Identifying Microgeographies Using Hierarchical Cluster Analysis: Startup Agglomeration and Venture Investment In U.S. Cities

42 Pages Posted: 17 Mar 2020

Date Written: February 17, 2020

Abstract

This paper advances a new technique for identifying, delineating, and analyzing microgeographies. It applies this technique to locate and measure agglomerations of high-growth, high-tech (HGHT) startup activity within 205 U.S. cities. Using data from 1995 to 2018 on venture-backed companies, I estimate the effect of startup agglomeration economies on ecosystem growth. I find that a one standard deviation increase in measures of startup agglomeration is associated with around a 12% increase in the next period's venture investment, and that optimal HGHT startup density appears to be around one every 2.5 hectares. I also simulate innovation districts for Houston, Texas. I find that an optimally sized and centrally located innovation district could increase Houston's venture investment by as much 15%, while the currently proposed 'Innovation Corridor' could reduce it by as much as 5%.

Keywords: Agglomeration, Micrography, Entrepreneurship, Startup, Hierarchical Cluster Analysis, Venture Capital, High-Growth High-Technology, Innovation District, Tax Increment Finance

JEL Classification: D61, L26, M13, R12, R58, G24, H00, G18, D49, D02

Suggested Citation

Egan, Edward, Identifying Microgeographies Using Hierarchical Cluster Analysis: Startup Agglomeration and Venture Investment In U.S. Cities (February 17, 2020). Available at SSRN: https://ssrn.com/abstract=3537162 or http://dx.doi.org/10.2139/ssrn.3537162

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