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This research will primarily focused on large and mid-sized Metropolitan Statistical Areas (MSAs), as that is where the greater majority of Venture Capital funding is located.
===Primary Data Set===The Hubs data setResearch Project is a full-length academic paper analyzing the effectiveness of "hubs", from SDC Platinuma component of the entrepreneurship ecosystem, has been constructed in the server: Data files are advancement and growth of entrepreneurial success in 128.42.44a metropolitan area.181/bulk/Hubs All files are It focuses on cities in 128the United States as the primary unit of analysis.42.44.182/bulk/Projects/Ecosystem/Hubs psql Hubs
The data set includes all United States Venture Capital transactions (moneytree) from the twenty-five year period of 1990 through 2015.
Data has been aggregated at the portfolio company, fund, and round level. It will be analyzed at the combined MSA level. We will be looking at in terms of number of companies funded in number of funds active, and flow of investment in a given MSA.
The Information on initial data set has now been uploaded work done prior to the database server, named Summer 2017 can be found at [[HubsSummer 2016]].There are 4 tables: *Rounds: Rounddate, coname, state, roundno, stage1, etc.*CombinedRounds: Coname, rounddate, discamount, fundname*Companies: LastInv, FirstInv, coname, MSA, MSACode, Address, state, datefounded, totalknownfunding, industry(major)*Funds: fundname, closingdate, lastinv, firstinv, msa, msacode, avinv, nocoinv, totalknowninv, address
Used variables'''Note on joining:''' The city-state-year ID from VC data is used as the master ID for joining datasets. Each table (e.g. income, nih, nsf, sbir, compustat) is first joined with the VC data on city-state-year ID and then the resulting tables are all joined together in the final table.
Companies: Coname, MSACode, Industry, state
MSALookupTable: MSACode, MSASuper
IndustryLookupTable: IndustryMajor, InduCode
->
CompanyInfo: Coname, MSASuper, InduCode, state (complete)
Rounds: coname, rounddate, stagecode, roundno CombinedRounds: coname, rounddate, discamount, fundname -> RoundInfoSuper: coname, rounddate, Database is '''nofundscities''', discamount -> RoundInfoSQL script is: Coname, roundyear, fundname, estamount (complete)merged_income.sql
Then take:
RoundInfo: Coname, roundyear, fundname, estamount
CompanyInfo: Coname, MSASuper, InduCode, state
FundInfo: fundname, msacode, state
->
SuperRoundInfo: Coname, CoMSASuper, CoInduCode, CoState, FundName, FundMSASuper, FundState, RoundYear, RoundEstAmount
->
MSAPortCos: Count(CoName) As NoPortCosFunded, CoMSASuper, RoundYear
...
'''Notes on Creation of Primary Data Set'''They contain:*City*State*city_state_id to uniquely identify each city*Income*Year*Code from the state code and Fips code
Raw tables* companies (last investment, first investment, company name, MSA, MSA code, address, state, date founded, known funding, industry) * funds (fund closing date, last investment, first investment, fund name, address, MSA, MSA code, Average investment, number companies invested (NoCos), known investment) * rounds (round date, company name, state, round number, stage 1, stage 2, stage 3) * combined rounds (company name, round date, disclosed amount, investor) * msalist (changes MSAs to CMSAs— combined MSAs)*industry list (changes 6 industry categories to 4— ICT, Life Sciences, Semiconductors, Other) ==Employment Data==
Process*cleaned tables to eliminate duplicationsData on employment was obtained from American Communities Survey, undisclosed variables*changed all original characters to include CMSA and Industry Codes (companyinfo3, fundinfocleanfinal, roundinfoclean) *matched funds to avoid any issues with names (i.e. Fund ABC L.P./Fund ABC LP/Fund ABC) *matched roundinfoclean investors to fundinfocleanfinal investors (roundinfo.txt >> cleanfundfinal.txt)*join by round and company conames*bridge years (1990-2016), stage, and cmsa* populate data with count of companies (Deal flow) and estimated amount ($)** data set in 181 hubs folder under summarycmsaUS Census Bureau.txt (38394)
Key decisionsRaw Data is in: E:\McNair\Projects\Hubs\Summer 2017\Employment Data by MSACleaned files are in Z:\Hubs\2017\clean data They contain:*Threw out undisclosed co through-out as no addressMSA code*MSA*Count is done by joining round and companyYear *Anything fund related must be disclosed fundEmployment rate of individuals 16 years or older*Near and far, and total invested, and fund counts, etc., are all done using disclosed funds that match onlyUnemployment rate of individuals 16 years or older
