Total Fertility Rate in India: Understanding the Numbers vs Reality

Total Fertility Rate (TFR) has emerged as a significant topic in India due to the gap between actual fertility behavior and statistical TFR calculations.
This is particularly relevant for a developing country like India, which is undergoing economic, social, and cultural transitions.
Understanding this gap is crucial for demographic planning, economic forecasting, and policy formulation.

Recent discussions highlight a large discrepancy between real fertility trends and TFR statistics.
This raises policy-relevant questions about population management, economic growth, and ageing population concerns.

The UN Population Fund (UNFPA) in its State of World Population 2025 report states:
- India’s TFR has fallen to 1.9, below the replacement level of 2.1.
- Sub-replacement fertility has shifted focus from concerns about rapid population growth to issues related to ageing populations.
Questions Raised by the Report:
- Does the current methodology for TFR calculation reflect real fertility preferences?
- Could the decline in TFR affect India’s economic trajectory, creating a need to raise fertility?

Definition: TFR is the average number of children a woman would have in her lifetime based on current age-specific fertility rates (ASFRs).
Reproductive Age Division: 15–49 years, divided into seven five-year cohorts: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49.
Age-Specific Fertility Rate (ASFR): Number of live births per 1,000 women in each cohort per year.
TFR Calculation:
- ASFR × 5 ÷ 1,000 for each cohort.
- Sum across all cohorts = Total Fertility Rate.
Synthetic Cohort Assumption: Assumes that younger women today will follow fertility patterns of older women in the future.

Assumption vs Reality:
- Real fertility preferences do not always follow synthetic cohort assumptions.
- Younger cohorts often delay childbirth, differing from older women.
Tempo Effect:
- TFR is sensitive to timing changes in childbearing, not just number of births.
- Postponed births are not captured in current data, artificially lowering TFR.
- This effect is more pronounced when birth postponement is common among younger cohorts.
Exclusion:
- Births below 15 and above 49 are excluded, which may be inaccurate in countries with early marriages.
Survey Limitations:
- Legal and social sensitivities may lead to concealed births or skipped questions by enumerators.

Urban Areas:
- Decline in ASFR among 15–19 and 20–24 cohorts.
- Increase in ASFR among 25–49 cohorts, indicating postponement of childbearing rather than an overall decline.
Rural Areas:
- Decline in 15–19 cohort, increase in 20–34 cohorts, suggesting delayed fertility.
- Reduction in ASFR among older cohorts (35–49) indicates declining fertility preference in older age groups.

Historical experience from Europe, the U.S., and newly industrialized economies shows that sub-replacement fertility does not hinder economic progress.
India’s demographic dividend has been underutilized due to youth unemployment and emerging challenges from automation and AI.
The current elderly population is largely independent of present fertility trends.
While state responsibility for elderly care remains, its urgency cannot be inferred solely from TFR decline.
Policy focus should be on creating social, educational, and economic conditions that naturally influence fertility decisions rather than reacting only to statistical measures.