Bibliographic record
Abstract
Rates of hiring and job separation fell by as much as a third in the U.S. between the late 1990s and the early 2010s. Half of this decline is associated with the declining incidence of jobs that start and end in the same calendar quarter, employment events that we call “single quarter jobs.” We investigate this unique subset of jobs and its decline using matched employer-employee data for the years 1996–2012. We characterize the worker demographics and employer characteristics of single quarter jobs, and demonstrate that changes over time in workforce and employer composition explain little of the decline in these jobs. We find that the decline in these jobs accounts for about a third of the decline in the fraction of the population that holds a job in the private sector that occurred from the mid-2000s to the early 2010s. We also find little evidence that single quarter jobs are stepping stones into longer-term employment. Finally, we show that the inclusion or exclusion of these single quarter jobs creates divergent trends in average earnings and the dispersion of earnings for the years 1996–2012. To the extent that administrative records measure the volatile tail of the employment distribution better than conventional household surveys, these findings show that measurement of short duration jobs matters for economic analysis.
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 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".