The Effect of COVID-19 on Consumer Goods Sector Performance: The Role of Firm Characteristics
Bibliographic record
Abstract
This study investigates the impact of the COVID-19 pandemic on company performance in the consumer goods industry. Additionally, it explores how company characteristics influence the relationship between the pandemic and company performance based on industry type and region. Analyzing data from 1491 companies across 79 countries between 2018 and 2022, we utilized ordinary least squares (OLS) with robust standard errors. Our findings confirm the pandemic’s overall adverse effect on the performance of consumer goods companies. However, variations emerged when examining diverse industries and regions. Notably, larger companies, particularly in the Americas, Europe, and Asia–Pacific, demonstrated greater resilience and performance during the pandemic. Furthermore, effective leveraging, especially in the Americas and Asia–Pacific, contributed to supporting performance amid the pandemic. These results hold crucial policy implications for companies aiming to enhance their performance in the face of health crises.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".