San Mateo County, California, is one of many “hourglass economies” in the US, meaning there are large shares of high- and low-income earners with few falling in the middle. This income disparity has resulted in inequities in residents’ ability to afford a home and access quality education and health care.

To understand and address these challenges, local leaders need reliable data on their communities and clear economic-mobility priorities to work toward. Through the Urban Institute’s Mobility Action Learning Network (MALN), San Mateo County’s Shared Prosperity Coordinating Council (SPCC)—a group of leaders from county and local government, nonprofit organizations, and the private sector—came together to meet these needs and advance their vision for shared prosperity. 

The SPCC partnered with the Urban Institute to develop an action plan for upward mobility, drawing on technical assistance focused on using data for decisionmaking. The plan was tailored to the community’s needs and leveraged local datasets with disaggregated information on resident participation across several safety net benefits and other key indicators of upward mobility and racial equity. The work highlighted in this case study offers lessons learned for local leaders, nonprofit practitioners, and cross-sector coalitions pursuing a variety of economic and upward mobility goals through data-informed strategies.

The Goal

The SPCC sought to build its capacity to use data in decisionmaking around economic mobility in San Mateo County. To achieve this, the SPCC leveraged support from the MALN to develop two tools.  

The Economic Mobility Action Plan includes three universal goals and three focus areas for action that would fill critical gaps San Mateo faces in key pillars of support for upward mobility. Though specific to local needs, the goals and focus areas are built upon the three-part definition of upward mobility and pillars in the Upward Mobility Framework (UMF).

The Equity Dashboard, which will launch later in 2025, will be a dynamic data tool the SPCC can use to track key indicators and measure progress toward its economic-mobility goals. It will offer disaggregated data about San Mateo to highlight demographic and geographic disparities and help the county target interventions where they are needed most.

How They Did It

Adapting the Upward Mobility Framework. The SPCC identified three universal goals in its action plan that are based on the UMF’s three-part definition of mobility from poverty:

• All individuals and families have a reliable income that covers their expenses while building wealth throughout their lifetime.
• All residents have voice and agency to influence larger policies and actions that affect their lives.
• Everyone feels the respect, dignity, and well-being that comes from a sense of belonging and contributing to and being appreciated across all settings and demographics.

By customizing the UMF to its local context, the SPCC is ensuring its work is both aligned with national best practices and deeply responsive to residents’ needs.

Using local data to understand upward mobility conditions in the community and identify goals. The SPCC leveraged local data—including a landscape analysis of administrative data and survey data it collects—to uncover inequities in the community and determine areas for action that would help San Mateo reach the three universal goals above. For example, the SPCC used local administrative data and gathered primary data to identify inequities in access to social services, create a unique measure of access to social services, and identify strategies to improve navigation of services in the community.

Engaging the community. Over three years, San Mateo County and its partners engaged more than 5,500 residents through surveys, workshops, focus groups, and listening sessions. These efforts were designed to reach historically underrepresented communities and ensured the action plan was grounded in community voice. Community feedback not only enriched SPCC’s plan but also fostered trust between the county, its partners, and the communities they serve.

Lessons Learned

The SPCC’s work highlights several important lessons for other communities looking to improve data-driven decisionmaking.

Adapt a national framework locally. The SPCC found value in using the UMF as a foundation but recognized the need to adapt and expand it to fit local needs. Integrating the framework with locally relevant data sources and community-identified priorities ensured SPCC’s approach was both evidence based and responsive to residents’ concerns.

Use data to drive decisionmaking. The SPCC’s reliance on diverse data sources allows it to refine focus areas and effectively measure progress toward its goals. For example, by providing real-time information on conditions for upward mobility, the Equity Data Dashboard will offer actionable insights for policymakers, service providers, and community leaders. 

Fill gaps without duplicating efforts. The SPCC conducted a landscape analysis to assess existing programs and pinpointed critical gaps in workforce development, civic representation, and access to social services. The analysis helped them identify areas for action as well as focus their efforts where they could add additional value and avoid replicating other initiatives’ work.