Tracking the trend of quinoa price in Bolivia: Structural breaks and persistence of shocks
Bibliographic record
Abstract
Quinoa has evolved considerably in the past decades, becoming consolidated as a fundamental pillar for Andean farming communities and emerging as a prominent actor in the global superfood market. Despite this, prices of this grain have been characterized by complex dynamics, with substantial fluctuations that directly affect smallholder income. The goal of this research is to analyze Bolivian quinoa price dynamics, identifying both the main events and factors that caused structural breaks in the price trend and the persistence of shocks in time. The approach employed combines, on the one hand, an analysis of the structural breaks by means of the Bai and Perron contrast, together with estimates of long memory using the 2ELW estimator. Also evaluated was the influence of exogenous variables that affect prices, for which the world commodity activity index (Index of Global Real Economic Activity), the Oceanic Niño Index and world quinoa production were considered. The findings show multiple structural breaks in the quinoa price series, related to certain key events. Among the latter are for example changes in research and development, the production and sales boom, and the boost prompted by State initiatives and international cooperation. These breaks are also related to different degrees of persistence in the shocks under the different regimes identified. Although the exogenous variables show no significant short-term effects, it is understood that they may have a relevant influence in different periods. The present study shows the complexity of Bolivian quinoa price dynamics, characterized by several structural breaks. To take proper advantage of this market, producers and policy makers must implement flexible strategies, as well as continuous monitoring of the sector's progress, considering the key factors that induced price trend changes over the years.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".