Productivity‐enhancing public investment and benefit taxation: the case of factor‐augmenting public inputs
Bibliographic record
Abstract
Government expenditure on public inputs such as human capital formation and public infrastructure can significantly affect productivity. An interesting and highly relevant policy question is whether such expenditure should be financed according to the benefit‐taxation principle. Focusing on factor‐augmenting public inputs, in this paper we derive the specification of the appropriate set of benefit taxes. Rather than fall on industries according to the degree to which the public input increases their productivities, these taxes must take the form of differential taxes on factor incomes. JEL Classification: H21, H54 Les investissements publics accroissant la productivité et l'imposition des avantages: le cas des intrants publics qui augmentent la productivité des facteurs de production. Les dépenses gouvernementales en intrants publics comme la formation de capital humain ou les infrastructures publiques peuvent affecter de manière significative la productivité. Une question intéressante et pertinente de politique publique est de savoir si des telles dépenses devraient être financées à l'aide d'un impôt sur les avantages ainsi dérivés. Mettant l'accent sur les intrants publics qui augmentent la productivité des facteurs, ce mémoire définit la spécification des impôts appropriés sur les avantages tirés de l'investissement public. Plutôt que de retomber sur les industries selon le degré d'accroissement de productivité qui s'ensuit, ces impôts doivent prendre la forme de taxes différentielles sur les revenus des divers facteurs de production.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".