Difference between revisions of "Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists"
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==Dataset build== | ==Dataset build== | ||
| − | Decisions: | + | ===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. | *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. | *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. | ||
| Line 40: | Line 42: | ||
*Determination of lead VC - see below | *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. | *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 | ||
| − | + | Variables: | |
| − | |||
*Startup name and ID, fund name and ID | *Startup name and ID, fund name and ID | ||
*Geocoded location of startup, state of incorporation of startup, year of founding (if available). | *Geocoded location of startup, state of incorporation of startup, year of founding (if available). | ||
Revision as of 13:50, 20 August 2017
| Academic Paper | |
|---|---|
| Title | Unobserved Complementarities between Entrepreneurs and VCs (Academic Paper) |
| Author | Ed Egan, Jeremy Fox, David Hsu |
| RAs | Amir Kazempour |
| Status | In development |
| © edegan.com, 2016 | |
Contents
Reference Papers
Jeremy's paper with David Hsu and Chenyu Yang is here:
Fox Hsu Yang (2015) - Unobserverd Heterogeneity in Matching Games with an Application to Venture Capital provides some notes.
Matlab Code
Abhijit Brahme (Work Log) contains his notes on working with the Matlab code. This needs a separate page.
Data specification
The data spec sent to Jeremy is in:
Z:\Projects\MatchingAcceleratorsToVCs
Data foundations
The database is vcdb2
This was built using:
Z:\VentureCapitalData\SDCVCData\vcdb2\ProcessData2.sql
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.
- Matching to a fund or a firm: For now, we will work with funds, though deals are sometimes transferred across funds within a firm (i.e. from Kliener fund IV to Kliener fund V), this is probably comparatively rare (check!).
- Dealing with the right censorship problem: We can likely address this with indicator variables to condition on, but may want to restrict estimation to dyads that don't have this issue. For now we will take portfolio companies that received their last investment and funds that made their last investment before 2012.
- Inadequate coverage in early years: VentureXpert's coverage is notably inferior prior to 1982. We should start with portco that received their first investment in 1985 and forward.
- 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
Variables:
- Startup name and ID, fund name and ID
- Geocoded location of startup, state of incorporation of startup, year of founding (if available).
- Exit indicator, exit value, exit type indicator
- alive2016 indicator, last round pre-2012 indicator
- Total invested (all SEL, across all funds), number of rounds (all SEL, across all funds), investment duration (yrs), date first inv, year first inv, total MOOMI (Money Out Over Money In)
- Number of funds investing
- As averages (?) and for lead:
- Fund ipo count, Fund M&A count, Fund investment count(calc at end), fund ipo rate, fund M&A rate, fund exit count, fund exit rate, fund ipo $, fund M&A $, fund exit $, fund fraction of MOOMI.
- Total invested by lead, number of rounds participation by lead, stage of participation of lead, location of lead, last investment pre-2012 indicator, lead fund type indicator (corp, priv, gov, etc.), lead fund size, lead fund vintage year.
- Indicator for transactional VC, amount of transactional VC.
- Possibly some dyadic variables like: Distance between lead and portco, industry preference match between lead and portco, maybe stage-match (doesn't make a lot of sense when collapsing rounds) between lead and port co.
Identifying lead VCs
Possible methods:
- Best performing participant (on exit count/value or fractional MOOMI) with tie-breaker
- Closest participant (using great circle distance)
- Most frequent participant with tie-breaker
- Participant with greatest investment with tie-breaker
- Participant in earliest round that stayed in for longest with tie-breaker
Minor Industry
Across all time and without regard to SEL vs. transaction, here's the minor industry list and counts:
indminorgroup | count -------------------------------+------- Industrial/Energy | 2871 Internet Specific | 8794 Biotechnology | 2592 Semiconductors/Other Elect. | 2402 Other Products | 4891 Computer Hardware | 2061 Computer Software and Services | 10550 Communications and Media | 3271 Medical/Health | 4373 Consumer Related | 3161
