Factors Analysis of the Electronics Industry Trade Imbalance between the US and China
Bibliographic record
Abstract
The US and China trade imbalance is a highly debated topic, and cause of trade conflict between the US and China. One particularly strong area of the trade imbalance is the electronics industry, which as of the year 2013 represented more than 45% of the total trade balance, the largest subsection of any industry. In order to understand the macroeconomic factors influencing overall trade balance as well as trade balance in the Electronics Industry, this study uses Ordinary Least Squares Regression analysis model to examine how macroeconomic factors such as Exchange Rate, China GDP, US GDP, and CPI affect the trade balance. The results are then compared to an equivalent analysis on the electronics industry using factors such as China Electronics Industry Production, US Electronics Industry Production, Exchange Rate, and CPI. The findings are surprising, showing that the same factors that are traditionally strongly correlated with a change in the overall trade balance, actually have an opposite effect on the Electronics industry trade balance. This paper explores not only what macro economic factors cause the trade imbalances, but also why they happen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".