updated analaysis with offshore considerations
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analysis/draft_appendix.md
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# Appendix: Heterogeneous Enforcement of Transparency
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## Evidence from the Texas Railroad Commission
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## Appendix A. Data Construction and Variables
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### A1. Data integration
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The analysis combines inspection and violation administrative records (2015-2025) into a district-year panel. Well-level linkage is done via `api_norm`.
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### A2. Core variables
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| Variable | Definition |
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| :--- | :--- |
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| `log_days_to_enf` | Log of district-year mean days from violation discovery to enforcement action |
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| `resolution_rate` | Share of violations compliant on re-inspection |
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| `compliance_rate` | Share of inspections marked compliant |
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| `violations_per_inspection` | Total violations divided by inspections |
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| `post_2019` | Indicator for years >= 2019 |
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| `post_trend` | Piecewise linear trend after policy (`max(year-2018,0)`) |
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| `offshore_jurisdiction` | Indicator for districts 02/03/04 |
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| `high_capacity` | District above median pre-policy inspection volume |
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| `low_baseline_compliance` | District below median pre-policy compliance |
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| `high_eji` | District above median EJ score |
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| `high_rural` | District above median RUCA |
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| `border_competition` | Operationalized border-proximity indicator |
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| `primary_basin` | Dominant basin category |
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## Appendix B. Econometric Specifications
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### B1. Interrupted panel (all districts; H1)
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\[
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Y_{dt}=\alpha_d + \beta_1 \text{YearNum}_t + \beta_2 \text{Post2019}_t + \beta_3 \text{PostTrend}_t + \varepsilon_{dt}
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\]
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### B2. District heterogeneity (H2)
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\[
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Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \varepsilon_{dt}
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\]
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### B3. Offshore moderation (H5)
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\[
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Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \phi(\text{Post2019}_t\times \text{Offshore}_d) + \varepsilon_{dt}
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\]
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All models report district-clustered standard errors.
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## Appendix C. Main Run Outputs
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### C1. H1 (all-district timing outcome)
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| Parameter | Coefficient | P-value |
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| :--- | ---: | ---: |
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| `post_2019` | 0.1514 | 0.3294 |
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| `post_trend` | -0.3603 | 0.0010 |
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Interpretation: no significant immediate level shift; significant post-policy acceleration slope.
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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.
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### C2. Event-study decomposition (all districts; ref=2018)
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| Year | Coefficient | P-value |
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| :--- | ---: | ---: |
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| 2015 | -0.4592 | 0.0658 |
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| 2016 | -0.3359 | 0.1615 |
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| 2017 | -0.0385 | 0.7502 |
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| 2019 | -0.1149 | 0.2843 |
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| 2020 | -0.1666 | 0.3878 |
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| 2021 | -0.4192 | 0.1072 |
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| 2022 | -0.5853 | 0.0333 |
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| 2023 | -0.4899 | 0.1160 |
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| 2024 | -0.7829 | 0.0057 |
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| 2025 | -1.4800 | <0.001 |
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Pre-policy years are jointly non-significant in this decomposition.
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The coefficient pattern reinforces parallel-pretrend credibility while showing that the post-policy effect strengthens in later years, consistent with delayed organizational adaptation.
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### C3. Offshore differential annual effects (ref=2018)
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| Year | Offshore differential coef | P-value |
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| :--- | ---: | ---: |
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| 2019 | 0.3479 | 0.1581 |
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| 2020 | 0.1796 | 0.7089 |
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| 2021 | 0.9121 | 0.1095 |
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| 2022 | 0.7532 | 0.0652 |
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| 2023 | 0.9166 | 0.0325 |
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| 2024 | 1.0693 | 0.0280 |
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| 2025 | 0.7233 | 0.2091 |
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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.
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### C4. H5 offshore moderator (conditional model)
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| Parameter | Coefficient | P-value |
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| :--- | ---: | ---: |
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| `post_2019:offshore_jurisdiction` | 0.3819 | <0.001 |
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See **Figure 4** in the main text (`district_treatment_effects_map_psj.png`) for the geographic distribution of district treatment effects.
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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.
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### C5. H3 moderator tests
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Main block:
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- H3a Capacity: -0.0188 (p=0.9415)
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- H3b Baseline performance: -0.0884 (p=0.7144)
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- H3e Border proximity: -0.2768 (p=0.3082)
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- H5 (same block estimate): 0.6317 (p=0.1055)
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Deep-dive block:
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- H3c EJ: 0.1818 (p=0.4866)
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- H3f Rurality: 0.2213 (p=0.4649)
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- H3e Border proximity: -0.3626 (p=0.1669)
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- H3d Geology: mixed basin interactions, with significant terms including:
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- `C(primary_basin)[0]:post_2019 = 0.5322` (p<0.001)
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- `C(primary_basin)[3]:post_2019 = -0.5707` (p<0.001)
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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.
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## Appendix D. Spatial Test (H4)
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Moran's I on district treatment effects:
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- Moran’s I = -0.0493
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- Permutation p-value = 0.8550
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Conclusion: no significant global spatial autocorrelation.
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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.
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## Appendix E. Robustness Tables
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### E1. Placebo policy years (all-district interrupted model)
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| Placebo year | Coefficient (`post`) | P-value |
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| :--- | ---: | ---: |
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| 2017 | 0.6565 | 0.0020 |
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| 2021 | -0.0245 | 0.9191 |
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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.
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### E2. Alternative outcomes (all-district interrupted model)
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| Outcome | `post` coef (p) | `post_trend` coef (p) |
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| :--- | :--- | :--- |
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| Resolution rate | 4.3721 (0.2104) | -2.9371 (0.1424) |
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| Compliance rate | -0.1311 (0.9316) | -0.5562 (0.1870) |
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| Violations per inspection | -0.0082 (0.6690) | 0.0106 (0.0600) |
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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.
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### E3. Sample restrictions (all-district interrupted model)
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| Restriction | `post_2019` coef (p) | `post_trend` coef (p) |
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| :--- | :--- | :--- |
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| Full sample | 0.1514 (0.3294) | -0.3603 (0.0010) |
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| Exclude extreme districts | 0.1917 (0.1930) | -0.2972 (0.0133) |
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| Exclude 2015-2016 | 0.1942 (0.1958) | -0.2313 (0.0950) |
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| Exclude 2020-2021 | 0.1516 (0.2959) | -0.3599 (0.0016) |
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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.
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### E4. Specification sensitivity
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| Specification | `post` effect | `post_trend` effect |
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| :--- | :--- | :--- |
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| Linear interrupted | -41.9298 (p=0.3104) | -67.0420 (p=0.0100) |
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| Winsorized interrupted | 0.2137 (p=0.1021) | -0.3147 (p=0.0016) |
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| Year FE + district post terms | 13 interaction terms | N/A |
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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.
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## Appendix F. Interpretation Notes
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1. The strongest system-wide evidence in this run is a **post-policy slope change**, not a one-time 2019 level shift.
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2. District heterogeneity is substantial and statistically material.
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3. Offshore jurisdiction contributes meaningfully in conditional models, but placebo behavior indicates caution in purely timing-based causal claims.
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4. Spatial diffusion is not supported by global autocorrelation tests.
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