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Record W2134962966 · doi:10.1257/mac.20140181

The Firm Size Distribution across Countries and Skill-Biased Change in Entrepreneurial Technology

2018· article· en· W2134962966 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.

Bibliographic record

VenueAmerican Economic Journal Macroeconomics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsMcGill University
Fundersnot available
KeywordsEconomicsDistribution (mathematics)Dispersion (optics)EconometricsTechnical changeConstruct (python library)Technological changeDemographic economicsMacroeconomicsProductivityMathematics

Abstract

fetched live from OpenAlex

Development is associated with systematic changes in the firm size distribution. I document that the mean and dispersion of firm size are larger in rich countries, and increased over time for US firms. To analyze the firm size-development link, I construct a frictionless general equilibrium model of occupational choice with skill-biased change in entrepreneurial technology (i.e., technical progress favors better entrepreneurs). The model accounts for key aspects of the US experience with only changes in aggregate technology. It attributes half the variation in mean and dispersion of firm size across countries to technical change. Distortions also affect the size distribution. (JEL J24, L11, L25, L26, O33)

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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