Susana Constenla-Villoslada

Susana combines econometrics and machine learning to deliver new solutions to persistent challenges in international development. She also specializes in integrating traditional economic data with emerging data sources, especially from remote sensing. Her work focuses on impact evaluation design and implementation, cross-sectional and forward-looking prediction for targeting and early warning, empirical resilience measurement, validation of new well-being and socioeconomic indicators.

Before joining the ISchool, Susana spent three years as a Research Analyst at the International Food Policy Research Institute in Washington, D.C. She holds an M.S. in Development Economics from Cornell University, which she obtained under a Fulbright Scholarship, and a B.S. in Agricultural Engineering, with honors, from the Polytechnic University of Madrid.

Publications:

Constenla-Villoslada, S., Liu, Y.,  McBride, L.,  Ouma, C.,  Mutanda, N., & Barrett, C.B. (2025). High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning, PNAS. U.S.A. 122 (23) e2416161122, https://doi.org/10.1073/pnas.2416161122.

Constenla-Villoslada, S., Liu, Y., Wen, J. et al. (2022). Large-scale land restoration improved drought resilience in Ethiopia’s degraded watersheds. Nature Sustainability. https://doi.org/10.1038.

Upton, J., Constenla-Villoslada, S. and Barrett, C.B. (2022). “Caveat utilitor: A comparative assessment of resilience measurement approaches”, Journal of Development Economics, Vol- 157, 102873.https://doi.org/10.1016/j.jdeveco.2022.102873.