Assessing the Effectiveness of Human Capital Investments on the Regional Unemployment Rate in the United States: 1990 and 2000
Bibliographic record
Abstract
This article evaluates the effect of human capital investment—for example, expenditures on education, training, and employment—on regional unemployment rates in the United States. State-level unemployment rates are estimated using the spatial lag fixed effects model with spatial correlation of regional unemployment rates for 1990 and 2000. The results show that unemployment rates can be decreased by a policy of state-level human capital investment. A $100 per capita human capital investment in a state is expected to decrease the unemployment rate by 0.63 percent. Human capital investment has a negative impact on a state's unemployment as long as the yearly average state net migration rate is greater than −1.6 percent. A maximum of 1.6 percent of a state's population can out-migrate on average in a year for human capital expenditures to be associated with a decrease in the state's unemployment rate. If a state's net migration rate is less than −1.6 percent of its population, human capital expenditures have a positive effect on unemployment.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".