American idle: the relative importance of dispositional and environmental predictors of state labor force participation rates in the USA
Bibliographic record
Abstract
Abstract This is the first study to determine the capacity of state-level IQ and Big Five personality to predict total, male, and female state labor force participation rates (LFPRs). It is based on residents aged 20–64 years in the 48 contiguous American states from 2013 to 2017. Five state environmental variables—White population percent, urban population percent, per capita personal income, political preference, and age distribution—were statistically controlled. Multiple regression analysis revealed that IQ independently predicted total, male, and female LFPRs with β coefficients of 0.67, 0.71, and 0.59, respectively. Neuroticism also independently predicted total LFPRs and male LFPRs with βs of − 0.20 and − 0.29. As well, lower Openness to Experience and higher liberal political preference were associated with higher female LFPRs, producing βs of − 0.37 and 0.47, respectively. No other variables emerged as independent predictors. Regarding total LFPR variance, the six dispositional variables jointly accounted for 23.0% with the five environmental variables controlled and 74.2% without. Corresponding values were 25.9% and 74.3% for male LFPRs, and 19.6% and 66.5% for female LFPRs. With the six dispositional variables controlled, the five environmental variables together could only account for 11.0% in total LFPRs, 10.8% in male LFPRs, and 16.5% in female LFPRs. Spatial autocorrelation was tested and found to be nonsignificant. These previously overlooked dispositional predictors of state LFPRs are especially important given the salience of LFPRs in economic functioning, declines in American LFPRs since 2000, and the 35th-place standing of the USA on LFPRs among nations by 2022.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".