COVID-19 and its effects on food producers: panel data evidence from Burkina Faso
Bibliographic record
Abstract
INTRODUCTION: Burkina Faso implemented stringent measures in response to the COVID-19 pandemic that profoundly affected its economy and might have exacerbated food insecurity. While prior studies have assessed the impact of these measures on consumers, there is a dearth of evidence of its effects on food producers in Sub-Saharan Africa. This study aims (i) to evaluate the repercussions of COVID-19 on the possession of food production assets and on the number of livestock owned; and (ii) to determine the correlation between the food insecurity experience scale (FIES) score, ownership of these assets, and the number of livestock owned. METHODS: This study employs a pre-post comparison design in two panels of randomly selected households in Burkina Faso. While Panel A was constituted of 384 households predominantly (76%) living in rural areas, Panel B comprised 504 households, only half of which (51%) lived in rural areas. All households were visited twice: in July 2019 and February 2021, for Panel A, and in February 2020 and February 2021, for Panel B. Panel B was added to the study before the pandemic thanks to additional funding; the timing of the survey was harmonized in both panels for the second round. Regression models were used with fixed effects at the household level, controlling for potential time-invariant confounding variables, and correlation coefficients between possession of production assets or number of livestock and FIES score were measured. RESULTS: Our findings indicate that the possession of some assets in Panel A (cart, livestock, bicycle, watch) was significantly reduced during the pandemic, as was the herd sizes among livestock-owning households in both panels. Households with fewer production assets and number of livestock were more likely to experience food insecurity. CONCLUSION: This study underscores the vulnerability of rural households in Burkina Faso to the economic disruptions caused by the COVID-19 pandemic. Addressing the challenges faced by farming and livestock-owning households is crucial for mitigating food insecurity and improving resilience in the face of ongoing crises.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".