The Effects of Some Social and Economic Indicators on the Gap Between the American Income Inequality Level and Its Optimal Level
Bibliographic record
Abstract
Since the last decades of the twentieth century, there has been a debate on the causes and consequences regarding the rise of inequality in its varied dimensions. Much discussion is dedicated to whether public policies should be aimed at reducing or mitigating the upward trend in inequality. This paper explores empirical evidence regarding the income inequality level that maximizes the per capita consumption of the U.S. economy from 1946 to 2015. Based on the cointegration equations empirical tests, we find a concave nonlinear relation between the log of per capita consumption and the log of the Gini Index. In this context, the optimal level of income inequality is 0.376. In addition, we test whether some determinants of inequality show a nonlinear relationship with the square of the difference between the current Gini index and its optimal level, (Gini – Gini*)2. The relation between (Gini-Gini*)2 and education shows an inverted U-shaped curve in which the threshold value wasn’t reached yet but, once the threshold value is reached, more education consumption could reduce income inequality, which can result in better equality of opportunity for most of the American population. However, the indicators of economic openness, taxes, and financial assets show U-shaped curves. Considering the analyzed period from 1946 to 2015, openness and, taxes have contributed to the increase in inequality since the mid-1970s. Besides, financial assets also have contributed to inequality since 2008. However, from 1946 to about 2007, these financial assets, which include credit, contributed to generating lower income inequality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".