Impact of Labor and Health on Economic Growth in Indonesia During the COVID-19 Pandemic: A Panel Data Regression Analysis
Bibliographic record
Abstract
The aim of this research is to explore the influence of labor force and health on Indonesia's economic growth amidst the COVID-19 pandemic.Employing time series data from 2018 to 2022, alongside cross-sectional data from all Indonesian provinces, the study utilizes Panel Data Regression Analysis as its primary method of investigation.This study's findings explain the t-count value of the labor variable of 4.925582 and the value of Prob.0.0000 (p-value 0.05), indicating that there is a positive influence of labor force (x) on economic growth (y) throughout the era of COVID-19.From formulation via the regression equation 𝑌 𝑡=𝑎+𝑥 𝑖 𝛽+𝜀 𝑡 found that economic growth = (-22.36519)+ 0.736771 labour -0.139982 health + e.It's showed that labor force has a positive impact on economic development, whereas health has a negative impact on economic growth.The f-test results in this analysis show that the fstatistical value is 2.352373 and the probability is 0.000257 (p 5%), implying that the Fixed Effect estimate, an independent variable consisting of labor and health combined, has a significant impact on Indonesian economic growth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".