The Firm Size Distribution and Productivity Growth
Bibliographic record
Abstract
Over the last 20 years, the annual average U.S. and Canadian productivity growth rates have been 2.3% and 1.3%, respectively. The objective of this paper is twofold. First, we empirically document the firm size distribution and the productivity for the two countries. Second, we quantitatively assesses how much different determinants of the firm size distribution contribute to this observed productivity difference between the two countries. For the empirical part, we show that U.S. firms are on average larger than their Canadian counterparts. This observation is particularly so in the manufacturing industry. Moreover, we show that small firms in the United States have growth rates that are higher than small firms in Canada, but larger firms in the two countries have similar growth rates. These observations suggest that small firms in the two countries may be the key source of the observed productivity growth gap. Given these observations, we build a model of firm size dynamics, which incorporates several determinants of the firm size distribution such as the tax structures and the financial market imperfections. We then calibrate the model for each country focusing on these determinants. The calibrated model is used to determine whether and how much the differences in these determinants can account for the differences in the firm size distributions and the productivity growth gap
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".