7,613
edits
Changes
Jump to navigation
Jump to search
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.
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.==Abstract==
=Data===Venture 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 Transactions Data Set==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.The main goal Hubs provide a very high degree of the data set agglomeration. Agglomeration is particularly important in entrepreneurship because it facilitates learning and failure is to aggregate company, fundfrequent. 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 round level data 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 be analyzed at estimate the effect of the introduction of a combined MSA 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 year levelHochberg (2015). The data set decision of a hub to locate itself in an area is compromised 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 major parts: steps. In the first step, a granular company/fund/round and hazard model is estimated which predicts the probability that a hub will come to an aggregated CMSA-Yeararea. The data includes all United States Venture Capital transactions (moneytree) from In the second stage these predicted probabilities are used to find a match for each treated region by finding the untreated region with the twenty-five most similar probability of founding an accelerator in that year period of 1990 through 2015when the treated region is on the common support.
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==Current Work==
Please note that this dataset is currently being constructed and has not been completely uploaded yet.===General Overview===
===General Procedure - Granular Table===Currently there are '''3''' major tasks being performed (list to be updated):#Start with separate raw datasets for Companies, Funds, and Rounds '''Creation of VC data table''': '''UPDATE: Complete''' (see completed work section below)#Add Data to Each Individual dataset (e'''Creation of Hubs Dataset''': '''UPDATE: See current work in progress for updates''' We will collect key variables for potential Hubs.g. add MSA code)#Clean and standardize names '''Hazard Rate Model''': '''UPDATE: (e.g. company or fund name7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for each dataset#Join the Datasets (here appropriate proportional rate hazard models with time varying covariates.''' In order to perform our diff-diff model, we need to exclude undisclosed companies)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.
===General Procedure - Granular 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
==Tables and Specific Procedure Used==
===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).
E:\McNair\Projects\Hubs\STEM grads for upload v2.xls
*University R&D spending, by university and year(2004 to 2014).
E:\McNair\Projects\Hubs\NSF spending for upload.xls
*Income per capital, by MSA and year(2000 to 2012)
E:\McNair\Projects\Hubs\Income per capita upload.xls
*Wages and salaries, by MSA and year(2000 to 2012)
E:\McNair\Projects\Hubs\Wage for upload v2.xls
The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment (view source)
Revision as of 10:56, 18 March 2019
, 10:56, 18 March 2019no edit summary
{{McNair ProjectsAcademicPaper|Project TitleHas title=The Impact of Entrepreneurship Hubs(Academic Paper)|Topic Area=Entrepreneurship Ecosystemson Urban Venture Capital Investment|OwnerHas author=Todd RachowinEd Egan, Ariel Sun|Start Term=Spring 2016|Status=Active|Deliverable=Academic PaperYael Hochberg|AudienceHas RAs=Academics|Keywords=Hubs, Incubators, Accelerators, Venture, Capital, Angel, InvestorHira Farooqi, Startups|Primary BillingHas paper status=AccNBER01Tabled
}}
=AbstractHubs Pages=*This page [[Hubs (Academic Paper)]] contains only the abstract and some useful refs*The main [[Hubs]] page is the place to go!*There is also [[Old Completed Work on Hubs]]*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]]
==Resources==
===Additional Resources===* A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].* Yael Hochberg and Fehder (2015), located in dropbox<includeonly>** Use this paper as a guideline on how to conduct the analysis[[Category*US Census Bureau data on employment by MSA: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_14_5YR_B23027&prodType=table*USPTO utility patents by MSA: http: McNair Projects]]//www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd.htm<*MSA level trends: http://www.metrotrends.org/includeonly><!-- flush -->data.cf
