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Are High-Tech Employment and Natural Amenities Linked?: Answers from a Smoothed Bayesian Spatial Model

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Author Info
Dorfman, Jeffrey H.
Patridge, Mark D.
Galloway, Hamilton
Abstract

We 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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Paper 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.

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Date of creation: 2008
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Handle: RePEc:ags:aaea08:6459

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Related research
Keywords: Bayesian econometrics; employment growth; high technology; smooth coefficient models; spatial modeling.; Labor and Human Capital; Resource /Energy Economics and Policy;

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  1. Koop, Gary & Tobias, Justin, 2004. "Semiparametric Bayesian Inference in Smooth Coefficient Models," Staff General Research Papers 12202, Iowa State University, Department of Economics.
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  2. Rosenthal, Stuart S. & Strange, William C., 2001. "The Determinants of Agglomeration," Journal of Urban Economics, Elsevier, vol. 50(2), pages 191-229, September. [Downloadable!] (restricted)
  3. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December. [Downloadable!] (restricted)
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  4. Edward L. Glaeser & Jed Kolko & Albert Saiz, 2000. "Consumer City," Harvard Institute of Economic Research Working Papers 1901, Harvard - Institute of Economic Research. [Downloadable!]
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  5. Klaus Desmet & Marcel Fafchamps, 2005. "Changes in the spatial concentration of employment across US counties: a sectoral analysis 1972--2000," Journal of Economic Geography, Oxford University Press, vol. 5(3), pages 261-284, June. [Downloadable!] (restricted)
  6. McGranahan, David A., 1999. "Natural Amenities Drive Rural Population Change," Agricultural Economics Reports 33955, United States Department of Agriculture, Economic Research Service. [Downloadable!]
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