MétaCan
Menu
Back to cohort
Record W3122446134 · doi:10.1257/mac.20180461

Advertising, Innovation, and Economic Growth

2021· article· en· W3122446134 on OpenAlex
Laurent Cavenaile, Pau Roldan-Blanco

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 · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrofoundationsEconomicsGrowth modelChannel (broadcasting)EconometricsEmpirical evidenceAdvertisingMicroeconomicsBusinessTelecommunicationsComputer scienceMacroeconomics

Abstract

fetched live from OpenAlex

This paper analyzes the implications of advertising for firm dynamics and economic growth through its interaction with R&D. We develop a model of endogenous growth with firm heterogeneity that incorporates advertising decisions and calibrate it to match several empirical regularities across firm size. Our model provides microfoundations for the empirically observed negative relationship between both firm R&D intensity and growth and firm size. In the calibrated model, about half of the deviation from proportional firm growth is attributed to our novel advertising channel. In addition, R&D and advertising are substitutes, a prediction for which we find evidence in the data. (JEL D22, E23, H25, L25, M37, O32)

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.226
Teacher spread0.214 · 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