VCDB20

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Project
VCDB20
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Project Information
Has title VCDB20
Has owner Ed Egan
Has start date
Has deadline date
Has project status
Has project output Data, Tool, How-to
Copyright © 2019 edegan.com. All Rights Reserved.

The VCDB20 project documents a build of my VCDB -- Venture Capital DataBase -- covering until the end of 2020. Each VCDB includes investments, funds, startups, executives, exits, locations, and more, derived from data from VentureXpert. This project updates vcdb4, which covered (almost) to the of Q3 2019, and replaces VCDB20H1 and [[VCDB20Q3], which were partial builds. See also: SDC Normalizer.

Data design

I followed the same data design as in VCDB20H1. Essentially the specification pulls everything, even things that aren't needed like incomplete M&As or withdrawn IPOs, or funds or investments that aren't venture capital, all the way to the present (the pull was done on 2020-01-04), and then place restrictions on the data later. Crucially, the pulls no longer use the venture-related flag, so the data contains private equity and other deals, as well as secondaries and purchases. Note that the M&As are pulled separately for public and private acquirers, and in chunks by year to keep the request sizes manageable.

Processing Steps

Source Files

Copy over and update the source files: 

Name
----
NormalizeFixedWidth.pl
RoundOnOneLine.pl
USFirmBranchOffices1980.rpt
USFirmBranchOffices1980.ssh
USFirms1980.rpt
USFirms1980.ssh
USFund1980.rpt
USFund1980.ssh
USFundExecs1980.rpt
USFundExecs1980.ssh
USIPO1980.rpt
USIPO1980.ssh
USMAPrivate.rpt
USMAPrivate00-10.ssh
USMAPrivate10-.ssh
USMAPrivate80-85.ssh
USMAPrivate85-00.ssh
USMAPublic.rpt
USMAPublic00-.ssh
USMAPublic80-00.ssh
USPortCo1980.rpt
USPortCo1980.ssh
USPortCoExecs1980.rpt
USPortCoExecs1980.ssh
USPortCoLongDesc1980.rpt
USPortCoLongDesc1980.ssh
USRound1980.rpt
USRound1980.ssh
USRoundOnOneLine1980.rpt
USRoundOnOneLine1980.ssh

Update the paths and dates in the ssh files then run them (see SDC Platinum).

Database import

Run the SDC Normalizer on each of the files. For most of them, that's straightforward. You can safely ignore the Access Violation error messages that occur at the end of some pulls. However, the following require attention:

  • Fix the header row in USFirms1980.txt before normalizing (the Capital Under Management column name is too long)
  • Remove double quotes from USFund1980-normal.txt, USFundExecs1980-normal.txt, USPortCo1980-normal.txt
  • The private and public M&A file sets have to be (separately) combined after they've been normalized. Then replace \tnp\t and \tnm\t with \t\t.
  • For RoundOnOneLine, remove the footer, run NormalizeFixedWidth.pl first then RoundOnOneLine.pl, and then fix the header.
  • The PortCo Long Description needs to be pre-processed from the command line and then post-processed in excel (see VCDB20H1 and Vcdb4#Long_Description).

Create the dbase as a researcher: 

createdb vcdb20

Move the files to //mother/bulk/vcdb20 and run the load script: 

E:\projects\vcdb20\Load.sql

Create the keys

Standardize company names using Hall normalization without a fuzzy algorithm (see The Matcher (Tool)) and matching them to themselves for PortCos, M&As, IPOs. It is crucial that the self-matches are made using mode=2, or you won't select a stdname and will generate duplicate entries (from the stdname permutations. 

perl .\Matcher.pl -mode=2 -file1="DistinctConame.txt" -file2="DistinctConame.txt"

The keys for each table as follows:

  • PortCo: Coname, statecode, datefirstinv
  • M&A (private targets): targetname, statecode, announceddate
  • IPOs: issuer, statecode, issuedate
  • Fund: Fundname
  • Firm: Firmname

Note that when normalizing Fundexecs use fundname, fundyear as the foreign keys. Generally, do not copy down keys unless they are foreign and blank in the source file.

Also, clean up the round table and create the SEL flags, as well as the PortCoSEL table. And make sure that the end of period date (plus 1) is updated in PortCoAliveDead.

Add the geocoding

Load in the old geocoding and create data files for new Google Maps API runs (see Geocode.py for directions). Note that I separate out Growth and non-growth PortCo addresses so that I can get the growth ones first for the PortCos, and that there are also firm and firm branch office addresses to process (US only). The limit for free is 2,500 calls/day but the cost per call is pretty low.

Note that PortCoGeoID and other tables are built in the BuildBaseTables.sql script.

Change to Load script:

  • Retrieve and load ZCTA Gazetteer from the U.S. Census
  • A small but meaningful proportion of US venture firms have zip codes but not addresses. I created the table firmbogeoplus to add longitude and latitudes for these firms in Load.sql (right after firmbogeo).

Load the Additions

These include:

Join in the exit data

Bring in and clean up the IPO and MA (private target) data, match it to the PortCos, and reconcile the conflicts. This creates two core tables, one key table, and two PortCo results tables:

  • IpoCleanNoDups
  • MACleanNoDups
  • ExitKeys (IPO and MA only, not PortCo)
  • PortCoExit
  • PortCoAliveDead

PortCoAliveDead follows the academic convention of marking a startup as alive if it has begun receiving investment and hasn't exited and if its last investment occurred less than five years ago.

Base Tables

The base tables are built using BuildBaseTables.sql. These provide common foundations for:

  • PortCo Geography
  • Other PortCo tables, including industry, id, cpi adjustments, geoids.
    • Exit, Alive/dead and patents & SBIR/STTR grant (note that these need updating) information is also included.
  • Firm variables (note that the full build is only done for firms that make growth investments).
  • Round Line Joiner (RLJoiner) tables.
  • PortCo People, including gender, dr., titles, serials.
  • Fund People: gender and dr.
  • The Master tables:
    • PortCoMaster
    • PortCoPeopleMaster
    • FirmGrowthMaster (the Firm master table, for growth investments)
    • RLMaster

Finally, this code also builds: MatchMostNumerous and MatchHighestRandom, which are used in Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists

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