ChatGPT said:
Great — I ran a short, evidence-based scenario forecast for how many Republican House seats could suffer (i.e., flip) from a measurable “Trump endorsement drag” in 2026. I’ll (A) show the key sources I used, (B) state the assumptions, (C) give three concrete scenarios with estimated seat losses, and (D) give the practical takeaway and next steps if you want a deeper run.
Sources I used (key load-bearing ones)
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Study showing a Trump endorsement can reduce general-election support for endorsed Republicans. Cambridge University Press & Assessment
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Washington Post / reporting and post-2022 analyses showing Trump endorsements helped win primaries but hurt some general races. The Washington Post
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Cook Political Report 2026 House ratings (counts of Solid / Lean / Toss-Up used as the competitive universe). Cook Political Report
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Forecasting pieces suggesting the GOP could be vulnerable in 2026 (context for baseline national environment). LSE Blogs
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Recent polling showing Trump’s approval among independents and overall is weak — important because endorsement effects depend on his popularity with swing voters. Reuters+1
What I assumed (be explicit so you can judge the numbers)
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Competitive Republican seats = the Cook snapshot I used: 31 Republican-leaning / toss-up seats (18 = Lean/Likely R + 13 = Toss-Up R). Those are the districts where a small swing is most likely to flip a seat. Cook Political Report
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The “Trump endorsement drag” is modeled as a general-election swing against a Trump-endorsed Republican relative to the same candidate without the endorsement. (Empirical work suggests the drag is real but varies by locale; the Cambridge PS study finds a statistically meaningful negative effect in some experimental settings). Cambridge University Press & Assessment
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Because exact seat-level responsiveness differs hugely, I ran three simple, transparent scenarios using plausible flip probabilities for competitive seats under small/medium/large average drag (these flip probabilities are illustrative but grounded in observed election sensitivity — small swings flip some very close races, larger swings flip many more). I’m explicit that these are scenario estimates, not precise forecasts.
Scenarios (conservative, medium, aggressive drag)
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Competitive Republican seats (universe): 31 (Cook’s Lean/Likely R + Toss-Up R). Cook Political Report
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Small drag (≈ −1.5% average effect)
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Assumed chance a competitive R seat flips because of endorsement = 20%.
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Estimated seats lost ≈ 6.2 → ~6 seats.
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Medium drag (≈ −3% average effect)
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Assumed flip chance per competitive R seat = 45%.
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Estimated seats lost ≈ 14.0 → ~14 seats.
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Large drag (≈ −5% average effect)
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Assumed flip chance per competitive R seat = 70%.
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Estimated seats lost ≈ 21.7 → ~22 seats.
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(Computation: estimated_losses = 31 * flip_probability — I kept assumptions explicit so you can change them.)
Why these scenarios make sense: small national swings (1–3 points) have historically decided many close House races; post-2022 analyses showed Trump endorsements cost Republicans in a number of close contests, so the medium scenario maps to that observed mid-range effect. The Washington Post+1
Practical takeaway
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If Trump’s endorsement were to impose even a modest general-election penalty on endorsed Republicans (≈ 1–3 points among general voters), it could cost the GOP half a dozen to a dozen+ House seats in the currently competitive set — potentially enough to flip the majority depending on margins elsewhere. Cook Political Report+1
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The most vulnerable seats are the Lean-and-Toss-Up Republicans listed by Cook and Inside Elections; an endorsement drag concentrates its damage there. Cook Political Report+1
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The size of the effect depends heavily on Trump’s popularity with independents and turnout among Democrats — both of which recent polls suggest are weak for Trump among independents, increasing the risk that his endorsement hurts more than helps. Reuters+1
Limits / caveats (important)
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This is a scenario model, not a full probabilistic simulation with district-level vote distributions. Exact seat outcomes depend on candidate quality, local issues, campaign spending, turnout, and whether the endorsed candidate pivots away from Trump afterward.
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The academic evidence shows the effect exists, but its magnitude varies across districts and cycles; in some safe Republican districts it’s negligible, in swing districts it can be decisive. Cambridge University Press & Assessment+1
Want to dive deeper?
I can immediately (pick one):
A. Run a Monte-Carlo style simulation using Cook’s district ratings and realistic swing distributions to produce a probability distribution of net seat changes under different drag sizes.
B. Produce a short list of the top 10 most at-risk Republican-held seats where a Trump endorsement would be likeliest to flip the race (using Cook / Inside Elections lists).
C. Do a district-level analysis for a specific state or region you care about.
Which one would you like me to run right now? (I’ll use current Cook/Inside Elections ratings and publicly available polling to keep it grounded.)
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