Impact Of Tariff On Income: Cross Country Analysis
Bibliographic record
Abstract
The purpose of the study is to assess the significance of import tariff on the economic growth of ten countries divided into two groups; developing and developed economies. The developed countries included in the sample are Australia, Japan, Canada, Turkey and United States. The developing group of countries consists of Pakistan, Sri Lanka, India, Bangladesh and Thailand. The time period taken span from 1998 till 2015.The cross-country analysis included in the study ranges from the application of OLS regression methods to country wise, unit root test and long run analysis. In addition, Panel Unit Root and Panel Cointegration Tests are also performed to enhance the analysis. The test results of Unit Root Test show that the series are non-stationary at level and on taking first difference these becomes stationary. After we established that the series are integrated of order 1 we proceeded with the Johansen test of Cointegration which established the long run associations among the variables. The Panel Cointegration (Larsson et al. 2001) technique is used to establish the long run association in a panel framework. The findings show long run associations among the variables. It is however reviewed that the policy variable import tariff cannot be used standalone to bring prosperity for the country specially in developing countries. The country needs support of infrastructure enhancement, technological advancement and education in order to fully reap the benefits of protection provided to the industries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".