Robust, contextualized data lay the foundation for local policymaking, providing the insights needed to design effective policy and assess outcomes. Data can shed light on the needs of a community and show differences across groups. They can then be used to inform the design and targeting of policies that increase upward mobility and racial equity in communities. 

The COVID-19 pandemic has significantly altered the conditions people experience across many policy domains, which has affected the outcomes captured by data collected during the pandemic. These effects have left many policymakers wondering whether data collected before and during the pandemic are still useful.

Despite the disruptions caused by COVID-19, data from before and during the pandemic hold value for policymakers, especially for understanding the effects of the pandemic and the policy changes made in response. Urban Institute’s Upward Mobility Framework project, which seeks to improve mobility from poverty and reduce racial inequities for people and communities, offers policymakers mobility metrics for every county and more than 450 cities across the country. These metrics include data on several predictors of upward mobility that have been both positively and negatively affected by the pandemic.

As local leaders continue to measure mobility from poverty and equity, and measure progress toward increasing them in their communities, it’s crucial to interpret data through the context of how the pandemic’s effects and the resulting policies have affected mobility conditions across various populations.

How has the COVID-19 pandemic affected mobility from poverty?

The pandemic has negatively affected people’s lives in many ways that are captured in the suite of mobility metrics, such as the following:

• The number of people experiencing a disability increased, which is likely to have ripple effects on key predictors, such as access to health services.
• Learning Loss increased across the country as classrooms went virtual and educators struggled to effectively engage young learners. The effects of learning loss may be evident in key predictors in the Upward Mobility Framework, such as effective public education and preparation for college.
• inflation, which could affect multiple metrics within the Rewarding Work pillar of the Upward Mobility Framework, including employment opportunities, access to jobs paying a living wage, opportunities for income, and financial security and wealth-building opportunities.

In response to these and other negative effects, federal, state, and local policymakers implemented some policies that had positive effects on mobility from poverty, including the following:

• As Americans relied more heavily on the internet for school, work, and social opportunities, the federal government and some local governments implemented policies, practices, and programs to increase digital access, which may continue to have a positive effect on upward mobility.
• The national eviction moratorium, which prevented more than a million evictions but ended in 2021, may continue to affect housing stability through the local rental and utility assistance programs that emerged as a result of eviction prevention measures.
• Many communities implemented policies to prevent COVID-19 transmission in jails by reducing jail population density, and some are considering making policy changes to sustain these reductions, which can have long-term effects on key predictors of upward mobility, such as just policing and safety from crime.

Both the negative and positive effects of the pandemic may impact our nation for decades, but with appropriate contextual knowledge and continuous tracking, prepandemic and pandemic-era data can remain incredibly valuable for practitioners interested in upward mobility and the resilience to, and fallout from, such a significant global event.

How are pandemic data still useful?

Although some of the pandemic’s impacts were short lived, others persist. When interpreting data collected during the pandemic, communities may want to consider which negative effects, such as learning loss and unemployment, could have lasting consequences and need targeted policy attention. Prepandemic data can serve as crucial reference points for understanding baseline mobility patterns and trends.

The pandemic has tended to exacerbate preexisting racial disparities, such as increasing numbers of maternal deaths and babies with low weights at birth. Other disparities have also been exacerbated; for instance, communities with inadequate structural supports for people with disabilities showed declines in several areas of mobility from poverty, such as employment opportunities and transportation access.

Considering trend data before and after 2020 provides a more holistic picture of mobility than do short-term fluctuations that neither reflect underlying patterns nor reveal systemic disparities that already existed. When comparing metrics from before and after the start of the pandemic, we can start to identify how the pandemic and policy changes have affected mobility conditions across populations and help policymakers understand what is most relevant, or requires the most attention, in their communities. With this analysis, policymakers and researchers can devise targeted strategies to address disparities and ensure all people have access to upward mobility.

As new short-term disruptions are inevitable, policymakers and other local leaders need to understand community resilience capability and how equitably residents with different identities experience that resilience. Concerns about how the pandemic has affected mobility metrics shouldn’t stop policymakers and researchers from using those metrics, seeking localized understandings, and planning accordingly.

As we navigate the path to recovery from the COVID-19 pandemic and work to build communities that support upward mobility, the mobility metrics continue to be an essential tool in shaping effective policies and interventions to build a more equitable future.