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Corporate Lobbying Dataset

Track corporate lobbying activity from US Senate Lobbying Disclosure Act (LDA) filings. See which companies spend on lobbying, which firms represent them, what policy issues they target, and which government entities they engage.

What’s Included

Section titled “What’s Included”

The Corporate Lobbying dataset provides:

  • Filing Details: Date, filing year, quarter, and unique filing identifier
  • Registrant Information: Lobbying firm name and client company
  • Financial Data: Income and expenses reported per filing
  • Issue Codes: Policy areas lobbied on (e.g., TAX, TRD, COM)
  • Government Entities: Bodies engaged (e.g., Senate, House)

Coverage

Section titled “Coverage”
Source Description Update Frequency
US Senate LDA Lobbying Disclosure Act filings Quarterly

Corporate lobbying data has 15+ years of historical filings available for long-horizon backtesting and historical analysis.

Quick Start

Section titled “Quick Start”
from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


df = fb.corporate_lobbying.ticker("AAPL", as_dataframe=True)
print(df)

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Key Fields

Section titled “Key Fields”
Field Description Example
clientName Company being represented Apple Inc.
registrantName Lobbying firm hired Lobbying Firm LLC
income Income reported by registrant (USD) 50000
expenses Expenses reported by registrant (USD) 75000
issueCodes Policy areas lobbied on [“TAX”, “TRD”, “COM”]
governmentEntities Government bodies engaged [“Senate”, “House”]
quarter Filing quarter Q3
filingYear Filing year 2025

Common Issue Codes

Section titled “Common Issue Codes”
Code Policy Area
TAX Taxation/Internal Revenue Code
TRD Trade (Domestic and Foreign)
COM Communications/Broadcasting/Radio/TV
CPT Computer Industry
ENV Environmental/Superfund
HCR Health Issues
DEF Defense
FIN Financial Institutions/Investments/Securities
IMM Immigration
TEC Telecommunications

Use Cases

Section titled “Use Cases”

Top Lobbying Spenders

Section titled “Top Lobbying Spenders”

Scan a list of tickers to find companies with the highest lobbying spend:

from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


def scan_lobbying_spend(symbols):
    """Find companies with the highest lobbying spend"""
    results = []


    for symbol in symbols:
        try:
            df = fb.corporate_lobbying.ticker(symbol, as_dataframe=True)


            total_spend = df["income"].sum() + df["expenses"].sum()
            results.append({
                "symbol": symbol,
                "filings": len(df),
                "total_spend": total_spend
            })
        except Exception:
            continue


    return sorted(results, key=lambda x: x["total_spend"], reverse=True)


tech_symbols = ["AAPL", "MSFT", "GOOGL", "AMZN", "META"]
spenders = scan_lobbying_spend(tech_symbols)


for s in spenders:
    print(f"{s['symbol']}: {s['filings']} filings, ${s['total_spend']:,.0f} total spend")

Quarterly Spend Trend

Section titled “Quarterly Spend Trend”

Track how a company’s lobbying spend changes quarter over quarter:

from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


def quarterly_lobbying_trend(symbol):
    """Analyze lobbying spend by quarter"""
    df = fb.corporate_lobbying.ticker(symbol, as_dataframe=True)


    df["period"] = df["filingYear"].astype(str) + "-" + df["quarter"]
    df["total_spend"] = df["income"] + df["expenses"]


    quarterly = df.groupby("period").agg(
        filings=("total_spend", "count"),
        total_spend=("total_spend", "sum"),
        registrants=("registrantName", "nunique")
    ).sort_index()


    return quarterly


trend = quarterly_lobbying_trend("AAPL")
for period, row in trend.iterrows():
    print(f"{period}: ${row['total_spend']:,.0f} across {row['registrants']} firms "
          f"({row['filings']} filings)")

Issue Code Analysis

Section titled “Issue Code Analysis”

Discover which policy areas a company focuses its lobbying efforts on:

from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


def analyze_issue_codes(symbol):
    """Break down lobbying by policy area"""
    df = fb.corporate_lobbying.ticker(symbol, as_dataframe=True)


    # Explode issueCodes list into individual rows
    exploded = df.explode("issueCodes")


    issue_counts = exploded.groupby("issueCodes").agg(
        filings=("issueCodes", "count"),
        total_income=("income", "sum")
    ).sort_values("filings", ascending=False)


    return issue_counts


issues = analyze_issue_codes("AAPL")
for code, row in issues.iterrows():
    print(f"{code}: {row['filings']} filings, ${row['total_income']:,.0f} income")
Section titled “Related Resources”