Labor Mobility of Scientists, Technological Diffusion, and the Firm's Patenting Decision

Kim, Jin‐Young; Marschke, Gerald

doi:10.2139/ssrn.288580

Cited by 125 publications

(157 citation statements)

References 23 publications

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“…To our knowledge, this is the first article that shows how the measures of knowledge spillovers drawn from patent citations (Jaffe et al 1993) and from high-skill labor mobility data (Kim and Marschke 2005) produce consistent and similar results.…”

Section: Introductionmentioning

confidence: 63%

“…The empirical literature has measured knowledge spillovers in two ways: (i) patent citations (Jaffe et al 1993) or (ii) the labor mobility of scientists and engineers (Almeida andKogut 1999, Kim andMarschke 2005). For robustness reasons, we employ both measures.…”

Section: Proxies For Knowledge Cluster Characteristicsmentioning

confidence: 99%

“…As Saxenian (1994) puts it, people perceive that they are employed "by the Valley" rather than by the individual firms. We adopt the measure employed by Kim and Marschke (2005), since Almeida and Kogut (1999) still use a patent-based proxy of scientist mobility. Thus, from the U.S. Current Population Survey (http://www.…”

Section: Proxies For Knowledge Cluster Characteristicsmentioning

confidence: 99%

“…census.gov/cps), for the years 1998-2000, and for each city, we calculate the average turnover experience (i.e., the share of mobile scientists) of all scientists and engineers, based on whether they change employers in the year before the survey and whether they remain in the same city. We use the same eight occupation categories for scientists and engineers used by Kim and Marschke (2005). We label this variable Mobilityspillover.…”

Section: Proxies For Knowledge Cluster Characteristicsmentioning

confidence: 99%

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Organizational Attributes and the Distribution of Rewards in a Region: Managerial Firms vs. Knowledge Clusters

Gambardella

Giarratana

2010

Organization Science

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T his paper expands the organization theory and evidence on regional industrial agglomerations. We define regional economic activities according to the attributes of the organizations that populate a region and investigate how organizational characteristics influence macro-outcomes at a regional economic level. We focus on two dimensions emerging from two widely known organizational forms: the managerial corporation and the knowledge cluster with a marked orientation toward interfirm knowledge spillovers. We use an original data set of 146 U.S. cities to obtain variations in the extent to which they are populated by managerial firms or knowledge clusters. By utilizing city-level measures of managerial salaries, we test how the intensity of managerial corporation versus knowledge cluster characteristics affects the mean and dispersion of the "rewards" of cities. Our evidence suggests that higher managerial corporate characteristics lower the variability of rewards, while they have no effect on the mean of rewards. Higher-knowledge cluster characteristics produce both higher dispersion and higher expected rewards. We explain these results by looking at the different learning mechanisms of the two organizational types. In so doing, we highlight the role of intra-and interfirm knowledge processes as important sources of differences in the rewards of the two models. From an empirical point of view, results are confirmed using both patent-based and skill mobility-based measures of knowledge spillovers.

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Section: Introductionmentioning

confidence: 63%

Section: Proxies For Knowledge Cluster Characteristicsmentioning

confidence: 99%

Section: Proxies For Knowledge Cluster Characteristicsmentioning

confidence: 99%

Section: Proxies For Knowledge Cluster Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Organizational Attributes and the Distribution of Rewards in a Region: Managerial Firms vs. Knowledge Clusters

Gambardella

Giarratana

2010

Organization Science

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“…These repeated encounters drive dynamic processes, generating vertical and horizontal connections that appear in 39 See, e.g., Zucker et al (1998) and Andersson et al (2004). productivity effects regarding the transmission of knowledge/information (Saxenian 1994;Porter 1998;Glaeser and Gottlieb 2009). Indeed, evidence also shows that firms are likely to patent more in regions characterized by higher labor mobility (Kim and Marschke 2005) and greater population density (Lööf and Nabavi 2012) .…”

Section: Agglomeration Economies Housing Markets and Infrastructurementioning

confidence: 99%

An Innovation Policy Framework: Bridging the Gap Between Industrial Dynamics and Growth

Braunerhjelm

Henrekson

2016

International Studies in Entrepreneurship

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: This paper examines policy measures that foster the creation of innovations with high inherent potential and that simultaneously provide the right incentives for individuals to create and expand firms that disseminate such innovations in the form of highly valued products. In so doing, we suggest an innovation policy framework based on two pillars: (i) the accumulation, investment, and upgrading of knowledge and (ii) the implementation of mechanisms that enable knowledge to be exploited such that growth and societal prosperity are encouraged. Knowledge is a necessary but far from sufficient condition for growth. To secure industrial dynamics and growth in the long term, institutions must be designed both to encourage sophisticated knowledge investments and to stimulate the creation, diffusion and productive use of knowledge in all sectors of the economy. We argue that the latter area has been overlooked in the policy discussion and that a coherent innovation policy framework must include tax policy, labor market regulation, savings channeling, competition policy, housing market regulation, and infrastructure to foster growth and future prosperity. Terms of use: Documents inJEL Classification: J24, O31, O32, O57.

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Hierarchical random‐effects model for the insurance pricing of vehicles belonging to a fleet

Desjardins

Dionne

2023

J of Applied Econometrics

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Summary We propose a count‐data model with hierarchical random effects for the posterior insurance ratemaking of vehicles belonging to a fleet, by allowing random effects for the fleet, the vehicles, and time. We derive a simple closed‐form ratemaking formula based on a hierarchical random‐effects specification. We estimate the corresponding econometric model and compute insurance premiums according to the past experience of both the vehicle and the fleet. Our model can be used in other count‐data applications with random individual and common effects on events involving many agents having activities with a principal in a hierarchical principal–agent environment, such as in education, health care management, finance, and business firms.

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Labor Mobility of Scientists, Technological Diffusion, and the Firm's Patenting Decision

Cited by 125 publications

References 23 publications

Organizational Attributes and the Distribution of Rewards in a Region: Managerial Firms vs. Knowledge Clusters

Organizational Attributes and the Distribution of Rewards in a Region: Managerial Firms vs. Knowledge Clusters

An Innovation Policy Framework: Bridging the Gap Between Industrial Dynamics and Growth

Hierarchical random‐effects model for the insurance pricing of vehicles belonging to a fleet

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