Poverty, Informality and the Optimal General Income Tax Policy
Bibliographic record
Abstract
Abstract This paper investigates the optimal general income tax and audit policies when poverty is considered a public bad in an economy with two types of individuals whose income may not be observed. Our results depend on whether poverty is measured in absolute or in relative terms. For a relative poverty measure, it is possible to characterize conditions under which both rich and poor agents face either positive, negative or zero marginal tax rates. There is distortion at the top as long as the rich can influence the welfare of the whole society through a measure of poverty and a distortion might be optimum to reduce aggregate poverty. Those that declare to be rich can be audited randomly, similar to their counterpart poor ones. Lastly, honesty may be punished as well as rewarded. With an absolute poverty measure, we replicate the results in the optimum tax literature, i.e., "no distortion and no auditing at the top".
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".