MétaCan
Menu
Back to cohort
Record W1519711094

Failure Rates for New Canadian Firms: New Perspectives on Entry and Exit

2000· preprint· en· W1519711094 on OpenAlexaboutno aff
John R. Baldwin, Lin Bian, Richard Dupuy, Guy Gellatly

Bibliographic record

VenueRePEc: Research Papers in Economics · 2000
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsCompetition (biology)Market failureDemographic economicsEconomicsBusinessMicroeconomicsDemography
DOInot available

Abstract

fetched live from OpenAlex

This study investigates the determinants of failure for new Canadian firms. It explores the role that certain factors play in conditioning the likelihood of survival - factors related to industry structure, firm demographics and macroeconomic cycles. It asks whether the determinants of failure are different for new start-ups than for firms that have reached adolescence, and if the magnitude of these differences is economically significant. It examines whether, after controlling for certain influences, failure rates differ across industries and provinces. Two themes figure prominently in this analysis. The first is the impact that certain industry characteristics - such as average firm size and concentration - have on the entry/exit process, either through their influence on failure costs or on the intensity of competition. The second centres on how the dimensions of failure evolve over time as new firms gain market experience.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.045
GPT teacher head0.300
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations83
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicFirm Innovation and GrowthFrench-language works237,207