Bribery, regulation and firm performance: evidence from a threshold model
Bibliographic record
Abstract
Abstract Firm-level bribery and regulation are two of the many determinants of firm performance. However, most of the existing studies examine the direct and linear effects of bribery and regulation and overlook their indirect effects. Using firm-level data, covering 20,343 firms in 78 developing countries, and employing a threshold model, the effects of firm performance’s standard determinants vary based on the bribery and regulation levels. Our findings reveal that the impact of bribery and regulation on firm performance varies significantly depending on corruption and regulation levels. Access to external finance improves firm performance if and only if the firms are exposed to bribes and firm-level regulation is below a given threshold. Furthermore, exports boost the performance of the firms that are exposed to more bribery and spend more time with regulation than those that face lower levels of regulation and bribery. While bribery harms firm performance, our findings reveal that spending time with regulation could improve firm performance if firms are exposed to low levels of bribery. Our findings confirm the ‘sand the wheels’ hypothesis and limiting firm-level bribery improves firm performance.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".