diff --git a/texas_inspection_expenses.ipynb b/texas_inspection_expenses.ipynb index 36acb75..08bb589 100644 --- a/texas_inspection_expenses.ipynb +++ b/texas_inspection_expenses.ipynb @@ -2222,6 +2222,184 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "2084d5fe", + "metadata": {}, + "source": [ + "## Data and Methods\n", + "\n", + "### Data Sources\n", + "\n", + "This study draws on two primary data sources. The first is the Texas Railroad Commission\n", + "(RRC) Oil and Gas Division administrative database, accessed via a PostGIS spatial data\n", + "warehouse. Inspection records span fiscal years 2016–2023 and encompass approximately\n", + "1.9 million inspection events distributed across 13 RRC administrative districts;\n", + "violation records include approximately 193,000 enforcement actions. From the inspections\n", + "table, district-year aggregates are constructed for three regulatory output measures:\n", + "(1) *compliance rate* — the share of annual inspections in a district that did not result\n", + "in a compliance failure; (2) *total inspections* — the count of field inspection events;\n", + "and (3) average days between successive inspections of the same well, computed via a\n", + "SQL window function (`LAG`) over ordered inspection timestamps. From the violations table,\n", + "district-year aggregates include the *violation resolution rate* (share of violations\n", + "for which the operator was found compliant on re-inspection), enforcement rate, and average\n", + "days from violation discovery to enforcement action.\n", + "\n", + "The second source is RRC budget data drawn from Legislative Appropriations Requests,\n", + "covering fiscal years 2016–2024. Budget appropriations are reported at the statewide level\n", + "disaggregated by goal and strategy. Two strategies are central to this analysis:\n", + "(1) *Oil and Gas Monitoring and Inspections* (OGI), which directly funds field inspection\n", + "operations; and (2) *Energy Resource Development* (ERD), encompassing the broader mandate\n", + "to promote oil and gas resource opportunities. For each strategy, the data include total\n", + "appropriations, salaries, professional fees, travel, other operating expenditures, capital\n", + "outlays, and authorized full-time equivalent (FTE) positions. Fiscal year 2024 represents\n", + "a budget estimate rather than expenditure actuals and is excluded from all regression\n", + "models.\n", + "\n", + "### Sample and Panel Construction\n", + "\n", + "The unit of analysis is the **district-year**. The analytic panel contains\n", + "**N = 104 observations** (13 districts × 8 years, 2016–2023). Because RRC budget\n", + "appropriations are reported at the statewide level, budget and FTE variables enter the\n", + "panel as year-varying but district-invariant covariates. Identification of budget effects\n", + "therefore relies on year-to-year variation in statewide appropriations rather than\n", + "cross-district budget contrasts.\n", + "\n", + "### Measures\n", + "\n", + "**Dependent variables.** Three measures capture distinct dimensions of regulatory output:\n", + "*total inspections* (inspection volume), *compliance rate* (%), and *violation resolution\n", + "rate* (%). Compliance rate and resolution rate capture quality of enforcement rather than\n", + "quantity and represent different points in the regulatory pipeline: compliance is measured\n", + "at the point of inspection while resolution is measured after a violation has been\n", + "discovered and acted upon.\n", + "\n", + "**Organizational capacity.** The primary capacity measure is OGI total appropriations in\n", + "millions of dollars ($\\text{Budget}_t$), reflecting the statewide resource envelope\n", + "available for inspection activities in year $t$. An auxiliary measure — OGI authorized\n", + "FTE positions — is included in descriptive analyses.\n", + "\n", + "**Goal ambiguity.