Last Saturday, the Department of Agriculture announced that they would be cancelling the Household Food Security Report, which until now has provided researchers with high-quality data about food insecurity across the country. This is a significant loss for policy analysts, policymakers, and the public, and it is another example of the current administration electing to allow public data to fall by the wayside. This press release announcing this decision is clear that this was a political decision rather than a policy decision, suggesting that this survey had only been conducted as a political tool.
Plenty has been said by advocates and analysts who work on food insecurity questions about how this data is important to their work. At Scioto Analysis, we don’t have the same level of specialist subject matter knowledge on food insecurity as people who work every day on this topic, so I’d suggest seeking out their thoughts about the direct significance of this change.
Instead, I wanted to talk today about why I think this is a dangerous decision in general, and how we can be more thoughtful about how we talk about policy changes that impact benefit enrollment.
As many others have pointed out, the loss of this survey has come after the Trump administration got work requirements for SNAP benefits passed as part of H.R. 1, the “Big Beautiful Bill” act. We’ve talked before about the impacts of work requirements on Medicaid, and it is not hard to see how work requirements for SNAP would similarly lead to fewer people receiving benefits. It follows that if fewer people receive SNAP benefits, then food insecurity rates might rise. However, we won't know by how much without this survey.
In practice, implementing work requirements has the same impact on enrollment as cutting benefits. Like how tariffs are essentially sales taxes, in their simplest forms we can understand what these policies are and what their goals are, and most importantly what the tradeoffs are in implementing these policies. Looking at these work requirements through this lens, we can ask the straightforward economic question “do the benefits of cutting SNAP benefits outweigh the costs?”
We can pretty easily understand what the benefits of cutting this program are: it reduces government spending, which we know creates drag on the economy. However, we now have lost information about what the costs of these cuts are going to end up being.
Advocates who work every day in this space might be able to collect their own data, or we might come up with estimation methods that are good enough to get an idea of what the costs of these cuts are, but it is undeniable that the quality of our information has just gone down significantly without our top federal source of information about food insecurity.
Not only do we lose our ability to fully understand the impacts of a timely federal policy change, analysts will no longer be able to study the impacts of other policy changes across the country.
Earlier this week, I was speaking to a group of students in a local MBA program about the future of work. Our conversation was focused almost exclusively on how access to data has been increasing over the years and how using all of this information in an intelligent way was going to be critical to their success going forward.
One student asked me near the end about how we answer questions in situations where we don’t have good data (in particular, she was interested in the lack of self-reported wellbeing data in the U.S.). I told her that this is one of the biggest roadblocks that analysts face.
We can collect new primary data, though this is often resource-intensive. We can also use proxy variables, which are related, measurable variables that can serve as an indirect stand-in for the variable of interest. Another option is to use imputation techniques to estimate and fill in missing values, or to shift to qualitative analysis like interviews or case studies, though these methods don’t allow us to fully understand the picture. Ultimately though, quantitative analysis is dependent on data.
When we have less information we end up making worse decisions on average. Removing a key data source because it might make one policy look bad is shortsighted. Policy analysis is not about deciding whether or not policy is good or bad, it is about understanding outcomes and informing future decisions. This decision makes everyone’s jobs more difficult.