Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets
AbstractAs 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 58 (2013)
Issue (Month): C ()
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Web page: http://www.elsevier.com/locate/csda
Approximate block diagonalization; Bayesian hierarchical modeling; Conditionally autoregressive models; Markov chain Monte Carlo; Origin–destination models; Spatial partitioning;
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