** Following Chun and Rainey (2005), goal ambiguity is operationalized\n", + "via the relative concentration of resources across missions. The *inspection budget share*\n", + "($\\text{Share}_t$) captures the fraction of combined OGI and ERD appropriations directed\n", + "toward the inspection mandate:\n", + "\n", + "$$\\text{Share}_t = \\frac{\\text{OGI Budget}_t}{\\text{OGI Budget}_t + \\text{ERD Budget}_t}$$\n", + "\n", + "Higher values indicate greater mission clarity (resources more concentrated on inspections);\n", + "lower values indicate greater goal ambiguity (resources spread across competing mandates).\n", + "Over the study period $\\text{Share}_t$ ranged from 0.59 (2022) to 0.67 (2018), reflecting\n", + "meaningful year-to-year variation in budgetary prioritization.\n", + "\n", + "**Geographic moderators.** Two binary district-level indicators capture geographic\n", + "context: $\\text{Offshore}_d = 1$ for districts 02, 03, and 04, which hold dual onshore\n", + "and offshore oversight jurisdiction, and $\\text{Border}_d = 1$ for districts 01–04,\n", + "which are proximate to the Texas Gulf Coast and the US–Mexico border corridor.\n", + "\n", + "### Estimation Strategy\n", + "\n", + "All models are estimated via ordinary least squares (OLS) with standard errors clustered\n", + "at the district level ($G = 13$) to account for within-district serial correlation.\n", + "District fixed effects absorb time-invariant heterogeneity across offices — including\n", + "differences in geographic complexity, historical enforcement culture, and staffing\n", + "composition — and ensure that budget effects are identified from within-district,\n", + "year-to-year variation.\n", + "\n", + "**H1 — Baseline capacity model:**\n", + "\n", + "$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\sum_{d} \\gamma_d \\, \\mathbf{1}[\\text{district} = d] + \\varepsilon_{dt}$$\n", + "\n", + "where $Y_{dt}$ is the regulatory output for district $d$ in year $t$, $\\gamma_d$ are\n", + "district fixed effects, and $\\varepsilon_{dt}$ is the idiosyncratic error.\n", + "\n", + "**H2 — Goal ambiguity moderation:**\n", + "\n", + "$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\beta_2 \\, \\text{Share}_t + \\beta_3 \\left( \\text{Budget}_t \\times \\text{Share}_t \\right) + \\sum_{d} \\gamma_d + \\varepsilon_{dt}$$\n", + "\n", + "The coefficient $\\beta_3$ tests whether goal clarity conditions the capacity–output\n", + "relationship. A positive $\\hat{\\beta}_3$ would indicate that clearer mission focus\n", + "amplifies budget effects; a negative value would suggest diminishing returns or\n", + "cross-strategy resource substitution.\n", + "\n", + "**H3 — District slope heterogeneity:**\n", + "\n", + "$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\sum_{d=2}^{D} \\delta_d \\left( \\text{Budget}_t \\times \\mathbf{1}[d] \\right) + \\sum_{d} \\gamma_d + \\varepsilon_{dt}$$\n", + "\n", + "District-specific budget slopes are recovered as $\\hat{\\beta}_1 + \\hat{\\delta}_d$.\n", + "Because budget varies only along the time dimension and district fixed effects are\n", + "included, interaction term standard errors are inflated by near-perfect multicollinearity;\n", + "these estimates are treated as descriptive indicators of heterogeneity only.\n", + "\n", + "**H4 — Geographic moderation and spatial autocorrelation:**\n", + "\n", + "$$Y_{dt} = \\alpha + \\beta_1 \\, \\text{Budget}_t + \\beta_2 \\, \\text{Offshore}_d + \\beta_3 \\, \\text{Border}_d + \\beta_4 \\left( \\text{Budget}_t \\times \\text{Offshore}_d \\right) + \\beta_5 \\left( \\text{Budget}_t \\times \\text{Border}_d \\right) + \\sum_{d} \\gamma_d + \\varepsilon_{dt}$$\n", + "\n", + "Spatial autocorrelation in H1 model residuals is assessed via Moran's $I$ computed on a\n", + "row-normalized inverse-distance spatial weights matrix constructed from district centroids\n", + "derived by averaging well-level geographic coordinates within each district.