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=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==
A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].==Current Work==
=Current Work=
==General Overview==
==Work In Progress==
Note: After Week of 7/11, work can be found at: [[Hubs: Hubs Scorecard]]
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[[Category: McNair Projects]]
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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
}}
=Hubs Pages=
*The main This page for Hubs can be found: [[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 current work in progress for building the Hubs datasheet variables for the scorecard go to: [[Hubs: Hubs Scorecard(Academic Paper)]]. This summarizes:*For a tracker of *Current work in progress for the dataset building for the Hubs scorecard go to : [[Hubs: Hubs Data BuildingScorecard]]*For a high-level overview *Tracking of the variables work in progress for the scorecard go to [[Hubs: Hubs DataBuilding]]
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 research paper will primarily be focused use a difference-in-difference approach to estimate the effect of the introduction of a hub on large seed and mid-sized Metropolitan Statistical Areas early stage venture capital investment in an area. The empirical methodology of the paper is closely aligned with the methodology in Fedher and Hochberg (MSAs2015). 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, as 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 is where 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 greater majority most similar probability of Venture Capital funding founding an accelerator in that year when the treated region is locatedon the common support.
===General Overview===
Currently there are '''3''' major tasks being performed (list to be updated):
#'''Creation of VC data table''': '''UPDATE: Complete''' (see completed worksection 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 collect key variables for potential Hubs based on certain characteristics. To do so:##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.
==Resources=Hubs Data==='''(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===
* A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].
* Yael Hochberg and Fehder (2015), located in dropbox
** Use this paper as a guideline on how to conduct the analysis
*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
