EMPLOYMENT OPPORTUNITIES

People experiencing periods of unemployment suffer a loss of income in the short term and potentially lower earnings once they find a new job. A job loss and associated unemployment and a struggle to find work contribute to a rise in depressive symptoms and anxiety as well as losses in self-esteem, life satisfaction, and sense of control. Those who become unemployed are also less likely to be socially engaged than those with jobs. Further, parental job loss and the attendant stress it brings spills over onto children, whose academic performance and behaviors suffer. People who have become so discouraged that they stop looking for work are jobless but no longer technically unemployed. As such, employment is a critical driver of mobility from poverty.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Employment-to-population ratio for adults ages 25 to 54.

This metric is the ratio of the number of employed adults ages 25 to 54 in a given jurisdiction to the total number of adults in that age range living there.

Validity: Employment captures what share of adults in a jurisdiction are engaging in work for pay. The employment-to-population ratio (EP) is a standard labor market metric reported monthly by the Bureau of Labor Statistics (BLS) and based on the Current Population Survey. The Working Group recommends applying the methodology used to compute the EP to similar data collected in the Census Bureau’s American Community Survey (ACS).

Availability: Data on employment are available from the ACS and Public Use Microdata Sample.

Frequency: New data for the metric are available annually. For subgroup analyses in less populated areas, several years of data may need to be pooled to obtain reliable estimates.

Geography: Data are available at the county and metropolitan levels.

Consistency: Information on employment and age is measured the same way across all geographies in the same year and over time in the ACS.

Subgroups: The metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populated areas and for certain demographic groups, several years of data may need to be pooled to obtain reliable estimates.

Limitations: The BLS reports the official EP monthly for those age 16 and up as well those age 20 and up. As such, the BLS-reported measure could be lower for jurisdictions that have many young adults attending college rather than working as well as for those that have many retirees. Consequently, for our purposes, we recommend computing the EP for adults ages 25 to 54 using data from the ACS rather than relying on BLS reports. Even when using ACS data, the EP can drop if unemployed people leave an area or if working people move in.

 

JOBS PAYING LIVING WAGES

Even if most community members are working, the jobs they hold may not pay them enough to escape poverty or offer prospects for advancement. Ideally, work should be both financially and personally rewarding while allowing workers to meet their family needs; in other words, they need access to jobs paying a living wage. Although many different attributes of a job can contribute to mobility, jobs that offer higher earnings tend to also offer employer benefits such as paid time off and health and pension benefits, and workers in better-paying jobs tend to have more stable employment. Further, children in families with higher-earning parents tend to be in better health and on better developmental trajectories than children with lower-earning parents. Earnings that equal or exceed the cost of a family’s basic needs for food, clothing, shelter, child care, health care, and transportation are an important threshold for predicting economic and social mobility.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Ratio of pay on an average job to the cost of living.

This metric shows what a typical job pays relative to the cost of living in a particular area. The metric is computed by dividing the average weekly earnings across all jobs in an area by the cost of meeting a family of three’s (one parent and two children) basic expenses in that area.

Validity: Employer-reported data on wages paid are a reliable indicator of what jobs pay, and the metric is based on data collected and disseminated by BLS. Data on what it costs to meet basic expenses requires detailed studies of the cost of food, clothing, shelter, health care, and work-related expenses for each jurisdiction. We rely on the work of well-regarded scholars at the Massachusetts Institute of Technology (MIT) to obtain estimates of the local cost of living.

Availability: Data on wages are available quarterly from the BLS’s Quarterly Census of Employment and Wages, and estimates of the cost of meeting a family’s basic needs, referred to as a living wage, are available annually from MIT.

Frequency: New data for the metric are available annually.

Geography: Data on wages are available at the county and metropolitan levels. Data on living wages are available at the county level.

Consistency: Information on quarterly wages is collected consistently by the BLS. MIT uses a consistent methodology to compute living wages by county.

Subgroups: The data cannot be disaggregated by demographics because they describe jobs rather than the people in them, but we can disaggregate by industry type.

