Bibliographic record
Abstract
Apart from this text, you may want to read Chari and Kehoe (1998). The problem of optimal taxation—how to raise a given a amount of revenue at least social cost—can be approached in different ways. The approach here, following Ramsey (1927) is to suppose that taxes must be proportional to income and/or consumption. In particular, lump-sum taxes are ruled out. No theoretical foundations are given for this particular taxation scheme. More precisely, the solution concept is the following. The Ramsey optimal allocation is that allocation that delivers the highest weighted sum of utilities among those allocations that form part of some competitive equilibrium. Notice that the proportionality of taxes is built in the concept of competitive equilibrium. Non-proportional taxes mean that people face different after-tax prices, and that is not consistent with competitive equilibrium. A competitive equilibrium consists of an allocation and after-tax prices. Alternatively, and perhaps more intuitively, we can think of it as having three parts: an allocation, pre-tax prices 1 and tax rates. In this context it is important to understand that a given allocation is not associated with a unique set of tax rates. It can easily happen that the same equilibrium allocation is supported by distinct tax rates. This phenomenon is known as tax equivalence. To see how this might work, consider an environment where a representative agent maximizes 1∑ t=0 βtu(ct, ℓt) subject to ht = 1 − ℓt (1 + τ ct)ct + kt+1 = (1 − τ kt)rtkt + (1 − τht)wtht, k0 given and some suitable No Ponzi Scheme constraint. The agent’s first order conditions are (1 − τht)wtuc,t = (1 + τ ct)uℓ,t and (1 + τ ct+1)uc,t = (1 + τ
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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 | 0.000 | 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) | 0.001 | 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".