Women’s exporting success: evidence from Canadian small and medium-sized enterprises
Bibliographic record
Abstract
Based on the Statistics Canada 2017 Survey on Financing and Growth of Small and Medium Enterprises, this chapter examines the role of gender on the export participation of Canadian small and medium-sized enterprises (SMEs), and identifies specific characteristics and business activities of women-owned SMEs that are associated with their export propensity and export intensity. It also provides empirical evidence of the benefits of online payments and innovations to the internationalization of women-owned SMEs. The study finds no statistically significant gender differences in the export propensity and the export intensity when business characteristics are controlled for. However, the impact of some characteristics on export propensity are significantly different between men and women-owned SMEs. Firstly, larger SMEs owned by women are less likely to export than men-owned and equally owned SMEs of the same size. Secondly, online payment and innovations play a more crucial role in facilitating exports for women-owned SMEs. Finally, for higher export intensity, the owner’s education level and managerial experience are much more important for women-owned SME exporters than for men-owned and equally owned exporters.
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 | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".