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Transitive properties: a spatial econometric analysis of new business creation around transit

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  • Kevin Credit

Abstract

This paper evaluates the relationship between transit station proximity and new business creation in five US regions with varying levels of maturity in rail transit development and/or entrepreneurial ecosystems: Boston, San Jose, Austin, Cleveland and Philadelphia. It tests a variety of spatial econometric models to find the best specification and compares the results with the kinds of non-spatial models currently used in the literature. This provides a better understanding of the role of various forms of spatial dependence in the transit – new business creation relationship and shows that existing models may overstate the impact of transit on new business creation. In addition, the paper teases out differences between regions, rail modes and business types that can be usefully applied to a variety of urban contexts.

Suggested Citation

  • Kevin Credit, 2019. "Transitive properties: a spatial econometric analysis of new business creation around transit," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(1), pages 26-52, January.
    • Handle: RePEc:taf:specan:v:14:y:2019:i:1:p:26-52
    DOI: 10.1080/17421772.2019.1523548
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    Cited by:

    1. Champagne, Marie-Pier & Dubé, Jean & Legros, Diègo, 2024. "Standing strong? The causal impact of metro stations on service firms’ survival," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    2. Oh, Seunghoon & Hofe, Rainer vom, 2023. "Transit-induced Agglomeration and Employment Opportunity: A Spatial Econometric Analysis of Skill- and Industry-specific Job Clusters in Philadelphia, PA," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 53(1), May.
    3. Elina Sukaryavichute & Elizabeth Delmelle & Colleen Hammelman, 2021. "Opportunities and challenges for small businesses in new transit neighborhoods: Understanding impacts through in‐depth interviews," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 1025-1041, June.
    4. Liu, Chang & Bardaka, Eleni, 2023. "Transit-induced commercial gentrification: Causal inference through a difference-in-differences analysis of business microdata," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    5. Oh, Seunghoon & Chen, Na, 2022. "Do public transit and agglomeration economies collectively enhance low-skilled job accessibility in Portland, OR?," Transport Policy, Elsevier, vol. 115(C), pages 209-219.
    6. Liu, Shuli & Wan, Yulai & Zhang, Anming, 2020. "Does China’s high-speed rail development lead to regional disparities? A network perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 299-321.
    7. Champagne, Marie-Pier & Dubé, Jean, 2023. "The impact of transport infrastructure on firms’ location decision: A meta-analysis based on a systematic literature review," Transport Policy, Elsevier, vol. 131(C), pages 139-155.
    8. Champagne, Marie-Pier & Dubé, Jean & Barla, Philippe, 2022. "Build it and they will come: How does a new public transit station influence building construction?," Journal of Transport Geography, Elsevier, vol. 100(C).

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