R&D Competition in the Chemicals Industry.
Bibliographic record
Abstract
This paper examines the effect of the Research and Experimentation Tax Credit on the Chemicals Industry. The subsidy program was put into place to counteract the underinvest-ment in private R&D caused by firms not internalizing the benefits of technological spillovers from their research. However, the rationale ignored the impact of competition in the product market. I propose and estimate a structural dynamic oligopoly model of competition in intel-lectual assets to capture the impact of interactions between firms in the industry. I estimate the dynamic parameters of the model using methods from Bajari, Benkard, and Levin (2007). In the data, I observe firms that persistently invest more in research and generate more in-novations than other firms that are observationally similar. I build upon previous estimators by incorporating this unobserved firm-level heterogeneity using techniques from Arcidiacono and Miller (2007). I find that increased investment in R&D by more advanced firms due to the subsidy, was largely offset by decreases by smaller firms because of the substitutability of knowledge in product market. This greatly reduced the effectiveness of the policy to spur innovation and limited its impact on social welfare. ∗I would like to thank Andrew Sweeting, Paul Ellickson, Chris Timmins, and Tracy Lewis for their helpful comments. All remaining errors are my own.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".