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

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

Source	Description	Update Frequency
US Senate LDA	Lobbying Disclosure Act filings	Quarterly

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

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

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech/v2"

response = requests.get(
    f"{BASE_URL}/lobbying/AAPL",
    headers={"Authorization": f"Bearer {API_KEY}"}
)

data = response.json()
for filing in data["data"]["filings"]:
    print(f"{filing['date']} Q{filing['quarter']}: {filing['registrantName']} "
          f"- ${filing['income']:,.0f} income, ${filing['expenses']:,.0f} expenses")

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

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

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

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

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

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")

Corporate Lobbying API Reference - Endpoint details, parameters, and response schema
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