Changes

The {{Project|Has project output=Data|Has sponsor=McNair Center|Has title=Hubs Research Project is a full-length academic paper analyzing the effectiveness of "hubs"|Has owner=Hira Farooqi, a component of the entrepreneurship ecosystem|Has keywords=Data|Has project status=Active|Does subsume=Hubs Analysis 2017, in the advancement and growth of entrepreneurial success in a metropolitan area. }}

This research will primarily focused on large '''Important Notice: The last update to the hubs data was done manually by Ed and midis in E:\projects\MeasuringHGHTEcosystems\HubsData-sized Metropolitan Statistical Areas (MSAs), as that is where the greater majority of Venture Capital funding is locatedRevisedSimplified. xlsx'''

The This page contains information about 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 companyused for this research project, fundincluding data sources, and round level. It will be analyzed at the combined MSA level. We will be looking at in terms location of number of companies funded in number of funds active, data on RDP and flow of investment in a given MSAdetails on data processing.

Companies'''Note on joining: Coname''' The city-state-year ID from VC data is used as the master ID for joining datasets. Each table (e.g. income, MSACodenih, Industrynsf, state MSALookupTable: MSACodesbir, MSASuper IndustryLookupTable: IndustryMajor, InduCode compustat) is first joined with the VC data on city-> CompanyInfo: Coname, MSASuper, InduCode, state (complete)-year ID and then the resulting tables are all joined together in the final table.

Rounds===Data by zip code===*Population data, 2000-2016 - US Census Bureau (E:\McNair\Hubs\summer 2017)https: coname//www2.census.gov/programs-surveys/popest/datasets/*Income data, rounddate, stagecode, roundno1998-2014 - The Internal Revenue Service (E:\McNair\Hubs\summer 2017) CombinedRoundshttps: coname//www.irs.gov/uac/about-irs*DCI index, rounddate, discamount, fundnameto assess the economic well-being of communities http://eig.org/dci/interactive-maps/u-s->zip-codes RoundInfoSuper*R&D Expenses, 1980-2016 - Wharton Research Data Services (E: coname, rounddate, '''nofunds''', discamount \McNair\Hubs\summer 2017) *Zipcode look-> RoundInfoup table obtained from https: Coname, roundyear, fundname, estamount //www.unitedstateszipcodes.org/zip-code-database/. It's available in (completeE:\McNair\Hubs\summer 2017).

'''Notes on Creation We have principle cities of Primary Data Set'''MSAs from the census:https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/delineation-files.html

Raw tables* companies (last investment, first investment, company name, MSA, MSA We might be able to go City -> FIPS place 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) ?

Process*cleaned tables to eliminate duplications, undisclosed variables*changed all original characters to include CMSA Cities and Industry Codes their FIPS codes (companyinfo3, fundinfocleanfinal, roundinfocleanwhich don't perfectly correspond) *matched funds to avoid any issues with names (iare available from https://www.e. Fund ABC L.Pcensus.gov/geo/reference/Fund ABC LPcodes/Fund ABC) *matched roundinfoclean investors to fundinfocleanfinal investors (roundinfo.txt >> cleanfundfinalplace.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 summarycmsa.txt (38394)html

Key decisionsThe Census claims to provide city to MSA here:*Threw out undisclosed co throughhttps://www.census.gov/geo/maps-out as no addressdata/data/ua_rel_download.html*Count However, there 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 onlyCBSA!

'''Glossary of Tables'''==COMPUSTAT Data== cleanco — used to remove duplicates from companies cleanedcompanies — clean set of companies with no duplicates cmsafunds- cmsas— list of all CMSAs in final The data set (for merging) cmsastats- statistics not including empty years (pre-merge) cmsastats2 - statistics separated by year-MSA cmsastats3— statistics separated by year-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 includes 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 on publicly traded firms in the US. It was obtained from companyround2 companyround3- combined company information with round information, cleaned up from companyround3 '''finaldataset'''- final statistics by CMSA-year, see section Final Primary the Wharton Research 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 Services (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 nearfarhttps://stage variables nearfar2 -wrds- added binomial variables for nearweb.wharton.upenn.edu/far and for each of the stages, used to build final dataset roundfund— not used— joined round to fund; dropwrds/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 tableindex.cfm?) 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-CMSA.

