The Role of Internal Control in Firms’ Coping with the Impact of the COVID-19 Pandemic: Evidence from China
Bibliographic record
Abstract
Internal control plays a role in risk prevention for firms when dealing with serious emergencies, which ensures the sustainable development of firms during a crisis. Based on the rapid outbreak of COVID-19 in China, this paper empirically tests whether internal control alleviates the negative impact of the pandemic on firm performance. Using a sample of Chinese listed firms from the first quarter of 2019 to the third quarter of 2020 and employing the difference-in-difference (DID) method, we find that the firms with a higher quality of internal control achieve better financial performance during the pandemic period; the more serious the pandemic is, the more obvious effect internal control plays. Furthermore, we consider the industry heterogeneity and firm heterogeneity of the risk resistance effect of internal control. In the manufacturing industry, which is a “disaster zone” of the pandemic, and the non-high-tech industry with a low degree of digitization, internal control can play a more important role in firms’ performance. Moreover, for state-owned enterprises, and firms with strong financing constraints, the role of internal control is more prominent. The above results provide empirical evidence for the risk prevention function of internal control and shed new light on the measures for firms to resist emergencies in the future.
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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 | 0.003 | 0.015 |
| 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 | 0.001 | 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".