6.3 KiB
Texas District Analysis: Regulatory Transparency and Enforcement in the Oil & Gas Industry
A research project examining how transparency disclosure reforms affect enforcement behavior in the Texas Railroad Commission (RRC), with a focus on district-level heterogeneity across 13 RRC regulatory districts from 2015–2025.
Research Overview
Core question: Does making well-level violation data publicly searchable change how quickly the RRC acts on violations?
The January 2019 RRC policy change — making well violation data publicly searchable — serves as the exogenous policy shock. The analysis tests whether and how this disclosure reform altered enforcement timing and compliance outcomes across districts, with particular attention to offshore-regulating districts (02, 03, 04) and structural moderators like basin composition, enforcement capacity, and environmental justice dimensions.
Key findings:
- No immediate post-2019 level shift in enforcement timing (coef=0.1514, p=0.33)
- Significant post-2019 trend acceleration: enforcement speed improves gradually over time (coef=−0.3603, p=0.001)
- Offshore-regulating districts show differential post-policy response (coef=0.3819, p<0.001), strongest in 2023–2024
- Basin composition is the clearest structural correlate of district-level heterogeneity
Data
All raw data originates from the Texas Railroad Commission and supplementary government sources:
| Source | Description | Size |
|---|---|---|
| Texas RRC | ~3.6M inspection records (pipe-delimited) | 424 MB |
| Texas RRC | ~368K violation records (pipe-delimited) | 66 MB |
| U.S. Census | Poverty rates and demographics by census tract | — |
| USDA RUCA (2020) | Rural-Urban Commuting Area classifications | 25 MB |
| USEIA | Shale basin and play shapefiles | — |
| Texas county shapefiles | County boundaries for spatial visualization | — |
Data covers approximately 1.01 million wells, 1.87 million inspections, and 191K violations within the 2015–2025 study window.
Note: Raw data files are large (several hundred MB each) and are excluded from version control via .gitignore. The data pipeline is fully documented in the rebuild/ notebooks.
Repository Structure
texas-district-analysis/
├── analysis/
│ ├── well_analyzer.py # Core analysis engine (PostgreSQL → metrics)
│ ├── updated_district_level_analysis_2015-2025_offshore_controls.ipynb # Main analysis notebook
│ ├── draft.md # Manuscript draft
│ ├── draft_appendix.md # Technical appendix with model specifications
│ ├── *.png # Figures (event study, district maps, etc.)
│ └── archive/ # Earlier notebook versions and alternate specs
├── data/
│ ├── INSPECTIONS.txt # Raw inspection records (pipe-delimited)
│ ├── VIOLATIONS.txt # Raw violation records (pipe-delimited)
│ ├── RUCA-codes-2020-tract.csv # RUCA classification by census tract
│ ├── district_by_county.csv # District–county crosswalk
│ └── {oil_gas_basin,shale_play,texas_county,texmex}_shape/ # ESRI shapefiles
├── rebuild/
│ ├── rrc_api_data.ipynb # Step 1: Fetch and process RRC API data
│ ├── create_violations_inspections.ipynb # Step 2: Build cleaned inspection/violation files
│ ├── add_census_data.ipynb # Step 3: Link census demographics
│ ├── add_shape_layers.ipynb # Step 4: Spatial feature engineering
│ ├── well_shape.ipynb # Step 5: Well geometry and shapefile creation
│ └── well-api-manual.pdf # RRC API technical documentation
├── papers/ # Manuscript versions (DOCX + PDF)
├── analysis_output.json # Pre-computed summary statistics
└── requirements.txt # Python dependencies
Analysis Pipeline
Raw RRC Data (API)
↓ rebuild/rrc_api_data.ipynb
Cleaned Inspections & Violations CSVs
↓ rebuild/create_violations_inspections.ipynb
Link Census Demographics
↓ rebuild/add_census_data.ipynb
Add Geographic Layers
↓ rebuild/add_shape_layers.ipynb
PostgreSQL Data Warehouse
↓ analysis/well_analyzer.py
District-Year Panel
↓ analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb
Econometric Models → Figures → Manuscript
Econometric Models
| Model | Description |
|---|---|
| 1 | Interrupted time-series (all districts pooled) |
| 2 | District-specific post-policy fixed effects |
| 3 | Offshore jurisdiction moderator (districts 02/03/04) |
| 4 | Spatial autocorrelation diagnostics (Moran's I, 5,000 permutations) |
| 5 | Structural moderators: capacity, baseline compliance, EJ, geology, rurality, border proximity |
Setup
Prerequisites
- Python 3.9+
- PostgreSQL (with PostGIS for spatial queries)
- The
well_analyzer.pymodule reads database credentials from environment variables
Install dependencies
pip install -r requirements.txt
Database configuration
Set the following environment variables before running analysis:
export PGHOST=localhost
export PGPORT=5432
export PGUSER=your_user
export PGPASSWORD=your_password
export PGDATABASE=texas_data
Run the data pipeline
Execute the notebooks in rebuild/ in order (steps 1–5) to populate the PostgreSQL database, then open analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb for the main analysis.
Key Statistics (2015–2025)
- 1,878,764 inspections across 420,185 unique wells
- Overall compliance rate: 89.9% (up from 88.4% in 2015 to 92.9% in 2024)
- 193,338 violations across 81,670 unique wells
- Mean days from violation discovery to enforcement action: 127 (median: 14)
- Compliance on re-inspection: 57.2%
- District compliance range: 81.2% (District 09) to 94.4% (District 8A)
Dependencies
pandas
numpy
sqlalchemy
psycopg2
scipy
statsmodels
matplotlib
seaborn
geopandas
shapely
libpysal
esda