Book-Value Wealth Taxation, Capital Income Taxation, and Innovation
Bibliographic record
Abstract
When is a wealth tax preferable to a capital income tax?When is the opposite true?More generally, can capital taxation be structured to improve productivity, incentivize innovation, and ultimately increase welfare?We study these questions theoretically in an infinite-horizon model with entrepreneurs and workers, in which entrepreneurial firms differ in their productivity and are subject to collateral constraints.The stationary equilibrium features heterogeneous returns and misallocation of capital.We show that increasing the wealth tax increases aggregate productivity.The gains result from the "use-it-or-lose-it" effect of wealth taxes when returns are heterogeneous, which causes a reallocation of capital from entrepreneurs with low productivity to those with high productivity.Furthermore, if the capital income tax is adjusted to balance the government's budget, aggregate capital, output, and wages also increase.We then study the welfare maximizing combination of wealth and capital income taxes and show that the optimal mix shifts towards a higher wealth tax and a lower capital income tax as the capital intensity of production increases.For a range of plausible parameter values, the optimal wealth tax is positive, whereas the capital income tax can be positive or negative (a subsidy).We then endogenize the entrepreneurial productivity distribution by introducing either ex ante innovation or entrepreneurial effort in production and show that this strengthens our results: by allowing entrepreneurs to keep more of the upside relative to a capital income tax, a wealth tax incentivizes more innovation and entrepreneurial effort, leading to larger increases in productivity, output, and welfare.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".