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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==Clinical Trials Data== CombinedRoundsData is in: coname, rounddate, discamount, fundname -> RoundInfoSuperZ: coname, rounddate, '''nofunds''', discamount ->\Hubs RoundInfoE: Coname, roundyear, fundname, estamount (complete)\McNair\Projects\Hubs\Summer 2017
Then take:Database is '''cities''' RoundInfoSQL script is: Coname, roundyear, fundname, estamount CompanyInfo: Coname, MSASuper, InduCode, state FundInfo: fundname, msacode, state -> SuperRoundInfo: Coname, CoMSASuper, CoInduCode, CoState, FundName, FundMSASuper, FundState, RoundYear, RoundEstAmount ->ctrials.sql MSAPortCosThe source file is: Count(CoName) As NoPortCosFunded, CoMSASuper, RoundYear ...
'''Notes on Creation of Primary Data Set'''*medclinical.txt
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) located in Z:\Hubs\2017
Process*cleaned tables to eliminate duplications, 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 (1990Date from 1999-2016), stage, and cmsa* populate data with count of companies (Deal flow) and estimated amount ($)** data set in 181 hubs folder under summarycmsa.txt (38394)2017
Key decisions:*Threw out undisclosed co through-out as no address*Count is done by joining round and company*Anything fund related must be disclosed fund*Near and far, and total invested, and fund counts, etc., are all done using disclosed funds that match only===Joined clinical trials table===
'''Glossary of Tables''' cleanco — used to remove duplicates 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 (pre-merge) cmsastats2 - statistics separated by year-MSAIt contains: cmsastats3— statistics separated by year-MSA-stage*city 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 '''finaldataset'''- 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 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*city_state_year matchfund2*noctrials - 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-CMSAtrials
===Hub Candidates Data Set===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
Characteristics/VariablesIt contains:*Year Foundedcity*Square footagestate*LinkedIN self-identifiers (what the organization classifies itself on its LinkedIN profile) city_state_id*Activeness on Twitter (binomial)city_state_year*Member Directory available online (binomial)year*Number of conference roomsnoctrials*Price ($/month) for Flex desk seedamtm*Offers Reserved desk (binomial)earlyamtm*Offers office space for rent (binomial) lateramtm*Offers community membership-- not for coworking but for community events, etc. (binomial)selamtm*Number of events offered per month (estimate)numseeds*Offers code academynumearly*Mission Statement/Vision (for qualitative or key-word analysis) numlater*numsel
These characteristics/variables will be used All the values of noctrials with missing values for years 1999-2017 are set equal to determine whether a candidate is or is not likely to be a Hub0.
As of March 10th 2016, the list contains 125 Hub candidates.==Population Data==Data is in: Z:\Hubs E:\McNair\Projects\Hubs\Summer 2017
===Supplementary Data Sets===SQL script is: '''Patent datapopulation.sql'''The source files are: to be pulled from USPTO or SDC Platinum*pop2000_2009.xlsx*pop2010_2016. xlsx
'''Number of STEM Graduate Students''' (NSF) and '''University R&D Spending''' (NSF)They contain:*University R&D Data found under file "NSF DATA_2004 to 2011.xlsx" in datasets folder (Ecosystem>>Hubs>>Datasets)State*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")City name ** not uploaded to server or matched yet to CMSA code, because of this discrepancy. Year **"Stem Grad Students.xlsx" contains categorized university by MSA, can be used for all university-based projectsPopulation Estimates
'''Per Capita Income''' and '''Employment Data''' (US Census Bureau): *"Per Capita Personal Income by MSA 2000-2012.xlsx" in datasets folder (Ecosystem>>Hubs>>Datasets>>Data Date 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 code2016
'''Firm Births''' (BDS)*in server 181, under ===Joined population table name "BDS"*includes birth, death, net(birth-death) and rate(death rate) for years 1990-2013 for every msa*includes code for CMSA but is not aggregated by CMSA** i.e. BDS statistics are still separate for all the smaller MSAs in New York's CMSA (code=1)==
