dadams
fff3019e27
- SQL year filters: BETWEEN 2016 AND 2025 - is_budget_year flag: year >= 2024 (covers both 2024 budget estimate and 2025 where no budget data exists) — both excluded from regressions - Data & Methods and Analysis cells updated to reflect 2016-2025 data range with 2016-2023 as the regression sample Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2024 lines
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2024 lines
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"cells": [
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"cell_type": "markdown",
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"id": "dc6818b8",
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"metadata": {},
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"source": [
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"# Texas RRC Inspection Expenses Analysis\n",
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"\n",
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"**Research question:** Does organizational capacity (budget, staffing) predict better regulatory outputs (inspections, compliance, enforcement), and how is that relationship moderated by goal ambiguity, district-level heterogeneity, and spatial/geographic factors?\n",
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"\n",
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"## Hypotheses\n",
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"- **H1 — Capacity → Outputs:** Higher OGI budget and FTE predict more inspections, higher compliance rates, and faster violation resolution.\n",
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"- **H2 — Goal Ambiguity:** When a larger share of RRC budget goes to the more ambiguous \"Energy Resource Development\" goal, the capacity → output relationship weakens.\n",
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"- **H3 — Multilevel / District Effects:** The capacity → output relationship varies across RRC districts (budget slope heterogeneity).\n",
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"- **H4 — Spatial & Geographic:** Offshore-jurisdiction and border districts moderate the capacity → output relationship; spatial autocorrelation in residuals is tested via Moran's I.\n",
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"\n",
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"**Data:**\n",
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"- PostgreSQL warehouse (`texas_data`): `inspections`, `violations`, `well_shape_tract`\n",
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"- `RRC Budget Data.xlsx`: statewide RRC budget by strategy, 2016–2024\n",
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"- Analysis period: 2016–2023 (2024 is budget estimate, excluded from regressions)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3ed415f0",
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"Requirement already satisfied: rfc3987-syntax>=1.1.0 in ./.venv/lib/python3.9/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (1.1.0)\n",
|
||
"Requirement already satisfied: babel>=2.10 in ./.venv/lib/python3.9/site-packages (from jupyterlab-server<3,>=2.28.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (2.18.0)\n",
|
||
"Requirement already satisfied: json5>=0.9.0 in ./.venv/lib/python3.9/site-packages (from jupyterlab-server<3,>=2.28.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (0.13.0)\n",
|
||
"Requirement already satisfied: requests>=2.31 in ./.venv/lib/python3.9/site-packages (from jupyterlab-server<3,>=2.28.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (2.32.5)\n",
|
||
"Requirement already satisfied: bleach[css]!=5.0.0 in ./.venv/lib/python3.9/site-packages (from nbconvert->jupyter->-r requirements.txt (line 1)) (6.2.0)\n",
|
||
"Requirement already satisfied: defusedxml in ./.venv/lib/python3.9/site-packages (from nbconvert->jupyter->-r requirements.txt (line 1)) (0.7.1)\n",
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||
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|
||
"Requirement already satisfied: nbclient>=0.5.0 in ./.venv/lib/python3.9/site-packages (from nbconvert->jupyter->-r requirements.txt (line 1)) (0.10.2)\n",
|
||
"Requirement already satisfied: beautifulsoup4 in ./.venv/lib/python3.9/site-packages (from nbconvert->jupyter->-r requirements.txt (line 1)) (4.14.3)\n",
|
||
"Requirement already satisfied: pandocfilters>=1.4.1 in ./.venv/lib/python3.9/site-packages (from nbconvert->jupyter->-r requirements.txt (line 1)) (1.5.1)\n",
|
||
"Requirement already satisfied: mistune<4,>=2.0.3 in ./.venv/lib/python3.9/site-packages (from nbconvert->jupyter->-r requirements.txt (line 1)) (3.2.0)\n",
|
||
"Requirement already satisfied: webencodings in ./.venv/lib/python3.9/site-packages (from bleach[css]!=5.0.0->nbconvert->jupyter->-r requirements.txt (line 1)) (0.5.1)\n",
|
||
"Requirement already satisfied: tinycss2<1.5,>=1.1.0 in ./.venv/lib/python3.9/site-packages (from bleach[css]!=5.0.0->nbconvert->jupyter->-r requirements.txt (line 1)) (1.4.0)\n",
|
||
"Requirement already satisfied: fastjsonschema>=2.15 in ./.venv/lib/python3.9/site-packages (from nbformat>=5.3.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (2.21.2)\n",
|
||
"Requirement already satisfied: urllib3<3,>=1.21.1 in ./.venv/lib/python3.9/site-packages (from requests>=2.31->jupyterlab-server<3,>=2.28.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (2.6.3)\n",
|
||
"Requirement already satisfied: charset_normalizer<4,>=2 in ./.venv/lib/python3.9/site-packages (from requests>=2.31->jupyterlab-server<3,>=2.28.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (3.4.4)\n",
|
||
"Requirement already satisfied: lark>=1.2.2 in ./.venv/lib/python3.9/site-packages (from rfc3987-syntax>=1.1.0->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (1.3.1)\n",
|
||
"Requirement already satisfied: cffi>=1.0.1 in ./.venv/lib/python3.9/site-packages (from argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (2.0.0)\n",
