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

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  • RICHARD SHEARMUR
  • PHILIPPE APPARICIO
  • PAULINE LIZION
  • MARIO POLÈSE

Abstract

ABSTRACT 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.

Suggested Citation

  • Richard Shearmur & Philippe Apparicio & Pauline Lizion & Mario Polèse, 2007. "Space, Time, and Local Employment Growth: An Application of Spatial Regression Analysis," Growth and Change, Wiley Blackwell, vol. 38(4), pages 696-722, December.
    • Handle: RePEc:bla:growch:v:38:y:2007:i:4:p:696-722
    DOI: 10.1111/j.1468-2257.2007.00393.x
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    4. Jean Bonnet & Sébastien Bourdin & Fatten Gazzah, 2017. "The entrepreuneurial context, a factor of Economic Growth in the Europe Union? A GWR analysis on the EU Regions," Working Papers halshs-01619798, HAL.
    5. Mohamed Amara & Mohamed Ayadi, 2011. "Local Employment Growth in the Coastal Area of Tunisia: A Dynamic Spatial Panel Approach," Working Papers 650, Economic Research Forum, revised 12 Jan 2011.
    6. Xinyi Wang & Fenzhen Su & Fengqin Yan & Xinjia Zhang & Xuege Wang, 2022. "Effects of Coastal Urbanization on Habitat Quality: A Case Study in Guangdong-Hong Kong-Macao Greater Bay Area," Land, MDPI, vol. 12(1), pages 1-24, December.
    7. Diana Gutiérrez-Posada & Fernando Rubiera-Morollon & Ana Viñuela, 2017. "Heterogeneity in the Determinants of Population Growth at the Local Level," International Regional Science Review, , vol. 40(3), pages 211-240, May.
    8. Ziying Jiang & Bo Xu, 2014. "Geographically weighted regression analysis of the spatially varying relationship between farming viability and contributing factors in Ohio," Regional Science Policy & Practice, Wiley Blackwell, vol. 6(1), pages 69-83, March.
    9. Katharina Pijnenburg, 2013. "Self-Employment and Economic Performance: A Geographically Weighted Regression Approach for European Regions," Discussion Papers of DIW Berlin 1272, DIW Berlin, German Institute for Economic Research.
    10. Jean Dubé & Mario Polèse, 2016. "The view from a lucky country: explaining the localised unemployment impacts of the Great Recession in Canada," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 9(1), pages 235-253.
    11. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    12. Yonghua Zou, 2015. "Re-examining the Neighborhood Distribution of Higher Priced Mortgage Lending: Global versus Local Methods," Growth and Change, Wiley Blackwell, vol. 46(4), pages 654-674, December.

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