---
license: other
license_name: public-domain
task_categories:
- tabular-classification
- tabular-regression
tags:
- executive-compensation
- sec-edgar
- esg
- governance
- quant
- finance
- proxy-statements
- united-states
pretty_name: SEC EDGAR Executive Compensation 2015-Present
size_categories:
- 100K<n<1M
---

# SEC EDGAR Executive Compensation 2015–Present

500K+ structured executive compensation records for S&P 500 + Russell 2000 companies — parsed from SEC EDGAR DEF 14A proxy filings, 2015–present.
CEO/CFO pay, stock awards, bonuses, non-equity incentives, and total compensation, linked by CIK, ticker, and fiscal year.
The most comprehensive open dataset for executive pay research, ESG governance scoring, and quant finance.

| 📊 Records | 📅 Coverage | 🏷️ License | 🔄 Updated |
|-----------|-------------|-----------|-----------|
| 500K+ compensation records | 2015–present | Public Domain | Annual |

**This repo contains a free 1,000-row sample.**
Full dataset (CSV + Parquet) → **[claritystorm.com/datasets/sec-edgar-exec-comp](https://claritystorm.com/datasets/sec-edgar-exec-comp)**

---

## Quick Start

```python
from datasets import load_dataset
import pandas as pd

# Load the 1,000-row sample
ds = load_dataset("claritystorm/sec-edgar-exec-comp")
df = ds["train"].to_pandas()

# CEO compensation trend by fiscal year
ceo = df[df["is_ceo"] == True]
print(ceo.groupby("fiscal_year")["total_compensation"].mean().round(0))

# Top 10 highest-paid CEOs in latest year
latest_year = ceo["fiscal_year"].max()
top_ceos = (ceo[ceo["fiscal_year"] == latest_year]
            [["company_name", "executive_name", "total_compensation"]]
            .sort_values("total_compensation", ascending=False)
            .head(10))
print(top_ceos)

# Pay mix: stock awards vs. salary
df["stock_pct"] = df["stock_awards"] / df["total_compensation"].replace(0, pd.NA)
print(df.groupby("fiscal_year")["stock_pct"].mean().round(3))
```

## Use Cases

- **ESG governance scoring** — CEO-to-median-worker pay ratios, pay-for-performance alignment, and board compensation governance
- **Quant factor research** — executive pay as an alpha signal; overpaid vs. underpaid CEO portfolios
- **Corporate governance AI** — train models to flag anomalous compensation structures or governance red flags
- **Pay equity research** — compare total compensation across sectors, company sizes, and fiscal years
- **Proxy advisor analytics** — replicate and extend institutional proxy voting research at scale
- **Financial NLP** — link to DEF 14A proxy text for compensation discussion and analysis (CD&A) NLP

## Schema (selected fields)

| Field | Type | Description |
|-------|------|-------------|
| cik | string | SEC Central Index Key (unique company identifier) |
| ticker | string | Stock ticker symbol |
| company_name | string | Company name as filed with SEC |
| fiscal_year | int | Fiscal year of the compensation disclosure |
| date_filed | string | Proxy filing date (YYYY-MM-DD) |
| executive_name | string | Executive's full name (normalized) |
| title | string | Executive's title/position |
| is_ceo | bool | True if title indicates Chief Executive Officer |
| is_cfo | bool | True if title indicates Chief Financial Officer |
| salary | float | Base salary ($) |
| bonus | float | Discretionary bonus ($) |
| stock_awards | float | Grant-date fair value of stock awards ($) |
| option_awards | float | Grant-date fair value of option awards ($) |
| non_equity_incentive | float | Non-equity incentive plan compensation ($) |
| total_compensation | float | Total reported compensation as filed ($) |
| total_comp_computed | float | ClarityStorm computed total (sum of components) |
| sector | string | Industry sector (where available) |

## ⬇️ Get the Full Dataset

| Tier | Price | Includes |
|------|-------|----------|
| Sample | Free | 1,000 rows, Public Domain (this repo) |
| Complete | $149 | Full 500K+ rows, CSV + Parquet, commercial license |
| Annual | $299/yr | Complete + annual updates (new proxy season each year) |

👉 **[Purchase at claritystorm.com/datasets/sec-edgar-exec-comp](https://claritystorm.com/datasets/sec-edgar-exec-comp)**

## Source

U.S. Securities and Exchange Commission (SEC), EDGAR — DEF 14A Proxy Statements.
SEC EDGAR data is a US federal government work in the **public domain** (17 U.S.C. 105).
Executive compensation for Named Executive Officers (NEOs) is required by SEC Regulation S-K Item 402.

Processed and structured by [ClarityStorm Data](https://claritystorm.com).