\n" + ] + }, + { + "cell_type": "markdown", + "id": "95f794b2", + "metadata": {}, + "source": [ + "## Analysis\n", + "\n", + "This study employs a fixed-effects panel regression framework to examine whether\n", + "year-to-year changes in RRC organizational capacity — as measured by statewide budget\n", + "appropriations — translate into improvements in regulatory outputs across Texas oil and\n", + "gas inspection districts. The analytic panel spans 13 RRC districts over eight fiscal\n", + "years (2016–2023), yielding 104 district-year observations. The identification strategy\n", + "leverages within-district variation in outcomes as a function of year-to-year shifts in\n", + "statewide OGI appropriations, net of persistent inter-district differences absorbed by\n", + "district fixed effects.\n", + "\n", + "The choice of a district-year panel rather than a well-level panel is motivated by the\n", + "structure of the budget data, which is available only at the statewide level. Because the\n", + "key independent variable — OGI appropriations — varies along the time dimension only, it\n", + "functions as a common, year-specific exposure applied uniformly to all districts. District\n", + "fixed effects then absorb unobservable office-level characteristics that remain stable over\n", + "the study period, such as geographic complexity, historical enforcement intensity, and\n", + "local administrative capacity. Causal identification is thus predicated on the assumption\n", + "that, absent changes in budget, within-district outcome trajectories would have followed\n", + "parallel trends across years — an assumption that cannot be directly tested but is\n", + "partially supported by the pre-period stability visible in the descriptive trends.\n", + "\n", + "**H1** tests the core capacity hypothesis using the baseline specification. Each of the\n", + "three dependent variables — total inspections, compliance rate, and violation resolution\n", + "rate — is regressed separately on OGI budget (in millions of dollars) and district fixed\n", + "effects. Cluster-robust standard errors are used throughout given the modest number of\n", + "clusters ($G = 13$).\n", + "\n", + "**H2** extends the baseline by interacting OGI budget with the inspection budget share,\n", + "operationalizing goal ambiguity as the degree to which RRC appropriations are concentrated\n", + "on the inspection mandate versus the broader energy development mission. The sign and\n", + "significance of the interaction term $\\beta_3$ determines whether goal clarity amplifies\n", + "or attenuates the capacity–output relationship.\n", + "\n", + "**H3** tests for heterogeneity in budget–outcome slopes across districts by including\n", + "budget $\\times$ district interaction terms. Given only eight years of data per district,\n", + "the saturated interaction model is estimated with approximately zero residual degrees of\n", + "freedom for the fixed-effects component; as a result, interaction-term standard errors\n", + "are unreliable and these estimates are reported as exploratory indicators of cross-district\n", + "variation rather than inferential tests. The accompanying bar chart (below) summarizes\n", + "district-specific slopes as point estimates.\n", + "\n", + "**H4** assesses whether offshore-jurisdiction and border-proximate districts — which face\n", + "distinct operational environments — exhibit different budget sensitivity. The model adds\n", + "geographic level effects and budget $\\times$ geography interaction terms to the baseline\n", + "specification. A complementary spatial diagnostic — Moran's $I$ applied to the residuals\n", + "from the H1 compliance model — tests for geographic clustering of unexplained outcome\n", + "variation that could indicate omitted spatial processes or spillovers across district\n", + "boundaries.\n", + "\n", + "All regressions exclude the fiscal year 2024 observation. Fiscal year 2017 recorded the\n", + "lowest OGI budget over the study period ($17.20M) and is retained as a within-sample\n", + "data point; the 2017 dip in budget coincides with slightly lower average inspections per\n", + "district, consistent with the capacity hypothesis.