Covid-19 pandemic and firm performance: evidence on industry differentials and impacting channels
Bibliographic record
Abstract
Purpose This study explores the impacts of Covid-19 on the performance of firms operating in different industries, and further discovers suspected impacting channels through which Covid-19 is significantly associated with firm performance. Design/methodology/approach A dataset of 402 listed firms from 2017Q1 to 2021Q4 is proceeded with high dimensional fixed effect (firm-quarter fixed effects) models and difference-in-difference models supported by propensity score matching. A thorough robustness testing procedure with a falsification test with a hypothetical event is applied. Findings The study asserts that the pandemic has remarkably hurt the businesses in industries that are more vulnerable to the coronavirus and governmental response policies. Adding to the confirmation of sales and expense channels, new channels – competition and short-term receivables –through which the negative impact of the pandemic is passed on firms is also examined. Originality/value First, this study is to be the first comprehensively investigate and affirm the varying impact of Covid-19 on the business performance of listed firms from different industries in Vietnam, providing additional insight into this research field in Vietnam and emerging economies. Second, the authors examine possible channels paving the way for the impact of Covid-19 on firms' performance and especially explore new channels associated with competition and short receivables. Third, the findings help to form the recommendations for Vietnamese firms, and the study could be replicated for other emerging countries under other similar infectious diseases-driven crises. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0072
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".