Patterns and Determinants of Fertility Differentials Among Reproductive-Age Women in Katsina Local Government Area, Katsina State

Abstract

This study examines the patterns and determinants of fertility differentials among reproductive- age women in Katsina Local Government Area (LGA), Katsina State, with a focus on understanding how socio-demographic factors such as age at marriage, educational attainment, and place of residence influence reproductive outcomes. Despite a national decline in the Total Fertility Rate (TFR) from 5.3 to 4.8 children per woman (NDHS, 2024), the unmet need for family planning has paradoxically increased from 18.9% to 21%, indicating a growing demand-supply gap in reproductive health services. The specific objectives include identifying key fertility patterns, analyzing socio-economic and cultural determinants of high fertility, and assessing the influence of reproductive agency and community norms. The theoretical framework is anchored in the Demographic Transition Theory (DTT) and the emerging concept of reproductive agency, which emphasizes individual autonomy in reproductive decision-making. Using a survey research design, 400 questionnaires were distributed, with 393 successfully retrieved, representing a response rate of 98.25%. Quantitative data were analyzed using descriptive statistics and chi- square tests to test hypotheses, while qualitative insights were drawn from a synthesis of recent literature and policy reports. Findings reveal that early marriage, low educational attainment, and rural residence are significantly associated with higher fertility. Hypothesis testing confirmed a statistically significant relationship between women’s education and fertility outcomes (?² = 29.43, p < 0.001), while age at marriage also showed a strong association (?² = 31.16, p < 0.001). The study concludes that improving reproductive health outcomes in Katsina LGA requires more than service expansion it demands a transformative approach centered on female empowerment, legal reform, and culturally sensitive programming. Policy interventions s

Keywords

References