===Resources===Data is in: * Yael Hochberg and Fehder (2015), located in dropbox Z:\Hubs\2017\clean data** Use this paper as a guideline on how to conduct the analysisThe file names are *US Census Bureau 1_population.txt - contains data on employment by MSA: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_14_5YR_B23027&prodType=tablepopulation estimates from 2000-2009*USPTO utility patents by MSA: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd 2_population.htm*MSA level trends: http://www.metrotrends.org/txt - contains data.cfon population estimates from 2010-2016
===The Target Dataset===
We will need to process the following variablesDatabase is '''cities'''SQL script is:*SuperMSA - combine SanFran and SanJose'''new_population.sql''', New York and Newark?, NC Research triangle, others?located in *CSV mapping msas to cmsas is in the folder (and a table in the dbase) Z:\Hubs\2017\sql scripts
Example datasetThey contain: MSA *City*State*city_state_id to uniquely identify each city*city_state_year to uniquely identify each city in each year*Population estimates*Year SeedVCInv SeedEarlyVCInv LaterVCInv NoDeals FundsInvested DistinctInvestors .... ----------------------------------------------------------------------------------------------------------------------------*Code from the state code and Fips code 1234 2001 1000000 20000000 30000000 4 7 7*State full name
Note that the unit of observation is MSA-YearRaw data was obtained from Census data, American Communities Survey.
Variables to be computed at the MSA levelRaw Data is in:*HubActive (binary)*NoHubsActive (Count)*HubSqFt*Other Hub Vars (build list!!!)*'''SeedVCInv''' (Seed/Start-up)*'''EarlyVCInv''' (Early Stage)*'''LaterStageVC''' (Later)*'''OtherStageVC''' (Buyout/Acq, Other)*'''NoDeals''' (done by local VCs?)**'''NoDealsNear'''**'''NoDealsFar'''*NoPortCosFunded*'''FundsInv''' (in an E:\McNair\Projects\Hubs\Summer 2017\MSA)**'''FundsInvFromNear''' (within MSA?)**'''FundsInvFromFar''' (outside MSA?)*DistinctInvestors (?)**DistinctInvestorsNear (within MSA?)**DistinctInvestorsFar (outside MSA?)*PatentCount*NoSTEMGrads*FirmBirths (BDS data)*UniRandDSpend*PerCapitaIncome*EmploymentIncome_raw.zip
We need to:
*Check funds invested means dollars invested
*Categorize near and far! Is it within MSA vs. not, within adjacent MSAs, etc.?
There may be a second dataset that has Hub-Industry-Year (where industry The master list with MSAs and principal cities is semiconductor/non-semiconductor?)titled '''list2.xls'''. It is located at: Z:\Hubs\2017
===Final Primary Data Set===This master list includes:*MSA code*MSA name*Principal City*State*Place code (city code)*State Code
*Deal is This master list was edited to associate each principal city with a round syndicate (near/far deal is one investor that unique state. E.g. if New York is near/far)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.
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
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:*Date from 1980-2017 (July). *427799 records*Fields include: fundname**R&D Expenditure**Address (inc. city, msacodezip, state)**Revenue of firms MSALookupTableDatabase is '''cities''' SQL script is: MSACodeCOMPUSTAT.sql Output file is COMPUSTATSummary.txt. It contains:*Variables: City, year, No.public firms, sum R&D, sum Sales, MSASuper 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 Z:\Hubs\2017 FundInfoDatabase is '''cities''' SQL script is: fundnamensf_2017.sql The source files are: nsf2017.txt, msacodecopied from table '''nsf''', 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 (completenumber 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
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 data
The name of the file is:
ctrialsSummary.txt
The Hubs candidate data set is a list name of potential hubs found in MSAs throughout the country. Researchers are currently pulling qualitative and quantitative information from the candidatejoined table is '''new_merged_ctrials'''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.
