One fun thing to do is to take these death probabilities and throw them into a Leslie matrix [1], along with assumptions for the age-specific birth rates. From there you can simulate the projected population growth (ignoring migration) with no more than a couple of lines of Python/R/whathaveyou.

It's also a great study case for understanding some basic principles of linear algebra: The dominant eigenvalue is the stable population growth rate, and the corresponding eigenvector is the stable age distribution.

[1] https://en.wikipedia.org/wiki/Leslie_matrix

Shameless self plug - I'm an Actuary, and as a way to learn React I put together a website that teaches you the basic concepts and math behind these actuarial table.

https://www.lifecontingencies.com

If anyone is interested in more life tables the Society of Actuaries has thousands to download and there is some simple code to read them in Python here:

https://www.joveactuarial.com/blog/mortality-tables/

I graphed the difference in years amongst men and women using good ol' fashioned awk and free graphing software:

https://imgur.com/a/3b1KrD7

It looks like at around age 18 - 30ish, the numbers start catching up, with a sharper reduction in expectancy differences happening around age 35.

From the PRB:

"Men are three times as likely as women to die from injuries (unintentional injuries, suicide, or homicide), and progress against those causes of death has been much slower than against other causes in the last 50 years. There is also evidence that men at all ages are less likely to seek medical care and less likely to comply with medical instructions than are women."

This data, unfortunately, is missing an important confounding variable besides male/female. In the US lifespan his highly correlated with income, and the trend is getting worse. "The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but increased by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the difference for both sexes)." [1]

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4866586/

Wow, as a guy I have a 1/5 chance of not even making it to retirement. (65) 1/3 chance of not living past 75. These are not good odds.

The death probability has a turning point from decreasing to increasing around age 10 for both male and female. Wonder why this specific age.

Based on this, by the time you're 30, around 2% of your male high school peers have passed away, but only 1% of your female ones. Puts an interesting perspective on things.

This is exactly the table I turned to in April 2020 to put my risk of dying from COVID-19 into perspective. In short, I had much bigger things to worry about over the next 10 years.

Great resource. I use this all the time for military clients near or past retirement eligibility, to estimate the lifetime financial penalty of discharging them before retirement, or of retiring them in a lower grade.

There is something I've never understood about this. Actually, I suspect that it is just plain old wrong.

It looks at what ages people died in the year 2019. If you die at age 80 in 2019, that means you were born in 1939. But if people born in the year 1939 are living to age 80... how do I put this... wouldn't you expect people born in, say, 1989 to live longer? Wouldn't people born 50 years later have longer life expectancies? Especially if you believe the stuff about accelerating rates of technogical growth.

I wish I was good enough at math to use these tables to figure out what percentage of the population is within a particular age range in any given year.

In particular, I'm curious to see what years the population of particular generations peaked.

Edit: Actually what I'm really looking to do is to correlate certain marketing demographics with generations. For example, "in what years did Generation X comprise the majority of living people in the 18-34 demographic?" (where Generation X is defined as people born 1961-1981).

obesity and smoking status are more important than sex differences.

Does this mean that until your 113th birthday you can expect to, on average, live to your next birthday?

I would love to see the same data for the last 2 years

Roughly 50% make it to age 80.

Roughly 20% make it to 90.

And around 1% make it to 100.

The road curves steeply at the end!

And coin toss of making to 80 (1/2). And if you make it to 90, every year is a round of russian roulette.

Great. I didn't need an existential crisis today :)

I really wonder about the quality of life as I age though. There is such a wide spread in my family. One of my grandfathers died of COVID early in the pandemic - he made it to 83.

I had always considered him too stubborn to die. He survived two congestive heart failures, two bouts with cancer, and a heart attack. He had never taken care of himself (obese all of his life, heavy smoker from teenager to age 50, emphysema (from smoking), etc.). He caught COVID while recovering in a physical rehab from his heart attack and that's what ended up doing him in.

His body was pretty much trashed by age 50 though. His ability to do things was pretty restricted from that point forward. As a child, he couldn't go on roller coasters with us, he had to be really careful with his diet, etc. He spent the last 3 years of his life in bed or wherever he could get on a walker.

Meanwhile, my other three grandparents are enjoying their late 70's and early 80's - they travel, go hiking, play golf, play with the grand children and great-grandchildren, etc.

I wonder how old I'll be when I get to the point where dying doesn't sound any worse than living.

To be fair, if you are reading this or wrote this, you are far from a "typical" male in this study. The typical includes a lot of low income and/or blue collar workers who cannot or will not take good care of their health and are engaged in high risk activities at work and after work. In all likelihood, the typical HackerNews user alarmed by a subject like "Actuarial Life Table" will have much better odds at living a long life... if only because you are paid tech wages.

This is why I "soft retired" early. People haven't said it to my face, but I get the definite impression many think I'm crazy for "abandoning" work. I'm through hiking the PCT this year, something I never thought I would do until much older.

I'm not really a brave guy, I fear change, but you got to take your chances while you're still alive.

I wonder how those odds change if you narrow down to your occupation/socioeconomic status/location.

I have always wondered what the income volatilities as you age are for the purposes of calculating how much I should be saving. My current strategy is to assume I will be unable to earn income and/or need to spend a lot on healthcare with increasing material odds starting at age 50 (since I might not have access to subsidized health insurance that comes with a job).