Impact of COVID-19 on Small and Medium Enterprises in South Asian Countries
Bibliographic record
Abstract
The research study finds the impact of COVID-19 on Small and Medium Enterprises (SMEs) in South Asian countries (excluding Afghanistan, due to the two-decades-long war on terrorism) by taking quarter-wise data from 2020 to 2021. By using the panel data random effect technique, the results demonstrate a negative relationship between COVID-19 spread and SMEs exports, as a one percent rise in the COVID-19 pandemic will result in a decline of 91 percent in exports of SMEs. The results also demonstrate that, with the exception of TTF (SME Financing as a % of Total Trade Finance), all explanatory variables are significant. SME Financing as a % of total trade finance (TTF) has a negative relationship with SMEs exports. Both BCS (Bank credit to SME sector) and NOB (percentage change in the number of SME borrowers) have a positive relationship with SMEE (Exports of SMEs), indicating that when BCS and NOB rise by one percent, SMEE will rise by .98 and 13.17 percent, respectively. The constant/intercept value shows that the SMEs exports will be 49.74 units when all other explanatory variables are set to zero. The research study also posed a policy recommendation in the situation of the COVID-19 epidemic, that what necessary and immediate action to be taken to save the lives and restore the economies of South Asian counties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".