The impact of direct and indirect taxes on economic growth: An empirical analysis related to Romania
Bibliographic record
Abstract
Abstract The purpose of this paper involved studying the impact of direct taxes and indirect taxes on the economic growth using an econometric Vector Autoregressive model (VAR) based on the statistical data related to Romania over the period of time 2009 (2nd quarter)-2017 (2nd quarter). Fiscal policy system involved a significant impact on the evolution of economic growth in the recent years in Romania, namely the years taken into consideration for this study. The econometric model used three endogenous variables, namely the level of direct taxes as percent of the Gross Domestic Product (%GDP), the level of indirect taxes as percent of the Gross Domestic Product (%GDP) and the economic growth rate over the analysed period of time. According to the econometric model presented in this paper, it was proved that a positive change in the structure of indirect taxes will have a strong positive influence on the economic growth over a medium-term period. On the other hand, economic growth will be negatively influenced in the next period of time after implementing a positive change in the structure of direct taxes, then returning to a positive influence over a medium term period and maintaining that influence in the future time periods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".