Transmission of Global Food Prices to Domestic Prices: Evidence from Sri Lanka
Bibliographic record
Abstract
Food prices have been increasing sharply since 2003. In the globalized world, the transmission of global foodprice increases to domestic market determines the decision of economic agents and policy makers of a domesticeconomy. The recent growth of global food prices affects the welfare of poor consumers and producers. In SriLanka, large segment of the population spends more than 50 percent of their income on food. Thus, this studyinvestigates and assesses how international food price surge affects domestic inflation process in Sri Lanka. Theempirical statistical results are derived by using a battery of parametric and non-parametric econometrictechniques using monthly data of price series for the period from 2003M1 to 2013M12. The co-integrationanalysis results confirm that global food prices, domestic prices are co-integrated. Therefore, Sri Lankangovernment needs to develop a safety net program for the poor and a longer term poverty reduction strategy.Policy attention needs to shift towards efforts to increase food production. The results of this study have variouspolicy implications for monetary policy, food and agricultural policy and trade policy for Sri Lanka.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".