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Urban Start-up Agglomeration and Venture Capital Investment (view source)
Revision as of 11:24, 10 August 2022
, 11:24, 10 August 2022no edit summary
This paper is published as:
[[Delineating Spatial Agglomerations|Egan, Edward J. and James A. Brander (2022), "New Method for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups.", Journal of Economic Geography, Manuscript: JOEG-2020-449.R2, forthcoming.]]
{{AcademicPaper
|Has title=Urban Start-up Agglomeration and Venture Capital Investment
|Has author=Ed Egan,Jim Brander
|Has RAs=Peter Jalbert, Jake Silberman, Christy Warden, Jeemin Sim
|Has paper status=Working paperPublished
}}
=New Submission=
A revised version of the paper, now co-authored with [[Jim Brander]] and based on the version 3 rebuild, was submitted to the Journal of Economic Geography. This is solely a methods paper, and is titled: '''A New Method for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups'''. The policy application would need to be written up as a separate paper.
==Acceptance==
On July 5th 2022, the paper was accepted to the Journal of Economic Geography:
* Manuscript ID JOEG-2020-449.R2
* Title: A New Method for Identifying and Delineating Spatial Agglomerations with Application to Clusters of Venture-Backed Startups
* Author(s): Edward J. Egan and James A. Brander.
* Editor: Bill Kerr, HBS: wkerr@hbs.edu
* Abstract:
This paper advances a new approach using hierarchical cluster analysis (HCA) for identifying and
delineating spatial agglomerations and applies it to venture-backed startups. HCA identifies
nested clusters at varying aggregation levels. We describe two methods for selecting a
particular aggregation level and the associated agglomerations. The “elbow method” relies
entirely on geographic information. Our preferred method, the “regression method”, also uses
venture capital investment data and identifies finer agglomerations, often the size of a small
neighborhood. We use heat maps to illustrate how agglomerations evolve and indicate how our
methods can aid in evaluating agglomeration support policies.
* Permanent link for code/data: https://www.edegan.com/wiki/Delineating_Spatial_Agglomerations
The paper is now in production. I will build a wiki page called [[Delineating_Spatial_Agglomerations]] that structures the documentation of the build process and shares code and some data or artifacts. Currently, that page redirects here.
== R&R ==
Files:
*Pdf: [[File:Egan Brander (2020) - A New Method for Identifying and Delineating Spatial Agglomerations (Submitted to JEG).pdf]]
*In E:\projects\agglomeration**Last document was Agglomeration Dec 15.docx**Build is Version 3-6-2-2. ** SQL file is: AgglomerationVcdb4.sql After some inquiries, we heard from Bill Kerr, the associate editor, that the paper had new reviews on Aug 11th. On Aug 23rd, we recieved an email titled "Journal of Economic Geography - Decision on Manuscript ID JOEG-2020-449" giving us an R&R. Overall, the R&R is very positive. Bill's comments:* Referees aligned on central issue of Census places* Too short: Wants application and suggests ("not contractual requirements"):** Diversity within and between in terms of types of VC investment (e.g., Biotech vs. ICT in Waltham)** Patent citiations made between VC backed firms Reviewer 1's comments (excluding minor things):* Explain projection (should have said it was WGS1984)* Starting units: Suggests MSA level. Suppose cities that are close... can we find cases?* Identify clusters that have grown over time* Maybe try a cluster-level analysis* Is ruling out the first second-difference too limiting? Can a city be a cluster? (Vegas, baby?, Or starting from CMSA, probably yes in some sense.)* Discuss cluster boundaries (they aren't hard and fast: "think of these clusters as the kernels or seeds of VC-backedstartup hotspots")
Reviewer 2's comments (excluding minor things):* Starting Units. Suggests MSA. * Explain R2 method better. He didn't say try cluster-level but that might be helpful to him too.* Change language (back) to microgeographies! (or startup neighborhoods). * Tighter connection to lit. He gives papers to start.* Discuss overlap of clusters (a la patent clustering). Check findings in Kerr and Kominers!!!* Discuss counterfactuals/cause-and-effect/application etc. Show/discuss that we didn't just find office parks. <pdf>File:JOEG1RndReviews.pdf</pdf> ===Notes for further improvement===
We might want to add some things in/back in. These include technical notes:
*We could think about commercial applications. Perhaps locating plants/facilities that are/aren't in clusters with a view to buying or selling them?
=Previous SSRN versionof the paper (uses v2 build)=
There are two 'final' papers based on the version 2 build. The one with Houston narrative as the motivation is available from SSRN: https://papers.ssrn.com/abstract=3537162
===Another round of refinements===
#The elbow method is pretty questionable has issues in its current form, so we are going to try using the elbow in the curvature (degree of concavity) instead.
#We might also try using elasticities...
#Rerun the distance calculations -- avghulldisthm and avgdisthm are only computed for layers that we select with some method (like max r2). However, this table hadn't been updated for the elbow method, perhaps as well as some other methods, so some distances would have been missing (and replaced with zeros in the STATA script).
=Old Work Using Circles=
See: [[Enclosing Circle Algorithm]]
==Very Old Summary==
