PhD in economics: cohesion and multidisciplinarity

At the end of the year, the doctoral students in CREST’s economics cluster organized a series of seminars for first-year doctoral students.

Cohesion & multidisciplinarity

Cohesion between doctoral students is essential. It fosters collaboration, the exchange of ideas and mutual support, creating an environment conducive to learning. Peer solidarity allows to share experiences, solve problems in groups and develop crucial skills. By fostering a team spirit, the cohesion between doctoral students contributes to their personal development and success in their doctoral studies.

These seminars provide another opportunity for first-year doctoral students to present their research. They highlight the theme of their research, as well as the specific questions they are tackling, and the methodology envisaged throughout their doctorate.

CREST doctoral students and researchers are invited to take in these seminars, where their experience in presenting their research enables young doctoral students to practice presenting their project clearly and concisely to people outside the field, thus providing a fresh and stimulating perspective.

The sessions also facilitate the establishment of links with researchers working in similar fields, giving PhD students the opportunity to obtain advice based on the experience of these researchers.

By emphasizing multidisciplinary within the same cluster, these seminars encourage doctoral students to benefit from reflections from the literature and approaches from other fields of research within CREST. This approach stimulates multidisciplinary reflection, enriching the research work of each student.

List of presentations

Camille Boissel Heterogeneous responses to labour policy
Sébastien Cerles A model of advertising
Aurélien Frot Job search biases in the data
Gaëtan Menard Productivity in health economics
Clément Montes A model of economic sanctions
Théo Roudil-Valentin Corporate taxation following shocks
Pedro Vergara Merino Econometrics of randomized experiment: theory and simulation
Vincent Verger Natural Language Processing applied to political economy
Yiyun Zheng A model of platforms and reputation

PolygramPodcast – Geoffrey Barrows

Green economy: an inclusive and sustainable economic growth for all?

An interview with Geoffrey Barrows, a CNRS researcher affiliated with CREST/Polytechnique, interested in environmental economics, international development and international trade.

Niger : chocs démographiques

Dans cet article du journal Les Echos, la journaliste Lucie Robequain, fait référence aux recherches de Pauline Rossi, enseignante en économie à l’École polytechnique et chercheuse au CREST.

CREST Conference on Risk & Insurance 14-15 sep 2023

The objective of the conference is to bring together leading academic scientists, researchers, and research scholars to present and exchange their research results on economic and financial decisions under risk, insurance markets and related public policies. It will be also a tribute to Professor Pierre Picard, an outstanding researcher of the field, who is Emeritus Professor at École polytechnique since September 2021.

This conference is organized as a workshop, based on plenary sessions held at ENSAE (Sep. 14) and Maison internationale (Cité internationale de Paris, Sep. 15), each presentation (12 in total) being made by a leading scientist. Presentations will represent the diversity of research on economic and financial decisions under risk, insurance markets and related public policies. All presentations will be made by invited speakers.

Date: September 14 and 15, 2023.

Participation to the conference is free, but registration is required. Please, click here to register.

Program of the conference

2023 France-Berkeley Fund: 2 CREST recipients

The France-Berkeley Fund

Established in 1993 as a partnership with the French Ministry of Foreign Affairs, the France-Berkeley Fund (FBF) promotes and supports scholarly exchange in all disciplines between faculty and research scientists at the University of California and their counterparts in France.

Through its annual grant competition, the FBF provides seed money for innovative, bi-national collaborations. The Fund’s core mission is to advance research of the highest caliber, to foster interdisciplinary inquiry, to encourage new partnerships, and to promote lasting institutional and intellectual cooperation between France and the United States.

2023-2024 Call: 2 CREST recipients

For the 2023-2024 call, 2 projects have been submitted and are getting funded:

• Decentralizing divorces
A project developed by Matias Nunez (CREST, CNRS Research fellow) and his counterpart Federico Echenique, Professor of Economics and Social Sciences at UC Berkeley.

This project focuses on the development of practical applications of mechanism design, a branch of economics concerned with developing well-functioning institutions that ensure efficient and fair outcomes. In particular, we will focus on legal settings where two persons need to reach an agreement while their preferences are misaligned. Examples are dissolution of partnerships, allocation of rights and duties among conflicting agents, and divorces. While a judge, legal experts and lengthy bargaining procedures are often needed in practice, we plan to develop economic tools to appraise reasonable compromises, reducing both cost and time.

• Towards Local, Distribution-Free and Efficient Guarantees in Aggregation and Statistical Learning
A project developed by Jaouad Mourtada (CREST, ENSAE Paris) and his counterpart Nikita Zhivotovskiy, Assistant Professor in Statistics at UC Berkeley.

Statistical learning theory is dedicated to the analysis of procedures for learning based on data. The general aim is to understand what guarantees on the prediction accuracy can be obtained, under which conditions and by which procedures. It can inform the design of sound and robust methods, that can withstand corruption in the data or departure from an idealized posited model, without sacrificing accuracy or efficiency in more favorable situations. In particular, the problem of aggregation can be formulated as follows: given a class of predictors and a sample, form a new predictor that is guaranteed to have an accuracy approaching that of the best predictor within the class, up to an error that should be as small as possible.
This problem can be cast in several settings and has been investigated through various angles in Statistics and Computer Science. While the topic is classical, it has seen a renewed interest through (for instance) the recent direction of robust statistical learning, which raises the question of the most general conditions under which a good accuracy can be achieved. Despite important progress, several important and basic questions have remained unanswered in the literature, which we aim to study.