LAC Main Seminar Series: Mind and machine: rooting out corrupt politicians

Convener: David Doyle, University of Oxford

Speaker: Nelson Ruiz, University of Essex

Bio

I am a Leverhulme Early Career Fellow, Senior Lecturer (~Associate Prof) and Head of the Political Economy Research Division at the Government Department at the University of Essex. I am also a Senior Visiting Fellow at the LSE Government Department, an Associate Member at Nuffield College University of Oxford, and an Associate Member at the Department of Politics and International Relations (DPIR). I completed my PhD at the London School of Economics and Political Science.  

My research interests are in the political economy of development. Within this field, the main topic I am interested in is the role of money in politics. I am also interested in studying corruption, clientelism, and political selection. Most of my research involves the use of causal inference methods.

Abstract

Despite its consequences, voters keep electing corrupt politicians. One common explanation is that voters simply lack information on whether candidates are corrupt, yet studies that deliberately provide such information find electoral accountability is weak. Can we root out corrupt politicians? We take a novel approach: first, we employ machine learning to identify widely available political/personal characteristics that are predictive of corrupt practices in Colombia. We then design an experiment that varies the provision of these predictors to study if voters discriminate corrupt from non-corrupt politicians. Voters mostly behave consistently with the machine learning results -using campaign financial and aesthetic cues to avoid corrupt candidates- however, when hiding the non-corrupt candidate’s ballot photo, this increases the likelihood of voting for a corrupt candidate. Compared to the established findings, we show it is possible for voters to root out corrupt politicians with a novel approach.