'''Glossary of Tables''' cleanco — used to remove duplicates The SQL file that merges employment data from companies cleanedcompanies — clean set of companies with no duplicates cmsafunds- cmsas— list of all CMSAs in final data set (for merging) cmsastats- statistics not including empty years ACS (pre-merge) cmsastats2 - statistics separated by year-MSA cmsastats3— statistics separated by year-Year) with the MSA-stage cmsastats4 cmsayears— empty merged table between year and cmsa cmsayearstage — empty merged table between cmsa/years and stage combinedrounds— raw sdc data for combined rounds combinedroundswamt— used to join rounds and combined rounds for roundinfo2 companies- raw SDC company data companyinfo — cleaned companies joined with state and CMSA information companyinfo2— companyinfo1 with original industry categories companyinfo3— companyinfo2 with updated industry categories and codes companyinfo4-- clean version of companyinfo3 companyround- combined company information with round information companyround2- combined company information with round information, cleaned up from companyround2 companyround3- combined company information with round information, cleaned up from companyround3 City file is titled '''finaldatasetEmployment.sql'''- final statistics by CMSA-year, see section Final Primary Data Set for more information fundinfo— funds joined with CMSA info. fundinfo2 - clean version of fundinfo1 fundinfoclean - used The file is located in process to clean fundinfo2: fundinfoclean2- used in process to clean fundinfo2 fundinfocleanfinal- used in process to clean fundinfo2 fundinfocleannodups- final clean set of fundinfo funds - raw SDC fund data Houston - analysis for Houston ecosystem team Houston2- analysis for Houston ecosystem team houston3- analysis for Houston ecosystem team industry — new industry codes (4)— used for all future data sets industrylist— lookup table for new industry codes (went from 6 to 4) joined1- used for matching process joined2- used for matching process matchfund2- used for matching process matchfunds- used for matching process matchroundfund - used for matching process matchroundfund2- used for matching process msalist — lookup table for MSA to CMSA (used for all future data sets) nearfar1-- beginning set before adding nearfar/stage variables nearfar2 -- added binomial variables for near/far and for each of the stages, used to build final dataset roundfund— not used— joined round to fund; drop/ignore roundinfo— round info cleaned up to include number of investors in a syndicate and estimated investment per member of syndicate roundinfo2— roundinfo1 including name of investors/funds roundinfo3— clean version of roundinfo2 roundinfoclean — final clean version of roundinfo3 (final roundinfo table) rounds — raw SDC round data stages — table for merging stage-year-CMSA superinfo — ignore/drop temp - used for matching process years — table for merging stage-year-CMSAZ:\Hubs\2017
===Hub Candidates Data Set===The final table is in db '''cities''' titled '''merged_employment'''.
The Hubs candidate data set is a list of potential hubs found in MSAs throughout the country. Researchers are currently pulling qualitative and quantitative information from the candidate's websites, in an attempt to categorize what can be identified as a hub. This is a difficult data set to pull, as there is little to no quantitative information available for this category of institution, and is dependent on accessibility of information to the public on the internet.It includes:*MSA*City*Year*Employment rate*Unemployment rate
Characteristics/Variables*Year Founded*Square footage*LinkedIN self-identifiers (what the organization classifies itself on its LinkedIN profile) *Activeness on Twitter (binomial)*Member Directory available online (binomial)*Number of conference rooms*Price ($/month) for Flex desk *Offers Reserved desk (binomial)*Offers office space for rent (binomial) *Offers community membership-- not for coworking but for community events, etc. (binomial)*Number of events offered per month (estimate)*Offers code academy*Mission Statement/Vision (for qualitative or key-word analysis) ===Joined employment table===
These characteristics/variables will be used to determine whether a candidate Data is or is not likely to be a Hubin: Z:\Hubs\clean data The file names are: EMP_05.txt - EMP_15. txt
