I am interested in a variety of topics related to economics and policy interventions. Here are some of my academic publications organized by theme. If you are interested in my work in progress or you do not have a subscription to the journals where I have published, feel free to contact me and I will be happy to provide the manuscripts. You can find related policy reports in the PPI section and media coverage in the Press section.

Policy Priority Inference (PPI)

This is a research program that uses agent-computing models to estimate the impact of public expenditure on development indicators. The problem of impact estimation sounds simple but, unfortunately, data on public expenditure is hard to come by in a form where one could link them to specific performance indicators, especially in a setting where one needs to consider hundreds of interdependent policy issues or indicators. Furthermore, even if such data become available, econometric and machine learning methods have a hard time establishing the relationship between expenditure programs and development. PPI tries to overcome these limitations by coupling data-science methods with theory-driven computational models of how expenditure contributes to development. It provides governments and other types of organizations with tools to better determine budgetary priorities when they want to reach a set of goals for their indicators.

Housing Markets

This research looks at problems related to housing markets and inequality problems arising from their dynamics.

Labor Flow Networks

This research was the first to introduce the idea of thinking about labour dynamics as workers flowing through networks (where such networks represent the structure of the labor market), coining the term Labor Flow Network (LFN). We have produced different types of models (mathematical and computational) to analyze highly granular dataset of employee-employer matched records and to understand better the consequences of economic shocks. Currently, I am working on developing models that can explain LFNs from bottom-up through agent computing; something extremely useful to understand the impact of strong shocks and to design nuanced policy interventions.

Vote Trading Networks

This research has produced a methodology to quantify the hidden behavior of vote trading (known as logrolling in the US) in congresses and parliaments. Our approach is the first to provide a systemic view of the problem, so researchers can use it to exploit large-scale roll call data from different institutions.