The Hubs candidate data set Raw Data 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.: E:\McNair\Projects\Hubs\Summer 2017 Z:\Hubs\2017

Characteristics/VariablesThe source file is RandDExpenditures.txt. It contains:*Year Founded*Square footage*LinkedIN selfDate from 1980-identifiers 2017 (what the organization classifies itself on its LinkedIN profileJuly) . *Activeness on Twitter (binomial)427799 records*Member Directory available online (binomial)Fields include:*Number of conference rooms*Price ($/month) for Flex desk R&D Expenditure*Offers Reserved desk (binomial)*Offers office space for rent Address (binomialinc. city, zip, state) *Offers community membership-- not for coworking but for community events, etc. (binomial)*Number Revenue of events offered per month (estimate)firms*Offers code academy *Mission Statement/Vision (for qualitative or key-word analysis) Database is '''cities'''

These characteristics/variables will be used to determine whether a candidate SQL script is or is not likely to be a Hub: COMPUSTAT. sql

As of March 10th 2016Output file is COMPUSTATSummary.txt. It contains:*Variables: City, year, the list contains 125 Hub candidatesNo.public firms, sum R&D, sum Sales, sum total assets*1979-2016*4440 cities

'''Where to find''': The Hubs data set can be found in the Ecosystem>>Hubs>>dataset folder. It is not currently located in the database due to a UTF8 issue Z:\Hubs\2017\Output_Files

===Supplementary NSF Data Sets===Data is in: E:\McNair\Projects\Hubs\Summer 2017 Z:\Hubs\2017 Database is '''Patent datacities''': to be pulled from USPTO or SDC Platinum.

'''Number of STEM Graduate Students''' (NSF) and '''University R&D Spending''' (NSF)SQL script is:*University R&D Data found under file "NSF DATA_2004 to 2011nsf_2017.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 projectssql

The source files are: nsf2017.txt, copied from table '''Per Capita Incomensf''' , and nsf_institution copied from table '''Employment Datansf_grants_institution''' (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 2000-2012the biotech db.xlsx" in datasets folder (Ecosystem>>Hubs>>datasets>>Data from Yael)**not uploaded to server or matched yet to CMSA code

'''Firm Births''' (BDS)They contain:*in server 181, under table name "BDS"Award ID*includes birth, death, net(birth-death) and rate(death rate) for years 1990-2013 for every msaAward Institution*Award Effective date*includes code for CMSA but is not aggregated by CMSAInstitution city*Award Value* i.e. BDS statistics are still separate for all the smaller MSAs in New York's CMSA (Organization state code=1)From 1900 - 2017

===Resources===* Yael Hochberg and Fehder (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://factfinderOutput file is nsfSummary.censustxt.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_14_5YR_B23027&prodType=tableIt contains:*USPTO tility patents by MSAVariables: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd.htmCity, State code year, nsf_nogrants, nsf_valuegrant *MSA level trends: http://www.metrotrends.org/data.cf1900-2017

We will need to process The script that cleans NIH data and generates the following variablessummary table is titled '''nihSummary'''. It is located here:*SuperMSA - combine SanFran and SanJose, New York and Newark?, NC Research triangle, others?

Note that ===Joined NIH table===The joined NIH table with the unit VC table is found in db '''cities'''. The table is named '''merged_nih'''.All the values of observation is MSAnih_valuegrant and nih_nogrants with missing values for years 1986-Year2015 are set equal to 0.The sql script is in Z:\HUbs\2017\sql scripts

Variables to be computed at the MSA level==Clinical Trials Data==Data is in:*HubActive (binary)*NoHubsActive (Count)*HubSqFt*Other Hub Vars (build list!!!) Z:\Hubs*'''SeedVCInv''' (Seed/Start-up)*'''EarlyVCInv''' (Early Stage)*'''LaterStageVC''' (Later)*'''OtherStageVC''' (Buyout/Acq, Other)*'''NoDeals''' (done by local VCs?)**'''NoDealsNear'''**'''NoDealsFar'''*NoPortCosFunded*'''FundsInv''' (in an MSA)**'''FundsInvFromNear''' (within MSA?)**'''FundsInvFromFar''' (outside MSA?)*DistinctInvestors (?)**DistinctInvestorsNear (within MSA?)**DistinctInvestorsFar (outside MSA?)*PatentCount*NoSTEMGrads*FirmBirths (BDS data)*UniRandDSpend*PerCapitaIncome*EmploymentE:\McNair\Projects\Hubs\Summer 2017

