Studying Firm Growth Distributions with a Large Administrative Employment Database
Bibliographic record
Abstract
Abstract This paper uses business tax administrative data to describe the annual firm growth rate distribution in Canada over the 2000–2009 period. This administrative tax database provides a unique lense to study firm growth as it allows us to look at the universe of Canadian employer firms and investigate the firm growth distribution across different dimensions. A non-normal, fat-tailed shape for the firm growth distributions holds across years, industries, regions, as well as firm size and age classes. The results show that the distributions of employment growth rates in Canada have more density in both the center and tails than a normal distribution. The evidence paints a picture of firm growth dynamics whereby most firms change very little each year, while a nontrivial amount also markedly grow or decline. A final finding is that young firms, aged four or less, represent a special case with an upwardly skewed distribution and a median growth rate greater than zero.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".