R&D Policy Competition with Process Innovation in a Multi-Product Duopoly
Bibliographic record
Abstract
This paper considers a reciprocal dumping model which consists of two countries, each owning a multi-product firm which sells products to both countries. The firms choose the R&D investment portfolio for their products, and a government may subsidize or tax its domestic firm for the R&D investment. It is shown that a firm invests more in R&D for its core (non-core) product if products are sufficiently differentiated (similar) to each other. Moreover, if a firm invests more in its non-core product than its core product, it does that to an extent such that the non-core product becomes the core product after the R&D process. Policy competition results in a unilateral incentive of a subsidy, and the stable optimal policy is always a subsidy. When two governments harmonize their policies, it is optimal for them to set subsidies to zero. The optimal subsidy in a duopoly is higher than that in a monopoly if and only if two governments' policies are strategic substitutes.
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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 | 0.001 |
| 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) | 0.000 | 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".