Firm Dynamics: Employment Growth Rates of Small Versus Large Firms in Canada
Bibliographic record
Abstract
This paper examines whether Canadian firms of different sizes (in terms of employment) grow at different rates year-on-year. The data are from Statistics Canada’s Longitudinal Employment Analysis Program and cover the 1999-to-2008 period. The methodology is similar to that used by Haltiwanger, Jarmin and Miranda (2010) for the United States: controls are used for firm age, and possible bias from short-term regression to the mean is removed by sizing firms according to their average number of employees in both previous and current years. The analysis shows that employment growth rates across the Canadian business sector does not vary much between firms of different size classes, except for the smallest and youngest firms. Employment growth rates rise with firm size for firms with fewer than 20 employees, but for larger firms, no relationship emerges between employment growth and firm size. These results are consistent with the average proportionate growth condition of Gibrat’s Law—the assertion of French economist Robert Gibrat that average employment growth is independent of firm size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".