Page 127 - IJPS-11-1

International Journal of
Population Studies Analysis of age-specific fertility in India

Kashmir, fertility reached its peak in the age group 30 – 34. (Chandola et al., 1999). In Pakistan, ASFR was studied
In all other bigger states or union territories, the highest using the Makeham curve fitting method (Luther, 1984).
fertility has been attained in the age group 25 – 29. Fertility, Similarly, Azzalini (2003; 2005) applied skew-normal
however, declines from age 30 in all the bigger states or distribution and skew-t distribution to study the pattern of
union territories, except Jammu and Kashmir where it ASFR. Mazzuco & Scarpa (2011) used a skew-symmetric
declines from age 35. The ASFR in the younger age group 15 model to fit the fertility pattern of different countries by
– 19 varies from 2.6 in Delhi to 22.3 in West Bengal. In the uni-modal and bimodal-fertility schedules. Skew-logistic
age group 30 – 34, the variation in the level of ASFR is from model was also used to study the ASFRs of Italy (Asili et al.,
41.9 in West Bengal to 147 in Bihar (SRS, 2020). 2014) and India (Mishra et al., 2017). Gaire & Aryal (2015)
Another important measure of fertility that has been proposed the Invers Gaussian model; Gaire et al. (2019)
used to measure the replacement level of fertility in any used the skew-log-logistic model; and Gaire et al., (2022)
region is the total fertility rate (TFR). TFR is measured by formulated the polynomial models to fit the ASFRs of
summing up all the ASFRs. According to SRS-2020, TFR Nepali mothers. Islam (2011) used a polynomial model to
for the country decreased to 2.0 in 2020 from 2.1 in 2019. fit the ASFRs and forward cumulative ASFRs of Indonesia
During 2020, Bihar reported the highest TFR (3.0), while and found that ASFRs follow the third-degree polynomial
Delhi, Tamil Nadu, and West Bengal reported the lowest model and forward cumulative ASFRs follow the second-
TFR (1.4). At present, the TFR among rural women is 2.2 degree polynomial model. Singh et al. (2015) fitted a third-
at the national level, which is higher than that of urban degree polynomial for different ages and their reciprocal
India (having a TFR of 1.6). At the national level, there is for the ASFRs of India.
an increasing trend in fertility in the more advanced age In India, the ASFR has been declining steadily in recent
group 30 – 44, while there is a decrease in fertility in the decades. However, there are still significant gaps in fertility
younger age group 15 – 29 (SRS, 2020). levels between different states and socioeconomic groups.
ASFRs offer a clear picture of the fertility patterns, as To achieve such a target, a better understanding of the
they provide information on the likelihood of a woman current pattern of ASFRs is required. Statistical models,
giving birth within a specific age range. In the Indian when well-constructed, can aid in this understanding as
context, analyzing the ASFRs can offer insight into the they provide better insight into some characteristics of
country’s fertility trend, including factors such as women’s the distributional pattern of fertility. A few studies have
education, healthcare, and family planning (Singh highlighted the use of polynomial model in ASFR modeling
et al., 2022). By examining the ASFR, policymakers can in India, but the available evidence is either restricted
make informed decisions regarding population policies, to single method or outdated and thus a comprehensive
healthcare, and education policies since regions with high analysis of ASFR using both deterministic and stochastic
ASFRs will cause significant population growth and other model is necessary.
health-related issues. Verma et al. (2019) proposed various In general, the ASFR follows a bell-shaped curve that
age-specific contraceptive policies to reduce fertility and depends on various factors, such as the age of women at
population growth rates in different age groups. Therefore, marriage, the proportion of married women at a specific
a thorough analysis of ASFRs in India is crucial in age, the proportion of widowhood and separated women,
understanding and addressing the country’s demographic post-partum abstinence and the level of contraceptive use
challenges. Considering the importance of ASFRs, several (Balasubramanian, 1980).
studies conducted to observe their pattern and trend are In demographic studies, deterministic and non-
discussed below. deterministic (stochastic) modeling techniques are
In the existing literature, various fertility models have employed. Deterministic models are generally used to
been proposed and implemented to study the behavior describe the functional relationship between the variables
of ASFRs. Some researchers have proposed deterministic under consideration. However, in non-deterministic
models, and others proposed stochastic models. Hoem models, the variables rely on probability distributions
et al. (1981) performed the curve fitting to the ASFR using (Islam, 2009). In this study, we proposed deterministic and
cubic spline, Hadwiger, Coal-Trussel, Beta, Gamma, Brass, non-deterministic models to study the recent pattern of
and Gompertz functions. Similarly, a generalized Hadwiger ASFRs in India. We considered eight deterministic models
model was used to fit the ASFRs of Hungary and Norway (viz. linear, second-degree, third-degree, fourth-degree,
(Gilje, 1969). However, the Hadwiger two-component and their reciprocal polynomial models), and six non-
mixture model was used to study the fertility pattern in the deterministic models (viz. skew-normal (type-1 and -2),
United Kingdom, Ireland, and the United States of America skew-t (type-3, -4, and -5) and skew-logistic model) to

Volume 11 Issue 1 (2025) 121 https://doi.org/10.36922/ijps.1338