Public R&D Subsidies and Productivity: Evidence from Firm-Level Data in Quebec
Bibliographic record
Abstract
This paper analyses empirically the impacts of public R&D grants on private R&D investments and on the productivity growth of the manufacturing firms in a context where fiscal incentives are present. Using the conditional semiparametric differenceindifferences estimator on longitudinal data from Quebec we show that firms that use public grants for R&D in conjunction with tax credits for R&D perform better in terms of R&D input additionality than firms that use only tax credits for R&D. We then use a production function to assess the effectiveness of public R&D grants in the productivity growth of firms. We find that for each additional dollar of public R&D grant, output increases by 0.134 dollars. We conclude that the additional return of direct subsidies is positive but lower than the return on the R&D financed by own funds or R&D tax credits.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".