FEC Political Contributions

Track political donations and their relationship to nonprofit leadership, policy decisions, and grant awards.

Overview

The Federal Election Commission (FEC) provides comprehensive data on political contributions. Integrating this with your nonprofit and grant data reveals important connections between:

Nonprofit leadership and political engagement
Political donations and federal grant awards
Policy advocacy and campaign finance
Jurisdiction politics and oral health funding

API Documentation: https://api.open.fec.gov/developers/

What You Get

FEC Data (NEW)

✅ Individual Contributions:

Contributor name, address, employer
Contribution amount and date
Recipient (candidate or committee)
Employer information (links to nonprofits!)

✅ Candidate Information:

Office (House, Senate, President)
Party affiliation
State and district
Election cycle

✅ Committee Finances:

PAC and Super PAC spending
Committee receipts and disbursements
Independent expenditures

Combined with Your Data

FEC Contributions     Your Nonprofit Data     IRS 990 / Grants.gov
─────────────────     ───────────────────     ───────────────────
John Smith      ────> Board Member at    ────> Received $2M
$5,000 to Sen X       MA Dental Clinic        federal grant
2024-06-15            Executive Director      2024-09-01
                      Salary: $150k

Use Cases

1. Track Politically Active Nonprofit Leaders

Find which nonprofit executives and board members make political donations:

from discovery.fec_integration import OpenFECAPI, PoliticalContributionMatcher

# Initialize API (get free key at https://api.data.gov/signup/)
api = OpenFECAPI(api_key="your_api_key_here")

# Load nonprofit officers (from IRS 990 data)
import pandas as pd
officers = pd.read_parquet("data/gold/states/MA/nonprofits_officers.parquet")

# Find their political contributions
matcher = PoliticalContributionMatcher(api)
contributions = matcher.find_nonprofit_leadership_contributions(
    officers_df=officers,
    state_code="MA",
    min_amount=200,
    election_cycle="2024"
)

print(f"Found {len(contributions):,} political contributions")
print(f"Total donated: ${contributions['contribution_amount'].sum():,.2f}")

2. Analyze Political Influence on Grant Awards

Compare political donations with federal grant awards:

# Load grants data
grants = pd.read_parquet("data/gold/states/MA/grants_revenue_sources.parquet")

# Analyze influence patterns
influence_analysis = matcher.analyze_political_influence(
    contributions_df=contributions,
    grants_df=grants
)

# Which nonprofits have politically active leadership AND received grants?
influential_orgs = influence_analysis[
    (influence_analysis['total_political_donations'] > 1000) &
    (influence_analysis['total_grants_received'] > 100000)
]

3. Map Donor Networks in Health Policy

Track which health policy advocates donate to which campaigns:

# Search for contributions from health sector
health_contributions = api.search_individual_contributions(
    contributor_employer="Health",
    contributor_state="MA",
    min_amount=1000,
    min_date="2024-01-01"
)

# Identify patterns
# - Which candidates get health sector donations?
# - Do dental/oral health professionals favor certain parties?
# - How does this correlate with oral health legislation?

API Access

Get Your Free API Key

Sign up at: https://api.data.gov/signup/
Receive API key via email (instant)
Use in API calls (see examples below)

Note: DEMO_KEY is available for testing but has strict limits (30 requests/hour)

Search Individual Contributions

api = OpenFECAPI(api_key="your_key")

# Search by contributor name
results = api.search_individual_contributions(
    contributor_name="John Smith",
    contributor_state="MA",
    min_amount=200
)

# Search by employer (find nonprofit employee donations)
results = api.search_individual_contributions(
    contributor_employer="Community Health Center",
    min_amount=500
)

# Search by amount and date range
results = api.search_individual_contributions(
    contributor_state="MA",
    min_amount=1000,
    max_amount=5000,
    min_date="2024-01-01",
    max_date="2024-12-31"
)

Search Candidates

# Find Massachusetts candidates
candidates = api.search_candidates(
    state="MA",
    office="S",  # Senate
    party="DEM",
    cycle=2024
)

# Find all House candidates
house_reps = api.search_candidates(
    state="MA",
    office="H",  # House
    cycle=2024
)

Data Schema

Contribution Fields

Field	Description	Example
`contributor_name`	Individual donor name	John Smith
`contributor_city`	City	Boston
`contributor_state`	State	MA
`contributor_employer`	Employer name	MA Dental Clinic
`contributor_occupation`	Job title	Executive Director
`contribution_receipt_amount`	Donation amount	2500.00
`contribution_receipt_date`	Date of contribution	2024-06-15
`committee_name`	Recipient committee	Smith for Senate
`candidate_name`	Candidate (if applicable)	Jane Doe

Candidate Fields

Field	Description	Example
`candidate_id`	FEC candidate ID	H0MA05123
`name`	Candidate name	Jane Doe
`office`	Office type	H, S, P
`state`	State	MA
`district`	Congressional district	05
`party`	Party affiliation	DEM, REP
`cycles`	Election cycles	[2024, 2022]

Bulk Data Download

For comprehensive historical analysis, download bulk files:

from discovery.fec_integration import FECBulkDataLoader

loader = FECBulkDataLoader()

# Download 2024 cycle data (WARNING: Large file ~3GB)
zip_file = loader.download_individual_contributions(cycle="2024")

# Parse with filters to reduce memory usage
contributions_df = loader.parse_individual_contributions(
    zip_path=zip_file,
    state_filter="MA",  # Only Massachusetts
    employer_filter="Health",  # Health sector only
    min_amount=200  # Significant contributions only
)

Warning: Bulk files are 1-5 GB. Use filters or the API for smaller queries.

