edegan.com - User contributions [en]

Seed Accelerator Data Assembly

2018-08-02T20:45:40Z

HiraF: /* Data assembly details */

==Useful pages==

*[[U.S. Seed Accelerators]]
**[[Accelerator Seed List (Data)]]
*[[Accelerator Demo Day]]
**[[Mechanical Turk (Tool)]]
*[[Crunchbase Accelerator Founders]]
**[[Crunchbase Data#Collecting Company Information]]
**[[Merging Existing Data with Crunchbase]]
*[[Whois Parser]]
*[[URL Finder (Tool)]]
*[[Industry Classifier]]
*[[Industry classifier yang]]
*[[VentureXpert Data]]

Please add (or subtract) other relevant (or irrelevant) pages!

==Database specification==

===Preamble===

We need to get the data into approximately 3NF to prevent errors and make it more useable.

Inputs:
E:\McNair\Projects\Accelerators\Summer 2018\The File To Rule Them All.xlsx
https://docs.google.com/spreadsheets/d/16Suyp364lMkmUuUmK2dy_9MeSoS1X4DfFl3dYYDGPT4/edit#gid=0

Recent work on the above from Connor:
*made the Demo Day Timing Google sheet as clean as possible (fixed dates, removed duplicates, created season column)
*recoded the employee count
*normalized the investment amount

Connor's next to do:
*Founders Experience: code job title
*Founders Education: remove unknowns, code degree and code major
*Fix multiple campus and cohorts (see below).

This is documented on [[U.S._Seed_Accelerators#Update_for_Hira]].

The current database work is in '''vcdb2'''. The code to build the relevant tables in vcdb2 is in:
E:\McNair\Projects\Accelerators\LoadAcceleratorDataV2.sql

Note that AddCBData.sql can be run on '''crunchbase2''' to get the relevant crunchbase data for import into vcdb2 (or elsewhere).

However, we should build a new database for this project:
createdb accelerator

As well as a new folder (Z:\accelerator) for the data.

The work in vcdb2 is essentially to build the data that goes into the File To Rule Them All.xlsx. We should start from there!

===Suggested Spec===

We need to address the issue with multiple campus and cohorts. This will require loading and manipulation of the data in SQL (Hira) as well as some manual fixes to the data (Connor).

Accelerator Table (fieldname colname/DISCARD, from sheet "Accelerators Final")
*acceleratorname (Primary Key) Accelerators
*url homepage_url
*cohorturl cohort page URL
*cohortlisting Break out cohorts on the website? (Y/N)
*type Type
*alive Alive
*typenote Type notes
*weeks Weeks
*durationnotes Duration Notes
*city city (note: address info is for the accelerator HQ here)
*state state/region (note: address info is for the accelerator HQ here)
*foundingdate Creation Date
*terms Terms of Joining Accelerator (non-equity pay/equity/free)
*equity Equity?
*equityamountdesc Equity Amount
*equityamount Equity Amount Normalized (only 1s, range is averaged)
*investmentamountdesc Investment Amount
*investmentamount Investment Amount Normalized (Midpoint)
*investmentnotes Investment Notes
*discard industry DISCARD
*industry Industry
*specgenindu Specified/General
*address Address (note: address info is for the accelerator HQ here)
*subtype DISCARD
*nonprofit nonprofit?
*studentfocus designed for students (Y/N)
*multicampus Multiple campuses? (Y/N)
*software tech (Y/N) DISCARD
*stagepref What stage do they look for in cohort companies (SEL)
*cbconame Name in Crunchbase
*uuid UUID

Note that we will assume that duration and terms are common across all cohorts run by the same accelerator. We might need to revisit this assumption.

