This page contains an income scaled for demographics calculator. It allows you to adjust average incomes for workforce and age breakdown of past years.
Enter a year and the tool will take the United States workforce weights per age of that year and apply it over time.
When you're satisfied with the result, click "Download Current to CSV" to export your current working data set.
Average Income Scaled for Demographics
Methodology and Source for the Income Adjusted by Workforce Calculator
Miriam King, Steven Ruggles, J. Trent Alexander, Sarah Flood, Katie Genadek, Matthew B. Schroeder, Brandon Trampe, and Rebecca Vick. Integrated Public Use Microdata Series, Current Population Survey: Version 3.0. [Machine-readable database]. Minneapolis: University of Minnesota, 2010.
The methodology is detailed in this post on importing this data into R to export it to a spreadsheet.
There are no tricks here, only a single adjustment: when a year doesn't have an age in the survey (for example, if a year doesn't have 88 year olds), we take the weight of 88 year olds in the original data and divide it equally for every other age.
This is barely a rounding error since all of the largest age groups in the workforce are represented, but to reproduce it exactly please follow the same step.
Here are some brief Q&As:
Why do you use average income and not median?
If you have a way to adjust demographic sizes for medians, please let us know.
There isn't a reversible way to add/subtract data points to get median numbers. In a sufficiently sized sample it's safer to assume average stays similar.
Does this Adjust for Male and Female Proportions in the Workforce?
No, this data is only adjusting for age. Age is a good proxy for experience, so it goes a long way to sanitizing this data.
How Do You Determine Who in the Workforce?
The Current Population Survey - a joint survey of the Bureau of Labor Statistics and Census Bureau does that. I am using the FULLPART variable from the IPUMS-CPS microdata.
What Do You Mean By 'Year'?
Surveys are conducted in March, so data is for the previous year: ie, '2015' means data from January to December of 2014.
March is convenient because most folks have tax returns in hand. It is easier to get better income numbers than in other months.
Adjusting Income for Changing Demographics to Avoid Bad Conclusions
We like to talk about statistical aberrations here such as Simpson's Paradox. If you're not careful, you might walk into similar traps.
There are a lot of scenarios (other than the health of the economy) that can drive changes in income numbers.
(A lot of those aren't accounted for in this post.)
One, is the need to adjust household income numbers for changing household sizes(!).
That isn't directly applicable to the individual income numbers of this post, but other variables could make the tool better. Think:
- Gender
- Educational status
- Types of jobs available
- Countless others
(Of course, as you add more and more variables each slice gets smaller and smaller. With more groups, the conclusions we can draw are less precise.)
That said, it's interesting to look at dates in the 70s.
There, we hit a demographic headwind as many young, inexperienced Boomers joined the workforce. That cleared up through the 80s and 90s... and today there are many more experienced, high-earning Boomers in the workforce.
As Boomers retire and more Millennials dominate the workforce, we might see headwinds on these numbers in the coming years. Be sure to check your income expectations against demographics using the income brackets by age calculators.
