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Stocks Weekly Government Contracts Compensation
Weekly disclosures of highly compensated officers at federal contract recipients mapped to tickers.
91,801 rows over 1,263 symbols, 15 columns, covering 2007-09-01 to 2026-03-01. The most recent observation is 2026-03-01.
Why It Matters
This dataset surfaces governance and contract-flow signals for government vendors by:
- Governance risk: Sudden changes in named officer compensation can flag governance shifts or compliance issues.
- Contract dependency: Officer disclosures often correlate with contract size and renewal risk for government vendors.
- Entity linkage: Mapping to public tickers enables alternative flow signals beyond traditional fundamentals.
Load It
Installation/Upgrade:
pip install --upgrade pwb-toolbox
Load the Dataset:
from pwb_toolbox import datasets as pwb_ds
df = pwb_ds.load_dataset("Stocks-Weekly-GovernmentContractsCompensation", symbols=["BA"])
print(df.iloc[0, :])
Example Output:
symbol BA
datetime 2025-11-01 00:00:00
recipient_uei P7SFAJZ6M9J6
recipient_name FRONTIER TECHNOLOGY INC
recipient_parent_name FRONTIER TECHNOLOGY INC
highly_compensated_officer_1_name RONALD D SHRODER
highly_compensated_officer_1_amount 1040191.0
highly_compensated_officer_2_name JOSE L HIDALGO
highly_compensated_officer_2_amount 792442.0
highly_compensated_officer_3_name JONATHAN M EHRLICH
highly_compensated_officer_3_amount 645433.0
highly_compensated_officer_4_name TIMOTHY M HINDS
highly_compensated_officer_4_amount 413336.0
highly_compensated_officer_5_name RUDY A FERNANDEZ
highly_compensated_officer_5_amount 330000.0
Columns
| Column Name | Description |
|---|---|
| symbol | Public company ticker linked to the contract recipient. |
| datetime | Week-ending snapshot date (YYYY-MM-DD). |
| recipient_uei | Unique entity identifier for the recipient. |
| recipient_name | Name of the government contract recipient. |
| recipient_parent_name | Parent entity name for the recipient. |
| highly_compensated_officer_1_name | Name of the highest compensated officer reported. |
| highly_compensated_officer_1_amount | Compensation for officer 1. |
| highly_compensated_officer_2_name | Name of the second highest compensated officer. |
| highly_compensated_officer_2_amount | Compensation for officer 2. |
| highly_compensated_officer_3_name | Name of the third highest compensated officer. |
| highly_compensated_officer_3_amount | Compensation for officer 3. |
| highly_compensated_officer_4_name | Name of the fourth highest compensated officer. |
| highly_compensated_officer_4_amount | Compensation for officer 4. |
| highly_compensated_officer_5_name | Name of the fifth highest compensated officer. |
| highly_compensated_officer_5_amount | Compensation for officer 5. |
Access
Browsing the card and the schema is open to anyone. Downloading the files needs an approved request, tied to a subscription: what each plan includes. The same subscription covers the other datasets in this organisation.
Elsewhere
- Dataset page and coverage charts
- The strategy catalogue, 3,806 papers and 4,837 replicated strategies
pwb-toolbox, the loader used in the snippet aboveawesome-systematic-trading, the replicated strategies with their measured Sharpe- Every dataset in this organisation
Papers With Backtest publishes 32 datasets on the Hub and codes the papers that use them. Every strategy in the catalogue is run over its own full history before it is published, which is where the numbers above come from.
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