Limitations: The metric can only be computed for the 365 largest counties and cannot be disaggregated by subgroups. The metric relies on MIT’s computations of “living wages.”

 

OPPORTUNITIES FOR INCOME

Income is a strong indication of a family’s material well-being. Families need a certain base level of income to meet their basic needs for food, clothing, shelter, health care, and any costs related to sustaining a job. Further, children raised in higher-income households demonstrate higher academic achievement and educational attainment, better physical and mental health, and fewer behavioral problems than their peers from lower-income households.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Household income at 20th, 50th, and 80th percentiles.

Household income is a standard measure of financial well-being. The Working Group recommended the metrics at these three levels to track how and for whom incomes are changing in a given place as well as whether incomes are rising across the board or are rising more for those with higher incomes. To identify income percentiles, all households are ranked by income from lowest to highest. The income level at the threshold between the poorest 20 percent of households and the richest 80 percent is the 20th percentile. Similarly, the threshold between the poorest and richest halves is the 50th percentile (or median), and the threshold between the poorest 80 percent and richest 20 percent is the 80th percentile.

Validity: These are well-established measures, and several federal agencies and many scholars frequently use them to assess families’ financial well-being.

Availability: Data on household income are available from the Census Bureau’s American Community Survey and Public Use Microdata Sample.

Frequency: New data for the metric are available annually. For subgroup analyses in less populated areas, several years of data may need to be pooled to obtain reliable estimates.

Geography: Data are available at the county and metropolitan levels.

Consistency: Income data are measured the same way across all geographies in the same year. The measure is fairly consistent over time, but changes in the phrasing and sequence of income source questions might affect comparisons over time. When such changes have occurred in other federal surveys, such as the Current Population Survey, the Census Bureau provides bridge-year data so users can assess the effects of survey changes.

Subgroups: The metric can be disaggregated by race or ethnicity, gender, and other demographic factors. For less populated areas and for certain demographic groups, several years of data may need to be pooled to obtain reliable estimates.

Limitations: The purchasing power of any particular level of income will vary based on the local cost of living. Also, because household sizes differ, the same income may be stretched across larger average households in some places relative to others. Like all metrics based on the characteristics of people living in an area, it can change because of residential mobility.

 

FINANCIAL SECURITY

Financial security extends beyond income and reflects the overall ability of a household to meet its current and future financial obligations and withstand potential financial shocks. Research finds that even a modest amount of savings can help buffer a short period of being unemployed or help face a medical emergency. Financial security can also include access to credit, debt loads, and financial management.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Share of households with debt in collections.

This metric accounts for the share of households in an area with debt that has progressed from being past due to being in collections.

Validity: Delinquent debt as measured by debt in collections is a valid and strong measure of financial distress.

Availability: Drawn directly from credit reports, the credit bureau data are national and uniform across the country. The data are restricted and are not accessible directly from credit bureaus but are made available publicly on the Urban Institute’s Debt in America website.

Frequency: New data for this metric are available annually.

Geography: Data on households with debt in collections are available by zip code or county.

Consistency: The share of households with debt in collections can be measured consistently for all geographies. The measure is likely to remain consistent over time unless the credit bureaus change the way overdue debt is captured in credit reporting.

Subgroups: The credit bureau data do not include information about race. But the debt value can be disaggregated by subarea when used in combination with the American Community Survey to identify the racial or ethnic composition of neighborhoods (zip codes) with more or less debt in collections. We distinguish zip codes that are majority non-Hispanic white or majority nonwhite. We define a majority as at least 60 percent of residents.

Limitations: Along with the limitations related to subgroups, these data do not capture “credit invisible” households, meaning those without a credit record. As a measure of financial well-being, even if few households have debt in collections, many may still have too little wealth or savings to be primed for upward mobility. This metric is somewhat sensitive to residential mobility. If many residents without overdue debt move in or out of a county or zip code, or if many residents with overdue debt move in or out, this metric could shift.

