Difference between revisions of "The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment"

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{{McNair Projects
+
{{AcademicPaper
|Project Title=Hubs(Academic Paper)
+
|Has title=The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment
|Topic Area=Entrepreneurship Ecosystems
+
|Has author=Ed Egan, Yael Hochberg
|Owner=Todd Rachowin, Ariel Sun
+
|Has RAs=Hira Farooqi,
|Start Term=Spring 2016
+
|Has paper status=Tabled
|Status=Active
 
|Deliverable=Academic Paper
 
|Audience=Academics
 
|Keywords=Hubs, Incubators, Accelerators, Venture, Capital, Angel, Investor, Startups
 
|Primary Billing=AccNBER01
 
 
}}
 
}}
 
 
=Hubs Pages=
 
=Hubs Pages=
*The main page for Hubs can be found: [[Hubs (Academic Paper)]]
+
*This page [[Hubs (Academic Paper)]] contains only the abstract and some useful refs
*For the current work in progress for building the Hubs datasheet for the scorecard go to: [[Hubs: Hubs Scorecard]]
+
*The main [[Hubs]] page is the place to go!
*For a tracker of work in progress for the dataset building for the scorecard go to [[Hubs: Hubs Data Building]]
+
*There is also [[Old Completed Work on Hubs]]
*For a high-level overview of the variables for the scorecard go to [[Hubs: Hubs Data]]
+
*For a high-level overview of the variables for the scorecard go to [[Hubs Scorecard (Academic Paper)]]. This summarizes:
 +
**Current work in progress for building the Hubs scorecard: [[Hubs: Hubs Scorecard]]
 +
**Tracking of work in progress for the scorecard [[Hubs: Hubs Data Building]]
  
=Abstract=
 
  
The Hubs Research Project is a full-length academic paper analyzing the effectiveness of "hubs", a component of the entrepreneurship ecosystem, in the advancement and growth of entrepreneurial success in a metropolitan area.
+
==Abstract==
  
This research will primarily be focused on large and mid-sized Metropolitan Statistical Areas (MSAs), as that is where the greater majority of Venture Capital funding is located.
+
Entrepreneurship hubs have recently emerged as a stable institutional form and as popular and important components of entrepreneurship ecosystems. Hubs are membership-based co-working flex-spaces with specialized services and resources for nascent start-up firms. Examples of hubs include the Capital Factory in Austin, Texas, 1871 in Chicago, Illinois, and 1776 in Philadelphia, Pennsylvania. Each of these hubs has around 50,000sqft of workspace for almost a thousand members working at hundreds of start-ups. Each also includes an accelerator program, has daily events, classes and meetings related to entrepreneurship, and hosts venture capitalists, angel investors, and service firms.
 +
Hubs provide a very high degree of agglomeration. Agglomeration is particularly important in entrepreneurship because it facilitates learning and failure is frequent. Entrepreneurs can then learn from other entrepreneurs as well as industry professionals; and when a start-up based in a hub fails, the firm’s human resources can be quickly and efficiently absorbed into another venture. We might therefore expect that the introduction of a hub will lead to a greater degree of entrepreneurial activity in a region.
 +
This paper will use a difference-in-difference approach to estimate the effect of the introduction of a hub on seed and early stage venture capital investment in an area. The empirical methodology of the paper is closely aligned with the methodology in Fedher and Hochberg (2015). The decision of a hub to locate itself in an area is expected to be highly correlated with existing characteristics of the area, unobserved in the data, which induces a significant endogeneity bias in the model. To rectify this issue the methodology proceeds in two steps. In the first step, a hazard model is estimated which predicts the probability that a hub will come to an area. In the second stage these predicted probabilities are used to find a match for each treated region by finding the untreated region with the most similar probability of founding an accelerator in that year when the treated region is on the common support.
  
A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].
+
==Current Work==
  
 +
===General Overview===
  
=Current Work=
 
==General Overview==
 
 
Currently there are '''3''' major tasks being performed (list to be updated):
 
Currently there are '''3''' major tasks being performed (list to be updated):
#'''Creation of VC data table''': '''UPDATE: Complete''' (see completed work)
+
#'''Creation of VC data table''': '''UPDATE: Complete''' (see completed work section below)
#'''Creation of Hubs Dataset''': '''UPDATE: See current work in progress for updates''' As of Spring 2016, a list of potential Hubs with a set of characteristics was created.  Many of these are not what will be defined as Hubs.  We will be creating a scorecard to help subjectively define Hubs based on certain characteristics. To do so:
+
#'''Creation of Hubs Dataset''': '''UPDATE: See current work in progress for updates''' We will collect key variables for potential Hubs.
##We will determine variables we would like to use for scorecard
 
##Create a process via Mechanical Turk to streamline the updating of the list
 
 
#'''Hazard Rate Model''': '''UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates.''' In order to perform our diff-diff model, we need to match MSAs.  In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.
 
