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Record W2557739864 · doi:10.3844/ajabssp.2016.148.156

An Empirical Analysis of Supply Response of Rubber in Malaysia

2016· article· en· W2557739864 on OpenAlexaff
Ghulam Mustafa, Ismail Abd Latif, Henry Egwuma

Bibliographic record

VenueAmerican Journal of Agricultural and Biological Sciences · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsNutrasource
FundersUniversiti Putra Malaysia
KeywordsEconomicsIncentiveFertilizerNatural rubberAgricultural economicsError correction modelEconometricsRelative priceMicroeconomicsCointegrationAgronomy

Abstract

fetched live from OpenAlex

Supply response of rubber to changes in economic incentives is analysed using co-integration approach. Time series data is taken for the period 1990 to 2014 and the vector error correction model framework has been applied. The empirical results confirmed the existence of a unique long-run equilibrium relationship among planted acreage, the relative price of rubber and price of fertilizer. Further, the estimates suggested that rubber supply is significantly influenced by the relative price of rubber and the price of fertilizer. The estimated short- and long-run elasticities of acreage with respect to relative price are respectively 0.04 and 0.77, while the short- and long-run elasticities of acreage with respect to fertilizer price are -0.20 and -0.28 respectively. The study recommends the design of an appropriate economic incentive structure to stimulate output and hence the income of farmers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.259
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2016
Admission routes1
Has abstractyes

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