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+# Texas Borderlands: Regulatory Enforcement Disparities
+
+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.
+
+## Research Questions
+
+- **RQ1:** Do border-exposed Texas Railroad Commission (RRC) districts differ from non-border districts in inspection intensity, violation detection, enforcement timing, and resolution rates?
+- **RQ2:** Did the 2019 disclosure reform change enforcement outcomes differently in border districts versus non-border districts?
+
+## Key Findings
+
+| Outcome | Border Districts | Non-Border Districts |
+|--------|-----------------|---------------------|
+| Inspections per well | 1.329 | 1.515 |
+| Violations per inspection | 0.130 | 0.098 |
+| Days to enforcement | 145.2 | 122.8 |
+| Resolution rate | 0.543 | 0.596 |
+
+**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*.
+
+## Project Structure
+
+```
+texas-borderlands/
+├── analysis/
+│   ├── borderlands.ipynb                   # Main analysis notebook
+│   ├── well_analyzer.py                    # WellAnalyzer class (data loading + metrics)
+│   └── output_borderlands/
+│       ├── rq1_results.csv                 # RQ1 regression results
+│       ├── rq2_results.csv                 # RQ2 FE interaction results
+│       ├── district_year_panel_borderlands.csv
+│       ├── border_vs_nonborder_trends.png
+│       ├── money_plot_timing_border_prepost2019.png
+│       ├── well_border_exposure_map.png
+│       ├── continuous_exposure_results.csv
+│       ├── cutoff_sensitivity_results.csv
+│       └── border_type_split_results.csv
+│
+├── data/
+│   ├── oil_gas_basin_shape/                # EIA TX shale basin boundaries
+│   ├── shale_play_shape/                   # EIA TX shale play delineations
+│   ├── texas_county_shape/                 # US Census TX county subdivisions (2025)
+│   ├── texmex_shape/                       # US Census TX-MX international boundary (2023)
+│   ├── competition_panel.csv
+│   └── district_competitor_links.csv
+│
+├── intro_thoery_methods_analysis_results_discussion.md  # Full paper draft
+├── appendix.md                             # Supplementary tables and robustness checks
+└── requirements.txt
+```
+
+## Tech Stack
+
+- **Python 3**
+- **pandas / numpy** — data manipulation and panel construction
+- **sqlalchemy / psycopg2** — PostgreSQL database access
+- **geopandas / shapely** — geospatial analysis and border proximity measurement
+- **scipy / statsmodels** — regression models (OLS, fixed effects)
+- **libpysal / esda** — spatial econometrics
+- **matplotlib / seaborn** — visualization
+- **python-dotenv** — environment configuration
+
+## Setup
+
+### 1. Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Configure the database connection
+
+Create a `.env` file in the project root (or set environment variables directly):
+
+```env
+PGHOST=localhost
+PGPORT=5432
+PGUSER=postgres
+PGPASSWORD=your_password
+PGDATABASE=texas_data
+```
+
+The database should have PostGIS enabled and contain the following tables:
+
+| Table | Description |
+|-------|-------------|
+| `well_shape_tract` (or similar) | Wells with location and demographic enrichment |
+| `inspections` | Inspection records with dates and district info |
+| `violations` | Violation records with enforcement and resolution dates |
+
+The `WellAnalyzer` class auto-detects the wells table name from a set of known aliases.
+
+### 3. Run the analysis
+
+Open and run the Jupyter notebook:
+
+```bash
+jupyter notebook analysis/borderlands.ipynb
+```
+
+Or use the `WellAnalyzer` class directly:
+
+```python
+from analysis.well_analyzer import WellAnalyzer
+
+analyzer = WellAnalyzer()
+analyzer.print_analysis()
+analyzer.export_analysis("output.json")
+```
+
+## Data
+
+### Primary Data (PostgreSQL)
+
+- **~1.01M wells** with geospatial coordinates and demographic/census tract enrichment
+- **~1.87M inspections** (2015–2025)
+- **~191.7K violations** (2015–2025)
+- **District-year panel:** 143 observations (13 RRC districts × 11 years)
+
+### Shapefiles
+
+| File | Source | Purpose |
+|------|--------|---------|
+| `texmex_shape/` | US Census Bureau (2023) | TX-MX border geometry for proximity calculations |
+| `texas_county_shape/` | US Census Bureau (2025) | State and county boundaries |
+| `oil_gas_basin_shape/` | US EIA | Texas shale basin delineations |
+| `shale_play_shape/` | US EIA | Texas shale play delineations |
+
+### Border Exposure Definitions
+
+- **District-level:** Binary — district centroid or wells within 50 km of any state/international border
+- **Well-level:** Binary flags at 25 km and 50 km buffers from TX-Mexico border
+- **Border subtypes:** TX-MX, TX-NM, TX-OK, TX-LA
+
+Border-exposed wells (50 km buffer): **169,520** of 1,010,432 total.
+
+## Empirical Design
+
+**Unit of analysis:** Texas RRC district × year (2015–2025)
+
+**Outcome variables:**
+- Inspection intensity (inspections per well)
+- Violation rate (violations per inspection)
+- Days to enforcement action
+- Resolution rate (compliance on reinspection)
+
+**RQ1 — Levels model:**
+```
+Y_{dt} = α + β·Border_d + γ·X_{dt} + ε_{dt}
+```
+
+**RQ2 — Fixed effects interaction model:**
+```
+Y_{dt} = α_d + δ_t + β·(Post2019_t × Border_d) + γ·X_{dt} + ε_{dt}
+```
+
+**Robustness checks:** Border-type splitting, continuous exposure shares, cutoff sensitivity (25/75/100 km thresholds).
+
+## Documentation
+
+- `intro_thoery_methods_analysis_results_discussion.md` — full paper draft covering theory, methods, results, and discussion
+- `appendix.md` — supplementary regression tables, robustness checks, and district profiles
+
+## License
+
+This project is for academic research purposes. Underlying data sources are public records from the Texas Railroad Commission and US federal agencies.