texas-borderlands/intro_thoery_methods_analysis_results_discussion.md

Introduction

Regulatory enforcement in borderlands jurisdictions is often expected to differ from interior jurisdictions due to administrative constraints, multi-jurisdictional exposure, and monitoring frictions. This manuscript analyzes Texas Railroad Commission district-year outcomes (2015-2025) to assess whether border-exposed districts show systematic enforcement gaps and whether those gaps changed after the 2019 disclosure reform.

The empirical design centers on two research questions from the notebook:

1. RQ1 (Border gaps): Do border-exposed Texas districts differ from non-border districts in enforcement intensity and pipeline outcomes?
2. RQ2 (Disclosure heterogeneity): Did the 2019 disclosure reform change enforcement outcomes differently in border districts versus non-border districts (level shift and post-policy trend differential)?

Theory

We use a borderlands governance framing with two linked mechanisms: capacity asymmetry and transparency-throughput effects. The corresponding hypotheses are:

1. H1 (Border inspection gap): Border districts have lower inspection intensity than non-border districts.
2. H2 (Border pipeline disadvantage): Border districts show weaker enforcement pipeline outcomes (higher violations per inspection and/or slower timing and/or lower resolution rates).
3. H3 (Disclosure heterogeneity in levels): Post-2019 level shifts differ between border and non-border districts (post_2019:border).
4. H4 (Disclosure heterogeneity in trends): Post-2019 trend shifts differ between border and non-border districts (post_trend:border).

This yields a core empirical claim: post-2019 border effects should be strongest in enforcement timing rather than in inspection coverage or resolution outcomes.

Methods

Data and Unit of Analysis

• Unit: district-year.
• Coverage: 13 Texas RRC districts, 2015-2025.
• Source tables: well_shape_tract, inspections, violations.
• Sample in current run: 1,010,432 wells; 1,867,859 inspections; 191,762 violations; 143 district-year observations.

Border Measurement: District Coding and Well Proximity

We use two complementary border constructions.

1. District-level baseline treatment (border_district): districts in the predefined border-adjacent set (01, 02, 06, 08, 8A, 09, 10) are coded 1; others are coded 0.
2. Well-level proximity treatment: each well is classified by spatial proximity to border segments, then rolled up to district-year exposure shares.

Well-level proximity was constructed from latitude/longitude and shapefiles as follows:

1. Texas-Mexico distance/flags from WellAnalyzer (within_25km_texmex, within_50km_texmex).
2. Additional state-border segments (TX-NM, TX-OK, TX-LA) built from Texas county boundary geometry and seed lines.
3. Distances computed in projected CRS (EPSG:5070), then threshold flags generated at 25 km and 50 km.
4. Composite exposure indicators created:
   • within_50km_state_border_any
   • well_border_exposed (1 if within 50 km of TX-MX or any TX-state border segment).

District-year well-proximity exposure is measured as:

ShareBorder_{dt} = \frac{BorderExposedInspections_{dt}}{Inspections_{dt}}

and an alternative district treatment is defined as border_exposure_district = 1 when ShareBorder_{dt} \ge 0.25.

Outcomes

InspectionIntensity_{dt} = \frac{Inspections_{dt}}{UniqueWells_{dt}}

ViolPerInsp_{dt} = \frac{Violations_{dt}}{Inspections_{dt}}

DaysToEnf_{dt} = \frac{1}{N_{dt}} \sum_{i=1}^{N_{dt}} (EnforcementDate_i - ViolationDiscoveryDate_i)

ResolutionRate_{dt} = \frac{CompliantOnReinspection_{dt}}{Violations_{dt}}

Exposure Definitions

• Baseline treatment: border_district (binary district border status).
• Additional robustness exposures:

1. Border-type indicators (TX-MX, TX-NM, TX-OK, TX-LA)
2. Continuous exposure share:

ShareBorder_{dt} = \frac{BorderExposedInspections_{dt}}{Inspections_{dt}}

3. Cutoff sensitivity with 25/75/100 km thresholds.

Estimating Equations

RQ1 levels:

Y_{dt} = \alpha + \beta_1 Border_d + \beta_2 \log(UniqueWells_{dt}) + \gamma_t + \varepsilon_{dt}

RQ2 FE interaction:

Y_{dt} = \alpha_d + \gamma_t + \theta_1(Post2019_t \times Border_d) + \theta_2(PostTrend_t \times Border_d) + \varepsilon_{dt}

Post2019_t = \mathbb{1}[t \ge 2019], \quad PostTrend_t = \max(0, t-2019)

Inference uses district-clustered standard errors (13 clusters), with emphasis on effect size and consistency across specifications.

