As a result of long-standing discrimination across multiple domains—such as education, labor markets, housing, and medical access—people with disabilities are twice as likely as nondisabled people to live in poverty. To support upward mobility from poverty, policymakers must specifically consider the unique challenges people with disabilities face when designing and implementing policies. When interventions don’t consider disabled people’s needs, they could be left behind.

Federal policies have contributed to limiting economic success for disabled people, creating clear barriers to upward mobility. For example, employers are allowed to pay some disabled employees a subminimum wage, and there are asset limits for people receiving Supplemental Security Income, both of which pose a barrier to building savings, further reducing opportunities for income and wealth. Disabled people often face higher costs to meet their needs (PDF) that strain their income, such as medical costs and higher housing costs for accessible housing caused by limited stock (only 6 percent of the national housing supply is considered accessible). 

To craft policies that address these barriers to upward mobility, local policymakers need data that include disabled people’s experiences. Accessing data that include information about disabilities is critical to successfully identifying and removing barriers to upward mobility. 

The Urban Institute’s Upward Mobility Data Dashboard provides disaggregation by disability for 2 of the 24 predictors: employment opportunities and preparation for college. The graph below shows an example of disaggregation for employment opportunities.

When first looking at the overall employment data for adults, the numbers suggest a strong employment metric for the community. However, when looking at the disaggregated data by disability, the data show that 47.2 percent of disabled adults are employed, compared to 87.4 percent of adults without a disability. Local leaders can use this type of data to inform the development of inclusive employment policies.

Including disability data can be challenging. The American Community Survey and the Community Population Survey both have historically provided data on several metrics that can be disaggregated by disability status and remain the foundational standard for national disability data. However, measuring disability status is complex, and most scales use a functional definition of disability—how the disability limits activities of daily living—which underestimates some portion of the disabled community. Existing data are often limited in how disability is defined and who is captured by that definition. There’s a wide range of disabilities, and there isn’t a universally accepted definition for “disability.” The current national question sets have been shown to perform particularly poorly in capturing neurological disabilities, developmental disabilities, psychiatric disabilities, and chronic illnesses.

Identifying and collecting data related to disability. Local leaders focused on upward mobility in their communities can bridge this data gap through creative solutions to identify, access, and collect data that include information about people with disabilities. This may be through existing, publicly available federal, state, or local data sources or through collaboration with community organizations, such as service providers, to help with original data collection efforts. 

State-level data is one starting point. There are some resources for state-level, disability-related data such as Cornell University’s Disability Statistics resource, the Centers for Disease Control and Prevention’s Disability and Health Data System, and the Lurie Institute for Disability Policy’s Disability Data Dashboard. State policymakers can also help support state- and local-level disability data collection efforts by passing legislation requiring data collection standards that include disability measures and tying resources for localities to this legislation.

Other examples of data policymakers might consider are data on Supplemental Security Income, accessible housing, access-a-ride programs, voting access programs (e.g., mail-in voting), and the ratio of staff within the public education system available to support students with disabilities to the number of students with disabilities (data are available in Urban’s Education Data Portal). These types of information can help policymakers draft disability-forward policies.

The Upward Mobility Initiative has several tools and resources local leaders can use to gather, understand and collect additional local disability data. Obtaining More Local Data provides additional sources of public local data that complement the Mobility Metrics, including by Framework pillar and geographic scale, and the Toolkit for Upward Mobility is a robust resource for local upward mobility planning. In particular, chapter 4 includes ways to identify supports for engaging in those efforts, from selecting a data collection lead who has familiarity with a range of different data sources and experience collecting data, to negotiating access to data from a variety of local partners through data sharing agreements. Understanding Community Resources: A Tool for Data Landscaping also offers advice on how to collaborate to share data. 

Understanding and gathering local data. As local data are collected, there will be gaps, and the toolkit can help leaders understand them and identify strategies and tools for filling them. For example, this worksheet can be used to determine if a survey is the right data collection tool, who to survey, and how.

To fully implement local data strategies, local leaders should engage community members in decisionmaking and center their expertise. Chapter 3 of the toolkit provides more information on this and how to use tools such as focus groups and data interactives, which are valuable for gaining context and more-detailed information from community members and other relevant groups (e.g., service providers). Engaging Communities in Measurement and Data-Driven Decisionmaking is a complementary toolkit for further community-engaged measurement. 

Addressing structural changes through policy. Looking at disability-specific measures is critical to understanding structural barriers to upward mobility from poverty for people with disabilities and identifying successful policy interventions to address those barriers. The inclusion of data disaggregated by disability status and data specific to accessibility will inform more-inclusive policymaking to better serve the community as a whole.