The Impact of Food Price Changes and Food Insecurity on Economic Welfare: A Case of Selected Southern African Countries
Bibliographic record
Abstract
Households are tremendously affected by changes in food prices. The extent of the impact depends on the income of households. This study is undertaken to analyse the impact of food price changes on food insecurity and economic welfare in selected southern African countries (Lesotho, Malawi, South Africa, Mozambique and Botswana). The Panel Auto Regressive Distributed Lag (PARDL) model is estimated using time series data from the period of 1980 to 2016. The findings of this study showed that food price changes positively affect economic welfare in the long run for the countries. Households that are net food sellers generate a higher income when prices go up. Therefore, food price changes are a gain for these households, especially producers and net sellers. Furthermore, the study revealed that inflation and net trade affect economic welfare for the countries in the short run. As a policy recommendation, the governments of these countries can subsidise food producers, most especially producers of staple foods that are seasonal; this can stabilize food price changes. As a result, both net sellers and net buyers of food can benefit from food prices. In other words, the benefit of food price can spread across to net buyers, not only net sellers. Also the governments of these countries can use monetary policy such as increase in interest rate to combat inflation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".