IDEAS home Printed from
   My bibliography  Save this paper

Recovering Technologies that Account for Generalized Managerial Preferences: An Application to Non-Risk-Neutral Banks


  • Joseph P. Hughes
  • William Lang
  • Loretta J. Mester
  • Choon-Geol Moon


The authors suggest that risk plays an important role in managerial production decisions. Managers make implicit and explicit decisions related to risk, return, and cost in setting target market, product, pricing and delivery decisions. Standard models of production and cost do not explicitly account for risk, assuming that managers are neutral toward risk. This simplification may undermine the model's usefulness when applied to an industry such as banking where risk plays an important economic role in the business. The standard model would label risk-averse banks at best, allocatively inefficient and at worst, technically inefficient. The authors attempt to maximize a managerial utility function, defined over profit, inputs, and outputs with respect to the mix of inputs and with respect to profit subject to the production constraint that the input mix must produce the given output vector. The solution to this utility maximization problem gives the manager's most preferred production plan. To the extent that managers have favored inputs whose employment they will increase at the expense of profit, the most preferred production plan will not be allocatively efficient. In fact, it may not even be technically efficient. The cost function that follows from the utility-maximizing production plan is sufficiently general to incorporate non-neutrality toward risk and to allow other managerial objectives in addition to profit maximization. Formulating the production plan from a model of constrained utility maximization suggests that the functional forms needed to implement the model can be derived by analogy to those of consumer theory. The Almost Ideal (AI) Demand System, adapted to accommodate generalized managerial preferences, yields input share equations and a profit (cost) function that, in the case of cost minimization, are identical to the translog cost function and input share equations. The model is estimated using 1989 and 1990 data from U.S. banks whose assets equal or exceed $1 billion. The AI System fits the data well; risk neutrality is conclusively rejected. The measure of scale economies obtained from the most preferred cost function is considerably larger than those obtained from more conventional cost functions, and it increases with bank asset size, suggesting that the diversification economies enjoyed by larger banks allow them to reduce the share of resources used to control risk which magnifies the diversification economies. When risk neutrality is imposed and financial capital is deleted from the model, the measure of scale elasticity drops considerably and resembles the magnitudes generally found in studies that employ the standard framework. Thus, this evidence provides some explanation of the seeming inconsistency between the actual merger wave that has been occurring and the standard literature that finds little motivation from cost savings.

Suggested Citation

  • Joseph P. Hughes & William Lang & Loretta J. Mester & Choon-Geol Moon, 1995. "Recovering Technologies that Account for Generalized Managerial Preferences: An Application to Non-Risk-Neutral Banks," Center for Financial Institutions Working Papers 95-16, Wharton School Center for Financial Institutions, University of Pennsylvania.
  • Handle: RePEc:wop:pennin:95-16
    Note: This paper is only available in hard copy

    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


    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:wop:pennin:95-16. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.