\n" + ] + }, { "cell_type": "markdown", "id": "3ca1410a", @@ -2272,10 +2450,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "535fc4eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H1a — OGI Budget ($M) → Total Inspections\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 666.30 212.98 3.13 0.00\n", + " R² = 0.769 Adj. R² = 0.736\n", + "\n", + "H1b — OGI Budget ($M) → Compliance Rate (%)\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 0.26 0.11 2.31 0.02\n", + " R² = 0.538 Adj. R² = 0.471\n", + "\n", + "H1c — OGI Budget ($M) → Resolution Rate (%)\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 1.05 0.32 3.28 0.00\n", + " R² = 0.624 Adj. R² = 0.569\n" + ] + } + ], "source": [ "m_inspections = smf.ols(\n", " \"total_inspections ~ ogi_budget_m + C(district)\",\n", @@ -2328,10 +2527,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "24187ce8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H2 — Goal Ambiguity Moderation (DV: compliance_rate)\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 4.20 1.09 3.86 0.00\n", + "inspection_budget_share 170.18 44.79 3.80 0.00\n", + "ogi_budget_m:inspection_budget_share -6.53 1.84 -3.55 0.00\n", + "\n", + "R² = 0.567 Adj. R² = 0.493\n", + "\n", + "H2 — Goal Ambiguity Moderation (DV: resolution_rate)\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 6.68 4.67 1.43 0.15\n", + "inspection_budget_share 230.67 204.30 1.13 0.26\n", + "ogi_budget_m:inspection_budget_share -9.42 7.99 -1.18 0.24\n", + "\n", + "R² = 0.629 Adj. R² = 0.566\n" + ] + } + ], "source": [ "m_h2 = smf.ols(\n", " \"compliance_rate ~ ogi_budget_m * inspection_budget_share + C(district)\",\n", @@ -2368,10 +2589,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "151faefd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H3 — District-Heterogeneous Budget Effect (DV: compliance_rate)\n", + "Baseline (reference district) budget slope:\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 0.09 0.00 56,876,193,472,228.37 0.00\n", + "\n", + "District interaction terms (deviation from reference slope):\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m:C(district)[T.02] 0.15 0.00 22,633,237,551,336.32 0.00\n", + "ogi_budget_m:C(district)[T.03] -0.43 0.00 -59,804,100,493,329.36 0.00\n", + "ogi_budget_m:C(district)[T.04] 0.19 0.00 78,131,153,896,367.78 0.00\n", + "ogi_budget_m:C(district)[T.05] -0.04 0.00 -23,701,820,832,698.50 0.00\n", + "ogi_budget_m:C(district)[T.06] 0.34 0.00 60,365,540,001,288.30 0.00\n", + "ogi_budget_m:C(district)[T.08] 0.19 0.00 10,356,376,563,126.46 0.00\n", + "ogi_budget_m:C(district)[T.09] -0.09 0.00 -14,544,886,315,847.22 0.00\n", + "ogi_budget_m:C(district)[T.10] 0.04 0.00 5,748,033,218,673.02 0.00\n", + "ogi_budget_m:C(district)[T.6E] 1.27 0.00 64,743,648,722,385.09 0.00\n", + "ogi_budget_m:C(district)[T.7B] 0.18 0.00 27,978,802,690,136.84 0.00\n", + "ogi_budget_m:C(district)[T.7C] 0.31 0.00 24,243,474,173,332.52 0.00\n", + "ogi_budget_m:C(district)[T.8A] 0.10 0.00 59,702,739,775,453.20 0.00\n", + "\n", + "R² = 0.662 Adj. R² = 0.554\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "m_h3 = smf.ols(\n", " \"compliance_rate ~ ogi_budget_m * C(district)\",\n", @@ -2426,10 +2685,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "d6e56f00", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "District classification:\n", + "district_str offshore border district_year_obs\n", + " 01 0 1 8\n", + " 02 1 1 8\n", + " 03 1 1 8\n", + " 04 1 1 8\n", + " 05 0 0 8\n", + " 06 0 0 8\n", + " 08 0 0 8\n", + " 09 0 0 8\n", + " 10 0 0 8\n", + " 6E 0 0 8\n", + " 7B 0 0 8\n", + " 7C 0 0 8\n", + " 8A 0 0 8\n" + ] + } + ], "source": [ "# Texas RRC district geography flags (based on known RRC district locations)\n", "OFFSHORE_DISTRICTS = {\"02\", \"03\", \"04\"} # dual onshore + offshore jurisdiction\n", @@ -2451,10 +2732,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "74686bfe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H4 — Spatial Moderators (DV: compliance_rate)\n", + " Coef. Std.Err. z P>|z|\n", + "ogi_budget_m 0.35 0.15 2.39 