A Plea for the Use of Laboratory Experiments in Basic Income Research
Bibliographic record
Abstract
We agree with the other participants in this debate that an experimental approach makes a significant contribution to our understanding of universal basic income (UBI) schemes, as there is a limit to what we can learn from surveys, simulations or studying existing welfare policies that only marginally resemble a UBI. However, we differ from others advocating the use of experiments in terms of the specific design of a UBI experiment. In particular, we want to urge a note of caution against conducting large-scale social or field experiments (along the lines of the famous negative income tax (NIT) experiments carried out in the US and Canada in the 1970s) advanced in recent years by Loek Groot (2004; 2006), Rafael Pinilla (2006), and many others. We think there are two distinct, if related, reasons why one might take a sceptical attitude towards field experiments in this particular context. First, field experiments are more susceptible to "political manipulation," defined as "external interference with the research process or its outcomes for political reasons," and its advocates are overly optimistic in thinking they can avoid political interference and manipulation of research into a controversial policy proposal such as UBI. Second, a field experiment design entails scientific limitations that impede a genuine understanding of the behavioural effects of UBI in a modern welfare state. While field experiments can teach us a lot about some of the central questions to be considered when implementing a UBI (Widerquist, 2006), they nevertheless face considerable constraints that affect both the scope of the research - the range of questions we can study in a single experiment - and the validity and robustness of the findings. Both concerns suggest we should investigate other possible experimental designs. We suggest that UBI researchers should embrace the methodology and design of rigorously controlled laboratory experiments, advanced in the past decades in cognitive psychology and behavioural economics and increasingly applied to political science, sociology and even social justice. In our view, laboratory experiments would help researchers obtain valuable empirical evidence about UBI that may be hard to attain in social experiments, without rendering research findings susceptible to the sort of adverse political manipulation that dealt a blow to the 1970s NIT experiments (Widerquist, 2005a).
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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 | 0.498 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.077 |
| Scholarly communication | 0.015 | 0.044 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.038 | 0.056 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".