Mathematical modeling of key fertility indicators based on Nepal census 2021 data

Abstract

Inefficient registration of vital events and the geographical remoteness of many regions in developing countries often hinder the regular availability of reliable data. Vital event data—such as statistics on mortality, fertility, and migration—are often limited and inadequate across developing countries. In many of these nations, civil registration and vital statistics (CRVS) systems function inefficiently, and reliable data are very limited. Hence, mathematical modeling of such data is of great importance—not only because these statistics serve as key development indicators, but also because they are widely used by policymakers and planners. Fertility and mortality figures are among the most critical vital statistics. In many cases, fertility is of particular interest to demographers, sometimes even more than mortality, because death occurs only once in an individual's lifetime, whereas fertility events can occur multiple times. In this paper, the evolving fertility dynamics of Nepal is explained by developing parsimonious mathematical models. Firstly, the behavior of Parity progression ratio (PPR) and Total Fertility Rate are elucidated. This is based on the number of children ever born to a married woman data of Census 2021. It is found that the PPR in younger women for the age group 20 to 35 is lower than that of women in age group 35 to 50. Further, the latent trends and gender disparity in the behavior of Age at First Marriage are also explained. The developed models have explained the inherent behaviors and patterns of these fertility measures with a high degree of accuracy, across different regions of Nepal. The methods developed here can also be used to explain the fertility scenario in other countries of south Asia and Africa.