Podcast — September 26, 2026
The Decisive | Season 7 | Ep.7 - U.S. Midterm Elections: Scenarios, Stakes and Significance
By Kristen Hallam and John Raines
The 2026 US midterm elections will shape control of Congress, influence policymaking at the federal and state levels, and help set the political landscape ahead of the 2028 presidential race.
In this episode of The Decisive, host Kristen Hallam speaks with John Raines, Head of North America Country Risk at S&P Global Market Intelligence, about how scenario-based analysis can provide a more useful view of the elections than headline polling alone. They examine the forces affecting key congressional and gubernatorial races, including the cost of living, energy prices, trade policy and growing debate over AI and data centers.
Kristen and John explore potential outcomes for the House and Senate and what each could mean for the US policy environment. Topics include the outlook for taxation, regulation, permitting, immigration enforcement and government spending, as well as the potential for legislative gridlock, funding disputes and increased congressional oversight. The episode also considers polling limitations, the possibility of delayed or contested results, and the implications for businesses navigating continued political uncertainty.
Listen to learn:
- Why election analysis benefits from scenarios rather than a single forecast
- What makes the 2026 midterms significant for US federal and state policy
- Which economic and policy issues could shape voter decisions
- Why businesses may need to prepare for several possible policy environments
More S&P Global Market Intelligence Content:
- Geopolitical Risk Brief: September 2026
- Picture This: 2026 US Midterm Elections Could Reshape Policy Risk
- US flash PMI signals fastest growth for over five years in September
For S&P Global subscribers (login required):
Credits:
- Host: Kristen Hallam
- Guest: John Raines
- Produced By: Kristen Hallam
- Edited By: Marz Marcello
- Published With Assistance From: Feranmi Adeoshun
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