 

WEALTH-BUILDING OPPORTUNITIES

The opportunity to build wealth, through savings, loans, business ownership, homeownership and more, affects the capacity of individuals’ and families’ to weather economic shocks and invest in their physical health, both of which affect economic mobility. Research finds that gaps in intergenerational wealth and disparate access to wealth-building opportunities can lead to a racial disparity in mobility outcomes. Families benefit from multiple sources of wealth, including checking and savings accounts, stocks, bonds, and businesses, but housing wealth is most accurately measured at the local level as compared to other forms of wealth.

To learn more about how this predictor is linked to upward mobility, read its assessment in the Evidence Resource Library.

Metric: Ratio of the Share of a Community's Housing Wealth Held by a Racial or Ethnic Group to the Share of Households of the Same Group.

This metric highlights racial and ethnic disparities in an important source of wealth—disparities that likely reflect structural inequities in wealth-building opportunities. The metric compares a racial or ethnic group’s share of primary-residence housing wealthⁱ in a community to its share of the population. For example, if Black homeowners have 15 percent of the primary-residence housing wealth but make up 45 percent of the population, then the metric is a ratio of 15%:45%. The greater the gap between these percentages, the more inequity in housing wealth in the community.

Validity: Although this ratio is not commonly used, each piece of the ratio is. The calculation of primary-residence housing wealth is consistent with the literature (Gallin et al. 2019). The share of racial and ethnic groups among the household population is commonly used. The juxtaposition of these two shares has been used to highlight housing wealth equity and homeownership wealth gaps (Neal, Young, and Choi 2021).

Availability: Data for this ratio are available annually from the Census Bureau’s American Community Survey.ⁱⁱ

Frequency: The data are collected annually. For subgroup analyses for less populated areas, it may be necessary to pool several years of data to obtain reliable estimates. 

Geography: Data are available at the county and metro levels.

Consistency: This metric is defined consistently across race and ethnic groups, is consistently measured over time, and is comparable across geography.

Structural equity* and subgroups: This metric captures the racial disparities apparent in ways wealth is shared across a given population. This helps elucidate any racial or ethnic disparities in comparative housing wealth and how it has been accessed or distributed in that community, rather than focusing on an aggregate measure of total homeownership. These disparities can signal instances of racism in achieving or benefiting from homeownership.

Structural relevance*: This ratio characterizes the distribution of aggregate housing wealth to describe a community-level condition, rather than an individual outcome, such as the average value of household wealth.

Limitations: This metric only captures relative wealth-building opportunities and does not capture absolute wealth-building opportunities. It is possible that a community has an equitable distribution of housing wealth in a community with very little housing wealth. This community will rate well under this metric. This metric will also be imprecise or suppressed for communities that lack racial or ethnic diversity. Although we refer to this metric as housing wealth, the data reflect homeowners’ self-assessments of the value of their homes and does not account for mortgage debt. Black and Latino households, on average, buy their homes with more debt (McCargo and Choi, 2020) so the racial housing wealth disparities are likely to widen if mortgage debt is incorporated. This metric also does not account for other financial costs and benefits of homeownership that could affect wealth building (Neal, Choi, and Walsh 2020). It also does not account for other important differences such as the average age of people in different racial and ethnic groups. One would expect older people to have higher-value homes than younger people, so some racial and ethnic disparities could be exaggerated by age differences. Therefore, this metric may not fully reflect the size of the actual housing wealth gap and could be misleading without a deeper understanding of homeownership and demographic circumstances in a community. Further, this metric focuses on only one form of wealth based on homeownership, and does not consider other forms of wealth like owning a business, stocks, or bonds.

* These selection criteria on structural equity and structural relevance were explicitly added in review of new metrics.

ⁱ Primary residence housing wealth is a product of the average owner-occupied home value and the total number of homeowners.

ⁱⁱ Data for this metric can also be accessed through Urban’s racial wealth equity tool, whose calculations and sample statistical program are publicly located on GitHub. See Michael Neal, Caitlin Young, and Jung Hyun Choi, “Tracking Homeownership Wealth Gaps: Is Housing Wealth Equitable in Your City?,” last updated November 24, 2021, https://apps.urban.org/features/tracking-housing-wealth-equity/.