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Actuarial Life Table

ssa.gov

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Re: Actuarial Life Table

#21

Post by siddboots »

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

Re: Actuarial Life Table

#22

Post by morepork »

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.

Re: Actuarial Life Table

#24

Post by amyjess »

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).

Re: Actuarial Life Table

#25

Post by bachmeier »

Spivakov wrote: post #6

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

While that's true, these are small death probabilities. The age 10 death probability is less than 1 in 10,000. It doesn't even reach 1 in 1000 until age 20 for men and age 34 for women. According to this data source[1] there are about 4 million 10-year olds. That works out to less than 400 deaths for the entire US in a year.

What I find interesting is the divergence at age 10 by gender. By the late teens, boys are about 2.5 times more likely to die, in spite of the probability being the same at age 10.

[1] https://www.statista.com/statistics/241488/population-of-the...

Re: Actuarial Life Table

#26

Post by zeroonetwothree »

hardtke wrote: post #10

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 ti…

Hard to say in which direction the causation lies.

Re: Actuarial Life Table

#27

Post by tfehring »

amyjess wrote: post #24

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 major…

There’s not enough data to answer those questions from these tables, the “number of lives” fields are just illustrative.

Re: Actuarial Life Table

#29

Post by lotsofpulp »

treespace8 wrote: post #28

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.

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).

Re: Actuarial Life Table

#30

Post by layer8 »

zeroonetwothree wrote: post #26
hardtke wrote: post #10

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 ti…

Hard to say in which direction the causation lies.

That’s actually a good point: People who die earlier due to genetics accumulate less wealth in their lifetime, and their children therefore inherit less. With that causality, people are poorer literally because they die earlier, rather than the other way around. ;)
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