Bibliographic record
Abstract
This study examines income elasticity of tax revenue for major OECD countries such as Korea, Canada, France, Japan, Norway, Spain, the United Kingdom and the United States, empirically analyzing their customs duties burden with the use of the annual data from 1990 to 2015, and clarifying their implications. Using the method of empirical analysis, the effect of tax law revision was not considered in estimating income elasticity of tax revenue (buoyancy). By comparing and analyzing the results with major OECD countries, policy implications of customs duties burden are clarified. According to the empirical results, elasticity of customs duties on the GDP of five countries, namely, Korea, Canada, Japan, Norway and the United States, showed a positive relationship between GDP and customs revenue. However, France, Spain and the United Kingdom reflected a negative relationship between GDP and customs revenue. After removing autocorrelation, almost all countries except Norway and Japan showed the same results as those before autocorrelation. Korea had a lower level of income tax and general consumption tax than the OECD average whereas it had a higher level of corporate tax, property tax, customs and import duties. Notably, the tax revenue as a percentage of total taxation of customs and import duties was the highest in Korea among OECD countries. Based on these results, implications of the need for an appropriate tax policy and directions for future research can be gleaned.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".