By George E. Rudebusch
How do Pay for Success (PFS) projects begin? Who initiates them? What separates projects that are likely to succeed from those that are not?
At the University of Virginia Pay for Success Lab, we conduct pre-feasibility analyses that answer all these essential questions related to PFS projects. Quality pre-feasibility analyses are powerful tools to shorten PFS project timelines, but these studies themselves take significant time to complete. Drawing from my experience, I present a case study below that illustrates engaging with stakeholders first accelerates the development of PFS projects. Before doing so, however, I first explain pre-feasibility analyses in greater detail, which accomplish three things—identify the problem, research solutions, and connect stakeholders.
First, pre-feasibility analyses identify issues communities could effectively address through a PFS framework. In cases where stakeholders have already identified a need, these analyses refine understanding about the issue. Second, pre-feasibility studies undertake initial research on the topic. What best practices have the literature identified? What social and economic benefits could an intervention provide? Is the problem solvable? Finally, pre-feasibility analyses initiate stakeholder engagement. We connect stakeholders to develop an intervention and a set of quantitative criteria to evaluate it.
Pre-feasibility analyses are powerful tools. Done well, they shorten PFS project timelines, quickening the delivery of solutions to pressing public problems.
Still, these analyses themselves take time to produce. And not every potential project fits with the PFS model. PFS projects succeed and fail according to data availability, stakeholder engagement, and consensus on outcome metrics. To accelerate pre-feasibility analyses, researchers should gain information about such factors from stakeholders themselves as soon as possible. The critical first step in pre-feasibility analysis is stakeholder outreach, as I learned from a recent project.
Recently, I worked on a project to reduce recidivism rates in Fairfax County, Virginia. My team and I began our analysis by combing through government data, PFS reports, and criminology literature. After a few weeks of research, we brainstormed solutions to reduce recidivism in Fairfax County. Viable programs incorporated best practices from the literature, while also factoring in characteristics specific to the population of ex-offenders reentering Northern Virginia.
We settled on a solution that would expand an existing program that provides comprehensive reentry services to incarcerated persons reentering into Fairfax County. After release, the nonprofit continued to deliver a suite of services, which included housing assistance, substance abuse therapy and job training, over two phases of intensity.
My colleagues and I were initially drawn to this program because it seemed a near-perfect fit for a PFS project. Using publicly available data, we found that participants in the program had recidivism rates well below the state average for comparable ex-offenders. Financials on the program similarly supported a PFS-funded expansion. Under conservative assumptions, we estimated that this project could generate an internal rate of return north of nine percent. After identifying and researching the issue and possible solutions, we were convinced this program was likely feasible for a PFS project.
The only remaining step in our pre-feasibility analysis was engaging with stakeholders – a step that ultimately led us to reverse our opinion about the PFS feasibility of this program. After speaking with several stakeholders, we drew three conclusions.
First, the stakeholders operated at arm’s length. No party with which we spoke coordinated to a sufficient degree to support a PFS project.
Second, the stakeholders, whether public or private, lacked data on key information, including: the criteria or process to select participants of the program; recidivism and employment rates; and program completion percentages among participants.
Finally, even if stakeholders had these data, they disagreed about basic definitions of success. There was little, if any, consensus on fundamental program characteristics, most notably a common definition of success. More worrisome for a PFS intervention, no stakeholder indicated the existing program was evaluated in an outcomes-based manner.
Without stakeholder coordination, rigorous data collection, and consensus on success, we concluded that the program could not be expanded using PFS funding.
Nevertheless, our analysis did bear one fruitful finding. Pre-feasibility analyses, indeed of any PFS project, should begin with stakeholder engagement. Is there interest among all stakeholders to coordinate effort and solve the issue at hand? Are they already collecting and ideally sharing relevant data? Is there a shared understanding about how to evaluate a solution? If these questions cannot be answered, there is little use of finishing a pre-feasibility analysis.
Pre-feasibility analyses are an important tool to identify good projects from bad. They can shorten development and implementation of PFS projects. And as learned from this case study, engaging with all stakeholders at the outset of a project will accelerate the delivery of PFS programs to communities with pressing public needs.
George E. Rudebusch is a Fellow in the University of Virginia Pay for Success Lab and is currently pursuing a Master of Public Policy / Juris Doctorate at the University of Virginia.