Page 70 - IJPS-11-3

International Journal of
Population Studies Age patterns of fertility in Ethiopia

As the fertility transition progresses, the ASFR assumes that regional fertility patterns align with national
shape becomes more sharply peaked, with higher trends. If these assumptions do not hold true in some
fertility concentrated in specific age groups. The model- regions, the estimates may not accurately reflect local
based results also show regional differences in fertility realities.
experiences. For instance, the Somali region exhibits a By addressing these limitations, future research can
flatter ASFR curve than other regions due to high fertility build upon the current study to provide more robust and
across consecutive age groups (20 – 24, 25 – 29, 30 – 34, comprehensive insights into fertility trends and transitions.
and 35 – 39), which is distinct from other regions. This
demonstrates the flexibility of the MFT in capturing 5. Conclusions
diverse fertility experiences across parts/regions with
varying fertility levels. This study aimed to develop an MFT for Ethiopia to
estimate ASFRs from TFR and to analyze changes in the
The age-specific fertility curves, when multiplied by
TFR, can be considered as density functions of age at age patterns of fertility across provinces from 2000 to 2016.
The models, developed using UN WPP 2022 ASFR and
childbearing (Brijesh et al., 2015). As the fertility transition TFR data of Ethiopia, were found suitable for estimating
progresses in Ethiopia, one can expect changes in ASFRs from the TFR of the regions of Ethiopia.
parameters, such as location, scale, and shape. Specifically,
the mean childbearing age of mothers is likely to increase, During the fertility transition, Ethiopia and its regions
and the childbearing duration will become narrower, displayed broad peak age-specific fertility patterns, with
causing the ASFR curve to become more peaked. The fertility concentrated heavily among the 20 – 24, 25 – 29,
transition may manifest as a shift from a broader peak to and 30 – 34 age groups. Exceptions included Addis Ababa,
a relatively sharp peak in ASFRs, an increase in the modal Dire Dawa, and Ethiopia (Urban) during the latest period
age of mothers, and a decrease from a high reproductive of 2016. On the other hand, Afar and Somali are seen to
life span to a lower one. have entirely different age-specific fertility patterns during

The study presents several strengths and limitations. the entire fertility transition period from 2000 to 2016.
One of its primary strengths is the innovative construction From the policy perspective, based on the findings of the
of an MFT, which is the first of its kind in Africa, providing present study, there is a pressing need for the government
plausible ASFRs derived solely from TFR data. The model- of Ethiopia to make sufficient efforts to reduce fertility,
based ASFRs provide information about ASFRs at sub- particularly in Afar and Somali. Generally, policy makers
national levels that enable policymakers to design and should give much focus to age groups 20-24, 25-29, and 30-34.
implement policies targeted at fertility-related programs. In addition, focusing on decreasing fertility among younger
It also facilitates understanding regional differences in age groups (15 to 25) accelerates the fertility transition.
fertility rates that can help tailor specific interventions. In
addition, the generalizability of the methodology allows for During the fertility transition period from 2000 to 2016,
its application in other African countries or regions with in regions with high urbanization, such as Ethiopia (urban),
similar demographic and socioeconomic contexts. It is also Addis Ababa, and Dire Diwa, a significant concentration of
possible to construct MFTs for other countries, given that fertility was observed in the age group of 25 – 29 compared
similar input data is available. The information on ASFRs to other groups. This pattern may be attributed to factors
would also help in informing fertility transition policies such as increased contraceptive use, more job opportunities,
by identifying age groups with high fertilities, facilitating higher educational levels, and overall urbanization and
appropriate policies like community-based programs, modernization of the provinces. These findings highlight
changes in laws regarding marriage age, or campaigns the considerable impact of urbanization on fertility patterns
promoting gender equity. Moreover, the knowledge of in these areas. It suggests the need for tailored policies to
ASFRs would enable improved health policy and family address the low fertility levels observed in Addis Ababa and
planning programs, allowing health policymakers to other future urban areas in the country.
tailor family planning and reproductive health services to Finally, caution should be exercised when using the
specific age groups, regions, or communities. MFT. As with every modeling approach, the model-based
However, the study also has limitations. The MFT was ASFRs may differ from actual ASFRs. The application
constructed by applying regressions based on actual and of the model fertility rates derived from a given TFR
projected data that are subject to change over time. As a is recommended when data are either unavailable or
result, the MFT may require revisions periodically when unreliable. This study can serve as an inspiration for
remarkably new data emerges. In addition, the model developing similar models or refining the current ones.

Volume 11 Issue 3 (2025) 64 https://doi.org/10.36922/ijps.4086