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Record W3125765484

Fiscal Transparency, Measurement and Determinants: Evidence from 27 Developing Countries

2015· preprint· en· W3125765484 on OpenAlexaff
Yves Tehou Tekeng, Mesbah Fathy Sharaf

Bibliographic record

VenueRePEc: Research Papers in Economics · 2015
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of AlbertaConcordia University
Fundersnot available
KeywordsTransparency (behavior)EconomicsOpenness to experienceDeveloping countryEndogeneityIndex (typography)Fiscal policyCorporate governancePublic economicsMacroeconomicsEconometricsFinanceEconomic growthPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.174
GPT teacher head0.323
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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