Disentangling the gender-differentiated determinants of home-based self-employment choices in Nigeria
Bibliographic record
Abstract
Abstract Understanding gender disparities in home-based self-employment (HBS) and their links to homeownership and socioeconomic factors is crucial for advancing sustainable development goals (SDGs) in Sub-Saharan Africa, especially Nigeria. This study uses data from the 2010/2011, 2012/13, 2015/16, and 2018/19 waves of the Nigerian General Household Survey (GHS). It employs random effect probit regression, the LASSO method for identifying predictors, and the Blinder–Oaxaca decomposition technique to analyse gender differences in nonlinear binary outcomes. The results show that female business owners are more likely to engage in HBS compared to males, highlighting the importance of gender equality (SDG 5) and decent work (SDG 8). While male entrepreneurs are mainly driven by profit, females prioritise balancing paid and unpaid work, reflecting motivations beyond profit within heterodox economics. Significant gender-differentiated impacts are observed in relation to monthly rent, post-secondary education, dwelling space, energy, and regional locations. Notably, the presence of children significantly increases female involvement in HBS, a trend not seen among males. Marriage also influences female participation, suggesting that marital circumstances and economic benefits play a role. These findings highlight the need for policies addressing gender-specific constraints, challenging traditional gender roles, and promoting inclusive human development within the SDG framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".