The Upward Mobility Framework defines upward mobility as having three essential, interconnected dimensions: economic success, power and autonomy, and dignity and belonging. It identifies more than 20 evidence-based predictors that are strongly associated with those dimensions of mobility. The Mobility Metrics are a suite of short- and medium-term measures of each predictor.

The Mobility Metrics visualized in the Upward Mobility Data Dashboard can be used in many ways  to create new or support existing local strategies that aim to increase upward mobility. The following are some of the ways the Mobility Metrics can be used:

• To compare a community’s metrics with peer communities to assess local obstacles to upward mobility and build public support for tackling them.
• To uncover persistent racial inequities and the structural barriers that perpetuate them.
• To prioritize predictors of upward mobility that a community can make the most progress on with focused attention and action.
• To highlight interconnections among predictors from different policy domains to recruit partners and identify the roles they can play in advancing mobility.
• To set targets for improving local metrics and narrowing inequities as part of a strategy to meaningfully change local investments, policies, and practices. 
• To monitor metrics over time to assess a community’s progress and hold local actors accountable.

Some predictors have two metrics because the first metric exhibited a known, clear gap in coverage that required a second metric to fill. Because it is difficult for any one metric to fully capture all aspects of a predictor, exploring additional local data can help uncover further insights into factors affecting a predictor in your community.

Some Mobility Metrics are measured using multiple data points and therefore have more than one value. This is because each metric is meant to capture one quantifiable aspect of a predictor, but to do so accurately requires breaking out the metric data into groups that vary across predictors. For example, household income is the Mobility Metric for the opportunities for income predictor. Instead of showing the overall average household income (one data point), the metric has three data points: household incomes at the 20th, 50th, and 80th percentiles of the income distribution.

Urban researchers well-versed in the underlying source data (one or more public datasets) calculated each metric in consultation with subject matter experts who understand the research underpinning the predictors and their connection to mobility. These experts advised researchers on reasonable interpretations, limits, and caveats for those data. 

The code used to create the metrics is available in our GitHub repository, which also includes documentation on how the data were sourced, how to run the code, and what decisions were made when calculating each metric.

Racial equity considerations are embedded in every element of the Upward Mobility Framework, as well as in the Mobility Metrics data. A forthcoming report will provide additional details on the connection between racial equity and upward mobility.

You may not see your city in the dashboard if it has fewer than 75,000 residents.

The Mobility Metrics are calculated for all counties and more than 480 cities across the United States. We use “city” to refer to incorporated places with more than 75,000 residents. Unfortunately, much of the data for the Mobility Metrics are not sufficiently accurate for places with smaller populations, so we do not report metrics for those cities.

If Mobility Metrics are not available for your city, you can explore one of the following alternatives:

• Examining the metrics for the county your city falls within. This can give you high-level information about your region that can be paired with local data. You can use the county metrics as a starting point for understanding upward mobility conditions in your community.
• Exploring alternative data sources for each predictor.
• Constructing your own metrics using the code available in our GitHub repository. You can leverage the code to replicate the metrics calculations for your city or to create similar calculations using different data sources.

Data for an individual metric may be marked as not available in the dashboard if (a) the data are missing from the original data source and cannot be calculated, or (b) the value has been suppressed because the sample size is too small to be an accurate representation.

For instance, Loving County, Texas, which had a population of 44 in 2022, is missing data for several metrics because there are too few residents to accurately calculate those metrics. We do not report metrics in these cases because imprecise estimates are not useful for evidence-based decisionmaking and may be misleading. 

When possible, we disaggregate Mobility Metrics data by race, ethnicity, and other categories. However, there are couple of reasons why we may not provide estimates for some racial and ethnic groups. First, if a racial or ethnic group is a small percentage of the local population, the sample size of the available data may be too small to provide statistically significant or reliable information. Small sample sizes also lead to concerns about privacy. Second, the data collection and documentation practices of the public sources we use limit our ability to disaggregate the data beyond the source’s categories. For example, the Centers for Disease Control and Prevention combines multiple smaller racial groups into an "other" category.