As of March 10th 2016, the list contains 125 Hub candidatesDatabase is '''cities'''SQL script is: '''new_employment.sql''' and it is located in Z:\Hubs\2017\sql scripts
===Supplementary Data Sets===They contain:*City*State'''Patent data''': *Code from the state code and Fips code*city_state_id to uniquely identify each city*city_state_year to be pulled from USPTO uniquely identify each city in each year*Employment rates of individuals of 16 years or older*Unemployment rates of individuals of 16 years or SDC Platinum. older*Year
'''Number of STEM Graduate Students''' (NSF) and '''University R&D Spending''' (NSF):*University R&D ==Schooling Data found under file "NSF DATA_2004 to 2011.xlsx" in datasets folder (Ecosystem>>Hubs>>Datasets)*R&D spending found at the university level for 2014 ("Stem Grad Students.xlsx) or at state level ("Science and Engineering Grad Students by State and Year 2000-2011.csv")** not uploaded to server or matched yet to CMSA code, because of this discrepancy. **"Stem Grad Students.xlsx" contains categorized university by MSA, can be used for all university-based projects==
'''Per Capita Income''' and '''Employment Data''' (on schooling was obtained from American Communities Survey, US Census Bureau): *"Per Capita Personal Income by MSA 2000-2012.xlsx" in datasets folder (Ecosystem>>Hubs>>Datasets>>Data from Yael)*"Wages and Salaries by MSA 2000-2012.xlsx" in datasets folder (Ecosystem>>Hubs>>datasets>>Data from Yael)**not uploaded to server or matched yet to CMSA code
'''Firm Births''' (BDS)Raw Data is in: E:\McNair\Projects\Hubs\Summer 2017\School Enrollment Data by MSACleaned files are in Z:\Hubs\2017\clean data They contain:*MSA code*MSA*in server 181, under table name "BDS"Year *includes birth, death, net(birth-death) Total number of population 3 years and rate(death rate) for years 1990-2013 for every msaover enrolled in school*includes code for CMSA but is not aggregated by CMSAPercent of population 3 years and over enrolled in public school** i.e. BDS statistics are still separate for all the smaller MSAs Percent of population 3 years and over enrolled in New York's CMSA (code=1)private school
===Resources===* Yael Hochberg and Fehder (Date from 2005-2015), located in dropbox** Use this paper as a guideline on how to conduct the analysis*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://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd.htm*MSA level trends: http://www.metrotrends.org/data.cf
===The Target Dataset===SQL file that merges schooling data from ACS (by MSA - Year) with the MSA-City file is titled '''schooling.sql'''. The file is located in: Z:\Hubs\2017
We will need to process the following variables:*SuperMSA - combine SanFran and SanJose, New York and Newark?, NC Research triangle, others?*CSV mapping msas to cmsas The final table is in the folder (and a table in the dbase)db '''cities''' titled '''merged_schooling'''.
Example dataset: MSA Year SeedVCInv SeedEarlyVCInv LaterVCInv NoDeals FundsInvested DistinctInvestors .... ---------------------------------------------------------------------------------------------------------------------------- 1234 2001 1000000 20000000 30000000 4 7 7===Joined schooling table===
Note that the unit of observation Database is MSA-Year'''cities'''SQL script which joins this table with VC table is: '''new_merged_schooling.sql'''The final table is in db '''cities''' titled '''new_merged_schooling'''.
Variables to be computed at the MSA levelIt contains:*HubActive (binary)City*NoHubsActive (Count)State*HubSqFtcity_state_id to uniquely identify each city*Other Hub Vars (build list!!!)city_state_year to uniquely identify each city in each year*'''SeedVCInv''' (Seed/Start-up)Total number of school enrollment*'''EarlyVCInv''' (Early Stage)*'''LaterStageVC''' (Later)*'''OtherStageVC''' (Buyout/Acq, Other)*'''NoDeals''' (done by local VCs?)**'''NoDealsNear'''Percentage enrolled in public schools**'''NoDealsFar'''*NoPortCosFunded*'''FundsInv''' (Percentage enrolled in an MSA)private schools**'''FundsInvFromNear''' (within MSA?)Year**'''FundsInvFromFar''' (outside MSA?)*DistinctInvestors (?)**DistinctInvestorsNear (within MSA?)**DistinctInvestorsFar (outside MSA?)*PatentCount*NoSTEMGrads*FirmBirths (BDS data)*UniRandDSpend*PerCapitaIncome*EmploymentCode from the state code and Fips code
We need to:*Check funds invested means dollars invested*Categorize near and far! Is it within MSA vs. not, within adjacent MSAs, etc.?==VC Data==
There may be a second dataset that has Hub-Industry-Year (where industry is semiconductor/non-semiconductor?).
