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Mobilite intergenerationnelle des gains et du revenu des hommes au Canada : etude basee sur les donnees longitudinales de l'impot sur le revenu

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  • Heisz, Andrew
  • Corak, Miles

Abstract

Le but de la presente analyse est d'etablir une estimation exacte du degre de mobilite intergenerationnelle du revenu au Canada. A partir des donnees sur l'impot sur le revenu pour quelque 400 000 paires pere-fils, nous en sommes arrives a une elasticite intergenerationnelle des gains dont la valeur est d'environ 0,2. La mobilite des gains tend a etre legerement superieure a celle du revenu; cependant, les methodes non parametriques revelent une non-linearite significative dans ces deux relations. Par ailleurs, la mobilite intergenerationnelle des gains est plus marquee a l'extremite inferieure de la distribution qu'a son extremite superieure et elle affiche une forme en V inverse ailleurs dans la distribution. Dans le cas du revenu, la mobilite intergenerationnelle suit essentiellement le meme profil, bien qu'elle soit beaucoup moins grande a l'extremite superieure de la distribution.

Suggested Citation

  • Heisz, Andrew & Corak, Miles, 1998. "Mobilite intergenerationnelle des gains et du revenu des hommes au Canada : etude basee sur les donnees longitudinales de l'impot sur le revenu," Direction des études analytiques : documents de recherche 1998113f, Statistics Canada, Direction des études analytiques.
    • Handle: RePEc:stc:stcp3f:1998113f
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    File URL: https://www150.statcan.gc.ca/n1/fr/catalogue/11F0019M1998113
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    Cited by:

    1. Klaus Adam & Albert Marcet & Juan Pablo Nicolini, 2016. "Stock Market Volatility and Learning," Journal of Finance, American Finance Association, vol. 71(1), pages 33-82, February.
    2. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    3. Chen, Zhenxi, 2016. "Regimes dependent speculative trading: Evidence from the United States housing market," FinMaP-Working Papers 66, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    4. Grosche, Stephanie & Heckelei, Thomas, 2014. "Price dynamics and financialization effects in corn futures markets with heterogeneous traders," Discussion Papers 172077, University of Bonn, Institute for Food and Resource Economics.
    5. Shintani, Mototsugu & Linton, Oliver, 2004. "Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos," Journal of Econometrics, Elsevier, vol. 120(1), pages 1-33, May.
    6. Zhenxi Chen & Thomas Lux, 2018. "Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 711-744, October.
    7. Ghonghadze, Jaba & Lux, Thomas, 2015. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," FinMaP-Working Papers 38, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    8. Katsuhiro Oshima, 2019. "Heterogeneous Beliefs, Monetary Policy, and Stock Price Volatility," KIER Working Papers 1013, Kyoto University, Institute of Economic Research.
    9. Zhenxi Chen & Weihong Huang & Huanhuan Zheng, 2018. "Estimating heterogeneous agents behavior in a two-market financial system," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 491-510, October.
    10. Klaus Adam & Albert Marcet & Johannes Beutel, 2017. "Stock Price Booms and Expected Capital Gains," American Economic Review, American Economic Association, vol. 107(8), pages 2352-2408, August.
    11. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy (IfW Kiel).
    12. Kukacka, Jiri & Barunik, Jozef, 2013. "Behavioural breaks in the heterogeneous agent model: The impact of herding, overconfidence, and market sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5920-5938.
    13. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
    14. Kristoufek, Ladislav & Vošvrda, Miloslav S., 2016. "Herding, minority game, market clearing and efficient markets in a simple spin model framework," FinMaP-Working Papers 68, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    15. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    16. Chen, Zhenxi, 2014. "Estimating heterogeneous agents behavior with different investment horizons in stock markets," FinMaP-Working Papers 5, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.

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