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Évaluer l’effet des politiques publiques locales avec les contrôles synthétiques et les modèles à facteurs : Une application au marché du poisson français

Listed author(s):
  • Laurent Gobillon

    ()

    (INED - Institut national d'études démographiques, PSE - Paris School of Economics, PSE - Paris-Jourdan Sciences Economiques - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR)

  • François-Charles Wolff

    ()

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes)

Cet article propose une évaluation de l’introduction d’une nouvelle technique de pêche aux Sables d’Olonne sur la qualité des poissons pêchés à partir de la méthode des contrôles synthétiques et de l’estimation de modèles à facteurs. L’estimation est menée sur des données agrégées au niveau des criées françaises sur la période 2009-2012. Notre estimateur préféré de l’effet de l’innovation à composition constante des espèces pêchées est obtenu avec le modèle à facteurs et correspond à un accroissement de 16,9% de la proportion de poissons présentant la meilleure qualité. La méthode des contrôles synthétiques conduit à un effet moindre de l’innovation (8,3%), mais elle repose sur une interpolation à partir des criées de contrôle qui est assez approximative.

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File URL: https://halshs.archives-ouvertes.fr/halshs-01183455/document
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Paper provided by HAL in its series PSE Working Papers with number halshs-01183455.

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Date of creation: Aug 2015
Handle: RePEc:hal:psewpa:halshs-01183455
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01183455
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  1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  2. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
  3. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, 07.
  4. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
  5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
  6. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
  7. Ahn, Seung Chan & Hoon Lee, Young & Schmidt, Peter, 2001. "GMM estimation of linear panel data models with time-varying individual effects," Journal of Econometrics, Elsevier, vol. 101(2), pages 219-255, April.
  8. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, 07.
  9. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
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