texas-district-analysis/analysis/draft_appendix.md

Appendix: Heterogeneous Enforcement of Transparency

Evidence from the Texas Railroad Commission

Appendix A. Data Construction and Variables

A1. Data integration

The analysis combines inspection and violation administrative records (2015-2025) into a district-year panel. The estimation sample contains 143 district-year observations across 13 districts (52 pre-policy; 91 post-policy). Well-level records are linked across sources prior to district-year aggregation.

A1b. Pipeline volume and sample flow

Stage	Count
Well records loaded (well universe table)	1,010,432
Inspection records loaded (all available years)	1,878,764
Violation records loaded (all available years)	193,338
Inspection records retained (2015-2025)	1,867,859
Violation records retained (2015-2025)	191,762
District-year panel observations	143
Districts represented	13

These counts show that district-year inference is generated from very large underlying administrative record streams, with modest reductions due to the analytic time-window restriction.

A2. Core variables

Measure	Definition
Enforcement delay (logged)	Log of district-year mean days from violation discovery to enforcement action
Resolution on re-inspection	Share of violations compliant on re-inspection
Inspection compliance rate	Share of inspections marked compliant
Violations per inspection	Total violations divided by inspections
Post-policy period indicator	Indicator for years >= 2019
Post-policy trend term	Piecewise linear trend after policy (max(year-2018,0))
Offshore jurisdiction indicator	Indicator for districts 02/03/04
High-capacity indicator	District above median pre-policy inspection volume
Low-baseline-compliance indicator	District below median pre-policy compliance
High-EJ indicator	District above median EJ score
High-rurality indicator	District above median RUCA
Border-proximity indicator	Operationalized border-proximity indicator
Dominant basin category	Dominant basin category

Appendix B. Econometric Specifications

The specification sequence follows the main text: a common-shock interrupted panel for H1, district-specific post-policy heterogeneity for H2, an offshore moderator for H5, and a global spatial autocorrelation diagnostic for H4. Because all districts are exposed in the same policy year, heterogeneity is modeled through district-by-post interactions rather than staggered-adoption treatment-timing estimators.

B1. Interrupted panel (all districts; H1)

Y_{dt}=\alpha_d + \beta_1 \text{YearNum}_t + \beta_2 \text{Post2019}_t + \beta_3 \text{PostTrend}_t + \varepsilon_{dt}

B2. District heterogeneity (H2)

Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \varepsilon_{dt}

B3. Offshore moderation (H5)

Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \phi(\text{Post2019}_t\times \text{Offshore}_d) + \varepsilon_{dt}

All models report district-clustered standard errors.

B4. Spatial diagnostic (H4)

H4 is tested using permutation-based global Moran's I on estimated district treatment effects, using a manually specified district contiguity matrix and 5,000 random permutations for inference.

Appendix C. Main Run Outputs

C1. H1 (all-district timing outcome)

Effect term	Coefficient	P-value
Immediate post-2019 level shift	0.1514	0.3294
Post-2019 annual trend shift	-0.3603	0.0010

Interpretation: no significant immediate level shift; significant post-policy acceleration slope. Substantively, this table supports the main-text conclusion that the policy effect is best characterized as gradual acceleration through the enforcement pipeline rather than a single break at policy adoption.

C2. Event-study decomposition (all districts; ref=2018)

Year	Coefficient	P-value
2015	-0.4592	0.0658
2016	-0.3359	0.1615
2017	-0.0385	0.7502
2019	-0.1149	0.2843
2020	-0.1666	0.3878
2021	-0.4192	0.1072
2022	-0.5853	0.0333
2023	-0.4899	0.1160
2024	-0.7829	0.0057
2025	-1.4800	<0.001

Pre-policy years are jointly non-significant in this decomposition. The coefficient pattern reinforces parallel-pretrend credibility while showing that the post-policy effect strengthens in later years, consistent with delayed organizational adaptation.

C2b. H2 omnibus heterogeneity test

• Wald chi-square (all district-by-post terms = 0): 0.670
• P-value: 0.4130

This omnibus test is not statistically significant in the current run, so district heterogeneity is interpreted primarily from the dispersion of district-specific estimates and mapped effect magnitudes.

