Changes

Coming back to it, the paper looks a little thin (though clearly the data is a monster already). I think it would benefit from a couple of extensions, particularly the inclusion of something that resembles an instrument. I have the following ideas, which might be feasible in the time we have: (Note: The defacto standard method of determining the lead investor is to see which (if any) investor was present from the first round.) ===Using Patents=== Patents might act to certify their patent-holders in the face of information asymmetries (see, for example, Hsu and Ziedonis, 2007). Thus firms with acquirers of targets with patents might value the certification of a venture capitalist less than when they consider targets without patents. Likewise, on average about 2/3rds of all patent citations are added by examiners (Alc�acer and Gittelman, 2006 and Cotropia et al., 2010). Thus citation counts might represent the search costs associated with finding information about patents. That is, patents with more citations are the ones that are easiest to find, and so mitigate information asymmetries the most successfully. ===VC Reputations=== We argue, explicitly, that VCs use their reputations to certify thier firms. We can calculate the defacto standard measures of reputation - the number of IPOs and the total number of successful exits, and use these to instrument our effects. This could be done for either the lead investor, or the most successful investor, or a weighted average of all investors (weighting by the number of rounds they participated in, or the proportional dollar value they may have provided). Likewise we can calculate the number of funds the lead investor had successfully raised at the time of the exit, or the average number of funds raised across all investors (again perhaps with a weighting).  ===VC Information Asymmetries=== Implicit in our argument is that VCs mitigate the information asymmetries between themselves and their portfolio firms effectively. We can refine this argument to consider the degree to which a VC is likely to be informed about their porfolio firm. ====Distances==== We can use the road or great-circle distance from the lead investor to the portfolio company as a measure of the information acquisition cost. We could also create a cruder but likely more meaningful version of this by creating a binary variable to see whether the lead investor was within a 20-minute drive of the portfolio company (this is the so called '20 minute rule' - discussed as important for monitoring in Tian, 2006). Alternatively we could consider the nearest investor, or the average of the nearest investors across all rounds, etc.  I can get 2,500 requests per IP address (I can run 3+ concurrently from Berkeley) from the Google Maps api, with responses including driving distances and estimated driving times. ====Active Monitoring==== I can also determine whether the lead VC has a board seat at the portfolio company at the time of the acquisition, as well as the fraction of invested firms with board seats, and the total number of board sets held by VCs (or the fraction), using the identities of the executives. Though this will be particularly difficult in terms of data, I plan on doing it for another project with Toby Stuart anyway.