Is ‘Mumpreneurship’ a White phenomenon? Self-employment among native and immigrant women in the U.S.
Bibliographic record
Abstract
In the context of persistent work and family conflict, self-employment is perceived as a solution for mothers to reconcile employment and childcare duties. The ‘mumpreneurship’ thesis states that the recent upsurge in female self-employment has been driven by mothers searching for the independence and flexibility that wage labour lacks to balance work and family demands. Although mumpreneurship has been portrayed as a growing popular phenomenon, most of the evidence is based on small qualitative studies and data for White women. This study examines the extent to which the concept of mumpreneurship can be applied to ethno-racial minorities and immigrant women using recent data from the U.S. Current Population Survey (2015-2020). We found that having children is associated with lower wage-employment and self-employment for all women, with the exception of native Black women. We find strong evidence for mumpreneurship among native-born White mothers, for whom self-employment is a preferred alternative over wage-employment. For all other racial minorities and immigrant mothers, children are not associated with women’s probability of self-employment. The findings suggest that mumpreneurship, as a strategy for combining work and family responsibilities, has been overstated, applying mainly to White women, but not to racial minority and immigrant women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".