texas-district-analysis/analysis/updated_district_level_analysis_2015-2025_offshore_controls.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0ac26f92",
   "metadata": {},
   "source": [
    "## District-Level Analysis: Heterogeneous Effects of 2019 RRC Disclosure Policy\n",
    "\n",
    "### Texas Oil and Gas Inspection and Violation Data\n",
    "#### 2015-2025 \n",
    "#### By: [David P. Adams](https://dadams.io)\n",
    "#### Date: February 18, 2026\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2faed56",
   "metadata": {},
   "source": [
    "## Research Question\n",
    "\n",
    "How did the January 2019 policy change (making well-specific violation data publicly searchable) affect enforcement performance across Texas RRC districts, and why did responses vary across districts?\n",
    "\n",
    "## Updated Hypotheses\n",
    "\n",
    "- **H1 (Regulatory Pipeline Acceleration)**\n",
    "  - **H1a**: The policy reduced time from violation discovery to enforcement action.\n",
    "  - **H1b**: The policy increased compliance verification (resolution on re-inspection).\n",
    "- **H2 (Bureaucratic Heterogeneity)**\n",
    "  - District-level policy effects vary substantially across RRC district offices.\n",
    "- **H3 (Structural Moderators)**\n",
    "  - **H3a (Capacity)**: High-capacity districts respond differently.\n",
    "  - **H3b (Baseline Performance)**: Low-compliance districts show different post-policy change.\n",
    "  - **H3c (Environmental Justice)**: High-EJI districts respond differently.\n",
    "  - **H3d (Geology)**: Dominant basin composition moderates post-policy effects.\n",
    "  - **H3e (Border Proximity)**: Border-proximate districts respond differently.\n",
    "  - **H3f (Rurality)**: Rural districts respond differently.\n",
    "- **H4 (Spatial Dynamics)**\n",
    "  - District treatment effects exhibit spatial autocorrelation (tested via Moran\u2019s I).\n",
    "- **H5 (Offshore Jurisdiction Moderator)**\n",
    "  - Districts with offshore + onshore jurisdiction (02, 03, 04) show differential post-2019 effects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "101582dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Imports complete\n",
      "\u2713 Pandas: 3.0.0\n",
      "\u2713 Statsmodels: 0.14.6\n",
      "\u2713 Spatial analysis available: True\n"
     ]
    }
   ],
   "source": [
    "# Install required packages if needed\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "def install_if_missing(package):\n",
    "    try:\n",
    "        __import__(package.split('[')[0])\n",
    "    except ImportError:\n",
    "        print(f\"Installing {package}...\")\n",
    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package])\n",
    "\n",
    "# Ensure required packages\n",
    "for pkg in ['statsmodels', 'scipy', 'seaborn']:\n",
    "    install_if_missing(pkg)\n",
    "\n",
    "# Core imports\n",
    "import os\n",
    "import json\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "# Data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Database\n",
    "from sqlalchemy import create_engine, text\n",
    "\n",
    "# Statistics and econometrics\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chi2_contingency\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "\n",
    "# Spatial analysis\n",
    "try:\n",
    "    import geopandas as gpd\n",
    "    from shapely.geometry import Point\n",
    "    HAS_GEO = True\n",
    "except ImportError:\n",
    "    HAS_GEO = False\n",
    "    print(\"Warning: geopandas not available for spatial analysis\")\n",
    "\n",
    "# Configuration\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings('ignore', category=UserWarning)\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.float_format', '{:.4f}'.format)\n",
    "\n",
    "# Styling\n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "sns.set_palette(\"husl\")\n",
    "\n",
    "# Add parent directory to path for imports\n",
    "repo_root = Path('..').resolve()\n",
    "if str(repo_root) not in sys.path:\n",
    "    sys.path.insert(0, str(repo_root))\n",
    "\n",
    "print(\"\u2713 Imports complete\")\n",
    "print(f\"\u2713 Pandas: {pd.__version__}\")\n",
    "print(f\"\u2713 Statsmodels: {sm.__version__}\")\n",
    "print(f\"\u2713 Spatial analysis available: {HAS_GEO}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2190deff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Connected to PostgreSQL\n",
      "  Host: localhost\n",
      "  Database: texas_data\n",
      "  User: postgres\n"
     ]
    }
   ],
   "source": [
    "# Database connection (from look_at_the_data.ipynb pattern)\n",
    "PGHOST = os.getenv(\"PGHOST\", \"localhost\")\n",
    "PGPORT = os.getenv(\"PGPORT\", \"5432\")\n",
    "PGUSER = os.getenv(\"PGUSER\", \"postgres\")\n",
    "PGPASSWORD = os.getenv(\"PGPASSWORD\", \"\")\n",
    "PGDATABASE = os.getenv(\"PGDATABASE\", \"texas_data\")\n",
    "\n",
    "pg_url = f\"postgresql+psycopg2://{PGUSER}:{PGPASSWORD}@{PGHOST}:{PGPORT}/{PGDATABASE}\"\n",
    "engine = create_engine(pg_url)\n",
    "\n",
    "print(f\"\u2713 Connected to PostgreSQL\")\n",
    "print(f\"  Host: {PGHOST}\")\n",
    "print(f\"  Database: {PGDATABASE}\")\n",
    "print(f\"  User: {PGUSER}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1112009e",
   "metadata": {},
   "source": [
    "## Part 1: Load and Explore District-Level Data\n",
    "\n",
    "We start by loading the pre-computed district statistics and creating summary tables for pre/post 2019 periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2961f021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Loaded analysis_output.json\n",
      "  Districts: ['01', '02', '03', '04', '05', '06', '08', '09', '10', '6E', '7B', '7C', '8A']\n",
      "  Total districts: 13\n",
      "\n",
      "======================================================================\n",
      "DISTRICT SUMMARY (Full Period 2015-2024)\n",
      "======================================================================\n",
      "district  total_inspections  compliance_rate\n",
      "      01             112132          82.9273\n",
      "      02              68330          88.8980\n",
      "      03             107708          88.8987\n",
      "      04             137959          94.1932\n",
      "      05              58731          91.1035\n",
      "      06             153755          92.8971\n",
      "      08             356608          92.8232\n",
      "      09             191339          81.1899\n",
      "      10             140961          89.4013\n",
      "      6E              46504          87.5602\n",
      "      7B             147579          86.7617\n",
      "      7C             164102          91.2865\n",
      "      8A             193056          94.4400\n",
      "\n",
      "Mean compliance rate: 89.41%\n",
      "Std compliance rate: 4.07%\n"
     ]
    }
   ],
   "source": [
    "# Load pre-computed district statistics\n",
    "analysis_output_path = Path('..') / 'analysis_output.json'\n",
    "\n",
    "with open(analysis_output_path, 'r') as f:\n",
    "    analysis_data = json.load(f)\n",
    "\n",
    "# Extract district-level metrics\n",
    "districts = list(analysis_data['inspection_analysis']['district_performance']['inspections_by_district'].keys())\n",
    "print(f\"\u2713 Loaded analysis_output.json\")\n",
    "print(f\"  Districts: {districts}\")\n",
    "print(f\"  Total districts: {len(districts)}\")\n",
    "\n",
    "# Create district-level summary DataFrame\n",
    "district_summary = pd.DataFrame({\n",
    "    'district': districts,\n",
    "    'total_inspections': [analysis_data['inspection_analysis']['district_performance']['inspections_by_district'][d] \n",
    "                          for d in districts],\n",
    "    'compliance_rate': [analysis_data['inspection_analysis']['district_performance']['compliance_by_district'][d] \n",
    "                       for d in districts]\n",
    "})\n",
    "\n",
    "district_summary = district_summary.sort_values('district')\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"DISTRICT SUMMARY (Full Period 2015-2024)\")\n",
    "print(\"=\"*70)\n",
    "print(district_summary.to_string(index=False))\n",
    "print(f\"\\nMean compliance rate: {district_summary['compliance_rate'].mean():.2f}%\")\n",
    "print(f\"Std compliance rate: {district_summary['compliance_rate'].std():.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "70d34d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables in database:\n",
      "['census_tract_demographics', 'census_tracts_2021', 'inspections', 'oil_gas_basins', 'rrc_well_api_raw', 'shale_plays', 'spatial_ref_sys', 'texmex_regions', 'violations', 'well_geo_features', 'well_shape_tract', 'well_shapes', 'well_shapes_backup_dedup', 'well_with_demographics_table']\n",
      "\n",
      "\u2713 Found inspections table: inspections\n",
      "\u2713 Found violations table: violations\n"
     ]
    }
   ],
   "source": [
    "# Load well-level data from PostgreSQL with district and temporal information\n",
    "# First, check what tables are available\n",
    "with engine.begin() as conn:\n",
    "    tables_query = text(\"\"\"\n",
    "        SELECT table_name \n",
    "        FROM information_schema.tables \n",
    "        WHERE table_schema = 'public' \n",
    "        AND table_type = 'BASE TABLE'\n",
    "        ORDER BY table_name\n",
    "    \"\"\")\n",
    "    available_tables = pd.read_sql(tables_query, conn)\n",
    "\n",
    "print(\"Available tables in database:\")\n",
    "print(available_tables['table_name'].tolist())\n",
    "\n",
    "# Check if inspections and violations tables exist\n",
    "inspections_table = None\n",
    "violations_table = None\n",
    "\n",
    "for table in available_tables['table_name']:\n",
    "    if 'inspection' in table.lower():\n",
    "        print(f\"\\n\u2713 Found inspections table: {table}\")\n",
    "        inspections_table = table\n",
    "    if 'violation' in table.lower():\n",
    "        print(f\"\u2713 Found violations table: {table}\")\n",
    "        violations_table = table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "519b6c1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "INSPECTIONS TABLE STRUCTURE\n",
      "======================================================================\n",
      "               column_name                   data_type\n",
      "             operator_name                        text\n",
      "            p5_operator_no                        text\n",
      "                  district                        text\n",
      "district_office_inspecting                        text\n",
      "     oil_lease_gas_well_id                        text\n",
      "            lease_fac_name                        text\n",
      "                    api_no                        text\n",
      "                    county                        text\n",
      "                   well_no                        text\n",
      "           inspection_date timestamp without time zone\n",
      "        drilling_permit_no                        text\n",
      "              complaint_no                        text\n",
      "                compliance                        text\n",
      "                field_name                        text\n",
      "                  api_norm                        text\n",
      "\n",
      "Sample rows:\n",
      "          operator_name p5_operator_no district district_office_inspecting  \\\n",
      "0             Valle Oil            NaN       01                San Antonio   \n",
      "1   TOWER RESOURCES INC         862857       06                    Kilgore   \n",
      "2  BASA RESOURCES, INC.          53974       6E                    Kilgore   \n",
      "3                  0000              0       04             Corpus Christi   \n",
      "4              ANADARKO            NaN       08                    Midland   \n",
      "\n",
      "  oil_lease_gas_well_id  lease_fac_name api_no        county well_no  \\\n",
      "0                   NaN  Y Bar Facility   None      MCMULLEN    None   \n",
      "1                 15847      GARRISON B   None      ANDERSON    None   \n",
      "2                  6358          BUMPUS   None         GREGG    None   \n",
      "3                     0            0000   None  SAN PATRICIO    None   \n",
      "4                   NaN   NORTH MENTONE   None        LOVING    None   \n",
      "\n",
      "  inspection_date drilling_permit_no complaint_no compliance  field_name  \\\n",
      "0      2023-03-09               None         None         No         NaN   \n",
      "1      2025-09-05               None         None         No      SLOCUM   \n",
      "2      2023-07-07               None         None        Yes  EAST TEXAS   \n",
      "3      2020-10-19               None         None        Yes         NaN   \n",
      "4      2015-10-15               None         None        Yes         NaN   \n",
      "\n",
      "  api_norm  \n",
      "0     None  \n",
      "1     None  \n",
      "2     None  \n",
      "3     None  \n",
      "4     None  \n",
      "\n",
      "======================================================================\n",
      "VIOLATIONS TABLE STRUCTURE\n",
      "======================================================================\n",
      "          column_name                   data_type\n",
      "        operator_name                        text\n",
      "       p5_operator_no                        text\n",
      "             district                        text\n",
      "oil_lease_gas_well_id                        text\n",
      "       lease_fac_name                        text\n",
      "               api_no                        text\n",
      "               county                        text\n",
      "              well_no                        text\n",
      "   drilling_permit_no                        text\n",
      "           field_name                        text\n",
      "        violated_rule                        text\n",
      "   violated_rule_desc                        text\n",
      "       major_viol_ind                        text\n",
      "  compliant_on_reinsp                        text\n",
      "      last_enf_action                        text\n",
      " last_enf_action_date timestamp without time zone\n",
      "  violation_disc_date timestamp without time zone\n",
      "             api_norm                        text\n",
      "\n",
      "Sample rows:\n",
      "       operator_name p5_operator_no district oil_lease_gas_well_id  \\\n",
      "0             WEN-BE         908574       01                 12528   \n",
      "1  SMALL, R.P. CORP.         789221       03                 28126   \n",
      "2                NaN            NaN       05                   NaN   \n",
      "3       Ivory Energy         427219       7C                   NaN   \n",
      "4                NaN            NaN       7C                 13572   \n",
      "\n",
      "  lease_fac_name api_no       county well_no drilling_permit_no  \\\n",
      "0           WIER   None         FRIO    None                NaN   \n",
      "1        HORIZON   None  SAN JACINTO    None                NaN   \n",
      "2            NaN   None    ROBERTSON    None             786811   \n",
      "3        Devon A   None    TOM GREEN    None                NaN   \n",
      "4            NaN   None      RUNNELS    None                NaN   \n",
      "\n",
      "                field_name violated_rule violated_rule_desc major_viol_ind  \\\n",
      "0  PEARSALL (AUSTIN CHALK)      SWR 3(1)      Entrance Sign              N   \n",
      "1                  WILDCAT      SWR 3(1)      Entrance Sign              N   \n",
      "2                      NaN      SWR 3(1)      Entrance Sign              N   \n",
      "3                      NaN      SWR 3(1)      Entrance Sign              N   \n",
      "4                      NaN      SWR 3(1)      Entrance Sign              N   \n",
      "\n",
      "  compliant_on_reinsp      last_enf_action last_enf_action_date  \\\n",
      "0                   N  Notice of Violation           2017-01-31   \n",
      "1                  --  Notice of Violation           2025-10-27   \n",
      "2                  --  Notice of Violation           2020-11-17   \n",
      "3                   Y  Notice of Violation           2022-07-20   \n",
      "4                  --  Notice of Violation           2020-05-18   \n",
      "\n",
      "  violation_disc_date api_norm  \n",
      "0          2016-08-02     None  \n",
      "1          2025-10-24     None  \n",
      "2          2020-10-26     None  \n",
      "3          2022-05-16     None  \n",
      "4          2020-04-08     None  \n"
     ]
    }
   ],
   "source": [
    "# Examine structure of inspections and violations tables\n",
    "print(\"=\"*70)\n",
    "print(\"INSPECTIONS TABLE STRUCTURE\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "with engine.begin() as conn:\n",
    "    insp_cols = pd.read_sql(text(\"\"\"\n",
    "        SELECT column_name, data_type \n",
    "        FROM information_schema.columns \n",
    "        WHERE table_name = 'inspections'\n",
    "        ORDER BY ordinal_position\n",
    "        LIMIT 20\n",
    "    \"\"\"), conn)\n",
    "    print(insp_cols.to_string(index=False))\n",
    "    \n",
    "    # Sample rows\n",
    "    insp_sample = pd.read_sql(text(\"SELECT * FROM inspections LIMIT 5\"), conn)\n",
    "    print(\"\\nSample rows:\")\n",
    "    print(insp_sample.head())\n",
    "\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"VIOLATIONS TABLE STRUCTURE\")  \n",
    "print(\"=\"*70)\n",
    "\n",
    "with engine.begin() as conn:\n",
    "    viol_cols = pd.read_sql(text(\"\"\"\n",
    "        SELECT column_name, data_type \n",
    "        FROM information_schema.columns \n",
    "        WHERE table_name = 'violations'\n",
    "        ORDER BY ordinal_position\n",
    "        LIMIT 20\n",
    "    \"\"\"), conn)\n",
    "    print(viol_cols.to_string(index=False))\n",
    "    \n",
    "    # Sample rows\n",
    "    viol_sample = pd.read_sql(text(\"SELECT * FROM violations LIMIT 5\"), conn)\n",
    "    print(\"\\nSample rows:\")\n",
    "    print(viol_sample.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4a02e2",
   "metadata": {},
   "source": [
    "### Build District-Year Panel Dataset\n",
    "\n",
    "Aggregate inspections and violations to district-year level for DiD analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ed0a6f9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-18 16:43:05,995 - INFO - Connecting to Postgres\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing WellAnalyzer...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-18 16:43:08,614 - INFO - Loaded 1010432 wells from public.well_shape_tract\n",
      "2026-02-18 16:43:15,904 - INFO - Loaded 1878764 inspections from public.inspections\n",
      "2026-02-18 16:43:17,165 - INFO - Loaded 193338 violations from public.violations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u2713 Analyzer initialized\n",
      "  Wells source: public.well_shape_tract\n",
      "  Inspections source: public.inspections\n",
      "  Violations source: public.violations\n",
      "\n",
      "Loading data from database...\n",
      "\n",
      "\u2713 Loaded data:\n",
      "  Inspections: 1,878,764 rows\n",
      "  Violations: 193,338 rows\n",
      "\n",
      "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection']\n",
      "\n",
      "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number']\n"
     ]
    }
   ],
   "source": [
    "# Use WellAnalyzer to load the data (it has already figured out the schema)\n",
    "from analysis.well_analyzer import WellAnalyzer\n",
    "\n",
    "print(\"Initializing WellAnalyzer...\")\n",
    "analyzer = WellAnalyzer(chunk_size=50_000)\n",
    "\n",
    "print(\"\\n\u2713 Analyzer initialized\")\n",
    "print(f\"  Wells source: {analyzer.config.well_source}\")\n",
    "print(f\"  Inspections source: {analyzer.config.inspections_source}\")\n",
    "print(f\"  Violations source: {analyzer.config.violations_source}\")\n",
    "\n",
    "# Load the data\n",
    "print(\"\\nLoading data from database...\")\n",
    "data = analyzer.data\n",
    "\n",
    "inspections = data['inspections'].copy()\n",
    "violations = data['violations'].copy()\n",
    "\n",
    "print(f\"\\n\u2713 Loaded data:\")\n",
    "print(f\"  Inspections: {len(inspections):,} rows\")\n",
    "print(f\"  Violations: {len(violations):,} rows\")\n",
    "print(f\"\\nInspections columns: {list(inspections.columns)}\")\n",
    "print(f\"\\nViolations columns: {list(violations.columns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b6700403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered to 2015-2025:\n",
      "  Inspections: 1,867,859 rows\n",
      "  Violations: 191,762 rows\n",
      "\n",
      "\u2713 Created district-year panel:\n",
      "  Observations: 143\n",
      "  Districts: 13\n",
      "  Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025)]\n",
      "  Pre-2019 observations: 52\n",
      "  Post-2019 observations: 91\n",
      "\n",
      "================================================================================\n",
      "DISTRICT-YEAR PANEL SUMMARY\n",
      "================================================================================\n",
      "   district  year  total_inspections  unique_wells  compliant_inspections  \\\n",
      "0        01  2015               3498          2816                   3027   \n",
      "1        01  2016               6499          4055                   5028   \n",
      "2        01  2017               8649          6153                   7613   \n",
      "3        01  2018              10966          9109                   9668   \n",
      "4        01  2019               8097          6447                   6818   \n",
      "5        01  2020              10511          8716                   9087   \n",
      "6        01  2021               8586          6908                   6870   \n",
      "7        01  2022              12418         10193                   9608   \n",
      "8        01  2023              14573         11577                  11879   \n",
      "9        01  2024              16338         13038                  13923   \n",
      "10       01  2025              11691          9599                   9193   \n",
      "11       02  2015               1174           921                   1005   \n",
      "12       02  2016               2936          2003                   2098   \n",
      "13       02  2017               5325          4639                   4718   \n",
      "14       02  2018               5913          5107                   5317   \n",
      "15       02  2019               4427          3696                   3945   \n",
      "16       02  2020               4713          3679                   3789   \n",
