Basic Income and the Pitfalls of Randomization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This essay evaluates the state of the debate around basic income, a controversial and much-discussed policy proposal. I explore its contested meaning and consider its potential impact. I provide a summary of the randomized guaranteed income experiments from the 1970s, emphasizing how experimental methods using scattered sets of isolated participants cannot capture the crucial social factors that help to explain changes in people’s patterns of work. In contrast, I examine a community experiment from the same period, where all residents of the town of Dauphin, Manitoba, were eligible for basic income payments. This “macro-experiment” sheds light on the community-level realities of basic income. I describe evidence showing that wages offered by Dauphin businesses increased. Additionally, labor market participation fell. By ignoring the social interactions that characterize real-world community contexts, randomized studies underestimate the decline in labor market participation and its impact on employers. These findings depend to a great extent on the details of the policy design, and as such I conclude that the oft-proposed right–left ideological alliance on basic income is unlikely to survive the move from basic income as a broad policy umbrella to basic income as a concrete policy option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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