Urban Start-up Agglomeration and Venture Capital Investment

Academic Paper
Title	Urban Start-up Agglomeration
Author	Ed Egan
RAs	Peter Jalbert, Jake Silberman, Christy Warden
Status	In development
© edegan.com, 2016

Summary

Agglomeration is generally thought to be one of the most important determinants of growth for urban entrepreneurship ecosystems. However, there is essentially no empirical evidence to support this. This paper takes advantage of geocoding and introduces a novel measure of agglomeration. This measure is the smallest circle area that covers all startup offices, subject to having at least N startups in each circle. Using GIS data on cities, this paper controls for the density and socio-demographics of an area to identify the effect of just agglomeration.

Description

Clusters of economic activity plays a significant role in the firms performance and growth. An important driver of growth is the knowledge spillover between firms. This includes among others the facilitation of information flow and ideas between firms which could be a milestone especially in the growth of startup firms or small businesses. This project focuses on the effects of agglomeration on the performance and growth of startup firms. It introduces a novel measure of agglomeration which can be used to empirically test the effects of clustering. This measure the is smallest total circle area that covers all of the startups in the sample such that there are at least n firms in each circle. The projects is based on the creation of an algorithm which gives an unbiased measure to be used in the empirical analysis. The regression we are interested in takes the following form:

The dependent variable is a measure of growth of the firms. This measure could be investment forwarded one period or growth in investment. The control variables include the number of the startups firms, m, the agglomeration measure, A and a vector of other control variables affecting the growth of firms at time t. Because of the endogeneity in the circle area or the measure of agglomeration, A, there is a need for an instrumental variable to get consistent estimates of the effects we are interested in. The proposed instrument is the presence of a river, or road in between the points representing geographical locations of the venture capital backed up firms. The instrument affects agglomeration without having a direct impact on the growth. This makes it good candidate for a valid instrument. The next tasks are determining the additional control variables to include in the regression, years to include in the analysis and methods of finding an unbiased measure of agglomeration.

Data

• SDC VentureXpert
• GIS City Data

Data on NSF, NIH, population, income, clinical trials, employment, schooling, R&D expenditures and revenue of firms can be found in Hubs. Data on the number of new vc backed firms in each city and year is in:

Z:\Hubs\2017\clean data

The name of the file is firm_nr.txt. Database is cities SQL script is: nr_firms.sql

Data on the circle area in each city and year is in:

Z:\Hubs\2017\clean data

The name of the file is circles.txt. Database is cities SQL script is: circles.sql

The script for joining the two tables on the VC table is in:

Z:\Hubs\2017\sql scripts

The name of the file is new_firm_nr_circles.sql

Also:

• Enclosing Circle Algorithm
• Normalizer
• Geocode.py

Unbiased measure

The number of startups affects the total area of the circles according to some function. The task is to find an unbiased measure of the area, which is not affected by the number of the startups, given the size and their distribution.

For the unbiased calculation of a measure in a different context see: http://users.nber.org/~edegan/w/images/d/d0/Hall_(2005)_-_A_Note_On_The_Bias_In_Herfindahl_Type_Measures_Based_On_Count_Data.pdf

GIS Resources

• https://www.census.gov/geo/maps-data/data/tiger-line.html
• https://www.census.gov/geo/maps-data/data/tiger.html
• http://postgis.net/features/
• https://en.wikipedia.org/wiki/GIS_file_formats