The American Dream by County: Opportunity Insights Data Explained

Where you are born in America still determines much of where you end up. The Opportunity Atlas, built by Raj Chetty and the team at Opportunity Insights, quantifies that reality with data covering 20 million Americans tracked from childhood into their mid-30s.

What the Opportunity Atlas Actually Measures

The Atlas tracks children born between 1978 and 1983, links them to IRS tax records, and measures their income in their mid-30s. The core metric is simple: given parents in the bottom income quintile, what percentage of children reach the top quintile as adults?

That figure varies from below 3% in some Deep South counties to above 15% in parts of the upper Midwest and Mountain West. The national average sits around 7.5%. That spread is the story.

The data also breaks out results by race, gender, and parental income level, which reveals patterns that a single average would obscure. Black men in particular face sharply lower upward mobility than white men in the same county, even after controlling for parental income.

The Geographic Patterns That Stand Out

The Southeast consistently produces the lowest mobility rates in the country. Counties in Georgia, Alabama, and the Carolinas rank near the bottom across almost every demographic group. Atlanta, despite its reputation as a Black economic hub, posts some of the lowest mobility rates for low-income Black children of any major metro.

The upper Midwest and Great Plains tell a different story. Counties in Minnesota, Wisconsin, Iowa, and the Dakotas show persistently high mobility rates. Salt Lake City and its surrounding counties rank among the best-performing metros in the country for children from low-income families.

The coasts are mixed. New York City and Los Angeles show moderate mobility overall, but the variation within those metros is extreme. Moving 20 miles in either city can mean crossing into a county with twice the upward mobility rate.

Tax burden matters here too. States with lower overall tax loads on working families tend to cluster toward the higher end of the mobility rankings. If you want to see how your state's tax structure affects household finances at different income levels, the True Cost of Living in High-Tax States breakdown is worth reading alongside the Atlas data.

Why Some Counties Perform and Others Don't

Chetty's research identifies five factors that correlate most strongly with high mobility: low income segregation, low income inequality, strong primary schools, high social capital, and stable two-parent family structures. None of these factors acts alone.

Commute time is a surprisingly strong predictor. Counties where the average commute exceeds 45 minutes show lower mobility even after controlling for income. The theory is that long commutes reduce parental time investment and weaken neighborhood social ties.

Property tax structure plays a direct role. Counties that fund schools primarily through local property taxes produce wide gaps between wealthy and low-income districts. New Jersey's effective property tax rate of 2.13% generates enormous school revenue in wealthy townships while leaving poorer districts underfunded relative to need. That funding gap shows up in the mobility data decades later.

For families making relocation decisions with an eye on long-term wealth and opportunity, the Capital Gains Tax by State analysis pairs well with the Atlas, since retained investment income compounds directly into the financial head start children receive.

How to Use This Data for Real Decisions

The Atlas is not a perfect decision tool. It measures outcomes for people who grew up in the 1980s and 1990s, and counties change. But the underlying drivers of mobility, school quality, neighborhood stability, income integration, change slowly. A county that ranked well 30 years ago tends to rank well today.

The practical use case is narrowing your relocation shortlist. If you have children and are choosing between two metros with similar job markets and cost-of-living profiles, the Atlas gives you a data-grounded way to compare what the environment has historically produced for kids at different income levels.

You can run a side-by-side comparison of tax burden, cost of living, and state-level policy factors using our state comparison calculator, which lets you filter by income level and family size.

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Key Takeaways

• A low-income child born in San Jose, CA has roughly a 12.9% chance of reaching the top income quintile. The same child born in Atlanta, GA has roughly a 4.5% chance, according to Opportunity Atlas data.
• The five strongest predictors of county-level mobility are income integration, income equality, school quality, social capital, and family stability. Commute time above 45 minutes correlates with lower mobility independently of income.
• Tax structure feeds the mobility gap indirectly. Property-tax-dependent school funding in high-tax states like New Jersey (2.13% effective rate) produces sharp district-level inequality that shows up in long-run outcome data.

Compare states head to head on taxes, cost of living, and economic conditions at Live or Die Here to find where the numbers actually work for your family.