Hubs

McNair Project

Hubs
Project Information
Project Title	Hubs
Owner	Hira Farooqi
Start Date
Deadline
Keywords	Data
Primary Billing
Notes
Has project status	Active
Copyright © 2016 edegan.com. All Rights Reserved.

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 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 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 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 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:

Companies: Coname, MSACode, Industry, state
MSALookupTable: MSACode, MSASuper
IndustryLookupTable: IndustryMajor, InduCode
-> 
CompanyInfo: Coname, MSASuper, InduCode, state (complete)

Funds: fundname, msacode, state
MSALookupTable: MSACode, MSASuper 
-> 
FundInfo: fundname, msacode, state (complete)

Rounds: coname, rounddate, stagecode, roundno
CombinedRounds: coname, rounddate, discamount, fundname
->
RoundInfoSuper: coname, rounddate, nofunds, discamount   
->
RoundInfo: Coname, roundyear, fundname, estamount (complete)

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

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)

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 (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)

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

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-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 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
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-CMSA

Hub Candidates Data Set

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.

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)

These characteristics/variables will be used to determine whether a candidate is or is not likely to be a Hub.

As of March 10th 2016, the list contains 125 Hub candidates.

Where to find: 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

Supplementary Data Sets

Patent data: to be pulled from USPTO or SDC Platinum.

Number of STEM Graduate Students (NSF) and University R&D Spending (NSF):

• University R&D 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 (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)

• in server 181, under 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

• 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://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

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:

MSA      Year       SeedVCInv      SeedEarlyVCInv      LaterVCInv     NoDeals   FundsInvested   DistinctInvestors   ....
----------------------------------------------------------------------------------------------------------------------------
1234     2001       1000000        20000000            30000000       4          7              7

Note that the unit of observation is MSA-Year.

Variables to be computed at the MSA level:

• 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 MSA)
  • FundsInvFromNear (within MSA?)
  • FundsInvFromFar (outside MSA?)
• DistinctInvestors (?)
  • DistinctInvestorsNear (within MSA?)
  • DistinctInvestorsFar (outside MSA?)
• PatentCount
• NoSTEMGrads
• FirmBirths (BDS data)
• UniRandDSpend
• PerCapitaIncome
• Employment

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 is semiconductor/non-semiconductor?).

Final Primary Data Set

• Deal is a round syndicate (near/far deal is one investor that is near/far).

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

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.html However, there is only CBSA!

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

COMPUSTAT Data

Data is in:

E:\McNair\Projects\Hubs\Summer 2017
Z:\Hubs\2017

Database is cities

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:

• Variables: City, year, No.public firms, sum R&D, sum Sales, sum total assets
• 1979-2016
• 4440 cities

NSF Data

Data is in:

E:\McNair\Projects\Hubs\Summer 2017
Z:\Hubs\2017

Database is cities

SQL script is: nsf_2017.sql

The source files are: nsf2017.txt, copied 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 code

From 1900 - 2017

Output file is nsfSummary.txt. It contains:

• Variables: City, State code year, nsf_nogrants, nsf_valuegrant
• 1900-2017

NIH Data

Data is in:

Z:\Hubs
E:\McNair\Projects\Hubs\Summer 2017

Database is cities SQL script is: nih2017.sql The source files are:

• nih_1986_2001.csv
• nih_2002_2012.txt
• nih_2013_2015

located 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:

E:\McNair\Projects\Hubs\Summer 2017

This table includes

• year
• city
• state
• country
• nogrants (number of grants)
• valuegrant
• city_state (the city-state ID that we'll merge on)

• Date from 1986-2015

Clinical Trials Data

Data is in:

Z:\Hubs
E:\McNair\Projects\Hubs\Summer 2017

Database is cities SQL script is: ctrials.sql The source file is:

• medclinical.txt

located in Z:\Hubs\2017

• Date from 1999-2017

Joined clinical trials data

The file which contains the number of trials in each city and year is located in:

Z:\Hubs\2017

The name of the file is

 ctrialsSummary.txt

It contains:

• city
• year
• city_state_year
• noctrials - number of trials

The ctrials is joined with vc_city_state_year. The joined SQL script is: new_ctrials.sql

It contains:

• city
• state
• city_state_id
• city_state_year
• year
• noctrials
• seedamtm
• earlyamtm
• lateramtm
• selamtm
• numseeds
• numearly
• numlater
• numsel

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 data

Data is in:

Z:\Hubs\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

The population table is joined on VC table called 'vc_city_state_year'. 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
  

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 data

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\merging_on_ID    

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 data

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 The final table 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\merging_on_ID    

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 data

Data is in:

Z:\Hubs\clean data

The file names are:

SCH_05.txt - SCH_15.txt

Database is cities SQL script is: new_merged_schooling.sql The final table is in db cities titled new_merged_schooling.

They contain:

• 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
 The file name is roundcitystateyear.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

Date from 1953-2017

The SQL file that merges VC data with the MSA-City file is titled vc.sql. The file is located in:

Z:\Hubs\2017

The final table is in db cities titled vc_city_state_year.

It includes:

• city
• state
• city_state_id
• city_state_year
• seedamtm
• earlyamtm
• lateramtm
• selamtm
• numseeds
• numearly
• numlater
• numsel
• year