The Evolution of Rotation Group Bias: Will the Real Unemployment Rate Please Stand Up?
Bibliographic record
Abstract
This paper documents that rotation group bias -the tendency for labor force statistics to vary systematically by month in sample in labor force surveys -in the Current Population Survey (CPS) has worsened considerably over time.The estimated unemployment rate for earlier rotation groups has grown sharply relative to the unemployment rate for later rotation groups; both should be nationally representative samples.The rise in rotation group bias is driven by a growing tendency for respondents to report job search in earlier rotations relative to later rotations.We investigate explanations for the change in bias.We find that rotation group bias increased discretely after the 1994 CPS redesign and that rising nonresponse is likely a significant contributor.Survey nonresponse increased after the redesign, and subsequently trended upward, mirroring the time pattern of rotation group bias.Consistent with this explanation, there is only a small increase in rotation group bias for households that responded in all eight interviews.An analysis of rotation group bias in Canada and the U.K. reveal no rotation group bias in Canada and a modest and declining bias in the U.K.There is not a "Heisenberg Principle" of rotation group bias, whereby the bias is an inherent feature of repeated interviewing.We explore alternative weightings of the unemployment rate by rotation group and find that, despite the rise in rotation group bias, the official unemployment does no worse than these other measures in predicting alternative measures of economic slack or fitting key macroeconomic relationships.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".