Investigation the Correlation between Purchasing Power Parity, Per Capita Gross Domestic Product and the Price Level Indices with Panel Data Analysis: Evidence from New Zealand, USA, Germany, Canada and Turkey
Bibliographic record
Abstract
Variables such as an Economy Purchasing Power Parity (PPP), per capita Real Gross Domestic Product (GDP) and Price Level Index are defined as the most important indicators of wealth. The Purchasing Power parity is a real variable. It shows the goods and services that people can buy with their existing incomes. Per capita income is obtained usually in one year period, in a country by dividing the total income to the country's population. Price level indices are indicative of the general price profile of countries. It is suggested in the literature that per capita GDP and Price Levels are indicators that affect PPP. Acting on this assumption, It is intended to be tested the relationship between Purchasing Power Parity and Per-capita Real Gross Domestic Product and Price Level indices by using annual data from 2005-2016 year for Turkey, Canada, New Zealand, Germany and US economy. Panel Data Analysis is used for this purpose. According to the results of the research, there is no relation between PPP and Real GDP and Price Levels. Keywords: USA, Canada, Germany, Turkey, New Zealand, PPP, per capita real GDP, the Price Level Index, Panel Data JEL Classifications: C4, E01, E31 DOI: https://doi.org/10.32479/ijefi.6889
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".