Progressive taxation, income inequality, and happiness.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Income inequality has become one of the more widely debated social issues today. The current article explores the role of progressive taxation in income inequality and happiness. Using historical data in the United States from 1962 to 2014, we found that income inequality was substantially smaller in years when the income tax was more progressive (i.e., a higher tax rate for higher income brackets), even when controlling for variables like stock market performance and unemployment rate. Time lag analyses further showed that higher progressive taxation predicted increasingly lower income inequality up to 5 years later. Data from the General Social Survey (1972-2014; N = 59,599) with U.S. residents (hereafter referred to as "Americans") showed that during years with higher progressive taxation rates, less wealthy Americans-those in the lowest 40% of the income distribution-tended to be happier, whereas the richest 20% were not significantly less happy. Mediational analyses confirmed that the association of progressive taxation with the happiness of less wealthy Americans can be explained by lower income inequality in years with higher progressive taxation. A separate sample of Americans polled online (N = 373) correctly predicted the positive association between progressive taxation and the happiness of poorer Americans but incorrectly expected a strong negative association between progressive taxation and the happiness of richer Americans. (PsycINFO Database Record
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it