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Roadmap for Pauline's JMP: 1) compelling reduced form, DiD, identification of the policy's causal effects 2) a rich and realistic search and matching model 3) a skillful structural estimation of the model 4) an insightful quantitative assessment of the reform,incl welfare effects
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Pauline's excellent empirical work starts by documenting that the reform had strong bite: French firms sharply cut the jobs offering low hours, shifting to jobs with at least 24 hours per week.
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To estimate causal effects, Pauline implements a firm-level DiD, by pre-reform share of low hours. (This intuitive heterogeneity is microfounded the model too!) In response to the reform, the treated firms cut low-hours jobs-but also overall jobs, while increasing average hours!
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Who drives this effect? Who gains or loses? Women -- who made up the lion's share of the part-time jobs pre-reform -- see their jobs and hours reduced most in the aggregate and at the firm level. Pauline puts it best: “Female workers were replaced by full-time male workers.”
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Pauline is both a labor and macroeconomist, so she asks: what are the aggregate effects? The DiD design can't answer those Qs. (It's in the year FEs!). Pauline constructs *and estimates* a rich and *realistic* search and matching model of the labor market, incl firm and job het.
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The estimated model, which also replicates the firm-level DiD estimates and the impacts of the reform in the data, uncovers the aggregate effects, including the net effects of cross-firm reallocation and welfare. Outstanding link b/w data and economics. Much more in the paper!
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