The large gap between real and calculated Total Fertility Rate is particularly relevant in a developing country such as India

The recent United Nations Population Fund's State of World Population 2025 report shows that India's current Total Fertility Rate (TFR) has fallen to 1.9. This sub-replacement fertility has shifted the focus of discourse from the belief that rapid population growth is the root of economic backwardness to new concerns about ageing populations.

The report brings urgent questions to the forefront. Does the methodology used to calculate TFR accurately reflect public perception? And could the decline in TFR pose risks to India's economic trajectory, thereby creating the need to raise the TFR?

Perceived meaning of TFR

In public discourse, TFR is interpreted as the average number of children a woman would bear by the end of her reproductive age. However, it is a statistical measure which calculates the average number of children a woman would have in her lifetime if she experienced the current birth rate trends at each age cohorts from 15 to 49 years throughout her reproductive life.

The reproductive age is divided into seven five-year age cohorts: 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, and 45-49. For each age cohort, the number of live births per 1,000 women in a given year is calculated; this is known as age-specific fertility rate (ASFR). These ASFRs are then converted into per-woman figures by multiplying each rate by five (since each cohort covers five years) and dividing by 1,000. Adding up the figures for all seven cohorts gives us the TFR.

This calculation assumes that that today's 15-19 age cohort will show fertility patterns similar to today's 45-49 cohort (or other older cohorts) once they themselves reach that age, and vice versa. This is known as the synthetic cohort assumption.

Limitation of TFR calculation

Real fertility preferences do not follow the synthetic cohort assumption. They often evolve differently, as future younger cohorts are likely to exhibit preferences that differ from those of today's older women.

Another important limitation is that it is a point-in-time measure, which is highly sensitive to a phenomenon known as the tempo effect. This refers to changes in the timing of childbearing, not necessarily reduction in the number of births. Since TFR is calculated from the ASFRs of women in a given cohort, births postponed are not captured in that year's data, so the fertility of women who delay childbirth is effectively 'missed' even though they may have the same number of children later. Thus, tempo effect artificially reduces the TFR and gives a false impression of lower fertility. The phenomena exacerbates when birth postponement takes place in the majority of the younger age cohorts. Interestingly, today's postponement will overestimate future TFR calculation. This large gap between real and calculated TFR is particularly relevant in India, which is simultaneously undergoing economic, social, and cultural transitions. These include rising female literacy and a more career-oriented outlook among women that creates a stronger incentive to delay childbirth.

The chart below shows urban age-specific fertility rate contribution to the total fertility rate

The ASFR trends presented reflect a shift in childbirth patterns, with fertility being postponed from younger to older cohorts. In urban areas, the decline in the share of ASFR among the youngest two cohorts (15-19 and 20-24) and the corresponding increase across the older cohorts (25-29 to 45-49) strongly indicates a postponement of fertility preferences rather than an overall decline in fertility.

The chart below shows rural age-specific fertility rate contribution to the total fertility rate

In rural areas, a decline in the share of ASFR is observed in the 15-19 age cohorts, along with an increase in the 20-24 to 30-34 age cohorts. This also suggests a postponement of fertility preferences. However, unlike in urban areas, this shift is largely confined to the middle age cohorts. The declining share of older cohorts (35-39 and above) points to a reduction in fertility preference in these age cohorts.

Another key limitation of the TFR is its exclusion of births to women below 15 and above 49, based on the assumption that such cases are negligible. This assumption is less valid in developing and Islamic countries where early marriages are common. Owing to legal and social sensitivities, births to underage girls are often concealed. Moreover, survey enumerators may also avoid uncomfortable questions, as noted in the 2020 DHS Methodological Report.

Given these problems, and amid growing calls to raise the TFR due to concerns over ageing population, it is important to recognise that historically, sub-replacement fertility has not constrained economic progress, as evidenced in Europe, the U.S., and several newly industrialised economies. Moreover, India has missed the opportunity to fully harness its demographic dividend, constrained by persistent youth unemployment, a challenge likely to intensify with automation and AI. More importantly, the current size of the elderly population remains largely independent of present fertility trends. While caring for the ageing population is a moral responsibility of the state, its urgency does not automatically increase simply because of a reported decline in TFR.

Anish Gupta teaches economics at the Delhi School of Economics; Shubham Sharma is a Research Associate at the Institute for Educational and Developmental Studies, Noida. Roshan Soni, from DSE, assisted with data.

Does India's 1.9 fertility rate reflect reality?