All candidates
3.69
90% 3.56–3.95
Politics Research · Campaign Finance
Forty-five years of congressional money
A broader donor base predicts different results for challengers and incumbents
We modeled 17,284 candidates in 8,642 contested House and Senate races from 1980 to 2024. Holding total fundraising fixed, a broader base of contributing organizations was associated with higher vote share for challengers and lower vote share for incumbents. We tested the model on elections held out of training.
What is being changed
For each candidate, we doubled the number of contributing organizations in the model while holding total dollars, the opponent, and all other inputs fixed. The figures below show how much the predicted vote share changed.
Challengers
+1.11
points of vote share
90% range +0.67 to +1.35
Incumbents
−0.48
points of vote share
90% range −0.94 to −0.12
Total fundraising held fixed; number of contributing organizations doubled.
Fundraising variables
Six fundraising changes modeled
The model uses 56 features. We classify 27 as factors a campaign can influence; the rest include seat history, the opponent, election timing, and incumbency. Bars show the change in expected vote share, with whiskers marking the 90% range.
Change in expected vote share, by lever
Everything else held exactly where it was, including the opponent
Challengers
Incumbents
Raising more money is the only tested change associated with higher vote share in every candidate group. The donor-breadth estimate is positive for challengers and negative for incumbents.
The money
Predicted vote share as fundraising increases
We varied each campaign's fundraising while holding other inputs fixed. Within the range represented in the data, higher fundraising predicted higher vote share. The size of the difference depended on incumbency and the amount already raised.
Expected vote share against money raised
1× is what the campaign actually raised
Open seats
Challengers
Incumbents
Open-seat candidates show the largest predicted gains throughout the curve. The range supported by the data is roughly half to twice the original fundraising amount; estimates beyond that range are illustrative.
What doubling the money is worth, by how much they had
Change in expected vote share, in points, split by what campaigns actually raised
The predicted gain from doubling fundraising is twice as large at $36,000 as at $590,000. It is largest in the lowest fundraising group, falls in the middle groups, and rises in the highest group.
Accuracy
How well the final model predicts
The model uses all 56 features, with training data from 1980–2014 and testing data from the 2016 and 2018 elections. The figures below show average error in vote-share points. Lower values indicate better predictions.
Incumbents
3.40
90% 3.32–3.72
Challengers
3.59
90% 3.38–3.93
Open seats
4.91
90% 4.35–5.63
Points of vote share off, on elections held out of training. For scale: knowing only the seat and who holds it, with no money at all, misses by 5.69 points. Actual vote shares in the test set run from 29% to 71% between the 10th and 90th percentile.
Sources
Election returns and campaign-finance records
The analysis uses publicly available election returns and FEC filings, with the sources and coverage listed below.
| Source | What it gave us | Coverage |
|---|---|---|
| MIT Election LabU.S. House returns | Votes per candidate per district | 1976–2018435 seats a cycle, complete |
| MIT Election LabU.S. Senate returns | Votes per candidate per state | 1976–2024complete |
| Wikipedia election boxes | House returns where MIT stops | 2022, 2024248 and 315 districts of 435 |
| FEC bulkCandidate master | Who ran, party, seat, incumbency, main committee | 1980–202464,133 candidacies |
| FEC bulkCandidate summary | Total raised, party money, self-funding, small-donor money | 1980–202466,007 candidate-cycles |
| FEC bulkCommittee contributions | PAC checks, outside support and attacks, with dates | 1980–20248.0 million transactions |
| FEC bulkIndividual contributions | Donations by size, count and timing | 1980–2024291 million transactions |
House results for 2020 do not exist in any source we hold, and 2022 and 2024 are roughly half covered. Those cycles are excluded from testing for that reason.
The full table
All modeled changes by candidate group
Change in expected vote share, in points, with the 90% range beneath. A figure is white where that whole range sits on one side of zero, and grey where the range still includes it.
| Lever | Challengers | Incumbents | Open seats |
|---|
Model details
Model specification and features
- What it predicts
- A candidate’s share of the vote against the other major party. A number like 53.2, not win or lose.
- Method
- Gradient-boosted trees, 400 rounds, learning rate 0.05.
- Trained on
- 14,506 candidates, 1980–2014
- Tested on
- 1,634 candidates in 2016 and 2018, never seen during training
- Error bars
- 40 refits, resampling whole races — never candidates, whose shares add to 100
- Excluded by design
- Calendar years and dates are excluded. With only about twenty elections in the data, the earlier model overfit each year's national conditions and performed poorly on new years. Timing enters as days before that race's election day, a measure comparable across cycles.
