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Record W3123421005 · doi:10.1093/ajae/aaw059

The Financialization of Food?

2016· article· en· W3123421005 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

VenueAmerican Journal of Agricultural Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsBank of Canada
Fundersnot available
KeywordsFinancializationEconomicsSpeculationVector autoregressionEquity (law)Futures contractMonetary economicsRecessionBusiness cycleFinancial economicsFinancial marketFinanceMacroeconomics

Abstract

fetched live from OpenAlex

Commodity‐equity return co‐movements rose dramatically during the Great Recession. This development took place following what has been dubbed the “financialization” of commodity markets. We first document changes since 1995 in the relative importance of financial institutions’ activity in agricultural futures markets. We then use a structural vector autoregression (VAR) model to ascertain the role of that activity in explaining correlations between weekly grain, livestock, and equity returns from 1995–2015. We provide robust evidence that, accounting for shocks that are idiosyncratic to agricultural markets, world business cycle shocks have a substantial and long‐lasting impact on the latter's co‐movements with financial markets. In contrast, changes in the intensity of financial speculation have an impact on cross‐market return linkages that is shorter‐lived and not statistically significant in all model specifications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.200

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 communication0.0000.000
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 head0.009
GPT teacher head0.180
Teacher spread0.170 · 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