|
||
"Requirement already satisfied: pycparser in ./.venv/lib/python3.9/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (2.23)\n",
|
||
"Requirement already satisfied: soupsieve>=1.6.1 in ./.venv/lib/python3.9/site-packages (from beautifulsoup4->nbconvert->jupyter->-r requirements.txt (line 1)) (2.8.3)\n",
|
||
"Requirement already satisfied: arrow>=0.15.0 in ./.venv/lib/python3.9/site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->jupyterlab->jupyter->-r requirements.txt (line 1)) (1.4.0)\n",
|
||
"Requirement already satisfied: asttokens>=2.1.0 in ./.venv/lib/python3.9/site-packages (from stack-data->ipython>=7.23.1->ipykernel->-r requirements.txt (line 2)) (3.0.1)\n",
|
||
"Requirement already satisfied: executing>=1.2.0 in ./.venv/lib/python3.9/site-packages (from stack-data->ipython>=7.23.1->ipykernel->-r requirements.txt (line 2)) (2.2.1)\n",
|
||
"Requirement already satisfied: pure-eval in ./.venv/lib/python3.9/site-packages (from stack-data->ipython>=7.23.1->ipykernel->-r requirements.txt (line 2)) (0.2.3)\n",
|
||
"Installing collected packages: pyparsing, pillow, kiwisolver, importlib-resources, fonttools, cycler, contourpy, matplotlib, seaborn\n",
|
||
"Successfully installed contourpy-1.3.0 cycler-0.12.1 fonttools-4.60.2 importlib-resources-6.5.2 kiwisolver-1.4.7 matplotlib-3.9.4 pillow-11.3.0 pyparsing-3.3.2 seaborn-0.13.2\n",
|
||
"\u001b[33mWARNING: You are using pip version 21.2.4; however, version 26.0.1 is available.\n",
|
||
"You should consider upgrading via the '/Users/dpadams/Repos/texas-inspection-expenses/.venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
|
||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%pip install -r requirements.txt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "49de2b5c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Matplotlib is building the font cache; this may take a moment.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"import warnings\n",
|
||
"from pathlib import Path\n",
|
||
"from urllib.parse import quote_plus\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import matplotlib.ticker as mticker\n",
|
||
"import statsmodels.formula.api as smf\n",
|
||
"from dotenv import load_dotenv\n",
|
||
"from sqlalchemy import create_engine, text\n",
|
||
"from scipy.spatial.distance import cdist\n",
|
||
"\n",
|
||
"warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
|
||
"pd.set_option(\"display.float_format\", \"{:,.2f}\".format)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "08420da3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Connected → texas_data on localhost:5433\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"load_dotenv(override=False)\n",
|
||
"\n",
|
||
"host = os.getenv(\"PGHOST\", \"localhost\")\n",
|
||
"port = os.getenv(\"5433\", \"5433\")\n",
|
||
"user = os.getenv(\"PGUSER\", \"postgres\")\n",
|
||
"password = quote_plus(os.getenv(\"PGPASSWORD\", \"\"))\n",
|
||
"database = os.getenv(\"PGDATABASE\", \"texas_data\")\n",
|
||
"\n",
|
||
"engine = create_engine(\n",
|
||
" f\"postgresql+psycopg2://{user}:{password}@{host}:{port}/{database}\"\n",
|
||
")\n",
|
||
"print(f\"Connected → {database} on {host}:{port}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b4e6c44f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1. Data Loading"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "43886f13",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# District-year inspection metrics aggregated in SQL.\n",
|
||
"# LAG() computes days since the previous inspection for the same well (api_norm).\n",
|
||
"insp_sql = \"\"\"\n",
|
||
"WITH lagged AS (\n",
|
||
" SELECT\n",
|
||
" district,\n",
|
||
" EXTRACT(year FROM inspection_date)::int AS year,\n",
|
||
" api_norm,\n",
|
||
" inspection_date,\n",
|
||
" CASE WHEN UPPER(compliance::text) IN ('YES', 'Y') THEN 1.0 ELSE 0.0 END AS is_compliant,\n",
|
||
" EXTRACT(EPOCH FROM (\n",
|
||
" inspection_date\n",
|
||
" - LAG(inspection_date) OVER (PARTITION BY api_norm ORDER BY inspection_date)\n",
|
||
" )) / 86400.0 AS days_since_prev\n",
|
||
" FROM inspections\n",
|
||
" WHERE inspection_date IS NOT NULL\n",
|
||
" AND district IS NOT NULL\n",
|
||
" AND EXTRACT(year FROM inspection_date) BETWEEN 2016 AND 2025\n",
|
||
")\n",
|
||
"SELECT\n",
|
||
" district,\n",
|
||
" year,\n",
|
||
" COUNT(*) AS total_inspections,\n",
|
||
" COUNT(DISTINCT api_norm) AS unique_wells,\n",
|
||
" ROUND(AVG(is_compliant)::numeric * 100, 2) AS compliance_rate,\n",
|
||
" ROUND(AVG(days_since_prev)::numeric, 1) AS avg_days_between_inspections\n",
|
||
"FROM lagged\n",
|
||
"GROUP BY district, year\n",
|
||
"ORDER BY district, year\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"insp = pd.read_sql(text(insp_sql), engine)\n",
|
||
"print(f\"Inspections panel: {len(insp):,} district-year rows | {insp['district'].nunique()} districts\")\n",
|
||
"insp.head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "3841e2f5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# District-year violation metrics. Blank last_enf_action strings treated as no action.\n",
|
||
"viol_sql = \"\"\"\n",
|
||
"SELECT\n",
|
||
" district,\n",
|
||
" EXTRACT(year FROM violation_disc_date)::int AS year,\n",
|
||
" COUNT(*) AS total_violations,\n",
|
||
" COUNT(DISTINCT api_norm) AS unique_wells_with_violations,\n",
|
||
" SUM(CASE WHEN major_viol_ind = 'Y' THEN 1 ELSE 0 END) AS major_violations,\n",
|
||
" ROUND(AVG(CASE WHEN compliant_on_reinsp = 'Y' THEN 1.0 ELSE 0.0 END)::numeric * 100, 2)\n",
|
||
" AS resolution_rate,\n",
|
||
" ROUND(AVG(CASE WHEN last_enf_action IS NOT NULL AND last_enf_action <> ''\n",
|
||
" THEN 1.0 ELSE 0.0 END)::numeric * 100, 2) AS enforcement_rate,\n",
|
||
" ROUND(AVG(\n",
|
||
" CASE WHEN last_enf_action_date IS NOT NULL\n",
|
||
" THEN EXTRACT(EPOCH FROM (last_enf_action_date - violation_disc_date)) / 86400.0\n",
