Impacts of government incentives to R&D, innovation and productivity : a microeconometric analysis of the Québec case
Bibliographic record
Abstract
There is a popular proverb that says, "No matter how long the night is, the morning is sure to come."As I conclude this thesis, I would like to send these encouraging words to all those who are in the course of writing their theses, and especially to those who are still at the beginning of this process.It is true that, as the saying goes, this process can be very lonely.Working on this project will remain a memorable accomplishment for me, because each day represented a huge challenge, especially due to the need to reconcile family, work and thesis writing.The joys, sorrows and feelings of discouragement were confronted with the vicissitudes of this long desert crossing.There is a great bench of people behind this work, and I would like to thank them all from the bottom of my heart.First and foremost, I would like to thank my advisor, Pierre Mohnen, who supported me over the years and gave me so much freedom to explore and discover new areas.Pierre, I would like to stress how much I enjoyed the way you have guided me through this research project.I was able to take advantage of your experience and availability, and these are capital elements when carrying out such a project.I don't think this is news to anyone who has already written a doctoral thesis.Since you were one of my favourite teachers during my master's studies at the University of Québec in Montreal, it was very easy for me to decide to come to Maastricht to complete my thesis.I would also like to highlight the contribution of Pierre Mohnen as co-author of a chapter in this thesis that was published as "Effectiveness of R&D Tax Incentives in Small and Large Enterprises in Québec" in the journal Small business economics ((2009) 33:91-107).I also thank the Institut de la Statistique du Québec (ISQ).Indeed, this thesis would not have been possible without the ISQ which provided me with access to the databases used in this project.This was a major challenge because most data was dictated by rules of strict confidentiality.To override this restriction, the ISQ had to make arrangements to allow access to the data while complying with federal and provincial rules and obtaining approval from both governments.Moreover, in addition to legal issues, there were all the difficulties related to data preparation, particularly with matching large and complex databases from different sources.Here again, I was able to benefit from the resources and expertise of the ISQ, and so, I want to thank any staff member at ISQ who, directly or indirectly, helped me in the various stages of this project.In this regard, I would especially like to thank Brigitte Poussart from the Science, Technology and Innovation team, whose contribution was crucial to the accomplishment of this project.She accompanied me through the long process to obtain authorizations from provincial and federal ii authorities to access the data.In addition to the logistical support, I also benefited from her knowledge and expertise, in particular, regarding the data sources used in this project and the creation of the final database.A thank you to Brigitte also for her meticulous proofreading and suggestions.Brigitte, I owe you one, and I am forever grateful for your contribution to this project.I would also like to thank other members of the Science, Technology
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".