英文文献：Are High-Tech Employment and Natural Amenities Linked?: Answers from a Smoothed Bayesian Spatial Model-高科技就业和自然设施有联系吗?:光滑贝叶斯空间模型的答案
英文文献作者：Dorfman, Jeffrey H.,Patridge, Mark D.,Galloway, Hamilton
英文文献摘要：
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.

我们调查了最近提出的理论，即高技术工人被吸引到高设施的位置，然后高技术工作跟随工人。利用一套跟踪美国县高科技就业增长的新数据集，我们估计了就业增长对当地自然设施水平反应的空间参数。我们通过一种合理的新模型，光滑系数模型来实现这一估计。该模型在空间设置中使用，以允许在其他标准回归模型中对关键变量进行平滑但非参数的响应函数。使用空间数据，可以进行灵活的建模，比如为每个位置估计独特的位置特定效应，以及对关键变量的响应，以使其随位置而变化。这种灵活性是通过非参数平滑实现的，而不是通过诸如地理加权回归等最近邻类型估计器。产生的模型可以在分析贝叶斯技术的直接应用中估计。我们的结果显示，便利设施对高科技就业增长肯定有显著影响;然而，影响因空间和舒适度的不同而不同。