CohortCompany Table (fieldname colname/DISCARD, from sheet "Cohorts Final")
*conamestd (primary key)
*coname
*conameorg
*colocation
*city
*state_code
*country_code
*address
*codescription
*short_desc
*long_desc
*cosectors
*costatus
*cofundingstage
*courl
*uuid
*category_list
*category_group_list
*founded_on
*employee_count
*emp_count_scale
*linkedin_url
*gotvc

We then need to deduplicate this table and make sure that conamestd is a valid primary key.

Note that we won't be taking the following from "Cohorts Final" for this table:
*year
*accelerator
*cohort
*quarter
*acclocation
*accperks DISCARD
*cofounder DISCARD

CohortParticipation
*Cohort
*year
*quarter
*accelerator (foreign key)
*conamestd (foreign key)

===Going further===

We should likely rebuild the cohort variable to make it a sole "primary key" for a cohort. This would mean turning each entry of cohort into something unique like: TechStars Boulder Fall 2017 or 1440 Cohort 1, so that this key could look up the year of the cohort, its quarter, etc. We could then break CohortParticipation into two tables:

CohortParticipation
*Cohort
*accelerator (foreign key)
*conamestd (foreign key)

Cohort
*Cohort
*year
*quarter

We could then add campus and a seperate table for campuses:

Campus Table
*CampusName (e.g., Techstars Boulder)
*Accelerator (foreign key)
*Address
*City
*State
*Zip
*Description

===Founders Information===

The three founder sheets turn into three tables nicely. We don't need to renormalize them for now, just fix up their variables and do some matching on employer.

Founders:
*Accelerator
*First Name
*Last Name
*Full Name
*Current Job
*Current Location

FoundersExperience:
*Accelerator
*First Name
*Last Name
*Full Name
*Employer
*Job Title
*Dates Employed
*Time Employed
*Location
*Extra Description

FoundersEducation:
*Accelerator
*First Name
*Last Name
*Full Name
*School Name
*Degree Name
*Major
*Attended
*Graduated
*Societies

==To do/For consideration==

Minh:
*Create a format for collecting timing data
*Put the timing job on Turk

Connor:
*Try for missing timings that we really (new process) need
*Col W should be headquarter address
*What stage- Clean up?

Maxine:
*Build the google URL finder
*Industry classification from description

Grace:
*Process and Join in new timing data - new date located in Z:/accelerator/Formatted Timing Info.txt
*Make a category group to minorcode lookup
*Run WHOIS crawler on all valid URLs (not facebook pages, etc.)
*Founders Experience: Match Employers to VC funds/firms, VC backed startups (requires data from Augi)

==Accelerator Data Assembly Progress (Hira) ==

*All data files are in Z:/accelerator
*The SQL file that loads all data is: LoadAccData.sql. It is located in E:\McNair\Projects\Accelerators\Summer 2018.

=== Data assembly details ===

The SQL file LoadAccData.sql currently loads data on Cohorts final and Founders from:
E:\McNair\Projects\Accelerators\Summer 2018\The File To Rule Them All.xlsx

It creates the following tables:

1) cohortsfinal - source file: Cohorts Final sheet in "The File to Rule Them All".

2) CohortCompany - this uses data in cohortsfinal and creates a table with the following:
*conamestd
*conameorg
*colocation
*city
*state_code
*country_code
*address
*codescription
*short_desc
*long_desc
*cosectors
*costatus
*cofundingstage
*courl
*uuid
*category_list
*category_group_list
*founded_on
*employee_count
*emp_count_scale
*linkedin_url
*gotvc

3)CohortParticipation - uses table cohortsfinal
*cohort
*year
*quarter
*accelerator
*conamestd

4) timing_final - This table is based on the most updated information on timing compiled in source file: Z:/accelerator/Formatted Timing Info.txt (by Grace). It includes:
*coname
*acceleratorname
*keyword
*url
*webpage
*predicted
*gooddata
*page_details
*full_date
*month
*year
*cohort_name
*notes
*prog_duration_wks
*actual_date
*actual_month
*actual_year
*season

