A Multivariate Spatial Analysis for Anticipating New Firm Counts

Abstract

This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.

Citation

Wang, Y., M. Kockelman, K. and Damien, P. (2016), "A Multivariate Spatial Analysis for Anticipating New Firm Counts", Spatial Econometrics: Qualitative and Limited Dependent Variables (Advances in Econometrics, Vol. 37), Emerald Group Publishing Limited, Leeds, pp. 167-193. https://doi.org/10.1108/S0731-905320160000037014