A Cautionary Note on Using (March) CPS and PSID Data to Study Worker Mobility
Bibliographic record
Abstract
The monthly Current Population Survey (CPS), with its Annual Demographic March supplement, and the Panel Study of Income Dynamics (PSID) are the leading sources of data on worker reallocation across occupations, industries, and firms. Much of the active current research is based on these data. In this paper, we contrast these datasets as sources of data for measuring the dynamics of worker mobility. We find that (i) (March) CPS data is characterized by a substantial amount of noise when it comes to identifying occupational and industry switches; (ii) March CPS data provides a poor measure of annual occupational mobility and, instead, most likely measures mobility over a much shorter period; (iii) (the changes in) the procedure to impute missing data has a dramatic effect on the interpretation of the CPS data in, e.g., the trend in occupational mobility. The most important shortcomings of the PSID are the facts that (i) occupational and industry affiliation data is available in most years at an annual frequency; (ii) the PSID’s sample, by design, excludes immigrants arriving to the U.S. after 1968; (iii) the Retrospective Occupation-Industry Files with reliable occupation and industry affiliation data are available only until
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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 | 0.150 | 0.494 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.006 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".