| Feature | What it is | Can a campaign change it? |
|---|---|---|
| The seat and who holds it — 4 features | ||
| prior_share | This party’s share of the seat at the last election | Fixed |
| incumbent | This candidate currently holds the seat | Fixed |
| open_seat | Nobody is defending the seat | Fixed |
| prior_contested | Whether last time’s race had both parties on the ballot | Fixed |
| The candidate, the seat’s drift, the calendar — 14 features | ||
| prior_runs | Times this person has been on a general election ballot before | Fixed |
| prior_wins | Times they have won one | Fixed |
| prior_best_share prior_mean_share | Their best and their average result in past races | Fixed |
| prior_share_2 | How the seat voted two elections ago | Fixed |
| seat_trend | The change between those two results: which way the seat is drifting | Fixed |
| seat_volatility seat_mean_share | How much the seat swings, and its long-run average | Fixed |
| rematch prior_pair_share | Whether these same two people have met before, and the result when they did | Fixed |
| is_midterm with_president midterm_exposed | Whether it is a midterm, whether the candidate shares the sitting president’s party, and both at once — the midterm penalty | Fixed |
| senate | A Senate seat rather than a House one | Fixed |
| How much money — 4 features | ||
| log_ttl_receipts | Everything the campaign raised | Can change |
| receipts_share | Their slice of all the money in the race | Can change |
| log_money_in_race log_opp_given_to | The size of the race, and what the opponent took in | Fixed |
| What kind of money — 14 features | ||
| log_given_to given_to_share | Checks written straight to the campaign by committees | Can change |
| log_outside_for log_outside_against log_opponent_attacked helped_me_share hurt_me_share | Money spent independently to support them, to attack them, and to attack their opponent. A campaign is forbidden to coordinate with any of it. | Fixed |
| log_party_money | What party committees gave | Can change |
| log_coordinated | Party spending arranged with the campaign | Can change |
| log_self_funded self_share | The candidate’s own money, gifts and loans together | Can change |
| log_unitemized unitemized_share | Donations under $200, which never appear as individual records and are recovered by subtraction | Can change |
| small_share_of_indiv | How much of their individual money came in small gifts | Can change |
| Individual donors — 5 features | ||
| log_indiv_total | All money from people, itemized | Can change |
| log_indiv_small log_indiv_large | Gifts under $500, and gifts of $2,000 or more | Can change |
| log_indiv_gifts log_avg_gift | How many donations arrived, and the average size of one | Can change |
| When the money arrived — 8 features | ||
| given_early_share given_late_share given_final_share | Share of committee money arriving over a year out, in the last six months, and in the final three weeks | Can change |
| given_mean_days_out | The dollar-weighted average number of days before election day | Can change |
| for_final_share against_final_share for_mean_days_out against_mean_days_out | The same timing for outside spending, in both directions | Fixed |
| Who sent it — 7 features | ||
| log_n_givers | How many separate organizations gave. The breadth measure, and the second most important feature in the model. | Can change |
| top_giver_share top5_share | What share came from the single largest backer, and from the largest five | Can change |
| from_small_share from_huge_share | Share from organizations that gave under $50k across all candidates that cycle, and from those that gave over $5m | Can change |
| log_pac_total log_mean_gift_from_org | Total committee money, and the average size of one organization’s support | Can change |
27 of the 56 are decisions. 29 are not. Everything marked fixed is settled before a campaign opens its office, or is money a campaign is legally barred from directing.
Limitations
These estimates describe associations. Candidates with many donors may have broader support for reasons the model cannot observe. The estimates do not show how a candidate's vote share would change if the campaign recruited more donors.
Earlier fundraising is associated with lower vote share in each group where the estimate is distinguishable from zero. Late donations may reflect rising support for a candidate. This design cannot separate that possibility from the effect of fundraising timing, so the finding does not support delaying fundraising.
The fundraising totals cover the full two-year cycle, including money received after election day. That portion accounts for 1.6% to 2.7% of the total. Its inclusion limits the model's use as a forecast based only on information available before the election.
Civly Fundraising
Broaden the base you already have
This analysis combines forty-five years of federal campaign-finance records with election returns. Civly uses these records for donor lists, prospecting, and compliance checks, including finding organizations that have supported similar candidates.
Civly Politics Research · prepared August 16, 2026. Sources: the MIT Election Data and Science Lab’s U.S. House and Senate returns, Wikipedia election boxes for the two House cycles MIT does not yet cover, and the Federal Election Commission’s bulk candidate master, candidate summary, committee-contribution and individual-contribution files. All sources are publicly available.
The panel covers contested U.S. House and Senate general elections from 1980 to 2024: 17,284 candidates in 8,642 races. The model is trained on 1980–2014 and every accuracy figure is scored on 2016 and 2018, held out entirely. Ranges are the 5th to 95th percentile across 40 refits that resample whole races. Each lever is measured by changing that one input for every real candidate and re-predicting, holding the rest of the race — including the opponent — exactly where it was.
Findings describe association in observed data. Nothing here establishes that any fundraising decision caused any outcome, and a campaign that already attracts many separate backers differs from one that does not in ways this model cannot observe. Not legal, compliance, or investment advice.