Determinants of Economic Growth in G20 Countries: A Panel Data Approach
Bibliographic record
Abstract
During last 10 years some G20 countries had economic instability. They have short and long term challenges such as unemployment, population ageing, globalization etc. In this study it is aimed to analyze macroeconomic indicators of G20 countries’ economic growth using panel data approach. Static linear panel data models were used for determining the effects of independent macro-economic variables on gross domestic product (GDP) of G20 countries including Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, Republic of Korea, Turkey, the United Kingdom and the United States of America. While dependent variable of analyze is gross domestic product (volume), the independent variables are current account balance, general government gross debt, general government revenue, general government total expenditure, gross national savings, inflation (average consumer prices), population, total investment, unemployment rate, volume of exports of goods and services, volume of imports of goods and services. The analysis proposed is based on a panel data (cross sectional time series data) approach. The dataset of this research involves 18 (unemployment rate variable of India was not available on our data set, therefore India was excluded from analysis) of G20 members (cross sectional units). The effects of 11 macroeconomic indicators on gross domestic product volume were examined by using panel data series. The findings of this paper would help G20 countries and investors for creating more effective macroeconomic strategies. For the government side, future rises, falls, and turning points of the macro indicators puts into perspective the effects of government policy created to deal with them. For the investors’ side, future values might increase the possibility of diligent investor in the financial market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".