#'''Hazard Rate Model''': '''UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates.''' In order to perform our diff-diff model, we need to match MSAs.  In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.
  
==Work In Progress==
 
Note: After Week of 7/11, work can be found at: [[Hubs: Hubs Scorecard]]
 
  
===Hubs Data===
+
==Resources==
'''(Weeks post Week of 7/11)'''
 
 
 
See [[Hubs: Hubs Scorecard]]
 
 
 
 
 
'''(Week of 7/11)'''
 
 
 
1) We published the twitter count on mechanical turk and received results.
 
 
 
2) We have audited the results and updated the amazon.
 
 
 
3) We are creating additional potential turks on the amazon site (See [[Hubs: Hubs Scorecard]])
 
 
 
4) We are finding more potential hubs from members of international national business innovation association
 
 
 
 
 
'''(Week of 7/4)'''
 
 
 
1) We have created the list and commented our thoughts after ---. For determining the variables, we have separated the list into two parts: a list of desired variables and ones that were previously collected, many of which are desired variables.
 
 
 
2) We have also created an example of how to write mechanical turks for collecting certain variables
 
 
 
=Completed Work=
 
==Venture Capital Data General Overview==
 
The main goal of the data set is to aggregate company, fund, and round level data to be analyzed at a combined MSA and year level. The data set is compromised of two major parts: a granular company/fund/round and an aggregated CMSA-Year.  The data includes all United States Venture Capital transactions (moneytree) from the twenty-five year period of 1990 through 2015.
 
 
 
The Hubs data set, from SDC Platinum, has been constructed in the server:
 
Data files are in 128.42.44.181/bulk/Hubs
 
All files are in 128.42.44.182/bulk/Projects/Hubs
 
psql Hubs2
 
 
 
See the server for the code and ~1st 5 rows of each table
 
 
 
===Procedure - Granular Table===
 
#Start with separate raw datasets for Companies, Funds, and Rounds
 
#Add Data to Each Individual dataset (e.g. add MSA code)
 
#Clean and standardize names (e.g. company or fund name) for each dataset
 
#Join the Datasets (here we need to exclude undisclosed companies)
 
 
 
===Procedure - CMSA-Year Table===
 
#Create a consistent CMSA-Year table to be used later
 
#Using the tables from the granular table, parse out the right data
 
#Join the parsed out data with the CMSA-Year Table
 
#Join these Tables
 
 
 
==VC Specific Tables and Procedure==
 
===Raw data tables===
 
#'''Funds''': fund name, first investment date, last investment date, fund closing date, address, known investment, average investment, number of companies invested, MSA, MSA code.
 
#'''Rounds''': round date, company name, state, round number, stage 1, stage 2, stage 3
 
#'''Combined Rounds''': company name, round date, disclosed amount, investor
 
#'''Companies''': company name, first investment, last investment, MSA, MSA code, address, state, date founded, known funding, industry
 
#'''MSA List''': MSA, MSA code, CMSA, CMSA code
 
#'''Industry List''': changes 6 industry categories to 4— ICT, Life Sciences, Semiconductors, Other
 
 
 
 
 
===Granular Table (Fund-Round-Company)===
 
The final table here contains all venture capital transactions by disclosed funds and portfolio companies, together with their CMSAs.
 
To get the table, we processed the raw data sets in the following steps:
 
#Clean '''Company''' data
 
##Import raw data companies
 
##Add variable 'CMSA' from data set MSA list, update variable 'industry' by joining data set industry list
 
##Remove duplicates and remove undisclosed companies
 
#Clean '''Fund''' data
 
##Import raw data funds
 
##Add variable 'CMSA'
 
##Remove duplicates and remove undisclosed funds
 
##Match fund names with itself using [The Matcher (Tool) |The Matcher] to get the standard fund names
 
#Clean '''Round''' data
 
##Import raw data rounds and combined rounds
 
##Add variables 'number of investment', 'estimated investment' and 'year'
 
##Remove duplicates and remove undisclosed funds
 
#'''Combine''' '''Companies''' and '''Rounds'''
 
##Combine cleaned companies and rounds data table on company names
 
##Add variable 'round number' and 'stage'
 
##Remove duplicates
 
#'''Combine''' '''Funds''' and '''rounds-companies'''
 
##Match fund names in rounds data table with standard fund names using [The Matcher (Tool) |The Matcher] to standardize fund names in rounds data table
 
##Join standard fund names to rounds-companies table
 
##Join cleaned funds table to rounds-companies table on standard fund names
 
 
 
 
 
===CMSA-Year Aggregated Table===
 
The final table contains number of companies and amount of investment, categorized by distance and stages, of each CMSA.
 
 
 
We processed data as follows:
 
#Create the '''CMSA-Year''' Table
 
##Create single variable tables: Distinct CMSA, year, stage, found year of fund and found year of company.
 
