Promise Neighborhoods are committed to ending intergenerational poverty by supporting children from cradle to career.
Inspired by Geoffrey Canada’s Harlem Children’s Zone and administered by the US Department of Education, Promise Neighborhoods take a place-based, data-driven approach to ensuring the long-term success of children, families, and neighborhoods.
Promise Neighborhood grantees work with schools and multiple nonprofit partners. Each site tracks community-level measures and uses real-time data to direct the work of their partners, all with the goal of building the most effective network of supports for kids. To help grantees in this effort, Urban Institute scholars provide guidance on data collection and performance measurement.
Here we share the stories of three Promise Neighborhoods that are using data to transform lives: one aims to close the disparity of opportunities for children in rural Indianola, Mississippi; another works to lift an isolated and forgotten Washington, DC, community out of poverty; and a third strives to meet the needs of residents from San Francisco’s gentrifying Mission District even as many are displaced from the neighborhood.
This project was funded by the US Department of Education. In addition, we thank the Promise Neighborhood implementation grantees from whom Urban Institute has gleaned much of the insight and experience outlined in our research reports: Indianola Promise Community; DC Promise Neighborhood Initiative; and Mission Promise Neighborhood.
As an organization, the Urban Institute does not take positions on issues, but it does empower and support its experts in sharing their own evidence-based views and policy recommendations that have been shaped by scholarship.
RESEARCH
SARAH GILLESPIE, Peter Tatian, JAY DEV, AND SARA MCTARNAGHAN
WRITING
MATTHEW JOHNSON, SERENA LEI, and Alexandra TILSLEY
Photography
MATTHEW JOHNSON and LYDIA THOMPSON
Design
CHRISTINA BAIRD and JOHN WEHMANN
Development
Christina Baird
Editorial
Michael Marazzi
Copyright © Urban Institute September 2016. View this project on GitHub