Market and Welfare Effects of Food Security Policies on Smallholder Rice Farmers and Consumers in Sierra Leone
Bibliographic record
Abstract
This research examines the market and welfare effects of three food security policy options in Sierra Leone in response to the high rate of rice importation and rising rice prices over the last decade. These policies aimed at curbing the rate of rice importation, promoting local rice production, and enhancing welfare of smallholder rice farmers in rural communities. The policies investigated included: 1) reinstating tariffs on rice imports, 2) promoting value-chain strengthening interventions that increase production of locally produced rice and 3) instituting a quota (or some quantity restriction) on rice imports. A log-linear comparative static displacement model was used to carry out the analysis. For the first policy, 20%, 30% and 40% shocks were introduced in the equilibrium system to represent decreases in the quantity of rice imported as a result of reinstating tariffs on imported rice. Results revealed that welfare of consumers and or smallholder farmers of locally produced rice was enhanced by 9.4% at a 10% tariff increase and 17.8% at a 20% tariff increase. Consumers of imported rice had their welfare enhanced by 3.5% at a 10% tariff increase while welfare was dis-enhanced by 5.4% at a 20% tariff increase. With a 10% increase in the supply of locally produced rice, there was a corresponding welfare enhancement on smallholder rice farmers and consumers by 14.43% and by 27% for a 20% increase in supply. Marginal increases were recorded for consumers of imported rice. The results show that the optimal policy in the current post-Ebola national recovery environment is one that increases local rice production through cultivation intensification and rice value chain efficiency.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".