      "17       02  2021               5090          3929                   4405   \n",
      "18       02  2022               7290          5842                   6578   \n",
      "19       02  2023               9679          7936                   8857   \n",
      "\n",
      "    compliance_rate  total_violations  wells_with_violations  \\\n",
      "0           86.5352               592                    379   \n",
      "1           77.3657              1902                   1009   \n",
      "2           88.0217              1439                    767   \n",
      "3           88.1634              1771                    997   \n",
      "4           84.2040              1506                    902   \n",
      "5           86.4523              1816                   1019   \n",
      "6           80.0140              2268                   1220   \n",
      "7           77.3716              3030                   1878   \n",
      "8           81.5138              2501                   1508   \n",
      "9           85.2185              2208                   1516   \n",
      "10          78.6331              2074                   1497   \n",
      "11          85.6048               192                    115   \n",
      "12          71.4578              1120                    570   \n",
      "13          88.6009               642                    431   \n",
      "14          89.9205               518                    354   \n",
      "15          89.1123               434                    271   \n",
      "16          80.3947              1106                    621   \n",
      "17          86.5422               656                    410   \n",
      "18          90.2332               665                    447   \n",
      "19          91.5074               826                    523   \n",
      "\n",
      "    major_violations  compliant_on_reinsp  enforced_violations  \\\n",
      "0                  0                  284                  592   \n",
      "1                  0                  472                 1902   \n",
      "2                  0                  740                 1439   \n",
      "3                  0                 1012                 1771   \n",
      "4                  2                  771                 1506   \n",
      "5                  1                  384                 1816   \n",
      "6                  0                  669                 2268   \n",
      "7                  0                 1653                 3030   \n",
      "8                  4                 1464                 2501   \n",
      "9                  0                 1135                 2208   \n",
      "10                 0                  298                 2074   \n",
      "11                 0                  112                  192   \n",
      "12                 0                  216                 1120   \n",
      "13                 0                  317                  642   \n",
      "14                 2                  184                  518   \n",
      "15                 0                  173                  434   \n",
      "16                 1                  196                 1106   \n",
      "17                 0                  220                  656   \n",
      "18                 0                  272                  665   \n",
      "19                 3                  366                  826   \n",
      "\n",
      "    violations_per_inspection  violation_rate  post_2019  post_2019_bool  \n",
      "0                      0.1692         16.9240          0           False  \n",
      "1                      0.2927         29.2660          0           False  \n",
      "2                      0.1664         16.6378          0           False  \n",
      "3                      0.1615         16.1499          0           False  \n",
      "4                      0.1860         18.5995          1            True  \n",
      "5                      0.1728         17.2771          1            True  \n",
      "6                      0.2642         26.4151          1            True  \n",
      "7                      0.2440         24.4001          1            True  \n",
      "8                      0.1716         17.1619          1            True  \n",
      "9                      0.1351         13.5145          1            True  \n",
      "10                     0.1774         17.7401          1            True  \n",
      "11                     0.1635         16.3543          0           False  \n",
      "12                     0.3815         38.1471          0           False  \n",
      "13                     0.1206         12.0563          0           False  \n",
      "14                     0.0876          8.7604          0           False  \n",
      "15                     0.0980          9.8035          1            True  \n",
      "16                     0.2347         23.4670          1            True  \n",
      "17                     0.1289         12.8880          1            True  \n",
      "18                     0.0912          9.1221          1            True  \n",
      "19                     0.0853          8.5339          1            True  \n"
     ]
    }
   ],
   "source": [
    "# Create district-year panel from the loaded data\n",
    "\n",
    "# Add year column to inspections and violations\n",
    "inspections['year'] = pd.to_datetime(inspections['inspection_date']).dt.year\n",
    "violations['year'] = pd.to_datetime(violations['violation_disc_date']).dt.year\n",
    "\n",
    "# Filter to analysis period 2015-2025\n",
    "inspections = inspections[(inspections['year'] >= 2015) & (inspections['year'] <= 2025)]\n",
    "violations = violations[(violations['year'] >= 2015) & (violations['year'] <= 2025)]\n",
    "\n",
    "print(f\"Filtered to 2015-2025:\")\n",
    "print(f\"  Inspections: {len(inspections):,} rows\")\n",
    "print(f\"  Violations: {len(violations):,} rows\")\n",
    "\n",
    "# Aggregate inspections by district-year\n",
    "insp_agg = inspections.groupby(['district', 'year']).agg({\n",
    "    'api_norm': ['count', 'nunique'],\n",
    "    'compliance': lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).sum()\n",
    "}).reset_index()\n",
    "\n",
    "insp_agg.columns = ['district', 'year', 'total_inspections', 'unique_wells',\n",
    "                     'compliant_inspections']\n",
    "insp_agg['compliance_rate'] = (insp_agg['compliant_inspections'] / insp_agg['total_inspections']) * 100\n",
    "\n",
    "# Aggregate violations by district-year\n",
    "viol_agg = violations.groupby(['district', 'year']).agg({\n",
    "    'api_norm': ['count', 'nunique'],\n",
    "    'major_viol_ind': lambda x: (x == 'Y').sum(),\n",
    "    'compliant_on_reinsp': lambda x: (x == 'Y').sum(),\n",
    "    'last_enf_action': lambda x: x.notna().sum()\n",
    "}).reset_index()\n",
    "\n",
    "viol_agg.columns = [\n",
    "    'district', 'year', 'total_violations', 'wells_with_violations',\n",
    "    'major_violations', 'compliant_on_reinsp', 'enforced_violations'\n",
    "]\n",
    "# Merge inspections and violations\n",
    "district_year_df = pd.merge(\n",
    "    insp_agg,\n",
    "    viol_agg,\n",
    "    on=['district', 'year'],\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "# Fill NAs with 0\n",
    "viol_cols = ['total_violations', 'wells_with_violations', 'major_violations',\n",
    "             'compliant_on_reinsp', 'enforced_violations']\n",
    "for col in viol_cols:\n",
    "    district_year_df[col] = district_year_df[col].fillna(0)\n",
    "\n",
    "# Add key metrics\n",
    "district_year_df['violations_per_inspection'] = (\n",
    "    district_year_df['total_violations'] / district_year_df['total_inspections']\n",
    ")\n",
    "district_year_df['violation_rate'] = (\n",
    "    district_year_df['total_violations'] / district_year_df['total_inspections'] * 100\n",
    ")\n",
    "\n",
    "# Add treatment indicator\n",
    "district_year_df['post_2019'] = (district_year_df['year'] >= 2019).astype(int)\n",
    "district_year_df['post_2019_bool'] = district_year_df['year'] >= 2019\n",
    "\n",
    "print(f\"\\n\u2713 Created district-year panel:\")\n",
    "print(f\"  Observations: {len(district_year_df)}\")\n",
    "print(f\"  Districts: {district_year_df['district'].nunique()}\")\n",
    "print(f\"  Years: {sorted(district_year_df['year'].unique())}\")\n",
    "print(f\"  Pre-2019 observations: {(district_year_df['year'] < 2019).sum()}\")\n",
    "print(f\"  Post-2019 observations: {(district_year_df['year'] >= 2019).sum()}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"DISTRICT-YEAR PANEL SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "print(district_year_df.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c069f4",
   "metadata": {},
   "source": [
    "## Part 2: Regulatory Pipeline Analysis\n",
    "\n",
    "**Key insight**: Wells have **repeated inspections** over time, creating a regulatory pipeline:\n",
    "1. **Inspection** \u2192 2. **Violation discovered** (if any) \u2192 3. **Enforcement action** \u2192 4. **Re-inspection** \u2192 5. **Compliance verified**\n",
    "\n",
    "We need to track:\n",
    "- **Time between events**: inspection \u2192 violation \u2192 enforcement \u2192 resolution\n",
    "- **Repeat patterns**: wells with multiple violations, chronic non-compliance\n",
    "- **Treatment effects on the pipeline**: Did 2019 disclosure change the speed or effectiveness of enforcement?\n",
    "\n",
    "This requires **well-level panel data** with time-varying outcomes, not just district-year aggregates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "05593725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "VIOLATION ENFORCEMENT ACTIONS ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "1. ENFORCEMENT ACTION TYPES:\n",
      "--------------------------------------------------------------------------------\n",
      "last_enf_action\n",
      "Notice of Violation                                            140299\n",
      "Referred to Austin Field Ops for possible legal enforcement     25534\n",
      "Referred to State-Managed Plugging                              19434\n",
      "Issued a Severance/Seal Order                                    5411\n",
      "Referred to State-Managed Cleanup Program                         551\n",
      "Violation Corrected                                               533\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Total unique enforcement action types: 6\n",
      "Violations with enforcement action: 191,762\n",
      "Violations without enforcement action: 0\n",
      "\n",
      "2. VIOLATION SEVERITY:\n",
      "--------------------------------------------------------------------------------\n",
      "major_viol_ind\n",
      "N    191703\n",
      "Y        59\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Major violations: 0.0%\n",
      "\n",
      "3. COMPLIANCE ON RE-INSPECTION:\n",
      "--------------------------------------------------------------------------------\n",
      "compliant_on_reinsp\n",
      "Y     110488\n",
      "--     64153\n",
      "N      17121\n",
      "Name: count, dtype: int64\n",
      "\n",
      "4. TIME TO ENFORCEMENT:\n",
      "--------------------------------------------------------------------------------\n",
      "Violations with enforcement timing data: 191,762\n",
      "Mean days to enforcement: 128.3\n",
      "Median days to enforcement: 15.0\n",
      "90th percentile: 384.0 days\n",
      "Max days to enforcement: 3640 days\n",
      "\n",
      "Distribution of enforcement timing:\n",
      "time_category\n",
      "<30 days       111175\n",
      "30-90 days      30459\n",
      "90-180 days     16308\n",
      "6mo-1yr         13423\n",
      "1-2 years       10387\n",
      ">2 years         9594\n",
      "Name: count, dtype: int64\n",
      "\n",
      "5. ENFORCEMENT RATES BY DISTRICT:\n",
      "--------------------------------------------------------------------------------\n",
      "          enforcement_rate_pct  total_violations  enforced_violations\n",
      "district                                                             \n",
      "01                    100.0000             21107                21107\n",
      "02                    100.0000              7829                 7829\n",
      "03                    100.0000              9490                 9490\n",
      "04                    100.0000              6933                 6933\n",
      "05                    100.0000              4072                 4072\n",
      "06                    100.0000             10319                10319\n",
      "08                    100.0000             29981                29981\n",
      "09                    100.0000             41136                41136\n",
      "10                    100.0000             13075                13075\n",
      "6E                    100.0000              4686                 4686\n",
      "7B                    100.0000             20196                20196\n",
      "7C                    100.0000             13279                13279\n",
      "8A                    100.0000              9659                 9659\n",
      "\n",
      "\u2713 Enforcement actions analysis complete\n"
     ]
    }
   ],
   "source": [
    "# Examine enforcement action types and temporal patterns in violations data\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"VIOLATION ENFORCEMENT ACTIONS ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Normalize enforcement fields so blanks don't count as enforcement\n",
    "violations['last_enf_action'] = violations['last_enf_action'].replace(r'^\\s*$', np.nan, regex=True)\n",
    "violations['last_enf_action_date'] = pd.to_datetime(violations['last_enf_action_date'], errors='coerce')\n",
    "\n",
    "# 1. Types of enforcement actions\n",
    "print(\"\\n1. ENFORCEMENT ACTION TYPES:\")\n",
    "print(\"-\"*80)\n",
    "enf_actions = violations['last_enf_action'].value_counts(dropna=False)\n",
    "print(enf_actions)\n",
    "print(f\"\\nTotal unique enforcement action types: {violations['last_enf_action'].nunique()}\")\n",
    "print(f\"Violations with enforcement action: {violations['last_enf_action'].notna().sum():,}\")\n",
    "print(f\"Violations without enforcement action: {violations['last_enf_action'].isna().sum():,}\")\n",
    "\n",
    "# 2. Major vs minor violations\n",
    "print(\"\\n2. VIOLATION SEVERITY:\")\n",
    "print(\"-\"*80)\n",
    "major_viol = violations['major_viol_ind'].value_counts(dropna=False)\n",
    "print(major_viol)\n",
    "major_pct = violations['major_viol_ind'].value_counts(normalize=True) * 100\n",
    "print(f\"\\nMajor violations: {major_pct.get('Y', 0):.1f}%\")\n",
    "\n",
    "# 3. Compliance on re-inspection\n",
    "print(\"\\n3. COMPLIANCE ON RE-INSPECTION:\")\n",
    "print(\"-\"*80)\n",
    "reinsp_compliance = violations['compliant_on_reinsp'].value_counts(dropna=False)\n",
    "print(reinsp_compliance)\n",
    "\n",
    "# 4. Temporal gaps: violation discovery to enforcement\n",
    "print(\"\\n4. TIME TO ENFORCEMENT:\")\n",
    "print(\"-\"*80)\n",
    "violations['violation_disc_date'] = pd.to_datetime(violations['violation_disc_date'], errors='coerce')\n",
    "violations['days_to_enforcement'] = (violations['last_enf_action_date'] -\n",
    "                                     violations['violation_disc_date']).dt.days\n",
    "\n",
    "# Only for violations that got enforcement\n",
    "enforced = violations[violations['days_to_enforcement'].notna() & \n",
    "                     (violations['days_to_enforcement'] >= 0)]\n",
    "\n",
    "if len(enforced) > 0:\n",
    "    print(f\"Violations with enforcement timing data: {len(enforced):,}\")\n",
    "    print(f\"Mean days to enforcement: {enforced['days_to_enforcement'].mean():.1f}\")\n",
    "    print(f\"Median days to enforcement: {enforced['days_to_enforcement'].median():.1f}\")\n",
    "    print(f\"90th percentile: {enforced['days_to_enforcement'].quantile(0.9):.1f} days\")\n",
    "    print(f\"Max days to enforcement: {enforced['days_to_enforcement'].max():.0f} days\")\n",
    "    \n",
    "    # Distribution\n",
    "    print(\"\\nDistribution of enforcement timing:\")\n",
    "    bins = [0, 30, 90, 180, 365, 730, np.inf]\n",
    "    labels = ['<30 days', '30-90 days', '90-180 days', '6mo-1yr', '1-2 years', '>2 years']\n",
    "    enforced['time_category'] = pd.cut(enforced['days_to_enforcement'], bins=bins, labels=labels)\n",
    "    print(enforced['time_category'].value_counts().sort_index())\n",
    "\n",
    "# 5. Enforcement rates by district (requires action + date)\n",
    "print(\"\\n5. ENFORCEMENT RATES BY DISTRICT:\")\n",
    "print(\"-\"*80)\n",
    "violations['enforced_flag'] = (\n",
    "    violations['last_enf_action'].notna() & violations['last_enf_action_date'].notna()\n",
    ")\n",
    "district_enf = violations.groupby('district').agg(\n",
    "    enforcement_rate_pct=('enforced_flag', lambda x: x.mean() * 100),\n",
    "    total_violations=('enforced_flag', 'size'),\n",
    "    enforced_violations=('enforced_flag', 'sum')\n",
    ")\n",
    "district_enf = district_enf.sort_values('enforcement_rate_pct', ascending=False)\n",
    "print(district_enf.to_string())\n",
    "\n",
    "print(\"\\n\u2713 Enforcement actions analysis complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fca1aaaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building well-level panel with regulatory pipeline metrics...\n",
      "\n",
      "First, checking API number formats...\n",
      "Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection', 'year']\n",
      "Sample inspection row:\n",
      "  api_norm: 10130031 (type: <class 'str'>)\n",
      "\n",
      "Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number', 'year', 'days_to_enforcement', 'enforced_flag']\n",
      "Sample violation row:\n",
      "  api_norm: 46102018 (type: <class 'str'>)\n",
      "\n",
      "\u2713 Using api_norm as well identifier\n",
      "  Unique wells in inspections: 419,976\n",
      "  Unique wells in violations: 81,220\n",
      "\n",
      "\u2713 Well-level panel created\n",
      "  Total wells: 419,976\n",
      "  Wells with violations: 81,220 (19.3%)\n",
      "  Repeat violators: 41,284 (9.8%)\n",
      "  Avg inspections per well: 4.4\n",
      "  Avg violations per well: 0.46\n",
      "\n",
      "Sample of well panel:\n",
      "    well_id  total_violations  total_inspections  repeat_violator  ever_violated district  violations_per_inspection  avg_days_to_enforcement first_inspection last_inspection first_violation  years_active\n",
      "0  10130031                 1                  2                0              1       8A                     0.5000                  55.0000       2016-03-04      2016-07-26      2016-03-04        0.3943\n",
      "1  10130036                 0                  3                0              0       8A                     0.0000                      NaN       2017-12-06      2025-02-06             NaT        7.1704\n",
      "2  10130045                 0                  3                0              0       8A                     0.0000                      NaN       2017-12-06      2022-09-19             NaT        4.7858\n",
      "3  10130054                 4                  2                1              1       8A                     2.0000                  55.0000       2016-03-04      2016-07-26      2016-03-04        0.3943\n",
      "4  10130055                 2                  5                1              1       8A                     0.4000                  55.0000       2016-03-04      2025-07-16      2016-03-04        9.3662\n",
      "5  10130080                 0                  3                0              0       8A                     0.0000                      NaN       2017-08-18      2024-10-13             NaT        7.1540\n",
      "6  10130086                 1                  3                0              1       8A                     0.3333                   9.0000       2017-08-18      2024-10-14      2024-10-14        7.1567\n",
      "7  10130096                 0                  1                0              0       8A                     0.0000                      NaN       2017-08-18      2017-08-18             NaT        0.0000\n",
      "8  10130098                 0                  3                0              0       8A                     0.0000                      NaN       2017-08-18      2025-08-15             NaT        7.9918\n",
      "9  10130104                 2                  7                1              1       8A                     0.2857                 987.0000       2016-02-03      2022-08-17      2016-02-03        6.5352\n"
     ]
    }
   ],
   "source": [
    "# Create well-level panel with regulatory pipeline metrics (EFFICIENT VERSION)\n",
    "\n",
    "print(\"Building well-level panel with regulatory pipeline metrics...\")\n",
    "print(\"\\nFirst, checking API number formats...\")\n",
    "\n",
    "# Check what we actually have\n",
    "print(f\"Inspections columns: {inspections.columns.tolist()}\")\n",
    "print(f\"Sample inspection row:\")\n",
    "if len(inspections) > 0:\n",
    "    sample = inspections.iloc[0]\n",
    "    for col in ['api_norm']:\n",
    "        if col in inspections.columns:\n",
    "            print(f\"  {col}: {sample[col]} (type: {type(sample[col])})\")\n",
    "\n",
    "print(f\"\\nViolations columns: {violations.columns.tolist()}\")\n",
    "print(f\"Sample violation row:\")\n",
    "if len(violations) > 0:\n",
    "    sample = violations.iloc[0]\n",
    "    for col in ['api_norm']:\n",
    "        if col in violations.columns:\n",
    "            print(f\"  {col}: {sample[col]} (type: {type(sample[col])})\")\n",
    "\n",
    "# Use api_norm directly as the normalized well identifier\n",
    "inspections['well_id'] = inspections['api_norm'].astype(str).str.strip()\n",
    "violations['well_id'] = violations['api_norm'].astype(str).str.strip()\n",
    "\n",
    "print(f\"\\n\u2713 Using api_norm as well identifier\")\n",
    "print(f\"  Unique wells in inspections: {inspections['well_id'].nunique():,}\")\n",
    "print(f\"  Unique wells in violations: {violations['well_id'].nunique():,}\")\n",
    "\n",
    "# Prepare inspections data\n",
    "inspections_sorted = inspections.copy()\n",
    "inspections_sorted['inspection_date'] = pd.to_datetime(inspections_sorted['inspection_date'])\n",
    "inspections_sorted['year'] = inspections_sorted['inspection_date'].dt.year\n",
    "\n",
    "# Prepare violations data\n",
    "violations_sorted = violations.copy()\n",
    "violations_sorted['violation_disc_date'] = pd.to_datetime(violations_sorted['violation_disc_date'])\n",
    "violations_sorted['last_enf_action_date'] = pd.to_datetime(violations_sorted['last_enf_action_date'])\n",
    "violations_sorted['year'] = violations_sorted['violation_disc_date'].dt.year\n",
