ANALISIS PENGARUH UNDERGROUND ECONOMY DAN VARIABEL MAKROEKONOMI TERHADAP PENDAPATAN NASIONAL \nTAHUN 2010.1 â 2017.4
Bibliographic record
Abstract
Gross Domestic Product (GDP) is the most credible calculations of national income. GDP represents the whole economic activities that occur in a region. In fact, GDP has a weakness that is escaped from underground economy activities. That weakness could affect the economic activities. Several previous studies showed a research gaps, there were differences in showing the influence of the underground economy on national income. \nThis study aims to estimate the size of underground economy activities and also analyze the influence of underground economy, investment, government foreign debt and exchange rate on national income. This study used secondary data in 2010 quarter 1 to 2017 quarter 4 obtained from Bank of Indonesia, the Central Statistics Agency and the Ministry of Finance. The currency demand approach method is used to estimate the underground economy, while the error correction model method is used to analyze the national income. \nThe result of the estimate shows that activity of underground economy is about 23,36 percent of GDP on average. Influence of underground economy toward national income shows insignificant result. Investment and exchange rates have a positive and significant influence on national income in the short and long term. Meanwhile, foreign debt has a positive and significant effect on national income only in the long run.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".