COVID-19 and small enterprises in the food supply chain: Early impacts and implications for longer-term food system resilience in low- and middle-income countries
Bibliographic record
Abstract
Food and nutrition security play an essential role in weathering and overcoming the COVID-19 pandemic-and in achieving sustainable development. In most low- and middle-income countries, micro, small, and medium-sized enterprises (MSMEs) play an essential role in food supply chains and thus in ensuring food and nutrition security. However, limited attention has been paid to how these critical food system actors are being impacted by the pandemic and associated measures. This paper helps fill that gap through analysis of data from 367 agri-food MSMEs in 17 countries, collected in May 2020 and capturing early impacts of the pandemic on their operations. About 94.3% of respondents reported that their firm's operations had been impacted by the pandemic, primarily through decreased sales as well as lower access to inputs and financing amid limited financial reserves. Difficulty with staffing was also widely cited. Eighty-four percent of firms reported changing their production volume as a result of the pandemic; of these, about 13% reported stopping production and about 82% reported decreasing production. Approximately 54% had changed product prices as a result of the pandemic. The probability of being severely impacted was significantly higher for firms with <50,000 USD in annual turnover; a larger decrease in consumer mobility for grocery/pharmacy shopping also increased the probability of a severe impact. Surprisingly, the youngest firms and those with the fewest employees (controlling for turnover) were less likely to be severely impacted. Over 80% of firms had taken actions to mitigate the pandemic's impact on their operations and/or staff, and about 44% were considering exploring new business areas, with some seeing opportunities for growth. We conclude by discussing implications for policy responses to address immediate challenges as well as increase long-term food system resilience to support further progress towards sustainable development.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".