If data for some metrics are not available for your community, consider pursuing one of the following strategies:  

• Leverage local data. Consult other data resources, including those that may be available only locally. In many cases, local partners can provide direct or indirect access to data—such as administrative data, historical records, survey data, and qualitative insights from the community—that help paint a fuller picture of a community’s upward mobility conditions.
• Conduct a survey. The dashboard may lack data for metrics and categories that are important to your community, and administering a survey is one way to gather that missing information or to supplement your data.
• Relax a metric’s definition. When data for a metric are suppressed because of insufficient data or weak data quality, it may be because the metric’s definition is too narrow, capturing only a small portion of a group. For instance, data on the number of 19- and 20-year-olds with a high school diploma or GED are often suppressed, because the limited age range is a relatively small group. Relaxing the metric’s definition to include a wider age range (e.g., 19- to 25-year-olds) or combining data from multiple five-year American Community Survey files could resolve this issue by providing more data for analysis.

If there are duplicate entries for your locality in the Upward Mobility Data Dashboard, it may be because it is one of the few places incorporated as both an independent city and a county in the United States.

Additionally, you may see different metrics values when viewing your locality as a county as opposed to a city. This is because of the underlying variation in the methods used to calculate the metrics at the county level versus the city level. 

Starting in 2022, Connecticut began using planning regions instead of counties as their geographic units. However, the nine planning regions do not map perfectly to the eight counties used previously. Given this discrepancy, we are able to show Mobility Metrics only for the eight counties in Connecticut before 2022, not for 2022 and later years. We are considering how to offer metrics for the nine planning regions in the future.

We plan to update the dashboard and Mobility Metrics data at least once a year in spring or summer. These updates may include adding the latest available years of data, adding earlier years of data, adding new categories, and adding new features and functionality to the dashboard. Subscribe to our newsletter to receive updates.

Many of the Mobility Metrics can be disaggregated by race and/or ethnicity, but limited data availability prevents us from credibly disaggregating others. Although we attempt to offer disaggregation by as many distinct races and ethnicities as possible, the data sources we rely on are often limited in scope. As such, these disaggregations may not provide a full picture of the demographics in your community. We encourage you to look to local data sources for additional information. If you’re interested in seeing which metrics can be disaggregated by categories other than race and/or ethnicity, or to find out the specific years for which disaggregated data are available, you can find that information in the Available Data section of the Upward Mobility Data Dashboard: Appendix.

Metrics disaggregated by race or ethnicity:

Rewarding Work 

• Employment opportunities: Share of adults ages 25 to 54 who are employed 
• Opportunities for income: Household income at 20th 50th, and 80th percentiles 
• Financial security: Share of adults with debt in collections 
• Wealth-building opportunities: Ratio of the share of total home values owned by a racial or ethnic group to the share of households of the same group 

High-Quality Education 

• Access to preschool: Share of 3- and 4-year-old children enrolled in nursery school or preschool 
• Effective public education: Average annual improvement in English Language Arts 
• School economic diversity: Share of students attending high-poverty schools, by race or ethnicity 
• Preparation for college: Share of 19- and 20-year-olds with a high school degree 
• Digital access: Share of households with a computer and broadband internet subscription in the home

Opportunity-Rich & Inclusive Neighborhoods 

• Housing stability: Number of public-school children who are ever homeless during the school year
• Economic inclusion: Share of people experiencing poverty who live in high-poverty neighborhoods 
• Racial diversity: Index of people’s exposure to neighbors of different races and ethnicities
• Transportation access: Transit trips index; Share of income spent on transportation 

Healthy Environment and Access to Good Health Care 

• Neonatal health: Share of infants with low birth weight 
• Environmental quality: Air quality 

Responsive and Just Governance 

• Descriptive representation: Ratio of the share of local, elected officials of a racial or ethnic group to the share of residents of the same group 
• Just policing: Juvenile arrests per 100,000 juveniles 

Metrics unable to be disaggregated by race or ethnicity: 

Rewarding Work 

• Jobs paying living wages: Pay on an average job compared with the cost of living 