===Final Primary Raw Data Set===is in: Z:\VentureCapitalData\SDCVCData\vcdb2 The file name is roundleveloutput2.txt
Table name: finaldataset
cmsa
year
totalamountinv--total amount invested
nearamountinv--amount invested from local funds
faramountinv-- amount invested from funds outside CMSA
earlyinv--amount invested in early stage companies
laterinv--amount invested in later stage companies
startupseedinv--amount invested in seed or startup stage companies
otherstageinv--amount invested in Acquisition/Buy-outs/Other stage companies
investingfund--distinct funds that are investing in that CMSA-year
investingfundnear--distinct funds from that CMSA that invested in that CMSA-year
investingfundfar--distinct funds from outside that CMSA that invested in that CMSA-year
deals--number of deals
neardeals--number of deals inside a CMSA
fardeals--number of deals from outside a CMSA --some of these deals might count in both categories, because of syndicate members being both inside and outside the CMSA
earlystagedeals--deals with earlystage companies
laterstagedeals--deals with later stage companies
startupseeddeals--deals with startup/seed companies
otherstagedeals--deals with companies in other stages
newportcosfunded--number of portfolio companies to receive their first investment in that year
===Data by zip code===
*Population data, 2000-2016 - US Census Bureau (E:\McNair\Hubs\summer 2017)
https://www2.census.gov/programs-surveys/popest/datasets/
*Income data, 1998-2014 - The Internal Revenue Service (E:\McNair\Hubs\summer 2017)
https://www.irs.gov/uac/about-irs
*DCI index, to assess the economic well-being of communities
http://eig.org/dci/interactive-maps/u-s-zip-codes
*R&D Expenses, 1980-2016 - Wharton Research Data Services (E:\McNair\Hubs\summer 2017)
*Zipcode look-up table obtained from https://www.unitedstateszipcodes.org/zip-code-database/. It's available in (E:\McNair\Hubs\summer 2017).
== Data by MSA ==[[category:Internal]]
no edit summary
{{Project|Has project output=Data|Has sponsor=McNair ProjectsCenter
|Has title=Hubs
|Has owner=Jake SilbermanHira Farooqi,
|Has keywords=Data
|Has project status=TabledActive|Does subsume=Hubs Analysis 2017,
}}
'''Important Notice: The Hubs Research Project is a full-length academic paper analyzing last update to the effectiveness of "hubs", a component of the entrepreneurship ecosystem, in the advancement data was done manually by Ed and growth of entrepreneurial success is in a metropolitan areaE:\projects\MeasuringHGHTEcosystems\HubsData-RevisedSimplified. xlsx'''
This page contains information about data used for this research project, including data sources, location of data on RDP and details on data processing.
===Data by zip code===*Population data, 2000-2016 - US Census Bureau (E:\McNair\Hubs\summer 2017)https://www2.census.gov/programs-surveys/popest/datasets/*Income data, 1998-2014 - The Internal Revenue Service (E:\McNair\Hubs\summer 2017)https://www.irs.gov/uac/about-irs*DCI index, to assess the economic well-being of communitieshttp://eig.org/dci/interactive-maps/u-s-zip-codes*R&D Expenses, 1980-2016 - Wharton Research Data Services (E:\McNair\Hubs\summer 2017)*Zipcode look-up table obtained from https://www.unitedstateszipcodes.org/zip-code-database/. It's available in (E:\McNair\Hubs\summer 2017). == Data by MSA == We have principle cities of MSAs from the census:https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/delineation-files.html We might be able to go City -> FIPS place code -> MSA? Cities and their FIPS codes (which don't perfectly correspond) are available from https://www.census.gov/geo/reference/codes/place.html The Census claims to provide city to MSA here: https://www.census.gov/geo/maps-data/data/ua_rel_download.htmlHowever, there is only CBSA! This might do it: https://www2.census.gov/geo/pdfs/maps-data/data/rel/explanation_ua_cbsa_rel_10.pdfWe can maybe track city to principal city to MSA ==COMPUSTAT Data==The data set includes information on publicly traded firms in the US. It was obtained from the Wharton Research Data Services (https://wrds-web.wharton.upenn.edu/wrds/index.cfm?). Raw