Bribery, insecurity, and firm performance: <scp>Evidence</scp> from the <scp>Boko Haram</scp> insurgency in <scp>Nigeria</scp>
Bibliographic record
Abstract
Abstract Research Summary During armed conflicts, when the rule of law collapses, bribery often becomes prevalent. Yet, the effect of bribery on firm performance under those circumstances remains unclear. Bribery could provide access to scarce resources, but it could also be the result of extortion. This study argues that bribes can improve firm performance during certain conflicts, when violence reduces public officials' ability to threaten firms. Using longitudinal data from businesses in Nigeria during the Boko Haram insurgency in 2009–2014, I find that firms exposed to Boko Haram attacks that bribed outperformed firms that did not bribe. Qualitative data suggest that the insurgency limited public officials' ability to threaten firms, making bribes less a means of rent extraction and more a way for firms to access resources. Managerial Summary In many economies, bribery is widespread despite being illegal. This study shows that during times of violent conflict bribery can be a way for firms to maintain operations despite the disruption. Using data from firms in Nigeria during the 2009–2014 Boko Haram insurgency, I find that firms that bribed tended to suffer less from the effects of the insurgency. Bribing firms were better able to secure protection and access transportation networks. At the same time, the conflict reduced local officials' ability to extort firms, making bribes less likely to involve extortion. These results highlight the extreme circumstances that firms face during violent conflicts and the illicit practices that may enable them to survive in the short run.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".