Examining the Effect of Financial Corruption on Budget Deficits in Selected Countries
Bibliographic record
Abstract
For many years, the budget deficit has been studied as an important variable in economics, and various schools and scholars have identified its causes and influential factors.However, since the 1990s, increasing attention has been directed toward the effect of institutional and political indicators on the budget deficit, and corruption has been introduced as one of the key contributors.This article, by introducing and calculating a composite index of financial corruption based on the deprivation theory and choosing the four economic dimensions of government spending, investment, income and economic freedom, investigates the effects of financial corruption on the budget deficit in 120 selected countries from 2007 to 2017 using the generalized torque method (GMM).The results show that, assuming other conditions constant, a one-unit increase in the corruption index increases the budget deficit by an average of 49/3 units, which indicates the positive and direct effect of corruption on the budget deficit.Also, according to the significance of the estimation, the variables of inflation, government spending, taxes and oil revenues have a positive effect on the budget deficit, while higher economic growth leads to a reduction in the budget deficit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".