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Record W2016729285 · doi:10.1287/mnsc.1100.1280

Noncompete Covenants: Incentives to Innovate or Impediments to Growth

2011· article· en· W2016729285 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueManagement Science · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of CanadaBrock University
KeywordsEntrepreneurshipEndogeneityEndowmentVenture capitalInstrumental variableIncentiveEnforcementEconomicsBusinessLabour economicsIntermediationScope (computer science)Metropolitan areaPanel dataFinancial intermediaryFinanceMarket economy

Abstract

fetched live from OpenAlex

We find that the enforcement of noncompete clauses significantly impedes entrepreneurship and employment growth. Based on a panel of metropolitan areas in the United States from 1993 to 2002, our results indicate that, relative to states that enforce noncompete covenants, an increase in the local supply of venture capital in states that restrict the scope of these agreements has significantly stronger positive effects on (i) the number of patents, (ii) the number of firm starts, and (iii) employment. We address potential endogeneity in the supply of venture capital by using endowment returns as an instrumental variable. Our results point to a strong interaction between financial intermediation and the legal regime in promoting entrepreneurship and economic growth. This paper was accepted by Gérard P. Cachon, entrepreneurship and innovation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.254
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it