This thesis reports on the economic analysis of trade secrets via data collected from prosecutions under the U.S. Economic Espionage Act (EEA.) Ratified in 1996, the EEA increases protection for trade secrets by criminalizing the theft of trade secrets. The empirical basis of the thesis is a unique database constructed using EEA prosecutions from 1996 to 2008. A critical and empirical analysis of these cases provides insight into the use of trade secrets. The increase in the criminal culpability of trade secret theft has important impacts on the use of trade secrets and the incentives for would-be thieves. A statistical analysis of the EEA data suggest that trade secrets are used primarily in manufacturing and construction. A cluster analysis suggests three broad categories of EEA cases based on the type of trade secret and the sector of the owner. A series of illustrative case studies demonstrates these clusters. A critical analysis of the damages valuations methods in trade secrets cases demonstrates the highly variable estimates of trade secrets. Given the criminal context of EEA cases, these valuation methods play an important role in sentencing and affect the incentives of the owners of trade secrets. The analysis of the lognormal distribution of the observed values is furthered by a statistical analysis of the EEA valuations, which suggests that the methods can result in very different estimates for the same trade secret. A regression analysis examines the determinants of trade secret intensity at the firm level. This econometric analysis suggests that trade secret intensity is negatively related to firm size. Collectively, this thesis presents an empirical analysis of trade secrets.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:552501 |
| Date | January 2010 |
| Creators | Searle, Nicola C. |
| Contributors | Reid, Gavin C.; Centre for Research into Industry, Enterprise, Finance and the Firm |
| Publisher | University of St Andrews |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10023/1632 |
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