Political connections and financing sources for firms under the COVID-19 pandemic
Bibliographic record
Abstract
Purpose This paper aims to analyze the relationship between political connections and firms’ financing sources under COVID-19. Design/methodology/approach This paper uses 2020–2021 private sector firm-level data collected by the regular and follow-up World Bank Enterprise Surveys through linear models with fixed effects. Findings The main result shows that political connections have a positive and significant effect on government support programs. In contrast, no significant impacts are detected in bank loans despite controlling for firms’ characteristics. Additionally, the severity of COVID-19 is positively associated with government support approval but negatively related to granted bank loans. Originality/value The study highlights the necessity for firms to diversify their funding sources during global crises like COVID-19. Moreover, advocating for inclusive policies is crucial, especially for smaller and foreign-owned firms that often face challenges in accessing government support. Engaging with policymakers and industry associations can amplify these firms’ voices, ensuring equitable access to financial assistance.
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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 | 0.000 | 0.000 |
| 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".