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Record W3210534777 · doi:10.1111/ssqu.13083

Earnings mobility and the Great Recession

2021· article· en· W3210534777 on OpenAlexaboutno aff
Brett Mullins, David L. Sjoquist, Sally Wallace

Bibliographic record

VenueSocial Science Quarterly · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsRecessionUnemploymentEconomicsDemographic economicsGreat recessionWageQuarter (Canadian coin)Labour economicsGeographyMacroeconomicsFinance

Abstract

fetched live from OpenAlex

Abstract Objective The Great Recession of 2007‐09 was a very significant economic event with substantial effects across the economy. An important but unexplored consequence of the Great Recession is its effect on income or earnings mobility. In this paper we explore the effect of the Great Recession on earnings mobility among low‐wage workers. Methods Using Georgia administrative data, we identify quarterly earnings of low‐income individuals over 14 years. We calculate earnings mobility indices for 7‐year periods and explore the differences in mobility between the pre‐ and post‐Great Recession periods. We also calculate earnings mobility indices for 51 overlapping three‐year (12‐quarter) intervals over the 2000 to 2015 period. Results We find that mobility is greater in the post‐Great Recession period. We also find that there is substantial variation in mobility indices in the post‐2007 period and that the variation in three‐year mobility indices is closely related to the unemployment rate. Conclusions The magnitude of earnings mobility was affected by the Great Recession and by the unemployment rate in general. An understanding of earnings mobility during times of economic upheaval helps policy makers better evaluate the overall impact of recessions.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.406
Teacher spread0.370 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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