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Record W1481416552 · doi:10.17016/feds.2011.60

Rising Inequality: Transitory or Permanent? New Evidence from a U.S. Panel of Household Income 1987-2006

2011· article· en· W1481416552 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFinance and Economics Discussion Series · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEconomicsEarningsEconometricsInequalityEconomic inequalityPermanent income hypothesisPanel dataPanel Study of Income DynamicsDemographic economicsLabour economicsMathematicsMacroeconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.128
GPT teacher head0.289
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it