US Census Income Dashboard

1994 Census — 32,561 adults — who earns over $50,000 a year?

Total records
32,561
adults surveyed
Earn >$50K
24.1%
7,841 people
Average age
38.6
years old
Avg hours / week
40.4
hours worked

The gender gap
is a marriage gap.

Married women and married men earn above $50K at virtually identical rates — around 45%. But unmarried women are at just 4.6%, vs 8.5% for unmarried men. The stronger pattern is linked to marital status, not sex — though this is an association, not a cause.

% Earning >$50K by Marital Status

n = 32,561

45% of married people earn over $50K vs 4.6% of never-married — but this headline overstates the marriage effect. Never-married people are much younger on average, so part of the gap is really an age gap. The chart to the right shows what remains once you hold age constant: still a very large premium, but not the whole story.

Marriage Premium Within Each Age Group

n = 32,561

Married people are about 8–9 years older on average than unmarried people — so some of the marital-status gap is really an age gap in disguise. But the income difference associated with marriage is large and consistent within every age group: at 36–45, unmarried adults earn over $50K at just 11% vs 52% for married adults the same age. Whether marriage leads to higher income, higher income leads to marriage, or both, cannot be determined from this data.

Why Do Married People Earn More? Matching like-for-like

n = 30,718 (excludes unknown occupation)

Part of the marriage premium is explained by observable differences: married people are more educated (30% hold a bachelor's degree vs 21% of unmarried), married men work ~4 more hours per week, and married people are 10 points more likely to hold an executive or professional role. But even after holding education, occupation type, and hours constant — comparing, say, two bachelor's-degree holders working full-time in equivalent jobs — the gap averages 32 percentage points and never falls below 4pp in any matched group.

Two explanations are consistent with what remains. The first is selection: financially stable people are more likely to marry and stay married, so higher income may precede marriage rather than follow from it. The second is a direct association: household specialisation, employer perception of married workers as more settled, or the motivation of supporting a family could each play a role. This dataset cannot separate the two — and both are likely present to some degree.

% Earning >$50K by Education Level

n = 32,561

Education is among the strongest predictors of income. Among those with a high school diploma, 16% earn over $50K; among doctoral and professional degree holders, 74% do. The steepest step up is at the bachelor's level, where rates more than double — though education, occupation, and family background are all intertwined, so education alone doesn't explain the gap.

† Preschool (n=51) and 1st–4th grade (n=168) — too few respondents to draw firm conclusions.

% Earning >$50K by Occupation

n = 30,718 (excludes unknown)

Executive/managerial (48%) and professional specialty (45%) have the highest rates of >$50K earners. Private household workers sit at just 0.7%. Note that the causal direction is unclear — people may enter higher-paying occupations because of prior education or background, rather than the occupation itself lifting their income.

† Private household service (n=149) — interpret with caution.

% Earning >$50K by Work Class

n = 28,704 (excludes unknown)

Incorporated self-employed workers lead at 56% — typically established business owners, who are also more likely to be older and more educated. Federal government workers follow at 39%. These are associations within this snapshot; they don't tell us whether the work class itself drives the income.

% Earning >$50K by Hours Worked / Week

n = 32,561

People working 51–60 hours per week are the most likely to earn over $50K (43%). The direction here is hard to read: higher-paying jobs may demand more hours, or workers may log more hours in pursuit of higher pay — the data cannot distinguish the two. The dip at 60+ likely reflects low-paid shift and manual roles where very long hours are common.