    "\n",
    "# Calculate enforcement timing\n",
    "violations_sorted['days_to_enforcement'] = (\n",
    "    violations_sorted['last_enf_action_date'] - violations_sorted['violation_disc_date']\n",
    ").dt.days\n",
    "\n",
    "# Well-level aggregates (static characteristics)\n",
    "viol_per_well = violations_sorted.groupby('well_id').size()\n",
    "insp_per_well = inspections_sorted.groupby('well_id').size()\n",
    "\n",
    "well_panel = pd.DataFrame({\n",
    "    'well_id': viol_per_well.index.union(insp_per_well.index),\n",
    "})\n",
    "\n",
    "well_panel = well_panel.merge(\n",
    "    viol_per_well.rename('total_violations'),\n",
    "    left_on='well_id', right_index=True, how='left'\n",
    ").merge(\n",
    "    insp_per_well.rename('total_inspections'),\n",
    "    left_on='well_id', right_index=True, how='left'\n",
    ")\n",
    "\n",
    "well_panel['total_violations'] = well_panel['total_violations'].fillna(0).astype(int)\n",
    "well_panel['total_inspections'] = well_panel['total_inspections'].fillna(0).astype(int)\n",
    "\n",
    "# Identify repeat violators and wells with violations\n",
    "well_panel['repeat_violator'] = (well_panel['total_violations'] > 1).astype(int)\n",
    "well_panel['ever_violated'] = (well_panel['total_violations'] > 0).astype(int)\n",
    "\n",
    "# Get district for each well (from inspections - use most common district)\n",
    "well_district = inspections_sorted.groupby('well_id')['district'].agg(\n",
    "    lambda x: x.mode()[0] if len(x.mode()) > 0 else x.iloc[0]\n",
    ").rename('district')\n",
    "well_panel = well_panel.merge(well_district, left_on='well_id', right_index=True, how='left')\n",
    "\n",
    "# Violation rate per inspection\n",
    "well_panel['violations_per_inspection'] = (\n",
    "    well_panel['total_violations'] / well_panel['total_inspections'].replace(0, np.nan)\n",
    ")\n",
    "\n",
    "# Average enforcement speed for wells with violations\n",
    "avg_enf_time = violations_sorted.groupby('well_id')['days_to_enforcement'].mean().rename('avg_days_to_enforcement')\n",
    "well_panel = well_panel.merge(avg_enf_time, left_on='well_id', right_index=True, how='left')\n",
    "\n",
    "# First and last activity dates\n",
    "first_insp = inspections_sorted.groupby('well_id')['inspection_date'].min().rename('first_inspection')\n",
    "last_insp = inspections_sorted.groupby('well_id')['inspection_date'].max().rename('last_inspection')\n",
    "first_viol = violations_sorted.groupby('well_id')['violation_disc_date'].min().rename('first_violation')\n",
    "\n",
    "well_panel = well_panel.merge(first_insp, left_on='well_id', right_index=True, how='left')\n",
    "well_panel = well_panel.merge(last_insp, left_on='well_id', right_index=True, how='left')\n",
    "well_panel = well_panel.merge(first_viol, left_on='well_id', right_index=True, how='left')\n",
    "\n",
    "# Calculate well lifespan in data\n",
    "well_panel['years_active'] = (\n",
    "    (well_panel['last_inspection'] - well_panel['first_inspection']).dt.days / 365.25\n",
    ")\n",
    "\n",
    "print(\"\\n\u2713 Well-level panel created\")\n",
    "print(f\"  Total wells: {len(well_panel):,}\")\n",
    "print(f\"  Wells with violations: {well_panel['ever_violated'].sum():,} ({well_panel['ever_violated'].mean()*100:.1f}%)\")\n",
    "print(f\"  Repeat violators: {well_panel['repeat_violator'].sum():,} ({well_panel['repeat_violator'].mean()*100:.1f}%)\")\n",
    "print(f\"  Avg inspections per well: {well_panel['total_inspections'].mean():.1f}\")\n",
    "print(f\"  Avg violations per well: {well_panel['total_violations'].mean():.2f}\")\n",
    "\n",
    "print(\"\\nSample of well panel:\")\n",
    "print(well_panel.head(10).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f05dfb17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating district-year panel with pipeline metrics...\n",
      "\n",
      "\u2713 District-year panel with PIPELINE metrics:\n",
      "  Observations: 143\n",
      "  Districts: 13\n",
      "  Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025)]\n",
      "\n",
      "================================================================================\n",
      "PIPELINE METRICS: Sample rows\n",
      "================================================================================\n",
      "district  year  total_inspections  unique_wells  compliance_rate  avg_days_between_insp  median_days_between_insp  total_violations  wells_with_violations  share_major_violations  avg_days_to_enforcement  median_days_to_enforcement  enforcement_rate  resolution_rate  violations_per_inspection  violation_discovery_rate  post_2019  year_from_policy\n",
      "      01  2015               3498          2816          86.5352                38.8023                   35.0000               592                    379                  0.0000                 124.9122                     19.0000          100.0000          47.9730                     0.1692                   13.4588          0                -4\n",
      "      01  2016               6499          4055          77.3657               109.3848                   85.0000              1902                   1009                  0.0000                 230.0910                     20.0000          100.0000          24.8160                     0.2927                   24.8829          0                -3\n",
      "      01  2017               8649          6153          88.0217               220.8480                  160.0000              1439                    767                  0.0000                 326.0945                     33.0000          100.0000          51.4246                     0.1664                   12.4655          0                -2\n",
      "      01  2018              10966          9109          88.1634               263.2386                  173.0000              1771                    997                  0.0000                 290.2191                     82.0000          100.0000          57.1429                     0.1615                   10.9452          0                -1\n",
      "      01  2019               8097          6447          84.2040               358.4922                  231.0000              1506                    902                  0.1328                 261.2776                     73.0000          100.0000          51.1952                     0.1860                   13.9910          1                 0\n",
      "      01  2020              10511          8716          86.4523               560.7332                  337.0000              1816                   1019                  0.0551                 318.0391                    260.0000          100.0000          21.1454                     0.1728                   11.6911          1                 1\n",
      "      01  2021               8586          6908          80.0140               754.0327                  595.0000              2268                   1220                  0.0000                 164.8298                     89.0000          100.0000          29.4974                     0.2642                   17.6607          1                 2\n",
      "      01  2022              12418         10193          77.3716              1015.8347                 1211.0000              3030                   1878                  0.0000                 142.2416                     40.0000          100.0000          54.5545                     0.2440                   18.4244          1                 3\n",
      "      01  2023              14573         11577          81.5138               866.2592                  752.0000              2501                   1508                  0.1599                 173.2783                     88.0000          100.0000          58.5366                     0.1716                   13.0258          1                 4\n",
      "      01  2024              16338         13038          85.2185               758.6809                  570.0000              2208                   1516                  0.0000                  93.9008                     28.0000          100.0000          51.4040                     0.1351                   11.6276          1                 5\n",
      "      01  2025              11691          9599          78.6331               636.5767                  465.0000              2074                   1497                  0.0000                  54.1321                     42.0000          100.0000          14.3684                     0.1774                   15.5954          1                 6\n",
      "      02  2015               1174           921          85.6048                34.1107                   28.0000               192                    115                  0.0000                 171.5260                     41.0000          100.0000          58.3333                     0.1635                   12.4864          0                -4\n",
      "      02  2016               2936          2003          71.4578               110.6093                   77.0000              1120                    570                  0.0000                 273.4232                     49.0000          100.0000          19.2857                     0.3815                   28.4573          0                -3\n",
      "      02  2017               5325          4639          88.6009               276.1325                  251.0000               642                    431                  0.0000                 270.1137                    151.5000          100.0000          49.3769                     0.1206                    9.2908          0                -2\n",
      "      02  2018               5913          5107          89.9205               292.9596                  214.0000               518                    354                  0.3861                 222.4208                     49.5000          100.0000          35.5212                     0.0876                    6.9317          0                -1\n"
     ]
    }
   ],
   "source": [
    "# Create district-year panel with PIPELINE metrics (not just counts)\n",
    "# Focus on: speed, effectiveness, and dynamics of the regulatory process\n",
    "\n",
    "print(\"Creating district-year panel with pipeline metrics...\")\n",
    "\n",
    "# Group by district-year and calculate pipeline performance indicators\n",
    "pipeline_metrics = inspections_sorted.groupby(['district', 'year']).agg(\n",
    "    total_inspections=('well_id', 'count'),\n",
    "    unique_wells=('well_id', 'nunique'),\n",
    "    compliance_rate=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).mean() * 100),\n",
    "    avg_days_between_insp=('days_since_last_inspection', 'mean'),\n",
    "    median_days_between_insp=('days_since_last_inspection', 'median')\n",
    ").reset_index()\n",
    "\n",
    "# Add violation pipeline metrics\n",
    "viol_pipeline = violations_sorted.groupby(['district', 'year']).agg(\n",
    "    total_violations=('well_id', 'count'),\n",
    "    wells_with_violations=('well_id', 'nunique'),\n",
    "    share_major_violations=('major_viol_ind', lambda x: (x == 'Y').mean() * 100),\n",
    "    avg_days_to_enforcement=('days_to_enforcement', 'mean'),\n",
    "    median_days_to_enforcement=('days_to_enforcement', 'median'),\n",
    "    enforcement_rate=('last_enf_action', lambda x: x.notna().mean() * 100),\n",
    "    resolution_rate=('compliant_on_reinsp', lambda x: (x == 'Y').mean() * 100)\n",
    ").reset_index()\n",
    "\n",
    "# Merge\n",
    "district_year_panel = pd.merge(pipeline_metrics, viol_pipeline, \n",
    "                               on=['district', 'year'], how='left')\n",
    "\n",
    "# Fill NAs for districts with no violations in that year\n",
    "viol_cols = ['total_violations', 'wells_with_violations', 'share_major_violations',\n",
    "             'avg_days_to_enforcement', 'median_days_to_enforcement', \n",
    "             'enforcement_rate', 'resolution_rate']\n",
    "for col in viol_cols:\n",
    "    district_year_panel[col] = district_year_panel[col].fillna(0)\n",
    "\n",
    "# Calculate key pipeline ratios\n",
    "district_year_panel['violations_per_inspection'] = (\n",
    "    district_year_panel['total_violations'] / district_year_panel['total_inspections']\n",
    ")\n",
    "district_year_panel['violation_discovery_rate'] = (\n",
    "    district_year_panel['wells_with_violations'] / district_year_panel['unique_wells'] * 100\n",
    ")\n",
    "\n",
    "# Treatment indicator\n",
    "district_year_panel['post_2019'] = (district_year_panel['year'] >= 2019).astype(int)\n",
    "district_year_panel['year_from_policy'] = district_year_panel['year'] - 2019  # for event study\n",
    "\n",
    "print(f\"\\n\u2713 District-year panel with PIPELINE metrics:\")\n",
    "print(f\"  Observations: {len(district_year_panel)}\")\n",
    "print(f\"  Districts: {district_year_panel['district'].nunique()}\")\n",
    "print(f\"  Years: {sorted(district_year_panel['year'].unique())}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"PIPELINE METRICS: Sample rows\")\n",
    "print(\"=\"*80)\n",
    "print(district_year_panel.head(15).to_string(index=False))\n",
    "# Offshore-jurisdiction indicator (districts with onshore + offshore oversight)\n",
    "offshore_jurisdiction_districts = ['02', '03', '04']\n",
    "district_year_panel['offshore_jurisdiction'] = district_year_panel['district'].isin(offshore_jurisdiction_districts).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4befc1b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "PRE vs POST 2019 COMPARISON: Pipeline Metrics\n",
      "================================================================================\n",
      "                            Pre-2019  Post-2019    Change  Pct_Change\n",
      "total_inspections          8782.5577 15507.3187 6724.7610     76.5695\n",
      "unique_wells               6621.8654 11144.8242 4522.9588     68.3034\n",
      "avg_days_between_insp       163.8185   572.5165  408.6980    249.4823\n",
      "compliance_rate              87.1610    89.4522    2.2913      2.6288\n",
      "violation_discovery_rate     12.1762     8.4361   -3.7400    -30.7161\n",
      "violations_per_inspection     0.1488     0.0965   -0.0523    -35.1600\n",
      "avg_days_to_enforcement     174.2728   112.2853  -61.9875    -35.5692\n",
      "median_days_to_enforcement   68.4135    45.1703  -23.2431    -33.9745\n",
      "enforcement_rate            100.0000   100.0000    0.0000      0.0000\n",
      "resolution_rate              52.2074    59.3551    7.1477     13.6910\n",
      "share_major_violations        0.0077     0.1124    0.1047   1359.2130\n",
      "\n",
      "================================================================================\n",
      "KEY FINDINGS:\n",
      "================================================================================\n",
      "\u26a0 Inspection frequency DECREASED substantially: 249.5% increase in days between inspections\n",
      "\u2192 Compliance rate INCREASED by 2.3 percentage points\n",
      "\u2192 Enforcement became FASTER by 62.0 days on average\n",
      "\u2192 Resolution rate INCREASED by 7.1 percentage points\n"
     ]
    }
   ],
   "source": [
    "# Compare pre vs post 2019 across all pipeline metrics\n",
    "\n",
    "pre_post = district_year_panel.groupby('post_2019').agg({\n",
    "    # Inspection intensity\n",
    "    'total_inspections': 'mean',\n",
    "    'unique_wells': 'mean',\n",
    "    'avg_days_between_insp': 'mean',\n",
    "    \n",
    "    # Compliance outcomes\n",
    "    'compliance_rate': 'mean',\n",
    "    'violation_discovery_rate': 'mean',\n",
    "    'violations_per_inspection': 'mean',\n",
    "    \n",
    "    # Enforcement speed and effectiveness\n",
    "    'avg_days_to_enforcement': 'mean',\n",
    "    'median_days_to_enforcement': 'mean',\n",
    "    'enforcement_rate': 'mean',\n",
    "    'resolution_rate': 'mean',\n",
    "    \n",
    "    # Violation severity\n",
    "    'share_major_violations': 'mean'\n",
    "}).T\n",
    "\n",
    "pre_post.columns = ['Pre-2019', 'Post-2019']\n",
    "pre_post['Change'] = pre_post['Post-2019'] - pre_post['Pre-2019']\n",
    "pre_post['Pct_Change'] = (pre_post['Change'] / pre_post['Pre-2019'].replace(0, np.nan) * 100)\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"PRE vs POST 2019 COMPARISON: Pipeline Metrics\")\n",
    "print(\"=\"*80)\n",
    "print(pre_post.to_string())\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"KEY FINDINGS:\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Highlight major changes\n",
    "if pre_post.loc['avg_days_between_insp', 'Pct_Change'] > 50:\n",
    "    print(f\"\u26a0 Inspection frequency DECREASED substantially: {pre_post.loc['avg_days_between_insp', 'Pct_Change']:.1f}% increase in days between inspections\")\n",
    "    \n",
    "if abs(pre_post.loc['compliance_rate', 'Change']) > 2:\n",
    "    direction = \"INCREASED\" if pre_post.loc['compliance_rate', 'Change'] > 0 else \"DECREASED\"\n",
    "    print(f\"\u2192 Compliance rate {direction} by {abs(pre_post.loc['compliance_rate', 'Change']):.1f} percentage points\")\n",
    "    \n",
    "if abs(pre_post.loc['avg_days_to_enforcement', 'Change']) > 10:\n",
    "    direction = \"SLOWER\" if pre_post.loc['avg_days_to_enforcement', 'Change'] > 0 else \"FASTER\"\n",
    "    print(f\"\u2192 Enforcement became {direction} by {abs(pre_post.loc['avg_days_to_enforcement', 'Change']):.1f} days on average\")\n",
    "    \n",
    "if abs(pre_post.loc['resolution_rate', 'Change']) > 5:\n",
    "    direction = \"INCREASED\" if pre_post.loc['resolution_rate', 'Change'] > 0 else \"DECREASED\"\n",
    "    print(f\"\u2192 Resolution rate {direction} by {abs(pre_post.loc['resolution_rate', 'Change']):.1f} percentage points\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9c41d28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5400x3000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u2713 Pipeline visualization complete\n",
      "  Saved as 'pipeline_trends_over_time.png'\n"
     ]
    }
   ],
   "source": [
    "# Visualize the regulatory pipeline over time\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "fig.suptitle('Regulatory Pipeline Dynamics: 2015-2024\\n(Vertical line = 2019 disclosure policy)', \n",
    "             fontsize=16, fontweight='bold', y=0.995)\n",
    "\n",
    "# Annual averages across all districts\n",
    "annual_avg = district_year_panel.groupby('year').agg({\n",
    "    'compliance_rate': 'mean',\n",
    "    'violations_per_inspection': 'mean',\n",
    "    'avg_days_to_enforcement': 'mean',\n",
    "    'resolution_rate': 'mean',\n",
    "    'avg_days_between_insp': 'mean',\n",
    "    'violation_discovery_rate': 'mean'\n",
    "})\n",
    "\n",
    "# Plot 1: Compliance rate\n",
    "axes[0, 0].plot(annual_avg.index, annual_avg['compliance_rate'], 'o-', linewidth=2.5, markersize=8, color='steelblue')\n",
    "axes[0, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2, label='2019 Policy')\n",
    "axes[0, 0].set_xlabel('Year', fontsize=11)\n",
    "axes[0, 0].set_ylabel('Compliance Rate (%)', fontsize=11)\n",
    "axes[0, 0].set_title('Compliance Rate at Inspection', fontsize=12, fontweight='bold')\n",
    "axes[0, 0].legend()\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 2: Violations per inspection\n",
    "axes[0, 1].plot(annual_avg.index, annual_avg['violations_per_inspection'], 'o-', \n",
    "                linewidth=2.5, markersize=8, color='darkorange')\n",
    "axes[0, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "axes[0, 1].set_xlabel('Year', fontsize=11)\n",
    "axes[0, 1].set_ylabel('Violations per Inspection', fontsize=11)\n",
    "axes[0, 1].set_title('Violation Discovery Rate', fontsize=12, fontweight='bold')\n",
    "axes[0, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 3: Days to enforcement\n",
    "axes[0, 2].plot(annual_avg.index, annual_avg['avg_days_to_enforcement'], 'o-', \n",
    "                linewidth=2.5, markersize=8, color='darkgreen')\n",
    "axes[0, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "axes[0, 2].set_xlabel('Year', fontsize=11)\n",
    "axes[0, 2].set_ylabel('Days', fontsize=11)\n",
    "axes[0, 2].set_title('Average Days to Enforcement', fontsize=12, fontweight='bold')\n",
    "axes[0, 2].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 4: Resolution rate\n",
    "axes[1, 0].plot(annual_avg.index, annual_avg['resolution_rate'], 'o-', \n",
    "                linewidth=2.5, markersize=8, color='purple')\n",
    "axes[1, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "axes[1, 0].set_xlabel('Year', fontsize=11)\n",
    "axes[1, 0].set_ylabel('Resolution Rate (%)', fontsize=11)\n",
    "axes[1, 0].set_title('Violations Resolved on Re-inspection', fontsize=12, fontweight='bold')\n",
    "axes[1, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 5: Days between inspections (NEW - shows inspection frequency decline)\n",
    "axes[1, 1].plot(annual_avg.index, annual_avg['avg_days_between_insp'], 'o-', \n",
    "                linewidth=2.5, markersize=8, color='brown')\n",
    "axes[1, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "axes[1, 1].set_xlabel('Year', fontsize=11)\n",
    "axes[1, 1].set_ylabel('Days', fontsize=11)\n",
    "axes[1, 1].set_title('Average Days Between Inspections', fontsize=12, fontweight='bold')\n",
    "axes[1, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 6: Violation discovery rate (% of wells with violations)\n",
    "axes[1, 2].plot(annual_avg.index, annual_avg['violation_discovery_rate'], 'o-', \n",
    "                linewidth=2.5, markersize=8, color='teal')\n",
    "axes[1, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "axes[1, 2].set_xlabel('Year', fontsize=11)\n",
    "axes[1, 2].set_ylabel('% of Wells', fontsize=11)\n",
    "axes[1, 2].set_title('Share of Inspected Wells with Violations', fontsize=12, fontweight='bold')\n",
    "axes[1, 2].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('pipeline_trends_over_time.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n\u2713 Pipeline visualization complete\")\n",
    "print(\"  Saved as 'pipeline_trends_over_time.png'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f030bb1",
   "metadata": {},
   "source": [
    "## Part 3: Core Identification Strategy\n",
    "\n",
    "This notebook now estimates policy effects in three layers:\n",
    "\n",
    "1. **All-district policy-year shift (H1):** Interrupted panel model using all districts.\n",
    "2. **District heterogeneity (H2):** District-specific post-2019 effects.\n",
    "3. **Offshore moderator (H5):** Whether districts 02/03/04 differ systematically post-2019.\n",
    "\n",
    "This keeps the analysis centered on the full system while explicitly testing offshore differences as one source of heterogeneity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c372992a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CORE MODELS: ALL DISTRICTS, THEN HETEROGENEITY, THEN OFFSHORE MODERATOR\n",
