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

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.

KEYWORDS:

• spatial models (C21)
• economic development (F63)
• urban, rural, regional, and transportation analysis (O18)
• planning policy (O21)

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author.

SUPPLEMENTAL DATA

Supplemental data for this article can be accessed at https://doi.org/10.1080/17421772.2019.1523548

ORCID

Kevin Credit http://orcid.org/0000-0002-3320-4670

Notes

1 Hecker provides an often-cited definition of high-technology employment based on NAICS industries with high concentrations of high-tech employment. The 14 industries identified as ‘Level 1’ are (by NAICS code): 3254, 3341, 3342, 3344, 3345, 3364, 5112, 5161, 5179, 5181, 5182, 5413, 5415 and 5417 (Hecker, 2005). For the purposes of this paper, new businesses in these sectors are classified as high-tech start-ups.

2 Transformed, as described in the Methods section below, for use in the final model.

3 This is the terminology used in GeoDa v.1.8.16.4 1, which was used to calculate all the smoothed variables used in the paper.

4 The negative binomial model is a special case of the Poisson model (with an additional parameter) that is used to model overdispersion in a Poisson distribution, that is, when the variance of the distribution is greater than its mean (Hilbe, 2014).

5 Note that this paper does not attempt to argue that the method used here is statistically preferable to either the spatial GLMM or the spatial filtering approaches for modeling spatial count data – a direct comparison of these methods is beyond the scope of this paper. Rather, this study simply presents an alternative method for modeling business data within traditional spatial econometric frameworks.

6 A first-order queen spatial eights matrix was used to calculate the spatial rate and SEB smoothing rates.

7 Many thanks to Dr LeSage for providing the MATLAB code to perform this analysis on his website (see http://www.spatial-econometrics.com) and via email. These posterior model probabilities were calculated using the lmarginal_cross_section function using the MATLAB code outlined in LeSage (2015).

8 All final models were estimated using the ‘lagsarlm’ function in the ‘spdep’ R package using the Monte Carlo approximate log-determinant method of weights matrix decomposition.

9 Significance measured at the p ≤ .05 level.

Additional information

Funding

This work was supported by the Ewing Marion Kauffman Foundation [grant number 20130782]. It was also supported by the Graduate School at Michigan State University under a Dissertation Completion Fellowship.