Threshold Effects of Economic-Policy Uncertainty on Food Security in Nigeria
Bibliographic record
Abstract
The study investigated the threshold effects of economic-policy uncertainty on food security in Nigeria, covering the period from 1970 to 2021. Summary statistics and unit root tests were employed for preliminary analysis, while the threshold regression model was used to realize the key objective of the study. The results revealed that adult population (ADULTPOP), environmental degradation (ENVT), exchange rate uncertainty (EXRU), financial deepening (FINDEEP), food security (FS), government expenditure in agriculture uncertainty (GEAU), global economic uncertainty (GEU), inflation (INF), and interest rate uncertainty (INRU) showed positive mean, maximum, and minimum values over the study period. Most variables exhibited low volatility, except for inflation (SD = 15.619) and interest rate uncertainty (SD = 8.435), which had relatively higher volatility. ADF and PP unit root tests indicated that ADULTPOP, FINDEEP, and FS had unit roots in levels, but became stationary after first differencing (integrated of order one). ENVT, EXRU, GEAU, GEU, INF, and INRU were stationary in level, indicating they were integrated of order zero. The result showed a threshold value of 0.077 for global economic uncertainty (GEU). Above this threshold, exchange rate uncertainty (EXRU) had a statistically significant effect on food security (p = 0.031). Non-threshold variables such as adult population (p = 0.000) and environmental degradation (p = 0.000) also had significant effects on food security. The study thus provided evidence of threshold effects of economic-policy uncertainty on food security. The study recommends that policymakers incorporate threshold values in policy implementation to mitigate risks linked to high economic-policy uncertainty. The Government is also advised to establish strategies for stabilizing exchange rates or alleviating their harmful effects on food supply, which may be crucial for achieving food security.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".