Crowding Out and Multiplier Effect in Indonesia
Bibliographic record
Abstract
The purpose of government policy from the issuance of SUN, which is anticipated to increase the amount of APBN funding available from the capital market, is affecting private investment and macroeconomic conditions. This research aims to find the correlation between SUN issuance, private bonds, Gross Domestic Product, inflation, and interest rates and finding out whether there is a multiplier effect in short term due crowding-out conditions in Indonesia. Canonical correlation is used to forecast and analyze correlations between sets of dependent and independent variables within a group. The degree of relationship between two sets of variables is measured by the canonical correlation, which characterizes an ideal linear combination of dependent and independent variables The government policy for issuing bonds (SUN) may alter the change with 48.437 percent on dependent variables (Gross Domestic Product, inflation, interest rates, and Indonesian Composite Index), and vice versa, changes in independent variables will also have an effect on changes with 38,666 percent in independent variables (Stocks, Government Bonds, And Corporate Bonds). The crowding out effect doesn’t produce the expected increase in the economic scale greater than one as predicted by Keynesian theory; however, Indonesia's crowding-out situation has a positive multiplier effect, leading to increases in Gross Domestic Product, Inflation and the Indonesian Composite Index.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".