Document athletics in README, About limitations, data dictionary

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01X9jG4CUFcH6n4vFvgtwndY
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emfurst 2026-10-17 11:40:48 -04:00
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# Admin Analytics
University of Delaware administrative cost benchmarking using public data (IRS 990, IPEDS, BLS CPI-U). Ingests data into a local DuckDB database and serves an interactive Dash dashboard for analysis.
University of Delaware administrative cost benchmarking using public data (IRS 990, IPEDS, EADA, BLS CPI-U). Ingests data into a local DuckDB database and serves an interactive Dash dashboard for analysis.
## Scope
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- **Executive compensation** from IRS 990 Schedule J filings by the University of Delaware (EIN 516000297) and UD Research Foundation (EIN 516017306)
- **Administrative cost ratios** from IPEDS finance surveys (expenses by function, staffing levels, enrollment)
- **Endowment performance** and **philanthropic giving** from IPEDS F2 (FASB) financial data
- **Intercollegiate athletics** spending from Equity in Athletics Disclosure Act (EADA) filings, set against university-wide spending and athletics leadership compensation
- **Administrative headcount** via web scraping, currently focused on the **College of Engineering line management** (COE Central, department offices) and the Provost's Office
### Changing the target institution
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- `UD_UNITID = 130943` -- IPEDS institution identifier. Change this to target a different institution. Look up UNITIDs at the [IPEDS Data Center](https://nces.ed.gov/ipeds/use-the-data).
- `UD_EINS = [516000297, 516017306]` -- IRS Employer Identification Numbers for 990 filings. Update these to the EINs of the target institution's nonprofit entities.
All IPEDS loaders accept a `unitid_filter` parameter. The scraper URLs in `src/admin_analytics/scraper/directory.py` are UD-specific and would need to be updated for a different institution.
All IPEDS and EADA loaders accept a `unitid_filter` parameter (EADA is keyed by the same UNITID). The scraper URLs in `src/admin_analytics/scraper/directory.py` are UD-specific and would need to be updated for a different institution.
Multi-institution comparisons (AAU peers, Carnegie peers) are planned for a future phase.
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Load data from public sources into the local DuckDB database (`data/admin_analytics.duckdb`).
```bash
# Ingest everything (IPEDS + IRS 990 + CPI + scraper)
# Ingest everything (IPEDS + IRS 990 + CPI + EADA + scraper)
uv run admin-analytics ingest all
# Or ingest individual sources
uv run admin-analytics ingest ipeds --year-range 2005-2024
uv run admin-analytics ingest irs990 --year-range 2019-2025
uv run admin-analytics ingest cpi
uv run admin-analytics ingest eada --year-range 2007-2025
uv run admin-analytics ingest scrape
```
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The dashboard must be restarted to pick up newly ingested data (DuckDB opens in read-only mode to avoid lock conflicts).
The dashboard has seven tabs:
The dashboard has eight tabs:
- **Executive Compensation** -- top earners from IRS 990 Schedule J, President and top-10 CAGR, trends by role, compensation breakdown by component, growth vs CPI-U (2015-2023)
- **Admin Cost Overview** -- admin cost ratios, expense breakdown by function, cost per student, admin-to-faculty ratio (IPEDS data, 2005-2024)
- **Staffing & Enrollment** -- staff composition, student-to-staff ratios, management vs faculty vs enrollment growth (indexed)
- **Endowment** -- endowment value trends, CAGR, investment return rate, CIO compensation vs endowment growth (IPEDS F2)
- **Philanthropy** -- total private gifts and grants, gift allocation, President and VP Development compensation growth vs fundraising (IPEDS F2 and IRS 990)
- **Athletics** -- athletics expenses by football, basketball, other sports, and spending not allocated by sport (FY2007-2025, FBS transition marked); growth vs university and instruction spending; cost per full-time undergraduate; share of university expenses; head coach and athletic director compensation (EADA, IPEDS, IRS 990)
- **Current Headcount** -- scraped UD staff directory data with overhead/non-overhead classification by unit
- **About** -- data sources, methodology, and limitations
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ipeds/ # IPEDS download, parsing, loading
irs990/ # IRS 990 XML download, parsing, title normalization
bls/ # BLS CPI-U download and loading
eada/ # EADA athletics download and loading
scraper/ # UD staff directory scraper and classifier
dashboard/ # Dash app, queries, page layouts
validation.py # Data validation queries
@ -118,5 +122,6 @@ tests/ # pytest test suite
|--------|-----------------|-------|
| [IPEDS](https://nces.ed.gov/ipeds/) | Institutional directory, expenses by function, staffing, enrollment | 2005-2024 |
| [IRS 990 e-file](https://www.irs.gov/charities-non-profits/form-990-series-downloads) | UD Foundation filings, executive compensation (Schedule J) | 2019-2025 index years (tax years 2017-2023) |
| [EADA](https://ope.ed.gov/athletics/) | Athletics expenses by sport, participants, coaching salaries, full-time undergraduate enrollment | FY2007-2025 (academic years 2006-07 to 2024-25) |
| [BLS CPI-U](https://www.bls.gov/cpi/) | Consumer Price Index for inflation adjustment | Full history |
| UD staff directories | Admin office headcounts and overhead classification | Current snapshot |