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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 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'''
===Primary Data Set===
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/Ecosystem/Hubs
psql Hubs
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.
The data set has now been uploaded to the database server, named Hubs.
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:Information on initial data work done prior to Summer 2017 can be found at [[Hubs Summer 2016]].
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.
Funds: fundname, msacode, state
MSALookupTable: MSACode, MSASuper
->
FundInfo: fundname, msacode, state (complete)
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).
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 ...== Data by MSA ==
'''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.
===Hub Candidates Data Set===
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 FoundedDate from 1980-2017 (July). *Square footage427799 records*LinkedIN self-identifiers (what the organization classifies itself on its LinkedIN profile) Fields include:*Activeness on Twitter (binomial)*R&D Expenditure*Member Directory available online *Address (binomialinc. city, zip, state)*Number *Revenue of conference roomsfirms*Price ($/month) for Flex desk Database is '''cities''' SQL script is: COMPUSTAT.sql*Offers Reserved desk (binomial)*Offers office space for rent (binomial) Output file is COMPUSTATSummary.txt. It contains:*Offers community membership-- not for coworking but for community eventsVariables: City, year, etcNo. (binomial)public firms, sum R&D, sum Sales, sum total assets*Number of events offered per month (estimate)1979-2016*Offers code academy4440 cities*Mission Statement/Vision (for qualitative or key-word analysis) It is located in Z:\Hubs\2017\Output_Files
These characteristics/variables will be used to determine whether a candidate ==NSF Data==Data is or in: E:\McNair\Projects\Hubs\Summer 2017 Z:\Hubs\2017 Database is not likely to be a Hub. '''cities'''
As of March 10th 2016, the list contains 125 Hub candidatesSQL script is: nsf_2017.sql
===Supplementary Data Sets===They contain:*Award ID*Award Institution*Award Effective date*Institution city*Award Value*Organization state code'''Patent data''': to be pulled from USPTO or SDC Platinum. From 1900 - 2017
'''Number of STEM Graduate Students''' (NSF) and '''University R&D Spending''' (NSF):*University R&D Data found under Output file "NSF DATA_2004 to 2011is nsfSummary.xlsx" in datasets folder (Ecosystem>>Hubs>>Datasets)txt. It contains:*R&D spending found at the university level for 2014 ("Stem Grad Students.xlsx) or at state level ("Science and Engineering Grad Students by Variables: City, State and Year 2000-2011.csv")** not uploaded to server or matched yet to CMSA codeyear, nsf_nogrants, because of this discrepancy. nsf_valuegrant **"Stem Grad Students.xlsx" contains categorized university by MSA, can be used for all university1900-based projects2017
'''Firm Births''' (BDS)==NIH Data==*Data is in server 181, under table name "BDS": *includes birth, death, net(birth-death) and rate(death rate) for years 1990-2013 for every msa Z:\Hubs*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) E:\McNair\Projects\Hubs\Summer 2017
===Resources===Database is '''cities'''* Yael Hochberg and Fehder (2015), located in dropboxSQL script is: nih2017.sql** Use this paper as a guideline on how to conduct the analysisThe source files are: *US Census Bureau data on employment by MSA: http://factfindernih_1986_2001.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_14_5YR_B23027&prodType=tablecsv*USPTO utility patents by MSA: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gdnih_2002_2012.htmtxt*MSA level trendsnih_2013_2015located in E: http://www.metrotrends.org/data.cf\McNair\Projects\Federal Grant Data\NIH
===The Target Dataset===
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 is in the folder (and a table in the dbase)
Example dataset Z: MSA Year SeedVCInv SeedEarlyVCInv LaterVCInv NoDeals FundsInvested DistinctInvestors .... ---------------------------------------------------------------------------------------------------------------------------- 1234 2001 1000000 20000000 30000000 4 7 7\Hubs\2017\sql scripts
Note that the unit of observation is MSA*Date from 1986-Year.2015
Variables to be computed at ===Joined NIH table===The joined NIH table with the MSA level:*HubActive (binary)*NoHubsActive (Count)*HubSqFt*Other Hub Vars (build list!!!)*VC table is found in db '''SeedVCInvcities''' (Seed/Start-up)*. The table is named '''EarlyVCInvmerged_nih''' (Early Stage).*'''LaterStageVC''' (Later)*'''OtherStageVC''' (Buyout/Acq, Other)*'''NoDeals''' (done by local VCs?)**'''NoDealsNear'''**'''NoDealsFar'''*NoPortCosFundedAll the values of nih_valuegrant and nih_nogrants with missing values for years 1986-2015 are set equal to 0.*'''FundsInv''' (The sql script is in an MSA)**'''FundsInvFromNear''' (within MSA?)**'''FundsInvFromFar''' (outside MSA?)*DistinctInvestors (?)**DistinctInvestorsNear (within MSA?)**DistinctInvestorsFar (outside MSA?)*PatentCount*NoSTEMGrads*FirmBirths (BDS data)*UniRandDSpend*PerCapitaIncome*Employment Z:\HUbs\2017\sql scripts
