Bibliographic record
Abstract
This paper introduces a new type of R&D subsidy, which is conditional on the success of the R&D project. In a threestage model, the government chooses a subsidy(ies) in the first stage; in the second stage, a monopolist chooses R&D effort which determines the size or the probability of success of the R&D project; in the last stage, the firm chooses its output. It is found that conditional subsidies can yield the same level of innovation and welfare as unconditional subsidies. However, when the probability of success is sufficiently low (be it endogenous or exogenous), conditional subsidies yield suboptimal levels of innovation and welfare. When the firm chooses the probability of success, conditional subsidies can have the advantage of a lower expected cost of the subsidy to the government. I consider the simultaneous use of conditional and unconditional subsidies, and show that different combinations of the two can lead to the same levels of innovation and welfare as unconditional subsidies alone. Finally, reverse conditional subsidies, which the firm gets only if the project fails, are considered. It is found that they yield the same level of innovation as unconditional subsidies, except when the probability of success is sufficiently high. Comparing conditional subsidies with reverse conditional subsidies, conditional subsidies yield higher (lower) welfare when the probability of success is high (low).
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".