Spatial Price Integration and Asymmetric Threshold Effects in Red Shallot Markets Between Urban and Rural Areas: Evidence from North Sumatra, Indonesia
Bibliographic record
Abstract
This study investigates spatial price integration and asymmetric threshold effects in red shallot markets between rural and urban areas in North Sumatra, Indonesia.Using monthly consumer price data from 2018 to 2024 across six markets, we apply Johansen cointegration, Granger causality, Vector Error Correction Models (VECM), Threshold Vector Autoregression (TVAR), Impulse Response Functions (IRF), and Forecast Error Variance Decomposition (FEVD).Results confirm long-run price equilibrium among market pairs, with VECM indicating that 38% of price deviations are corrected monthly.Granger causality reveals directional asymmetries: short-distance markets exhibit rural-to-urban predictability, while long-distance pairs show bidirectional influence.TVAR identifies a non-linear threshold near IDR 1,200/kg, above which price transmission intensifies.IRF shows rural markets adjust more slowly to shocks from urban centers, particularly beyond 180km.FEVD results indicate that rural markets account for 22-47% of urban price variance under specific spatial conditions.These findings highlight how distance, asymmetry, and threshold dynamics shape price integration in perishable crop markets.The study offers new empirical insights for spatial food market governance and supports targeted infrastructure and information interventions to promote sustainable rural-urban integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".