Modelling the Dynamic Impact of Replanting Subsidy on Tea Production in Sri Lanka: Policy Analysis Using the ARDL Model Approach
Bibliographic record
Abstract
The tea industry in Sri Lanka plays a vital role in the economy with its direct and indirect contribution to the gross domestic products, employment, and foreign exchange earnings. The successive governments have introduced subsidy schemes for replanting to increase tea production. However, according to the authors' knowledge, there are no comprehensive quantitative studies undertaken to effectively investigate and quantify the effect of tea replanting subsidy scheme on tea production of the various geographic elevations of tea cultivating lands. The significant contribution of this paper is the quantification of the impact of tea replanting subsidy schemes on tea production in the short-run and long-run at different altitudes. This research takes time series data from 1970 to 2018 of three different heights or elevations-high, medium, and low. The Autoregressive Distributed Lag (ARDL) Model examines the short-run and long-run dynamics of the subsidy scheme on tea production. The results reveal that there is a cointegration between tea production and three variables; tea replanting subsidy, tea prices and tea bearing area; in all three elevations. But tea replanting subsidy is not significant in long run for all three elevations, separately. In the short-run analysis, the impact of replanting subsidy is significant only for tea production in low heights with one-year time lag. Since the study reveals that tea replanting subsidy increases, the tea production of low elevation also increases, and almost 60% of the tea extent and 73% of the total tea production gained from low heights, we recommend that the government continue the tea replanting subsidy schemes as it is benefitted by whole tea industry in long run.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".