|
||
" END\n",
|
||
" )::numeric, 1) AS avg_days_to_enforcement\n",
|
||
"FROM violations\n",
|
||
"WHERE violation_disc_date IS NOT NULL\n",
|
||
" AND district IS NOT NULL\n",
|
||
" AND EXTRACT(year FROM violation_disc_date) BETWEEN 2016 AND 2025\n",
|
||
"GROUP BY district, year\n",
|
||
"ORDER BY district, year\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"viol = pd.read_sql(text(viol_sql), engine)\n",
|
||
"print(f\"Violations panel: {len(viol):,} district-year rows\")\n",
|
||
"viol.head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "9e196cac",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Budget long: 18 rows (2 strategies × 9 years)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "year",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "strategy",
|
||
"rawType": "object",
|
||
"type": "string"
|
||
},
|
||
{
|
||
"name": "total_budget",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "salaries",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "other_personnel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "professional_fees",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "travel",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "other_operating",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "capital_exp",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "fte",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
}
|
||
],
|
||
"ref": "d1e3d3b9-d373-4e93-9e56-96a3f8488e43",
|
||
"rows": [
|
||
[
|
||
"0",
|
||
"2016",
|
||
"Energy Resource Development",
|
||
"11708475.0",
|
||
"7669719.0",
|
||
"398589.0",
|
||
"3366389.0",
|
||
"16477.0",
|
||
"210293.0",
|
||
"0.0",
|
||
"130.6"
|
||
],
|
||
[
|
||
"1",
|
||
"2017",
|
||
"Energy Resource Development",
|
||
"10911094.0",
|
||
"7273775.0",
|
||
"389348.0",
|
||
"3118066.0",
|
||
"6792.0",
|
||
"77855.0",
|
||
"0.0",
|
||
"120.3"
|
||
],
|
||
[
|
||
"2",
|
||
"2018",
|
||
"Energy Resource Development",
|
||
"9846886.0",
|
||
"7292933.0",
|
||
"282337.0",
|
||
"977645.0",
|
||
"28694.0",
|
||
"1045727.0",
|
||
"0.0",
|
||
"131.0"
|
||
],
|
||
[
|
||
"3",
|
||
"2019",
|
||
"Energy Resource Development",
|
||
"11123757.0",
|
||
"8068497.0",
|
||
"217988.0",
|
||
"1493755.0",
|
||
"73651.0",
|
||
"988740.0",
|
||
"13232.0",
|
||
"137.4"
|
||
],
|
||
[
|
||
"4",
|
||
"2020",
|
||
"Energy Resource Development",
|
||
"17280569.0",
|
||
"9707894.0",
|
||
"236356.0",
|
||
"5989236.0",
|
||
"41752.0",
|
||
"1165481.0",
|
||
"54037.0",
|
||
"153.4"
|
||
],
|
||
[
|
||
"5",
|
||
"2021",
|
||
"Energy Resource Development",
|
||
"16237704.0",
|
||
"10887561.0",
|
||
"237777.0",
|
||
"3562816.0",
|
||
"5614.0",
|
||
"1446301.0",
|
||
"10140.0",
|
||
"168.1"
|
||
],
|
||
[
|
||
"6",
|
||
"2022",
|
||
"Energy Resource Development",
|
||
"25583205.0",
|
||
"11166309.0",
|
||
"246340.0",
|
||
"12560550.0",
|
||
"37731.0",
|
||
"1246443.0",
|
||
"19985.0",
|
||
"157.1"
|
||
],
|
||
[
|
||
"7",
|
||
"2023",
|
||
"Energy Resource Development",
|
||
"26903564.0",
|
||
"11056060.0",
|
||
"252933.0",
|
||
"12846821.0",
|
||
"56650.0",
|
||
"2287481.0",
|
||
"48344.0",
|
||
"151.3"
|
||
],
|
||
[
|
||
"8",
|
||
"2024",
|
||
"Energy Resource Development",
|
||
"35533565.0",
|
||
"13183578.0",
|
||
"229161.0",
|
||
"15140585.0",
|
||
"144641.0",
|
||
"6425653.0",
|
||
"0.0",
|
||
"186.0"
|
||
],
|
||
[
|
||
"9",
|
||
"2016",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"18471666.0",
|
||
"15080122.0",
|
||
"685768.0",
|
||
"1546321.0",
|
||
"22630.0",
|
||
"208311.0",
|
||
"121363.0",
|
||
"256.7"
|
||
],
|
||
[
|
||
"10",
|
||
"2017",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"17204058.0",
|
||
"15086262.0",
|
||
"686194.0",
|
||
"176786.0",
|
||
"19654.0",
|
||
"230525.0",
|
||
"272461.0",
|
||
"249.5"
|
||
],
|
||
[
|
||
"11",
|
||
"2018",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"17562431.0",
|
||
"13083406.0",
|
||
"430429.0",
|
||
"1147080.0",
|
||
"57312.0",
|
||
"1040639.0",
|
||
"649172.0",
|
||
"229.9"
|
||
],
|
||
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|
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||
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[
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[
|
||
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||
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||
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||
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||
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[
|
||
"15",
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||
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||
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|
||
"25914265.0",
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||
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[
|
||
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||
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|
||
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|
||
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||
"18622389.0",
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||
"457753.0",
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||
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|
||
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|
||
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|
||
[
|
||
"17",
|
||
"2024",
|
||
"Oil/Gas Monitoring & Inspections",
|
||
"38506556.0",
|
||
"20834721.0",
|
||
"361687.0",
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||
"8851915.0",
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||
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||
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|
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"<style scoped>\n",
|
||
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|
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|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>year</th>\n",
