IDEAS home Printed from
   My bibliography  Save this paper

Efficient Allocations in a Dynamic Moral Hazard Economy


  • Noah Williams

    () (Dept. of Economics Princeton University)


I analyze the implications of moral hazard in dynamic economy with production. In particular, I add agency frictions to a benchmark stochastic growth model, by assuming that firms observe output but hours worked and productivity are unobservable. I cast the problem as a continuous time principal agent model and study the contracting problem that results. I solve for the optimal contract using some recent results on the validity of the first-order approach in continuous time, which makes the analysis tractable. I show that the dynamic agency frictions introduce both a "labor wedge" which distorts the allocation of labor within a period and an "intertemporal wedge" distorting the allocation of consumption over time. I analyze the quantitative importance of moral hazard in this economy for consumption and output dynamics and asset prices.

Suggested Citation

  • Noah Williams, 2006. "Efficient Allocations in a Dynamic Moral Hazard Economy," 2006 Meeting Papers 138, Society for Economic Dynamics.
  • Handle: RePEc:red:sed006:138

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    More about this item


    moral hazard; dynamic contracting;

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:red:sed006:138. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christian Zimmermann). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.