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US Bill Proposes Ban on Lawmakers Betting on Their Own Elections

Cointelegraph · Turner Wright

North Carolina Representative Don Davis has introduced legislation to prevent politicians and their immediate relatives from betting on their own election outcomes using prediction market platforms. The proposed No Betting on Your Own Race Act aims to combat insider trading and conflicts of interest, imposing fines to curb lawmakers from profiting financially off their election campaigns.

Key Provisions of the Bill

The No Betting on Your Own Race Act, introduced by Don Davis, prohibits federal candidates, their campaigns, spouses, and children from "buying, selling, acquiring, disposing of, or holding contracts" related to their own election outcomes. The proposed legislation includes a civil penalty of $10,000 for each violation or triple any financial gain obtained. While the text doesn't explicitly name prediction market platforms such as Kalshi and Polymarket, it references "political event contracts," indicating the rules are aimed at activities on such platforms.

Context and Future Outlook

In August, Republican House candidate Laurie Buckhout faced a three-year suspension and a $2,590 penalty on the Kalshi platform over trading contracts linked to her election race, but she did not face criminal or civil charges. Davis’ bill is not expected to be addressed before the 2026 midterm elections due to the congressional recess; both House and Senate will be out of session until November, with only some pro forma House sessions occurring. Meanwhile, event contracts on US elections remain available on Kalshi and Polymarket, offering favorable odds on Democrats reclaiming Congress in 2027.

Why it matters

Passing this bill could close a loophole that allows politicians to exploit insider information and influence prediction markets for personal gain during elections. As platforms trading political event contracts gain popularity, such restrictions promote transparency and public trust in the electoral process and legislative institutions.

Prepared from the source material with AI-assisted editing and checked against the supplied facts.

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