Data is in: E:\McNair\Projects\Hubs\Summer 2017 FundsZ:\Hubs\2017 The source file is RandDExpenditures.txt. It contains: fundname*Date from 1980-2017 (July). *427799 records*Fields include:**R&D Expenditure**Address (inc. city, msacodezip, state)**Revenue of firms Database is '''cities''' SQL script is: COMPUSTAT.sql Output file is COMPUSTATSummary.txt. It contains:*Variables: City, year, No.public firms, sum R&D, sum Sales, sum total assets*1979-2016*4440 cities It is located in Z:\Hubs\2017\Output_Files ==NSF Data==Data is in: E:\McNair\Projects\Hubs\Summer 2017 MSALookupTableZ: MSACode\Hubs\2017 Database is '''cities''' SQL script is: nsf_2017.sql The source files are: nsf2017.txt, copied from table '''nsf''', MSASuper and nsf_institution copied from table '''nsf_grants_institution''' from the biotech db. They contain:*Award ID*Award Institution*Award Effective date*Institution city*Award Value*Organization state codeFrom 1900 - 2017 Output file is nsfSummary.txt. It contains:*Variables: City, State code year, nsf_nogrants, nsf_valuegrant *1900-2017 ===Joined NSF table===The joined nsf table with the VC table is found in db '''cities'''. The table is named '''merged_nsf'''.All the values of nogrants and valuegrant with missing values for years 1990-2017 are set equal to 0.The sql script is in Z:\HUbs\2017\sql scripts ==NIH Data==Data is in: Z:\Hubs E:\McNair\Projects\Hubs\Summer 2017 Database is '''cities'''SQL script is: nih2017.sqlThe source files are: *nih_1986_2001.csv*nih_2002_2012.txt*nih_2013_2015located in E:\McNair\Projects\Federal Grant Data\NIH The script that cleans NIH data and generates the summary table is titled '''nihSummary'''. It is located here: Z:\Hubs\2017\sql scripts This table includes*year*city*state*country*nogrants (number of grants)*valuegrant*city_state *Date from 1986-2015 ===Joined NIH table===The joined NIH table with the VC table is found in db '''cities'''. The table is named '''merged_nih'''.All the values of nih_valuegrant and nih_nogrants with missing values for years 1986-2015 are set equal to 0.The sql script is in Z:\HUbs\2017\sql scripts ==Clinical Trials Data==Data is in: Z:\Hubs E:\McNair\Projects\Hubs\Summer 2017 Database is '''cities'''SQL script is: ctrials.sqlThe source file is: *medclinical.txt located in Z:\Hubs\2017 *Date from 1999-2017 ===Joined clinical trials table=== The file which contains the number of trials in each city and year is located in: Z:\Hubs\2017 The file is in: Z:\Hubs\2017\clean dataThe name of the file is: ctrialsSummary.txt It contains:*city*year*city_state_year*noctrials - number of trials The ctrials is joined with VC table. The joined SQL script is: '''new_ctrials.sql''' and it is located in Z:\Hubs\2017\sql scripts The name of the joined table is '''new_merged_ctrials'''. It contains:*city*state*city_state_id*city_state_year*year*noctrials*seedamtm*earlyamtm*lateramtm*selamtm*numseeds*numearly*numlater*numsel All the values of noctrials with missing values for years 1999-2017 are set equal to 0. ==Population Data==Data is in: Z:\Hubs E:\McNair\Projects\Hubs\Summer 2017 Database is '''cities''' SQL script is: '''population.sql'''The source files are: *pop2000_2009.xlsx*pop2010_2016.xlsx They contain:*State*City name *Year *Population Estimates Date from 2000-2016 ===Joined population table=== Data is in: Z:\Hubs\2017\clean dataThe file names are 1_population.txt - contains data on population estimates from 2000-2009 2_population.txt - contains data on population estimates from 2010-> 2016 Database is '''cities'''SQL script is: '''new_population.sql''', located in FundInfoZ:\Hubs\2017\sql scripts The population table is joined on VC table. The table is called '''new_merged_population'''. They contain: fundname*City*State*city_state_id to uniquely identify each city*city_state_year to uniquely identify each city in each year*Population estimates*Year*Code from the state code and Fips code*State full name ==Income Data== Raw data was obtained from Census data, msacodeAmerican Communities Survey. Raw Data is in: E:\McNair\Projects\Hubs\Summer 2017\MSA Income_raw.zip Date from 2005-2015 The