|
||
" <th>strategy</th>\n",
|
||
" <th>total_budget</th>\n",
|
||
" <th>salaries</th>\n",
|
||
" <th>other_personnel</th>\n",
|
||
" <th>professional_fees</th>\n",
|
||
" <th>travel</th>\n",
|
||
" <th>other_operating</th>\n",
|
||
" <th>capital_exp</th>\n",
|
||
" <th>fte</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>11,708,475.00</td>\n",
|
||
" <td>7,669,719.00</td>\n",
|
||
" <td>398,589.00</td>\n",
|
||
" <td>3,366,389.00</td>\n",
|
||
" <td>16,477.00</td>\n",
|
||
" <td>210,293.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>130.60</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>10,911,094.00</td>\n",
|
||
" <td>7,273,775.00</td>\n",
|
||
" <td>389,348.00</td>\n",
|
||
" <td>3,118,066.00</td>\n",
|
||
" <td>6,792.00</td>\n",
|
||
" <td>77,855.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>120.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>9,846,886.00</td>\n",
|
||
" <td>7,292,933.00</td>\n",
|
||
" <td>282,337.00</td>\n",
|
||
" <td>977,645.00</td>\n",
|
||
" <td>28,694.00</td>\n",
|
||
" <td>1,045,727.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>131.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>11,123,757.00</td>\n",
|
||
" <td>8,068,497.00</td>\n",
|
||
" <td>217,988.00</td>\n",
|
||
" <td>1,493,755.00</td>\n",
|
||
" <td>73,651.00</td>\n",
|
||
" <td>988,740.00</td>\n",
|
||
" <td>13,232.00</td>\n",
|
||
" <td>137.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>17,280,569.00</td>\n",
|
||
" <td>9,707,894.00</td>\n",
|
||
" <td>236,356.00</td>\n",
|
||
" <td>5,989,236.00</td>\n",
|
||
" <td>41,752.00</td>\n",
|
||
" <td>1,165,481.00</td>\n",
|
||
" <td>54,037.00</td>\n",
|
||
" <td>153.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>16,237,704.00</td>\n",
|
||
" <td>10,887,561.00</td>\n",
|
||
" <td>237,777.00</td>\n",
|
||
" <td>3,562,816.00</td>\n",
|
||
" <td>5,614.00</td>\n",
|
||
" <td>1,446,301.00</td>\n",
|
||
" <td>10,140.00</td>\n",
|
||
" <td>168.10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>25,583,205.00</td>\n",
|
||
" <td>11,166,309.00</td>\n",
|
||
" <td>246,340.00</td>\n",
|
||
" <td>12,560,550.00</td>\n",
|
||
" <td>37,731.00</td>\n",
|
||
" <td>1,246,443.00</td>\n",
|
||
" <td>19,985.00</td>\n",
|
||
" <td>157.10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>26,903,564.00</td>\n",
|
||
" <td>11,056,060.00</td>\n",
|
||
" <td>252,933.00</td>\n",
|
||
" <td>12,846,821.00</td>\n",
|
||
" <td>56,650.00</td>\n",
|
||
" <td>2,287,481.00</td>\n",
|
||
" <td>48,344.00</td>\n",
|
||
" <td>151.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>Energy Resource Development</td>\n",
|
||
" <td>35,533,565.00</td>\n",
|
||
" <td>13,183,578.00</td>\n",
|
||
" <td>229,161.00</td>\n",
|
||
" <td>15,140,585.00</td>\n",
|
||
" <td>144,641.00</td>\n",
|
||
" <td>6,425,653.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>186.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>2016</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>18,471,666.00</td>\n",
|
||
" <td>15,080,122.00</td>\n",
|
||
" <td>685,768.00</td>\n",
|
||
" <td>1,546,321.00</td>\n",
|
||
" <td>22,630.00</td>\n",
|
||
" <td>208,311.00</td>\n",
|
||
" <td>121,363.00</td>\n",
|
||
" <td>256.70</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>2017</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>17,204,058.00</td>\n",
|
||
" <td>15,086,262.00</td>\n",
|
||
" <td>686,194.00</td>\n",
|
||
" <td>176,786.00</td>\n",
|
||
" <td>19,654.00</td>\n",
|
||
" <td>230,525.00</td>\n",
|
||
" <td>272,461.00</td>\n",
|
||
" <td>249.50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>2018</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>17,562,431.00</td>\n",
|
||
" <td>13,083,406.00</td>\n",
|
||
" <td>430,429.00</td>\n",
|
||
" <td>1,147,080.00</td>\n",
|
||
" <td>57,312.00</td>\n",
|
||
" <td>1,040,639.00</td>\n",
|
||
" <td>649,172.00</td>\n",
|
||
" <td>229.90</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>21,951,747.00</td>\n",
|
||
" <td>14,878,875.00</td>\n",
|
||
" <td>340,135.00</td>\n",
|
||
" <td>2,895,436.00</td>\n",
|
||
" <td>187,048.00</td>\n",
|
||
" <td>1,185,772.00</td>\n",
|
||
" <td>1,255,930.00</td>\n",
|
||
" <td>255.60</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>26,057,560.00</td>\n",
|
||
" <td>17,228,302.00</td>\n",
|
||
" <td>417,683.00</td>\n",
|
||
" <td>4,822,351.00</td>\n",
|
||
" <td>106,428.00</td>\n",
|
||
" <td>1,398,705.00</td>\n",
|
||
" <td>896,846.00</td>\n",
|
||
" <td>284.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>28,756,689.00</td>\n",
|
||
" <td>17,155,864.00</td>\n",
|
||
" <td>426,139.00</td>\n",
|
||
" <td>8,212,873.00</td>\n",
|
||
" <td>34,762.00</td>\n",
|
||
" <td>1,394,783.00</td>\n",
|
||
" <td>230,439.00</td>\n",
|
||
" <td>277.80</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>25,914,265.00</td>\n",
|
||
" <td>17,834,460.00</td>\n",
|
||
" <td>391,138.00</td>\n",
|
||
" <td>4,007,178.00</td>\n",
|
||
" <td>154,334.00</td>\n",
|
||
" <td>1,255,945.00</td>\n",
|
||
" <td>694,706.00</td>\n",
|
||
" <td>264.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>34,330,858.00</td>\n",
|
||
" <td>18,622,389.00</td>\n",
|
||
" <td>457,753.00</td>\n",
|
||
" <td>8,945,350.00</td>\n",
|
||
" <td>149,418.00</td>\n",
|
||
" <td>2,428,330.00</td>\n",
|
||
" <td>2,234,623.00</td>\n",
|
||
" <td>271.20</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>Oil/Gas Monitoring & Inspections</td>\n",
|
||
" <td>38,506,556.00</td>\n",
|
||
" <td>20,834,721.00</td>\n",
|
||
" <td>361,687.00</td>\n",
|
||
" <td>8,851,915.00</td>\n",
|
||
" <td>316,806.00</td>\n",
|
||
" <td>4,112,998.00</td>\n",
|
||
" <td>2,659,208.00</td>\n",
|
||
" <td>280.80</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" year strategy total_budget salaries \\\n",
|
||
"0 2016 Energy Resource Development 11,708,475.00 7,669,719.00 \n",
|
||
"1 2017 Energy Resource Development 10,911,094.00 7,273,775.00 \n",
