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*select MSAs
===The Target Dataset===
We will need to process the following variables:
*SuperMSA - combine SanFran and SanJose, New York and Newark?, NC Research triangle, others?
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
*SeedEarlyVCInv
*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?).
