Are High-Tech Employment and Natural Amenities Linked?: Answers from a Smoothed Bayesian Spatial Model
AbstractWe investigate the recently advanced theory that high-technology workers are drawn to high amenity locations and then the high-technology jobs follow the workers. Using a novel data set that tracks high-technology job growth by U.S. county, we estimate spatial parameters of the response of job growth to the level of local natural amenities. We achieve this estimation with a reasonably new class of models, smooth coefficient models. The model is employed in a spatial setting to allow for smooth, but nonparametric response functions to key variables in an otherwise standard regression model. With spatial data this allows for flexible modeling such as a unique place-specific effects to be estimated for each location, and also for the responses to key variables to vary by location. This flexibility is achieved through the non-parametric smoothing rather than by nearest-neighbor type estimators such as in geographically weighted regressions. The resulting model can be estimated in a straightforward application of analytical Bayesian techniques. Our results show that amenities can definitely have a significant effect on high-technology employment growth; however, the effect varies over space and by amenity level.
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Bibliographic InfoPaper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida with number 6459.
Date of creation: 2008
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Bayesian econometrics; employment growth; high technology; smooth coefficient models; spatial modeling.; Labor and Human Capital; Resource /Energy Economics and Policy;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-11-18 (All new papers)
- NEP-ECM-2008-11-18 (Econometrics)
- NEP-GEO-2008-11-18 (Economic Geography)
- NEP-LAB-2008-11-18 (Labour Economics)
- NEP-URE-2008-11-18 (Urban & Real Estate Economics)
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