Data Requirements for Constructing Accurate Life Tables

Life tables look deceptively simple, just rows of numbers showing how many people survive to each age. But behind every reliable life table sits a serious data engine: counts of who is alive, who has died, and at what age. Without good quality inputs, the whole structure collapses. So before we admire the elegance of a life expectancy figure like 70.8 years for India, it is worth asking a harder question: what kind of data does a demographer actually need to build one, and where does that data come from?

The core data a life table needs 🔗

At its foundation, a life table is a structured representation of mortality, showing how a hypothetical group of people would thin out as they age. To construct it, demographers need two non-negotiable inputs: the population count by age (and usually by sex), and the number of deaths by age (and sex) for a defined time period. From these, they calculate age-specific death rates (ASDRs), which become the engine of the entire table.

An ASDR is the number of deaths in a particular age group divided by the mid-year population in that same age group, typically expressed per 1,000 people. These rates are then converted into probabilities of dying, which feed into other life table columns such as survivors at age x, person-years lived, and ultimately life expectancy. As the U.S. Social Security Administration’s actuarial documentation explains, the fundamental step in life table construction is developing probabilities of death that accurately reflect the underlying pattern of mortality in the population.

Why age-specific data matters more than totals 🔗

A crude death rate, the total deaths divided by total population, hides far too much. Mortality is heavily skewed by age. Infants face elevated risks, young adults face very low ones, and risk climbs sharply after middle age. A life table only becomes meaningful when it captures this age pattern, which is why ASDRs are the starting point. Without disaggregated data by age, you cannot model survival curves at all.

Sex also matters. Male and female mortality patterns differ substantially at every age, so most countries publish life tables separately for men and women. According to public health methodology resources, life tables are usually constructed separately for men and women because of their substantially different mortality rates. Sex-disaggregated data is therefore not optional, it is part of the basic requirement.

Where the data comes from in India 🔗

India faces a familiar developing-country problem: the official Civil Registration System (CRS), which legally records every birth and death, has historically been incomplete. To fill the gap, the Office of the Registrar General of India launched the Sample Registration System (SRS) in the mid-1960s. The SRS has since become the workhorse for national life table construction.

The Sample Registration System (SRS) 🔗

The SRS is a large-scale demographic survey based on a dual-record system. Local enumerators continuously record births and deaths in a representative sample of villages and urban blocks, while an independent supervisor conducts a retrospective survey every six months. The two records are matched, and any discrepancies are verified through home visits. This dual collection method generates a count of all births and deaths within the sample unit for each six-month period, which is then used to calculate fertility, mortality, and child mortality rates at the state level.

For life table construction, the SRS provides the crucial ASDR series. India uses the MORTPAK 4 software, a mortality measurement package developed by the United Nations, to generate abridged life tables using ASDRs derived from SRS data. The result is the abridged life table published periodically by the Registrar General, which gives life expectancy at birth and at various ages for India and major states.

The Civil Registration System (CRS) 🔗

The CRS, established under the Registration of Births and Deaths Act of 1969, is meant to capture every vital event in the country. Its coverage has improved sharply in recent years. The 2023 CRS report showed significant progress, with 98.4% of births and 97.2% of deaths registered nationally. In principle, a near-complete CRS would make a sample-based system unnecessary, since you would already have every death recorded by age and sex.

In practice, however, the CRS still has problems with the quality of demographic detail. A peer-reviewed comparison of mortality data in India found that key CRS limitations are uniform low reporting of child deaths, low female death registration in many states, and critical gaps in cause-of-death ascertainment. So while CRS numbers may look complete in aggregate, the breakdowns needed for a life table, particularly child and female deaths, are often unreliable at the state level.

Census and survey data 🔗

Population counts come from the decennial Census of India, which provides the denominator for death rates. Between census years, projections are used. Surveys such as the National Family Health Survey (NFHS) and Demographic and Health Surveys also contribute mortality estimates, especially for infant and child mortality, through birth histories and sibling survival methods.

Cohort versus period data: a fundamental choice 🔗

The type of life table you want to build determines the kind of data you need. There are two basic approaches, and they make very different demands on the data system.

Cohort life tables and longitudinal data 🔗

A cohort life table follows a real group of people, all born in the same year, throughout their entire lives. To build one for a 1925 birth cohort, you would need mortality data for that group from 1925 all the way until the last member dies. This is longitudinal data in the strictest sense.

The data demand is enormous. As the UK’s Office for National Statistics explains, cohort life expectancy at age 65 in 2020 would be worked out using the mortality rate for age 65 in 2020, age 66 in 2021, age 67 in 2022, and so on. Building a complete cohort table requires waiting until the entire cohort has died, which means roughly a century of consistent record-keeping. For most ages of interest, modern cohort tables therefore combine observed historical rates with projected future rates.

The advantage is realism: cohort tables capture the actual mortality experience of a generation, including the effects of wars, famines, pandemics, and medical advances. The disadvantage is impracticality. They are rarely available in full, and their data demands are beyond what most countries, including India, can meet historically.