|
||
"2 2018 Energy Resource Development 9,846,886.00 7,292,933.00 \n",
|
||
"3 2019 Energy Resource Development 11,123,757.00 8,068,497.00 \n",
|
||
"4 2020 Energy Resource Development 17,280,569.00 9,707,894.00 \n",
|
||
"5 2021 Energy Resource Development 16,237,704.00 10,887,561.00 \n",
|
||
"6 2022 Energy Resource Development 25,583,205.00 11,166,309.00 \n",
|
||
"7 2023 Energy Resource Development 26,903,564.00 11,056,060.00 \n",
|
||
"8 2024 Energy Resource Development 35,533,565.00 13,183,578.00 \n",
|
||
"9 2016 Oil/Gas Monitoring & Inspections 18,471,666.00 15,080,122.00 \n",
|
||
"10 2017 Oil/Gas Monitoring & Inspections 17,204,058.00 15,086,262.00 \n",
|
||
"11 2018 Oil/Gas Monitoring & Inspections 17,562,431.00 13,083,406.00 \n",
|
||
"12 2019 Oil/Gas Monitoring & Inspections 21,951,747.00 14,878,875.00 \n",
|
||
"13 2020 Oil/Gas Monitoring & Inspections 26,057,560.00 17,228,302.00 \n",
|
||
"14 2021 Oil/Gas Monitoring & Inspections 28,756,689.00 17,155,864.00 \n",
|
||
"15 2022 Oil/Gas Monitoring & Inspections 25,914,265.00 17,834,460.00 \n",
|
||
"16 2023 Oil/Gas Monitoring & Inspections 34,330,858.00 18,622,389.00 \n",
|
||
"17 2024 Oil/Gas Monitoring & Inspections 38,506,556.00 20,834,721.00 \n",
|
||
"\n",
|
||
" other_personnel professional_fees travel other_operating \\\n",
|
||
"0 398,589.00 3,366,389.00 16,477.00 210,293.00 \n",
|
||
"1 389,348.00 3,118,066.00 6,792.00 77,855.00 \n",
|
||
"2 282,337.00 977,645.00 28,694.00 1,045,727.00 \n",
|
||
"3 217,988.00 1,493,755.00 73,651.00 988,740.00 \n",
|
||
"4 236,356.00 5,989,236.00 41,752.00 1,165,481.00 \n",
|
||
"5 237,777.00 3,562,816.00 5,614.00 1,446,301.00 \n",
|
||
"6 246,340.00 12,560,550.00 37,731.00 1,246,443.00 \n",
|
||
"7 252,933.00 12,846,821.00 56,650.00 2,287,481.00 \n",
|
||
"8 229,161.00 15,140,585.00 144,641.00 6,425,653.00 \n",
|
||
"9 685,768.00 1,546,321.00 22,630.00 208,311.00 \n",
|
||
"10 686,194.00 176,786.00 19,654.00 230,525.00 \n",
|
||
"11 430,429.00 1,147,080.00 57,312.00 1,040,639.00 \n",
|
||
"12 340,135.00 2,895,436.00 187,048.00 1,185,772.00 \n",
|
||
"13 417,683.00 4,822,351.00 106,428.00 1,398,705.00 \n",
|
||
"14 426,139.00 8,212,873.00 34,762.00 1,394,783.00 \n",
|
||
"15 391,138.00 4,007,178.00 154,334.00 1,255,945.00 \n",
|
||
"16 457,753.00 8,945,350.00 149,418.00 2,428,330.00 \n",
|
||
"17 361,687.00 8,851,915.00 316,806.00 4,112,998.00 \n",
|
||
"\n",
|
||
" capital_exp fte \n",
|
||
"0 0.00 130.60 \n",
|
||
"1 0.00 120.30 \n",
|
||
"2 0.00 131.00 \n",
|
||
"3 13,232.00 137.40 \n",
|
||
"4 54,037.00 153.40 \n",
|
||
"5 10,140.00 168.10 \n",
|
||
"6 19,985.00 157.10 \n",
|
||
"7 48,344.00 151.30 \n",
|
||
"8 0.00 186.00 \n",
|
||
"9 121,363.00 256.70 \n",
|
||
"10 272,461.00 249.50 \n",
|
||
"11 649,172.00 229.90 \n",
|
||
"12 1,255,930.00 255.60 \n",
|
||
"13 896,846.00 284.00 \n",
|
||
"14 230,439.00 277.80 \n",
|
||
"15 694,706.00 264.00 \n",
|
||
"16 2,234,623.00 271.20 \n",
|
||
"17 2,659,208.00 280.80 "
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"BUDGET_PATH = Path(\"RRC Budget Data.xlsx\")\n",
|
||
"raw = pd.read_excel(BUDGET_PATH, header=None)\n",
|
||
"YEARS = [2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]\n",
|
||
"COLS = slice(1, 10) # spreadsheet columns 1-9 map to years 2016-2024\n",
|
||
"\n",
|
||
"# ── Section 1: Energy Resource Development (rows 7-18) ──────────────────────\n",
|
||
"erd = pd.DataFrame({\n",
|
||
" \"year\": YEARS,\n",
|
||
" \"strategy\": \"Energy Resource Development\",\n",
|
||
" \"total_budget\": raw.iloc[1, COLS].values.astype(float),\n",
|
||
" \"salaries\": raw.iloc[7, COLS].values.astype(float),\n",
|
||
" \"other_personnel\": raw.iloc[8, COLS].values.astype(float),\n",
|
||
" \"professional_fees\": raw.iloc[9, COLS].values.astype(float),\n",
|
||
" \"travel\": raw.iloc[13, COLS].values.astype(float),\n",
|
||
" \"other_operating\": raw.iloc[16, COLS].values.astype(float),\n",
|
||
" \"capital_exp\": raw.iloc[17, COLS].values.astype(float),\n",
|
||
" \"fte\": raw.iloc[18, COLS].values.astype(float),\n",
|
||
"})\n",
|
||
"\n",
|
||
"# ── Section 2: Oil/Gas Monitoring & Inspections (rows 20-31) ────────────────\n",
|
||
"ogi = pd.DataFrame({\n",
|
||
" \"year\": YEARS,\n",
|
||
" \"strategy\": \"Oil/Gas Monitoring & Inspections\",\n",
|
||
" \"total_budget\": raw.iloc[2, COLS].values.astype(float),\n",
|
||
" \"salaries\": raw.iloc[20, COLS].values.astype(float),\n",
|
||
" \"other_personnel\": raw.iloc[21, COLS].values.astype(float),\n",
|
||
" \"professional_fees\": raw.iloc[22, COLS].values.astype(float),\n",
|
||
" \"travel\": raw.iloc[26, COLS].values.astype(float),\n",
|
||
" \"other_operating\": raw.iloc[29, COLS].values.astype(float),\n",
|
||
" \"capital_exp\": raw.iloc[30, COLS].values.astype(float),\n",
|
||
" \"fte\": raw.iloc[31, COLS].values.astype(float),\n",
|
||
"})\n",
|
||
"\n",
|
||
"budget_long = pd.concat([erd, ogi], ignore_index=True)\n",
|
||
"print(f\"Budget long: {len(budget_long)} rows (2 strategies × {len(YEARS)} years)\")\n",
|
||
"budget_long\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "896d152b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# ── Wide budget: one row per year with ogi_ / erd_ prefixed columns ──────────\n",
|
||
"ogi_wide = ogi.drop(columns=\"strategy\").add_prefix(\"ogi_\")\n",
|
||
"erd_wide = erd.drop(columns=\"strategy\").add_prefix(\"erd_\")\n",
|
||
"\n",
|
||
"budget_wide = (\n",
|
||
" ogi_wide\n",
|
||
" .merge(erd_wide, left_on=\"ogi_year\", right_on=\"erd_year\")\n",
|
||
" .rename(columns={\"ogi_year\": \"year\"})\n",
|
||
" .drop(columns=\"erd_year\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# ── Merge inspections + violations, then join statewide budget on year ────────\n",
|
||
"panel = (\n",
|
||
" insp\n",
|
||
" .merge(viol, on=[\"district\", \"year\"], how=\"left\")\n",
|
||
