A mortality table is a statistical chart that shows the probability of dying at each age, starting from birth and continuing through the oldest possible age. It tracks a hypothetical group of 100,000 people born at the same time, showing how many survive to each birthday, how many die in each year of life, and how many years, on average, a person at any given age can expect to live. Insurance companies use mortality tables to price life insurance policies, pension funds use them to plan for future payouts, and governments use them to track public health trends.
How a Mortality Table Is Structured
Every mortality table starts with the same premise: imagine 100,000 people born on the same day. The table then follows this group year by year, applying real-world death rates to calculate how many people survive to each age. The Social Security Administration publishes one of the most widely referenced versions in the United States, and its structure is standard across most life tables.
The core columns in a mortality table each answer a specific question:
- Age (x): The exact age being measured, from 0 (birth) through the end of the table.
- Probability of death (qx): The chance that someone who just turned age x will die before their next birthday. This is the foundational number. Everything else in the table is calculated from it.
- Number surviving (lx): How many of the original 100,000 are still alive at the start of age x. Each year’s survivors equal last year’s survivors minus last year’s deaths.
- Number dying (dx): How many people die between age x and age x+1. This is simply the number of survivors multiplied by the probability of death.
- Life expectancy (ex): The average number of years remaining for someone who has reached age x.
So if you look up age 65 in a U.S. mortality table, you’d find the probability of a 65-year-old dying within the next year, how many of the original 100,000 made it to 65, and that the average 65-year-old can expect about 19.7 more years of life (based on 2024 data). The math cascades: each row depends on the row before it, creating a complete picture of survival from birth to the oldest ages.
Period Tables vs. Cohort Tables
There are two fundamentally different ways to build a mortality table, and they can produce noticeably different results.
A period life table takes death rates from a single year (or short span of years) and applies them to every age as if those rates will never change. Period life expectancy at age 65 in 2020, for instance, uses the 2020 death rates for ages 65, 66, 67, and so on. It’s a snapshot of mortality conditions right now, frozen in time. This makes period tables useful for comparing health trends across years, regions, or countries, because they’re based entirely on observed data with no guesswork. Most official government life tables, including those published by the CDC and the UK’s Office for National Statistics, are period tables.
A cohort life table follows an actual birth year group through time. For someone born in 1955, it would use the real death rate at age 40 in 1995, at age 50 in 2005, at age 65 in 2020, and so on. For years that haven’t happened yet, it relies on projections of how death rates will likely improve. Because medical advances and public health improvements tend to lower death rates over time, cohort life expectancy is almost always higher than period life expectancy. Demographers consider cohort tables a more accurate reflection of how long people will actually live, but the trade-off is that they require assumptions about the future.
How Life Insurance Companies Use Them
Mortality tables are the backbone of life insurance pricing. When you apply for a policy, the insurer needs to estimate the likelihood that it will have to pay out the death benefit in any given year. That probability, drawn from mortality data, directly determines your premium.
Insurers don’t rely on a single generic table. They build or select tables stratified by age, sex, smoking status, health history, and sometimes occupation or lifestyle factors. A 40-year-old nonsmoking woman will have a very different probability of death than a 40-year-old male smoker, and the premiums reflect that gap precisely. The tables also allow actuaries to project expected cash flows: how much the company will collect in premiums versus how much it will pay in claims over any time horizon. This projection determines how much money the insurer must hold in reserves to stay solvent.
In the life settlements market, where existing life insurance policies are bought and sold, actuaries use mortality assumptions to estimate how long the insured person is likely to live. That survival estimate determines what a buyer should pay for the policy today, since it dictates how many premium payments the buyer will need to make before collecting the death benefit.
Their Role in Pensions and Retirement
Pension funds face a challenge that’s essentially the mirror image of life insurance: instead of paying when someone dies, they pay for as long as someone lives. Underestimating how long retirees will survive means the fund runs short of money.
The IRS requires defined benefit pension plans to use specific mortality tables when calculating their funding obligations. These tables are based on the actual mortality experience of pension plan participants, not the general population, because people with pensions tend to live longer than average. The tables also incorporate projected trends in mortality improvement, meaning they account for the expectation that people will continue living longer in the future.
Under federal law, a pension plan’s funding target is the present value of all benefits that have been earned by participants as of the start of each plan year. Mortality tables feed directly into that calculation. If updated tables show that retirees are living longer than previously expected, the plan’s liabilities increase, and the employer may need to contribute more money. The tables are revised periodically to reflect real-world changes in how long pension recipients are actually surviving.
Mortality Tables in Medical Research
Outside of finance, mortality tables play a central role in clinical research through a method called survival analysis. When researchers want to know whether a treatment extends life, or whether exposure to a substance increases the risk of death, they use life table methods to compare survival between groups over time.
The approach works by dividing a study period into intervals (often months or years) and calculating the proportion of patients who survive each interval. This produces a survival curve that plots time against cumulative survival probability. Researchers can then compare curves between groups, for example, patients who received a new cancer drug versus those who received standard treatment.
One practical strength of this method is that it handles incomplete data well. If a patient drops out of a study or is still alive when the study ends, the analysis can account for that missing information rather than discarding the patient’s data entirely. From these comparisons, researchers calculate a hazard ratio, which quantifies how much higher (or lower) the risk of death is in one group compared to another. A hazard ratio of 3 for a toxic exposure, for example, means the exposed group has three times the risk of death compared to the unexposed group.
What Recent U.S. Data Shows
Mortality tables are living documents, updated as death rates change. The most recent CDC data shows U.S. life expectancy at birth reached 79.0 years in 2024, up from 78.4 in 2023. That 0.6-year increase was driven largely by declining deaths from unintentional injuries, COVID-19, heart disease, cancer, and homicide.
The gap between men and women narrowed slightly. Female life expectancy rose to 81.4 years, while male life expectancy climbed to 76.5, a difference of 4.9 years compared to 5.3 years the year before. At age 65, the average American can now expect to live an additional 19.7 years: 20.8 years for women and 18.4 years for men. These numbers feed directly into the mortality tables that insurers, pension funds, and government agencies use to make financial and policy decisions.
A Brief Origin Story
The concept dates back to 1662, when a London merchant named John Graunt published the first systematic analysis of birth and death records. He collected data on causes of death, documented that more boys are born than girls, and described how a group of people “dies out” over time. His work wasn’t a true life table in the modern sense, but it established the template for analyzing demographic data numerically.
The first mortality table built on actual age-at-death data came from astronomer Edmond Halley (the same Halley behind the comet) around 1693. He used death records from a population thought to be stable and estimated survival probabilities by age. That framework, tracking a cohort from birth through death using observed probabilities, remains the foundation of every mortality table produced today.