How Should We Tackle The National Debt? Notes from our August 12, 2025 meeting

 

The Fairfax Alliance hosted an interactive exercise at our August 12 meeting considering the federal budget deficit using: Debt Fixer | Committee for a Responsible Federal Budget.  The primary exercise was to examine common-ground approaches to reaching consensus, with a side-benefit of learning about key components of the federal budget and the degree to which recent proposed policy changes might affect the deficit and debt.  The Debt Fixer app prompts users to choose from some 90 policy options with the objective to reduce the deficit to 100% of GDP.  For this Fairfax Alliance application, a team of Reds and a team of Blues each developed an “advance team” proposal.  Those proposals were merged before the in-person meeting to select only those policy options both teams agreed upon, a combination which achieved about two-thirds of the deficit reduction objective.  The meeting attendees were divided into two self-selected breakout groups (i.e., Reds and Blues mixed randomly) and given the challenge to choose policies to close the remaining gap.  One caveat: both groups had to agree to choose two policies that only the Red advance team picked as well as two policies that only the Blue advance team picked.

 The breakout groups prompted lively discussion and some good-natured horse-trading.  Both groups achieved the Debt Fixer deficit-reduction goal and gained appreciation for the relative magnitude of different federal budget policy proposals.  Perhaps the primary point of consensus was that the pop-up explanations of policies in the Debt Fixer app were helpful, but left many wanting more information.  Additional areas of consensus related to the import of good data and analysis, particularly when the policy topics are complex, inter-related, and continually changing.  For instance, when considering behavioral incentives (such as taxes on unhealthy behavior) how much of the intended behavioral response is accounted for in revenue estimates?  The exercise reinforced for many the complexity of our legal and social ecosystems and the degree to which data-driven disagreements among experts are essential to examining all sides of an issue and providing effectively vetted guidance for decisionmakers. 

The Fairfax Alliance hosted an interactive exercise at our August 12 meeting considering the federal budget deficit using: Debt Fixer | Committee for a Responsible Federal Budget.  The primary exercise was to examine common-ground approaches to reaching consensus, with a side-benefit of learning about key components of the federal budget and the degree to which recent proposed policy changes might affect the deficit and debt.  The Debt Fixer app prompts users to choose from some 90 policy options with the objective to reduce the deficit to 100% of GDP.  For this Fairfax Alliance application, a team of Reds and a team of Blues each developed an “advance team” proposal.  Those proposals were merged before the in-person meeting to select only those policy options both teams agreed upon, a combination which achieved about two-thirds of the deficit reduction objective.  The meeting attendees were divided into two self-selected breakout groups (i.e., Reds and Blues mixed randomly) and given the challenge to choose policies to close the remaining gap.  One caveat: both groups had to agree to choose two policies that only the Red advance team picked as well as two policies that only the Blue advance team picked.

The breakout groups prompted lively discussion and some good-natured horse-trading.  Both groups achieved the Debt Fixer deficit-reduction goal and gained appreciation for the relative magnitude of different federal budget policy proposals.  Perhaps the primary point of consensus was that the pop-up explanations of policies in the Debt Fixer app were helpful, but left many wanting more information.  Additional areas of consensus related to the import of good data and analysis, particularly when the policy topics are complex, inter-related, and continually changing.  For instance, when considering behavioral incentives (such as taxes on unhealthy behavior) how much of the intended behavioral response is accounted for in revenue estimates?  The exercise reinforced for many the complexity of our legal and social ecosystems and the degree to which data-driven disagreements among experts are essential to examining all sides of an issue and providing effectively vetted guidance for decisionmakers.