PENGARUH PERANG DAGANG AS-CHINA TERHADAP \nPERTUMBUHAN EKONOMI (STUDI KASUS NEGARA MITRA \n \nDAGANG AS-CHINA TAHUN 2016-2019)
Bibliographic record
Abstract
This study aims to analyze how the effect of US-China trade war on economic growth in 17 \ntrading partner countries of the United States and China, and analyze the effects of export \nperformance, Foreign Direct Investment, and exchange rates on economic growth in 17 \ntrading partner countries of the United States and China in the period before the US-China \ntrade war (2016-2017) and after the US-China trade war (2018-2019). The samples in this \nstudy examined 17 trading partner countries of the United States and China, including Japan, \nHong Kong, South Korea, Taiwan, Australia, Vietnam, Malaysia, Brazil, India, Russia, \nThailand, Singapore, Indonesia, Canada, Philippines, Saudi Arabia, and United Arab \nEmirates. The variables that used in this study are ecomonic growth, export performance, \nForeign Direct Investment, exchange rate, and dummy variable in the period before and after \nthe US-China trade war. The method in this study uses panel data regression method \nwith dummy variables in the period before and after the US-China trade war which is \nprocessed by Microsoft Excel 2010 and E-Views 10 program. \nThe results of this analysis show that the US-China trade war dummy has an effect on \neconomic growth in 17 trading partner countries of the United States and China. Export \nperformance and FDI have positive and significant impact on economic growth in 17 trading \npartner countries of the United States and China in the period before and after the US-China \ntrade war. Meanwhile, the exchange rate has no significant effect on economic growth in 17 \n \ntrading partner countries of the United States and China in the period before and after the US- \nChina trade war. \n \nKeywords: Economic Growth, Export Performance, Foreign Direct Investment, Exchange \nRate, Trade War. \n \nPenelitian ini bertujuan untuk menganalis bagaimana pengaruh perang dagang \nAS-China terhadap pertumbuhan ekonomi di 17 negara mitra dagang Amerika \nSerikat dan China, dan menganalisis pengaruh kinerja ekspor, Foreign Direct \nInvestment, dan nilai tukar terhadap pertumbuhan ekonomi di 17 negara mitra \ndagang Amerika Serikat dan China pada masa sebelum perang dagang AS-China \n(2016-2017) dan sesudah perang dagang AS-China (2018-2019). Sampel dalam \npenelitian ini meneliti 17 negara mitra dagang Amerika Serikat dan China yaitu \nnegara Jepang, Hongkong, Korea Selatan, Taiwan, Australia, Vietnam, Malaysia, \nBrasil, India, Rusia, Thailand, Singapura, Indonesia, Kanada, Filipina, Saudi \nArabia, dan Uni Emirat Arab. Variabel dalam penelitian ini adalah pertumbuhan \nekonomi, kinerja ekspor, Foreign Direct Investment, nilai tukar, dan variabel \ndummy periode sebelum dan sesudah perang dagang AS-China. Metode dalam \npenelitian ini menggunakan metode regresi data panel dengan variabel dummy \nsebelum dan sesudah perang dagang AS-China yang diolah dengan Microsoft \nExcel 2010 dan program E-Views 10. \nHasil analisis penelitian menunjukkan dummy perang dagang AS-China \nberpengaruh terhadap pertumbuhan ekonomi di 17 negara mitra dagang Amerika \nSerikat dan China. Kinerja ekspor dan FDI berpengaruh positif dan signifikan \nterhadap pertumbuhan ekonomi di 17 negara mitra dagang Amerika Serikat dan \nChina pada masa sebelum dan sesudah perang dagang AS-China. Sedangkan nilai \ntukar tidak berpengaruh signifikan terhadap pertumbuhan ekonomi di 17 negara \nmitra dagang Amerika Serikat dan China pada masa sebelum dan sesudah perang \ndagang AS-China. \n \nKata Kunci: Pertumbuhan Ekonomi, Kinerja Ekspor, Foreign Direct Invesment, \nNilai Tukar, Perang Dagang.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".