5) Founders - source file: "The File to Rule Them All - Founders main sheet"
*Accelerator
*First_Name
*Last_Name
*Full_Name
*Current_Job
*Current_Location

6) founders_experience - source file: "The File to Rule Them All - Founders experience sheet"
*Accelerator
*First_Name
*Last_Name
*Full_Name
*Employer
*VC
*VC_backed_startup
*OLD_Job_Title
*NEW_Job_Title
*Dates_Employed
*Time_Employed
*Location
*Extra_Description

7) additional_timing_info - source file: "merging_work.xlxs" located in: E:\Projects\McNair\Seed DB
8) additional_timing_info2 - source file: "formatted timing info2.txt" located in E:\Projects\McNair\Accelerators\Summer 2018. This was collected through MTurks.
Tables 7 and 8 include columns:
*coname
*acceleratorname
*cohort_name
*date
*month
*year
*season

9) timing_combined - This table combines all timing information we have and appends tables 4, 7 and 8.
10) cohortcompanies_wtiming - merges data in tables cohortcompany and timing_combined

Seed Accelerator Data Assembly

2018-08-02T18:24:23Z

HiraF: /* Data assembly details */

2017-08-04T19:47:38Z

HiraF: /* Analysis Stage 1 */

2017-07-20T21:57:16Z

HiraF:

Hubs Analysis 2017

2017-07-20T21:56:38Z

HiraF:

Hubs Analysis 2017

2017-07-20T21:54:40Z

HiraF: /* Analysis Stage 1 */

2017-07-20T21:37:50Z

HiraF:

Hira Farooqi (Work Log)

2017-07-20T21:36:42Z

HiraF:

Dylan Dickens (Research Plan)

2017-07-20T21:33:20Z

HiraF:

[[Category:Work Log]]

Hubs

2017-07-18T16:22:21Z

HiraF:

Hira Farooqi (Work Log)

2017-07-18T16:07:07Z

HiraF:

Hira Farooqi (Work Log)

2017-07-17T18:53:28Z

2017-06-28T22:50:22Z

HiraF: /* Income Data */

{{McNair Projects
|Has title=Hubs
|Has owner=Hira Farooqi,
|Has keywords=Data
|Has project status=Active
}}
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 file is nsf2017.txt, copied from table titled '''nsf''' in the biotech db.

It contains:
*Award ID
*Award Institution
*Award Effective date
*Institution city
*Award Value
From 1900 - 2017

Output file is nsfSummary.txt. It contains:
*Variables: City, year, nogrants, valuegrant
*1900-2017

Cities are not unique. Eg. NEW YORK and New York are two different cities. Need to merge their data.
*3854 cities

==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

*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

==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

==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
Cleaned files are in
Z:\Hubs\2017\merging_on_ID

They contain:
*MSA code
*MSA
*Year
*Total Household Income

Date from 2005-2015

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
*Year
*Total Household Income

The table includes 8780 observations

Hira Farooqi (Work Log)

2017-06-27T22:32:57Z

HiraF:

Hira Farooqi (Work Log)

2017-06-27T22:32:33Z

HiraF:

Hubs

2017-06-27T22:28:28Z

HiraF: /* Income Data */

{{McNair Projects
|Has title=Hubs
|Has owner=Hira Farooqi,
|Has keywords=Data
|Has project status=Active
}}
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 file is nsf2017.txt, copied from table titled '''nsf''' in the biotech db.

It contains:
*Award ID
*Award Institution
*Award Effective date
*Institution city
*Award Value
From 1900 - 2017

Output file is nsfSummary.txt. It contains:
*Variables: City, year, nogrants, valuegrant
*1900-2017

Cities are not unique. Eg. NEW YORK and New York are two different cities. Need to merge their data.
*3854 cities

==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

*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

==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

==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
Cleaned files are in
E:\McNair\Projects\Hubs\Summer 2017\MSA_Income_cleaning

They contain:
*MSA
*Year
*Total Household Income

Date from 2005-2015