A Study on the Impact of COVID-19 on Korea’s Exports in the Pharmaceutical Industry: Comparison between OECD and Non-OECD Countries
Bibliographic record
Abstract
Purpose – This paper empirically analyzes the impact of the COVID-19 pandemic on Korea’s exports in the pharmaceutical industry, and this estimation result is compared with that of manufacturing. Moreover, to distinguish those impacts between OECD and non-OECD countries, we divide 76 countries into two groups, OECD and non-OECD countries, and compare the estimation results. Design/Methodology/Approach – We used the panel data of 76 countries and quarterly periods from the first quarter of 2020 to last quarter of 2021 for estimation. The econometric technique employed in the study is PPML (Poisson pseudo maximum likelihood). Findings – We show that the COVID-19 pandemic has positively influenced Korea’s exports in the pharmaceutical industry. Moreover, the impact turns out to be statistically more significant in non-OECD countries than OECD countries. The supplementary finding of our study is that there is no clear evidence on the linkage between COVID-19 and Korea’s exports in the manufacturing sector. Research Implications – Unlike existing papers have focused on showing the negative relationship between COVID-19 and international trade, this paper has showed the positive linkage between them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".