Tests Run in Notebook

The notebook estimated the following test families:

1. Descriptive border-gap tests:
   • Border vs non-border means for inspection intensity, violations per inspection, days to enforcement, and resolution rate.
2. RQ1 levels regressions (border gaps):
   • Outcomes: inspection_intensity, violations_per_inspection.
   • Specification: border_district + log_unique_wells + C(year).
3. RQ2 FE interaction regressions (post-2019 heterogeneity):
   • Outcomes: inspection_intensity, violations_per_inspection, avg_days_to_enforcement, resolution_rate.
   • Specification: C(district) + C(year) + post_2019:border_district + post_trend:border_district.
4. Border-type robustness tests:
   • District profiles for TX-MX, TX-NM, TX-OK, TX-LA exposure.
   • RQ1-style levels with has_tx_* indicators.
   • RQ2-style FE interactions with post_2019:has_tx_* and post_trend:has_tx_*.
5. Continuous-exposure robustness tests:
   • Replace binary border indicator with share_border_exposed_insp in both RQ1-style and RQ2-style specifications.
6. Cutoff-sensitivity tests:
   • Recompute proximity exposure from minimum distance to any border at 25 km, 75 km, and 100 km.
   • Estimate RQ1-style models for inspection intensity and RQ2-style timing interaction models.
7. Visualization and reporting tests:
   • Border/non-border trend plots.
   • Main timing figure with district-year group means and 95% confidence intervals.
8. Competition/reaction-function scaffolding (not estimated as causal model):
   • District-to-competitor jurisdiction link table and template generated for future interstate stringency integration.

Analysis

Descriptive Border Gaps

Outcome	Non-border	Border
Inspection intensity	1.515	1.329
Violations per inspection	0.098	0.130
Mean days to enforcement	122.8	145.2
Mean resolution rate	0.596	0.543

Descriptively, border districts show weaker enforcement conditions across coverage, detection conditional on inspection, timing, and follow-through.

Main Regression Evidence

Model	Coefficient	p-value	N
RQ1: border_district on inspection_intensity	-0.1755	0.0999	143
RQ1: border_district on violations_per_inspection	0.0434	0.0949	143
RQ2: post_2019:border on inspection_intensity	-0.1191	0.0753	143
RQ2: post_2019:border on violations_per_inspection	0.0040	0.8881	143
RQ2: post_2019:border on avg_days_to_enforcement	-74.5893	0.0156	143
RQ2: post_2019:border on resolution_rate	0.0404	0.4520	143

The most stable differential post-2019 effect is a border-specific improvement in enforcement timing.

Results

Hypothesis Tests

Hypothesis	Test evidence	Decision (current run)
H1: Border districts have lower inspection intensity	RQ1: border_district -> inspection_intensity = -0.1755, p = 0.0999; descriptives 1.329 (border) vs 1.515 (non-border)	Partial support
H2: Border districts have weaker pipeline outcomes	Descriptives: 0.130 vs 0.098 violations/inspection, 145.2 vs 122.8 days, 0.543 vs 0.596 resolution; RQ1 border_district -> violations_per_inspection = 0.0434, p = 0.0949	Supported descriptively, mixed regression support
H3: Border-specific post-2019 level shift	RQ2 post_2019:border -> avg_days_to_enforcement = -74.5893, p = 0.0156; other outcomes null	Supported for timing only
H4: Border-specific post-2019 trend shift	RQ2 post_trend:border terms: inspection p = 0.8181, violations p = 0.8350, timing p = 0.9252, resolution p = 0.3404	Not supported in baseline model

The hypothesis tests indicate the clearest inferential signal is a border-specific post-2019 timing level shift, consistent with "faster pipeline, not wider pipeline."

Figure Callouts

Figure 1 (group trends): analysis/output_borderlands/border_vs_nonborder_trends.png
Figure 2 (main timing figure with CI): analysis/output_borderlands/money_plot_timing_border_prepost2019.png

Figure 2 uses district-year means with equal district weighting:

\bar{Y}_{gt} = \frac{1}{n_{gt}} \sum_{d \in g} Y_{dt}, \quad
CI_{95\%} = \bar{Y}_{gt} \pm 1.96 \cdot \frac{s_{gt}}{\sqrt{n_{gt}}}

Discussion

The findings are consistent with a transparency-throughput mechanism: disclosure-era pressure appears to accelerate processing where baseline constraints are stronger, but this does not map cleanly to expansion of enforcement reach or follow-through. The strongest claim supported by this design is "faster pipeline, not wider pipeline."

The contribution is a boundary condition argument: transparency reforms can produce uneven administrative effects across territorial governance contexts, with timing responsiveness exceeding capacity expansion.

The design does not identify interstate strategic competition. A full Neil Woods-style test requires district-year competitor stringency series and explicit enforcement-gap dynamics. That's the next step in the research agenda, but the current analysis provides a necessary first step by establishing the presence of border-specific enforcement gaps and their heterogeneous response to disclosure reform.