Investigation with panel data analysis of the effect on economic growth of employment in agriculture and industrial sector: example of some OECD countries (1993-2017)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose-Economic growth is one of the biggest indicators of the strength of a country. Countries provide economic growth by generating resources with their advanced technology. In this study, for some OECD countries (Germany, Belgium, Canada and Turkey) was investigated effect on the economic growth of the employment in the agriculture and industrial sector using panel data analysis. In the study, annual data were used from the years 1993-2017. Methodology-The data were taken from the official web address of the World Bank. Firstly, the data to be used in the model were examined by "unit root tests" to determine whether these series are stationary. According to the results of the unit root test applied to the levels of the variables, it was seen that the series were not stationary but contained unit root. For this reason, the primary differences of the series were taken and found to be stationary. Then the co-integration test was performed. Findings-The results of the cointegration tests performed indicate that there is a cointegration and there is a long-term relationship between the variables. In the study, classical, fixed effect and random effective regression models were used. The Hausman test was applied to determine the correct regression to be used, resulting in the appropriate model being the random effect model. Conclusion-After the Haussmann test, the most appropriate model was obtained as a random effect model.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it