The Effect of ESG Activities on Financial Performance during the COVID-19 Pandemic—Evidence from Korea
Bibliographic record
Abstract
This study examines the effect of a firm’s environmental, social, and governance (ESG) activities on its financial performance during the acute uncertainty caused by the COVID-19 pandemic. Due to the COVID-19 pandemic, most Korean firms suffered unexpected difficulties in their business activities in early 2020, and their financial performance deteriorated significantly. The purpose of this study is to empirically analyze whether a firm’s ESG activities affect its financial performance during a business crisis. The results show that, in the first quarter of 2020, when the impact of the COVID-19 pandemic occurred, firms’ earnings dropped significantly; however, we found that the higher the performance of ESG activities, the smaller the decline in earnings. The results imply that, in an environment of uncertainty, the performance of a firm’s ESG activities is reflected in its financial outcomes. This result implies that trust and bond between firms and stakeholders, as formed through investments in social capital, are rewarded when the overall level of sustainability in markets is negatively impacted. In addition, our results suggest that the performance of nonfinancial activities is useful information for stakeholders’ decision making in relation to market uncertainty.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".