Age-specific fertility rate (ASFR) is the number of live births to women in a specific age group per 1,000 women in that age group in a given year. It’s usually calculated for single years of age, 5-year age categories or the age range 15 to 49. ASFRs are calculated for a given country, territory, or geographic area.
The age pattern of fertility, or the frequency with which women of various ages within the reproductive years have children, is measured by age-specific fertility rates, or ASFRs. These measures can also compute the total fertility rate (TFR). ASFRs usually remain unaffected by shifts in the age mix of the population, as a result, this indicator is considered as a better tool for comparing various populations or sub-groups and tracking trends over time than crude birth rates. In order to make well-informed decisions about fertility planning, policymakers must understand fertility patterns and trends within a community, which is why ASFRs are crucial.
Fig: 1
This article outlines the Age-Specific Fertility Rates (ASFR) in India across various age groups over nearly three decades, based on the Demographic and Health Surveys (DHS) conducted between 1992-93 and 2019-21. The ASFR is a crucial demographic indicator that measures the number of births per 1,000 women in specific age groups, providing insights into the fertility patterns within a population.
The data reveals a clear downward trend in fertility rates across all age groups over the years, reflecting significant changes in reproductive behaviour, likely driven by various socio-economic, cultural, and policy factors. In the youngest age group (15-19 years), the fertility rate has consistently declined from 130 births per 1,000 women in 1992-93 to 43 births in 2019-21. This dramatic reduction suggests a delay in the age of marriage and first childbirth, possibly due to increased educational attainment among women and improved access to family planning services.
In the 20-24 age group, which traditionally has the highest fertility rates, a substantial decrease is also observed. The rate dropped from 210 in 1998-99 to 165 in 2019-21. Although this age group remains in the peak childbearing period, the decline indicates a shift toward lower fertility, even during the most fertile years.
For women aged 25-29, the fertility rate fell from 143 in 1998-99 to 122 in 2019-21, further supporting the trend of reduced fertility at prime reproductive ages. In older age groups, the fertility rates have also seen notable reductions, with the 30-34 age group declining from 69 in 1998-99 to 50 in 2019-21 and the 35-39 age group decreasing from 28 to 14 over the same period. This indicates a shortening of the reproductive window, where women have fewer children and complete their families earlier.
The fertility rates in the oldest age groups (40-44 and 45-49) have always been low but show a slight decrease over time, underscoring a further decline in late-age fertility. For instance, the rate in the 40-44 age group has dropped from 8 in 1998-99 to just 3 in 2019-21 (fig: 1).
These declining fertility rates across all age groups reflect India’s demographic transition towards lower fertility, which is likely influenced by factors such as increased urbanization, higher female education levels, greater labour force participation among women, and enhanced access to contraceptive methods. The overall trend points to a shift towards smaller family sizes, delayed childbearing, and greater reproductive health awareness, all of which are crucial for the ongoing demographic and socio-economic changes in the country.
References
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About Author: Pankaj Chowdhury is a former Research Assistant at the International Economic Association. He holds a Master’s degree in Demography & Biostatistics from the International Institute for Population Sciences and a Bachelor’s degree in Statistics from Visva-Bharati University. His primary research interests focus on exploring new dimensions of in computational social science and digital demography.
Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of 360 Analytika.
Acknowledgement: The author extends his gratitude to the Demographic and Health Surveys (DHS) Program for providing data support.
This article is posted by Sahil Shekh, Editor-in-Chief at 360 Analytika.