double checked, updated narrative

analysis/draft.md

@@ -4,9 +4,9 @@
 
 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.
 
-While targeted transparency is increasingly utilized as a regulatory tool to improve accountability, its actual impact is mediated by the bureaucratic discretion of local field offices. Because policy implementation often experiences a lag, we utilize an Interrupted Time Series design to capture gradual enforcement acceleration, while explicitly modeling the structural, spatial, and demographic factors that drive street-level bureaucratic heterogeneity.
+While targeted transparency is increasingly utilized as a regulatory tool to improve accountability, its actual impact is mediated by the bureaucratic discretion of local field offices. Because implementation effects often unfold with delay, we use an interrupted time-series framework to distinguish immediate level shifts from post-policy trend change. We then model structural, spatial, and demographic sources of district-level heterogeneity.
 
-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.
+Our core empirical results indicate a two-part pattern. First, enforcement timing improves gradually after the policy (significant post-policy trend improvement) rather than through a sharp immediate level break in 2019. Second, district responses vary substantially, and offshore-jurisdiction districts (02/03/04) exhibit systematically different post-policy dynamics once district-specific post effects are modeled. The paper contributes evidence on how transparency reforms operate in decentralized enforcement systems where local administrative discretion remains central.
 
 ## Theory and Hypotheses
 
@@ -24,7 +24,21 @@ We test:
 
 ## Data and Measures
 
-We construct a district-year panel (2015-2025, 13 RRC districts) from administrative inspection and violation records. Well-level records are linked across sources prior to district-year aggregation.
+We construct a district-year panel (2015-2025, 13 RRC districts) from administrative inspection and violation records. Well-level records are linked across sources prior to district-year aggregation. The analysis sample includes 143 district-year observations (52 pre-policy, 91 post-policy).
+
+To give scale to the regulatory pipeline, Table A summarizes the analytic data volumes used in this run.
+
+**Table A. Pipeline data volume (analysis window: 2015-2025)**
+
+| Component | Count |
+| :--- | ---: |
+| Well records loaded (well universe table) | 1,010,432 |
+| Inspection records (analysis window) | 1,867,859 |
+| Violation records (analysis window) | 191,762 |
+| District-year observations | 143 |
+| Districts | 13 |
+
+These volumes indicate a high-throughput enforcement system in which timing outcomes are estimated on a relatively small district-year panel built from large underlying administrative flows.
 
 Primary outcomes:
 
@@ -52,7 +66,7 @@ $$
 Y_{dt} = \alpha_d + \gamma_t + \sum_{d} \theta_d \bigl(\mathrm{District}_d \times \mathrm{Post2019}_t\bigr) + \varepsilon_{dt}
 $$
 
-This yields district-specific post-policy effects and a joint heterogeneity test.
+This yields district-specific post-policy effects and a joint heterogeneity test. In the current run, the omnibus test of district-by-post terms is not statistically significant (Wald chi-square = 0.670, p=0.4130), so H2 evidence is interpreted primarily from the pattern and magnitude of district-specific estimates rather than a single global rejection.
 Because all districts are exposed in the same year, this is not a staggered-adoption DiD problem. Still, recent DiD work highlights that pooled average effects can mask meaningful treatment-effect heterogeneity, so we estimate district-specific post effects directly rather than rely on a single pooled interaction (de Chaisemartin & D'Haultfœuille, 2020; Goodman-Bacon, 2021; Sun & Abraham, 2021).
 
 ### Model 3: Offshore moderation (H5)
@@ -66,7 +80,7 @@ This specification tests whether offshore-regulating districts differ systematic
 
 ### Spatial diagnostic (H4)
 
-After estimating district treatment effects, we test for global spatial autocorrelation using permutation-based Moran's I (Anselin, 1995). This assesses whether high- and low-response districts are geographically clustered in ways consistent with diffusion or regional administrative spillovers.
+After estimating district treatment effects, we test for global spatial autocorrelation using permutation-based Moran's I (Anselin, 1995). The statistic is computed from a manually specified district contiguity matrix and evaluated with randomization inference (5,000 permutations), assessing whether high- and low-response districts are geographically clustered in ways consistent with diffusion or regional administrative spillovers.
 
 All models use district-clustered standard errors.
 
@@ -122,7 +136,7 @@ Figure 2 complements Table 2 by displaying the full time path (including pre-pol
 
 ### 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).
+District-level post-policy responses are substantively heterogeneous in magnitude and direction, ranging from substantial acceleration (e.g., District 09) to substantial slowdown (e.g., Districts 03 and 04). The omnibus joint test of district-by-post terms in this specification is not statistically significant (Wald chi-square = 0.670, p=0.4130), so this heterogeneity is treated as descriptive/structural variation rather than a decisive global rejection test.
 
 From district effect summaries used in mapping:
 
@@ -193,11 +207,7 @@ Moran’s I on district effects:
 - \(I = -0.0493\), permutation p=0.8550.
 
 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.
-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
 
@@ -241,7 +251,7 @@ Table 4 consolidates robustness evidence in one place: level-shift estimates are
 
 ## 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.
+The transparency reform is associated with a gradual statewide acceleration in enforcement timing rather than a single immediate break at implementation. District responses still diverge notably in estimated magnitudes and signs, and offshore jurisdiction explains a meaningful share of that variation 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.
 

analysis/draft_appendix.md

@@ -5,7 +5,21 @@
 
 ### 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`.
+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`.
+
+### A1b. Pipeline volume and sample flow
+
+| Stage | Count |
+| :--- | ---: |
+| Well records loaded (well universe table) | 1,010,432 |
+| Inspection records loaded (all available years) | 1,878,764 |
+| Violation records loaded (all available years) | 193,338 |
+| Inspection records retained (2015-2025) | 1,867,859 |
+| Violation records retained (2015-2025) | 191,762 |
+| District-year panel observations | 143 |
+| Districts represented | 13 |
+
+These counts show that district-year inference is generated from very large underlying administrative record streams, with modest reductions due to the analytic time-window restriction.
 
 ### A2. Core variables
 
@@ -51,7 +65,7 @@ All models report district-clustered standard errors.
 
 ### B4. Spatial diagnostic (H4)
 
-H4 is tested using permutation-based global Moran's I on estimated district treatment effects.
+H4 is tested using permutation-based global Moran's I on estimated district treatment effects, using a manually specified district contiguity matrix and 5,000 random permutations for inference.
 
 ## Appendix C. Main Run Outputs
 
@@ -83,6 +97,13 @@ Substantively, this table supports the main-text conclusion that the policy effe
 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.
 
+### C2b. H2 omnibus heterogeneity test
+
+- Wald chi-square (all district-by-post terms = 0): 0.670
+- P-value: 0.4130
+
+This omnibus test is not statistically significant in the current run, so district heterogeneity is interpreted primarily from the dispersion of district-specific estimates and mapped effect magnitudes.
+
 ### C3. Offshore differential annual effects (ref=2018)
 
 | Year | Offshore differential coef | P-value |