Rising Inequality: Transitory or Permanent? New Evidence from a U.S. Panel of Household Income 1987-2006
Bibliographic record
Abstract
We use a new and large panel dataset of household income to shed light on the permanent versus transitory nature of rising inequality in individual male labor earnings and in total household income, both before and after taxes, in the United States over the period 1987-2006. Due to the quality and the significant size of our dataset, we are able to conduct our analysis using rich and precisely estimated error-components models of income dynamics. Our main specification finds evidence for a quadratic heterogeneous income profiles component and a random walk component in permanent earnings, and for a moving-average component in autoregressive transitory earnings. We find that the increase in inequality over our sample period was entirely permanent for male earnings, and predominantly permanent for household income. We also show that the tax system, though reducing inequality, nonetheless did not materially affect its increasing trend. Furthermore, we compare our model-based findings against those of simpler, non-model based inequality decomposition methods. We show that the results for the trends in the evolution of the permanent and transitory variances are remarkably similar across methods, whereas the results for the shares of those variances in cross-sectional inequality differ widely. Further investigation into the sources of these differences suggests that simpler methods produce erroneous decompositions because they cannot flexibly capture the relative degree of persistence of the transitory component of income.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".