" .merge(budget_wide, on=\"year\", how=\"left\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# ── Derived columns ───────────────────────────────────────────────────────────\n",
|
||
"panel[\"violations_per_inspection\"] = panel[\"total_violations\"] / panel[\"total_inspections\"]\n",
|
||
"panel[\"ogi_budget_m\"] = panel[\"ogi_total_budget\"] / 1_000_000 # dollars → millions\n",
|
||
"panel[\"erd_budget_m\"] = panel[\"erd_total_budget\"] / 1_000_000\n",
|
||
"panel[\"post_2019\"] = (panel[\"year\"] >= 2019).astype(int)\n",
|
||
"# 2024 = budget estimate; 2025 = no budget data — exclude both from regressions\n",
|
||
"panel[\"is_budget_year\"] = (panel[\"year\"] >= 2024).astype(int)\n",
|
||
"\n",
|
||
"# Goal ambiguity: share of combined budget going to the inspection mission.\n",
|
||
"# Higher share = clearer mission focus; lower share = more goal ambiguity.\n",
|
||
"panel[\"inspection_budget_share\"] = (\n",
|
||
" panel[\"ogi_total_budget\"] / (panel[\"ogi_total_budget\"] + panel[\"erd_total_budget\"])\n",
|
||
")\n",
|
||
"\n",
|
||
"# Fill violation NaNs for districts with zero violations in a given year\n",
|
||
"fill_cols = [\n",
|
||
" \"total_violations\", \"unique_wells_with_violations\", \"major_violations\",\n",
|
||
" \"resolution_rate\", \"enforcement_rate\", \"avg_days_to_enforcement\",\n",
|
||
" \"violations_per_inspection\",\n",
|
||
"]\n",
|
||
"panel[fill_cols] = panel[fill_cols].fillna(0)\n",
|
||
"\n",
|
||
"print(f\"Analysis panel: {len(panel):,} rows | \"\n",
|
||
" f\"{panel['district'].nunique()} districts | \"\n",
|
||
" f\"{panel['year'].nunique()} years\")\n",
|
||
"panel.head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3558bae7",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2. Exploratory Overview"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "92921756",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ogi_budget_m ogi_fte total_inspections compliance_rate total_violations resolution_rate avg_days_to_enf\n",
|
||
"year \n",
|
||
"2016 18.47 256.70 18,277.85 83.11 3,398.15 36.78 131.86\n",
|
||
"2017 17.20 249.50 20,138.54 86.52 2,915.69 59.02 185.01\n",
|
||
"2018 17.56 229.90 25,703.54 90.17 3,197.62 59.46 207.25\n",
|
||
"2019 21.95 255.60 25,058.46 89.85 2,550.77 61.44 170.36\n",
|
||
"2020 26.06 284.00 27,669.46 89.57 2,750.92 56.81 154.66\n",
|
||
"2021 28.76 277.80 24,115.54 88.76 2,556.38 66.18 118.82\n",
|
||
"2022 25.91 264.00 32,023.54 89.82 2,819.92 67.85 91.50\n",
|
||
"2023 34.33 271.20 33,805.69 91.62 2,598.62 69.65 105.15\n",
|
||
"2024 38.51 280.80 36,552.77 92.58 2,221.15 65.13 76.93\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1600x800 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Year-level means across districts\n",
|
||
"yearly = panel.groupby(\"year\").agg(\n",
|
||
" ogi_budget_m = (\"ogi_budget_m\", \"first\"),\n",
|
||
" ogi_fte = (\"ogi_fte\", \"first\"),\n",
|
||
" total_inspections = (\"total_inspections\", \"mean\"),\n",
|
||
" compliance_rate = (\"compliance_rate\", \"mean\"),\n",
|
||
" total_violations = (\"total_violations\", \"mean\"),\n",
|
||
" resolution_rate = (\"resolution_rate\", \"mean\"),\n",
|
||
" avg_days_to_enf = (\"avg_days_to_enforcement\",\"mean\"),\n",
|
||
").round(2)\n",
|
||
"\n",
|
||
"print(yearly.to_string())\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 3, figsize=(16, 8))\n",
|
||
"axes = axes.flatten()\n",
|
||
"\n",
|
||
"yearly[\"ogi_budget_m\"].plot(ax=axes[0], marker=\"o\", title=\"OGI Budget ($M)\")\n",
|
||
"axes[0].yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f\"${x:.0f}M\"))\n",
|
||
"\n",
|
||
"yearly[\"ogi_fte\"].plot(ax=axes[1], marker=\"o\", title=\"OGI FTE Positions\")\n",
|
||
"\n",
|
||
"yearly[\"total_inspections\"].plot(ax=axes[2], marker=\"o\", title=\"Avg Inspections / District\")\n",
|
||
"\n",
|
||
"yearly[\"compliance_rate\"].plot(ax=axes[3], marker=\"o\", title=\"Avg Compliance Rate (%)\")\n",
|
||
"\n",
|
||
"yearly[\"resolution_rate\"].plot(ax=axes[4], marker=\"o\", title=\"Avg Resolution Rate (%)\")\n",
|
||
"\n",
|
||
"yearly[\"avg_days_to_enf\"].plot(ax=axes[5], marker=\"o\", title=\"Avg Days to Enforcement\")\n",
|
||
"\n",
|
||
"for ax in axes:\n",
|
||
" ax.axvline(2019, color=\"red\", linestyle=\"--\", alpha=0.5, label=\"2019 policy\")\n",
|
||
" ax.set_xlabel(\"Year\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "5d2671c9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 900x700 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"corr_cols = [\n",
|
||
" \"ogi_budget_m\", \"ogi_fte\", \"inspection_budget_share\",\n",
|
||
" \"total_inspections\", \"compliance_rate\",\n",
|
||
" \"total_violations\", \"resolution_rate\", \"avg_days_to_enforcement\",\n",
|
||
"]\n",
|
||
"corr = panel[corr_cols].corr().round(2)\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(9, 7))\n",
|
||
"im = ax.imshow(corr, cmap=\"RdBu_r\", vmin=-1, vmax=1)\n",
|
||
"ax.set_xticks(range(len(corr_cols)))\n",
|
||
"ax.set_yticks(range(len(corr_cols)))\n",
|
||
"ax.set_xticklabels(corr_cols, rotation=45, ha=\"right\", fontsize=9)\n",
|
||
"ax.set_yticklabels(corr_cols, fontsize=9)\n",
|
||
"for i in range(len(corr_cols)):\n",
|
||
" for j in range(len(corr_cols)):\n",
|
||
" ax.text(j, i, corr.iloc[i, j], ha=\"center\", va=\"center\", fontsize=8)\n",
|
||
"plt.colorbar(im, ax=ax)\n",
|
||
"ax.set_title(\"Correlation Matrix — Key Variables\")\n",
|
||
"plt.tight_layout()\n",
|
||
"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 2016–2025 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; years 2024 and\n",
|
||
"2025 are included in descriptive analyses but excluded from regression models\n",
|
||
"because budget actuals are unavailable for those years). 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 fiscal years 2024 (budget estimate) and 2025 (no\n",
|
||
"budget data), retaining 2016–2023 as the regression sample. Fiscal year 2017 recorded the\n",
|
||
"lowest OGI budget over the study period ($17.20M) and is retained as a within-sample\n",
