New measures of the costs of unemployment: Evidence from the subjective well-being of 2.3 million Americans
Bibliographic record
Abstract
By exploiting two very large samples of US subjective well-being data we are able to obtain comparable estimates of the monetary and other costs of unemployment on the unemployed themselves, while simultaneously estimating the effects of local employment on the subjective well-being of the rest of the population. For those who are unemployed, the subjective well-being consequences can be divided into income and non-income effects, with the latter being five times larger than the former. This is similar to what has been found in many countries, as is our finding that the non-income effects are lower for individuals living in areas of high unemployment. Most importantly, we are able to use the large sample size and variety of questions in the BRFSS and Gallup daily polls to reconcile, and extend to the United States, what had previously seemed to be contradictory results on the size and nature of the spillover effects of unemployment on subjective well-being. At the population level the spillover effects are twice as large as the direct effects, making the total well-being costs of unemployment fifteen times larger than those directly due to the lower incomes of the unemployed.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".