Opportunity-Rich & Inclusive Neighborhoods 

• Housing affordability: Number of affordable and available housing units per 100 households with low, very low, and extremely low incomes 
• Social capital: Number of membership associations per 10,000 people; Economic connectedness index 

Healthy Environment and Access to Good Health Care 

• Access to health services: Number of people per primary care physician 
• Safety from trauma: Deaths caused by injury per 100,000 people 

Responsive and Just Governance 

• Political participation: Share of the voting-age population who turns out to vote 
• Safety from crime: Numbers of reported property crimes and reported violent per 100,000 people
• Just policing: Juvenile arrests per 100,000 juveniles

The years vary by metric. View “Upward Mobility Data Dashboard Appendix” to see the years of data available for each metric.

The categories vary by metric. View “Upward Mobility Data Dashboard Appendix” to see how the data can be disaggregated and what categories are available for each metric.

The national medians offer a benchmark against which you can compare your community’s performance on a metric. We calculate a national median for each metric by sorting all counties in the nation based on their value for a metric and weighting those values by each county’s population. The value that falls in the middle is the national median, and it is calculated for every year of available data for each metric. We base these medians on counties—not cities—because the entire US population lives in a county but not everyone lives in a city. This ensures that the medians are representative of the national population.

We do not provide a national median if the counties that have weak data quality or unavailable data for a metric in a given year have a combined population that makes up 15 percent or more of the US population.

Urban researchers familiar with the source data calculated each metric in collaboration with field experts who understand the research underpinning the predictors and their connection to mobility. These experts guided researchers on sensible interpretations, limitations, and cautions for the data. They also determined the data quality. Their reasoning is outlined in the data documentation.

A data quality index is provided to communicate each metric’s reliability. When viewing the metrics, you will see one of the following designations for each data point:

• Strong or acceptable: This indicates that the calculated metric is of high quality or has limited issues; that is, there are no significant concerns regarding measurement error, missing data, sample size, or precision. Community partners can act on this information but should corroborate it with local data sources.
• Weak: This indicates that the calculated metric has serious issues, including critical concerns with measurement error, missing data, sample size, or precision. A community partner should not take action based on this metric.
• N/A: This indicates that the metric is not available; that is, a metric for the county or city could not be responsibly calculated based on available data or the data were missing.

Data that are of weak quality may be unreliable because of measurement error, missing data, small sample sizes, or a lack of precision. Though you should not act based on such data alone, the data may serve as a signal of your community’s upward mobility conditions. You should validate such data by examining related metrics, corroborating with alternative data sources, or confirming any insights with qualitative information.

Confidence intervals are used in statistical methods to convey how precise an estimate is within a range. Researchers use them because counts and percentages calculated from surveys, vital statistics data, and other surveillance systems are not a perfect reflection of a population.

Narrower confidence intervals imply a greater likelihood that the estimate is close to the true value. Conversely, broader confidence intervals imply it is less likely that the estimate is close to the true value. For example, a 95 percent confidence interval of 0.1 to 0.5 indicates that, if the researcher were to calculate the estimate from an infinite number of samples of the same size drawn from the same base population, the estimate would fall between 0.1 and 0.5 95 percent of the time.

A confidence interval’s width can also indicate precision. For example, a narrow confidence interval indicates a higher degree of precision. A good rule of thumb when comparing your community’s estimate with that of another community is to evaluate the overlap in the confidence intervals. If the intervals do not overlap, then the estimates are statistically significantly different from one another. If they overlap, then it is possible that the difference between the estimates is the result of a sampling error, meaning the estimates are not statistically significantly different. The less the confidence intervals overlap, the more likely that the observed difference is a true difference. This also applies when comparing estimates across data categories.

