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Record W2762797004 · doi:10.1002/jae.2407

Commodity Price Volatility and the Sources of Growth

2014· article· en· W2762797004 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

VenueJournal of Applied Econometrics · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsTrinity College
FundersEconomic and Social Research Council
KeywordsEconomicsVolatility (finance)EconometricsHuman capitalMonetary economicsEconomic growth

Abstract

fetched live from OpenAlex

Summary This paper studies the impact of the growth and volatility of commodity terms of trade (CToT) on economic growth, total factor productivity, physical capital accumulation and human capital acquisition. We use the standard system generalized methods of moments (GMM) approach as well as the dynamic common correlated effects pooled mean group (CCEPMG) methodology for estimation to account for cross‐country heterogeneity, cross‐sectional dependence and feedback effects. Using both annual data for 1970–2007 and 5‐year non‐overlapping observations, we find that while CToT growth enhances real output per capita, CToT volatility exerts a negative impact on economic growth operating mainly through lower accumulation of physical and human capital. Productivity, however, is not affected by either the growth or the volatility of CToT. Our results also indicate that the negative growth effects of CToT volatility offset the positive impact of commodity booms. Therefore, we argue that volatility, rather than abundance per se, drives the ‘resource curse’ paradox. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.179
Teacher spread0.160 · 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