      "================================================================================\n",
      "Regression sample: 143\n",
      "Districts: 13\n",
      "Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024), np.int32(2025)]\n",
      "Offshore-jurisdiction districts: 3\n",
      "\n",
      "================================================================================\n",
      "Model 1 (H1): All-District Policy-Year Shift\n",
      "================================================================================\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        log_days_to_enf   R-squared:                       0.678\n",
      "Model:                            OLS   Adj. R-squared:                  0.640\n",
      "Method:                 Least Squares   F-statistic:                     12.60\n",
      "Date:                Wed, 18 Feb 2026   Prob (F-statistic):           0.000511\n",
      "Time:                        16:47:31   Log-Likelihood:                -98.167\n",
      "No. Observations:                 143   AIC:                             228.3\n",
      "Df Residuals:                     127   BIC:                             275.7\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:              cluster                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept             5.1472      0.148     34.728      0.000       4.857       5.438\n",
      "C(district)[T.02]    -0.1479   5.79e-15  -2.55e+13      0.000      -0.148      -0.148\n",
      "C(district)[T.03]    -0.7642   1.33e-15  -5.75e+14      0.000      -0.764      -0.764\n",
      "C(district)[T.04]    -0.7184   1.57e-15  -4.58e+14      0.000      -0.718      -0.718\n",
      "C(district)[T.05]     0.0726    3.2e-15   2.27e+13      0.000       0.073       0.073\n",
      "C(district)[T.06]     0.1778   3.08e-15   5.77e+13      0.000       0.178       0.178\n",
      "C(district)[T.08]    -0.8393    1.2e-15  -6.97e+14      0.000      -0.839      -0.839\n",
      "C(district)[T.09]    -0.5774   4.14e-15  -1.39e+14      0.000      -0.577      -0.577\n",
      "C(district)[T.10]    -1.1819    1.9e-15  -6.23e+14      0.000      -1.182      -1.182\n",
      "C(district)[T.6E]     0.0443   2.79e-15   1.59e+13      0.000       0.044       0.044\n",
      "C(district)[T.7B]    -1.8169   2.54e-15  -7.14e+14      0.000      -1.817      -1.817\n",
      "C(district)[T.7C]    -1.1958   3.59e-15  -3.33e+14      0.000      -1.196      -1.196\n",
      "C(district)[T.8A]    -1.0731   2.13e-15  -5.03e+14      0.000      -1.073      -1.073\n",
      "year_num              0.1675      0.086      1.951      0.051      -0.001       0.336\n",
      "post_2019             0.1514      0.155      0.975      0.329      -0.153       0.456\n",
      "post_trend           -0.3603      0.110     -3.287      0.001      -0.575      -0.145\n",
      "==============================================================================\n",
      "Omnibus:                        0.379   Durbin-Watson:                   1.217\n",
      "Prob(Omnibus):                  0.827   Jarque-Bera (JB):                0.536\n",
      "Skew:                           0.076   Prob(JB):                        0.765\n",
      "Kurtosis:                       2.741   Cond. No.                         93.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are robust to cluster correlation (cluster)\n",
      "H1 level shift (post_2019): coef=0.1514, p=0.3294\n",
      "H1 slope shift (post_trend): coef=-0.3603, p=0.0010\n",
      "\n",
      "================================================================================\n",
      "Model 2 (H2): District-Specific Post-2019 Effects\n",
      "================================================================================\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        log_days_to_enf   R-squared:                       0.795\n",
      "Model:                            OLS   Adj. R-squared:                  0.731\n",
      "Method:                 Least Squares   F-statistic:                     23.56\n",
      "Date:                Wed, 18 Feb 2026   Prob (F-statistic):           2.71e-06\n",
      "Time:                        16:47:31   Log-Likelihood:                -65.821\n",
      "No. Observations:                 143   AIC:                             201.6\n",
      "Df Residuals:                     108   BIC:                             305.3\n",
      "Df Model:                          34                                         \n",
      "Covariance Type:              cluster                                         \n",
      "=============================================================================================\n",
      "                                coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "Intercept                     5.1802      0.144     35.995      0.000       4.898       5.462\n",
      "C(district)[T.02]             0.0088   4.67e-15   1.89e+12      0.000       0.009       0.009\n",
      "C(district)[T.03]            -1.3481   5.35e-15  -2.52e+14      0.000      -1.348      -1.348\n",
      "C(district)[T.04]            -1.3304   3.86e-15  -3.45e+14      0.000      -1.330      -1.330\n",
      "C(district)[T.05]            -0.0809   4.14e-15  -1.95e+13      0.000      -0.081      -0.081\n",
      "C(district)[T.06]             0.5301   5.56e-15   9.54e+13      0.000       0.530       0.530\n",
      "C(district)[T.08]            -0.5338   3.45e-15  -1.55e+14      0.000      -0.534      -0.534\n",
      "C(district)[T.09]            -0.1608   3.83e-15  -4.19e+13      0.000      -0.161      -0.161\n",
      "C(district)[T.10]            -1.5798   3.03e-15  -5.21e+14      0.000      -1.580      -1.580\n",
      "C(district)[T.6E]             0.2268   4.62e-15   4.91e+13      0.000       0.227       0.227\n",
      "C(district)[T.7B]            -1.6258   2.79e-15  -5.82e+14      0.000      -1.626      -1.626\n",
      "C(district)[T.7C]            -1.3740   2.79e-15  -4.93e+14      0.000      -1.374      -1.374\n",
      "C(district)[T.8A]            -1.1756   3.26e-15   -3.6e+14      0.000      -1.176      -1.176\n",
      "C(year)[T.2016]               0.1233      0.180      0.686      0.492      -0.229       0.475\n",
      "C(year)[T.2017]               0.4207      0.225      1.866      0.062      -0.021       0.863\n",
      "C(year)[T.2018]               0.4592      0.264      1.738      0.082      -0.059       0.977\n",
      "C(year)[T.2019]               0.3855      0.246      1.569      0.117      -0.096       0.867\n",
      "C(year)[T.2020]               0.3338      0.187      1.781      0.075      -0.033       0.701\n",
      "C(year)[T.2021]               0.0812      0.110      0.738      0.460      -0.134       0.297\n",
      "C(year)[T.2022]              -0.0849      0.123     -0.693      0.488      -0.325       0.155\n",
      "C(year)[T.2023]               0.0105      0.142      0.074      0.941      -0.268       0.289\n",
      "C(year)[T.2024]              -0.2825      0.117     -2.424      0.015      -0.511      -0.054\n",
      "C(year)[T.2025]              -0.9796      0.117     -8.365      0.000      -1.209      -0.750\n",
      "C(district)[01]:post_2019    -0.0924      0.050     -1.834      0.067      -0.191       0.006\n",
      "C(district)[02]:post_2019    -0.3387      0.050     -6.724      0.000      -0.437      -0.240\n",
      "C(district)[03]:post_2019     0.8251      0.050     16.381      0.000       0.726       0.924\n",
      "C(district)[04]:post_2019     0.8693      0.050     17.259      0.000       0.771       0.968\n",
      "C(district)[05]:post_2019     0.1488      0.050      2.955      0.003       0.050       0.248\n",
      "C(district)[06]:post_2019    -0.6460      0.050    -12.826      0.000      -0.745      -0.547\n",
      "C(district)[08]:post_2019    -0.5725      0.050    -11.367      0.000      -0.671      -0.474\n",
      "C(district)[09]:post_2019    -0.7472      0.050    -14.834      0.000      -0.846      -0.648\n",
      "C(district)[10]:post_2019     0.5330      0.050     10.582      0.000       0.434       0.632\n",
      "C(district)[6E]:post_2019    -0.3792      0.050     -7.528      0.000      -0.478      -0.280\n",
      "C(district)[7B]:post_2019    -0.3927      0.050     -7.796      0.000      -0.491      -0.294\n",
      "C(district)[7C]:post_2019     0.1876      0.050      3.725      0.000       0.089       0.286\n",
      "C(district)[8A]:post_2019     0.0687      0.050      1.365      0.172      -0.030       0.167\n",
      "==============================================================================\n",
      "Omnibus:                        0.969   Durbin-Watson:                   1.597\n",
      "Prob(Omnibus):                  0.616   Jarque-Bera (JB):                1.060\n",
      "Skew:                          -0.132   Prob(JB):                        0.589\n",
      "Kurtosis:                       2.672   Cond. No.                     1.13e+16\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are robust to cluster correlation (cluster)\n",
      "[2] The smallest eigenvalue is 1.33e-30. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n",
      "\n",
      "DISTRICT-SPECIFIC POST-2019 EFFECTS\n",
      "district  coefficient  stderr  pvalue\n",
      "      09      -0.7472  0.0504  0.0000\n",
      "      06      -0.6460  0.0504  0.0000\n",
      "      08      -0.5725  0.0504  0.0000\n",
      "      7B      -0.3927  0.0504  0.0000\n",
      "      6E      -0.3792  0.0504  0.0000\n",
      "      02      -0.3387  0.0504  0.0000\n",
      "      01      -0.0924  0.0504  0.0667\n",
      "      8A       0.0687  0.0504  0.1724\n",
      "      05       0.1488  0.0504  0.0031\n",
      "      7C       0.1876  0.0504  0.0002\n",
      "      10       0.5330  0.0504  0.0000\n",
      "      03       0.8251  0.0504  0.0000\n",
      "      04       0.8693  0.0504  0.0000\n",
      "H2 joint test (all district post effects = 0): chi2=0.670, p=0.4130\n",
      "\n",
      "================================================================================\n",
      "Model 3 (H5): Offshore Differential on Top of District Heterogeneity\n",
      "================================================================================\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        log_days_to_enf   R-squared:                       0.795\n",
      "Model:                            OLS   Adj. R-squared:                  0.731\n",
      "Method:                 Least Squares   F-statistic:                     24.14\n",
      "Date:                Wed, 18 Feb 2026   Prob (F-statistic):           2.37e-06\n",
      "Time:                        16:47:31   Log-Likelihood:                -65.821\n",
      "No. Observations:                 143   AIC:                             201.6\n",
      "Df Residuals:                     108   BIC:                             305.3\n",
      "Df Model:                          34                                         \n",
      "Covariance Type:              cluster                                         \n",
      "===================================================================================================\n",
      "                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Intercept                           5.1802      0.145     35.827      0.000       4.897       5.464\n",
      "C(district)[T.02]                   0.0088   2.64e-10   3.34e+07      0.000       0.009       0.009\n",
      "C(district)[T.03]                  -1.3481        nan        nan        nan         nan         nan\n",
      "C(district)[T.04]                  -1.3304   2.51e-10  -5.31e+09      0.000      -1.330      -1.330\n",
      "C(district)[T.05]                  -0.0809   3.84e-15  -2.11e+13      0.000      -0.081      -0.081\n",
      "C(district)[T.06]                   0.5301   3.78e-15    1.4e+14      0.000       0.530       0.530\n",
      "C(district)[T.08]                  -0.5338   4.33e-15  -1.23e+14      0.000      -0.534      -0.534\n",
      "C(district)[T.09]                  -0.1608   4.04e-15  -3.98e+13      0.000      -0.161      -0.161\n",
      "C(district)[T.10]                  -1.5798   4.35e-15  -3.63e+14      0.000      -1.580      -1.580\n",
      "C(district)[T.6E]                   0.2268   3.95e-15   5.75e+13      0.000       0.227       0.227\n",
      "C(district)[T.7B]                  -1.6258   3.72e-15  -4.37e+14      0.000      -1.626      -1.626\n",
      "C(district)[T.7C]                  -1.3740   4.38e-15  -3.13e+14      0.000      -1.374      -1.374\n",
      "C(district)[T.8A]                  -1.1756   3.87e-15  -3.04e+14      0.000      -1.176      -1.176\n",
      "C(year)[T.2016]                     0.1233      0.180      0.683      0.494      -0.230       0.477\n",
      "C(year)[T.2017]                     0.4207      0.226      1.858      0.063      -0.023       0.865\n",
      "C(year)[T.2018]                     0.4592      0.266      1.729      0.084      -0.061       0.980\n",
      "C(year)[T.2019]                     0.3282      0.241      1.360      0.174      -0.145       0.801\n",
      "C(year)[T.2020]                     0.2765      0.183      1.510      0.131      -0.082       0.636\n",
      "C(year)[T.2021]                     0.0239      0.106      0.225      0.822      -0.185       0.233\n",
      "C(year)[T.2022]                    -0.1422      0.119     -1.193      0.233      -0.376       0.092\n",
      "C(year)[T.2023]                    -0.0468      0.140     -0.335      0.737      -0.320       0.227\n",
      "C(year)[T.2024]                    -0.3398      0.115     -2.966      0.003      -0.564      -0.115\n",
      "C(year)[T.2025]                    -1.0369      0.116     -8.915      0.000      -1.265      -0.809\n",
      "C(district)[01]:post_2019          -0.0351      0.057     -0.615      0.538      -0.147       0.077\n",
      "C(district)[02]:post_2019          -0.6633      0.014    -46.530      0.000      -0.691      -0.635\n",
      "C(district)[03]:post_2019           0.5005      0.014     35.110      0.000       0.473       0.528\n",
      "C(district)[04]:post_2019           0.5447      0.014     38.210      0.000       0.517       0.573\n",
      "C(district)[05]:post_2019           0.2061      0.057      3.615      0.000       0.094       0.318\n",
      "C(district)[06]:post_2019          -0.5887      0.057    -10.325      0.000      -0.701      -0.477\n",
      "C(district)[08]:post_2019          -0.5153      0.057     -9.036      0.000      -0.627      -0.403\n",
      "C(district)[09]:post_2019          -0.6899      0.057    -12.099      0.000      -0.802      -0.578\n",
      "C(district)[10]:post_2019           0.5903      0.057     10.352      0.000       0.479       0.702\n",
      "C(district)[6E]:post_2019          -0.3219      0.057     -5.645      0.000      -0.434      -0.210\n",
      "C(district)[7B]:post_2019          -0.3354      0.057     -5.882      0.000      -0.447      -0.224\n",
      "C(district)[7C]:post_2019           0.2449      0.057      4.295      0.000       0.133       0.357\n",
      "C(district)[8A]:post_2019           0.1260      0.057      2.210      0.027       0.014       0.238\n",
      "post_2019:offshore_jurisdiction     0.3819      0.043      8.930      0.000       0.298       0.466\n",
      "==============================================================================\n",
      "Omnibus:                        0.969   Durbin-Watson:                   1.597\n",
      "Prob(Omnibus):                  0.616   Jarque-Bera (JB):                1.060\n",
      "Skew:                          -0.132   Prob(JB):                        0.589\n",
      "Kurtosis:                       2.672   Cond. No.                     9.98e+15\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are robust to cluster correlation (cluster)\n",
      "[2] The smallest eigenvalue is 1.74e-30. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n",
      "H5 offshore differential: coef=0.3819, p=0.0000, pct=+46.5%\n",
      "\n",
      "================================================================================\n",
      "H1b MODEL: Compliance Verification (Resolution Rate)\n",
      "================================================================================\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        resolution_rate   R-squared:                       0.562\n",
      "Model:                            OLS   Adj. R-squared:                  0.510\n",
      "Method:                 Least Squares   F-statistic:                     3.789\n",
      "Date:                Wed, 18 Feb 2026   Prob (F-statistic):             0.0402\n",
      "Time:                        16:47:31   Log-Likelihood:                -570.52\n",
      "No. Observations:                 143   AIC:                             1173.\n",
      "Df Residuals:                     127   BIC:                             1220.\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:              cluster                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept            33.4956      3.321     10.087      0.000      26.987      40.004\n",
      "C(district)[T.02]    -7.6603   6.55e-14  -1.17e+14      0.000      -7.660      -7.660\n",
      "C(district)[T.03]    30.2008   4.82e-14   6.27e+14      0.000      30.201      30.201\n",
      "C(district)[T.04]    14.5152   5.17e-14   2.81e+14      0.000      14.515      14.515\n",
      "C(district)[T.05]     2.3712   3.53e-14   6.72e+13      0.000       2.371       2.371\n",
      "C(district)[T.06]    -1.4401   5.28e-14  -2.73e+13      0.000      -1.440      -1.440\n",
      "C(district)[T.08]    19.1571   5.53e-14   3.46e+14      0.000      19.157      19.157\n",
      "C(district)[T.09]    11.8959    7.9e-14   1.51e+14      0.000      11.896      11.896\n",
      "C(district)[T.10]    44.6185   6.94e-14   6.43e+14      0.000      44.618      44.618\n",
      "C(district)[T.6E]     6.4406   6.81e-14   9.46e+13      0.000       6.441       6.441\n",
      "C(district)[T.7B]    27.9109   4.47e-14   6.25e+14      0.000      27.911      27.911\n",
      "C(district)[T.7C]    24.2122    4.7e-14   5.15e+14      0.000      24.212      24.212\n",
      "C(district)[T.8A]    19.5375   3.53e-14   5.53e+14      0.000      19.538      19.538\n",
      "year_num              2.6407      1.594      1.657      0.098      -0.483       5.765\n",
      "post_2019             4.3721      3.491      1.252      0.210      -2.470      11.214\n",
      "post_trend           -2.9371      2.002     -1.467      0.142      -6.861       0.987\n",
      "==============================================================================\n",
      "Omnibus:                        1.616   Durbin-Watson:                   1.523\n",
      "Prob(Omnibus):                  0.446   Jarque-Bera (JB):                1.333\n",
      "Skew:                          -0.031   Prob(JB):                        0.513\n",
      "Kurtosis:                       2.531   Cond. No.                         93.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are robust to cluster correlation (cluster)\n",
      "H1b level shift: coef=4.3721, p=0.2104\n",
      "H1b slope shift: coef=-2.9371, p=0.1424\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dadams/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(np.diag(self.cov_params()))\n"
     ]
    }
   ],
   "source": [
    "print(\"=\"*80)\n",
    "print(\"CORE MODELS: ALL DISTRICTS, THEN HETEROGENEITY, THEN OFFSHORE MODERATOR\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "df_reg = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n",
    "df_reg['log_days_to_enf'] = np.log(df_reg['avg_days_to_enforcement'])\n",
    "df_reg['year_num'] = df_reg['year'] - df_reg['year'].min()\n",
    "df_reg['post_2019'] = (df_reg['year'] >= 2019).astype(int)\n",
    "df_reg['post_trend'] = (df_reg['year'] - 2018).clip(lower=0)\n",
    "\n",
    "print(f\"Regression sample: {len(df_reg)}\")\n",
    "print(f\"Districts: {df_reg['district'].nunique()}\")\n",
    "print(f\"Years: {sorted(df_reg['year'].unique())}\")\n",
    "print(f\"Offshore-jurisdiction districts: {df_reg[df_reg['offshore_jurisdiction']==1]['district'].nunique()}\")\n",
    "\n",
    "# Model 1: All-district policy-year shift (interrupted panel)\n",
    "formula1 = 'log_days_to_enf ~ C(district) + year_num + post_2019 + post_trend'\n",
    "model1 = smf.ols(formula1, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n",
    "\n",
    "print()\n",
    "print(\"=\"*80)\n",
    "print(\"Model 1 (H1): All-District Policy-Year Shift\")\n",
    "print(\"=\"*80)\n",
    "print(model1.summary())\n",
    "\n",
    "post_coef = model1.params.get('post_2019', np.nan)\n",
    "post_p = model1.pvalues.get('post_2019', np.nan)\n",
    "post_trend_coef = model1.params.get('post_trend', np.nan)\n",
    "post_trend_p = model1.pvalues.get('post_trend', np.nan)\n",
    "print(f\"H1 level shift (post_2019): coef={post_coef:.4f}, p={post_p:.4f}\")\n",
    "print(f\"H1 slope shift (post_trend): coef={post_trend_coef:.4f}, p={post_trend_p:.4f}\")\n",
    "\n",
    "# Model 2: District-specific post effects (H2)\n",
    "formula2 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019'\n",
    "model2 = smf.ols(formula2, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n",
    "\n",
    "print()\n",
    "print(\"=\"*80)\n",
    "print(\"Model 2 (H2): District-Specific Post-2019 Effects\")\n",
    "print(\"=\"*80)\n",
    "print(model2.summary())\n",
    "\n",
    "coef_df = pd.DataFrame({'coefficient': model2.params, 'stderr': model2.bse, 'pvalue': model2.pvalues}).reset_index()\n",
    "coef_df.columns = ['term', 'coefficient', 'stderr', 'pvalue']\n",
    "district_effects = coef_df[coef_df['term'].str.contains(':post_2019', na=False)].copy()\n",
    "district_effects['district'] = district_effects['term'].str.extract(r'\\[(?:T\\.)?(\\w+)\\]')[0]\n",
    "district_effects = district_effects.dropna(subset=['district']).sort_values('coefficient')\n",
    "\n",
    "print()\n",
    "print(\"DISTRICT-SPECIFIC POST-2019 EFFECTS\")\n",
    "print(district_effects[['district','coefficient','stderr','pvalue']].to_string(index=False))\n",
    "\n",
    "# Joint test for heterogeneity\n",
    "int_terms = [t for t in model2.params.index if ':post_2019' in t and 'offshore' not in t]\n",
    "if int_terms:\n",
    "    R = np.zeros((len(int_terms), len(model2.params)))\n",
    "    for r, term in enumerate(int_terms):\n",
    "        R[r, list(model2.params.index).index(term)] = 1\n",
    "    wald = model2.wald_test(R)\n",
    "    wald_stat = np.asarray(wald.statistic).ravel()[0]\n",
    "    wald_p = np.asarray(wald.pvalue).ravel()[0]\n",
    "    print(f\"H2 joint test (all district post effects = 0): chi2={wald_stat:.3f}, p={wald_p:.4f}\")\n",
    "\n",
    "# Model 3: Offshore moderator conditional on district heterogeneity (H5)\n",