We need toDatabase is '''cities'''SQL script is:ctrials.sql*Check funds invested means dollars invested*Categorize near and far! Is it within MSA vs. not, within adjacent MSAs, etc.?The source file is:

Table nameThe file which contains the number of trials in each city and year is located in: Z: finaldataset\Hubs\2017 cmsaThe 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 totalamountinvZ:\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-total amount invested 2016 ===Joined population table=== Data is in: Z:\Hubs\2017\clean dataThe file names are nearamountinv1_population.txt -contains data on population estimates from 2000-amount invested from local funds2009 faramountinv2_population.txt -contains data on population estimates from 2010- amount invested 2016  Database is '''cities'''SQL script is: '''new_population.sql''', located in Z:\Hubs\2017\sql scripts The population table is joined on VC table. The table is called '''new_merged_population'''. They contain:*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 funds outside CMSA Census data, American Communities Survey. Raw Data is in: earlyinvE:\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-amount invested NJ. So '''list''' was edited to put New York with NY.   Cleaned Income data files are in early stage companies laterinvZ:\Hubs\2017\merging_on_ID  They contain:*MSA code*MSA*Year *Total Household Income  The MSA-City-amount invested State look up file is titled '''msa_city_state_wcode.txt'''. It is located in later stage companies startupseedinvZ:\Hubs\2017\merging_on_ID  The SQL file that merges income data from ACS (by MSA -Year) with the MSA-amount invested 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 MSACleaned files are in seed 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 startup stage companies older  otherstageinvDate from 2005-2015 The SQL file that merges employment data from ACS (by MSA -amount invested 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 Acquisition/Buy: Z:\Hubs\clean data The file names are: EMP_05.txt -outs/Other stage companies 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. Raw Data is in: investingfundE:\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-distinct funds that City file is titled '''schooling.sql'''. The file is located in: Z:\Hubs\2017 The final table is in db '''cities''' titled '''merged_schooling'''. It includes:*MSA*City*Year*Total*Percent_public_schooling*Percent_private_schooling ===Joined schooling table=== Data is in: Z:\Hubs\clean dataThe file names are investing : SCH_05.txt - SCH_15.txt  Database is '''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'''. 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 that CMSA-private schools*Year*Code from the state code and Fips code ==VC Data==   Raw Data is in: Z:\VentureCapitalData\SDCVCData\vcdb2 The file name is roundleveloutput2.txt It contains:*city*state*year investingfundnear*seedamtm -seed, amount in millions*earlyamtm -distinct funds from that CMSA that invested early, amount in that CMSAmillions*lateramtm -year late, amount in millions investingfundfar*selamtm -seed early late, amount in millions*numseeds -distinct funds number of seeds*numearly *numlater*numsel*numdeals*numalive  Date from outside that CMSA that invested 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 CMSAdata is merged on)*year*seedamtm - Seed Amount*earlyamtm -year Early Investment Amount*lateramtm - Late Investment Amount*selamtm - Seed early or late amount*numseeds - Number of seed investments deals*numearly -Number of early investments*numlater -number Number of late investments*numsel *numdeals - Number of deals (first contracts) neardeals*numalive - Number of start ups alive*income - Income per capita in each city-year*sbir_nogrants -number Number of deals inside a CMSA SBIR grants fardeals*sbir_valuegrant -Value of SBIR grants*emp -number Employment stats of deals from outside a CMSA each city-year*unemp -Rate of unemployment*popestimate -some Population estimate of these deals might count each city-year*private - Enrollment in private schools*public - Enrollment in both categories, because public schools*total - *numfirms - Number of syndicate members being both inside and outside the CMSApublicly traded firms earlystagedeals*randd -R&D expenditure of publicly traded firms*revenue -deals with earlystage companiesRevenue of PTF*totalassets laterstagedeals*nsf_nogrants -Number of NSF grants*valuegrant -deals with later stage companies Value of NSF grants startupseeddeals*nih_nogrants -Number of NIH grants*nih_valuegrant -deals with startup/seed companies Value of NIH grants otherstagedeals*noctrials -NUmber of clinical trials == Defining Hubs == '''Summer 2016''' -deals with companies 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 other stages a sample size of ~ 30. The master list and the final hubs list is titled '''Hubs Data v2_'16'''. It is located here: newportcosfundedZ:\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-number 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 portfolio companies to receive their first investment in that yearthese characteristics.   [[category:Internal]]