The Role of Social Welfare Policies in Stabilizing Income and Employment of Those Who Graduate from a High School in a Bad Economy
Bibliographic record
Abstract
We address the role of welfare policies in stabilizing long-term income and employment status of those who graduated from a high school in recession. Our analyses involve the following three questions: (i) how do the w elf are policies affect the decision-making of the unlucky cohorts on whether they make further human capital investment or start job search?; (ii) is the further human investment more effective to improve their lifetime income and employment status than the skill accumulation through learning-by-doing?; and (iii) what are the cost-effective policies to enhance their well-being? The empirical findings, regarding these questions, using the National Longitudinal Survey of Children and Youth (NLSCY) of Canada are as follows. First, the recession cohorts are less likely to choose further educational investment. The enrollment in post-secondary educational institutio ns tends to decrease more than their full-time employment in economic downturn. Second, the effect of some forms of the subsidy to educational investment is amplified in a bad economy. The probability of educational in vestment of the RESP (Registered Educational Savings Plan) users is higher in recession. Third, the difference i n income and employment stability is explained by the difference in educational attainment better than by the e conomic state at graduation. This implies that the low income and the unstable employment of recession cohort s is due to less post-secondary education, which allows lower accessibility to the occupations and the industries that offer higher wage and stable employment. The overall results indicate that the subsidy to post-secondary educational investment can be effective to raise the welfare of the hapless cohorts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".