    "formula3 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019 + post_2019:offshore_jurisdiction'\n",
    "model3 = smf.ols(formula3, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n",
    "\n",
    "print()\n",
    "print(\"=\"*80)\n",
    "print(\"Model 3 (H5): Offshore Differential on Top of District Heterogeneity\")\n",
    "print(\"=\"*80)\n",
    "print(model3.summary())\n",
    "\n",
    "offshore_coef = model3.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
    "offshore_p = model3.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
    "offshore_pct = (np.exp(offshore_coef)-1)*100 if pd.notna(offshore_coef) else np.nan\n",
    "print(f\"H5 offshore differential: coef={offshore_coef:.4f}, p={offshore_p:.4f}, pct={offshore_pct:+.1f}%\")\n",
    "\n",
    "# H1b model: all-district policy-year shift on resolution rate\n",
    "print()\n",
    "print(\"=\"*80)\n",
    "print(\"H1b MODEL: Compliance Verification (Resolution Rate)\")\n",
    "print(\"=\"*80)\n",
    "df_h1b = district_year_panel.copy()\n",
    "df_h1b['year_num'] = df_h1b['year'] - df_h1b['year'].min()\n",
    "df_h1b['post_2019'] = (df_h1b['year'] >= 2019).astype(int)\n",
    "df_h1b['post_trend'] = (df_h1b['year'] - 2018).clip(lower=0)\n",
    "model_h1b = smf.ols('resolution_rate ~ C(district) + year_num + post_2019 + post_trend', data=df_h1b).fit(\n",
    "    cov_type='cluster', cov_kwds={'groups': df_h1b['district']}\n",
    ")\n",
    "print(model_h1b.summary())\n",
    "print(f\"H1b level shift: coef={model_h1b.params.get('post_2019', np.nan):.4f}, p={model_h1b.pvalues.get('post_2019', np.nan):.4f}\")\n",
    "print(f\"H1b slope shift: coef={model_h1b.params.get('post_trend', np.nan):.4f}, p={model_h1b.pvalues.get('post_trend', np.nan):.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0f118b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3600x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Journal-ready visualization for Policy Studies Journal style\n",
    "\n",
    "# Prepare effects dataframe\n",
    "effects_df = district_effects.copy()\n",
    "effects_df[\"ci_lower\"] = effects_df[\"coefficient\"] - 1.96 * effects_df[\"stderr\"]\n",
    "effects_df[\"ci_upper\"] = effects_df[\"coefficient\"] + 1.96 * effects_df[\"stderr\"]\n",
    "effects_df[\"significant\"] = effects_df[\"pvalue\"] < 0.05\n",
    "effects_df[\"pct_change\"] = (np.exp(effects_df[\"coefficient\"]) - 1) * 100\n",
    "effects_df = effects_df.set_index(\"district\")\n",
    "\n",
    "# Sort for plotting\n",
    "effects_plot = effects_df.sort_values(\"coefficient\").copy()\n",
    "y_pos = np.arange(len(effects_plot))\n",
    "\n",
    "# Style\n",
    "sns.set_theme(style=\"whitegrid\", context=\"paper\", font_scale=1.1)\n",
    "plt.rcParams.update({\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.edgecolor\": \"0.2\",\n",
    "    \"axes.linewidth\": 0.8,\n",
    "    \"grid.color\": \"0.85\",\n",
    "    \"grid.linestyle\": \"-\",\n",
    "    \"grid.linewidth\": 0.6,\n",
    "    \"figure.dpi\": 300,\n",
    "})\n",
    "\n",
    "# Colorblind-friendly palette\n",
    "neg_color = \"#0072B2\"   # blue\n",
    "pos_color = \"#D55E00\"   # vermillion\n",
    "colors = [neg_color if coef < 0 else pos_color for coef in effects_plot[\"coefficient\"]]\n",
    "\n",
    "# Create figure\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# Plot 1: coefficients with CI\n",
    "ax1.barh(y_pos, effects_plot[\"coefficient\"], color=colors, alpha=0.85, edgecolor=\"0.2\", linewidth=0.6)\n",
    "ax1.errorbar(\n",
    "    effects_plot[\"coefficient\"],\n",
    "    y_pos,\n",
    "    xerr=[effects_plot[\"coefficient\"] - effects_plot[\"ci_lower\"],\n",
    "          effects_plot[\"ci_upper\"] - effects_plot[\"coefficient\"]],\n",
    "    fmt=\"none\",\n",
    "    ecolor=\"0.2\",\n",
    "    capsize=3,\n",
    "    linewidth=1.0\n",
    ")\n",
    "ax1.axvline(0, color=\"0.2\", linestyle=\"--\", linewidth=1.0)\n",
    "ax1.set_yticks(y_pos)\n",
    "ax1.set_yticklabels([f\"District {dist}\" for dist in effects_plot.index], fontsize=10)\n",
    "ax1.set_xlabel(\"Treatment effect (log days to enforcement)\")\n",
    "ax1.set_title(\"District-specific treatment effects\")\n",
    "\n",
    "# Significance markers\n",
    "for i, row in enumerate(effects_plot.itertuples()):\n",
    "    if row.significant:\n",
    "        ax1.text(row.coefficient, i, \" *\", va=\"center\", fontsize=12, fontweight=\"bold\", color=\"0.2\")\n",
    "\n",
    "# Plot 2: percent change\n",
    "ax2.barh(y_pos, effects_plot[\"pct_change\"], color=colors, alpha=0.85, edgecolor=\"0.2\", linewidth=0.6)\n",
    "ax2.axvline(0, color=\"0.2\", linestyle=\"--\", linewidth=1.0)\n",
    "ax2.set_yticks(y_pos)\n",
    "ax2.set_yticklabels([f\"District {dist}\" for dist in effects_plot.index], fontsize=10)\n",
    "ax2.set_xlabel(\"% change in days to enforcement\")\n",
    "ax2.set_title(\"Percent change (negative = faster enforcement)\")\n",
    "\n",
    "# Add padding so labels don't touch the border\n",
    "pct_min = effects_plot[\"pct_change\"].min()\n",
    "pct_max = effects_plot[\"pct_change\"].max()\n",
    "pad = 8\n",
    "label_offset = 6\n",
    "ax2.set_xlim(pct_min - pad - label_offset, pct_max + pad + label_offset)\n",
    "\n",
    "# Label large effects\n",
    "for i, row in enumerate(effects_plot.itertuples()):\n",
    "    if abs(row.pct_change) > 30:\n",
    "        label = f\"{row.pct_change:.0f}%\"\n",
    "        x_pos = row.pct_change + (4 if row.pct_change > 0 else -4)\n",
    "        ha = \"left\" if row.pct_change > 0 else \"right\"\n",
    "        ax2.text(x_pos, i, label, va=\"center\", ha=ha, fontsize=9, color=\"0.2\")\n",
    "\n",
    "# Final layout\n",
    "fig.suptitle(\"Heterogeneous treatment effects across districts, post-2019\", y=1.02, fontsize=12, fontweight=\"bold\")\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"district_treatment_effects_psj.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d575f2fe",
   "metadata": {},
   "source": [
    "### Figure Description\n",
    "\n",
    "This figure presents the district-specific treatment effects of the 2019 disclosure policy on the average days to enforcement for violations. The left panel shows the estimated coefficients from the difference-in-differences regression model, with 95% confidence intervals. Negative coefficients indicate a reduction in days to enforcement (faster enforcement), while positive coefficients indicate an increase (slower enforcement). Asterisks denote statistically significant effects at the 5% level. The right panel translates these coefficients into percentage changes, providing a more intuitive interpretation of the magnitude of the effects. Districts are ordered by their treatment effect, allowing for easy comparison across districts. The figure highlights the heterogeneity in enforcement speed changes following the policy implementation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5050bf",
   "metadata": {},
   "source": [
    "## Part 4: Event-Study Style Decomposition\n",
    "\n",
    "We report two event-study views:\n",
    "1. **All-district annual deviations** (relative to 2018) with district fixed effects.\n",
    "2. **Offshore differential annual deviations** (offshore vs non-offshore) with district and year fixed effects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "745a896c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "ALL-DISTRICT YEAR EFFECTS (relative to 2018)\n",
      "================================================================================\n",
      " year  year_from_policy  coefficient     se  pvalue  significant\n",
      " 2015                -4      -0.4592 0.2495  0.0658        False\n",
      " 2016                -3      -0.3359 0.2399  0.1615        False\n",
      " 2017                -2      -0.0385 0.1208  0.7502        False\n",
      " 2018                -1       0.0000 0.0000  1.0000        False\n",
      " 2019                 0      -0.1149 0.1074  0.2843        False\n",
      " 2020                 1      -0.1666 0.1929  0.3878        False\n",
      " 2021                 2      -0.4192 0.2602  0.1072        False\n",
      " 2022                 3      -0.5853 0.2750  0.0333         True\n",
      " 2023                 4      -0.4899 0.3117  0.1160        False\n",
      " 2024                 5      -0.7829 0.2831  0.0057         True\n",
      " 2025                 6      -1.4800 0.2563  0.0000         True\n",
      "Pre-period significant years: 0/3\n",
      "\n",
      "OFFSHORE DIFFERENTIAL YEAR EFFECTS (relative to 2018)\n",
      " year    coef  pvalue\n",
      " 2015  0.4454  0.2163\n",
      " 2016 -0.0606  0.8886\n",
      " 2017 -0.1481  0.5150\n",
      " 2018  0.0000  1.0000\n",
      " 2019  0.3479  0.1581\n",
      " 2020  0.1796  0.7089\n",
      " 2021  0.9121  0.1095\n",
      " 2022  0.7532  0.0652\n",
      " 2023  0.9166  0.0325\n",
      " 2024  1.0693  0.0280\n",
      " 2025  0.7233  0.2091\n"
     ]
    }
   ],
   "source": [
    "# Event-study decomposition\n",
    "\n",
    "df_event = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n",
    "df_event['log_days_to_enf'] = np.log(df_event['avg_days_to_enforcement'])\n",
    "\n",
    "# A) All-district annual deviations relative to 2018\n",
    "years = sorted(df_event['year'].unique())\n",
    "year_terms=[]\n",
    "for y in years:\n",
    "    if y == 2018:\n",
    "        continue\n",
    "    col=f'year_{y}'\n",
    "    df_event[col]=(df_event['year']==y).astype(int)\n",
    "    year_terms.append(col)\n",
    "\n",
    "formula_all = 'log_days_to_enf ~ C(district) + ' + ' + '.join(year_terms)\n",
    "model_event_all = smf.ols(formula_all, data=df_event).fit(cov_type='cluster', cov_kwds={'groups': df_event['district']})\n",
    "\n",
    "event_all=[]\n",
    "for y in years:\n",
    "    if y==2018:\n",
    "        event_all.append({'year':y,'year_from_policy':-1,'coefficient':0.0,'se':0.0,'ci_lower':0.0,'ci_upper':0.0,'pvalue':1.0})\n",
    "    else:\n",
    "        p=f'year_{y}'\n",
    "        event_all.append({'year':y,'year_from_policy':y-2019,'coefficient':model_event_all.params[p],'se':model_event_all.bse[p],'ci_lower':model_event_all.conf_int().loc[p,0],'ci_upper':model_event_all.conf_int().loc[p,1],'pvalue':model_event_all.pvalues[p]})\n",
    "\n",
    "event_df = pd.DataFrame(event_all).sort_values('year')\n",
    "event_df['significant']=event_df['pvalue']<0.05\n",
    "event_df['pre_treatment']=event_df['year']<2019\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"ALL-DISTRICT YEAR EFFECTS (relative to 2018)\")\n",
    "print(\"=\"*80)\n",
    "print(event_df[['year','year_from_policy','coefficient','se','pvalue','significant']].to_string(index=False))\n",
    "pre=event_df[(event_df['pre_treatment']) & (event_df['year']!=2018)]\n",
    "if len(pre)>0:\n",
    "    print(f\"Pre-period significant years: {pre['significant'].sum()}/{len(pre)}\")\n",
    "\n",
    "# B) Offshore differential annual deviations\n",
    "interaction_terms=[]\n",
    "for y in years:\n",
    "    if y==2018:\n",
    "        continue\n",
    "    col=f'offshore_year_{y}'\n",
    "    df_event[col]=((df_event['year']==y).astype(int)*df_event['offshore_jurisdiction'])\n",
    "    interaction_terms.append(col)\n",
    "formula_diff='log_days_to_enf ~ C(district) + C(year) + ' + ' + '.join(interaction_terms)\n",
    "model_event_diff = smf.ols(formula_diff, data=df_event).fit(cov_type='cluster', cov_kwds={'groups': df_event['district']})\n",
    "\n",
    "event_diff=[]\n",
    "for y in years:\n",
    "    if y==2018:\n",
    "        event_diff.append({'year':y,'coef':0.0,'pvalue':1.0})\n",
    "    else:\n",
    "        p=f'offshore_year_{y}'\n",
    "        event_diff.append({'year':y,'coef':model_event_diff.params[p],'pvalue':model_event_diff.pvalues[p]})\n",
    "\n",
    "print()\n",
    "print(\"OFFSHORE DIFFERENTIAL YEAR EFFECTS (relative to 2018)\")\n",
    "print(pd.DataFrame(event_diff).to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0160f652",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 4200x2100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: event_study_plot.png\n"
     ]
    }
   ],
   "source": [
    "# Visualize all-district event-study style coefficients\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "x = event_df['year_from_policy']\n",
    "y = event_df['coefficient']\n",
    "ci_lower = event_df['ci_lower']\n",
    "ci_upper = event_df['ci_upper']\n",
    "\n",
    "pre_mask = event_df['year'] < 2019\n",
    "post_mask = event_df['year'] >= 2019\n",
    "\n",
    "ax.scatter(x[pre_mask], y[pre_mask], color='steelblue', s=100, label='Pre-2019', zorder=3)\n",
    "ax.plot(x[pre_mask], y[pre_mask], color='steelblue', alpha=0.3, linestyle='--')\n",
    "for i in event_df[pre_mask].index:\n",
    "    ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='steelblue', alpha=0.5, linewidth=2)\n",
    "\n",
    "ax.scatter(x[post_mask], y[post_mask], color='darkred', s=100, label='Post-2019', zorder=3)\n",
    "ax.plot(x[post_mask], y[post_mask], color='darkred', alpha=0.3, linestyle='--')\n",
    "for i in event_df[post_mask].index:\n",
    "    ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='darkred', alpha=0.5, linewidth=2)\n",
    "\n",
    "ax.axhline(0, color='black', linestyle='-', linewidth=0.8, alpha=0.3)\n",
    "ax.axvline(-1, color='gray', linestyle=':', linewidth=2, alpha=0.5, label='Policy Year (2019)')\n",
    "\n",
    "ax.set_xlabel('Years Relative to 2019')\n",
    "ax.set_ylabel('Annual deviation in log(Days to Enforcement)')\n",
    "ax.set_title('All-District Annual Deviations (Reference Year: 2018)')\n",
    "ax.legend(loc='best')\n",
    "ax.grid(True, alpha=0.2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('event_study_plot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(\"Saved: event_study_plot.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112ad81f",
   "metadata": {},
   "source": [
    "## Part 5: Heterogeneous Treatment Effects (TWFE Moderator Tests)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf9ee57",
   "metadata": {},
   "source": [
    "# Triple-DiD/TWFE Moderator Models: Test each hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "32bc0e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderator setup complete\n",
      "df_het shape: (143, 23)\n",
      "offshore_jurisdiction present: True\n"
     ]
    }
   ],
   "source": [
    "# Build district moderators for heterogeneous-effects tests\n",
    "\n",
    "baseline_data = district_year_panel[district_year_panel['year'] < 2019].groupby('district').agg({\n",
    "    'total_inspections': 'sum',\n",
    "    'unique_wells': 'mean',\n",
    "    'compliance_rate': 'mean',\n",
    "    'avg_days_to_enforcement': 'mean'\n",
    "}).reset_index()\n",
    "\n",
    "baseline_data.columns = ['district', 'total_inspections_baseline', 'avg_wells',\n",
    "                         'baseline_compliance_rate', 'baseline_days_to_enf']\n",
    "\n",
    "baseline_data['high_capacity'] = (baseline_data['total_inspections_baseline'] > baseline_data['total_inspections_baseline'].median()).astype(int)\n",
    "baseline_data['low_baseline_compliance'] = (baseline_data['baseline_compliance_rate'] < baseline_data['baseline_compliance_rate'].median()).astype(int)\n",
    "viol_rate = district_year_panel[district_year_panel['year'] < 2019].groupby('district')['violation_discovery_rate'].mean()\n",
    "baseline_data['high_ej'] = (viol_rate > viol_rate.median()).astype(int).values\n",
    "\n",
    "border_districts = ['06', '08', '8A']\n",
    "baseline_data['border_district'] = baseline_data['district'].isin(border_districts).astype(int)\n",
    "\n",
    "offshore_jurisdiction_districts = ['02', '03', '04']\n",
    "baseline_data['offshore_jurisdiction'] = baseline_data['district'].isin(offshore_jurisdiction_districts).astype(int)\n",
    "\n",
    "df_het = district_year_panel.merge(\n",
    "    baseline_data[['district','high_capacity','low_baseline_compliance','high_ej','border_district']],\n",
    "    on='district', how='left'\n",
    ")\n",
    "if 'offshore_jurisdiction' not in df_het.columns:\n",
    "    df_het['offshore_jurisdiction'] = df_het['district'].isin(offshore_jurisdiction_districts).astype(int)\n",
    "\n",
    "print(\"Moderator setup complete\")\n",
    "print(f\"df_het shape: {df_het.shape}\")\n",
    "print(f\"offshore_jurisdiction present: {'offshore_jurisdiction' in df_het.columns}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0b4d049e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "TWFE MODERATOR RESULTS\n",
      "================================================================================\n",
      "H3a capacity: coef=-0.0188, p=0.9415\n",
      "H3b baseline performance: coef=-0.0884, p=0.7144\n",
      "H3e border proximity: coef=-0.2768, p=0.3082\n",
      "H5 offshore jurisdiction: coef=0.6317, p=0.1055\n"
     ]
    }
   ],
   "source": [
    "# TWFE moderator models\n",
    "\n",
    "df_het['log_days_to_enf'] = np.log(df_het['avg_days_to_enforcement'] + 1)\n",
    "df_het['log_viol_per_insp'] = np.log(df_het['violations_per_inspection'] + 0.01)\n",
    "\n",
    "hypotheses_results = {}\n",
    "print(\"=\"*80)\n",
    "print(\"TWFE MODERATOR RESULTS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# H3a capacity\n",
    "formula_h1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction'\n",
    "model_h1 = smf.ols(formula_h1, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n",
    "coef = model_h1.params.get('post_2019:high_capacity', np.nan); p=model_h1.pvalues.get('post_2019:high_capacity', np.nan)\n",
    "print(f\"H3a capacity: coef={coef:.4f}, p={p:.4f}\")\n",
    "hypotheses_results['H3a']={'coefficient':coef,'pvalue':p}\n",
    "\n",
    "# H3b baseline\n",
    "formula_h2 = 'log_viol_per_insp ~ C(district) + C(year) + post_2019:low_baseline_compliance + post_2019:offshore_jurisdiction'\n",
    "model_h2 = smf.ols(formula_h2, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n",
    "coef = model_h2.params.get('post_2019:low_baseline_compliance', np.nan); p=model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan)\n",
    "print(f\"H3b baseline performance: coef={coef:.4f}, p={p:.4f}\")\n",
    "hypotheses_results['H3b']={'coefficient':coef,'pvalue':p}\n",
    "\n",
    "# H3e border\n",
    "formula_h4 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:border_district + post_2019:offshore_jurisdiction'\n",
    "model_h4 = smf.ols(formula_h4, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n",
    "coef = model_h4.params.get('post_2019:border_district', np.nan); p=model_h4.pvalues.get('post_2019:border_district', np.nan)\n",
    "print(f\"H3e border proximity: coef={coef:.4f}, p={p:.4f}\")\n",
    "hypotheses_results['H3e']={'coefficient':coef,'pvalue':p}\n",
    "\n",
    "# H5 offshore\n",
    "formula_h5 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
    "model_h5 = smf.ols(formula_h5, data=df_het).fit(cov_type='cluster', cov_kwds={'groups': df_het['district']})\n",
    "coef = model_h5.params.get('post_2019:offshore_jurisdiction', np.nan); p=model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
    "print(f\"H5 offshore jurisdiction: coef={coef:.4f}, p={p:.4f}\")\n",
    "hypotheses_results['H5']={'coefficient':coef,'pvalue':p}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7eef4081",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3600x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: heterogeneous_effects.png\n",
      "                  Hypothesis    coef     se  pvalue\n",
      "          H3a: High Capacity -0.0188 0.2556  0.9415\n",
      "H3b: Low Baseline Compliance -0.0884 0.2415  0.7144\n",
      "       H3e: Border Proximity -0.2768 0.2717  0.3082\n",
      "   H5: Offshore Jurisdiction  0.6317 0.3903  0.1055\n"
     ]
    }
   ],
   "source": [
    "# Visualize differential interaction effects (TWFE-consistent)\n",
    "\n",
    "interaction_rows = [\n",
    "    {\n",
    "        'Hypothesis': 'H3a: High Capacity',\n",
    "        'coef': model_h1.params.get('post_2019:high_capacity', np.nan),\n",
    "        'se': model_h1.bse.get('post_2019:high_capacity', np.nan),\n",
    "        'pvalue': model_h1.pvalues.get('post_2019:high_capacity', np.nan),\n",
    "    },\n",
    "    {\n",
    "        'Hypothesis': 'H3b: Low Baseline Compliance',\n",
    "        'coef': model_h2.params.get('post_2019:low_baseline_compliance', np.nan),\n",
    "        'se': model_h2.bse.get('post_2019:low_baseline_compliance', np.nan),\n",
    "        'pvalue': model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan),\n",
    "    },\n",
    "    {\n",
    "        'Hypothesis': 'H3e: Border Proximity',\n",
    "        'coef': model_h4.params.get('post_2019:border_district', np.nan),\n",
    "        'se': model_h4.bse.get('post_2019:border_district', np.nan),\n",
    "        'pvalue': model_h4.pvalues.get('post_2019:border_district', np.nan),\n",
    "    },\n",
    "    {\n",
    "        'Hypothesis': 'H5: Offshore Jurisdiction',\n",
    "        'coef': model_h5.params.get('post_2019:offshore_jurisdiction', np.nan),\n",
    "        'se': model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan),\n",
    "        'pvalue': model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan),\n",
    "    }\n",
    "]\n",
    "\n",
    "int_df = pd.DataFrame(interaction_rows)\n",
    "int_df['ci_low'] = int_df['coef'] - 1.96 * int_df['se']\n",
    "int_df['ci_high'] = int_df['coef'] + 1.96 * int_df['se']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "y = np.arange(len(int_df))\n",
    "colors = ['darkred' if (c > 0) else 'steelblue' for c in int_df['coef']]\n",
    "\n",
    "ax.barh(y, int_df['coef'], color=colors, alpha=0.75)\n",
    "ax.errorbar(\n",
    "    int_df['coef'], y,\n",
    "    xerr=[int_df['coef'] - int_df['ci_low'], int_df['ci_high'] - int_df['coef']],\n",
    "    fmt='none', ecolor='black', capsize=4\n",
    ")\n",
    "ax.axvline(0, color='black', linestyle='--', linewidth=1)\n",
    "ax.set_yticks(y)\n",
    "ax.set_yticklabels(int_df['Hypothesis'])\n",
    "ax.set_xlabel('Differential effect on log(days to enforcement)')\n",
    "ax.set_title('TWFE Interaction Effects (95% CI)')\n",
    "ax.grid(True, axis='x', alpha=0.3)\n",
    "\n",
    "for i, pval in enumerate(int_df['pvalue']):\n",
    "    if pd.notna(pval) and pval < 0.05:\n",
    "        ax.text(int_df.loc[i, 'coef'], i, ' *', va='center', fontsize=14, color='black')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('heterogeneous_effects.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(\"Saved: heterogeneous_effects.png\")\n",
    "print(int_df[['Hypothesis', 'coef', 'se', 'pvalue']].to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5ecdabd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "DISTRICT PERFORMANCE ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "High Performers (enforcement improved >40%): ['09', '06', '08']\n",
      "Low Performers (enforcement worsened >30%): ['10', '03', '04']\n",
      "\n",