Database is '''Where to findcities''': 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
The population table is joined on VC table. The table is called '''new_merged_population'''.
==Income Data==
Date from 2005-2015
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 (by 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 Database is '''cities'''SQL script is: merged_income.sql They contain:*City*State*city_state_id to uniquely identify each city*Income*Year*Code from the state code and Fips code ==Employment Data== Data on employment was obtained from American Communities Survey, US Census Bureau. Raw Data is in: E:\McNair\Projects\Hubs\Summer 2017\Employment Data by zip MSACleaned files are in Z:\Hubs\2017\clean data They contain:*MSA code*MSA*Year *Employment rate of individuals 16 years or older*Unemployment rate of individuals 16 years or older Date from 2005-2015 The SQL file that merges employment data from ACS (by MSA - Year) with the MSA-City file is titled '''Employment.sql'''. The file is located in: Z:\Hubs\2017 The final table is in db '''cities''' titled '''merged_employment'''. It includes:*MSA*City*Year*Employment rate*Unemployment rate ===Joined employment table=== Data is in: *Population Z:\Hubs\clean data The file names are: EMP_05.txt - EMP_15.txt Database is '''cities'''SQL script is: '''new_employment.sql''' and it is located in Z:\Hubs\2017\sql scripts The final table which is joined on VC is in db cities titled '''new_merged_employment'''. They contain:*City*State*Code from the state code and Fips code*city_state_id to uniquely identify each city*city_state_year to uniquely identify each city in each year*Employment rates of individuals of 16 years or older*Unemployment rates of individuals of 16 years or older*Year ==Schooling Data== Data on schooling was obtained from American Communities Survey, US Census Bureau.httpsRaw Data is in://www2 E:\McNair\Projects\Hubs\Summer 2017\School Enrollment Data by MSACleaned files are in Z:\Hubs\2017\clean data They contain:*MSA code*MSA*Year *Total number of population 3 years and over enrolled in school*Percent of population 3 years and over enrolled in public school*Percent of population 3 years and over enrolled in private school Date from 2005-2015 The SQL file that merges schooling data from ACS (by MSA - Year) with the MSA-City file is titled '''schooling.censussql'''. The file is located in: Z:\Hubs\2017 The final table is in db '''cities''' titled '''merged_schooling'''.gov/programs-surveys/popest/datasets/ It includes:*MSA*City*Year*Total*Percent_public_schooling*Income Percent_private_schooling ===Joined schooling table=== Data is in: Z:\Hubs\clean data The file names are: SCH_05.txt - SCH_15.txt Database is '''cities'''SQL script which joins this table with VC table is: '''new_merged_schooling.sql'''The Internal Revenue Servicefinal table is in db '''cities''' titled '''new_merged_schooling'''. It contains:*City*State*city_state_id to uniquely identify each city*city_state_year to uniquely identify each city in each year*Total number of school enrollment*Percentage enrolled in public schools*Percentage enrolled in private schools*Year*Code from the state code and Fips code ==VC Data== Raw Data is in: https Z://www\VentureCapitalData\SDCVCData\vcdb2 The file name is roundleveloutput2.irs.gov/uac/abouttxt It contains:*city*state*year*seedamtm - seed, amount in millions*earlyamtm -irsearly, amount in millions*DCI indexlateramtm - late, to assess the economic wellamount in millions*selamtm - seed early late, amount in millions*numseeds -being number of communitiesseedshttp*numearly *numlater*numsel*numdeals*numalive Date from 1948-2017 The table is in db '''cities''' titled '''new_vc'''. It includes://eig*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.org/dci/interactivetxt''' 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 -maps/uNumber of SBIR grants*sbir_valuegrant -sValue of SBIR grants*emp -zipEmployment stats of each city-codesyear*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 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 (this is important as our dependent variable is VC 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. [[category:Internal]]
