Can “Self-Financing” Redeem the Basic Income Guarantee? Disincentives, Efficiency Cost, Tax Burdens, and Attitudes
Bibliographic record
Abstract
Recent proposals have been advanced for “self-financing” a basic income guarantee (BIG). This article critically assesses claims that this method of finance would alleviate the economic issues of incentives and efficiency and the political issues of taxpayer burdens and support that challenge all major BIG schemes. The self-financing methods are shown merely to obscure the adverse impacts on marginal effective tax rates that would otherwise require explicit increases in income tax rates. Increased efficiency costs are shown by examples to be extremely large. The self-financing structure is also found to concentrate burdens on middle-income taxpayers. Public opinion surveys further suggest scant public support for the taxes that would be needed to finance such schemes. The article reviews the economic basis for categorical treatment, with BIG benefits focused on those who are unable to work and policies supporting work, earnings, and human capital investment for employable persons. This approach could be financed with lesser taxpayer burdens while according more closely with public values about work; it would also directly address the long-run causes of poverty rather than just the symptoms.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".