texas-borderlands/appendix.md

Appendix

A1. Data Construction and Scope

• Analysis window: 2015-2025.
• District-year panel size: 143 rows (13 districts x 11 years).
• Primary identifiers: api_norm, district, year.
• Border geometry includes TX-MX plus TX-NM, TX-OK, TX-LA boundary proximity.

Build validation counts

Metric	Value
Wells loaded	1,010,432
Inspections (2015-2025)	1,867,859
Violations (2015-2025)	191,762
Border-exposed wells (any 50 km border)	169,520
Panel observations	143
Districts	13

Border subtype counts (50 km)

Border subtype	Wells
TX-MX	40,339
TX-NM	81,567
TX-OK	19,643
TX-LA	29,675

A2. Supplementary Equations

Border-type FE interaction model

Y_{dt} = \alpha_d + \gamma_t + \sum_k \lambda_k(Post2019_t \times Type_{kd}) + \sum_k \phi_k(PostTrend_t \times Type_{kd}) + \varepsilon_{dt}

where k \in \{\text{TX-MX}, \text{TX-NM}, \text{TX-OK}, \text{TX-LA}\}.

Continuous exposure FE interaction model

Y_{dt} = \alpha_d + \gamma_t + \eta_1(Post2019_t \times ShareBorder_{dt}) + \eta_2(PostTrend_t \times ShareBorder_{dt}) + \varepsilon_{dt}

Cutoff-specific exposure

ShareBorder^{(c)}_{dt}, \quad c \in \{25,75,100\}

substituted into the same FE interaction framework.

A3. Main FE Interaction Table (RQ2)

Outcome	post_2019 x border	p-value	post_trend x border	p-value	N
inspection_intensity	-0.1191	0.0753	-0.0052	0.8181	143
violations_per_inspection	0.0040	0.8881	-0.0012	0.8350	143
avg_days_to_enforcement	-74.5893	0.0156	-1.1587	0.9252	143
resolution_rate	0.0404	0.4520	-0.0186	0.3404	143

A4. Border-Type Timing Interactions (Money Plot Companion)

Term	Coefficient	p-value
post_2019:has_tx_mex	4.0900	0.9062
post_2019:has_tx_nm	-18.7442	0.6013
post_2019:has_tx_ok	-14.2446	0.8134
post_2019:has_tx_la	-43.6598	0.6415
post_trend:has_tx_mex	-0.0148	0.9991
post_trend:has_tx_nm	22.9067	0.0189
post_trend:has_tx_ok	-16.7188	0.0794
post_trend:has_tx_la	0.6415	0.9551

A5. Continuous Exposure Results

Family	Outcome	Term	Coef	p-value	N
RQ1 levels continuous	inspection_intensity	share_border_exposed_insp	0.2095	0.4757	143
RQ1 levels continuous	avg_days_to_enforcement	share_border_exposed_insp	103.4683	0.5710	143
RQ1 levels continuous	violations_per_inspection	share_border_exposed_insp	-0.1585	0.0144	143
RQ1 levels continuous	resolution_rate	share_border_exposed_insp	-0.0420	0.8619	143
RQ2 FE continuous	avg_days_to_enforcement	post_2019:share_border_exposed_insp	-109.4067	0.4449	143
RQ2 FE continuous	avg_days_to_enforcement	post_trend:share_border_exposed_insp	13.9623	0.7415	143
RQ2 FE continuous	resolution_rate	post_2019:share_border_exposed_insp	-0.0322	0.8163	143
RQ2 FE continuous	resolution_rate	post_trend:share_border_exposed_insp	-0.0979	0.0423	143

A6. Cutoff Sensitivity (Timing-Focused Terms)

Cutoff km	Term	Coef	p-value	N
25	post_2019:share_border_25km	-101.9283	0.7010	143
75	post_2019:share_border_75km	-75.6591	0.5116	143
100	post_2019:share_border_100km	-4.4795	0.9474	143

