ArticleBMC women's health2026
The relationship between women's occupation and fertility outcomes in The Gambia, 2019-2020.
Article in BMC women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWomen's occupation and labour force participation are assumed to lower fertility levels as they compete with women's household obligations. While research has demonstrated this to be true in industrialized nations, developing countries, such as The Gambia, require further investigation.
methodsSecondary data extracted from the Gambia Demographic Health Survey (GDHS) 2019-2020 were used. A weighted sample of 11,865 women of reproductive age (15-49 years) was included in the study. At the bivariate level, one-way ANOVA and t-tests were used to examine the relationship between the dependent variable, children ever born, and the independent variable, women's occupation, as well as each control variable. Multivariable analysis, comprising Poisson regression models, was used to assess the impact of the independent variable on the dependent variable while controlling for confounders.
resultsThe analysis revealed that women's occupational type was significantly associated with fertility in The Gambia. After adjustment for socio-demographic characteristics, women in professional, technical, managerial, and clerical occupations had approximately 12.8% fewer children than non-working women (IRR = 0.872, p < 0.01), while women in agricultural occupations had approximately 6.3% more children (IRR = 1.063, p < 0.01). Age, education, wealth status, contraceptive use, religion, and marital status were also significantly associated with children ever born.
conclusionThe findings show that measures aimed at lowering fertility should be multifaceted, focusing on variables that were found to be significant in this study. These could be effective tools for empowering Gambian women and lowering fertility. There is also a need for further primary data-based research into the relationship.
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