      "================================================================================\n",
      "COMPARING HIGH vs LOW PERFORMERS\n",
      "================================================================================\n",
      "\n",
      "--- HIGH PERFORMERS ---\n",
      "district  coefficient  pct_change  total_inspections_baseline  baseline_compliance_rate  baseline_days_to_enf\n",
      "      09      -0.7472    -52.6291                       62196                   82.3120              238.4897\n",
      "      06      -0.6460    -47.5879                       37386                   88.9838              474.9800\n",
      "      08      -0.5725    -43.5906                       60999                   88.2270              135.3338\n",
      "\n",
      "--- LOW PERFORMERS ---\n",
      "district  coefficient  pct_change  total_inspections_baseline  baseline_compliance_rate  baseline_days_to_enf\n",
      "      10       0.5330     70.4019                       39620                   88.9370               49.3502\n",
      "      03       0.8251    128.2135                       32975                   94.0868               61.9163\n",
      "      04       0.8693    138.5278                       32081                   92.7315               62.7780\n",
      "\n",
      "================================================================================\n",
      "AVERAGE CHARACTERISTICS BY PERFORMANCE\n",
      "================================================================================\n",
      "\n",
      "High Performers (n=3):\n",
      "  Avg baseline inspections: 53,527\n",
      "  Avg baseline compliance: 86.5%\n",
      "  Avg baseline days to enforcement: 282.9\n",
      "  Avg wells: 10,516\n",
      "\n",
      "Low Performers (n=3):\n",
      "  Avg baseline inspections: 34,892\n",
      "  Avg baseline compliance: 91.9%\n",
      "  Avg baseline days to enforcement: 58.0\n",
      "  Avg wells: 5,946\n",
      "\n",
      "================================================================================\n",
      "PRE/POST COMPARISON BY PERFORMANCE GROUP\n",
      "================================================================================\n",
      "\n",
      "High Performers:\n",
      "  Days to enforcement: 282.9 \u2192 106.8 (-62.3%)\n",
      "  Compliance rate: 86.5% \u2192 89.3% (+2.8pp)\n",
      "  Violations per inspection: 0.178 \u2192 0.109 (-38.7%)\n",
      "  Violation discovery rate: 12.8% \u2192 8.9% (-3.9pp)\n",
      "  Total inspections: 160,581 \u2192 537,179 (+234.5%)\n",
      "\n",
      "Low Performers:\n",
      "  Days to enforcement: 58.0 \u2192 97.2 (+67.6%)\n",
      "  Compliance rate: 91.9% \u2192 90.3% (-1.6pp)\n",
      "  Violations per inspection: 0.079 \u2192 0.079 (+0.8%)\n",
      "  Violation discovery rate: 7.8% \u2192 7.7% (-0.1pp)\n",
      "  Total inspections: 104,676 \u2192 279,637 (+167.1%)\n",
      "\n",
      "================================================================================\n",
      "KEY INSIGHTS\n",
      "================================================================================\n",
      "\n",
      "High performers improved enforcement speed dramatically, while:\n",
      "  \u2022 Maintaining or improving compliance\n",
      "  \u2022 Potentially reducing inspection intensity (may be more targeted)\n",
      "  \u2022 Finding fewer violations (better deterrence?)\n",
      "\n",
      "Low performers got slower at enforcement, while:\n",
      "  \u2022 May have experienced increased workload\n",
      "  \u2022 Different regulatory priorities\n",
      "  \u2022 Resource constraints\n",
      "\n",
      "\u2713 District performance comparison complete\n"
     ]
    }
   ],
   "source": [
    "# Deep dive: What distinguishes high vs low performing districts?\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"DISTRICT PERFORMANCE ANALYSIS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Get treatment effects from model 2\n",
    "district_treatment_effects = effects_df.copy()\n",
    "district_treatment_effects = district_treatment_effects.reset_index()\n",
    "\n",
    "# Classify districts by performance\n",
    "district_treatment_effects['performance'] = 'Middle'\n",
    "district_treatment_effects.loc[district_treatment_effects['coefficient'] < -0.4, 'performance'] = 'High Performers'\n",
    "district_treatment_effects.loc[district_treatment_effects['coefficient'] > 0.3, 'performance'] = 'Low Performers'\n",
    "\n",
    "high_performers = district_treatment_effects[district_treatment_effects['performance'] == 'High Performers']['district'].tolist()\n",
    "low_performers = district_treatment_effects[district_treatment_effects['performance'] == 'Low Performers']['district'].tolist()\n",
    "\n",
    "print(f\"\\nHigh Performers (enforcement improved >40%): {high_performers}\")\n",
    "print(f\"Low Performers (enforcement worsened >30%): {low_performers}\")\n",
    "\n",
    "# Compare characteristics\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"COMPARING HIGH vs LOW PERFORMERS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Merge treatment effects with baseline characteristics\n",
    "comparison_df = district_treatment_effects.merge(baseline_data, on='district', how='left')\n",
    "\n",
    "# Compare high vs low performers\n",
    "high_perf_stats = comparison_df[comparison_df['performance'] == 'High Performers']\n",
    "low_perf_stats = comparison_df[comparison_df['performance'] == 'Low Performers']\n",
    "\n",
    "print(\"\\n--- HIGH PERFORMERS ---\")\n",
    "print(high_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n",
    "                       'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n",
    "\n",
    "print(\"\\n--- LOW PERFORMERS ---\")\n",
    "print(low_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n",
    "                      'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n",
    "\n",
    "# Statistical comparison\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"AVERAGE CHARACTERISTICS BY PERFORMANCE\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "for group_name, group_data in [('High Performers', high_perf_stats), \n",
    "                                ('Low Performers', low_perf_stats)]:\n",
    "    print(f\"\\n{group_name} (n={len(group_data)}):\")\n",
    "    print(f\"  Avg baseline inspections: {group_data['total_inspections_baseline'].mean():,.0f}\")\n",
    "    print(f\"  Avg baseline compliance: {group_data['baseline_compliance_rate'].mean():.1f}%\")\n",
    "    print(f\"  Avg baseline days to enforcement: {group_data['baseline_days_to_enf'].mean():.1f}\")\n",
    "    print(f\"  Avg wells: {group_data['avg_wells'].mean():,.0f}\")\n",
    "\n",
    "# Look at pre/post trends for these districts\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"PRE/POST COMPARISON BY PERFORMANCE GROUP\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "for group_name, districts in [('High Performers', high_performers), \n",
    "                               ('Low Performers', low_performers)]:\n",
    "    group_data = district_year_panel[district_year_panel['district'].isin(districts)]\n",
    "    \n",
    "    pre = group_data[group_data['year'] < 2019].agg({\n",
    "        'avg_days_to_enforcement': 'mean',\n",
    "        'compliance_rate': 'mean',\n",
    "        'violations_per_inspection': 'mean',\n",
    "        'total_inspections': 'sum',\n",
    "        'violation_discovery_rate': 'mean'\n",
    "    })\n",
    "    \n",
    "    post = group_data[group_data['year'] >= 2019].agg({\n",
    "        'avg_days_to_enforcement': 'mean',\n",
    "        'compliance_rate': 'mean',\n",
    "        'violations_per_inspection': 'mean',\n",
    "        'total_inspections': 'sum',\n",
    "        'violation_discovery_rate': 'mean'\n",
    "    })\n",
    "    \n",
    "    print(f\"\\n{group_name}:\")\n",
    "    print(f\"  Days to enforcement: {pre['avg_days_to_enforcement']:.1f} \u2192 {post['avg_days_to_enforcement']:.1f} \"\n",
    "          f\"({((post['avg_days_to_enforcement']/pre['avg_days_to_enforcement'])-1)*100:+.1f}%)\")\n",
    "    print(f\"  Compliance rate: {pre['compliance_rate']:.1f}% \u2192 {post['compliance_rate']:.1f}% \"\n",
    "          f\"({post['compliance_rate']-pre['compliance_rate']:+.1f}pp)\")\n",
    "    print(f\"  Violations per inspection: {pre['violations_per_inspection']:.3f} \u2192 {post['violations_per_inspection']:.3f} \"\n",
    "          f\"({((post['violations_per_inspection']/pre['violations_per_inspection'])-1)*100:+.1f}%)\")\n",
    "    print(f\"  Violation discovery rate: {pre['violation_discovery_rate']:.1f}% \u2192 {post['violation_discovery_rate']:.1f}% \"\n",
    "          f\"({post['violation_discovery_rate']-pre['violation_discovery_rate']:+.1f}pp)\")\n",
    "    print(f\"  Total inspections: {pre['total_inspections']:,.0f} \u2192 {post['total_inspections']:,.0f} \"\n",
    "          f\"({((post['total_inspections']/pre['total_inspections'])-1)*100:+.1f}%)\")\n",
    "\n",
    "# Key differences summary\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(\"KEY INSIGHTS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "print(\"\\nHigh performers improved enforcement speed dramatically, while:\")\n",
    "print(\"  \u2022 Maintaining or improving compliance\")\n",
    "print(\"  \u2022 Potentially reducing inspection intensity (may be more targeted)\")\n",
    "print(\"  \u2022 Finding fewer violations (better deterrence?)\")\n",
    "\n",
    "print(\"\\nLow performers got slower at enforcement, while:\")\n",
    "print(\"  \u2022 May have experienced increased workload\")\n",
    "print(\"  \u2022 Different regulatory priorities\")\n",
    "print(\"  \u2022 Resource constraints\")\n",
    "\n",
    "print(\"\\n\u2713 District performance comparison complete\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5413bdc",
   "metadata": {},
   "source": [
    "## Part 6: Spatial Analysis\n",
    "\n",
    "### Geographic Patterns in Treatment Effects\n",
    "\n",
    "Now that we've identified massive heterogeneity in how districts responded to the 2019 policy, let's examine **spatial patterns**:\n",
    "\n",
    "1. **Spatial autocorrelation**: Do neighboring districts have similar treatment effects?\n",
    "2. **Geographic clusters**: Are high/low performers geographically clustered?\n",
    "3. **Spillover effects**: Does one district's response affect its neighbors?\n",
    "\n",
    "This helps answer: Is the heterogeneity random or geographically structured?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "370786d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "SPATIAL MAPPING OF TREATMENT EFFECTS\n",
      "================================================================================\n",
      "\n",
      "District Treatment Effects Summary:\n",
      "district  coefficient  pct_change      effect_category\n",
      "      09      -0.7472    -52.6291   Strong Improvement\n",
      "      06      -0.6460    -47.5879   Strong Improvement\n",
      "      08      -0.5725    -43.5906   Strong Improvement\n",
      "      7B      -0.3927    -32.4766 Moderate Improvement\n",
      "      6E      -0.3792    -31.5572 Moderate Improvement\n",
      "      02      -0.3387    -28.7299 Moderate Improvement\n",
      "      01      -0.0924     -8.8223            No Change\n",
      "      8A       0.0687      7.1150            No Change\n",
      "      05       0.1488     16.0496     Moderate Decline\n",
      "      7C       0.1876     20.6369     Moderate Decline\n",
      "      10       0.5330     70.4019       Strong Decline\n",
      "      03       0.8251    128.2135       Strong Decline\n",
      "      04       0.8693    138.5278       Strong Decline\n"
     ]
    }
   ],
   "source": [
    "# Spatial map of district-specific treatment effects\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"SPATIAL MAPPING OF TREATMENT EFFECTS\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# Prepare data for mapping\n",
    "map_data = effects_df.reset_index()\n",
    "map_data['treatment_effect_pct'] = map_data['pct_change']\n",
    "map_data['effect_category'] = pd.cut(map_data['pct_change'], \n",
    "                                      bins=[-100, -40, -10, 10, 40, 200],\n",
    "                                      labels=['Strong Improvement', 'Moderate Improvement', \n",
    "                                             'No Change', 'Moderate Decline', 'Strong Decline'])\n",
    "\n",
    "print(\"\\nDistrict Treatment Effects Summary:\")\n",
    "print(map_data[['district', 'coefficient', 'pct_change', 'effect_category']].sort_values('pct_change').to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "661a7a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading district-by-county CSV from: ../data/district_by_county.csv\n",
      "Loading county shapefile from: ../data/texas_county_shape/tl_2025_48_cousub/tl_2025_48_cousub.shp\n",
      "\u2713 district_by_county: 254 rows; columns: ['county', 'district_code', 'FIPS', 'district_name']\n",
      "\u2713 districts_shapefile: 862 geometries; columns: ['STATEFP', 'COUNTYFP', 'COUSUBFP', 'COUSUBNS', 'GEOID', 'GEOIDFQ', 'NAME', 'NAMELSAD', 'LSAD', 'CLASSFP', 'MTFCC', 'FUNCSTAT', 'ALAND', 'AWATER', 'INTPTLAT', 'INTPTLON', 'geometry']\n",
      "\u2713 Loaded district-by-county mapping and county shapes\n"
     ]
    }
   ],
   "source": [
    "# Load district-to-county mapping and county shapefile\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "# Relative paths to data (use parent directory '..')\n",
    "csv_path = Path('..') / 'data' / 'district_by_county.csv'\n",
    "shp_path = Path('..') / 'data' / 'texas_county_shape' / 'tl_2025_48_cousub' / 'tl_2025_48_cousub.shp'\n",
    "\n",
    "print(f'Loading district-by-county CSV from: {csv_path}')\n",
    "print(f'Loading county shapefile from: {shp_path}')\n",
    "\n",
    "# Read CSV (ensure FIPS is string to preserve leading zeros)\n",
    "district_by_county = pd.read_csv(csv_path, dtype={'FIPS': str})\n",
    "# Keep original column names; later cell pads FIPS to COUNTYFP as needed\n",
    "\n",
    "# Read county shapefile as GeoDataFrame\n",
    "districts_shapefile = gpd.read_file(shp_path)\n",
    "\n",
    "# Basic checks\n",
    "print(f'\u2713 district_by_county: {len(district_by_county):,} rows; columns: {list(district_by_county.columns)}')\n",
    "print(f'\u2713 districts_shapefile: {len(districts_shapefile):,} geometries; columns: {list(districts_shapefile.columns)}')\n",
    "\n",
    "# Provide an alias if calling code expects 'districts_by_county' (some cells used plural name)\n",
    "districts_by_county = district_by_county.copy()\n",
    "\n",
    "# Align column names if necessary (no overwrite if already present)\n",
    "if 'COUNTYFP' not in district_by_county.columns and 'FIPS' in district_by_county.columns:\n",
    "    district_by_county['COUNTYFP'] = district_by_county['FIPS'].astype(str).str.zfill(3)\n",
    "\n",
    "print('\u2713 Loaded district-by-county mapping and county shapes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8d81a177",
   "metadata": {},
   "outputs": [],
   "source": [
    "# district_by_county FIPS are 3-digit county codes (no state), so pad to match COUNTYFP\n",
    "district_by_county['COUNTYFP'] = district_by_county['FIPS'].astype(str).str.zfill(3)\n",
    "\n",
    "districts_map = districts_shapefile.merge(\n",
    "    district_by_county,\n",
    "    on='COUNTYFP',\n",
    "    how='left'\n",
    ")\n",
    "districts_map = districts_map.dissolve(by='district_code')\n",
    "districts_map = districts_map.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "24fedc28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outline-only map of districts with labels\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 12))\n",
    "districts_map.boundary.plot(ax=ax, color='black', linewidth=0.5)\n",
    "\n",
    "# Use representative points for label placement inside polygons\n",
    "label_points = districts_map.representative_point()\n",
    "for x, y, label in zip(label_points.x, label_points.y, districts_map['district_code']):\n",
    "    ax.text(x, y, str(label), fontsize=7, ha='center', va='center')\n",
    "\n",
    "ax.set_title('District Boundaries', fontsize=14)\n",
    "ax.axis('off')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dd678dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2250x2700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize treatment effects by district\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "\n",
    "# Normalize district codes for a clean join\n",
    "def normalize_district_code(val):\n",
    "    s = str(val).strip()\n",
    "    s = s.replace('District', '').replace('district', '').strip()\n",
    "    m = re.match(r'^0*(\\d+[A-Za-z]?)$', s)\n",
    "    if m:\n",
    "        return m.group(1).lstrip('0') or '0'\n",
    "    return s\n",
    "\n",
    "map_data['district_code'] = map_data['district'].apply(normalize_district_code)\n",
    "districts_map['district_code'] = districts_map['district_code'].apply(normalize_district_code)\n",
    "\n",
    "plot_df = districts_map.merge(\n",
    "    map_data[['district_code', 'pct_change']],\n",
    "    on='district_code',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "# Publication styling\n",
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 300,\n",
    "    \"axes.edgecolor\": \"0.2\",\n",
    "    \"axes.linewidth\": 0.8,\n",
    "    \"font.size\": 10\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7.5, 9))\n",
    "ax.set_axis_off()\n",
    "\n",
    "# Diverging color scale centered at 0\n",
    "vmin = plot_df['pct_change'].min()\n",
    "vmax = plot_df['pct_change'].max()\n",
    "vabs = max(abs(vmin), abs(vmax))\n",
    "\n",
    "plot_df.plot(\n",
    "    column='pct_change',\n",
    "    cmap='RdBu_r',\n",
    "    vmin=-vabs,\n",
    "    vmax=vabs,\n",
    "    legend=True,\n",
    "    ax=ax,\n",
    "    missing_kwds={\"color\": \"lightgrey\", \"label\": \"No data\"},\n",
    "    edgecolor=\"0.4\",\n",
    "    linewidth=0.4\n",
    ")\n",
    "\n",
    "# Refined labels\n",
    "label_points = plot_df.representative_point()\n",
    "for x, y, label in zip(label_points.x, label_points.y, plot_df['district_code']):\n",
    "    ax.text(x, y, str(label), ha='center', va='center', fontsize=8, color='black')\n",
    "\n",
    "ax.set_title(\n",
    "    'District Treatment Effects (Percent Change in Days to Enforcement)',\n",
    "    fontsize=12, fontweight='bold', pad=12\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c33eccb",
   "metadata": {},
   "source": [
    "Figure Caption and Description\n",
    "\n",
    "Title: District Treatment Effects on Enforcement Speed (Percent change in days to enforcement, post\u20112019 vs. pre\u20112019)\n",
    "What it shows: Choropleth of RRC districts colored by the percent change in average days from violation discovery to enforcement following the January 2019 disclosure policy (negative = faster enforcement; positive = slower enforcement).\n",
    "Color scale & interpretation: Diverging RdBu_r palette centered at 0 (symmetric vmin/vmax). In this scheme, red tones indicate negative percent changes (faster enforcement / improvement) and blue tones indicate positive percent changes (slower enforcement / decline). Light gray denotes districts with no estimate.\n",
    "Data & method: District\u2011specific treatment effects come from a district \u00d7 post\u20112019 DiD on log(days to enforcement), converted to percent change (pct_change) and joined to district geometries by district_code. The map uses a symmetric color range so equal\u2011magnitude increases and decreases are visually comparable.\n",
    "Labels & layout: District codes are placed at polygon representative points for identification; the map emphasizes spatial pattern and relative magnitudes rather than statistical significance.\n",
    "Caveat / footnote: Percent changes are derived from log\u2011scale coefficients; model standard errors are clustered at the district level and confidence intervals are reported elsewhere\u2014interpret magnitudes together with statistical precision. Data sources: RRC inspection/violation records (2015\u20132025) and county shapefiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "737e2034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "SPATIAL DYNAMICS: MORAN'S I\n",
      "================================================================================\n",
      "N districts: 13\n",
      "Moran's I: -0.0493\n",
      "Permutation p-value: 0.8550\n"
     ]
    }
   ],
   "source": [
    "# Spatial dynamics: Moran's I for district treatment effects\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"SPATIAL DYNAMICS: MORAN'S I\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "neighbors = {\n",
    "    '01': ['02', '05'], '02': ['01', '05'], '03': ['04', '05', '8A'], '04': ['03', '06', '08'],\n",
    "    '05': ['01', '02', '03', '6E', '7C'], '06': ['04', '6E'], '6E': ['05', '06', '7B', '7C'],\n",
    "    '7B': ['6E', '7C', '09'], '7C': ['05', '6E', '7B', '09'], '08': ['04', '8A'],\n",
    "    '8A': ['03', '08'], '09': ['7B', '7C', '10'], '10': ['09']\n",
    "}\n",
    "\n",
    "valid = map_data[['district','pct_change']].dropna().sort_values('district')\n",
    "districts = valid['district'].tolist(); y = valid['pct_change'].astype(float).to_numpy(); n=len(districts)\n",
    "idx = {d:i for i,d in enumerate(districts)}\n",
    "W = np.zeros((n,n), dtype=float)\n",
    "for d in districts:\n",
    "    i=idx[d]\n",
    "    nbrs=[k for k in neighbors.get(d,[]) if k in idx]\n",
    "    if nbrs:\n",
    "        w=1.0/len(nbrs)\n",
    "        for k in nbrs:\n",
    "            W[i, idx[k]] = w\n",
    "S0=W.sum(); z=y-y.mean()\n",
    "moran_i = (n/S0)*((z@W@z)/(z@z)) if (S0>0 and np.dot(z,z)>0) else np.nan\n",
    "\n",
    "rng=np.random.default_rng(42); B=5000\n",
    "if pd.notna(moran_i):\n",
    "    sims=[]\n",
    "    for _ in range(B):\n",
    "        zp=rng.permutation(z); sims.append((n/S0)*((zp@W@zp)/(zp@zp)))\n",
    "    sims=np.array(sims)\n",
    "    p=(np.sum(np.abs(sims)>=abs(moran_i))+1)/(B+1)\n",
    "else:\n",
    "    p=np.nan\n",
    "\n",
    "print(f\"N districts: {n}\")\n",
    "print(f\"Moran's I: {moran_i:.4f}\")\n",
    "print(f\"Permutation p-value: {p:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a166e911",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eea68f63",
   "metadata": {},
   "source": [
    "### Spatial Spillovers Summary\n",
    "\n",
    "The formal spatial test (Moran's I) indicates **no statistically significant global spatial autocorrelation** in district treatment effects.\n",
    "\n",
    "- Moran's I: **-0.0493**\n",
    "- Permutation p-value: **0.8550**\n",
    "\n",
    "Interpretation:\n",
    "- The sign is slightly negative, but the estimate is far from statistically significant.\n",
    "- We do not find evidence that high- or low-response districts are spatially clustered in a systematic way.\n",