Period life tables and cross-sectional data 🔗

A period (or current) life table takes a shortcut. It uses cross-sectional data, the mortality rates observed for people of all ages during a single year or short window, and applies them to a hypothetical cohort. So an SRS-based 2020 life table uses the death rate for one-year-olds in 2020, for two-year-olds in 2020, and so on, even though these are different people.

This is the form used almost universally for official statistics. The Wikipedia entry on life tables notes that the term life table primarily refers to period life tables, as cohort life tables can only be constructed using data up to the current point. Period tables are quick to produce, only need one year of data, and are extremely useful for tracking short-term changes, including the mortality shock from events like COVID-19.

The trade-off is that they assume current mortality conditions will hold forever, which they will not. A 2020 period life expectancy assumes today’s one-year-olds will face today’s mortality at every future age, ignoring future improvements (or shocks).

Challenges in collecting accurate life table data 🔗

Even with established systems like the SRS and CRS, building a truly accurate life table is harder than it looks. Several challenges affect data quality in real-world settings.

Incomplete and uneven coverage 🔗

National averages mask significant state-level gaps. The 2023 CRS report shows that while many states have achieved complete death registration, states like Uttar Pradesh and Assam still register only around 69% of deaths. When deaths go unrecorded, ASDRs are artificially low and life expectancy is overestimated.

Missing age and sex information 🔗

Even when a death is registered, the age or sex field is sometimes left blank. A study of CRS completeness from 2005 to 2015 found that critical differences between Indian states remain, with poor availability of reporting by age and sex. For life table construction, this is fatal: a death without an age cannot be assigned to an age group, and the ASDR breaks down.

Age misreporting and digit preference 🔗

In populations with limited literacy or weak documentation, reported ages cluster around round numbers (40, 50, 60) due to a phenomenon called digit preference. This distorts the age distribution of both populations and deaths. Researchers reviewing the SRS have noted discrepancies in published age-specific rates, particularly at infant ages. One IIPS analysis pointed out that the published IMR and U5MR in many state-level SRS reports differ substantially from the rates estimated from the ASDRs published by the same office, suggesting internal inconsistencies.

Regional and rural-urban differences 🔗

Mortality patterns vary sharply across India. Kerala’s infant mortality rate stands at 5 per 1,000 live births while it is 37 in Chhattisgarh, Madhya Pradesh, and Uttar Pradesh. A national life table averages over this enormous variation, hiding state-level realities. Constructing reliable state and district-level life tables requires sufficient sample sizes in each region, which the SRS, designed for state-level estimates, cannot always provide for smaller geographies.

Processing and methodological choices 🔗

Raw death and population counts are not the end of the story. Demographers must decide how to handle the youngest age groups, where deaths are unevenly distributed across the year, what separation factors to apply, and how to smooth irregular rates from small samples. The choice between an abridged life table (using 5-year age groups) and a complete one (single-year groups) depends on the granularity of the available data and the size of the population.

Why this matters beyond the textbook 🔗

Life tables are not just academic exercises. They drive pension calculations, health insurance pricing, public health budgeting, and assessments of pandemic impact. Excess deaths during COVID-19 became a major debate precisely because India’s underlying mortality data is patchy. Better life tables require better data systems, and the slow strengthening of both CRS and SRS over decades is what makes today’s estimates possible at all.

The honest takeaway is that every life table is only as good as the death registration and population data behind it. Improvements in age-sex reporting, expanded sample coverage, and integration between CRS, SRS, and census data are not bureaucratic details, they are the foundation on which all mortality estimates rest.

What do you think? If the CRS in India now covers over 97% of deaths, should the country gradually move away from the SRS, or does the SRS still offer something the CRS cannot? And when reading a published life expectancy figure, how much weight should you give to the data system that produced it?

References

1. https://www.ssa.gov/oact/NOTES/as116/as116_IV.html
2. https://www.healthknowledge.org.uk/public-health-textbook/health-information/3a-populations/life-tables-demographic-applications
3. https://ghdx.healthdata.org/series/india-sample-registration-system-srs
4. https://www.downtoearth.org.in/health/soe-2026-indias-life-expectancy-trajectory-so-far
5. https://www.dataforindia.com/crs-srs-explainer/
6. https://pmc.ncbi.nlm.nih.gov/articles/PMC7430426/
7. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/methodologies/periodandcohortlifeexpectancyexplained
8. https://en.wikipedia.org/wiki/Life_table
9. https://www.researchgate.net/publication/397452185_Civil_Registration_System_CRS_Report_-India_2023_-Trends_Progress_and_Challenges_in_Birth_Death_Registration
10. https://pubmed.ncbi.nlm.nih.gov/31496526/
11. https://iipsindia.ac.in/sites/default/files/SRS_Data.pdf
12. https://claritydeskhub.com/sample-registration-system-srs-statistical-report-upsc/