Integration with Your Data

Complete Political-Financial Picture

┌─────────────────────────────────────────────────────────────┐
│                     YOUR DATA ECOSYSTEM                     │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  FEC Contributions          Nonprofit Data       Grants     │
│  ─────────────────          ──────────────       ──────     │
│  John Smith           ────> Board Member    <──── $2M       │
│  $5k to Sen X                MA Dental           Federal    │
│  2024-06-15                  Exec Director        Grant     │
│                              Salary: $150k        2024-09   │
│                                                             │
│  Voter Data                 Jurisdiction         Meetings   │
│  ───────────                ────────────         ────────   │
│  District: D+15       ────> Boston, MA     <──── Minutes    │
│  Dem majority                90,000 pop           discussing│
│  High turnout                                    fluoride  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Analysis Questions You Can Answer

Political Influence:
- Do nonprofits with politically active leaders receive more federal grants?
- What's the timeline from donation → grant award?
- Which party's donors get more oral health funding?
Advocacy Networks:
- Which dental health advocates donate to which candidates?
- Do oral health nonprofits coordinate political giving?
- How does political engagement correlate with policy success?
Jurisdiction Politics:
- Do Democratic or Republican areas get more oral health grants?
- Does legislator party affiliation affect local fluoridation votes?
- Political polarization of oral health policy?
Follow the Money:
- Corporate → Political donations → Policy decisions
- Nonprofit leaders → Campaign contributions → Federal grants
- Special interest influence on public health policy

Example Analysis

Which MA Health Nonprofits Have Political Connections?

import pandas as pd
from discovery.fec_integration import OpenFECAPI, PoliticalContributionMatcher

# 1. Load your data
nonprofits = pd.read_parquet("data/gold/states/MA/nonprofits_organizations.parquet")
officers = pd.read_parquet("data/gold/states/MA/nonprofits_officers.parquet")
grants = pd.read_parquet("data/gold/states/MA/grants_revenue_sources.parquet")

# 2. Find political contributions from nonprofit leaders
api = OpenFECAPI(api_key="your_key")
matcher = PoliticalContributionMatcher(api)

contributions = matcher.find_nonprofit_leadership_contributions(
    officers_df=officers,
    state_code="MA",
    min_amount=200,
    election_cycle="2024"
)

# 3. Analyze influence
influence = matcher.analyze_political_influence(
    contributions_df=contributions,
    grants_df=grants
)

# 4. Results
print(f"Nonprofits with political donations: {len(influence):,}")
print(f"Total donated: ${influence['total_political_donations'].sum():,.2f}")
print(f"Total grants received: ${influence['total_grants_received'].sum():,.2f}")
print(f"ROI ratio: {influence['total_grants_received'].sum() / influence['total_political_donations'].sum():.1f}x")

Rate Limiting

OpenFEC API limits:

API Key	Rate Limit
`DEMO_KEY`	30 requests/hour
Registered key	1,000 requests/hour

Our implementation includes automatic rate limiting (0.2s between requests).

Privacy & Ethics

Important Considerations:

Public Data: FEC contributions are public record
Correlation ≠ Causation: Political donations don't prove influence
Context Matters: Contributions may be personal, not institutional
Transparency: Be clear about limitations in your analysis
Fairness: Present data without partisan bias

Best Practices:

Focus on patterns, not individual actors
Provide full context for findings
Acknowledge limitations of correlation analysis
Use for transparency, not to imply wrongdoing

Next Steps

Get API Key: https://api.data.gov/signup/

Test Integration:

python discovery/fec_integration.py --api-key YOUR_KEY --state MA --employer "Health"

Match to Your Data:
- Link FEC contributions to nonprofit officers
- Compare with grant awards
- Analyze political patterns
Build Dashboard:
- "Political Connections" widget
- Transparency reporting
- Influence analysis visualizations

Grants.gov API: Federal grant opportunities
IRS Form 990 (your existing data): Nonprofit finances
Voter Data: Jurisdiction political demographics
OpenStates: State legislator information

Additional Resources

OpenFEC API Documentation
FEC Bulk Data Downloads
FEC File Format Documentation
OpenSecrets.org: Campaign finance research
FollowTheMoney.org: State-level money tracking

Overview​

What You Get​

FEC Data (NEW)​

Combined with Your Data​

Use Cases​

1. Track Politically Active Nonprofit Leaders​

2. Analyze Political Influence on Grant Awards​

3. Map Donor Networks in Health Policy​

API Access​

Get Your Free API Key​

Search Individual Contributions​

Search Candidates​

Data Schema​

Contribution Fields​

Candidate Fields​

Bulk Data Download​

Integration with Your Data​

Complete Political-Financial Picture​

Analysis Questions You Can Answer​

Example Analysis​

Which MA Health Nonprofits Have Political Connections?​

Rate Limiting​

Privacy & Ethics​

Next Steps​

Related Integrations​

Additional Resources​