Competitive Intensity as Driver of Innovation and Productivity Growth: A Synthesis of the Literature
Bibliographic record
Abstract
The objective of the report is to survey and assess the existing economic theoretical literature and empirical evidence on the linkages between open and competitive markets (competitive intensity) and innovation and productivity growth. The report is divided into three main parts. The first part examines the state of economic theory on the relationship between competitive intensity, innovation and productivity. The second section examines relevant empirical work that has been done on the role of firm dynamics in sustaining a competitive environment. The third section surveys evidence of linkages provided by the international case studies of the effects of open and competitive markets on innovation and productivity. The report concludes that the weight of the evidence indicates that competitive intensity has a strong positive effect on innovation and productivity. Accordingly, Canada should pay closer attention to the competitive implications of public policy than has been the case in the past. The international experience provides strong support for this conclusion. While there can be negative implications for certain groups from such policy changes, the evidence shows that they are often smaller than anticipated. Restrictions on competition should only be allowed when it can be demonstrated that they are needed to achieve overriding societal interests.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".