Human capital investment by the poor: Informing policy with laboratory experiments
Bibliographic record
Abstract
The purpose of the study is to better understand human capital investment decisions of the working poor, and to collect information that can be used to design a policy to induce the poor to invest in human capital. We use laboratory experimental methodology to elicit the preferences and observe the choices of the target population of a proposed government policy. We recruited 256 subjects in Montreal, Canada; 72 percent had income below 120 percent of the Canadian poverty level. The combination of survey measures and actual decisions allows us to better understand individual heterogeneity in responses to different subsidy levels. In particular, participants chose between various cash alternatives and educational subsidies, for themselves and for a family member, allowing for the construction of two measures of willingness to invest in education. Two behavioral characteristics, patience and attitude towards risk, are key to understanding the determinants of educational investment for the low-income individuals in this experiment. The decision to save for a family member’s education is somewhat different from that of investing in one’s own education. Patient participants were more likely to save for a family member’s education, but in contrast to investing in one’s own education, a subject’s attitude towards risk played no role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".