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==Dataset build==
===Decisions=== Decisions we need to make:
*Will we need synthetic matches? If so what we do we do for outcomes? Can still do dyadic and left/right pair variables.
*Granularity of industry: To start let's use minor industry group (see below). We use a much finer grained industry definition and aggregate back up to balance out the counts somewhat later.
*Determination of lead VC - see below
*How to collapse VC rounds (date, amount, etc.): We will use only seed, early, later stage investment and insist on the presence of seed/early for inclusion. We can then have date first, investment duration (to date last), total investment.
 
===Objective dataset description===
 
Unit of observation - a startup-fund match.
 
Constraints:
*PortCo name disclosed
*PortCo date of first investment >= 1/1/1985
*PortCo date of last investment <= 1/1/2012
*PortCo received at least one round of Seed or Early stage investment
*Matched VC is not undisclosed
*Matched VC fund has completed
The objective isVariables:*Unit of observation - a startup-fund match.
*Startup name and ID, fund name and ID
*Geocoded location of startup, state of incorporation of startup, year of founding (if available).

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