Inflation, Interest, and the Secular Rise in Wealth Inequality in the U.S.: Is the Fed Responsible?
Bibliographic record
Abstract
Two hallmarks of U.S. monetary policy since the 1981-1982 recession have been declining interest rates and moderation in inflation.Coincident with these trends has been a surge in U.S. wealth inequality, with the Gini coefficient up by 0.070 between 1983 and 2019.This paper analyzes the connection between these two developments on the basis of the Survey of Consumer Finances.Contrary to expectations, the paper finds that these two monetary effects have reduced wealth inequality rather than increasing it.The effect is sizeable, with the Gini coefficient declining by 0.045 over these years.Asset price changes and debt devaluation accounted for 72.6 percent of the advance of mean wealth over 1983-2019.They also would have led to a 204.9 percent gain in median wealth, compared to the actual rise of 23.4 percent.Moreover, they have helped lower the racial wealth gap rather than enlarging it.These results are at odds with previous literature in which estimates range from a weak negative effect on inequality to neutral, small positive, and strong positive.In terms of methodology, this paper differs from previous work by focusing on only the direct effects of interest rate changes and inflation on the household balance sheet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".