Nearly six in 10 young adults live within 10 miles of where they grew up, and eight in 10 live within 100 miles, according to a new study by researchers at the U.S. Census Bureau and Harvard University.

Even the prospect of higher earnings in more distant locations does little to change these patterns.

The new study examines the migration patterns of young adults and explores where people go between childhood and young adulthood. It also analyzes how those patterns differ across demographic groups and whether people move greater distances to take advantage of job opportunities.

The research used newly constructed and publicly available statistics on the migration flows of young adults in the United States. You can explore the patterns at migrationpatterns.org.

The data show a key pattern: most young adults do not move far from their childhood home.

About the Data

The dataset for this project includes migration between commuting zones (CZs) for young adults for all 741 CZs in the United States. CZs are collections of counties that serve as a measure of local labor markets.

The data provide not only aggregate migration patterns but also migration flows broken down into four race/ethnicity categories and five quintiles of parental income.

The final dataset draws upon anonymized decennial census, survey and tax data for people born from 1984 to 1992, to measure migration between locations in childhood and young adulthood. Childhood locations are measured at age 16 and locations in young adulthood are measured at age 26.

Interactive data visualizations can be found at migrationpatterns.org, where the full dataset is also available for download.

Migration Patterns

The data show a key pattern: most young adults do not move far from their childhood home.

Figure 1 illustrates these patterns for individuals who grew up in Indianapolis: 73% remained there as young adults. Among those who left, nearby Terre Haute, Indiana, was a more common destination than, for example, New York City.

Figure 1. Migration Patterns of Young Adults Who Grew Up in Indianapolis, IN

Those who grew up in Dubuque, Iowa, follow similar patterns (Figure 2). More children moved to nearby Waterloo (3.59%) or Des Moines (4.12%) than cross state lines to Chicago (2.3%), which is only slightly further away.

Figure 2. Migration Patterns of Young Adults Who Grew Up in Dubuque, IA

Migration Patterns Vary By Race/Ethnicity

There are significant differences in migration patterns by race/ethnicity.

For example, Black young adults moved, on average, 60 fewer miles than White young adults — 130 miles vs. 190 miles. This is because White young adults were more likely to leave their childhood CZ and, when they did, they traveled farther.

Table 1 provides insight into migration patterns by race/ethnicity, listing the 10 most common destinations among those who left the area in which they grew up.

For White young adults, New York City, Los Angeles, and Chicago were the most common destinations, followed by Denver. For Black young adults, the most common destination was Atlanta, followed by Houston, Washington, D.C., and New York City.

While Denver was the fourth most popular destination for White young adults, it was not a top- 10 destination for Black, Hispanic or Asian young adults. Similarly, while Atlanta was the most common destination for Black young adults, it wasn’t among the top 10 destinations for White, Hispanic or Asian young adults.

Table 1. Top 10 Destinations of Young Adults Who Leave Their Childhood Commuting Zones

Two Case Studies of U.S. Migration

The migration data in this project helps paint a more vivid picture of major migration patterns within the United States. Here, we highlight two such patterns: net inflow of Black individuals to the South and diminished out-migration of White individuals from Appalachia.

The New Great Migration

It is a well-documented fact that there has been a recent net-inflow of Black individuals into the American South, a pattern coined the New Great Migration.

By linking young adults to their parents, we can see that this migration is primarily driven by individuals who grew up in affluent families.

For example, we look at migration patterns for Black young adults who grew up in St. Louis. We compare individuals whose parents were in the top 20% of the income distribution (Figure 3) to individuals whose parents were in the bottom 20% of the income distribution (Figure 4).

We find those raised in higher-income families were twice as likely to move to large cities in the South such as Atlanta (1.92% vs 0.88%), Houston (1.22% vs 0.65%) and Dallas (1.48% vs. 0.60%). They were also more than 10 times as likely to move to Washington, D.C. (1.48% vs. 0.13%).

By contrast, migration rates to nearby destinations differed far less across levels of family income. For example, Black individuals who grew up in high-income households were no more likely than their low-income counterparts to move within 250 miles of St. Louis.