We determine peer community suggestions by using data on demographics, housing, population, and geographic proximity to calculate an overall measure of similarity between two cities or two counties. (Cities are not suggested as peers for counties and vice versa.) The methodology and approach we use borrow largely from the Peer City Identification Tool developed by the Federal Reserve Bank of Chicago. The variables used to determine similarity are as follows:  

• Demographics 
  • total population     
  • population density (population divided by area square feet)
  • population as a share of the total metropolitan statistical area population 
  • share of the population that is foreign born 
  • share of the population ages 18 and under
  • share of the population ages 65 and over
  • share of the population that is non-Hispanic white 
• Housing 
  • vacancy rate
  • median monthly housing costs (the average contract rent for renters and the average principal interest tax and insurance costs for homeowners)
• Population changes 
  • change in the population since 2010
  • change in the share of the population with a bachelor’s degree since 2010
• Geographic proximity 
  • Distance between county or city centroids 

You can download data for the specific communities and predictors that you’ve selected using the “Export All Data” button on the dashboard. Visit our data dictionary if you need help understanding the variables in these files.   

Datasets containing Mobility Metrics data for all cities and counties are available on the Urban Institute Data Catalog. The code used to produce these datasets is available on the GitHub repository.  

To learn how to navigate the GitHub repository and leverage its content, use the comprehensive README, which outlines the structure, content, and standards used to construct the repository.  

The code used to calculate each metric can be leveraged to: 

• recalculate the source data to different or more-specific geographical boundaries where possible, 
• disaggregate the data in different ways, or 
• repurpose the structure or processes of the calculations for other data projects.

Community members can use the Mobility Metrics to help communicate, validate, interrogate, and better understand local upward mobility conditions. Residents’ engagement with the metrics and the Upward Mobility Framework will enhance the relevance and impact of a community’s mobility work by incorporating lived experiences.

We welcome questions and feedback at [email protected]. If you encounter issues while using the dashboard, please select the “See a problem?” button.

We provide national medians for at least some years for the following metrics:

Rewarding Work

• Employment opportunities: Share of adults ages 25 to 54 who are employed
• Jobs paying living wages: Pay on an average job compared with the cost of living
• Opportunities for income:  Household income at 20th 50th, and 80th percentiles
• Financial security: Share of adults with debt in collections

High-Quality Education

• Access to preschool: Share of 3- and 4-year-old children enrolled in nursery school or preschool
• Effective public education: Average annual improvement in English Language Arts
• School economic diversity: Share of students attending high-poverty schools, by race or ethnicity
• Preparation for college: Share of 19- and 20-year-olds with a high school degree
• Digital access: Share of households with a computer and broadband internet subscription in the home

Opportunity-Rich & Inclusive Neighborhoods

• Housing affordability: Number of affordable and available housing units per 100 households with low, very low, and extremely low incomes
• Housing stability: Share of public-school children who are ever homeless during the school year; Number of public-school children who are ever homeless during the school year
• Economic inclusion: Share of people experiencing poverty who live in high-poverty neighborhoods
• Racial diversity: Index of people’s exposure to neighbors of different races and ethnicities
• Social capital: Number of membership associations per 10,000 people; Economic connectedness index
• Transportation access: Transit trips index; Share of income spent on transportation

Healthy Environment and Access to Good Health Care

• Access to health services: Number of people per primary care physician
• Environmental quality: Air quality
• Safety from trauma: Deaths caused by injury per 100,000 people

Responsive and Just Governance

• Political participation: Share of the voting-age population who turns out to vote
• Descriptive representation: Ratio of the share of local, elected officials of a racial or ethnic group to the share of residents of the same group

We are unable to provide national medians for the following metrics:

Rewarding Work

• Wealth-building opportunities: Ratio of the share of total home values owned by a racial or ethnic group to the share of households of the same group

High-Quality Education

• School economic diversity: Share of students attending high-poverty schools, by race or ethnicity

Opportunity-Rich & Inclusive Neighborhoods

• Racial diversity: Index of people's exposure to neighbors of different races and ethnicities

Heathy Environment and Access to Good Health Care

• Neonatal health: Share of infants with low birth weight

Responsive and Just Governance

• Safety from crime: Numbers of reported property crimes and reported violent crimes per 100,000 people
• Just policing: Juvenile arrests per 100,000 juveniles