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# Texas Borderlands: Regulatory Enforcement Disparities
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An empirical research project examining whether oil and gas regulatory enforcement in Texas differs systematically between border-proximate and interior districts — and whether a 2019 disclosure reform produced heterogeneous effects across those regions.
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## Research Questions
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- **RQ1:** Do border-exposed Texas Railroad Commission (RRC) districts differ from non-border districts in inspection intensity, violation detection, enforcement timing, and resolution rates?
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- **RQ2:** Did the 2019 disclosure reform change enforcement outcomes differently in border districts versus non-border districts?
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## Key Findings
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| Outcome | Border Districts | Non-Border Districts |
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|--------|-----------------|---------------------|
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| Inspections per well | 1.329 | 1.515 |
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| Violations per inspection | 0.130 | 0.098 |
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| Days to enforcement | 145.2 | 122.8 |
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| Resolution rate | 0.543 | 0.596 |
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**Post-2019 reform effect:** Enforcement processing time in border districts improved by **~75 days** (p=0.016) relative to non-border districts — but inspection reach and resolution rates did not converge. Conclusion: *faster pipeline, not wider pipeline*.
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## Project Structure
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```
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texas-borderlands/
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├── analysis/
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│ ├── borderlands.ipynb # Main analysis notebook
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│ ├── well_analyzer.py # WellAnalyzer class (data loading + metrics)
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│ └── output_borderlands/
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│ ├── rq1_results.csv # RQ1 regression results
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│ ├── rq2_results.csv # RQ2 FE interaction results
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│ ├── district_year_panel_borderlands.csv
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│ ├── border_vs_nonborder_trends.png
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│ ├── money_plot_timing_border_prepost2019.png
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│ ├── well_border_exposure_map.png
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│ ├── continuous_exposure_results.csv
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│ ├── cutoff_sensitivity_results.csv
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│ └── border_type_split_results.csv
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│
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├── data/
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│ ├── oil_gas_basin_shape/ # EIA TX shale basin boundaries
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│ ├── shale_play_shape/ # EIA TX shale play delineations
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│ ├── texas_county_shape/ # US Census TX county subdivisions (2025)
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│ ├── texmex_shape/ # US Census TX-MX international boundary (2023)
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│ ├── competition_panel.csv
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│ └── district_competitor_links.csv
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│
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├── intro_thoery_methods_analysis_results_discussion.md # Full paper draft
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├── appendix.md # Supplementary tables and robustness checks
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└── requirements.txt
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```
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## Tech Stack
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- **Python 3**
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- **pandas / numpy** — data manipulation and panel construction
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- **sqlalchemy / psycopg2** — PostgreSQL database access
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- **geopandas / shapely** — geospatial analysis and border proximity measurement
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- **scipy / statsmodels** — regression models (OLS, fixed effects)
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- **libpysal / esda** — spatial econometrics
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- **matplotlib / seaborn** — visualization
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- **python-dotenv** — environment configuration
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## Setup
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### 1. Install dependencies
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```bash
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pip install -r requirements.txt
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```
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### 2. Configure the database connection
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Create a `.env` file in the project root (or set environment variables directly):
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```env
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PGHOST=localhost
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PGPORT=5432
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PGUSER=postgres
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PGPASSWORD=your_password
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PGDATABASE=texas_data
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```
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The database should have PostGIS enabled and contain the following tables:
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| Table | Description |
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|-------|-------------|
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| `well_shape_tract` (or similar) | Wells with location and demographic enrichment |
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| `inspections` | Inspection records with dates and district info |
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| `violations` | Violation records with enforcement and resolution dates |
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The `WellAnalyzer` class auto-detects the wells table name from a set of known aliases.
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### 3. Run the analysis
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Open and run the Jupyter notebook:
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```bash
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jupyter notebook analysis/borderlands.ipynb
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```
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Or use the `WellAnalyzer` class directly:
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```python
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from analysis.well_analyzer import WellAnalyzer
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analyzer = WellAnalyzer()
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analyzer.print_analysis()
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analyzer.export_analysis("output.json")
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```
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## Data
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### Primary Data (PostgreSQL)
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- **~1.01M wells** with geospatial coordinates and demographic/census tract enrichment
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- **~1.87M inspections** (2015–2025)
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- **~191.7K violations** (2015–2025)
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- **District-year panel:** 143 observations (13 RRC districts × 11 years)
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### Shapefiles
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| File | Source | Purpose |
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|------|--------|---------|
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| `texmex_shape/` | US Census Bureau (2023) | TX-MX border geometry for proximity calculations |
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| `texas_county_shape/` | US Census Bureau (2025) | State and county boundaries |
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| `oil_gas_basin_shape/` | US EIA | Texas shale basin delineations |
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| `shale_play_shape/` | US EIA | Texas shale play delineations |
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### Border Exposure Definitions
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- **District-level:** Binary — district centroid or wells within 50 km of any state/international border
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- **Well-level:** Binary flags at 25 km and 50 km buffers from TX-Mexico border
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- **Border subtypes:** TX-MX, TX-NM, TX-OK, TX-LA
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Border-exposed wells (50 km buffer): **169,520** of 1,010,432 total.
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## Empirical Design
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**Unit of analysis:** Texas RRC district × year (2015–2025)
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**Outcome variables:**
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- Inspection intensity (inspections per well)
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- Violation rate (violations per inspection)
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- Days to enforcement action
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- Resolution rate (compliance on reinspection)
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**RQ1 — Levels model:**
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```
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Y_{dt} = α + β·Border_d + γ·X_{dt} + ε_{dt}
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```
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**RQ2 — Fixed effects interaction model:**
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```
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Y_{dt} = α_d + δ_t + β·(Post2019_t × Border_d) + γ·X_{dt} + ε_{dt}
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```
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**Robustness checks:** Border-type splitting, continuous exposure shares, cutoff sensitivity (25/75/100 km thresholds).
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## Documentation
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- `intro_thoery_methods_analysis_results_discussion.md` — full paper draft covering theory, methods, results, and discussion
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- `appendix.md` — supplementary regression tables, robustness checks, and district profiles
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## License
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This project is for academic research purposes. Underlying data sources are public records from the Texas Railroad Commission and US federal agencies.
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