Page 69 - IJPS-11-3

International Journal of
Population Studies Age patterns of fertility in Ethiopia

regression models could have helped validate the region- approach using TFRs as input to obtain plausible ASFRs.
specific ASFRs reported by surveys like the EDHS or The use of a long series of ASFRs and corresponding TFRs
various censuses. is an advantage.
The model-based patterns of fertility for Ethiopia and The cubic polynomials developed in this study are well-
its regions generally exhibit a uni-modal distribution, with suited for modeling fertility over time due to their flexibility
maximum fertility among women aged 25 – 29, followed in capturing the typical fertility transition pattern, which
by a steady decline. This indicates that the fertility patterns includes periods of high fertility, potential stalling, and
in Ethiopia have a right-skewed distribution where most subsequent decline. This makes the cubic polynomial a
births occur within younger age groups and few in older useful tool for modeling the non-linear nature of fertility
age groups (Figure 3). transitions over time.
The fertility patterns are broad due to high fertility The fertility schedules estimated for various regions
among women aged 20 – 34, except in urban areas. The and periods using the MFT of Ethiopia were consistent
fertility patterns for Ethiopia (urban) and cities such as with expectations, reflecting the rise in modernization and
Addis Ababa, Dire Dawa, and Harari had relatively sharp the ongoing fertility transition of the various provinces in
peaks, indicating low fertility rates. In particular, Addis Ethiopia. At the early stages of fertility transition (with a
Ababa and Ethiopia (urban) showed very sharp peaks TFR of about seven), the majority of births occur at ages 20
attributable to smaller fertilities. – 24 and 25 – 29. In addition, births are also significant at
During the study period, fertility declines were observed ages 15 – 19, 30 – 34, 35 – 39, and 40 – 44, indicating early
in most regions, indicating ongoing fertility transition. entry to childbearing and late exit. In this stage, births are
Tigray, Amhara, Oromiya, Benshangul Gumuz, SNNP, predominantly concentrated in the 20s. However, as TFR
Gambela, Dire Dawa, Ethiopia (total), Ethiopia (rural), decreases, the majority of births shift to ages 30 – 34 and
and Ethiopia (urban) showed fertility declines during the 25 – 29, while the values of other age groups decline. At the
study period. However, fertility transition setbacks were later stage of the fertility transition, the majority of births
observed in urban places such as Addis Ababa and Harari. are concentrated in the late twenties and early thirties,
indicating late entry to childbearing and early exit. This
Notably, the Afar and Somali regions showed an
increment in ASFRs during the study period, with the phenomenon is expected and is known as childbearing
transition characterization (El-Khorazaty & Horne, 1992).
Somali region displaying a broader peak than others due
to high fertility among women aged 20 – 39. The model-based ASFRs for Ethiopia and its regions
exhibited a uni-modal distribution similar to those depicted
4. Discussion in the EDHS results (Central Statistical Agency [Ethiopia]

In countries like Ethiopia, producing fertility indicators & ICF, 2016, p. 78). In addition, the ASFR curves are broad
is crucial for effective interventions to ensure fertility peaked with high values during the study period, except
transition. However, fertility indicators can be biased due for urban Ethiopia, due to the high fertility of women aged
to issues related to fertility data, such as birth omissions, 20 – 34. This finding aligns with EDHS reports, which
age misreporting, and small sample sizes. This study aimed consistently show higher fertility for this age group across
to overcome these challenges by developing plausible all survey periods. The broad peaks associated with high
ASFR estimates using a model-based approach. Model- ASFR values imply that fertility remains high in most
based ASFRs can serve multiple purposes. They can be parts of the country. In contrast, urban areas show sharp
used to evaluate the quality of empirical data, correct peaks and lower ASFRs, indicating declining fertility in
irregularities in data, and make predictions (Sloggett, urbanized areas.
2015). Understanding the pattern of fertility and its stable The difference in fertility in urban Ethiopia and other
features is essential for producing acceptable indicators. regions of the country may be attributable to differences
Previous studies by major researchers such as Hadwiger in family planning utilization. Urban Ethiopia has higher
(1940), Hoem et al. (1981), Gompertz (1825), Brass (1975, family planning use compared to rural areas, contributing
1978), Pasupuleti and Pathak (2010), Chandola et al. to lower fertility rates in cities such as Addis Ababa, Dire
(1999), Schmertmann (2003), Peristera & Kostaki (2007), Dawa, and Harari (Central Statistical Agency [Ethiopia] &
and Gayawan et al. (2010) focused on modeling ASFR ICF International, 2012, p. 98; Central Statistical Agency
as a function of maternal age during birth. Each study [Ethiopia] & ICF., 2016, p. 112; Central Statistical Authority
built upon previous work by proposing new models that [Ethiopia] & ORC Macro, 2001, p. 55; Central Statistical
better fit the available fertility data. This study differs in its Authority [Ethiopia] & ORC Macro, 2006, p. 63).

Volume 11 Issue 3 (2025) 63 https://doi.org/10.36922/ijps.4086