Bibliographic record
Abstract
Chapter 2 examines the incidence of R&D tax credits on workers’ wages and the underlying mechanisms. Leveraging a regression kink design with matched employer–employee tax records, I show that R&D tax credits substantially increase firms’ R&D expenditures. Responses are concentrated among R&D-intensive firms, where higher spending leads to gains in profitability, productivity, and wages. These benefits accrue primarily to incumbent workers, with high-skill, long-tenured, and older employees experiencing the largest earnings increases: a 10 percent rise in expenditure limit raises their annual wages by 1.2–1.9 percent. In contrast, entrants and less-skilled, younger, or short-tenured workers experience no significant wage effects. The results are consistent with a rent-sharing framework and highlight the role of R&D policy in shaping within-firm wage inequality. Chapter 3 connects changes in employer characteristics through job transitions to employee earnings following mergers and acquisitions (M&As). Using firm balance sheet data linked to individual earnings data in Canada and a matched difference-in-differences design, we find that workers at target firms experience a post-M&A earnings decline, driven largely by those who leave. Although movers transition to larger and more profitable firms, they face wage losses, likely reflecting the erosion of firm-specific human capital or backloaded contracts. The evidence suggests that the loss of match-specific premiums is the primary mechanism behind post-M&A wage declines. Chapter 4 examines the impact of corporate M&As on firm profitability and markups. Using financial data (2010 - 2018) for 10 European countries and a matched difference-in-differences design, we find that acquirers’ and targets’ markups remain unchanged, while their profitability declines substantially. Heterogeneity analyses across sectors and deal types confirm that these patterns are inconsistent with a market power channel. Instead, the evidence indicates that acquisitions are associated with weaker medium-run performance rather than increasing market power.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".