    "- This supports the view that district responsiveness is primarily administrative/institutional rather than geographically diffused.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e48f8f",
   "metadata": {},
   "source": [
    "# Part 7: Robustness Checks and Sensitivity Analysis\n",
    "\n",
    "Now that we've established the heterogeneous treatment effects, let's validate our findings through several robustness tests:\n",
    "\n",
    "1. **Placebo tests**: Run DiD with fake policy dates (should show no effect)\n",
    "2. **Alternative outcomes**: Test with other measures (compliance rate, violations per inspection)\n",
    "3. **Sample restrictions**: Exclude outlier districts and re-run\n",
    "4. **Time sensitivity**: Test different time windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "88017799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "ROBUSTNESS TEST 1: PLACEBO TESTS (ALL DISTRICTS)\n",
      "================================================================================\n",
      "Placebo 2017: post coef=0.6565, p=0.0020\n",
      "Placebo 2021: post coef=-0.0245, p=0.9191\n",
      " placebo_year  coefficient  pvalue  significant\n",
      "         2017       0.6565  0.0020         True\n",
      "         2021      -0.0245  0.9191        False\n"
     ]
    }
   ],
   "source": [
    "## Robustness Test 1: Placebo Policy Years (All-District Model)\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"ROBUSTNESS TEST 1: PLACEBO TESTS (ALL DISTRICTS)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "\n",
    "def fit_shift(df, policy_year):\n",
    "    d=df.copy()\n",
    "    d['year_num']=d['year']-d['year'].min()\n",
    "    d['post']=(d['year']>=policy_year).astype(int)\n",
    "    d['post_trend']=(d['year']-(policy_year-1)).clip(lower=0)\n",
    "    m=smf.ols('log_days_to_enf ~ C(district) + year_num + post + post_trend', data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n",
    "    return m\n",
    "\n",
    "placebo_results=[]\n",
    "for py in [2017,2021]:\n",
    "    m=fit_shift(df_reg, py)\n",
    "    coef=m.params.get('post', np.nan); p=m.pvalues.get('post', np.nan)\n",
    "    print(f\"Placebo {py}: post coef={coef:.4f}, p={p:.4f}\")\n",
    "    placebo_results.append({'placebo_year':py,'coefficient':coef,'pvalue':p,'significant':(p<0.05) if pd.notna(p) else False})\n",
    "\n",
    "placebo_df=pd.DataFrame(placebo_results)\n",
    "print(placebo_df.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eb74242a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "ROBUSTNESS TEST 2: ALTERNATIVE OUTCOMES (ALL DISTRICTS)\n",
      "================================================================================\n",
      "                  outcome  post_coef  post_p  post_trend_coef  post_trend_p\n",
      "    Resolution Rate (H1b)     4.3721  0.2104          -2.9371        0.1424\n",
      "          Compliance Rate    -0.1311  0.9316          -0.5562        0.1870\n",
      "Violations per Inspection    -0.0082  0.6690           0.0106        0.0600\n"
     ]
    }
   ],
   "source": [
    "## Robustness Test 2: Alternative Outcomes (All-District Model)\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"ROBUSTNESS TEST 2: ALTERNATIVE OUTCOMES (ALL DISTRICTS)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "def fit_alt(dep, data):\n",
    "    d=data.copy()\n",
    "    d['year_num']=d['year']-d['year'].min()\n",
    "    d['post']=(d['year']>=2019).astype(int)\n",
    "    d['post_trend']=(d['year']-2018).clip(lower=0)\n",
    "    m=smf.ols(f'{dep} ~ C(district) + year_num + post + post_trend', data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n",
    "    return m\n",
    "\n",
    "rows=[]\n",
    "\n",
    "m=fit_alt('resolution_rate', district_year_panel)\n",
    "rows.append({'outcome':'Resolution Rate (H1b)','post_coef':m.params.get('post',np.nan),'post_p':m.pvalues.get('post',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "m=fit_alt('compliance_rate', district_year_panel)\n",
    "rows.append({'outcome':'Compliance Rate','post_coef':m.params.get('post',np.nan),'post_p':m.pvalues.get('post',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "d=district_year_panel.copy(); d['violations_per_inspection']=(d['total_violations']/d['total_inspections']).replace([np.inf,-np.inf],np.nan)\n",
    "d=d.dropna(subset=['violations_per_inspection'])\n",
    "m=fit_alt('violations_per_inspection', d)\n",
    "rows.append({'outcome':'Violations per Inspection','post_coef':m.params.get('post',np.nan),'post_p':m.pvalues.get('post',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "alt_df=pd.DataFrame(rows)\n",
    "print(alt_df.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "98be850c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS (ALL DISTRICTS)\n",
      "================================================================================\n",
      "              restriction  post_coef  post_p  post_trend_coef  post_trend_p\n",
      "              Full sample     0.1514  0.3294          -0.3603        0.0010\n",
      "Exclude extreme districts     0.1917  0.1930          -0.2972        0.0133\n",
      "        Exclude 2015-2016     0.1942  0.1958          -0.2313        0.0950\n",
      "        Exclude 2020-2021     0.1516  0.2959          -0.3599        0.0016\n"
     ]
    }
   ],
   "source": [
    "## Robustness Test 3: Sample Restrictions (All-District Model)\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS (ALL DISTRICTS)\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "\n",
    "def fit_main(df):\n",
    "    d=df.copy()\n",
    "    d['year_num']=d['year']-d['year'].min()\n",
    "    d['post_2019']=(d['year']>=2019).astype(int)\n",
    "    d['post_trend']=(d['year']-2018).clip(lower=0)\n",
    "    m=smf.ols('log_days_to_enf ~ C(district) + year_num + post_2019 + post_trend', data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n",
    "    return m\n",
    "\n",
    "rows=[]\n",
    "m=fit_main(df_reg); rows.append({'restriction':'Full sample','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "if 'district_effects' in locals() and len(district_effects)>=4:\n",
    "    ex = district_effects.sort_values('coefficient').head(2)['district'].tolist() + district_effects.sort_values('coefficient').tail(2)['district'].tolist()\n",
    "    d=df_reg[~df_reg['district'].isin(ex)].copy(); m=fit_main(d)\n",
    "    rows.append({'restriction':'Exclude extreme districts','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "d=df_reg[df_reg['year']>=2017].copy(); m=fit_main(d)\n",
    "rows.append({'restriction':'Exclude 2015-2016','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "d=df_reg[~df_reg['year'].isin([2020,2021])].copy(); m=fit_main(d)\n",
    "rows.append({'restriction':'Exclude 2020-2021','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "restrict_df=pd.DataFrame(rows)\n",
    "print(restrict_df.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2dc752a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\n",
      "================================================================================\n",
      "                         spec  post_coef  post_p  post_trend_coef  post_trend_p  n_interactions\n",
      "           Linear interrupted   -41.9298  0.3104         -67.0420        0.0100             NaN\n",
      "Year FE + district post terms        NaN     NaN              NaN           NaN         13.0000\n",
      "       Winsorized interrupted     0.2137  0.1021          -0.3147        0.0016             NaN\n"
     ]
    }
   ],
   "source": [
    "## Robustness Test 4: Specification Sensitivity\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "spec=[]\n",
    "\n",
    "# Linear interrupted model\n",
    "lin=df_reg.copy(); lin['year_num']=lin['year']-lin['year'].min(); lin['post_2019']=(lin['year']>=2019).astype(int); lin['post_trend']=(lin['year']-2018).clip(lower=0)\n",
    "m=smf.ols('avg_days_to_enforcement ~ C(district) + year_num + post_2019 + post_trend', data=lin).fit(cov_type='cluster', cov_kwds={'groups': lin['district']})\n",
    "spec.append({'spec':'Linear interrupted','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "# Year FE heterogeneity model summary stat\n",
    "m=smf.ols('log_days_to_enf ~ C(district) + C(year) + C(district):post_2019', data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n",
    "int_terms=[t for t in m.params.index if ':post_2019' in t]\n",
    "spec.append({'spec':'Year FE + district post terms','post_coef':np.nan,'post_p':np.nan,'post_trend_coef':np.nan,'post_trend_p':np.nan,'n_interactions':len(int_terms)})\n",
    "\n",
    "# Winsorized interrupted model\n",
    "from scipy.stats import mstats\n",
    "w=df_reg.copy(); w['log_w']=mstats.winsorize(w['log_days_to_enf'], limits=[0.05,0.05]); w['year_num']=w['year']-w['year'].min(); w['post_2019']=(w['year']>=2019).astype(int); w['post_trend']=(w['year']-2018).clip(lower=0)\n",
    "m=smf.ols('log_w ~ C(district) + year_num + post_2019 + post_trend', data=w).fit(cov_type='cluster', cov_kwds={'groups': w['district']})\n",
    "spec.append({'spec':'Winsorized interrupted','post_coef':m.params.get('post_2019',np.nan),'post_p':m.pvalues.get('post_2019',np.nan),'post_trend_coef':m.params.get('post_trend',np.nan),'post_trend_p':m.pvalues.get('post_trend',np.nan)})\n",
    "\n",
    "spec_df=pd.DataFrame(spec)\n",
    "print(spec_df.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "59216419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\n",
      "================================================================================\n",
      "         Category                         Metric   Value        P-value\n",
      "Main all-district       H1 level shift post_2019  0.1514         0.3294\n",
      "Main all-district      H1 slope shift post_trend -0.3603         0.0010\n",
      " H2 heterogeneity District post effects included      13 see joint test\n",
      "      H5 offshore Offshore differential (model3)  0.3819         0.0000\n",
      "\n",
      "Placebo summary:\n",
      " placebo_year  coefficient  pvalue  significant\n",
      "         2017       0.6565  0.0020         True\n",
      "         2021      -0.0245  0.9191        False\n",
      "\n",
      "Alternative outcomes summary:\n",
      "                  outcome  post_coef  post_p  post_trend_coef  post_trend_p\n",
      "    Resolution Rate (H1b)     4.3721  0.2104          -2.9371        0.1424\n",
      "          Compliance Rate    -0.1311  0.9316          -0.5562        0.1870\n",
      "Violations per Inspection    -0.0082  0.6690           0.0106        0.0600\n",
      "\n",
      "Sample restriction summary:\n",
      "              restriction  post_coef  post_p  post_trend_coef  post_trend_p\n",
      "              Full sample     0.1514  0.3294          -0.3603        0.0010\n",
      "Exclude extreme districts     0.1917  0.1930          -0.2972        0.0133\n",
      "        Exclude 2015-2016     0.1942  0.1958          -0.2313        0.0950\n",
      "        Exclude 2020-2021     0.1516  0.2959          -0.3599        0.0016\n",
      "\n",
      "Specification summary:\n",
      "                         spec  post_coef  post_p  post_trend_coef  post_trend_p  n_interactions\n",
      "           Linear interrupted   -41.9298  0.3104         -67.0420        0.0100             NaN\n",
      "Year FE + district post terms        NaN     NaN              NaN           NaN         13.0000\n",
      "       Winsorized interrupted     0.2137  0.1021          -0.3147        0.0016             NaN\n"
     ]
    }
   ],
   "source": [
    "## Comprehensive Robustness Summary\n",
    "\n",
    "print(\"=\"*80)\n",
    "print(\"COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "rows=[]\n",
    "rows.append({'Category':'Main all-district','Metric':'H1 level shift post_2019','Value':f\"{model1.params.get('post_2019',np.nan):.4f}\", 'P-value':f\"{model1.pvalues.get('post_2019',np.nan):.4f}\"})\n",
    "rows.append({'Category':'Main all-district','Metric':'H1 slope shift post_trend','Value':f\"{model1.params.get('post_trend',np.nan):.4f}\", 'P-value':f\"{model1.pvalues.get('post_trend',np.nan):.4f}\"})\n",
    "rows.append({'Category':'H2 heterogeneity','Metric':'District post effects included','Value':f\"{len([t for t in model2.params.index if ':post_2019' in t])}\", 'P-value':'see joint test'})\n",
    "rows.append({'Category':'H5 offshore','Metric':'Offshore differential (model3)','Value':f\"{model3.params.get('post_2019:offshore_jurisdiction',np.nan):.4f}\", 'P-value':f\"{model3.pvalues.get('post_2019:offshore_jurisdiction',np.nan):.4f}\"})\n",
    "\n",
    "summary_df=pd.DataFrame(rows)\n",
    "print(summary_df.to_string(index=False))\n",
    "\n",
    "if 'placebo_df' in locals():\n",
    "    print() \n",
    "    print('Placebo summary:')\n",
    "    print(placebo_df.to_string(index=False))\n",
    "if 'alt_df' in locals():\n",
    "    print() \n",
    "    print('Alternative outcomes summary:')\n",
    "    print(alt_df.to_string(index=False))\n",
    "if 'restrict_df' in locals():\n",
    "    print() \n",
    "    print('Sample restriction summary:')\n",
    "    print(restrict_df.to_string(index=False))\n",
    "if 'spec_df' in locals():\n",
    "    print() \n",
    "    print('Specification summary:')\n",
    "    print(spec_df.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a6ca56",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0c3516db",
   "metadata": {},
   "source": [
    "# Part 8: Deep Dive - Demographics and Geographic Features\n",
    "\n",
    "Given strong district heterogeneity and weak support for most structural moderators in the core models, this section explores whether district demographics and geology help explain residual variation in post-2019 district effects.\n",
    "\n",
    "Focus areas:\n",
    "1. Demographics: rurality (RUCA), minority share, poverty, EJ context.\n",
    "2. Geologic structure: dominant basin and basin diversity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dd43ab4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "EXPLORING POSTGIS TABLES\n",
      "================================================================================\n",
      "\n",
      "1. Inspections table columns:\n",
      "--------------------------------------------------------------------------------\n",
      "               column_name                   data_type\n",
      "             operator_name                        text\n",
      "            p5_operator_no                        text\n",
      "                  district                        text\n",
      "district_office_inspecting                        text\n",
      "     oil_lease_gas_well_id                        text\n",
      "            lease_fac_name                        text\n",
      "                    api_no                        text\n",
      "                    county                        text\n",
      "                   well_no                        text\n",
      "           inspection_date timestamp without time zone\n",
      "        drilling_permit_no                        text\n",
      "              complaint_no                        text\n",
      "                compliance                        text\n",
      "                field_name                        text\n",
      "                  api_norm                        text\n",
      "\n",
      "2. Demographics table (well_with_demographics_table):\n",
      "--------------------------------------------------------------------------------\n",
      "               column_name        data_type\n",
      "           canonical_api10             text\n",
      "              api10_number             text\n",
      "                api_number             text\n",
      "        census_tract_geoid             text\n",
      "                  latitude double precision\n",
      "                 longitude double precision\n",
      "                      geom     USER-DEFINED\n",
      "                tract_name             text\n",
      "            ruca_code_2020 double precision\n",
      "             ruca_category             text\n",
      "  ruca_primary_description             text\n",
      "ruca_secondary_description             text\n",
      "        ej_composite_score double precision\n",
      "              pct_minority double precision\n",
      "              pct_hispanic double precision\n",
      "              poverty_rate double precision\n",
      "         unemployment_rate double precision\n",
      "          less_than_hs_pct double precision\n",
      " linguistic_isolation_rate double precision\n",
      "   renter_cost_burden_rate double precision\n",
      "\n",
      "Sample demographics data:\n",
      "api_norm ruca_code_2020 ruca_category pct_minority poverty_rate\n",
      "70430256           None          None         None         None\n",
      "70430240           None          None         None         None\n",
      "70430233           None          None         None         None\n",
      "70230275           None          None         None         None\n",
      "70230267           None          None         None         None\n",
      "\n",
      "3. Geography table (well_geo_features):\n",
      "--------------------------------------------------------------------------------\n",
      "    column_name    data_type\n",
      "             id       bigint\n",
      "canonical_api10         text\n",
      "   api10_number         text\n",
      "     api_number         text\n",
      "           geom USER-DEFINED\n",
      "     basin_name         text\n",
      "      play_name         text\n",
      "    texmex_name         text\n",
      "       api_norm         text\n",
      "\n",
      "Sample geography data:\n",
      "api_norm basin_name play_name\n",
      "    None          8      None\n",
      "    None          2      None\n",
      "    None          2      None\n",
      "    None          8      None\n",
      "    None          8      None\n",
      "\n",
      "================================================================================\n",
      "\u2713 Table exploration complete\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "## Step 1: Explore the PostGIS tables to understand their structure\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"EXPLORING POSTGIS TABLES\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# Check column names in inspections table\n",
    "print(\"\\n1. Inspections table columns:\")\n",
    "print(\"-\" * 80)\n",
    "insp_cols_query = \"\"\"\n",
    "SELECT column_name, data_type \n",
    "FROM information_schema.columns \n",
    "WHERE table_name = 'inspections' \n",
    "ORDER BY ordinal_position;\n",
    "\"\"\"\n",
    "insp_cols = pd.read_sql(insp_cols_query, engine)\n",
    "print(insp_cols.to_string(index=False))\n",
    "\n",
    "# Check demographics table\n",
    "print(\"\\n2. Demographics table (well_with_demographics_table):\")\n",
    "print(\"-\" * 80)\n",
    "demo_cols_query = \"\"\"\n",
    "SELECT column_name, data_type \n",
    "FROM information_schema.columns \n",
    "WHERE table_name = 'well_with_demographics_table' \n",
    "ORDER BY ordinal_position \n",
    "LIMIT 20;\n",
    "\"\"\"\n",
    "demo_cols = pd.read_sql(demo_cols_query, engine)\n",
    "print(demo_cols.to_string(index=False))\n",
    "\n",
    "# Get sample from demographics\n",
    "demo_sample_query = \"\"\"\n",
    "SELECT api_norm, ruca_code_2020, ruca_category, pct_minority, poverty_rate\n",
    "FROM well_with_demographics_table \n",
    "LIMIT 5;\n",
    "\"\"\"\n",
    "demo_sample = pd.read_sql(demo_sample_query, engine)\n",
    "print(\"\\nSample demographics data:\")\n",
    "print(demo_sample.to_string(index=False))\n",
    "\n",
    "# Check geography table  \n",
    "print(\"\\n3. Geography table (well_geo_features):\")\n",
    "print(\"-\" * 80)\n",
    "geo_cols_query = \"\"\"\n",
    "SELECT column_name, data_type \n",
    "FROM information_schema.columns \n",
    "WHERE table_name = 'well_geo_features' \n",
    "ORDER BY ordinal_position;\n",
    "\"\"\"\n",
    "geo_cols = pd.read_sql(geo_cols_query, engine)\n",
    "print(geo_cols.to_string(index=False))\n",
    "\n",
    "# Get sample from geography\n",
    "geo_sample_query = \"\"\"\n",
    "SELECT api_norm, basin_name, play_name\n",
    "FROM well_geo_features \n",
    "LIMIT 5;\n",
    "\"\"\"\n",
    "geo_sample = pd.read_sql(geo_sample_query, engine)\n",
    "print(\"\\nSample geography data:\")\n",
    "print(geo_sample.to_string(index=False))\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"\u2713 Table exploration complete\")\n",
    "print(\"=\" * 80)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "22144c31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\n",
      "================================================================================\n",
      "\n",
      "Strategy: Join demographics/geography with inspections using api_norm\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "Querying demographics by district...\n",
      "--------------------------------------------------------------------------------\n",
      "\u2713 SUCCESS: Loaded demographics for 13 districts\n",
      "\n",
      "District Demographics:\n",
      "district  n_wells     avg_income  avg_ruca_code  avg_pct_minority  avg_poverty_rate  avg_ej_score  pct_metropolitan  pct_micropolitan  pct_rural\n",
      "      01    32382 -44687864.0623         7.1255            0.2672            0.1763        0.4970            0.2918            0.0131     0.6812\n",
      "      02    17223    -97913.4176         7.6108            0.2012            0.1395        0.4509            0.2435            0.0013     0.7482\n",
      "      03    16826  -3375276.0532         5.2039            0.2434            0.1228        0.4918            0.5231            0.1091     0.3582\n",
      "      04    21238  -1887605.1984         4.5855            0.2127            0.2489        0.5924            0.5313            0.2355     0.2222\n",
      "      05    10101     55658.2253         8.7672            0.1831            0.1270        0.4605            0.0660            0.1075     0.8140\n",
      "      06    24743    -78226.5235         5.9712            0.2200            0.1473        0.4934            0.2813            0.2452     0.4627\n",
      "      08   106371 -56597541.7157         5.9276            0.2474            0.1141        0.4963            0.3765            0.1595     0.4615\n",
      "      09    47422  -1558781.8820         4.6279            0.1320            0.0984        0.3763            0.6023            0.0345     0.3442\n",
      "      10    30981     68603.5885         8.5872            0.1545            0.1066        0.4574            0.0487            0.1349     0.7726\n",
      "      6E     6249     56451.2206         3.0494            0.2382            0.1971        0.5343            0.7531            0.1037     0.1413\n",
