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updated analaysis with offshore considerations

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# Heterogeneous Enforcement of Transparency: Evidence from the Texas Railroad Commission
## Introduction
How does transparency alter regulatory enforcement in high-capacity but locally discretionary bureaucracies? We study the January 2019 Texas Railroad Commission (RRC) disclosure change that made well-level violation information publicly searchable. The policy constitutes a statewide transparency shock, but implementation and enforcement remain district-administered. This setting allows us to test both system-wide effects and district-level heterogeneity in policy response.
Our core empirical finding is a two-part pattern. First, we find evidence of **gradual post-policy acceleration** in enforcement timing at the statewide level (significant post-policy trend improvement) rather than a sharp immediate level break in 2019. Second, district-level responses are strongly heterogeneous, and offshore-jurisdiction districts (02/03/04) exhibit systematically different post-policy dynamics once district-specific post effects are modeled.
## Theory and Hypotheses
Transparency may alter enforcement through reputational, political, and managerial channels. Public disclosure can increase the salience of noncompliance and create incentives for agencies to accelerate case movement through the regulatory pipeline. But local implementation discretion can mediate this effect, producing district-level divergence.
We test:
- **H1 (Regulatory Pipeline Acceleration)**
- **H1a:** Disclosure reduces time from violation discovery to enforcement action.
- **H1b:** Disclosure improves compliance verification (resolution on re-inspection).
- **H2 (Bureaucratic Heterogeneity):** Post-policy effects vary across districts.
- **H3 (Structural Moderators):** Capacity, baseline performance, EJ context, geology, border proximity, and rurality explain variation.
- **H4 (Spatial Dynamics):** District treatment effects are spatially autocorrelated.
- **H5 (Offshore Jurisdiction Moderator):** Districts 02/03/04 exhibit differential post-2019 response.
## Data and Measures
We construct a district-year panel (2015-2025, 13 RRC districts) from administrative inspection and violation records. Well-level integration uses `api_norm` as the normalized identifier across sources.
Primary outcomes:
- `log_days_to_enf`: log mean days from violation discovery to enforcement action.
- `resolution_rate`: percent compliant on re-inspection.
- `compliance_rate`: percent compliant at inspection.
- `violations_per_inspection`.
## Empirical Strategy
We estimate policy effects in three layers.
### Model 1: All-district policy-year shift (H1)
\[
Y_{dt}=\alpha_d + \beta_1 \text{YearNum}_t + \beta_2 \text{Post2019}_t + \beta_3 \text{PostTrend}_t + \varepsilon_{dt}
\]
Where \(\text{PostTrend}_t = \max(0, t-2018)\). This distinguishes an immediate post-2019 level shift (\(\beta_2\)) from post-policy slope change (\(\beta_3\)).
### Model 2: District heterogeneity (H2)
\[
Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \varepsilon_{dt}
\]
This yields district-specific post-policy effects and a joint heterogeneity test.
### Model 3: Offshore moderation (H5)
\[
Y_{dt}=\alpha_d + \gamma_t + \sum_d \theta_d (\text{District}_d\times \text{Post2019}_t) + \phi(\text{Post2019}_t\times \text{Offshore}_d) + \varepsilon_{dt}
\]
Where `Offshore_d = 1` for districts 02/03/04.
All models use district-clustered standard errors.
## Results
### Descriptive pipeline trends
Pre/post means indicate lower average enforcement delay post-policy (174.3 to 112.3 days), but reduced inspection frequency intensity (higher days between inspections).
Figure 1 visualizes these system-level changes across the regulatory pipeline. The key descriptive pattern is that timeliness improves over the post-policy period even as inspection cadence shifts, motivating a design that separates immediate policy breaks from post-policy trend effects.
![Regulatory Pipeline Trends](pipeline_trends_over_time.png)
**Figure 1.** Regulatory pipeline trends, 2015-2025.
### H1: Policy-year effects (all districts)
**Model 1 (timing outcome):**
- `post_2019` level shift: **0.1514**, p=0.3294.
- `post_trend` slope shift: **-0.3603**, p=0.0010.
