The Economics group at CREST is composed of 50 researchers and faculties from Ecole Polytechnique, ENSAE Paris, ENSAI, CNRS and Télécom ParisTech. CREST is hosting more than 40 PhD students and postdocs working closely with our researchers. It has been created in 2015 from the merging of the Economics department at Ecole polytechnique and the former CREST.

All members of CREST’s Economics and Sociology clusters belong to the Department of Economics and Sociology of Institut Polytechnique de Paris.

Research in economics at CREST covers all fields with a particular emphasis in Econometric Theory, Microeconomic Theory, Macroeconomics and Applied Economics: Public Economics, Development Economics, Environment Economics, Labor Economics, Political Economy. The group currently hosts two ERC grant and 8 ANR projects. Faculties are also involved in the ECODEC-Labex and the EUR “Data Science for Economics, Finance and Management” projects, jointly with economists from HEC Paris. 

Economists within CREST share a strong emphasis on quantitative methods and data analysis and combine fundamental research and more applied work. Fundamental research in econometric theory, mechanism design or public economics is meant to push the research frontier but also contribute to public debates.

Economists at CREST are taking a central role in developing quantitative methods in the framing, analysis and resolution of public policy problems. The group is a founding member of Institut des Politiques Publiques, the leading institution for public policy evaluation in France.

We also want to contribute to the major societal challenge of the twenty-first century, fighting climate change. Research in environmental economics contributes to the interdisciplinary Energy for Climate center as well as other international research networks such as CLIMATE-KIC.

Finally, we believe that the big data revolution calls for novel analytical tools that can discipline the statistical analysis of these data and help reach a causal interpretation of statistical correlations. Research on this topic contributes to the Hi! Paris center on data analytics and AI for Science, Business and Society.