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Urban Start-up Agglomeration and Venture Capital Investment (view source)
Revision as of 13:15, 27 October 2020
, 13:15, 27 October 2020→New Work
I also wanted to fix confusion between CSAs (Combined Statistical Areas)[https://en.wikipedia.org/wiki/Combined_statistical_area] and CMSAs (Consolidated Metropolitan Statistical Areas)[https://www2.census.gov/geo/pdfs/reference/GARM/Ch13GARM.pdf]. CMSA redirects to CSA on Wikipedia. However, it is actually not clear if these are the same things. OMB is the originator of both terms[https://www.census.gov/programs-surveys/metro-micro/about/masrp.html].
====Guzman and Stern====
Ed's suggested paragraph:
We use data on venture capital-backed startups as their venture capital investment provides a direct performance measure to identify their clusters. However, other entrepreneurial firms may have clusters that influence clusters of venture-backed firms. In particular, pre-venture capital startups, firms that have experienced an acquisition or an initial public offering, and high-growth startups that do not raise venture capital (see Guzman and Stern, 2015, and related papers) may all affect the agglomeration of venture-backed startups. We leave it to future research to examine the relationships between clusters of different types of startups.
A two-sentence version:
We use data on venture capital-backed startups as their venture capital investment provides a direct performance measure to identify their clusters. However, we expect that other entrepreneurial firms, such as those identified from business registration data in Guzman and Stern (2015), may have a second-order effect on the clustering of venture-backed firms.
A hard-and-fast two-sentence version, with a one-sentence pillow:
Guzman and Stern (2015) use business registration to find non-venture-backed startups. We do not use this data as it does not have a performance measure to demonstrate the representative layer's selection. However, understanding the relationships between clusters of different types of startups is an exciting topic for future research.
The objective:
"I think less is more. We just want to try to immunize ourselves against a referee who thinks we might be unaware of that data. We need a sentence saying it exists and another sentence saying why we don’t use it. And citing one source is probably enough."
We should cite their Science paper:
Guzman, Jorge and Stern, Scott (2015), "Where is Silicon Valley?", Science, Vol. 347, No.6222, pp. 606-609, American Association for the Advancement of Science
Bibtex:
@article {Guzman606,
author = {Guzman, Jorge and Stern, Scott},
title = {Where is Silicon Valley?},
volume = {347},
number = {6222},
pages = {606--609},
year = {2015},
doi = {10.1126/science.aaa0201},
publisher = {American Association for the Advancement of Science},
issn = {0036-8075},
URL = {https://science.sciencemag.org/content/347/6222/606},
eprint = {https://science.sciencemag.org/content/347/6222/606.full.pdf},
journal = {Science}
}
=====Previous attempts to include Guzman and Stern=====
From version 8:
Finally, Guzman and Stern (2015a,b) and related papers use business registration data to suggest that non-venture high-growth, high-technology startups outnumber their venturebacked counterparts by around three or four to one. These papers map and analyze nonventure startup activity within states, cities, zip codes, and for an individual street. A natural extension to this paper would consider agglomeration economies of non-venture startups, as well as their effects on the agglomeration of venture-backed firms.
From version 4.1:
Catalini et al. (2019) suggest that there are high-growth, high-technology startups that are not supported by venture capital. These firms may have been turned down by venture capitalists, may never approach venture capitalists, or may not have had their venture investment recorded at the time the authors conducted their study. Regardless, according to Catalini et al. (2019), non-venture HGHT startups outnumber venture-backed firms by around three or four to one. One obvious question is then whether they enhance or detract from agglomeration economies among venture-backed startups.
A natural extension of this paper would consider the impact of these firms. Catalini et al. (2019) derives its data from the processing of business registration data, which is explored for Massachusetts in Guzman & Stern (2015a), California in Guzman & Stern (2015b), firms leaving Delaware in Guzman (2017), and 15 U.S. states in Guzman & Stern (2016). This data provides full addresses, and so readily lends itself to Geographic Information System mapping, as well as a hierarchical cluster analysis decomposition to create microgeographies.
=====Comments from the Associate Editor and two reviewers at Management Science=====
Associate Editor:
Multiple referees recommend supplementing your analyses with additional datasets. An expensive but informative path would be the National Establishment Time Series, which has street addresses for every business that reports data to Dun & Bradstreet; this would solve your longitudinal address problem. Alternatively, at no cost you can take advantage of Guzman & Stern’s Startup Cartography project data, which provides counts of the high-potential startups you are probably trying to capture with VX (and also see high-potential startups that don’t raise VC).
Reviewer 2:
First, the analysis only considers startup ventures which have already received venture funding—an important component of the ecosystem, for sure, but not necessarily the most important for agglomeration purposes. This results in an incomplete and possibly distorted picture of what agglomeration actually means. if a district has 50 startups that have not yet received VC funding and one that has, would that qualify as agglomeration? And what about companies that have been acquired or have gone public? At a minimum, a more accurate count of active ventures would be required to draw any legitimate conclusions about the effects of agglomeration, along the lines of what Andrews, Fazio, Guzman, Liu, & Stern (2019) have done with the Startup Cartography Project, especially if it is true that “non-venture high-growth, high-technology startups outnumber their venture-backed counterparts by around three or four to one” (p. 5).
Reviewer 4:
The authors note the challenges of using VC database addresses. This needs to be more closely considered given the persistence/agglomeration application currently contemplated. If ala Guzman’s work, you have a venture being ported to Redwood City for a big financing round and you also assign all prior rounds to Redwood City, you create quite a biased sample to extra growth around winner places.
===Back and Forth===
