Basic Income in a Small Town: Understanding the Elusive Effects on Work
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the impact of a guaranteed annual income experiment from the 1970s called the Manitoba Basic Annual Income Experiment (Mincome). We examine Mincome’s “saturation” site located in Dauphin, Manitoba, where all town residents were eligible for payments. Would people work less if their basic needs were guaranteed outside the market? Never before or since the Dauphin experiment has a rich country tested a guaranteed annual income at the level of an entire town. A community-level experiment accounts for the fact that people make decisions in a social context, not in isolation. Using hitherto unanalyzed data we find an 11.3 percentage point reduction in labor market participation, and nearly 30 percent of that fall can be attributed to “community context” effects. Additionally, we show that withdrawals were driven disproportionately by young and single-headed households. Participants who provide qualitative explanations for work withdrawals typically cite care work, disability and illness, uneven employment opportunities, or educational investment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it