Rank your income to specific age groups to see if you’re in the 1%, to see where you stand today, or to see where you project yourself to be in the future. Read more about the households that make up the top 1% by income earnings.
Simply add up your households annual income, such as your salary, your spouse’s salary, bonus, business income, and windfall events, then enter it into the calculator. The age ranges are based off of the age of the head of household, who is typically the primary income earner. The way that the age ranges work is to limit the results to the age groups that you are interested in comparing yourself to, so that the results are more relevant. So for example, if you are 35, I would recommend setting the minimum to 30 and the maximum to 40. The calculator will then update to show only the household incomes of households with heads within that age range.
Income Summary Statistics for Households Aged 30 to 40Percentile Rank : An income of $0.00 for ages 30 to 40 ranks at the 0.03%
Median Income : $54,784.70
Mean Income : $80,908.00
Income 25th - 75th Percentile Ranges : $29,421.41 to $94,351.42
Income Percentiles by AgeFor reference, here is how much you would have to earn to rank at certain percentiles for ages 30 to 40
This is where your income would rank if there were 100 households within the nation who's head of households were between the ages of 30 to 40. 99 households would be have higher incomes than you. 0 households would have lower incomes than your household.
Share These Results :These results are based off of 1019 individual samples where the head of household was age 30 to 40 and are weighted to represent 23058840 American households. The SCF is known to be slightly biased towards higher incomes values, which the Federal Reserve attempts to correct for by adjusting the weighting of each individual response. Keep this in mind if the number of responses your output is based off of is low, or if you are looking at the tail ends of the data--like the top 1% or bottom 1%.
The numbers are based off of the results of the 2013 Survey of Consumer Finances by the Federal Reserve. I used R to separate one of the five imputations with the sample replicatant weights from the Federal Reserve. If you want to do your own analysis check out the raw data, and also check out this guide on how to import the data into R http://www.r-bloggers.com/analyze-the-survey-of-consumer-finances-scf-with-r/. The number of samples per age vary quite a bit, so you might get unusual results for certain ages.