The effects of unions on research and development: an empirical analysis using multi‐year data
Bibliographic record
Abstract
A link between unionization and research and development rates (research and development expenditures divided by output) is tested for in thirteen aggregate Canadian industries. A balanced panel of thirteen industries covering 1968 to 1986 reveals a negative relationship between industry unionization rates and research and development. The results hold when a number of techniques are used to control for unobserved industry heterogeneity and non‐linear responses to unionization. In an industry that moves from the 25th to the 75th percentile of unionization, research and development is predicted to fall by about 40 per cent. JEL Classification: J51 Les effets des syndicats sur l'intensité de la recherche et développement: une analyse empirique sur plusieurs années. Ce mémoire examine le lien entre le degré de syndicalisation et les taux de dépenses en recherche et développement en proportion de la production dans treize industries canadiennes. Pour ces treize secteurs industriels, entre 1968 et 1986, il semble qu'il y ait une relation négative entre les taux de syndicalisation et les taux de dépenses en recherche et développement. Ces résultats sont robustesmême quand on utilise des techniques pour tenir compte de l'hétérogéneité des secteurs et des réponses non linéaires à la syndicalisation. Pour une industrie qui voit son taux de syndicalisation passer du 25e au 75e percentile, on prévoit une chute de 40% dans la recherche et développement.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".