Measuring Underemployment at the County Level
Bibliographic record
Abstract
As labor markets tightened in the last half of the nineties, economic development and community leaders sought to identify more locally available workers than were indicated by published statistics. Using results from commissioned surveys, they pointed to large numbers of part-time workers who desired full-time work, and to full-time workers who were qualified for better jobs. These statistics were often used to negate low official unemployment rates that deterred firms, concerned by the ostensible shortage of workers, from locating in their counties. We have conducted a larger, statewide, survey of underemployment and linked it to the detailed demographic and labor force data from the 2000 Census. We used the results to identify variations in the number and type of underemployed persons around the state, with emphasis on the differences between urbanized and rural areas. Over a quarter of full-time workers reported underemployment, including a third of workers in exurban counties. However, forty to fifty percent of underemployment is reportedly by choice, with the highest rates in the small urban and exurban regions. Of those that are not underemployed by choice, over ninety percent of respondents in some regions cited lack of job opportunities. We find that between fourteen and forty percent of part-time workers prefer full-time work, with the highest rates in rural Appalachian counties. We provide some of the reasons underemployed people cite as constraints to better employment. Also, we used the survey results and the recent Census information to predict the number and type of underemployed persons in each county. The model can be used to update predictions as new local demographic and labor force estimates are released annually from the Census Bureau’s forthcoming American Community Surveys.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".