Startup Firms from Research in US Universities

Abstract

In this study the annual number of startups initiated and cumulative operational startups per university are modeled as a function of characteristics of the university itself, its faculty, and general financial conditions.I employ annual university-level data from the surveys by AUTM on the number of startups initiated and operational, the presence of a medical school, land-grant and private status, the size and age of the TTO, the number of invention disclosures, and the levels of federal and industrial funding, as well as data on the size and quality of the life science and engineering faculties from the 1994 Survey by the National Research Council (NRC).The quality measure is a weighted average of the NRC ranks of the individual department in each university, and varies from one to five.I also use annual state-level data on venture capital funding from the National Venture Capital Association Yearbook, and the annual NASDAQ composite index.I test four different specifications of the model, involving four different samples: all U.S. universities in the AUTM surveys; all universities except the University of California (UC) system, omitted because all ten of its campuses report as one, so these observations are substantial outliers; all universities who created their TTOs before passage of the Bayh-Dole Act in 1980; 1 and all universities who created their TTOs after passage of Bayh-Dole.2 1 Those universities with TTOs created before the passage of the Bayh-Dole Act are: Boston, Colorado State, Cornell, Harvard, Iowa State, Johns Hopkins, Kansas State, Montana State, Oregon State, Stanford, Tufts and Washington State Universities, the California and Massachusetts Institutes of Technology, the State University of New York system, the University of California system, and the Universities of Georgia, Iowa, Minnesota, Rochester, Southern California, Utah, Virginia and Wisconsin.2 I thank Bruno Cassiman for suggesting that I stratify the data by the ages of the TTOs in a different context.There is insufficient data to allow for finer partitions by TTO age, but using the Bayh-Dole Act to partition the data seems the most reasonable and intuitive approach.

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