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@@ -84,8 +84,7 @@ Pre/post means indicate lower average enforcement delay post-policy (174.3 to 11
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. 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.](pipeline_trends_over_time.png)
**Figure 1.** Regulatory pipeline trends, 2015-2025.
### H1: Policy-year effects (all districts) ### H1: Policy-year effects (all districts)
@@ -113,16 +112,24 @@ Event-study decomposition (relative to 2018) corroborates this dynamic pattern:
**Table 2. Event-study coefficients (all districts, reference year = 2018)** **Table 2. Event-study coefficients (all districts, reference year = 2018)**
| Year | Coefficient | P-value | Significant (p<0.05) | | Year | Coefficient | P-value |
| :--- | ---: | ---: | :--- | | :--- | ---: | ---: |
| 2022 | -0.5853 | 0.0333 | Yes | | 2015 | -0.4592 | 0.0658 |
| 2024 | -0.7829 | 0.0057 | Yes | | 2016 | -0.3359 | 0.1615 |
| 2025 | -1.4800 | <0.001 | Yes | | 2017 | -0.0385 | 0.7502 |
| 2018 (reference) | 0.0000 | 1.0000 |
| 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 |
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. 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.](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. 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.
@@ -137,14 +144,13 @@ From district effect summaries used in mapping:
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. 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.](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. 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)[^1].](district_treatment_effects_map_psj.png)
**Figure 4.** Geographic distribution of district treatment effects (percent change in days to enforcement).
**Figure note.** Districts are shaded by the estimated percent change in days to enforcement after 2019 (negative values indicate faster enforcement; positive values indicate slower enforcement), using a diverging scale centered at zero so improvements and slowdowns are visually comparable. Estimates come from the district-by-post model on logged enforcement delay and are converted to percent changes; district labels indicate RRC district codes. Magnitudes should be interpreted with the coefficient uncertainty reported in the corresponding model tables. [^1]: Districts are shaded by the estimated percent change in days to enforcement after 2019 (negative values indicate faster enforcement; positive values indicate slower enforcement), using a diverging scale centered at zero so improvements and slowdowns are visually comparable. Estimates are derived from the district-by-post model on logged enforcement delay and converted to percent changes; labels indicate RRC district codes. Interpret magnitudes alongside the coefficient uncertainty reported in the corresponding model tables.
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. 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.
@@ -186,11 +192,9 @@ Overall, H3 receives limited support except partial geology effects.
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. 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.](heterogeneous_effects.png)
**Figure 5.** Moderator interaction estimates.
![Demographics and Geography](district_demographics_geography.png) ![Figure 6. Demographic/geographic correlates of district effects.](district_demographics_geography.png)
**Figure 6.** Demographic/geographic correlates of district effects.
### H4: Spatial dynamics ### H4: Spatial dynamics
@@ -198,8 +202,7 @@ Moran’s I on district effects:
- $I = -0.0493$, permutation p=0.8550. - $I = -0.0493$, permutation p=0.8550.
No evidence of statistically significant global spatial autocorrelation. No evidence of statistically significant global spatial autocorrelation. In this run, the spatial evidence is inferential (permutation Moran’s I) rather than model-based spatial plotting; Figure 4 provides the relevant geographic context for district-level effect dispersion.
In this run, the spatial evidence is inferential (permutation Moran’s I) rather than model-based spatial plotting; Figure 4 provides the relevant geographic context for district-level effect dispersion.
## Robustness ## Robustness
@@ -239,7 +242,9 @@ Across variants, the post-policy slope result is more stable than the immediate
| Linear interrupted | -41.9298 (0.3104) | -67.0420 (0.0100) | Same directional pattern | | 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 | | 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. 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. Across the full robustness suite, the most consistent result is the negative post-policy trend in enforcement timing, not a one-time level break at policy adoption. Placebo tests show a significant 2017 pseudo-break ($\hat\beta=0.6565$, $p=0.0020$) but a null 2021 pseudo-break ($\hat\beta=-0.0245$, $p=0.9191$), indicating that single-year level-shift estimates can absorb unrelated temporal structure. Alternative-outcome models similarly show limited generalization of effects beyond timing: resolution-rate and compliance-rate level/trend terms are not statistically significant, while violations per inspection shows only a marginal post-trend signal.
Sample-restriction and specification-sensitivity checks reinforce this interpretation. In the interrupted-panel framework table, the post-policy trend term remains negative across all restrictions (full sample, excluding extreme districts, excluding early years, excluding 2020-2021) and is generally significant or marginal, whereas the immediate post-policy level term remains weak and unstable. Alternative functional forms (linear and winsor-adjusted interrupted models) replicate the same hierarchy: slope effects are directionally stable and comparatively robust, while level effects are sensitive. Taken together, these checks support a cautious but coherent conclusion: the policy's strongest empirical signature is gradual acceleration in enforcement timing rather than a uniform, immediate break or broad cross-outcome improvement.
## Discussion ## Discussion