Impact of COVID-19 and the Russia-Ukraine Crisis on Micro Small and Medium-Sized Enterprises (MSMEs) in Malawi
Bibliographic record
Abstract
The study was undertaken to assess the impact of COVID-19 (the pandemic) and the Russia Ukraine crisis on MSMEs in Malawi, the role regional integration plays in post pandemic recovery, and how technology and innovation are being leveraged by MSMEs to address the impacts of the pandemic and the Russia-Ukraine crisis. The basis of the study is a survey of 503 MSMEs which was conducted from 14th November to 22nd November 2022 across the three regions covering all the 28 districts and the cities of Blantyre, Lilongwe, Mzuzu, and Zomba. The 503 businesses were selected randomly to participate in a telephone survey where 107 businesses were closed at the time of the survey, and 396 businesses are operational. Most of the businesses are micro and small, operate in the service sector, and equally owned by men, women, the youth and the elderly. Over a quarter of the businesses were established in the last three years. The study therefore recommends supporting productive and competitive businesses and building capacity of the MSME sector to produce products for the export markets. Improving access to capital, uninterrupted electricity supply, and stabilising the Malawian Kwacha would enhance performance of the MSME sector. There is a requirement for a platform to disseminate information and studies about existing opportunities in regional markets and Africa to enhance integration of MSME sector.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".