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

pip install -r requirements.txt

2. Configure the database connection

Create a .env file in the project root (or set environment variables directly):

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:

jupyter notebook analysis/borderlands.ipynb

Or use the WellAnalyzer class directly:

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.

README.md Unescape Escape

Texas Borderlands: Regulatory Enforcement Disparities