Distribution of Firms by Size: Observations and Evidence from Selected Countries
Bibliographic record
Abstract
It is commonly remarked that Small and Medium-Sized Enterprises (SMEs) form the backbone of many different economies around the world, but the extent to which such national trends form part of a quantifiable larger global pattern has rarely, if ever, been examined. It is not unusual to hear business leaders, elected officials, public policymakers and researchers in a given region claim that small businesses represent a surprisingly large share of the local economy. They typically argue that SMEs constitute the majority of all firms, and have done so for an extended period of time. The claim is repeated across many national jurisdictions, but each statement is only ever considered in isolation. Few attempts have been made to compare the proportionate distribution of SMEs in one nation-state with those in other jurisdictions. Large-scale trends, however, are often the aggregate sum of many local occurrences. Is it possible that what seems to be an isolated regional phenomenon is, in fact, a common pattern across much of the world? This paper examines the number of micro, small, medium and large-sized enterprises from a selection of different countries. Using definitions and data provided by the national statistical agency in each nation, it seeks to compare the relative proportion of firms by size, and to determine if there are any common patterns. It then suggests some indicative theories about SME distribution for future research to test.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".