How Do Household Coping Strategies Evolve With Increased Food Insecurity? An Examination of Nigeria's Food Price Shock of 2015–2018
Bibliographic record
Abstract
ABSTRACT Faced with a significant devaluation of its currency and a surge in food prices, the Nigerian government prohibited the use of foreign currency for food imports. This essentially blocked the importation of numerous food items under the guise of stimulating the domestic output of these staples. Consequently, food prices in Nigeria increased despite a global decline in food prices, and the incidence and severity of food insecurity escalated. This study examines the changes in the types and severity of coping mechanisms for food insecurity resulting from the food price shock caused by the oil price crash, currency devaluation, and restrictions on foreign exchange. Nigeria's General Household Survey Panel data from 2012 and 2015, during periods of high oil prices, is compared with data from 2018 when oil prices had remained low, the currency had been devalued, and the treasury had been depleted. Alongside detailed descriptive statistics, logistic and hurdle regressions are employed for statistical analysis. Findings indicate a rise in the percentage of Nigerian households grappling with food insecurity from 2015 to 2018. During this period, 68.7% of households resorted to at least one coping mechanism, 31.8% adopted six or more coping strategies, and 43.2% resorted to severe coping strategies. The issue stems not primarily from natural disasters or conflicts but from a failure in macroeconomic and agricultural economic policies. Our findings confirm that these policies come at great cost, particularly to female‐headed households, single‐parent households, households headed by elderly people, and other vulnerable populations, pushing them deeper into food insecurity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".