Bibliographic record
Abstract
The first chapter examines the impacts of stringent size requirements to qualify for R&D tax credits on firms' growth decisions. I leverage a 2004 eligibility change in Canada's largest R&D program, which allowed firms below the initial threshold to increase production and R&D spending while maintaining eligibility for a 35 percent subsidy. Using firm-level data, I find no short-term impact on R&D spending, but a significant 8% increase in productivity per worker post-reform. The results are driven by less financially constrained firms, emphasizing that firms were artificially limiting their growth to stay eligible. Looking at mechanisms to explain how firms decided to grow, I find that firms tended to increase their current production but did not conduct more long-term investment. The second chapter builds on the first to understand how productivity gains impacted firms' pay and hiring policies. On average, firms raised earnings by around 2 percent, translating to about 1,100 CAD per worker. Using the reform as an instrument for firms' value-added level, I recover rent-sharing elasticities that range between 0.17 and 0.23. No employment effect is detectable post-reform. Within firms, the lower earnings quartiles saw larger increases. The productivity gains also led firms to pay new hires about 2.2 percent more and to recruit from better firms, though they did not hire better individual workers. Combining these results with Chapter 1 suggests that firms restricted their production to maintain eligibility for the generous R&D program, possibly by reducing the number of hours worked. The third chapter quantifies the reallocation effect of high-tech industrial policies targeting a segment of the IT sector. Exploiting a 2008 reform in Quebec that introduced a wage subsidy for all IT firms, with stringent criteria for non-software firms, I find that IT sector earnings increased significantly, although with no observable employment effect. Breaking down the IT sector between software and non-software firms, I find that most of the employment gains in the software industry are offset by declines in non-software industries. Heterogeneity by age group confirms that younger workers gained more and were more prone to reallocate across targeted and non-targeted industries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".