Advanced Search
MyIDEAS: Login

Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets

Contents:

Author Info

  • Chakraborty, A.
  • Beamonte, M.A.
  • Gelfand, A.E.
  • Alonso, M.P.
  • Gargallo, P.
  • Salvador, M.
Registered author(s):

    Abstract

    As a result of increased mobility patterns of workers, explaining labor flows and partitioning regions into local labor markets (LLMs) have become important economic issues. For the former, it is useful to understand jointly where individuals live and where they work. For the latter, such markets attempt to delineate regions with a high proportion of workers both living and working. To address these questions, we separate the problem into two stages. First, we introduce a stochastic modeling approach using a hierarchical spatial interaction specification at the individual level, incorporating individual-level covariates, origin (O) and destination (D) covariates, and spatial structure. We fit the model within a Bayesian framework. Such modeling enables posterior inference regarding the importance of these components as well as the O–D matrix of flows. Nested model comparison is available as well. For computational convenience, we start with a minimum market configuration (MMC) upon which our model is overlaid. At the second stage, after model fitting and inference, we turn to LLM creation. We introduce a utility with regard to the performance of an LLM partition and, with posterior samples, we can obtain the posterior distribution of the utility for any given LLM specification which we view as a partition of the MMC. We further provide an explicit algorithm to obtain good partitions according to this utility, employing these posterior distributions. However, the space of potential market partitions is huge and we discuss challenges regarding selection of the number of markets and comparison of partitions using this utility. Our approach is illustrated using a rich dataset for the region of Aragón in Spain. In particular, we analyze the full dataset and also a sample. Future data collection will arise as samples of the working population so assessing population level inference from the sample is useful.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312003209
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 58 (2013)
    Issue (Month): C ()
    Pages: 292-307

    as in new window
    Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:292-307

    Contact details of provider:
    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Approximate block diagonalization; Bayesian hierarchical modeling; Conditionally autoregressive models; Markov chain Monte Carlo; Origin–destination models; Spatial partitioning;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:292-307. 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: (Zhang, Lei).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.