How do Gender and Immigrant Background Affect a Company Owner's Decision to Engage in Direct or Indirect Exporting?
Bibliographic record
Abstract
This research focuses on understanding the effects of gender and immigrant ownership on the export behavior of small and medium-sized enterprises (SMEs). Prior studies indicate that female and male entrepreneurs have different qualities or experiences that might result in different export strategies. In addition, there is evidence that business owners with an immigrant background have export-enabling characteristics. Drawing on insights from social capital theory, I investigate the separate and joint effects of gender and immigrant background on the likelihood of SMEs to engage in direct exporting—i.e., selling goods or services directly to foreign customers—as opposed to indirect exporting—i.e., using an intermediary to sell goods or services to foreign customers—or not exporting at all. I analyzed a sample of 78 SMEs. The results show that female-majority-owned SMEs are less likely to export directly compared to male-majority-owned SMEs. Immigrant-owned SMEs are more likely to export directly, and particularly when they have male owners. Female-majority-owned SMEs’ propensity to export directly is not affected when their owners have an immigrant background. I will discuss the theoretical implications of these findings and show how they may serve as a guide to improve the design and implementation of policies targeted at immigrant export businesses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".