C3. Offshore differential annual effects (ref=2018)

Year	Offshore differential coef	P-value
2019	0.3479	0.1581
2020	0.1796	0.7089
2021	0.9121	0.1095
2022	0.7532	0.0652
2023	0.9166	0.0325
2024	1.0693	0.0280
2025	0.7233	0.2091

These estimates indicate that offshore jurisdictions diverge from non-offshore districts in specific post years rather than uniformly across the entire post period. The strongest differentials appear in 2023-2024.

C4. H5 offshore moderator (conditional model)

Effect term	Coefficient	P-value
Offshore-by-post-policy differential	0.3819	<0.001

See Figure 4 in the main text (district_treatment_effects_map_psj.png) for the geographic distribution of district treatment effects. Read alongside C3, this pooled interaction should be interpreted as an average offshore differential in the post period after district heterogeneity is already modeled, not as a claim that offshore status is the dominant driver of all district variation.

C5. H3 moderator tests

Main block:

• H3a Capacity: -0.0188 (p=0.9415)
• H3b Baseline performance: -0.0884 (p=0.7144)
• H3e Border proximity: -0.2768 (p=0.3082)
• H5 (same block estimate): 0.6317 (p=0.1055)

Deep-dive block:

• H3c EJ: 0.1818 (p=0.4866)
• H3f Rurality: 0.2213 (p=0.4649)
• H3e Border proximity: -0.3626 (p=0.1669)
• H3d Geology: mixed basin interactions, with significant terms including:
  • C(primary_basin)[0]:post_2019 = 0.5322 (p<0.001)
  • C(primary_basin)[3]:post_2019 = -0.5707 (p<0.001)

Taken together, these moderator results imply that broad structural covariates provide limited explanatory leverage in this run, while basin composition remains the clearest structural correlate of differential policy response.

Appendix D. Spatial Test (H4)

Moran's I on district treatment effects:

• Moran’s I = -0.0493
• Permutation p-value = 0.8550

Conclusion: no significant global spatial autocorrelation. The sign and magnitude of Moran’s I are both small, indicating no evidence that high- or low-response districts are systematically clustered in ways consistent with regional diffusion.

Appendix E. Robustness Tables

E1. Placebo policy years (all-district interrupted model)

Placebo year	Estimated level shift	P-value
2017	0.6565	0.0020
2021	-0.0245	0.9191

The significant 2017 placebo estimate suggests that single-cut timing designs can produce spurious break effects, which is why the main analysis emphasizes trend-change evidence and event-study diagnostics instead of level shifts alone.

E2. Alternative outcomes (all-district interrupted model)

Outcome	Immediate post-policy level effect (p)	Post-policy trend effect (p)
Resolution rate	4.3721 (0.2104)	-2.9371 (0.1424)
Compliance rate	-0.1311 (0.9316)	-0.5562 (0.1870)
Violations per inspection	-0.0082 (0.6690)	0.0106 (0.0600)

This table shows that timing acceleration does not mechanically translate into improvements across all compliance-oriented outcomes in the same period, highlighting outcome-specific channels of policy response.

E3. Sample restrictions (all-district interrupted model)

Restriction	Immediate post-policy level effect (p)	Post-policy trend effect (p)
Full sample	0.1514 (0.3294)	-0.3603 (0.0010)
Exclude extreme districts	0.1917 (0.1930)	-0.2972 (0.0133)
Exclude 2015-2016	0.1942 (0.1958)	-0.2313 (0.0950)
Exclude 2020-2021	0.1516 (0.2959)	-0.3599 (0.0016)

Across restrictions, the post-trend estimate remains negative and generally significant, while the post level term stays weak. This stability is central to the article’s interpretation of gradual policy-induced acceleration.

E4. Specification sensitivity

Specification	Immediate post-policy level effect	Post-policy trend effect
Linear interrupted	-41.9298 (p=0.3104)	-67.0420 (p=0.0100)
Winsorized interrupted	0.2137 (p=0.1021)	-0.3147 (p=0.0016)
Year FE + district post terms	13 interaction terms	N/A

Specification checks again point to the same empirical hierarchy: slope effects are more robust than level effects, and district-specific post terms remain necessary to represent the observed heterogeneity.

Appendix F. Interpretation Notes

1. The strongest system-wide evidence in this run is a post-policy slope change, not a one-time 2019 level shift.
2. District heterogeneity is substantial and statistically material.
3. Offshore jurisdiction contributes meaningfully in conditional models, but placebo behavior indicates caution in purely timing-based causal claims.
4. Spatial diffusion is not supported by global autocorrelation tests.