Fiscal Transparency, Measurement and Determinants: Evidence from 27 Developing Countries
Bibliographic record
Abstract
Fiscal transparency has been consistently identified as a key feature of efficient fiscal policy and a prerequisite for good public governance. However, measuring fiscal transparency remains an empirical challenge, and extant literature on developing countries is still sparse. To that end, this paper examines the determinants of fiscal transparency in developing countries. We add to the existing literature by proposing a new replicable index of fiscal transparency that is consistent with the definition provided by the International Monetary Fund and the World Bank. Additional determinants of fiscal transparency, which are exclusively relevant in the study of developing countries, are also examined. In particular, we introduce such factors as natural resources, the openness of the economy, the literacy rate of the population, and the quality of institutions. Because of possible endogeneity arising from interdependence among some variables, two-stage least squares (2SLS) is used to ensure that the estimators are consistent. As a robustness check, the same estimation procedure was replicated by replacing our index of fiscal transparency with respectively the index of Andreula et al. (2009) and the Open Budget Index, both of which use a significant similar number of developing countries of our selected sample. The paper found that the level of natural resources and the openness of the capital account negatively affect fiscal transparency. However, the quality of institutions and literacy were found to positively affect fiscal transparency. These results were robust to the measure of fiscal transparency. The findings of this paper provide an explanation of why, after a decade of fiscal transparency programs, many developing countries are still lagging behind, thereby losing the potential benefits mentioned in the literature. These findings could help guide policies directed at improving the level of fiscal transparency in developing countries.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".