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A Data Driven Approach to Grant Making

Tags: Regional Partnerships

A Data Driven Approach to Grant Making

By Hannah Grace Bauman


In 2019, the High Country Regional Council hoped to effectively allocate funding to support at-risk middle school youth. The Council recognized that a gap in middle school programming existed in every county across the region, and sensed variations in levels of need, but were unclear on the extent of those variations. Rather than guess which counties needed more funding based on anecdotes and personal connections, the Council sought to identify a need-based distribution of funds informed by three objective metrics:

  • School-aged children living in poverty
  • Pupil enrollment
  • Graduation rates

This method aimed to maximize the impact of funding by ensuring that programming reached counties with higher levels of poverty, a greater number of students and lower graduation rates. To determine the distribution of funding based on these metrics, El Pomar staff created a report that displayed each of these metrics in Clear Creek, Eagle, Lake, Pitkin and Summit Counties. El Pomar staff then computed equally weighted averages based on the metrics and split $1 million dollars over five years between the counties. These computations relied on data from the Annie E. Casey Foundation Kids Count Data Center.

The first metric, school aged children living in poverty, provided the Council a way to determine which counties maintain the greatest economic need. This method indicated to council members that Lake, Clear Creek and Eagle Counties had the highest percentages of school-aged youth living in poverty. 

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School-Aged Children in Poverty (Percent) 2018 - KIDS COUNT Data Center

The second metric, pupil enrollment, allowed the Council to verify where students are concentrated. In Eagle County, there are more than 6,800 students, compared to only 760 students in Clear Creek County. Thus, the Council determined that Eagle County should receive more funding to serve a greater number of students.

Finally, graduation rates enabled Council members to identify which counties struggled with their educational outcomes. In order to allocate more funding to counties with lower graduation rates, the Council learned that Pitkin and Summit Counties had the highest graduation rates (at 95.5% and 95% respectively) while Lake County (55.4%) and Eagle County (74.9%) performed poorly in comparison.

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High School Graduation Rates (Percent) 2018 - KIDS COUNT Data Center

By using these metrics and computing equally weighted averages, the Council determined that Eagle and Lake Counties would receive more funding than Clear Creek, Pitkin and Summit Counties. Each county still received robust funding to address gaps in programming for at-risk middle schoolers, but the dollars were objectively distributed based on levels of need, as displayed in the data. This approach allowed the Council to grant dollars not based on bias or subjective information but instead based on concrete quantitative data. In doing so, the Council ensured that funding was appropriately allocated between communities based on needs and enrollment.

If your Council or organization is interested in this grant making approach or wishes to learn more about the demographics in your region, please visit the Kids Count Data Center here.




Hannah Grace Bauman joined El Pomar Foundation as a member of the 2019 Fellowship class. As a Fellow, Hannah Grace works on American Council of Young Political Leaders, Regional Partnerships, Forum for Civic Advancement and the Western Legislative Academy. In addition, Hannah Grace supports the High Country region. Read more about Hannah Grace here