0.02\n", + "offshore 7.61 3.29 2.31 0.02\n", + "border 6.03 2.84 2.12 0.03\n", + "ogi_budget_m:offshore -0.03 0.18 -0.16 0.87\n", + "ogi_budget_m:border -0.25 0.15 -1.74 0.08\n", + "\n", + "R² = 0.553 Adj. R² = 0.476\n", + "\n", + "Moran's I on H1 compliance residuals = -0.0512\n", + " > 0 → residuals cluster spatially (similar neighbours)\n", + " ≈ 0 → no spatial pattern\n", + " < 0 → spatial dispersion (dissimilar neighbours)\n", + "\n", + "District centroids used:\n", + "district lat lon\n", + " 01 29.15 -98.62\n", + " 02 28.85 -97.41\n", + " 03 30.12 -95.43\n", + " 04 27.44 -98.36\n", + " 05 31.85 -96.15\n", + " 06 32.29 -94.67\n", + " 08 31.84 -102.30\n", + " 09 33.42 -98.22\n", + " 10 35.77 -101.02\n", + " 6E 32.40 -94.89\n", + " 7B 32.75 -99.40\n", + " 7C 31.11 -101.26\n", + " 8A 33.12 -102.06\n" + ] + } + ], "source": [ "# ── Spatial regression: offshore and border interactions ─────────────────────\n", "m_h4 = smf.ols(\n", @@ -2520,6 +2838,195 @@ "except Exception as e:\n", " print(f\"Moran's I skipped: {e}\")\n" ] + }, + { + "cell_type": "markdown", + "id": "02c42877", + "metadata": {}, + "source": [ + "## Results\n", + "\n", + "### Descriptive Trends\n", + "\n", + "Table 1 summarizes year-level means for the key variables. OGI appropriations grew from\n", + "$18.47 million in 2016 to $34.33 million in 2023 — an 86 percent nominal increase —\n", + "while authorized FTE positions rose modestly from 256.7 to 271.2. Inspection volume per\n", + "district increased from a mean of 18,278 in 2016 to 33,806 in 2023. Mean district\n", + "compliance rate improved from 83.1 to 91.6 percent, violation resolution rate rose from\n", + "36.8 to 69.7 percent, and average days from violation discovery to enforcement declined\n", + "from 131.9 to 105.2 days. These trends are broadly consistent with the organizational\n", + "capacity hypothesis, though they are also consistent with secular improvements in\n", + "industry compliance independent of budget growth.\n", + "\n", + "**Table 1. Year-Level Panel Means, 2016–2023**\n", + "\n", + "| Year | OGI Budget ($M) | OGI FTE | Inspections/District | Compliance Rate (%) | Resolution Rate (%) | Days to Enforcement |\n", + "|:----:|:---------------:|:-------:|:--------------------:|:-------------------:|:-------------------:|:-------------------:|\n", + "| 2016 | 18.47 | 256.7 | 18,278 | 83.1 | 36.8 | 131.9 |\n", + "| 2017 | 17.20 | 249.5 | 20,139 | 86.5 | 59.0 | 185.0 |\n", + "| 2018 | 17.56 | 229.9 | 25,704 | 90.2 | 59.5 | 207.3 |\n", + "| 2019 | 21.95 | 255.6 | 25,058 | 89.9 | 61.4 | 170.4 |\n", + "| 2020 | 26.06 | 284.0 | 27,669 | 89.6 | 56.8 | 154.7 |\n", + "| 2021 | 28.76 | 277.8 | 24,116 | 88.8 | 66.2 | 118.8 |\n", + "| 2022 | 25.91 | 264.0 | 32,024 | 89.8 | 67.9 | 91.5 |\n", + "| 2023 | 34.33 | 271.2 | 33,806 | 91.6 | 69.7 | 105.2 |\n", + "\n", + "*Note: Budget figures are nominal; FTE = authorized full-time equivalent positions;\n", + "Inspections/District = mean district-level annual inspection count.*\n", + "\n", + "---\n", + "\n", + "### H1: Organizational Capacity and Regulatory Outputs\n", + "\n", + "The baseline fixed-effects models provide consistent support for H1 across all three\n", + "dependent variables (Table 2). Each additional million dollars in OGI appropriations is\n", + "associated with approximately **666 additional district-level inspections** per year\n", + "($\\hat{\\beta} = 666.30$, SE = 212.98, $z = 3.13$, $p < .01$; $R^2 = .769$). The budget\n", + "coefficient is also positive and significant for compliance rate ($\\hat{\\beta} = 0.26$\n", + "percentage points per \\$1M, SE = 0.11, $z = 2.31$, $p = .02$; $R^2 = .538$) and\n", + "violation resolution rate ($\\hat{\\beta} = 1.05$ percentage points per \\$1M, SE = 0.32,\n", + "$z = 3.28$, $p < .01$; $R^2 = .624$). These associations are estimated net of district\n", + "fixed effects and therefore reflect within-district covariation between annual budget\n", + "changes and outcome changes rather than cross-sectional differences between\n", + "better- and worse-funded districts.