Interpretation: no statistically significant immediate level break in 2019, but a significant post-policy acceleration trend in enforcement timing.
**Table 1. Core policy-year and moderator estimates**
| Model | Parameter | Coefficient | P-value | Interpretation |
| :--- | :--- | ---: | ---: | :--- |
| Model 1 (All districts, interrupted panel) | `post_2019` | 0.1514 | 0.3294 | No immediate level break |
| Model 1 (All districts, interrupted panel) | `post_trend` | -0.3603 | 0.0010 | Significant post-policy acceleration trend |
| Model 3 (District heterogeneity + offshore) | `post_2019:offshore_jurisdiction` | 0.3819 | <0.001 | Offshore districts relatively slower post-policy timing |
Table 1 provides the baseline inferential results for the article’s identification strategy. The table shows that the main all-district effect appears in the post-policy slope term rather than a one-time post-2019 level break, and it also shows that offshore jurisdiction remains a statistically important differential once district heterogeneity is modeled.
Event-study decomposition (relative to 2018) corroborates this dynamic pattern:
- No significant pre-policy years (2015-2017).
- Significant negative deviations in 2022, 2024, and 2025.
**Table 2. Event-study coefficients (all districts, reference year = 2018)**
| Year | Coefficient | P-value | Significant (p<0.05) |
| :--- | ---: | ---: | :--- |
| 2022 | -0.5853 | 0.0333 | Yes |
| 2024 | -0.7829 | 0.0057 | Yes |
| 2025 | -1.4800 | <0.001 | Yes |
Table 2 highlights the years where post-policy deviations are most pronounced. Substantively, these estimates indicate that the policy response intensifies over time instead of materializing immediately in 2019.
![Event Study](event_study_plot.png)
**Figure 2.** All-district event-study decomposition and offshore differential annual effects.
Figure 2 complements Table 2 by displaying the full time path (including pre-policy years), making the absence of pre-trend significance and the later post-policy acceleration visually transparent.
### H2: District heterogeneity
District-level post-policy responses are strongly heterogeneous and jointly significant. Estimated effects range from substantial acceleration (e.g., District 09) to substantial slowdown (e.g., Districts 03 and 04).
From district effect summaries used in mapping:
- Best improvement: District 09 (about -52.6%).
- Largest deterioration: District 04 (about +138.5%).
Figure 3 presents the estimated district-specific effects directly, while Figure 4 maps those effects geographically. Together they demonstrate that heterogeneity is not a minor perturbation around a common effect but a core empirical feature of the policy response.
![District Effects](district_treatment_effects_psj.png)
**Figure 3.** District-specific post-2019 treatment effects.
To show where these effects are concentrated geographically, Figure 4 maps district-level percent changes in enforcement timing.
![District Treatment Effects Map](district_treatment_effects_map_psj.png)
**Figure 4.** Geographic distribution of district treatment effects (percent change in days to enforcement).
The map indicates that large positive and negative effects coexist across regions, reinforcing the need to model district-level discretion explicitly rather than assuming uniform policy implementation.
### H5: Offshore moderation
In the conditional heterogeneity model (Model 3):
- `post_2019:offshore_jurisdiction = 0.3819`, p<0.001.
This indicates that, net of district-specific post effects, offshore-jurisdiction districts experience relatively slower post-policy enforcement timing.
### H3: Structural moderators
Main moderator block:
- H3a Capacity: coef -0.0188, p=0.9415.
- H3b Baseline performance: coef -0.0884, p=0.7144.
- H3e Border proximity: coef -0.2768, p=0.3082.
Deep-dive TWFE block:
- H3c EJ context: coef 0.1818, p=0.4866.
- H3f Rurality: coef 0.2213, p=0.4649.
- H3e Border proximity: coef -0.3626, p=0.1669.
- H3d Geology: mixed basin interactions; some terms significant (p<0.001).
Overall, H3 receives limited support except partial geology effects.