We need to==Clinical Trials Data==Data is in:*Check funds invested means dollars invested Z:\Hubs*Categorize near and far! Is it within MSA vs. not, within adjacent MSAs, etc.? E:\McNair\Projects\Hubs\Summer 2017
There may be a second dataset that has Hub-Industry-Year (where industry is semiconductor/non-semiconductor?)*medclinical.txt
===Final Primary Data Set===located in Z:\Hubs\2017
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===Joined clinical trials table===
===Data by zip code===The file which contains the number of trials in each city and year is located in:*Population data, 2000-2016 - US Census Bureau (E Z:\McNair\Hubs\summer 2017)https The file is in://www2.census.gov/programs-surveys/popest/datasets/*Income data, 1998-2014 - The Internal Revenue Service (E Z:\McNair\Hubs\summer 2017)\clean datahttpsThe name of the file is://www ctrialsSummary.irs.gov/uac/about-irstxt It contains:*DCI index, to assess the economic well-being of communitiescityhttp://eig.org/dci/interactive-maps/u-s-zip-codes*year*R&D Expenses, 1980-2016 - Wharton Research Data Services (E:\McNair\Hubs\summer 2017)city_state_year*Zipcode looknoctrials -up number of trials The ctrials is joined with VC table obtained from https. The joined SQL script is://www'''new_ctrials.unitedstateszipcodes.org/zip-code-database/. Itsql'''s available and it is located in (E Z:\McNair\Hubs\summer 2017)\sql scripts The name of the joined table is '''new_merged_ctrials'''.
== Data by MSA ==It contains:*city*state*city_state_id*city_state_year*year*noctrials*seedamtm*earlyamtm*lateramtm*selamtm*numseeds*numearly*numlater*numsel
==COMPUSTAT Data==All the values of noctrials with missing values for years 1999-2017 are set equal to 0.
SQL script is: COMPUSTAT.sql
The source file is RandDExpenditures.txt. It contains:
*Date from 1980-2017 (July). All COMPUSTAT.
*427799 records
*Fields include:
**R&D Expenditure
**Address (inc. city, zip, state)
Output file is COMPUSTATSummary.txt. It contains[[category:*Variables: City, year, No.public firms, sum R&D, sum Sales, sum total assets*1979-2016*4440 citiesInternal]]
no edit summary
{{Project|Has project output=Data|Has sponsor=McNair ProjectsCenter
|Has title=Hubs
|Has owner=Hira Farooqi,
|Has keywords=Data
|Has project status=Active
|Does subsume=Hubs Analysis 2017,
}}
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. It focuses on cities in the United States as the primary unit of analysis.
This might do it: https://www2.census.gov/geo/pdfs/maps-data/data/rel/explanation_ua_cbsa_rel_10.pdf
We can maybe track city to principal city to MSA
The source files are: nsf2017.txt, copied from table '''nsf''', and nsf_institution copied from table '''Where to findnsf_grants_institution''': The Hubs data set can be found in from the Ecosystem>>Hubs>>dataset folderbiotech db. It is not currently in the database due to a UTF8 issue
===Joined NSF table===The joined nsf table with the VC table is found in db '''Per Capita Incomecities''' and . The table is named '''Employment Datamerged_nsf''' (US Census Bureau): *"Per Capita Personal Income by MSA 2000-2012.xlsx" in datasets folder (Ecosystem>>Hubs>>Datasets>>Data from Yael)*"Wages All the values of nogrants and Salaries by MSA 2000valuegrant with missing values for years 1990-20122017 are set equal to 0.xlsx" The sql script is in datasets folder (Ecosystem>>Hubs>>datasets>>Data from Yael)**not uploaded to server or matched yet to CMSA code Z:\HUbs\2017\sql scripts
The script that cleans NIH data and generates the summary table is titled '''nihSummary'''. It is located here:
This table includes
*year
*city
*state
*country
*nogrants (number of grants)
*valuegrant
*city_state
Database is '''cities'''
SQL script is: ctrials.sql
The source file is:
*Deal is a round syndicate (near/far deal is one investor that is near/far).Date from 1999-2017
==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 data
The 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
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 Census data, American 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 (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 data
The 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 MSA
Cleaned 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:
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.
Raw Data is in:
E:\McNair\Projects\Hubs\Summer 2017\School Enrollment Data by MSA
Cleaned 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.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 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 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 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
*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 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.