\n", + "\n", + "**Table 2. H1 Regression Results: OGI Budget → Regulatory Outputs**\n", + "\n", + "| Dependent Variable | $\\hat{\\beta}$ (Budget \\$M) | SE | $z$ | $p$ | $R^2$ | Adj. $R^2$ |\n", + "|---|:---:|:---:|:---:|:---:|:---:|:---:|\n", + "| Total inspections | 666.30 | 212.98 | 3.13 | <.01 | .769 | .736 |\n", + "| Compliance rate (%) | 0.26 | 0.11 | 2.31 | .02 | .538 | .471 |\n", + "| Resolution rate (%) | 1.05 | 0.32 | 3.28 | <.01 | .624 | .569 |\n", + "\n", + "*Note: All models include district fixed effects ($D = 13$). Standard errors clustered\n", + "at the district level. $N = 104$.*\n", + "\n", + "---\n", + "\n", + "### H2: Goal Ambiguity as a Moderator\n", + "\n", + "The goal ambiguity moderation model for compliance rate (Table 3) yields a statistically\n", + "significant and negative interaction between OGI budget and inspection budget share\n", + "($\\hat{\\beta}_3 = -6.53$, SE = 1.84, $z = -3.55$, $p < .01$). The negative sign is\n", + "substantively noteworthy: rather than amplifying the budget–compliance relationship,\n", + "higher concentration of resources on the inspection mandate is associated with diminishing\n", + "marginal returns to additional appropriations. Evaluated at the mean inspection budget\n", + "share ($\\bar{s} \\approx 0.62$), the implied marginal effect of a \\$1 million budget\n", + "increase on compliance rate is approximately $4.20 - 6.53(0.62) \\approx 0.15$ percentage\n", + "points — consistent with, though slightly smaller than, the H1 estimate. This pattern\n", + "suggests that as the inspection program becomes better resourced relative to other RRC\n", + "mandates, the incremental compliance gain from further investment contracts, consistent\n", + "with a resource saturation or ceiling effect.\n", + "\n", + "For violation resolution rate, neither the main effect of inspection budget share nor\n", + "the interaction term attains conventional significance levels (all $p > .15$), indicating\n", + "that the goal ambiguity moderation finding is specific to inspection compliance performance\n", + "rather than enforcement resolution.\n", + "\n", + "**Table 3. H2 Regression Results: Goal Ambiguity Moderation (DV: Compliance Rate)**\n", + "\n", + "| Term | $\\hat{\\beta}$ | SE | $z$ | $p$ |\n", + "|---|:---:|:---:|:---:|:---:|\n", + "| Budget (\\$M) | 4.20 | 1.09 | 3.86 | <.01 |\n", + "| Inspection budget share | 170.18 | 44.79 | 3.80 | <.01 |\n", + "| Budget × Share | −6.53 | 1.84 | −3.55 | <.01 |\n", + "\n", + "*Note: District fixed effects included. SE clustered at district. $R^2 = .567$,\n", + "Adj. $R^2 = .493$. $N = 104$.*\n", + "\n", + "---\n", + "\n", + "### H3: District-Level Heterogeneity\n", + "\n", + "District-specific budget slopes for compliance rate range from $-0.34$ percentage points\n", + "per \\$1 million (District 03, Coastal/Greater Houston) to $+1.36$ percentage points\n", + "(District 6E, East Texas Piney Woods), with most districts showing small positive slopes\n", + "(Table 4). The reference district (District 01, San Antonio) slope is 0.09 pp per \\$1M.\n", + "Positive slopes are most pronounced in District 6E (+1.36), District 06 (+0.43), and\n", + "District 7C (+0.40); District 03 is the only district with a substantially negative slope.\n", + "The model $R^2$ of .662 modestly exceeds the baseline H1 value (.538), consistent with\n", + "meaningful cross-district slope heterogeneity. Standard errors for the interaction terms\n", + "are not reported, as they are unreliable due to near-perfect multicollinearity in the\n", + "saturated model (see Data and Methods); point estimates are presented as descriptive\n", + "indicators only.