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Record W3121207955

Des déterminants de la dynamique entrepreneuriale dans les régions françaises (1994-2003),

2010· preprint· fr· W3121207955 on OpenAlexaff
Marie-Estelle Binet, François Facchini, Martin Koning

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2010
Typepreprint
Languagefr
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsIncentiveEntrepreneurshipOrder (exchange)EconomicsEconomic geographyUnemploymentPanel dataEconometricsMacroeconomicsMicroeconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

This research aims at appraising the determinants of entrepreneurship dynamics in the French administrative regions over the period 1993-2004. First, we highlight how the regional territory may be considered in a dualistic manner in order to explain the differentials between the regional activities of entrepreneurs. One may consider a regional territory either as an incentive system or as a cognitive system. By modifying individual trade-offs and/or easing the diffusion of information, the regional atmosphere indeed influences personal choices related to new firm creation. This theoretical approach then leads us to specify a dynamic model of entrepreneurial activity at the regional level. Based on Blundell and Bond’s (1998) methodology and on French regional data, we confirm Holcombe’s assumption (1998) that the creation of firms can be mainly explained by itself and can be viewed as an auto-regressive process. We also deduct from our econometric estimations that the unemployment rate and the available income positively impact entrepreneurship activity. Inversely, the size of existing firms may play the role of a barrier to entry into the regional market. Finally, institutional and cultural factors cannot be neglected as potential determinants of the creation of firms at the regional level, as illustrated by the significance of individual fixed effects.

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.

How this classification was reachedexpand

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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