MétaCan
Menu
Back to cohort
Record W1772563446 · doi:10.1111/1756-2171.12100

Building new plants or entering by acquisition? Firm heterogeneity and entry barriers in the U.S. cement industry

2015· article· en· W1772563446 on OpenAlexaff
Hector Perez‐Saiz

Bibliographic record

VenueThe RAND Journal of Economics · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsBank of Canada
FundersFundación Caja MadridUS-UK Fulbright CommissionNational Science Foundation
KeywordsIndustrial organizationBusinessPermissiveCementEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

I estimate a model of entry for the cement industry that considers two options of expansion: building a plant or acquiring an incumbent. The model takes into account that there is a transfer of the buyer firm‐level characteristics to the acquired plants, which affects profits from the acquisition. Estimates show that a less‐permissive Reagan–Bush administration's merger policy would decrease the number of acquired plants by 71%, greenfield entry would increase by 9.2% and consumer surplus would decrease by 23.5%. Results suggest that regulators should be concerned about policies that negatively affect the efficient reallocation of assets between incumbents and entrants.

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)0.003
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.060
GPT teacher head0.257
Teacher spread0.197 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe RAND Journal of EconomicsSame topicFirm Innovation and GrowthFrench-language works237,207