Bibliographic record
Abstract
Income inequality may hinder economic growth is a widespread concern. The results from previous literature are mixed. Although both USA and China is an excellent case study by itself, it is even interesting to compare them given they are the two largest economies in the world, and yet completely different from each other. We employ annual data from 1980 to 2012 and apply cointegration to study the effects of income inequality on real GDP per capita and real GDP of both USA and China. We also include the exchange rate into the model to examine possible effects of depreciation on growth. The main findings are: first, depreciation does not affect the growth of USA. Second, depreciation promotes growth of China in the short-run, but may hurt its growth in the long-run. Third, income inequality will hurt growth of USA in the short-run, while it encourages its growth in the long-run. Finally, income inequality may promote growth of China in both short-run and long-run.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".