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drafts are done. going to convert to docx and share

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analysis/draft_appendix.md
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# 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 linkage is done via `api_norm`.
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
@@ -45,21 +46,21 @@ The specification sequence follows the main text: a common-shock interrupted pan
### 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.
@@ -124,18 +125,20 @@ These estimates indicate that offshore jurisdictions diverge from non-offshore d
| :--- | ---: | ---: |
| 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.
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)
@@ -199,7 +202,7 @@ Specification checks again point to the same empirical hierarchy: slope effects
## 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.
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.