Integrity Score Methodology
The Integrity Score is a composite metric designed to measure the transparency and independence of elected officials in relation to campaign finance. It ranges from 0 to 100 and is computed from six sub-metrics, each capturing a different dimension of financial accountability. This page documents exactly how each sub-metric works, where the data comes from, and how the final score is calculated.
Donor-Vote Alignment (30%)
Measures how closely an official's voting record aligns with the interests of their top donors. A high score indicates that voting behavior is not systematically correlated with donor industry preferences.
Data Sources
Congressional voting records, FEC contribution data, CRP industry classifications, and bill-industry mapping from legislative analysis.
How It's Computed
For each vote, we identify industries with a financial stake in the outcome. We then compare the official's vote to the aggregate position of their top donors in that industry. The metric is the percentage of votes where the official voted against the majority position of their largest donor industries. Higher percentages indicate greater independence from donor influence.
Small-Dollar Ratio (20%)
Tracks the proportion of an official's total fundraising that comes from small-dollar individual contributions (under $200). A higher ratio suggests broader grassroots support rather than reliance on large donors or PACs.
Data Sources
FEC individual contribution records and committee summary filings for the current election cycle.
How It's Computed
Total small-dollar contributions (individual donations under $200, including unitemized contributions reported in committee summaries) divided by total contributions received. The resulting ratio is scaled to a 0-100 score, with 100 representing a campaign funded entirely by small-dollar donors.
Disclosure Compliance (15%)
Evaluates the completeness and timeliness of an official's financial disclosures. Officials who file on time and provide detailed, accurate reports earn higher scores.
Data Sources
FEC filing records, congressional financial disclosure reports, and filing deadline compliance data.
How It's Computed
Points are assigned based on: on-time filing of FEC reports (40%), completeness of donor employer/occupation data in itemized receipts (30%), timely filing of personal financial disclosures (20%), and whether any amendments were required due to errors (10%). The final score reflects a weighted sum of these four sub-components.
Revolving Door Index (15%)
Assesses the extent to which an official's staff and former colleagues have moved between government service and lobbying or private-sector positions in related industries. A high score indicates fewer revolving-door connections.
Data Sources
Lobbying disclosure filings (Senate Office of Public Records), congressional staff directories, and public employment records.
How It's Computed
We track the number of former staffers who registered as lobbyists within two years of leaving the official's office, weighted by how recently the transition occurred and the relevance of their lobbying clients to the official's committee assignments. The score is inversely proportional to the revolving-door activity: fewer transitions yield a higher score.
Leadership PAC Transparency (10%)
Evaluates how transparently an official operates their leadership PAC. Some leadership PACs are used to fund personal expenses, support allies, or channel money in ways that reduce public accountability.
Data Sources
FEC committee filings for leadership PACs, disbursement records, and connected organization data.
How It's Computed
We analyze leadership PAC disbursements for: the ratio of candidate contributions to total spending (higher is better), the percentage of spending on overhead vs. direct political activity, and the presence of payments to family members or personal businesses. Each factor is weighted and combined into a composite transparency score.
Outside Income & Assets (10%)
Examines potential conflicts of interest arising from an official's personal financial holdings, outside income, and business relationships. A high score indicates fewer apparent conflicts between personal financial interests and legislative responsibilities.
Data Sources
Annual congressional financial disclosure reports, which include assets, income, liabilities, transactions, and positions held outside of Congress.
How It's Computed
We cross-reference an official's reported financial holdings and outside income sources with their committee assignments and voting record. Potential conflicts are identified when an official holds significant assets in industries directly affected by their legislative work. The score is reduced proportionally to the number and severity of identified conflicts.
Weighted Scoring Formula
The overall Integrity Score is a weighted average of the six sub-metrics. Each sub-metric produces a score from 0 to 100, which is then multiplied by its weight:
Integrity Score =
(Donor-Vote Alignment × 0.30)
+ (Small-Dollar Ratio × 0.20)
+ (Disclosure Compliance × 0.15)
+ (Revolving Door Index × 0.15)
+ (Leadership PAC Transparency × 0.10)
+ (Outside Income & Assets × 0.10)
The weights reflect the relative importance of each factor in assessing financial accountability. Donor-Vote Alignment receives the highest weight because it most directly measures whether campaign contributions appear to influence legislative behavior. The weights are subject to review and may be adjusted as the methodology evolves.
Seed Data Limitations
The current Integrity Scores are computed from seed data and should be treated as illustrative rather than definitive. As the project matures and more complete data pipelines are established, scores will become more accurate and comprehensive. In particular, the Revolving Door Index and Outside Income & Assets metrics rely on data sources that are not yet fully automated. We are actively working to improve data coverage and update frequency.
Transparency & Open Source
We believe the methodology behind any accountability score should itself be transparent. The scoring algorithms, data processing pipelines, and this documentation are all open source and available for public review.
View source on GitHub