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Record W2903180626 · doi:10.1515/jgd-2018-0011

Output Effects of Global Food Commodity Shocks

2018· article· en· W2903180626 on OpenAlexaff
Bilge Erten, Kerem Tuzcuoglu

Bibliographic record

VenueJournal of Globalization and Development · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsBank of Canada
FundersWorld Bank Group
KeywordsEconomicsFood pricesConsumption (sociology)CommoditySupply shockFood securityFood processingMonetary economicsInternational economicsAgricultureMonetary policy

Abstract

fetched live from OpenAlex

Abstract The dramatic fluctuations in global food prices over the past two decades have generated significant concern about their destabilizing macroeconomic effects. While the pass-through effects of international food prices on domestic prices have been widely documented, these estimates have not taken into account reverse causality, omitted variable bias, or differences in sources of international food price fluctuations. We use sign restrictions to identify relevant demand and supply shocks that explain the volatility in global food prices. We quantify their dynamic effects on several components of food exporters’ and food importers’ domestic output, including household consumption, government consumption, investment, and net exports. Our findings reveal that identifying the sources of the shocks driving global food prices is crucial to evaluating their domestic effects. Expansions in global economic activity that increase global food prices stimulate the domestic output of both food-importing and food-exporting economies; however, disruptions in global food commodity markets that lead to rising real food prices have large contractionary effects for food importers due to deteriorating trade balances and falling household consumption. We also document that the adverse effects of unfavorable global food shocks on household consumption are greater for food-importing countries with relatively high shares of household food expenditures and large food trade deficits.

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 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)0.000
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 score0.152
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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 teacher head, 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

Citations3
Published2018
Admission routes1
Has abstractyes

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