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Space, Time, and Local Employment Growth: An Application of Spatial Regression Analysis

机译：时空和当地就业增长：空间回归分析的应用

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摘要

Local and regional employment growth is generally studied either by searching for local qualitative explanatory factors such as governance, synergy between firms, and milieu effects, or by searching for general growth factors using statistical techniques. The body of work that relies on this approach has tended, in keeping with economics' nomothetic tradition, to assume that local and regional growth factors are constant over space. The focus of this paper is on exploring the spatial stationarity of employment growth factors in Canada, but it also seeks to clarify some of the broad principles behind spatial regression techniques in order to provide a point of entry and a conceptual framework for empirical researchers. To do so, we apply a recently developed technique, Geographically Weighted Regression (GWR), and we explore the method's advantages and limits for answering our research question. We find evidence that growth factors differ across Canada, but we also conclude that the GWR technique, given the number and shape of regions available for our analysis and given certain limitations that are currently inherent to the method, can only provide tentative and exploratory results.
机译：通常通过搜索当地的定性解释因素（例如治理，公司之间的协同效应和环境效应）或通过使用统计技术搜索一般的增长因素来研究本地和区域就业增长。依赖于这种经济学方法的工作原理，依赖这种方法的工作趋向于假定局部和区域性增长因素在整个空间中是恒定的。本文的重点是探索加拿大就业增长因素的空间平稳性，但它也试图阐明空间回归技术背后的一些广泛原则，以便为经验研究者提供切入点和概念框架。为此，我们采用了最近开发的技术，即地理加权回归（GWR），并探讨了该方法的优点和局限性来回答我们的研究问题。我们发现有证据表明加拿大各地的增长因素有所不同，但我们还得出结论，鉴于可用于我们的分析的区域的数量和形状以及该方法当前固有的某些局限性，GWR技术只能提供试验性和探索性结果。

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