FERTILITY PATTERNS AND MATERNAL CHARACTERISTICS ACROSS NIGERIA'S GEOPOLITICAL ZONES: A LONGITUDINAL STUDY FROM 2003 TO 2018
A Longitudinal Study from 2003 to 2018
Abstract
This study examines the reproductive and demographic traits of mothers in Nigeria while utilizing secondary data from the Nigeria Demographic and Health Survey (NDHS). The study uses Structural Equation Modeling (SEM) to examine the relationship between socioeconomic status, reproductive behaviour, and geo-cultural factors affecting fertility patterns in Nigeria. The results show that the average age of mothers at first birth has gradually increased, while the proportion of mothers with no formal education remains significant. The model reveals significant insights into how these latent constructs interact. The model indicates that higher SES leads to fewer children, while reproductive behaviours like age at first birth and marital status positively influence the total number of children ever born. The model fit indices: RMSEA of 0.072; TLI of 0.903; and CFI of 0.947, demonstrate a reasonable fit, suggesting that the model adequately captures the underlying relationships, but some coefficients suggest measurement issues. The study emphasizes the importance of cultural norms and socioeconomic conditions in shaping reproductive choices.
References
Agbor, I. (2016). Culture, Child Preference and Fertility Behaviour: Implications for Population Growth in Cross River State, Nigeria. British Journal of Education Society & Behavioural Science, 17(3), 1–21. https://doi.org/10.9734/bjesbs/2016/27289 DOI: https://doi.org/10.9734/BJESBS/2016/27289
Ahinkorah, B.O., Seidu, A.A., Armah-Ansah, E.K., Ameyaw, E.K., Budu, E. and Yaya, S. (2021). Socio-economic and demographic factors associated with fertility preferences among women of reproductive age in Ghana: evidence from the 2014 Demographic and Health Survey. Reproductive Health, 18, 1-10. https://doi.org/10.1186/s12978-020-01057-9 DOI: https://doi.org/10.1186/s12978-020-01057-9
Ajzen, I. and Klobas, J. (2013). Fertility intentions: An approach based on the theory of planned behaviour. Demographic Research, 29, 203-232. https://doi.org/10.4054/DemRes.2013.29.8 DOI: https://doi.org/10.4054/DemRes.2013.29.8
Alkema, L., Raftery, A.E., Gerland, P., Clark, S.J., Pelletier, F., Buettner, T. and Heilig, G.K. (2011). Probabilistic projections of the total fertility rate for allcountries. Demography, 48(3), 815-839. https://doi.org/10.1007/s13524-011-0040-5 DOI: https://doi.org/10.1007/s13524-011-0040-5
Ariho, P., Kabagenyi, A. and Nzabona, A. (2018). Determinants of change in fertility pattern among women in Uganda during the period 2006–2011. Fertility Research and Practice, 4(4). https://doi.org/10.1186/s40738-018-0049-1 DOI: https://doi.org/10.1186/s40738-018-0049-1
Asemota, O.A. and Klatsky, P. (2015, January). Access to infertility care in the developing world: the family promotion gap. In Seminars in Reproductive Medicine, 3(01), 017-022. Thieme Medical Publishers. DOI: https://doi.org/10.1055/s-0034-1395274
Avogo, W.A. and Somefun, O.D. (2019). Early marriage, cohabitation, and childbearing in West Africa. Journal of Environmental and Public Health, 2019(1), 9731756. https://doi.org/10.1155/2019/9731756 DOI: https://doi.org/10.1155/2019/9731756
Bau, N. and Fernández, R. (2023). Culture and the Family. In Handbook of the Economics of the Family: North-Holland, 1(1), 1-48. DOI: https://doi.org/10.1016/bs.hefam.2023.01.001
Bennett, L.R. (2013). Early marriage, adolescent motherhood, and reproductive rights for young Sasak mothers in Lombok. Wacana Journal of the Humanities of Indonesia, 15(1), 66. https://doi.org/10.17510/wjhi.v15i1.105 DOI: https://doi.org/10.17510/wjhi.v15i1.105
Boker, S.M., Neale, M.C., Maes, H.H., Spiegel, M., Brick, T.R., Estabrook, R., Bates, T.C., Gore, R.J, Hunter, M.D., Pritikin, J.N., Zahery, M. and Kirkpatrick, R.M. (2023). OpenMx: Extended Structural Equation Modelling. R package version 2.21.8. https://CRAN.R-project.org/package=OpenMx
Bollen, K.A., Glanville, J.L. and Stecklov, G. (2007). Socio-economic status, permanent income, and fertility: A latent-variable approach. Population studies, 61(1), 15-34. https://doi.org/10.1080/00324720601103866 DOI: https://doi.org/10.1080/00324720601103866
Bongaarts, J. (2008). Fertility Transitions in Developing Countries: Progress or Stagnation? Studies in Family Planning, 39(2), 105–110. https://doi.org/10.1111/j.1728-4465.2008.00157.x DOI: https://doi.org/10.1111/j.1728-4465.2008.00157.x
Cleland, J. (1996). A regional review of fertility trends in developing countries: 1960 to 1995. The Future Population of the World, 47-72.
Cleland, J. (2001). The effects of improved survival on fertility: A reassessment. Population and development review, 27, 60-92.https://www.jstor.org/stable/3115250
De la Croix, D. and Pommeret, A. (2021). Childbearing postponement, its option value, and the biological clock. Journal of Economic Theory, 193, 105231. https://doi.org/10.1016/j.jet.2021.105231 DOI: https://doi.org/10.1016/j.jet.2021.105231
Epskamp, S. (2022). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages' Output. R package version 1.1.6. https://CRAN.R-project.org/package=semPlot
Epstein, D.A., Lee, N.B., Kang, J.H., Agapie, E., Schroeder, J., Pina, L.R., ... Munson, S. (2017). Examining menstrual tracking to inform the design of personal informatics tools. In Proceedings of the 2017 CHI conference on human factors in computing systems, pp: 6876-6888. DOI: https://doi.org/10.1145/3025453.3025635
Fagbamigbe, A.F. and Idemudia, E.S. (2016). Survival analysis and prognostic factors of timing of first childbirth among women in Nigeria. BMC pregnancy and childbirth, 16, 1-12. https://doi.org/10.1186/s12884-016-0895-y DOI: https://doi.org/10.1186/s12884-016-0895-y
Feyisetan, B.J. and Bankole, A. (2009). Fertility transition in Nigeria: trends and prospect. asdf, 461.
Fox, J., Nie, Z. and Byrnes, J. (2022). sem: Structural Equation Models. R Package version 3.1.15. https://CRAN.R-project.org/package=sem
Gayawan, E., Adebayo, S.B., Ipinyomi, R.A. and Oyejola, B.A. (2010). Modelling fertility curves in Africa. Demographic Research, 22, 211–236. http://www.jstor.org/stable/26349558 DOI: https://doi.org/10.4054/DemRes.2010.22.10
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