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Recursive Contracts, Lotteries and Weakly Concave Pareto Sets

Author

Listed:
  • Harold Cole

    (University of Pennsylvania)

  • Felix Kubler

    (University of Mannheim)

Abstract
Marcet and Marimon (1994, revised 1998, revised 2011) developed a recursive saddle point method which can be used to solve dynamic contracting problems that include participation, enforcement and incentive constraints. Their method uses a recursive multiplier to capture implicit prior promises to the agent(s) that were made in order to satisfy earlier instances of these constraints. As a result, their method relies on the invertibility of the derivative of the Pareto frontier and cannot be applied to problems for which this frontier is not strictly concave. In this paper we show how one can extend their method to a weakly concave Pareto frontier by expanding the state space to include the realizations of an end of period lottery over the extreme points of a flat region of the Pareto frontier. With this expansion the basic insight of Marcet and Marimon goes through -- one can make the problem recursive in the Lagrangian multiplier which yields significant computational advantages over the conventional approach of using utility as the state variable. The case of a weakly concave Pareto frontier arises naturally in applications where the principal's choice set is not convex but where randomization is possible. (Copyright: Elsevier)

Suggested Citation

  • Harold Cole & Felix Kubler, 2012. "Recursive Contracts, Lotteries and Weakly Concave Pareto Sets," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(4), pages 479-500, October.
    • Handle: RePEc:red:issued:11-266
    DOI: 10.1016/j.red.2012.05.001
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    References listed on IDEAS

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    Cited by:

    1. Matthias Messner & Nicola Pavoni & Christopher Sleet, 2012. "Recursive Methods for Incentive Problems," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(4), pages 501-525, October.
    2. Marimon, Ramon & Werner, Jan, 2021. "The envelope theorem, Euler and Bellman equations, without differentiability," Journal of Economic Theory, Elsevier, vol. 196(C).
    3. Albert Marcet & Ramon Marimon, 2019. "Recursive Contracts," Econometrica, Econometric Society, vol. 87(5), pages 1589-1631, September.
    4. Nicola Pavoni & Christopher Sleet & Matthias Messner, 2018. "The Dual Approach to Recursive Optimization: Theory and Examples," Econometrica, Econometric Society, vol. 86(1), pages 133-172, January.
    5. Neele Balke & Thibaut Lamadon, 2020. "Productivity Shocks, Long-Term Contracts and Earnings Dynamics," NBER Working Papers 28060, National Bureau of Economic Research, Inc.
    6. Golosov, M. & Tsyvinski, A. & Werquin, N., 2016. "Recursive Contracts and Endogenously Incomplete Markets," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 725-841, Elsevier.
    7. Mele, Antonio, 2014. "Repeated moral hazard and recursive Lagrangeans," Journal of Economic Dynamics and Control, Elsevier, vol. 42(C), pages 69-85.
    8. Gaetano Bloise & Paolo Siconolfi, 2022. "A Negishi Approach to Recursive Contracts," Econometrica, Econometric Society, vol. 90(6), pages 2821-2855, November.
    9. Matthias Messner & Nicola Pavoni & Christopher Sleet, "undated". "Contractive Dual Methods for Incentive Problems," GSIA Working Papers 2012-E26, Carnegie Mellon University, Tepper School of Business.
    10. François Le Grand & Xavier Ragot, 2022. "Managing Inequality Over Business Cycles: Optimal Policies With Heterogeneous Agents And Aggregate Shocks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 511-540, February.
    11. YiLi Chien & Harold L. Cole & Hanno Lustig, 2014. "Implications of Heterogeneity in Preferences, Beliefs and Asset Trading Technologies for the Macroeconomy," NBER Working Papers 20328, National Bureau of Economic Research, Inc.
    12. François Le Grand & Xavier Ragot, 2022. "Managing Inequality Over Business Cycles: Optimal Policies With Heterogeneous Agents And Aggregate Shocks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 511-540, February.
    13. Messner Matthias & Pavoni Nicola & Sleet Christopher, "undated". "Recursive Methods for Dynamic Incentive Problems," GSIA Working Papers 2012-E13, Carnegie Mellon University, Tepper School of Business.
    14. Messner Matthias & Pavoni Nicola & Sleet Christopher, "undated". "On the Dual Approach to Recursive Optimization," GSIA Working Papers 2012-E12, Carnegie Mellon University, Tepper School of Business.
    15. Lilia Maliar & Serguei Maliar, 2016. "Ruling Out Multiplicity of Smooth Equilibria in Dynamic Games: A Hyperbolic Discounting Example," Dynamic Games and Applications, Springer, vol. 6(2), pages 243-261, June.
    16. Frech, Maria & Maideu-Morera, Gerard, 2024. "The Hidden Demand for Flexibility: a Theory for Gendered Employment Dynamics," TSE Working Papers 24-1588, Toulouse School of Economics (TSE).
    17. Thibaut Lamadon, 2014. "Productivity Shocks, Dynamic Contracts and Income Uncertainty," 2014 Meeting Papers 243, Society for Economic Dynamics.
    18. Yili Chien & Harold Cole & Hanno Lustig, 2016. "Implications of Heterogeneity in Preferences, Beliefs and Asset Trading Technologies in an Endowment Economy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 215-239, April.
    19. Balke, Neele & Lamadon, Thibaut, 2021. "Productivity shocks, long-term contracts and earnings dynamics," Working Paper Series 2021:19, IFAU - Institute for Evaluation of Labour Market and Education Policy.

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    More about this item

    Keywords

    Recursive contracting; recursive multipliers; lotteries;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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