IDEAS home Printed from
   My bibliography  Save this paper

Neural-Network Modeling for Labor-Force Migration: a Competitive-Learning Approach


  • Thomas G. Wier

    () (Northeastern State University)

  • Vir V. Phoha

    () (Northeastern State University)


This study is an examination of the appropriateness of using a competitive learning neural-net algorithm to model labor-force migration. In the past, there have been a number of studies to model labor-force migration using cross-sectional regression analysis. These studies model the propensity to migrate based upon income differential, and they model this behavior by estimating the propensity to migrate between two cities or states given a number of different factors. All of the studies include income as a significant factor to be examined in the human-capital decision to migrate. This study is a new "computational regional science" approach. A neural-network algorithm based on competitive learning, developed and successfully applied to an image restoration problem, is used as a pilot for modeling regional science problems in general using neural networks. It first converts this problem to an optimization problem, which is solved using the algorithm of Phoha and Oldham (1996). The labor force migration problem lends itself easily to an optimization problem, which can then be solved by the competitive learning algorithm. This study assumes that interregional income differentials determine migration probability and should also be equilibrated by migration. The human-capital model of migration is based upon this income maximization thesis. This algorithm adjusts the income levels locally, but also simultaneously minimizes a global optimization function of the differences in income levels between cities. The resulting method, in terms of economic analysis, takes a general-equilibrium approach to migration. Rather than examining the variation in migration given different location factors like income, the model simulates the optimizing behavior of migration by equilibrating income based on an actual set of initial income conditions in a realistic spatial matrix of cities. By simulating this behavior, using both local equilibria information -- which is distance sensitive -- and a global equilibria -- which is not --, the actual equilibrating behavior of income can be compared to the model output to assess the efficiency of actual labor-force mobility in equilibrating economic conditions over space.

Suggested Citation

  • Thomas G. Wier & Vir V. Phoha, 1999. "Neural-Network Modeling for Labor-Force Migration: a Competitive-Learning Approach," Computing in Economics and Finance 1999 1043, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:1043

    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.

    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:sce:scecf9:1043. 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: (Christopher F. Baum). 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.