2016
DOI: 10.2139/ssrn.2758854
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Revisiting Labor Mobility in Innovation Markets

Abstract: It is now widely asserted that legal regimes that enforce contractual and other limitations on labor mobility deter technological innovation. First, recent empirical studies purport to show relationships between bans on enforcing noncompete agreements, increased employee movement, and increased innovation. We find that these studies misconstrue legal differences across states and otherwise are flawed, incomplete, or limited in applicability. Second, scholars have largely adopted the view that California's poli… Show more

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Cited by 8 publications
(7 citation statements)
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“…This literature looks at the effect of NCCs on the innovation process (e.g. Gilson 1999, Fallick, Fleischman, and Rebitzer 2006, Marx, Strumsky, and Fleming 2009, Samila and Sorenson 2011, Marx, Singh, and Fleming 2015, and Barnett and Sichelman 2016, entrepreneurship (e.g., Stuart and Sorenson 2003a, and Stuart and Sorenson 2003b, Samila and Sorenson 2011, and Starr, Balasubramanian, and Sakakibara 2017, employee mobility (e.g., Fallick, Fleischman, and Rebitzer 2006, Marx, Strumsky, and Fleming 2009, and Jeffers 2018, firm-sponsored versus employee-paid training (e.g., Garmaise 2011, Starr, Prescott, and, Starr 2018, andStarr, Ganco, andCampbell 2018), wages (e.g., Mukherjee and Vasconcelos 2011 and Balasubramanian, Chang, Sakakibara, Sivadasa, and Starr 2018), firm's output (e.g., Bishara 2011, Bishara and Orozco 2012, Lobel and Amir 2014, and Anand, Hasan, Sharma, and Wang 2018, as well as on the firms' financial reporting choices (e.g., Chen, Zhang, and Zhou 2018). Our paper contributes to this literature by furthering our understanding of how participants of the labor force respond to NCCs.…”
mentioning
confidence: 99%
“…This literature looks at the effect of NCCs on the innovation process (e.g. Gilson 1999, Fallick, Fleischman, and Rebitzer 2006, Marx, Strumsky, and Fleming 2009, Samila and Sorenson 2011, Marx, Singh, and Fleming 2015, and Barnett and Sichelman 2016, entrepreneurship (e.g., Stuart and Sorenson 2003a, and Stuart and Sorenson 2003b, Samila and Sorenson 2011, and Starr, Balasubramanian, and Sakakibara 2017, employee mobility (e.g., Fallick, Fleischman, and Rebitzer 2006, Marx, Strumsky, and Fleming 2009, and Jeffers 2018, firm-sponsored versus employee-paid training (e.g., Garmaise 2011, Starr, Prescott, and, Starr 2018, andStarr, Ganco, andCampbell 2018), wages (e.g., Mukherjee and Vasconcelos 2011 and Balasubramanian, Chang, Sakakibara, Sivadasa, and Starr 2018), firm's output (e.g., Bishara 2011, Bishara and Orozco 2012, Lobel and Amir 2014, and Anand, Hasan, Sharma, and Wang 2018, as well as on the firms' financial reporting choices (e.g., Chen, Zhang, and Zhou 2018). Our paper contributes to this literature by furthering our understanding of how participants of the labor force respond to NCCs.…”
mentioning
confidence: 99%
“…The extent of knowledge spillovers depends on the strength of intellectual property rights and substitute legal appropriability mechanisms such as trade secrecy and covenants not to compete (Barnett & Sichelman, ; Png, ; Starr, ). By combining data on such laws with the Cattell dataset, researchers can investigate the effect of legal appropriability mechanisms on clustering and spillovers.…”
Section: Future Researchmentioning
confidence: 99%
“…We use these 10 states as one of our control groups in the analysis that follows. However, Barnett and Sichelman (2016) argue that the Stuart and Sorenson (2003) classification oversimplifies and misstates the dissimilarity in the strength of state-by-state enforcement of non-competes by classifying enforcement strength as a binary variable: "enforcing" or "nonenforcing." Barnett and Sichelman (2016) argue that the binary classification is highly inaccurate in that, with the exception of California, North Dakota, and…”
Section: Non-enforcing Statesmentioning
confidence: 99%
“…The misclassification of states identified by Barnett and Sichelman (2016) may not be random and could introduce systematic error. As a result, we also use a control group consisting of two states (California and North Dakota) since only these states can be classified as non-enforcing states in the entire sample period used in our study.…”
Section: California and North Dakotamentioning
confidence: 99%
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