Earnings mobility and the Great Recession
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".