Unité mixte de recherche 7235

Structural gravity equations with intensive and extensive margins

Matthieu Crozet, Pamina Koenig

[en] New trade models with heterogeneous firms have had a consequent influence on gravity equations. According to Chaney (2007) and Melitz and Ottaviano (2005), the theoretical relationship between trade costs and trade flows is the sum of the effect of trade costs on the number of exporting firms (the extensive margin) and the value of individual exports (the intensive margin). The distinctive effect of distance on the two margins deeply modifies predictions of the trade literature, among which the sectoral effect of trade policies. Using French firms-level export data to 61 countries, on the period 1989-1992, we provide unbiased structural estimates of the three parameters governing trade elasticities with respect to distance. This dissection of the gravity equation provides consistent evidence in favor of heterogeneous firms models of trade. [/en]


mardi 17 mai 2022

Rencontres économiques

9h30 à 11h30

Comment se débarrasser des énergies fossiles et développer les énergies renouvelables dans un monde en crise ?

jeudi 19 mai 2022


Christophe Blot

Are all central bank asset purchases the same?

lundi 23 mai 2022

Law, Institutions and Economics in Nanterre (LIEN)

Clara Jean (Grenoble Ecole de Management)

The Value of Your Data: Privacy and Personal Data Exchange Networks

mardi 24 mai 2022

Recherche et Economie et Socioéconomie Politique, des Institutions et des Régulations (RESPIR)

Alice Sindzingre (CEPN) et Fabrice Tricou

De 13h30 à 15h30

Six forms of hierarchy for a theoretical analysis of capitalism

lundi 30 mai 2022

Law, Institutions and Economics in Nanterre (LIEN)

Antoine Dubus (ETH Zurich)

Salle G110

Data Driven Mergers and Acquisitions with Information Synergies

mardi 31 mai 2022

Series of Webinars on Economics of Environment, Energy and Transport (SWEEET)

Juan Pablo Montero (PUC)


jeudi 9 juin 2022


Rémi Generoso


jeudi 9 juin 2022

Groupe de travail « Intelligence artificielle »

Hugo Le Picard (IFRI)

Salle G614B

Le deep learning au service de l’analyse des énergies renouvelables en Afrique

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