Quiz: US Income Percentile Calculator

Estimate where your annual income ranks among US earners. Enter your income and choose household or individual, and the calculator fits a log-normal income-distribution model to estimate your percentile.

Quick Facts

Model
Log-normal approximation of the income distribution
Percentile = Φ((ln(income) − ln(median)) / σ), the standard normal CDF applied on a log scale.
Why log-normal
Income is right-skewed
Economists commonly approximate income and wealth distributions as log-normal because raw income is strongly right-skewed but roughly symmetric once log-transformed.

Your Results

Calculated
Estimated percentile
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Share of earners at or below you
Top share
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Share of earners above you
Multiple of median
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Your income ÷ assumed median
90th percentile reference
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Approx. income at the 90th percentile

Ready

Enter your annual income and choose household or individual, then press Calculate.

How the US Income Percentile Calculator works

This tool estimates where an annual income ranks among US earners by fitting a log-normal curve to the income distribution — a standard approximation economists use because income is strongly right-skewed (a long tail of high earners) but becomes roughly bell-shaped once you take its logarithm.

The formula

For an income I, an assumed median income M, and a log-standard-deviation σ (sigma, which controls how spread out incomes are):

z = (ln(I) − ln(M)) / σ

Percentile = Φ(z) × 100

where Φ is the cumulative distribution function of the standard normal distribution — the same function used for z-scores and bell-curve probabilities in statistics. The calculator uses different median and sigma values for household income (all earners in a home combined) and individual income (one wage earner), since the two have different centers and spreads.

Worked example

Using the household-income parameters (median around $75,000, σ ≈ 0.62), an income of $100,000 gives z = ln(100000/75000) / 0.62 ≈ 0.463, which maps to roughly the 68th percentile — higher than about two-thirds of household incomes, or the top 32%. Doubling that income to $200,000 pushes z to about 1.58, close to the 94th percentile, illustrating how income percentiles compress as income rises: each additional dollar buys less percentile movement near the top.

Why household and individual income differ

Household income sums every earner living in one home, so it is typically higher and more spread out than any single person's pay. Individual income reflects one wage or salary. Selecting the matching type keeps the percentile comparison meaningful — comparing a two-earner household's combined income against individual-income benchmarks (or vice versa) produces a misleading rank.

Limits of the log-normal approximation

A log-normal curve is a convenient, widely used approximation, not a lookup of exact government percentile tables. It fits the middle of the distribution reasonably well, but real income data has a fatter top tail (closer to a Pareto distribution) above roughly the 95th percentile, so estimates for very high incomes are directional rather than precise. Treat the result as a planning estimate, and consult primary sources such as the US Census Bureau for exact official statistics.

Frequently Asked Questions

How does this calculator estimate a percentile?
It models US income as log-normal, a standard simplification economists use because raw income is strongly right-skewed but roughly symmetric on a log scale. The calculator computes z = (ln(income) - ln(median)) / sigma using an assumed median and log-standard-deviation (sigma) for household or individual income, then converts z to a percentile with the standard normal cumulative distribution function.
Are the median and percentile figures exact Census statistics?
No. The median and sigma values are approximate reference parameters in the general range reported for US household and individual income in recent years, used to calibrate the log-normal curve. This tool produces a statistical estimate of your rank, not an official Census Bureau percentile lookup, and works best as a planning reference rather than a precise figure.
Why does the calculator ask for household versus individual income?
Household income (all earners combined) and individual income (one person) have different medians and spreads, so mixing them gives a misleading rank. Selecting the matching type applies the median and sigma calibrated for that income concept.
Why might the top and bottom of the distribution be less accurate?
A log-normal curve fits the broad middle of the US income distribution reasonably well, but the real top tail (roughly the top 1-5%) is fatter than log-normal predicts, closer to a Pareto distribution. Percentile estimates near the extremes should be read as directional rather than precise.

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