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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".