##Create the cross production tables: CMSA-year, CMSA-year-fund year founded and CMSA-year-company year founded
 
#Draw data from cleaned companies, funds and rounds tables
 
##Create a table with 'CMSA', 'number of companies' and 'year Founded' from cleaned companies table and join it to CMSA -year founded
 
##Create a table with 'Company CMSA', 'round year', 'disclosed amount' from rounds-companies combined table, and add stage binary variables. Join it to CMSA-year-company year founded
 
##Create a table with 'CMSA', 'fund year', 'number of investors' from cleaned funds table and join it to CMSA-year-fund year founded
 
#Create '''near-far''' and stages table
 
##Add fund data to rounds-companies
 
##Create near-far and stages binary variable
 
##Count investment and deals by CMSA and year, categorized by near-far and stages
 
#Combine all tables by CMSA and round-year
 
 
 
==Supplementary Data Sets==
 
 
 
Supplementary data sets are cleaned and joined back to CMSAyear table on CMSA and year:
 
 
 
#Number of STEM graduate student, by university and year(2005 to 2014).
 
#University R&D spending, by university and year(2004 to 2014).
 
#Income per capital, by MSA and year(2000 to 2012)
 
#Wages and salaries, by MSA and year(2000 to 2012)
 
 
 
 
 
The datasets can respectively be found at:
 
E:\McNair\Projects\Hubs\STEM grads for upload v2.xls
 
E:\McNair\Projects\Hubs\NSF spending for upload.xls
 
E:\McNair\Projects\Hubs\Income per capita upload.xls
 
E:\McNair\Projects\Hubs\Wage for upload v2.xls
 
 
 
=Resources=
 
  
 
===Additional Resources===
 
===Additional Resources===
 +
* A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].
 
* Yael Hochberg and Fehder (2015), located in dropbox
 
* Yael Hochberg and Fehder (2015), located in dropbox
 
** Use this paper as a guideline on how to conduct the analysis
 
** Use this paper as a guideline on how to conduct the analysis
Line 161: Line 39:
 
*USPTO utility patents by MSA: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd.htm
 
*USPTO utility patents by MSA: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd.htm
 
*MSA level trends: http://www.metrotrends.org/data.cf
 
*MSA level trends: http://www.metrotrends.org/data.cf
 
 
 
 
<includeonly>
 
[[Category: McNair Projects]]
 
</includeonly><!-- flush flush --><!-- flush flush --><!-- flush flush -->
 

Latest revision as of 10:56, 18 March 2019

Academic Paper
Title The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment
Author Ed Egan, Yael Hochberg
RAs Hira Farooqi
Status Tabled
© edegan.com, 2016

Hubs Pages


Abstract

Entrepreneurship hubs have recently emerged as a stable institutional form and as popular and important components of entrepreneurship ecosystems. Hubs are membership-based co-working flex-spaces with specialized services and resources for nascent start-up firms. Examples of hubs include the Capital Factory in Austin, Texas, 1871 in Chicago, Illinois, and 1776 in Philadelphia, Pennsylvania. Each of these hubs has around 50,000sqft of workspace for almost a thousand members working at hundreds of start-ups. Each also includes an accelerator program, has daily events, classes and meetings related to entrepreneurship, and hosts venture capitalists, angel investors, and service firms. Hubs provide a very high degree of agglomeration. Agglomeration is particularly important in entrepreneurship because it facilitates learning and failure is frequent. Entrepreneurs can then learn from other entrepreneurs as well as industry professionals; and when a start-up based in a hub fails, the firm’s human resources can be quickly and efficiently absorbed into another venture. We might therefore expect that the introduction of a hub will lead to a greater degree of entrepreneurial activity in a region. This paper will use a difference-in-difference approach to estimate the effect of the introduction of a hub on seed and early stage venture capital investment in an area. The empirical methodology of the paper is closely aligned with the methodology in Fedher and Hochberg (2015). The decision of a hub to locate itself in an area is expected to be highly correlated with existing characteristics of the area, unobserved in the data, which induces a significant endogeneity bias in the model. To rectify this issue the methodology proceeds in two steps. In the first step, a hazard model is estimated which predicts the probability that a hub will come to an area. In the second stage these predicted probabilities are used to find a match for each treated region by finding the untreated region with the most similar probability of founding an accelerator in that year when the treated region is on the common support.

Current Work

General Overview

Currently there are 3 major tasks being performed (list to be updated):

  1. Creation of VC data table: UPDATE: Complete (see completed work section below)
  2. Creation of Hubs Dataset: UPDATE: See current work in progress for updates We will collect key variables for potential Hubs.
  3. Hazard Rate Model: UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates. In order to perform our diff-diff model, we need to match MSAs. In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.


Resources

Additional Resources