Page 126 - IJPS-11-1

International Journal of

Population Studies

RESEARCH ARTICLE
Analysis of age-specific fertility in India:

Deterministic and non-deterministic modeling
approaches

1
Diptismita Jena , Prafulla Kumar Swain *, Manas Ranjan Tripathy ,
2†
1†
Prashant Verma , and Pravat Kumar Sarangi 1
3
1 Department of Statistics, Ravenshaw University, Cuttack, Odisha, India
2 Department of Statistics, Utkal University, Bhubaneswar, Odisha, India
3 Department of Statistics, University of Allahabad, Prayagraj, Uttar Pradesh, India

Abstract
The main objective of this study is to investigate the pattern of age-specific fertility
rates (ASFRs) in India using deterministic and non-deterministic approaches.
Toward this end, we proposed statistical polynomial regression models to study the
† These authors contributed eqully to distributional pattern of ASFRs for total, rural, and urban women in India. Further, a
this work. comparative study considering selected skewed regression models was undertaken.
*Corresponding author: For this study, secondary data on ASFR were collected from Sample Registration
Prafulla Kumar Swain System, Statistical Report-2020, and from National Family Health Survey 5 (NFHS-5;
(prafulla86@gmail.com) 2019 – 2021). It was found that all three subcategories of ASFRs, namely, the total,
Citation: Jena, D., Swain, P.K., rural, and urban ASFRs, followed the reciprocal biquadratic polynomial model.
Tripathy, M.R., Verma, P. & On the other hand, all three subcategories of ASFR follow the skew-normal type 2
Sarangi, P.K. (2025). Analysis
of age-specific fertility in India: distribution. Similar findings were also obtained and validated based on NFHS-5
Deterministic and non-deterministic data. Further, the chosen statistical models’ validity and stability were tested using
modeling approaches. International various model validation techniques and model selection criteria.
Journal of Population Studies,
11(1): 120-135.
https://doi.org/10.36922/ijps.1338 Keywords: Age-specific fertility rate; Polynomial regression model; Skewed regression
Received: July 20, 2023 model; Cross validity prediction power; Shrinkage; Coefficient of determination
1st revised: September 4, 2023
2nd revised: September 17, 2023
Accepted: October 20, 2023 1. Introduction
Published Online: December 1, Like in many other developing countries, fertility is an essential issue with significant
2023 social, economic, and demographic consequences in India. The fertility rate is an
Copyright: © 2023 Author(s). important metric that describes changing patterns of the demographic configuration of
This is an Open-Access article a nation concerning its population size, composition, and growth rate. The age of women
distributed under the terms of the
Creative Commons Attribution is an essential factor affecting fertility levels. Further, age-specific fertility rates (ASFRs)
License, permitting distribution, provide a clear picture of the fertility behavior of women in different age groups. ASFR
and reproduction in any medium, is defined as total number of babies born per 1000 women in a specific age group in a
provided the original work is
properly cited. particular region. To monitor and detect the change in population in any region, for
example, district, state, or country, it is crucial to study its fertility and, more specifically,
Publisher’s Note: AccScience
Publishing remains neutral with its age-specific fertility behavior.
regard to jurisdictional claims in
published maps and institutional According to Sample Registration System (SRS-2020) in the states of Andhra Pradesh
affiliations. and West Bengal, fertility reached its peak in the age group 20 – 24. In Jammu and

Volume 11 Issue 1 (2025) 120 https://doi.org/10.36922/ijps.1338