Steering US Credit Programs Toward Data-Driven Performance Management

I recently had the opportunity to speak at an event to discuss a highly informative new report on US federal lending, authored by Tom Stanton, Alan Rhinesmith, and Mike Easterly, focusing on the importance of borrower outcomes rather than simple measures of loan volume.¹

Three primary thoughts occurred to me as I considered the report’s findings.

1. There’s a clear need for evidence-based credit program performance management.
2. As the world’s largest financial institution, it is imperative the US government better coordinate its lending activities.
3. It’s great to focus on outcomes, but there’s quite a bit of space to explore and fill between originations and outcomes.

First, it’s helpful to consider performance management in the context of the input, output, outcome, and impact rubric of the Government Performance and Results Act of 1993. A number of other statutes enacted during the 1990s (such as the Federal Credit Reform Act and Chief Financial Officers Act) collectively placed substantial emphasis on budgetary inputs and the proper stewardship of taxpayer dollars.

For discretionary credit programs, focus on budgeting inputs (along with always-tight appropriations allocations) began a race to the bottom by credit agencies in terms of subsidy rates. The lower the rate, the greater the volume permitted. And with loan volume as the primary output in the rubric, loan production has increasingly become a key objective for some agencies.

Over time, technology and process improvements have resulted in better data being collected and available to analysis by agencies. This has better positioned agencies to assess program outcomes in a more data-driven and rigorous way.

And outcomes – such as whether a borrower has repaid a loan – needs to extend into broader impacts, questions like:

• Was a borrower made better off through the provision of a loan?
• To what extent have societal benefits been generated?
• Has the private credit marketplace been strengthened by learning more about a particular market?

All of this information – from inputs to impacts – forms the basis for robust benefit/cost analyses that is the centerpiece of any informed public policy debate.

Second, to the extent we think of the US government as the world’s largest financial institution, it surely has the largest lending division on the planet. And there are myriad challenges in considering federal credit as a single system.

Clearly, opportunities for shared services exist (for example, predictive modeling, loan performance analytics, servicing, and asset disposition, to name a few) but we need to avoid one-size-fits-all solutions. The report is instructive in describing the disparate public policy justifications for credit initiatives, and assessing program effectiveness is highly dependent on those reasons.

Being mindful of those differences leads us to impacts – for example:

• If attempting to fill a market gap, has the market adjusted or resolved the failure?
• If directed to a particular industry sector, was it strengthened?
• If trying to resolve resource allocation challenges, has society benefited?

All too often, there’s a tendency to search for omnipotent solutions, but the report stresses the multi-faceted approaches needed in the realm of federal credit. There are no simple solutions for the world’s largest financial institution.

Third, key activities take place in the space between originations and outcomes. Much is happening in this space as, for instance, loan servicers have morphed into technology firms, some of which are highly entrepreneurial. They are using ever-more plentiful data in new and creative ways. Federal agencies would be wise to get plugged in to these developments.

At the Golub Center, we’ve begun to explore ways to improve borrower outcomes through the use of data analytics. We’re seeking to address questions like how best to tailor support to borrowers and develop data-driven, dynamic models to get borrowers on the right path and to keep them there. The notion of bringing a more quantitative and data-rich view of borrowers can help to enable successful outcomes.

Relatedly, we’ve also been considering how public policy challenges are defined in terms of credit assistance programs. If defined incorrectly, it can lead to misinformed policy where even the types of data collected and analytics performed can be askew. The policy narrative needs to reflect the fact that needs of borrowers and taxpayers are aligned.

While staff operating government loan programs should be passionate about their agency’s mission, they must remain dispassionate about the tools being used to accomplish that mission. The huge volume of credit now outstanding puts both borrowers and taxpayers at risk, so federal credit agencies need to be focused on outcomes and impacts to determine whether the benefits of a program – or even an individual loan – is ultimately worth the cost.

¹ Stanton, Thomas H., Alan B. Rhinesmith, and Michael E. Easterly, “Federal Credit Programs, Borrower Outcomes Matter More Than Volume,” May 2017. http://thomas-stanton.com/wp-content/uploads/2017/06/Federal-Credit-Programs-borrower-outcomes-as-a-focus.pdf