master list with MSAs and principal cities is titled '''list2.xls'''. It is located at: Z:\Hubs\2017 This master list includes:*MSA code*MSA name*Principal City*State*Place code (city code)*State Code This master list was edited to associate each principal city with a unique state. E.g. if New York is the principal city located in New York-New Jersey MSA, it was associated with state NY-NJ. So '''list''' was edited to put New York with NY. Cleaned Income data files are in Z:\Hubs\2017\merging_on_ID They contain:*MSA code*MSA*Year *Total Household Income The MSA-City-State look up file is titled '''msa_city_state_wcode.txt'''. It is located in Z:\Hubs\2017\merging_on_ID The SQL file that merges income data from ACS (completeby MSA - Year)with the MSA-City file is titled '''income.sql'''. It is located here: Z:\Hubs\2017\sql scripts The final income table is in db '''cities''' titled '''merged_income'''. It includes:*MSA*City*State*Year*Total Household Income The table includes 8780 observations ===Joined income table=== Data is in: Z:\Hubs\clean dataThe file names are: INC_05.txt - INC_15.txt
Date from 2005-2015
The final table which is joined on VC is in db cities titled '''Where to findnew_merged_employment''': The Hubs data set can be found in the Ecosystem>>Hubs>>dataset folder. It is not currently in the database due to a UTF8 issue
It includes:
*MSA
*City
*Year
*Total
*Percent_public_schooling
*Percent_private_schooling
Data is in:
Z:\Hubs\clean data
The file names are:
SCH_05.txt - SCH_15.txt
It contains:*Deal city*state*year*seedamtm - seed, amount in millions*earlyamtm - early, amount in millions*lateramtm - late, amount in millions*selamtm - seed early late, amount in millions*numseeds - number of seeds*numearly *numlater*numsel*numdeals*numalive Date from 1948-2017 The table is in db '''cities''' titled '''new_vc'''. It includes:*city*state*city_state_id*city_state_year*seedamtm*earlyamtm*lateramtm*selamtm*numseeds*numearly*numlater*numsel*numdeals*numalive*year ==Final Joined Data set == The final data set is in file '''final.txt''' and is located here: Z:\Hubs\2017 It includes:*city*state*city_state_year - (ID that data is merged on)*year*seedamtm - Seed Amount*earlyamtm - Early Investment Amount*lateramtm - Late Investment Amount*selamtm - Seed early or late amount*numseeds - Number of seed investments *numearly - Number of early investments*numlater - Number of late investments*numsel *numdeals - Number of deals (first contracts)*numalive - Number of start ups alive*income - Income per capita in each city-year*sbir_nogrants - Number of SBIR grants*sbir_valuegrant - Value of SBIR grants*emp - Employment stats of each city-year*unemp - Rate of unemployment*popestimate - Population estimate of each city-year*private - Enrollment in private schools*public - Enrollment in public schools*total - *numfirms - Number of publicly traded firms*randd - R&D expenditure of publicly traded firms*revenue - Revenue of PTF*totalassets *nsf_nogrants - Number of NSF grants*valuegrant - Value of NSF grants*nih_nogrants - Number of NIH grants*nih_valuegrant - Value of NIH grants*noctrials - NUmber of clinical trials == Defining Hubs == '''Summer 2016''' - Last year a round syndicate master list of 125 "potential" hubs was used. A scorecard was developed which filtered these 125 candidate hubs to determine which of these should be included in the study sample. This method resulted in a sample size of ~ 30. The master list and the final hubs list is titled '''Hubs Data v2_'16'''. It is located here: Z:\Hubs\2017\hubs_data '''Summer 2017''' - In order to obtain a more statistically significant sample of hubs, we developed 5 criteria which produce a more relaxed definition of hubs than last year. These include *Availability of co-working space*Coding classes or tech events*Some focus on the tech sector (near/far deal this is one investor that important as our dependent variable is near/farVC funding)*Presence of an accelerator*Availability of mentorship for members. We will review the 125 candidate hubs and select those which satisfy a subset or all of these characteristics.
