7.7 KiB
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. Well-level linkage is done via api_norm.
A2. Core variables
| Variable | Definition |
|---|---|
log_days_to_enf |
Log of district-year mean days from violation discovery to enforcement action |
resolution_rate |
Share of violations compliant on re-inspection |
compliance_rate |
Share of inspections marked compliant |
violations_per_inspection |
Total violations divided by inspections |
post_2019 |
Indicator for years >= 2019 |
post_trend |
Piecewise linear trend after policy (max(year-2018,0)) |
offshore_jurisdiction |
Indicator for districts 02/03/04 |
high_capacity |
District above median pre-policy inspection volume |
low_baseline_compliance |
District below median pre-policy compliance |
high_eji |
District above median EJ score |
high_rural |
District above median RUCA |
border_competition |
Operationalized border-proximity indicator |
primary_basin |
Dominant basin category |
Appendix B. Econometric Specifications
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.
Appendix C. Main Run Outputs
C1. H1 (all-district timing outcome)
| Parameter | Coefficient | P-value |
|---|---|---|
post_2019 |
0.1514 | 0.3294 |
post_trend |
-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.
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)
| Parameter | Coefficient | P-value |
|---|---|---|
post_2019:offshore_jurisdiction |
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 | Coefficient (post) |
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 | post coef (p) |
post_trend coef (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 | post_2019 coef (p) |
post_trend coef (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 | post effect |
post_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
- The strongest system-wide evidence in this run is a post-policy slope change, not a one-time 2019 level shift.
- District heterogeneity is substantial and statistically material.
- Offshore jurisdiction contributes meaningfully in conditional models, but placebo behavior indicates caution in purely timing-based causal claims.
- Spatial diffusion is not supported by global autocorrelation tests.