I am interested in issues of governance, in particular corruption and accountability, and the interface between civil society, democratic backsliding, and digital media. My dissertation explores how individuals, in their roles as ordinary citizens or managers of firms, use connections to politicians and bureaucrats to obtain privileged access to public services or public procurement contracts. In my job market paper, I build and test an argument to explain how networks of political favoritism operate in public contracting and the role bureaucratic capacity plays. Using data on all public contracts awarded by Guatemalan municipalities in the period 2013-19, and firm connections, I show that more capable bureaucracies increase the likelihood of well connected firms winning contracts through less competitive processes.
My second major research interest is in the application of big data analytics to digital media to study the interface between corruption, civil society and democratic backsliding. As an original contributor to the Machine Learning for Peace Project (MLP), I apply the latest machine learning techniques to create and analyze an unprecedented high-frequency dataset of civic space events. My colleagues and I have used that data to study everything from the effects of repressive legal changes on the content and slant of media coverage to exploring differences in reporting between international and local media across more than 40 countries. As part of the MLP project, I am combining big data with field projects in El Salvador and Guatemala.
I also have ongoing projects on the reintegration of deported migrants and the gradient of state presence through its territory.