A7. District Border-Type Profile

District	Wells	Dominant type	TX-MX share	TX-NM share	TX-OK share	TX-LA share
01	31,898	TX-MX	0.2313	0.0000	0.0000	0.0000
02	17,099	NONE	0.0000	0.0000	0.0000	0.0000
03	16,700	TX-LA	0.0000	0.0000	0.0000	0.1166
04	20,973	TX-MX	0.6384	0.0000	0.0000	0.0000
05	9,938	TX-OK	0.0000	0.0000	0.0022	0.0000
06	24,422	TX-LA	0.0000	0.0000	0.0293	0.5235
08	105,931	TX-NM	0.0001	0.1905	0.0000	0.0000
09	46,485	NONE	0.0000	0.0000	0.0000	0.0000
10	29,621	TX-OK	0.0000	0.0009	0.3020	0.0000
6E	6,235	NONE	0.0000	0.0000	0.0000	0.0000
7B	21,230	NONE	0.0000	0.0000	0.0000	0.0000
7C	43,061	NONE	0.0000	0.0000	0.0000	0.0000
8A	42,005	TX-NM	0.0000	0.4182	0.0000	0.0000

A8. Figures and Output Artifacts

• Border exposure map: analysis/output_borderlands/well_border_exposure_map.png
• Border vs non-border trends: analysis/output_borderlands/border_vs_nonborder_trends.png
• Main timing figure: analysis/output_borderlands/money_plot_timing_border_prepost2019.png
• Timing CI table: analysis/output_borderlands/money_plot_timing_ci_by_year.csv

A9. Optional Prior Artifact (Not Estimated in Current Causal Scope)

analysis/output_borderlands/competition_asymmetry_results.csv contains:

• gap_pos = 0.5837 (p < 0.001)
• gap_neg = 0.4993 (p = 0.0027)

These estimates are not part of the current notebook's identified model scope and should not be interpreted as a completed reaction-function test in this manuscript version.

A10. Estimation Workflow and Test Inventory

Border treatment construction

Two border definitions were used:

1. District-level baseline treatment (border_district), with border-adjacent districts coded as 01, 02, 06, 08, 8A, 09, 10.
2. Well-level proximity treatment, rolled up to district-year exposure.

Well-level proximity workflow:

1. Import TX-MX proximity flags from WellAnalyzer (within_25km_texmex, within_50km_texmex).
2. Build additional TX-NM, TX-OK, TX-LA border segments from county boundary geometry plus seed lines.
3. Compute distances in EPSG:5070 and generate threshold flags.
4. Build composite indicators:
   • within_50km_state_border_any
   • well_border_exposed (within 50 km of TX-MX or any TX-state border segment).

District-year exposure share:

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

Alternative district coding:

border\_exposure\_district_{dt} = \mathbb{1}[ShareBorder_{dt} \ge 0.25]

Tests run

1. Descriptive border-gap comparisons by group means.
2. RQ1 levels regressions:
   • Outcomes: inspection_intensity, violations_per_inspection.
   • Model: border_district + log_unique_wells + C(year).
3. RQ2 FE interaction regressions:
   • Outcomes: inspection_intensity, violations_per_inspection, avg_days_to_enforcement, resolution_rate.
   • Model: C(district) + C(year) + post_2019:border_district + post_trend:border_district.
4. Border-type robustness:
   • District border-type profile (TX-MX, TX-NM, TX-OK, TX-LA).
   • RQ1-style levels with has_tx_*.
   • RQ2-style FE interactions with post_2019:has_tx_* and post_trend:has_tx_*.
5. Continuous exposure robustness:
   • Replace binary border term with share_border_exposed_insp in RQ1-style and RQ2-style specifications.
6. Cutoff sensitivity robustness:
   • Recompute exposure using minimum distance to any border at 25/75/100 km.
   • Estimate RQ1-style inspection-intensity models and RQ2-style timing interaction models.
7. Figures/reporting:
   • Border vs non-border trend plots.
   • Main timing figure with district-year means and 95% CIs.
8. Reaction-function scaffolding (not estimated):
   • Create district-competitor link table and district-year competitor template for future data integration.