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Code and data for detecting "u-turns" among inventors. If two inventors have been "lumped" into a single inventor ID, or if a single inventor patents concurrently with multiple assignees, it may appear that the inventor is moving repeatedly when in fact something is going on. Uturns.py detects these "u-turns" in the patent data and labels them so that researchers can either exclude them from inventor mobility tables or check them manually. The input file Order.inventor.geo.assignee.combo.disambig.txt.7z is a compressed file of patent data with the pipe-delimited format patent #|inventor ID|assigneeid|application date| internal ID | grant date | inventor name | last name | first name | city/state | country | state | city | inventor # on patent | inventor ID | county | fips | fips state | fips county | latitude | longitude | zip code | msa. The output file is pipe-delimited with patent # | inventor ID | internal ID | application date | grant date | inventor name | last name | uturn identifier. Note that records are only output by Uturns.py for inventors with u-turns. Also, patent assignees are notoriously difficult to consolidate due to slight variations in naming. The sheer number of assignee variations make it impossible to check all by hand. However, we use the aforementioned u-turns to identify possibly-missed consolidations, i.e. if an inventor u-turns between two companies with very similar names. We use soundex to identify very similar names within u-turns and then check all of these by hand. The resulting file, myassigneeids.dta, contains assignee names and IDs based on this hand-check of u-turns with very similar names.&nbsp

We estimate the firm-level returns to retaining employees using difference-in-differences analysis and a natural experiment where the enforcement of employee noncompete agreements was inadvertently reversed in Michigan. We find that noncompete enforcement boosted the short-term value of publicly traded companies by approximately 9%. The effect is increasing in local competition and growth opportunities, and offset by patenting.

When start-up innovation involves a potentially disruptive technology—initially lagging in the predominant performance metric, but with a potentially favorable trajectory of improvement—incumbents may be wary of engaging in cooperative commercialization with the start-up. While the prevailing theory of disruptive innovation suggests that this will lead to (exclusively) competitive commercialization and the eventual replacement of incumbents, we consider a dynamic strategy involving product market entry before switching to a cooperative commercialization strategy. Empirical evidence from the automated speech recognition industry from 1952 to 2010 confirms our main hypothesis., Wharton School. Mack Institute for Innovation Management, Sloan School of Management (Roberts E-Center Fund)