|
||
"data point; the 2017 dip in budget coincides with slightly lower average inspections per\n",
|
||
"district, consistent with the capacity hypothesis.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ca1410a",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H1: Organizational Capacity → Policy Outputs\n",
|
||
"\n",
|
||
"**Prediction:** Higher OGI budget and FTE predict more inspections, higher compliance rates, and faster violation resolution.\n",
|
||
"\n",
|
||
"**Model:** OLS with district fixed effects and year 2016–2023 (excluding 2024 budget estimate). Budget varies only over time, so it identifies via year-to-year changes in statewide RRC allocations; district FE absorbs persistent cross-district differences.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "463387d3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1500x400 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"actuals = panel[panel[\"is_budget_year\"] == 0].copy()\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
|
||
"\n",
|
||
"actuals.plot.scatter(x=\"ogi_budget_m\", y=\"total_inspections\",\n",
|
||
" alpha=0.4, ax=axes[0], title=\"Budget → Inspections\")\n",
|
||
"actuals.plot.scatter(x=\"ogi_budget_m\", y=\"compliance_rate\",\n",
|
||
" alpha=0.4, ax=axes[1], title=\"Budget → Compliance Rate (%)\")\n",
|
||
"actuals.plot.scatter(x=\"ogi_budget_m\", y=\"resolution_rate\",\n",
|
||
" alpha=0.4, ax=axes[2], title=\"Budget → Resolution Rate (%)\")\n",
|
||
"\n",
|
||
"for ax in axes:\n",
|
||
" ax.set_xlabel(\"OGI Budget ($M)\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "535fc4eb",
|
||
"metadata": {},
|
||
"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 ($M) → Resolution Rate (%)\n",
|
||
" Coef. Std.Err. z P>|z|\n",
|
||
"ogi_budget_m 1.05 0.32 3.28 0.00\n",
|
||
" R² = 0.624 Adj. R² = 0.569\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"m_inspections = smf.ols(\n",
|
||
" \"total_inspections ~ ogi_budget_m + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"m_compliance = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"m_resolution = smf.ols(\n",
|
||
" \"resolution_rate ~ ogi_budget_m + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"# Detect actual column names — statsmodels uses z/P>|z| with robust SEs in some versions\n",
|
||
"_tbl = m_inspections.summary2().tables[1]\n",
|
||
"_t = \"t\" if \"t\" in _tbl.columns else \"z\"\n",
|
||
"_p = \"P>|t|\" if \"P>|t|\" in _tbl.columns else \"P>|z|\"\n",
|
||
"display_cols = [\"Coef.\", \"Std.Err.\", _t, _p]\n",
|
||
"\n",
|
||
"print(\"H1a — OGI Budget ($M) → Total Inspections\")\n",
|
||
"print(m_inspections.summary2().tables[1][display_cols].loc[[\"ogi_budget_m\"]])\n",
|
||
"print(f\" R² = {m_inspections.rsquared:.3f} Adj. R² = {m_inspections.rsquared_adj:.3f}\\n\")\n",
|
||
"\n",
|
||
"print(\"H1b — OGI Budget ($M) → Compliance Rate (%)\")\n",
|
||
"print(m_compliance.summary2().tables[1][display_cols].loc[[\"ogi_budget_m\"]])\n",
|
||
"print(f\" R² = {m_compliance.rsquared:.3f} Adj. R² = {m_compliance.rsquared_adj:.3f}\\n\")\n",
|
||
"\n",
|
||
"print(\"H1c — OGI Budget ($M) → Resolution Rate (%)\")\n",
|
||
"print(m_resolution.summary2().tables[1][display_cols].loc[[\"ogi_budget_m\"]])\n",
|
||
"print(f\" R² = {m_resolution.rsquared:.3f} Adj. R² = {m_resolution.rsquared_adj:.3f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "56add68a",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H2: Goal Ambiguity Moderates Capacity Effects\n",
|
||
"\n",
|
||
"**Prediction:** When a larger share of the combined RRC budget flows to the broader \"Energy Resource Development\" goal (lower `inspection_budget_share`), the capacity → output link weakens.\n",
|
||
"\n",
|
||
"**Operationalization:**\n",
|
||
"`inspection_budget_share = ogi_budget / (ogi_budget + erd_budget)`\n",
|
||
"\n",
|
||
"A negative interaction coefficient `ogi_budget_m × inspection_budget_share` would be unexpected (higher share → weaker effect). A positive coefficient supports H2 — clearer mission focus amplifies the budget → compliance relationship.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "24187ce8",
|
||
"metadata": {},
|
||
"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",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"key_rows = [\"ogi_budget_m\", \"inspection_budget_share\", \"ogi_budget_m:inspection_budget_share\"]\n",
|
||
"print(\"H2 — Goal Ambiguity Moderation (DV: compliance_rate)\")\n",
|
||
"print(m_h2.summary2().tables[1][display_cols].loc[key_rows])\n",
|
||
"print(f\"\\nR² = {m_h2.rsquared:.3f} Adj. R² = {m_h2.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"# ── Same model with resolution rate as DV ────────────────────────────────────\n",
|
||
"m_h2_res = smf.ols(\n",
|
||
" \"resolution_rate ~ ogi_budget_m * inspection_budget_share + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"print(\"\\nH2 — Goal Ambiguity Moderation (DV: resolution_rate)\")\n",
|
||
"print(m_h2_res.summary2().tables[1][display_cols].loc[key_rows])\n",
|
||
"print(f\"\\nR² = {m_h2_res.rsquared:.3f} Adj. R² = {m_h2_res.rsquared_adj:.3f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b6583857",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H3: District Multilevel Effects\n",
|
||
"\n",
|
||
"**Prediction:** The budget → output slope varies across RRC districts — some districts translate budget increases into better outputs more effectively than others.\n",
|
||
"\n",
|
||
"**Model:** Interaction `ogi_budget_m × C(district)` — the reference district captures the baseline budget slope; interaction terms show how each other district's slope differs.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "151faefd",
|
||
"metadata": {},
|
||
"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": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"m_h3 = smf.ols(\n",
|
||
