Urban Institute
Research Analyst
Rayanne Hawkins
Urban Institute
Business Operations Manager

Data considerations for PFS projects

February 20, 2018 - 2:12pm

Data review, collection, and analysis are the backbone of all pay for success (PFS) projects—or social impact bonds (SIB), as they’re termed elsewhere. Our experiences working with jurisdictions developing PFS projects have yielded the following data-focused recommendations. Data can help PFS partners use evidence to select programs on the front end, and through evaluation, generate evidence on the back end.

Use data to help identify the target population. Review existing research about the suggested intervention, and subset of people it is most effective at helping. Local data can then help identify people with similar demographic characteristics who could be expected to benefit from the program that are living in the area where the program will be implemented.  Using data in this targeted way can help PFS partners understand whether the intervention is a good fit for their community, or in other words, the likelihood of it working in their jurisdiction.

Make sure that the outcomes you want to achieve are attainable. An early selling point of the pay for success field was that reallocating resources through PFS could generate cashable savings to government departments and agencies. If cashable savings are a must for the jurisdiction, due diligence around which populations are the costliest, as well as which interventions have the potential to yield cost savings, is essential. This will take targeted research into potential interventions, as well as the ability to access and analyze individual-level social services data. However, as the field evolves, its thinking on cashable savings has shifted. For example, it might be unrealistic to aim to close a jail or prison in a criminal justice project. A more realistic hope could be that a PFS project can potentially save a jurisdiction from building a new correctional facility. Regardless of the resulting cash savings, governments need to focus on their willingness to pay for outcomes improvements.

Link high-quality data sets from different social sectors to monitor trends in social service usage. For instance, permanent supportive housing (PSH) has been a common intervention in PFS projects to date due to its strong evidence base. It targets individuals who touch several social sectors, including criminal justice, homelessness, and healthcare. Partners who have worked on existing PFS supportive housing projects have indicated that data helped project stakeholders realize that frequent utilizers of one system were not necessary frequent utilizers of other systems. A high utilizer of the health system has a lower chance of being arrested than a comparable average utilizer simply because they may be in the hospital very often. Similarly, high utilizers of the jail system have a lower chance of accessing mainstream healthcare or homelessness services because those needs are met to a degree while they are in jail. This type of data drilldown can provide a more granular view of sub-populations, which can then better inform project planning. 

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As an organization, the Urban Institute does not take positions on issues. Scholars are independent and empowered to share their evidence-based views and recommendations shaped by research. Photo via Shutterstock.