International Trade on IPR, Economic Growth, and FDI in Indonesia’s Manufacturing Sector
Bibliographic record
Abstract
Purpose – Indonesia has always experienced a deficit in the balance of payments in the Intellectual Property Rights (IPR) sector. However, it is hoped that the high value of Indonesian IPR imports will be beneficial in increasing the attractiveness of FDI, encouraging economic growth, and increasing IPR exports. The purpose of this research is to examine the causal relationship between IPR imports (MIPR), IPR exports (XIPR), incoming FDI in the manufacturing sector (FDI), and economic growth (GGDP) in the case of Indonesia. Design/Methodology/Approach – The data used in this research are time series data from the first quarter of 2004 to the fourth quarter of 2021. These data are analyzed using the VECM approach. Findings – The study results show that MIPR has dominated Indonesia’s total trade in this sector. The contribution of import value is 95 percent compared to exports, which are only 5 percent. The VECM estimation results show a one-way Granger causality relationship from the independent variables (FDI, GGDP, and XIPR) to MIPR as a dependent variable. In a short-term causality relationship, IPR imports encourage FDI inflows into Indonesia. Then, FDI in the manufacturing sector significantly impacts Indonesia’s IPR exports. Research Implications – Indonesia’s outflow of money to pay for the use of foreign IPR can encourage the entry of FDI in the manufacturing sector, resulting in a technology transfer that produces domestic IPR, which can be sold abroad.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".