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Datasets:
mliliu
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dime-recipients

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
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parquet
Languages:
English
Size:
100K - 1M
Tags:
political-science
campaign-finance
ideology-scores
elections
united-states
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metadata 
license: other
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - political-science
  - campaign-finance
  - ideology-scores
  - elections
  - united-states
size_categories:
  - 100K<n<1M

DIME Recipients Database with Campaign Finance Ideology Scores

Dataset Description

This dataset contains comprehensive information about political recipients (candidates and committees) in the United States from 1980-2024, including their campaign finance-based ideology scores from the Database on Ideology, Money in Politics, and Elections (DIME).

Key Features

  • 479,502 recipients across 1980-2024
  • Campaign Finance (CF) ideology scores for ideological positioning
  • Multiple office levels: Federal (House, Senate), State, Local
  • Party affiliations with cleaned coding
  • Financial data: Receipts, contributions, expenditures

Dataset Source

  • Original Source: Stanford University - Adam Bonica
  • Website: https://data.stanford.edu/dime
  • Primary Citation: Bonica, Adam. 2014. "Mapping the Ideological Marketplace." American Journal of Political Science 58(2): 367-386.

Dataset Structure

Basic Statistics

  • Total Records: 479,502
  • Unique Recipients: 216,371
  • Time Coverage: 1980-2024
  • Total Receipts: $1,114,933,155,977
  • Individual Contributions: $573,053,829,718

Key Columns

Identifiers

  • bonica.rid: Unique recipient identifier (primary key)
  • name: Recipient name (candidate or committee)
  • bonica.cid: Contributor identifier (for matching with contributions)

Political Information

  • party: Party code (100=Democrat, 200=Republican, 328=Independent)
  • recipient.cfscore: Campaign Finance ideology score (-2 to +2, negative=liberal, positive=conservative)
  • nimsp.office: Office sought (house, senate, state:lower, local:council, etc.)
  • state: State abbreviation
  • district: Congressional district (for House candidates)

Financial Data

  • total.receipts: Total money raised
  • total.indiv.contribs: Individual contribution amounts
  • total.pac.contribs: PAC contribution amounts
  • num.givers: Number of contributors

Additional Scores

  • recipient.cfscore.dyn: Dynamic CF score (time-varying)
  • dwnom1: DW-NOMINATE score (for legislators)
  • composite.score: Composite ideology measure

Data Distribution

Party Distribution

  • I: 173,888 (36.3%)
  • D: 154,373 (32.2%)
  • R: 149,643 (31.2%)
  • O: 1,036 (0.2%)
  • L: 226 (0.0%)

Office Distribution

  • : 169,451
  • house: 115,899
  • senate: 42,089
  • local:other: 16,436
  • local:council: 15,877

CF Score Distribution

  • Mean: 0.171
  • Std Dev: 1.020
  • Range: -6.864 to 6.714
  • Median: 0.074

Usage Examples

Basic Loading 

from datasets import load_dataset
import pandas as pd

# Load full dataset
dataset = load_dataset("mliliu/dime-recipients")
df = dataset['train'].to_pandas()

print(f"Dataset shape: {df.shape}")
print(f"CF scores available: {df['recipient.cfscore'].notna().sum():,}")

Filtering Examples 

# Recent federal candidates only
federal_recent = df[
    (df['cycle'] >= 2016) & 
    (df['nimsp.office'].isin(['house', 'senate'])) &
    (df['recipient.type'] == 'cand')
]

# Major party candidates with CF scores  
major_parties = df[
    df['party'].isin(['100', '200']) &  # Dem/Rep
    df['recipient.cfscore'].notna()
]

# Senate candidates by ideology
senate_liberal = df[
    (df['nimsp.office'] == 'senate') &
    (df['recipient.cfscore'] < -0.5)
]

Ideology Analysis 

import matplotlib.pyplot as plt

# Plot ideology distribution by party
dem_scores = df[df['party'] == '100']['recipient.cfscore'].dropna()
rep_scores = df[df['party'] == '200']['recipient.cfscore'].dropna()

plt.hist(dem_scores, alpha=0.7, label='Democrats', bins=50)
plt.hist(rep_scores, alpha=0.7, label='Republicans', bins=50)
plt.xlabel('CF Score (Liberal ← → Conservative)')
plt.ylabel('Frequency')
plt.legend()
plt.show()

Pre-processed Versions Available

This dataset has been optimized and filtered into several versions:

  • dime_recent.parquet: Records from 2010+ (277,297 rows)
  • dime_federal_candidates.parquet: House + Senate candidates (157,988 rows)
  • dime_house_candidates.parquet: House candidates only (115,899 rows)
  • dime_senate_candidates.parquet: Senate candidates only (42,089 rows)
  • dime_major_parties.parquet: Democrat + Republican only (304,016 rows)

Data Quality Notes

Missing Data Rates

  • cf_score: 0.0% missing
  • party: 0.0% missing
  • state: 0.0% missing
  • district: 0.0% missing

Data Cleaning Applied

  • Party codes standardized (100→D, 200→R, 328→I, etc.)
  • CF scores converted to numeric format
  • Office types extracted from NIMSP data
  • Decade groupings added for temporal analysis

Methodology: Campaign Finance Scores

The CF scores are estimated using a Bradley-Terry model applied to campaign contribution patterns:

  1. Contributors make donations reflecting ideological preferences
  2. Recipients receive donations from ideologically-aligned contributors
  3. Scaling algorithm positions recipients on liberal-conservative dimension
  4. Scores range from -2 (very liberal) to +2 (very conservative)

Key advantages:

  • Covers candidates, PACs, and committees
  • Available for all time periods
  • Not dependent on roll-call votes
  • Captures fundraising-based ideology

Licensing and Citation

Usage Rights

  • ✅ Academic Research: Permitted
  • ❓ Redistribution: Contact original authors
  • ❓ Commercial Use: Requires permission

Required Citation 

@article{bonica2014mapping,
  title={Mapping the ideological marketplace},
  author={Bonica, Adam},
  journal={American Journal of Political Science},
  volume={58},
  number={2},
  pages={367--386},
  year={2014}
}

Additional References

For methodology details:

  • Bonica, Adam. 2016. "Avenues of influence: on the political expenditures of corporations and their directors and executives." Business and Politics 18(4): 367-394.

Technical Details

File Formats

  • Parquet: 37.2 MB (recommended for analysis)
  • CSV.gz: 28.8 MB (human-readable)
  • Sharded: Available for distributed processing

Performance Benchmarks

  • Loading time: ~3-5 seconds for full dataset
  • Memory usage: ~500MB RAM for full dataset in pandas
  • Query performance: Optimized with column indices

Contact

For questions about this dataset preparation:

  • Dataset processing: Created for academic research
  • Original data: Contact Adam Bonica (Stanford)
  • Usage questions: See DIME project documentation

Data card generated on 2025-08-03 Processing pipeline version: 1.0