil fuel industries are at risk of losing market value due to anticipated breakthroughs in renewable technology and governments stepping up climate policies in the light of the Paris commitments to limit global warming to 1.5 or 2 degrees Celsius. Stranded assets arise due to...

To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict...

We propose a new method to design a short survey measure of a complex concept such as women’s agency. The approach combines mixed-methods data collection and machine learning. We select the best survey questions based on how strongly correlated they are with a “gold standard” measure of...

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a...

In this article, we combine machine learning techniques with statistical moments of the gasoline price distribution. By doing so, we aim to detect and predict cartels in the Brazilian retail market. In addition to the traditional variance screen, we evaluate how the standard deviation,...

Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted...