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Record W2725160416 · doi:10.1111/joie.12132

Do Multi‐Plant Firms Reduce Misallocation? Evidence from Canadian Manufacturing

2017· article· en· W2725160416 on OpenAlex
Pavel Ševčík

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Industrial Economics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAllocative efficiencyProductivityDispersion (optics)Industrial organizationEconomicsBusinessMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Using Canadian plant‐level data, this paper shows that, depending on the industry, the differences in the average plant‐level productivity and cross‐plant allocation of resources between multi‐plant and single‐plant firms account for 1 to 15 per cent of the industry‐level TFP. A large part of this contribution stems from more efficient cross‐plant allocation of resources, measured by the covariance between plant size and productivity, in the pool of plants in multi‐plant firms compared to the pool of plants in single‐plant firms. There is less dispersion in the marginal products of the inputs, and thus less misallocation, in industries in which multi‐plant firms account for a larger share of output. The patterns found in the cross‐plant distribution of productivity and size are also consistent with better allocative efficiency among plants in multi‐plant firms than among plants in single‐plant firms.

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)0.001
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 score0.342
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

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

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 head0.178
GPT teacher head0.277
Teacher spread0.100 · 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