Asymmetric Effects of Economic Policy Uncertainty on Food Security in Nigeria
Bibliographic record
Abstract
This study investigates the asymmetric effects of economic policy uncertainty (EPU) on food security in Nigeria, utilizing annual time series data from 1970 to 2021. The study used descriptive statistics, unit root tests, the nonlinear autoregressive distributed lag (NARDL) model and its associated Bounds tests to analyze the data. The analysis reveals that adult population, environmental degradation, exchange rate uncertainty (EXRU), financial deepening, food security (FS), government expenditure in agriculture uncertainty (GEAU), inflation, and interest rate uncertainty (INRU) exhibit positive mean values over the period, with varying degrees of volatility. Cointegration tests indicate a long-term relationship between EPU variables (GEAU, INRU, and EXRU) and food security. The study finds that cumulative positive and negative EPU variables have significant effects on food security in the short run. Specifically, negative GEAU, positive INRU, positive and negative EXRU have significant effects in the short run. In the long run, negative GEAU, positive and negative EXRU have significant effects on food security. Additionally, the research highlights asymmetric effects, showing that the influence of GEAU and EXRU on food security differs in the short- and long-run. The study underscores the importance of increased government expenditure on agriculture, control of exchange rate and interest rate uncertainty, and the reduction in economic policy uncertainty to mitigate risks in the agricultural sector and enhance food security. Recommendations include strategies to stabilize exchange rates to safeguard food supply and overall food security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".