My research fields are in Political Economy, Development Economics, Conflict and Machine Learning. I am particularly interested in how instability arises and how it affects long run economic development. Most of my research uses simple difference-in-difference estimates but I am increasingly interested in adapting machine learning methods for social science. This has three elements: the use for feature of extraction from text and images, the use of supervised learning for forecasting and nowcasting and causal inference.
My research has been published in top journals in economics and political science like the American Economic Review, the American Review of Political Science, the Journal of the European Economic Association, the American Journal of Economics: Macro and the Journal of Public Economics.
My research has no particular geographic focus but I am involved in both academic and policy work on Northern Africa. I am particularly interested in countries in Europe, Latin America and Northern Africa because of their cultural and geographic proximity to Spain.
Much of my academic research uses simple theory to make sense of the data. I believe theory is essential to form hypothesis and understand the data. At the same time data can help us develop better theories.
I am also increasingly interested in the use of statistical models used in the empirical analysis. The perfect example is the Latent Dirichlet Allocation which is a statistical model of text generation which can help reveal underlying semantic structures in written text.
I strongly believe in the idea that structural estimation, theory, natural experiments, case studies, RCTs and reduced form work should complement each other to help us understand society. But it is not always clear whether we focus on the right mechanisms and channels. This is where I think forecasting has a role to play even if the ultimate goal is to identify causal mechanisms.