Mr. Lorenzo Casaburi :: Center for International Development at Harvard University (CID):: Research Programs :: Sustainability Science Program :: People :: Research Fellows

Sustainability Science Program

Mr. Lorenzo Casaburi
Center for International Development
Kennedy School of Government
Harvard University
79 JFK Street
Cambridge, MA 02138 USA
Email: casaburi "at" fas.harvard.edu
Group affiliation: 2008-9 Doctoral Fellow

Lorenzo Casaburi is a Doctoral Fellow in the Sustainability Science Program at Harvard's Center for International Development and a doctoral candidate in the Economics Department at Harvard University. His main field of interest is development economics. He is currently focusing on the process of technology diffusion in social networks. Other areas being explored include the impact of improved access to water on child health and household welfare in rural areas and the determinants of agricultural productivity in sugar production. These projects are based on fieldwork and data from Western Kenya. Casaburi is a recepient of the Giorgio Ruffolo Pre-doctoral Fellowship in Sustainability Science (2007). Before joining Harvard Economics Ph.D. he worked as a research evaluation consultant at the Poverty Action Lab at the Massachusetts Institute of Technology and for several nongovernmental organizations in Kenya focusing on the evaluation of health and education interventions in rural areas. At the University of Bologna he studied how manufacturing firms in Italy and in developing countries react to increased international integration. Casaburi holds a degree in Economics magna cum laude from the University of Bologna (2004) and was a visiting student at the Department of Economics at the University of California at Berkeley in 2003-04. His faculty host at Harvard is Michael Kremer.

Learning in Networks and Technology Diffusion: Theory and Evidence from a Randomized Experiment in Kenya. The research focuses on the process of diffusion of new technologies in social networks. A simple theory will be developed to evaluate how information flows across neighbors (i.e. agents connected in the network) and how network structure affects the learning process. The research explores how agents weigh potentially redundant information. Preliminary findings of the model are compared with data from a field experiment in Kenya. The intervention has already promoted the adoption of a chlorine based product in rural areas.