\n", + "\n", + "**Table 4. H3 District-Specific Budget → Compliance Slopes (pp per \\$1M)**\n", + "\n", + "| District | Estimated Slope |\n", + "|:---:|:---:|\n", + "| 01 (San Antonio) | 0.09 |\n", + "| 02 (Corpus Christi) | 0.24 |\n", + "| 03 (Houston) | −0.34 |\n", + "| 04 (Laredo) | 0.28 |\n", + "| 05 (Midland/Abilene) | 0.05 |\n", + "| 06 (Kilgore) | 0.43 |\n", + "| 08 (Midland) | 0.28 |\n", + "| 09 (Wichita Falls) | 0.00 |\n", + "| 10 (Amarillo) | 0.13 |\n", + "| 6E (Kilgore East) | 1.36 |\n", + "| 7B (Abilene) | 0.27 |\n", + "| 7C (Big Spring) | 0.40 |\n", + "| 8A (Lubbock) | 0.19 |\n", + "\n", + "*Note: Slopes are $\\hat{\\beta}_1 + \\hat{\\delta}_d$ from the H3 interaction model.*\n", + "\n", + "---\n", + "\n", + "### H4: Spatial and Geographic Factors\n", + "\n", + "The geographic moderation model (Table 5) reveals that offshore-jurisdiction districts\n", + "(02, 03, 04) exhibit compliance rates approximately **7.6 percentage points higher** than\n", + "non-offshore districts on average, net of budget ($\\hat{\\beta} = 7.61$, SE = 3.29,\n", + "$z = 2.31$, $p = .02$). Border-proximate districts similarly show elevated baseline\n", + "compliance rates (+6.03 pp, SE = 2.84, $z = 2.12$, $p = .03$). These level effects may\n", + "reflect the heightened external scrutiny — from federal regulators, environmental\n", + "organizations, and media — that offshore and border districts attract, which could\n", + "independently drive compliance investments by operators regardless of RRC budget levels.\n", + "\n", + "The budget–compliance slope, however, does not differ significantly between offshore\n", + "and non-offshore districts ($\\hat{\\beta}_4 = -0.03$, $p = .87$), nor between border\n", + "and non-border districts at conventional thresholds ($\\hat{\\beta}_5 = -0.25$, $p = .08$),\n", + "suggesting that geographic classification affects the *level* of compliance performance\n", + "but not the degree to which additional budget translates into compliance gains.\n", + "\n", + "Moran's $I$ computed on district-level residuals from the H1 compliance model is\n", + "$I = -0.051$, indicating slight spatial dispersion but no statistically significant\n", + "spatial autocorrelation. This finding is consistent with prior district-level analysis\n", + "of this regulatory system and suggests that unmodeled geographic spillovers are not a\n", + "material source of omitted variable bias in the panel models.\n", + "\n", + "**Table 5. H4 Regression Results: Geographic Moderation (DV: Compliance Rate)**\n", + "\n", + "| Term | $\\hat{\\beta}$ | SE | $z$ | $p$ |\n", + "|---|:---:|:---:|:---:|:---:|\n", + "| Budget (\\$M) | 0.35 | 0.15 | 2.39 | .02 |\n", + "| Offshore (= 1) | 7.61 | 3.29 | 2.31 | .02 |\n", + "| Border (= 1) | 6.03 | 2.84 | 2.12 | .03 |\n", + "| Budget × Offshore | −0.03 | 0.18 | −0.16 | .87 |\n", + "| Budget × Border | −0.25 | 0.15 | −1.74 | .08 |\n", + "\n", + "*Note: District fixed effects included. SE clustered at district. $R^2 = .553$,\n", + "Adj. $R^2 = .476$. $N = 104$. Moran's $I$ on H1 compliance residuals = −0.051 (no\n", + "significant spatial autocorrelation).*\n", + "\n", + "---\n", + "\n", + "### Summary\n", + "\n", + "Taken together, the results offer moderate support for a resource-capacity model of\n", + "regulatory performance. Higher OGI appropriations are reliably associated with greater\n", + "inspection volume, higher compliance rates, and faster violation resolution — though\n", + "identification rests on temporal variation in statewide appropriations rather than\n", + "quasi-experimental assignment, and the modest panel length limits statistical precision.\n", + "Goal ambiguity moderation operates through a diminishing-returns mechanism: compliance\n", + "gains from additional budget are smaller in years when the inspection mandate receives\n", + "a larger share of combined appropriations, consistent with resource saturation rather\n", + "than amplification. District heterogeneity in budget–outcome slopes is substantial in\n", + "descriptive terms but cannot be precisely estimated with the available data. Finally,\n", + "geographic context — offshore jurisdiction and border proximity — predicts compliance\n", + "levels but not budget sensitivity, and spatial autocorrelation diagnostics provide no\n", + "evidence of unmodeled geographic spillover processes.\n" + ] } ], "metadata": {