Do Accounting and Audit Quality Affect World Bank Lending?
Bibliographic record
Abstract
ABSTRACT We investigate the role of accounting and audit quality in the allocation of international development aid loans provided by the World Bank. This aid is crucial to improve governance functions, infrastructure, and capital markets, and the accounting and audit environments in a country can provide the World Bank with confidence that aid is being used as intended rather than being diverted for personal or political gain. We find that development aid loans are higher for countries with stronger accounting quality, where IFRS use is mandated, and where the audit environment is stronger. However, we also find that United States geo-political interests influence these results. Specifically, the World Bank appears to “overlook” accounting and audit quality in countries where geo-political interests are relatively aligned with those of the U.S. Finally, we find that accounting and auditing matter only in countries with relatively high corruption levels, indicating that the World Bank has greater trust that accounting and auditing are of relatively high quality in low-corruption countries. Data Availability: All data are publicly available.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".