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Record W2906076126 · doi:10.22004/ag.econ.277212

Decontrolling, Price Transmission and Market Integration of Sugar Sector in India vis-a-vis Global market - A cointegration Analysis

2018· preprint· en· W2906076126 on OpenAlexaboutno aff
P. Murali, R Sendhil, D. Puthira Prathap, V. Venkatasubrmanian, Bharat Ram

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationMarket integrationSugarEconomicsError correction modelAcknowledgementInternational economicsBusinessAgricultural economicsMacroeconomicsEconometricsBiology

Abstract

fetched live from OpenAlex

The study analyses the extent, pattern and degree of the spatial integration of sugar markets in India, as well as relationship of white sugar export prices of India and global market. The pattern and degree of integration were assessed by testing for the existence of the Law Of One Price (LOOP) and ascertaining the speed of adjustment toward long-run equilibrium, using various tests by using cointegrated methods. Results have shown that only 4 of 11 sugar markets are integrated into a common sugar market. The supply of sugar appears to be the most important factor shaping the long-run behaviour of its price levels in India. No single market is found to be the price leader. The prices of sugar exported by India to the global market were not cointegrated and did not conform LOOP. Decontrolling of sugar sector from clutches of monthly release mechanism and export quota in India, plays an insignificant role in determining the relationship of sugar prices in the global market. The study suggests that improving the extent of market integration by focusing on the communication and other market related infrastructure and sugar policy reforms for more integration of domestic and global market. Acknowledgement : The presenting author is thankful to Director, ICAR- Sugarcane Breeding Institute for kind support to submit the paper to 30th International Conference of Agricultural Economists (ICAE 2018) in Vancouver, British Columbia, Canada, 28 July 2 August 2018.

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)0.003
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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