Bibliographic record
Abstract
The paper explores the role of R&D investments reducing fixed production costs in entry deterrence. An incumbent monopolist performs R&D to reduce its fixed production costs. There is a potential entrant, which can also perform R&D for the same purpose. There are bidirectional technological spillovers between the incumbent and the potential entrant. It is shown that deterrence, which takes the form of underinvestment in R&D by the incumbent, is more likely when the spillover from the incumbent to the potential entrant is high, when the spillover from the potential entrant to the incumbent is low, and when the fixed cost is intermediate. The comparative statics of the model depend heavily on which of two cases obtains: the first case is when separation between deterrence and accommodation is dictated by the relative profitability of these strategies; the second case is when separation between these two strategies is dictated by the positivity of R&D investments. The role of two policy tools, R&D subsidies and intellectual property protection, is examined. R&D subsidies, while they generally facilitate entry, move R&D investments in socially undesirable directions, except when accommodation is the equilibrium with and without the subsidy. As for intellectual property rights, they have no effect on R&D investments (except under deterrence) and tend to reduce entry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".