Figure 3. Migration Patterns of Black Young Adults From Low-Income Households in St. Louis, MO

Figure 4. Migration Patterns of Black Young Adults From High-Income Households in St. Louis, MO

Migration from Appalachia

There has been considerable academic interest in rates of migration to and from Appalachia. Our results show that, given the region’s relatively low average income, rates of out-migration by White individuals were unexpectedly low. This was largely driven by young adults born into low-income families. Those individuals left the region at lower rates than White young adults living in other places with similar levels of mean income.

Figure 5 plots the fraction of White young adults from low-income families who remained within the commuting zone where they grew up. The rate of staying within a CZ, referred to in the Figure as the stay rate, is plotted in relation to the mean income of the CZ. While more affluent CZs had higher stay rates on average, the commuting zones in Appalachia had above-average stay rates at all income levels.

This pattern dissipated when young adults from high income families were examined (Figure 6). Among those from affluent families, individuals born in low-income commuting zones consistently had lower rates of out-migration. That said, rates of out-migration from Appalachia were similar to migration rates from other CZs with comparable levels of mean income.

Figure 5. Share of White Young Adults From Low-Income Families Living in Their Childhood Commuting Zone by CZ Mean Income

Figure 6. Share of White Young Adults from High-Income Families Living in Their Childhood Commuting Zone by CZ Mean Income

Migration and Economic Opportunity

This project explores not only migration patterns but also how they change in response to new economic opportunities.

We study this question by looking at the geographic variation in the recovery from the Great Recession. By examining differences in the rates of local wage growth between 2010 and 2017, we can examine whether young adults migrated to places that were offering higher wages.

The potential for better pay has a clear and detectable impact on migration decisions. We find individuals moved to places that offered higher wages.

We also find that the degree of responsiveness to higher wages differed across demographic groups. Average migration responses differed by both race/ethnicity and parent income.

We find that, compared to White young adults, Black young adults were less likely to relocate in response to the prospect of higher earnings. We also find that young adults raised in high-income families were more likely than those raised in low-income families to migrate in response to higher wage offers.

While some young adults may change their migration decisions in response to economic opportunities, the magnitudes of these effects are not particularly large.

For example, consider a case where a commuting zone experiences a $1600 increase in average annual wages. (This increase is on par with the type of wage growth successful cities experienced during the recovery from the Great Recession.) We estimate that this increase in wage growth would, on average, lead to a 1% increase in the number of residents.

This means that 99% of the residents of the CZ would have lived there even if it hadn’t experienced the strong wage growth. It also means that 99% of the benefits of the local wage growth flows to those same individuals.

Given that eight in 10 individuals live within 100 miles of their childhood CZ, these results also mean that benefits of wage growth primarily flow to individuals who grew up in or nearby the affected CZ.

Taken together, these findings have implications for assessing the impact of local investment.

We can think of an individual’s “radius of economic opportunity” as the geographic area within which they might benefit from economic growth. If individuals are highly mobile and highly responsive to wage opportunities, that radius is quite large. If the geographic mobility is limited, that radius may be quite small.

Our results suggest that individuals who benefit most from local wage growth are those who grew up nearby, and that those born in a given place are unlikely to benefit from local investment in faraway locations.

In other words, for many, particularly Black and Hispanic individuals and/or those from low-income families, the “radius of economic opportunity” is quite limited.

Nathaniel Hendren is an Economics professor at Harvard University, co-director of Opportunity Insights, and co-director of Policy Impacts. Opportunity Insights and Policy Impacts are nonpartisan, not-for-profit organizations located at Harvard University.

Sonya R. Porter is a principal sociologist and demographer in the U.S. Census Bureau’s Center for Economic Studies.

Ben Sprung Keyser is a Ph.D. student in economics at Harvard University and co-director of Policy Impacts.

Related Statistics

Our email newsletter is sent out on the day we publish a story. Get an alert directly in your inbox to read, share and blog about our newest stories.

Contact our Public Information Office for media inquiries or interviews.

There’s No Place Like (Close to) Home