" \"compliance_rate ~ ogi_budget_m * C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"coef_table = m_h3.summary2().tables[1]\n",
|
||
"\n",
|
||
"# Baseline budget slope (reference district)\n",
|
||
"baseline_row = coef_table.loc[[\"ogi_budget_m\"]]\n",
|
||
"print(\"H3 — District-Heterogeneous Budget Effect (DV: compliance_rate)\")\n",
|
||
"print(f\"Baseline (reference district) budget slope:\")\n",
|
||
"print(baseline_row[display_cols])\n",
|
||
"\n",
|
||
"# District-specific deviations from baseline\n",
|
||
"interaction_rows = coef_table[coef_table.index.str.contains(\"ogi_budget_m:C\")]\n",
|
||
"print(\"\\nDistrict interaction terms (deviation from reference slope):\")\n",
|
||
"print(interaction_rows[display_cols].round(4))\n",
|
||
"print(f\"\\nR² = {m_h3.rsquared:.3f} Adj. R² = {m_h3.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"# ── Plot district-specific budget slopes ─────────────────────────────────────\n",
|
||
"districts = actuals[\"district\"].unique()\n",
|
||
"slopes = {}\n",
|
||
"for d in districts:\n",
|
||
" key = f\"ogi_budget_m:C(district)[T.{d}]\"\n",
|
||
" base = m_h3.params.get(\"ogi_budget_m\", 0)\n",
|
||
" delta = m_h3.params.get(key, 0)\n",
|
||
" slopes[str(d)] = base + delta\n",
|
||
"\n",
|
||
"slope_df = pd.Series(slopes).sort_values()\n",
|
||
"fig, ax = plt.subplots(figsize=(10, 4))\n",
|
||
"slope_df.plot.barh(ax=ax, color=[\"#d62728\" if v < 0 else \"#1f77b4\" for v in slope_df])\n",
|
||
"ax.axvline(0, color=\"black\", linewidth=0.8)\n",
|
||
"ax.set_xlabel(\"Budget slope (compliance rate pp per $M)\")\n",
|
||
"ax.set_title(\"H3 — District-Specific Budget → Compliance Slopes\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5bb27b3e",
|
||
"metadata": {},
|
||
"source": [
|
||
"## H4: Spatial and Geographic Factors\n",
|
||
"\n",
|
||
"**Predictions:**\n",
|
||
"- Offshore-jurisdiction districts (02, 03, 04) show a different budget → output relationship due to dual onshore/offshore oversight burden.\n",
|
||
"- Border-proximate districts show a different relationship due to cross-jurisdiction enforcement complexity.\n",
|
||
"- Spatial autocorrelation in H1 residuals (Moran's I) would indicate unmodeled geographic spillovers.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "d6e56f00",
|
||
"metadata": {},
|
||
"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",
|
||
"BORDER_DISTRICTS = {\"01\", \"02\", \"03\", \"04\"} # south / gulf coast proximity\n",
|
||
"\n",
|
||
"actuals = actuals.copy()\n",
|
||
"actuals[\"district_str\"] = actuals[\"district\"].astype(str).str.strip()\n",
|
||
"actuals[\"offshore\"] = actuals[\"district_str\"].isin(OFFSHORE_DISTRICTS).astype(int)\n",
|
||
"actuals[\"border\"] = actuals[\"district_str\"].isin(BORDER_DISTRICTS).astype(int)\n",
|
||
"\n",
|
||
"print(\"District classification:\")\n",
|
||
"print(\n",
|
||
" actuals.groupby([\"district_str\", \"offshore\", \"border\"])\n",
|
||
" .size()\n",
|
||
" .reset_index(name=\"district_year_obs\")\n",
|
||
" .to_string(index=False)\n",
|
||
")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "74686bfe",
|
||
"metadata": {},
|
||
"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",
|
||
" \"compliance_rate ~ ogi_budget_m + offshore + border \"\n",
|
||
" \"+ ogi_budget_m:offshore + ogi_budget_m:border + C(district)\",\n",
|
||
" data=actuals,\n",
|
||
").fit(cov_type=\"cluster\", cov_kwds={\"groups\": actuals[\"district\"]})\n",
|
||
"\n",
|
||
"spatial_rows = [\n",
|
||
" \"ogi_budget_m\", \"offshore\", \"border\",\n",
|
||
" \"ogi_budget_m:offshore\", \"ogi_budget_m:border\",\n",
|
||
"]\n",
|
||
"available = [r for r in spatial_rows if r in m_h4.params.index]\n",
|
||
"print(\"H4 — Spatial Moderators (DV: compliance_rate)\")\n",
|
||
"print(m_h4.summary2().tables[1][display_cols].loc[available])\n",
|
||
"print(f\"\\nR² = {m_h4.rsquared:.3f} Adj. R² = {m_h4.rsquared_adj:.3f}\")\n",
|
||
"\n",
|
||
"# ── Moran's I on H1 residuals ─────────────────────────────────────────────────\n",
|
||
"# Compute district centroids from well lat/lon joined via inspections\n",
|
||
"centroids_sql = \"\"\"\n",
|
||
"SELECT\n",
|
||
" i.district,\n",
|
||
" AVG(w.latitude) AS lat,\n",
|
||
" AVG(w.longitude) AS lon\n",
|
||
"FROM inspections i\n",
|
||
"JOIN well_shape_tract w USING (api_norm)\n",
|
||
"WHERE w.latitude IS NOT NULL\n",
|
||
" AND w.longitude IS NOT NULL\n",
|
||
" AND i.district IS NOT NULL\n",
|
||
"GROUP BY i.district\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"try:\n",
|
||
" centroids = pd.read_sql(text(centroids_sql), engine)\n",
|
||
"\n",
|
||
" # Average H1 compliance residuals to district level\n",
|
||
" resid_df = actuals[[\"district\", \"compliance_rate\"]].copy()\n",
|
||
" resid_df[\"resid\"] = m_compliance.resid.reindex(actuals.index).values\n",
|
||
" resid_by_district = resid_df.groupby(\"district\")[\"resid\"].mean().reset_index()\n",
|
||
"\n",
|
||
" centroids = centroids.merge(resid_by_district, on=\"district\").dropna()\n",
|
||
"\n",
|
||
" # Row-normalised inverse-distance weights matrix\n",
|
||
" coords = centroids[[\"lon\", \"lat\"]].values\n",
|
||
" D = cdist(coords, coords)\n",
|
||
" np.fill_diagonal(D, np.inf)\n",
|
||
" W = 1 / D\n",
|
||
" W = W / W.sum(axis=1, keepdims=True)\n",
|
||
"\n",
|
||
" z = centroids[\"resid\"].values\n",
|
||
" z = z - z.mean()\n",
|
||
" n = len(z)\n",
|
||
" morans_i = (n / W.sum()) * (z @ W @ z) / (z @ z)\n",
|
||
"\n",
|
||
" print(f\"\\nMoran's I on H1 compliance residuals = {morans_i:.4f}\")\n",
|
||
" print(\" > 0 → residuals cluster spatially (similar neighbours)\")\n",
|
||
" print(\" ≈ 0 → no spatial pattern\")\n",
|
||
" print(\" < 0 → spatial dispersion (dissimilar neighbours)\")\n",
|
||
"\n",
|
||
" print(\"\\nDistrict centroids used:\")\n",
|
||
" print(centroids[[\"district\", \"lat\", \"lon\"]].round(2).to_string(index=False))\n",
|
||
"\n",
|
||
"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": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|