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Record W2046696915 · doi:10.1017/s1365100512000090

PUBLIC INFRASTRUCTURE AND EXTERNALITIES IN U.S. MANUFACTURING: EVIDENCE FROM THE PRICE-AUGMENTING AIM COST FUNCTION

2012· article· en· W2046696915 on OpenAlex
Guohua Feng, Apostolos Serletis

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

VenueMacroeconomic Dynamics · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExternalityProxy (statistics)Public infrastructureProductivityEconomicsFlexibility (engineering)Function (biology)Industrial organizationProduction (economics)Index (typography)Production functionMicroeconomicsEconometricsComputer scienceMacroeconomics

Abstract

fetched live from OpenAlex

In this paper, we propose a price-augmenting asymptotically ideal model (AIM) cost function to investigate the effects of public infrastructure on the performance of the U.S. manufacturing industry, using KLEMS data over the period from 1953 to 2001. In doing so, we make a distinction between the productivity effect and the production factor effect of public infrastructure. This distinction allows us to focus on the more interesting productivity effect by incorporating public infrastructure into the AIM cost function through the efficiency index. Moreover, we specify the growth rate of the efficiency index as a Box–Cox function of public infrastructure and a time trend, a proxy for other technology. The excellent flexibility of our price-augmenting AIM cost function offers many insights regarding the effects of infrastructure on the U.S. manufacturing sector.

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 score0.041
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.047
GPT teacher head0.218
Teacher spread0.172 · 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