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Bernard Fortin
Researcher at Laval University
Publications - 148
Citations - 4939
Bernard Fortin is an academic researcher from Laval University. The author has contributed to research in topics: Labour supply & Conformity. The author has an hindex of 28, co-authored 145 publications receiving 4496 citations. Previous affiliations of Bernard Fortin include Université de Montréal & Institute for the Study of Labor.
Papers
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Identification of peer effects through social networks.
TL;DR: Durlauf et al. as discussed by the authors considered an extended version of the linear-in-means model where interactions are structured through a social network and provided easy-to-check necessary and sufficient conditions for identification of peer effects.
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Marriage Market, Divorce Legislation, and Household Labor Supply
TL;DR: In this paper, the authors provide a theoretical framework for analyzing the impact of the marriage market and divorce legislation on household labor supply and show that their model imposes new restrictions on the labor supply functions and eases the identification of individual preferences and the intra-household decision process.
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A test of the unitary and collective models of household labour supply
Bernard Fortin,Guy Lacroix +1 more
TL;DR: In this paper, a nonlinear unrestricted household labour supply system is proposed and the set of parametric restrictions imposed by each model is derived and the results generally reject the income pooling restrictions embodied in the unitary model.
Posted Content
The effect of taxes on labor supply in the underground economy
Thomas Lemieux,Bernard Fortin +1 more
TL;DR: In this article, the authors used micro data from a randomized survey carried out in the metropolitan area of Quebec City, Canada, to analyze the decision to evade taxes and work in the underground economy.
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Tax evasion and social interactions
TL;DR: In this article, the authors extend the standard tax evasion model by allowing for social interactions and show that intrinsic nonlinearity between individual and group responses is sufficient to identify the model without imposing any exclusion restrictions.