Government Revenue Structure and Fiscal Performance in the G7: Evidence from a Panel Data Analysis
Bibliographic record
Abstract
In a global context characterized by budgetary pressures, aging populations, and accelerated economic transitions, the capacity of countries to mobilize stable and sustainable tax revenues represents a crucial pillar for maintaining macroeconomic stability and social cohesion. This research investigated the determinants of total tax revenues in the developed economies of the G7 group (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) during the period 2000–2022, employing both static and dynamic panel econometric approaches. The estimated model considered total tax revenues as the dependent variable, while the explanatory variables encompassed the main categories of government revenues: direct taxes (personal and corporate income), indirect taxes (consumption, trade, and other taxes), social contributions, grants, other non-tax revenues, and institutional quality indicators (regulatory quality and control of corruption). The empirical findings revealed that all tax components analyzed exert a positive and significant influence on total tax revenues, with particularly strong effects observed for consumption taxes, social contributions, and personal income taxes. Based on these results, the study provides policy recommendations aimed at diversifying revenue sources, balancing direct and indirect taxation, and broadening the tax base equitably. The study advances the literature on international taxation by offering an integrated and comparative analysis of the revenue structures in advanced economies, while also identifying relevant pathways for sustainable tax reforms in a dynamic global environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".