Specific Inputs, Value-Added, and Production Linkages in Tax-Incidence Theory
Bibliographic record
Abstract
Factors of production that cannot be moved from one activity to another due to their intrinsic nature, location preferences, mobility restrictions, or licensing requirements were featured prominently in the tax literature of the 1970s. Immobile factors, however, often produce inputs for other sectors. Several examples of this type that enhance and enrich some well-known existing models are presented. The value-adding process and cross-sector connections are explicitly specified. The new tax-incidence results often resemble those in mobile-factors-only (mfo) models in spite of one or more sector-specific inputs. Numerical examples, based on stylized U.S. data, illustrate the results and highlight the difficulties that arise in defining equivalent specifications. Goods mobility offsets some effects of factor immobility, but the computed tax elasticities are rarely the same as in mfo models. The Marshallian short-run/long-run distinction, blurred somewhat by production linkages, does not disappear.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".