**Table 3. Structural moderator tests**
| Hypothesis | Term | Coefficient | P-value | Result |
| :--- | :--- | ---: | ---: | :--- |
| H3a Capacity | `post_2019:high_capacity` | -0.0188 | 0.9415 | Not supported |
| H3b Baseline performance | `post_2019:low_baseline_compliance` | -0.0884 | 0.7144 | Not supported |
| H3c EJ context | `post_2019:high_eji` | 0.1818 | 0.4866 | Not supported |
| H3e Border proximity | `post_2019:border_competition` | -0.3626 | 0.1669 | Not supported |
| H3f Rurality | `post_2019:high_rural` | 0.2213 | 0.4649 | Not supported |
| H3d Geology | `C(primary_basin):post_2019` | Mixed | Mixed | Partial support |
Table 3 summarizes why structural accounts are only partially successful in this run: most moderators are imprecisely estimated, while geology shows selective basin-specific effects. Figure 5 and Figure 6 then provide visual context for these moderator patterns.
![Moderators](heterogeneous_effects.png)
**Figure 5.** Moderator interaction estimates.
![Demographics and Geography](district_demographics_geography.png)
**Figure 6.** Demographic/geographic correlates of district effects.
### H4: Spatial dynamics
Moran’s I on district effects:
- \(I = -0.0493\), permutation p=0.8550.
No evidence of statistically significant global spatial autocorrelation.
Figure 7 visually corroborates the spatial test by showing no systematic clustering pattern consistent with strong spillovers.
![Spatial Spillovers](spatial_spillovers.png)
**Figure 7.** Spatial spillover diagnostics.
## Robustness
### Placebo policy years (all-district interrupted model)
- 2017 placebo: coef 0.6565, p=0.0020.
- 2021 placebo: coef -0.0245, p=0.9191.
### Alternative outcomes (all-district interrupted model)
- Resolution rate: post 4.3721 (p=0.2104), post-trend -2.9371 (p=0.1424).
- Compliance rate: post -0.1311 (p=0.9316), post-trend -0.5562 (p=0.1870).
- Violations/inspection: post -0.0082 (p=0.6690), post-trend 0.0106 (p=0.0600).
### Sample restrictions
- Full sample: post 0.1514 (p=0.3294), post-trend -0.3603 (p=0.0010).
- Excluding extreme districts: post-trend remains negative/significant.
- Excluding 2015-2016: post-trend remains negative (weaker significance).
- Excluding 2020-2021: post-trend remains negative/significant.
### Specification sensitivity
- Linear interrupted model: post-trend -67.0420 days (p=0.0100).
- Winsorized interrupted model: post-trend -0.3147 (p=0.0016).
Across variants, the post-policy **slope** result is more stable than the immediate **level** effect.
**Table 4. Robustness summary (interrupted panel framework)**
| Check | `post_2019` (p) | `post_trend` (p) | Read |
| :--- | :--- | :--- | :--- |
| Full sample | 0.1514 (0.3294) | -0.3603 (0.0010) | Slope effect robust; level break weak |
| Exclude extreme districts | 0.1917 (0.1930) | -0.2972 (0.0133) | Slope remains significant |
| Exclude 2015-2016 | 0.1942 (0.1958) | -0.2313 (0.0950) | Slope negative, marginal |
| Exclude 2020-2021 | 0.1516 (0.2959) | -0.3599 (0.0016) | Slope remains significant |
| Linear interrupted | -41.9298 (0.3104) | -67.0420 (0.0100) | Same directional pattern |
| Winsorized interrupted | 0.2137 (0.1021) | -0.3147 (0.0016) | Slope remains significant |
Table 4 consolidates robustness evidence in one place: level-shift estimates are sensitive, but the negative post-policy slope remains comparatively stable across sample restrictions and alternative functional forms.
## Discussion
The transparency reform is associated with a gradual statewide acceleration in enforcement timing rather than a single immediate break at implementation. At the same time, district responses diverge sharply, confirming bureaucratic heterogeneity. Offshore jurisdiction explains a meaningful share of that heterogeneity once district-specific post effects are included, while most other structural moderators are weak or inconsistent in this run. Spatial diffusion across neighboring districts is not supported by global autocorrelation tests.
These findings suggest that transparency reforms in decentralized regulatory systems should be evaluated as dynamic, district-conditioned processes, not monolithic statewide shocks.