      "      7B    21726   -224729.5548         8.4244            0.0904            0.1225        0.4211            0.1217            0.0478     0.8103\n",
      "      7C    43167   -220859.9070         9.6275            0.4354            0.1125        0.5370            0.0455            0.0004     0.9525\n",
      "      8A    42122   -713670.0572         7.4827            0.1952            0.1369        0.5159            0.1708            0.2073     0.6198\n",
      "\n",
      "================================================================================\n",
      "Querying geographic features by district...\n",
      "================================================================================\n",
      "\u2713 SUCCESS: Loaded geography for 13 districts\n",
      "\n",
      "District Geography:\n",
      "district  n_wells_with_geo primary_basin  n_basins  n_plays\n",
      "      01             31838             8         3        2\n",
      "      02             17102             8         1        1\n",
      "      03             16649             8         2        3\n",
      "      04             20925             8         1        1\n",
      "      05              9662             2         4        2\n",
      "      06             24410             2         2        2\n",
      "      08            105874             5         3        2\n",
      "      09             42771             3         3        1\n",
      "      10             11638             0         2        0\n",
      "      6E              6237             2         1        1\n",
      "      7B             20343             3         2        1\n",
      "      7C             42263             5         2        2\n",
      "      8A             40740             5         3        1\n",
      "\n",
      "================================================================================\n",
      "\u2713\u2713\u2713 District aggregation COMPLETE \u2713\u2713\u2713\n",
      "  Demographics: 13 districts\n",
      "  Geography: 13 districts\n"
     ]
    }
   ],
   "source": [
    "## Step 2: Aggregate demographics and geographic features by district\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# All relevant tables use api_norm as the normalized well identifier\n",
    "\n",
    "print(\"\\nStrategy: Join demographics/geography with inspections using api_norm\")\n",
    "\n",
    "# Demographics by district (via inspections)\n",
    "print(f\"\\n{'-'*80}\")\n",
    "print(\"Querying demographics by district...\")\n",
    "print(f\"{'-'*80}\")\n",
    "\n",
    "demo_district_query = \"\"\"\n",
    "WITH well_districts AS (\n",
    "    SELECT DISTINCT \n",
    "        api_norm,\n",
    "        district\n",
    "    FROM inspections\n",
    "    WHERE district IS NOT NULL AND api_norm IS NOT NULL\n",
    ")\n",
    "SELECT \n",
    "    wd.district,\n",
    "    COUNT(DISTINCT wd.api_norm) as n_wells,\n",
    "    AVG(d.median_household_income) as avg_income,\n",
    "    AVG(d.ruca_code_2020::numeric) as avg_ruca_code,\n",
    "    AVG(d.pct_minority) as avg_pct_minority,\n",
    "    AVG(d.poverty_rate) as avg_poverty_rate,\n",
    "    AVG(d.ej_composite_score) as avg_ej_score,\n",
    "    -- RUCA categories (using 2020 codes)\n",
    "    AVG(CASE WHEN d.ruca_code_2020 <= 3 THEN 1.0 ELSE 0.0 END) as pct_metropolitan,\n",
    "    AVG(CASE WHEN d.ruca_code_2020 BETWEEN 4 AND 6 THEN 1.0 ELSE 0.0 END) as pct_micropolitan,\n",
    "    AVG(CASE WHEN d.ruca_code_2020 >= 7 THEN 1.0 ELSE 0.0 END) as pct_rural\n",
    "FROM well_districts wd\n",
    "LEFT JOIN well_with_demographics_table d \n",
    "    ON wd.api_norm = d.api_norm\n",
    "GROUP BY wd.district\n",
    "ORDER BY wd.district;\n",
    "\"\"\"\n",
    "\n",
    "try:\n",
    "    district_demographics = pd.read_sql(demo_district_query, engine)\n",
    "    if district_demographics is not None and len(district_demographics) > 0:\n",
    "        print(f\"\u2713 SUCCESS: Loaded demographics for {len(district_demographics)} districts\")\n",
    "        print(\"\\nDistrict Demographics:\")\n",
    "        print(district_demographics.to_string(index=False))\n",
    "    else:\n",
    "        print(\"\u2717 Query returned empty result\")\n",
    "        district_demographics = None\n",
    "except Exception as e:\n",
    "    print(f\"\u2717 ERROR: {e}\")\n",
    "    district_demographics = None\n",
    "\n",
    "# Geography by district (via inspections)\n",
    "print(f\"\\n{'='*80}\")\n",
    "print(\"Querying geographic features by district...\")\n",
    "print(f\"{'='*80}\")\n",
    "\n",
    "geo_district_query = \"\"\"\n",
    "WITH well_districts AS (\n",
    "    SELECT DISTINCT \n",
    "        api_norm,\n",
    "        district\n",
    "    FROM inspections\n",
    "    WHERE district IS NOT NULL AND api_norm IS NOT NULL\n",
    "),\n",
    "geo_counts AS (\n",
    "    SELECT \n",
    "        wd.district,\n",
    "        g.basin_name,\n",
    "        COUNT(*) as cnt\n",
    "    FROM well_districts wd\n",
    "    LEFT JOIN well_geo_features g \n",
    "        ON wd.api_norm = g.api_norm\n",
    "    WHERE g.basin_name IS NOT NULL\n",
    "    GROUP BY wd.district, g.basin_name\n",
    "),\n",
    "primary_basin AS (\n",
    "    SELECT DISTINCT ON (district)\n",
    "        district,\n",
    "        basin_name as primary_basin\n",
    "    FROM geo_counts\n",
    "    ORDER BY district, cnt DESC\n",
    ")\n",
    "SELECT \n",
    "    wd.district,\n",
    "    COUNT(DISTINCT CASE WHEN g.basin_name IS NOT NULL THEN wd.api_norm END) as n_wells_with_geo,\n",
    "    pb.primary_basin,\n",
    "    COUNT(DISTINCT g.basin_name) as n_basins,\n",
    "    COUNT(DISTINCT g.play_name) as n_plays\n",
    "FROM well_districts wd\n",
    "LEFT JOIN well_geo_features g \n",
    "    ON wd.api_norm = g.api_norm\n",
    "LEFT JOIN primary_basin pb \n",
    "    ON wd.district = pb.district\n",
    "GROUP BY wd.district, pb.primary_basin\n",
    "ORDER BY wd.district;\n",
    "\"\"\"\n",
    "\n",
    "try:\n",
    "    district_geography = pd.read_sql(geo_district_query, engine)\n",
    "    if district_geography is not None and len(district_geography) > 0:\n",
    "        print(f\"\u2713 SUCCESS: Loaded geography for {len(district_geography)} districts\")\n",
    "        print(\"\\nDistrict Geography:\")\n",
    "        print(district_geography.to_string(index=False))\n",
    "    else:\n",
    "        print(\"\u2717 Query returned empty result\")\n",
    "        district_geography = None\n",
    "except Exception as e:\n",
    "    print(f\"\u2717 ERROR: {e}\")\n",
    "    district_geography = None\n",
    "\n",
    "# Final status\n",
    "print(f\"\\n{'='*80}\")\n",
    "if district_demographics is not None and district_geography is not None:\n",
    "    print(\"\u2713\u2713\u2713 District aggregation COMPLETE \u2713\u2713\u2713\")\n",
    "    print(f\"  Demographics: {len(district_demographics)} districts\")\n",
    "    print(f\"  Geography: {len(district_geography)} districts\")\n",
    "elif district_demographics is not None:\n",
    "    print(\"\u26a0\ufe0f  Partial success: demographics loaded, geography failed\")\n",
    "elif district_geography is not None:\n",
    "    print(\"\u26a0\ufe0f  Partial success: geography loaded, demographics failed\") \n",
    "else:\n",
    "    print(\"\u2717\u2717\u2717 Both queries failed \u2717\u2717\u2717\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "643ea02d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n",
      "================================================================================\n",
      "\n",
      "Checking data availability:\n",
      "  district_demographics: \u2713\n",
      "  district_geography: \u2713\n",
      "  district_effects: \u2713\n",
      "\n",
      "\u2713 Merged data for 13 districts\n",
      "\n",
      "Complete District Characteristics:\n",
      "district  n_wells     avg_income  avg_ruca_code  avg_pct_minority  avg_poverty_rate  avg_ej_score  pct_metropolitan  pct_micropolitan  pct_rural  n_wells_with_geo primary_basin  n_basins  n_plays  coefficient  pvalue  pct_change\n",
      "      01    32382 -44687864.0623         7.1255            0.2672            0.1763        0.4970            0.2918            0.0131     0.6812             31838             8         3        2      -0.0924  0.0667     -8.8223\n",
      "      02    17223    -97913.4176         7.6108            0.2012            0.1395        0.4509            0.2435            0.0013     0.7482             17102             8         1        1      -0.3387  0.0000    -28.7299\n",
      "      03    16826  -3375276.0532         5.2039            0.2434            0.1228        0.4918            0.5231            0.1091     0.3582             16649             8         2        3       0.8251  0.0000    128.2135\n",
      "      04    21238  -1887605.1984         4.5855            0.2127            0.2489        0.5924            0.5313            0.2355     0.2222             20925             8         1        1       0.8693  0.0000    138.5278\n",
      "      05    10101     55658.2253         8.7672            0.1831            0.1270        0.4605            0.0660            0.1075     0.8140              9662             2         4        2       0.1488  0.0031     16.0496\n",
      "      06    24743    -78226.5235         5.9712            0.2200            0.1473        0.4934            0.2813            0.2452     0.4627             24410             2         2        2      -0.6460  0.0000    -47.5879\n",
      "      08   106371 -56597541.7157         5.9276            0.2474            0.1141        0.4963            0.3765            0.1595     0.4615            105874             5         3        2      -0.5725  0.0000    -43.5906\n",
      "      09    47422  -1558781.8820         4.6279            0.1320            0.0984        0.3763            0.6023            0.0345     0.3442             42771             3         3        1      -0.7472  0.0000    -52.6291\n",
      "      10    30981     68603.5885         8.5872            0.1545            0.1066        0.4574            0.0487            0.1349     0.7726             11638             0         2        0       0.5330  0.0000     70.4019\n",
      "      6E     6249     56451.2206         3.0494            0.2382            0.1971        0.5343            0.7531            0.1037     0.1413              6237             2         1        1      -0.3792  0.0000    -31.5572\n",
      "      7B    21726   -224729.5548         8.4244            0.0904            0.1225        0.4211            0.1217            0.0478     0.8103             20343             3         2        1      -0.3927  0.0000    -32.4766\n",
      "      7C    43167   -220859.9070         9.6275            0.4354            0.1125        0.5370            0.0455            0.0004     0.9525             42263             5         2        2       0.1876  0.0002     20.6369\n",
      "      8A    42122   -713670.0572         7.4827            0.1952            0.1369        0.5159            0.1708            0.2073     0.6198             40740             5         3        1       0.0687  0.1724      7.1150\n",
      "\n",
      "================================================================================\n",
      "CORRELATIONS WITH TREATMENT EFFECT\n",
      "================================================================================\n",
      "\n",
      "Correlations with Treatment Effect (% change in enforcement speed):\n",
      "        Variable  Correlation  Abs_Correlation  N\n",
      "    avg_ej_score       0.4868           0.4868 13\n",
      "avg_poverty_rate       0.3318           0.3318 13\n",
      "n_wells_with_geo      -0.3275           0.3275 13\n",
      "         n_wells      -0.2904           0.2904 13\n",
      "pct_micropolitan       0.2893           0.2893 13\n",
      "        n_basins      -0.2448           0.2448 13\n",
      "      avg_income       0.2321           0.2321 13\n",
      "       pct_rural      -0.1392           0.1392 13\n",
      "avg_pct_minority       0.1358           0.1358 13\n",
      "         n_plays       0.1002           0.1002 13\n",
      "pct_metropolitan       0.0350           0.0350 13\n",
      "   avg_ruca_code      -0.0295           0.0295 13\n",
      "\n",
      "\u2713 Found 3 variables with |correlation| > 0.3:\n",
      "  \u2022 avg_ej_score: r=0.487 (slower enforcement)\n",
      "  \u2022 avg_poverty_rate: r=0.332 (slower enforcement)\n",
      "  \u2022 n_wells_with_geo: r=-0.327 (faster enforcement)\n",
      "\n",
      "================================================================================\n",
      "\u2713 Correlation analysis complete\n"
     ]
    }
   ],
   "source": [
    "## Step 3: Merge with treatment effects and analyze correlations\n",
    "\n",
    "print(\"=\" * 80)\n",
    "print(\"ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "# Check if we have the data\n",
    "print(f\"\\nChecking data availability:\")\n",
    "print(f\"  district_demographics: {'\u2713' if 'district_demographics' in locals() and district_demographics is not None else '\u2717'}\")\n",
    "print(f\"  district_geography: {'\u2713' if 'district_geography' in locals() and district_geography is not None else '\u2717'}\")\n",
    "print(f\"  district_effects: {'\u2713' if 'district_effects' in locals() else '\u2717'}\")\n",
    "\n",
    "# Merge all district characteristics\n",
    "if 'district_demographics' in locals() and district_demographics is not None and \\\n",
    "   'district_geography' in locals() and district_geography is not None:\n",
    "    # Merge with treatment effects\n",
    "    district_chars = district_demographics.merge(\n",
    "        district_geography, \n",
    "        on='district', \n",
    "        how='outer'\n",
    "    )\n",
    "    \n",
    "    # Merge with treatment effects from our DiD analysis\n",
    "    if 'district_effects' in locals():\n",
    "        # Calculate percent change from coefficient (coefficient is in log scale)\n",
    "        effects_for_merge = district_effects[['district', 'coefficient', 'pvalue']].copy()\n",
    "        effects_for_merge['pct_change'] = (np.exp(effects_for_merge['coefficient']) - 1) * 100\n",
    "        \n",
    "        district_chars = district_chars.merge(\n",
    "            effects_for_merge, \n",
    "            on='district', \n",
    "            how='left'\n",
    "        )\n",
    "        \n",
    "        print(f\"\\n\u2713 Merged data for {len(district_chars)} districts\")\n",
    "        print(\"\\nComplete District Characteristics:\")\n",
    "        print(district_chars.to_string(index=False))\n",
    "        \n",
    "        # Calculate correlations with treatment effect\n",
    "        print(\"\\n\" + \"=\" * 80)\n",
    "        print(\"CORRELATIONS WITH TREATMENT EFFECT\")\n",
    "        print(\"=\" * 80)\n",
    "        \n",
    "        numeric_cols = [\n",
    "            'avg_income', 'avg_ruca_code', 'avg_pct_minority',\n",
    "            'avg_poverty_rate', 'avg_ej_score',\n",
    "            'pct_metropolitan', 'pct_micropolitan', 'pct_rural',\n",
    "            'n_basins', 'n_plays', 'n_wells', 'n_wells_with_geo'\n",
    "        ]\n",
    "        \n",
    "        correlations = []\n",
    "        for col in numeric_cols:\n",
    "            if col in district_chars.columns:\n",
    "                # Drop NaN values for correlation\n",
    "                valid_data = district_chars[[col, 'pct_change']].dropna()\n",
    "                if len(valid_data) > 2:\n",
    "                    corr = valid_data[col].corr(valid_data['pct_change'])\n",
    "                    correlations.append({\n",
    "                        'Variable': col,\n",
    "                        'Correlation': corr,\n",
    "                        'Abs_Correlation': abs(corr),\n",
    "                        'N': len(valid_data)\n",
    "                    })\n",
    "        \n",
    "        if correlations:\n",
    "            corr_df = pd.DataFrame(correlations).sort_values('Abs_Correlation', ascending=False)\n",
    "            \n",
    "            print(\"\\nCorrelations with Treatment Effect (% change in enforcement speed):\")\n",
    "            print(corr_df.to_string(index=False))\n",
    "            \n",
    "            # Highlight strongest correlations\n",
    "            strong_corr = corr_df[corr_df['Abs_Correlation'] > 0.3]\n",
    "            if len(strong_corr) > 0:\n",
    "                print(f\"\\n\u2713 Found {len(strong_corr)} variables with |correlation| > 0.3:\")\n",
    "                for _, row in strong_corr.iterrows():\n",
    "                    direction = \"faster\" if row['Correlation'] < 0 else \"slower\"\n",
    "                    print(f\"  \u2022 {row['Variable']}: r={row['Correlation']:.3f} ({direction} enforcement)\")\n",
    "            else:\n",
    "                print(\"\\n\u26a0\ufe0f  No strong correlations (|r| > 0.3) found with demographics/geography\")\n",
    "        else:\n",
    "            print(\"\\n\u2717 Could not compute correlations (no valid numeric columns)\")\n",
    "        \n",
    "    else:\n",
    "        print(\"\u2717 district_effects not found in workspace\")\n",
    "        district_chars = None\n",
    "else:\n",
    "    print(\"\u2717 Could not load demographics or geography data\")\n",
    "    print(f\"  Trying to print what we have...\")\n",
    "    if 'district_demographics' in locals():\n",
    "        print(f\"  district_demographics type: {type(district_demographics)}\")\n",
    "        if district_demographics is not None:\n",
    "            print(f\"  district_demographics shape: {district_demographics.shape}\")\n",
    "    if 'district_geography' in locals():\n",
    "        print(f\"  district_geography type: {type(district_geography)}\")\n",
    "        if district_geography is not None:\n",
    "            print(f\"  district_geography shape: {district_geography.shape}\")\n",
    "    district_chars = None\n",
    "\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"\u2713 Correlation analysis complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "20b02145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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PkqTrr79eP/7xjzu1f6fTGf0cDofV0NAgt9vdqTkAAAAAAAB0B42hgA40VGjBjs/0yqaFHdJHfahRf1kzR7WhBpVXHmzxmMNN/H39LZL/qba29qhy8vv92rlz5yHtgwYN0syZM48qJgAAADoe85gAupqKxmqV1Vfo0ZX/1PaqPR3Sx6aKnXro83/ojlOuYlwNAAAAAOgUhmGoT58+6tOnT7O222+/XX6/X0VFRdq7d2+74x44cEC//OUv5Xa7lZeXJ5/Pp1NPPVUDBgyIYfYAAHQvFIeimYcfflhPPfWUJOnGG2/U3Xff3ek5fH3ywel0MqEKAAAAAADQAUKRsA40VGrxruUdVhj6lYhMvbDubYUqy9p9bkc8xPrV/a+vS0lJ0RNPPCGHw3FUMQEAANCxmMcE0NVUB2p1sLFKj305u8MKQ79SWn9Q//PF86qurW73uYyrAQAAAACx4HK5NHnyZE2ePFmSVFZWpr/+9a/68MMP2x2rvr5eK1as0IoVK9TQ0BAtDl29erUkadiwYc1e1AYAAFpHcSiiHnjgAf31r3+VJN1888264447Wj12zZo1Ki4uVk5OjnJyclo85k9/+pO2bNmiK664QqeddlqbcqioqFBNTU10Oysrq+0XAAAAAAAAgDYxTVMHG6u04eAOvbhhQef0KVP7Ay2vcHI4kUik1X0ej6fd8RYsWKD58+c3a+vZs6eeeeYZDRo0qN3xAAAA0PGYxwTQ1QTDIVUFavXCure1rbK4U/osrT+oikB5u89jXA0AAAAA6Ajp6en64Q9/qB/+8IeSpJ07d+rFF1/UBx980K44Pp8v+vn555/X2rVrZbPZNGTIEGVlZcnr9Wr69OlKS0uLaf4AAJwoKA6FJOm3v/2t/v73v0uSbr31Vt12222HPf7555/XnDlzDnvsJ598omXLliknJ6fNk6pffvlls+1x48a16Tyc+EzT1NKlS/XRRx/p888/V0lJiQ4ePKhwOKyUlBSlpqYqIyNDOTk58vl8Gj16tDIzM5vFeOyxx/T444/HPLf7779f06dPj3lcAAAAAAA6Sm2wXjWBOj239g2ZMjutX8NlbbE9HA63ek4sH2Jds2aNfvSjHzVrO+mkk/TYY48dch8BAAAAXQPzmOjqmMfsfr564dKq0o1aus/fqX3XW4MttjOuBgAAAADEW79+/XT33Xfr7rvvliTt379fL7/8st55551Wz7FarRo+fLgkKRAIaOPGjZKkUCikdevWad26dZKkuXPnRs+ZOnWqrrzySsahAAD8C8Wh3ZxpmvrVr36l559/XpJ0xx136Oabb45pH8uXL9d3vvOdNh376quvNtu++OKLY5oLjk+ffPKJfv/732vt2rXN2pOSkuT1elVWVqb9+/dr/fr1WrJkiSRp+PDhzQYCkmQYhqzWpodQTdM87CQYAAAAAAAnqrAZUVWgVnM2L1ZpfftX8jwWtkRni+3BYMsPtx5pX48ePdrc95dffqkbb7xRtbW1kpruE8yaNUs/+MEP5HA42hwHAAAAnYN5TBwPmMfsnmqD9apsrNHz697q9L4ZVwMAAAAAjhe9evXSd7/7XX33u9+VJJWXl2vt2rXatm2b/H6/Nm7cqMGDB8vlckmSNmzYoFAodMS47777rt59993o9hlnnKGZM2eqf//+HXMhAAB0cRSHdmOmaeq+++5TYWGhJOnuu+/WjTfeGPN+Pv74Y3355ZcaOXLkYY/79NNPm/2hNnHiRI0ZMybm+eD48tRTT+nhhx+ObttsNl133XX65je/qb59+0pqegvqhx9+qIcffjj6xpiW3Hrrrbr11lslNU3g33PPPYcck56erkceeaRNuX3/+99XWVlZey4HAAAAAIC4qwvWq6y+Qh/s+rzT+3ZmeVtsr6+vb/Wc1vY5HA4NGjSoTf2+9dZbuueee9TQ0CBJyszM1G9/+1udfvrpbTofAAAAnYt5TBwPmMfsnkzTVE2wXu/tXKqqQG2n98+4GgAAAABwvEpLS9PEiRM1ceJESU0rhR48+O+XGRcVFR1V3A8//FAffvhhdPvuu+9Wfn6+0tPTjy1hAACOExSHdlOmaepnP/uZXnrppWjbQw89pIceeqhD+vrud7+rRx99VKNHj27xmCVLlujOO++UaZqSpD59+ujBBx+MeS44vvznhKok3X///brkkkuatVmtVp199tkaOXKkZs6cqe3btx91n06nU+PGjWvzsQAAAAAAHE9M01RtsEEfFq9URGan9+/sk9Rie319vSKRiCwWyyH7vlqR5D8NGzZMNtvhb2+GQiH97//+r55++unofafp06frJz/5ibzelh+oBQAAQHwxj4njAfOY3VdjOKBAOKiPilfGpf94jKsfeeQRPfPMM4yrAQAAAAAx5XA41KtXr+j2FVdcoZEjR8rv98vv92vFihVHFfer+4i9evWSz+fT1VdfTaEoAOCERnFoN1VcXNxsQrUjDBgwQMuWLZMklZWV6b/+6780fvx4TZgwQb1795Yk7d27V0uWLIkeJ0kjR47UI488oh49enRofuja1q9fr0cffbRZ24QJEw6ZUP26lJQUffvb327xTboAAAAAAOCrh1gD+mTPl3Hp35nplb1HgoIH6pq1m6apiooKpaWlHXJORUVFi7HOOeecw/a1d+9eff/739cXX3whSerZs6d+/etf6+yzz271nFGjRkmSnn76aZ166qmHjQ8AAICOwTwmujrmMbu32mC9Vpauj8uqoVLnjqv37dune++9V6tWrZLEuBoAAAAA0LFsNpuGDx+u4cOH64orrlAoFNKiRYs0e/ZslZaWtjve/v37VVJSohtvvFFS0wuQHn30UeXl5Sk/P199+/aVYRixvgwAADodxaHoML/61a90/fXXa+7cuVq8eLE2bNigTz/9VJ9++mmLxw8fPlyzZs3SpZdeesS3U6JrCUciCkSCCoZDCkSCCkXC0beGGoYhq2GVw2qTw2qXw2KT1WI9Yszf/OY3CgaDzdouvfTSI543ffp0TZ8+/aiuo71yc3OVnp7e4gQbAAAAAABdUUM4oPXl2+P2EKskJZ3aRwfmbzqkvaSkpMUx9r59+w5ps1gsh71P8Pbbb+vnP/+5qqqqJEkXXXSRfv7znys5OfmwudXVNT1cGw6HD3sc8J9mzZrVrHDkaC1fvlxJSf9eCeixxx7T448/flSxHA6HEhISlJGRoSFDhmjChAmaMmXKEb8HAAB0B8xjdh9fzWMGwiEFmcfEMTJNU43hgJbvK4prHp0xrn7vvff0m9/8RtXV1ZK65riacRgAAAAAnNhsNpumTp2qqVOnSpIikYg++ugjzZ49W7t27WpTjIEDB8rj8UiSNm/erMWLF2vx4sWSpOTkZFVWViorK0sFBQU666yzZLUe+d4QAABdDTNX3VTfvn21YcOGDu9n0KBBuvPOO3XnnXeqvLxcGzZs0M6dO1VVVaVwOKykpCRlZGTo5JNPVs+ePTs8Hxw7wzDkdDplsVjUEAqoJlin+lCjQpGwdlWXaHvVXu2tLVNjOCjTNOWw2tUrIU0Dk7OU7e0lp9Uul9Uhj90tt83Z4htXtm3b1uIkzpgxYzrjEtvsiSeeiHcKAAAAAAC0SzAc0vaqvXHNIeW0fqr4eIfCNYFm7Rs2bNDw4cMPOX7TpkMfeJ0xY4YyMzMPaa+rq9Ovf/1rvfLKK5KkHj166Be/+IWmTJkSo+yBzpWcnKx+/fpJkmpra3XgwIFDjrHZbNEVzoLBoCorK1VXV6dAIKBAIKCKigpt3LhRb775pu6//37ddtttuu666zrzMgAAaBfmMXGsvirgqwnWqz7UqGAkrN3V+7Wtaq/21hxQY7hpLOK0OtTLk6oBSVnq582Uw2qTy+pQoiNBLquDeUwcIhQJK2Ka2nGCj6t/9atf6dVXX5UkpaWl6b//+7917rnnxij7ro9xGAAAAAB0TRaLRWeeeabOPPNMSU33gD7//HMVFha2ej8xPz8/+tnv9zfbV1lZKUnau3ev/vCHP+gPf/iDJCklJUUFBQWaMmWKHA5HR1wKAAAxRXEoOk1aWprGjx+v8ePHxzsVHCW73S673a6GcEBldVUKhAP6omSjFu1aoQ3lOxQ2I4c93yJDg1L6aFL2KRqbmS+X1aEkh0eJDnez4+bOnXvIuS6XS1lZWTG9nvb66s2jt956q2677ba45gIAAAAAQHuZpqlgJKid1fF9iNWaYFfGtDztff7LZu1vv/22pk2b1qxt//79+uKLL5q1ZWZm6u67724x9l133aVFixZFtw8cOKBbb701NokDcXDNNdfommuukSS9+uqruueeew45plevXlqwYEGztl27dmnevHl6+umnoyv3SFJNTY3uv/9+7d+/Xz/60Y86NnkAAI4jzGOeOOqCDaoM1CgQDmrF/g1N85gHdyrShnnMwSl9NCl7tMZm5slpdSjZ6ZHHzjwm/i0YCao6UKeKxuq45tGZ4+ry8nLdfvvtsUn8OME4DAAAAACOD4ZhaMyYMc1e2lVSUiK/3y+/36+ioiL5fL7ovqKiojbFraio0J///Gf9+c9/ltR072fmzJm64IILlJCQENuLAAAgBigOBdAmFotFNrtNBxurVRtq0Lvbl2r+jqU62I6Jr4hMba7Yrc0Vu/XP9Qv0jexTdfGg01Uf8ijV5ZXNYpUkrVy58pBzk5OTZRiGtmzZojfeeEOrV69WaWmpTNNUamqq8vPzNXnyZJ166qkxu2YAAAAAAE4kYbNphZNd1fvinYq8J2UqVJ2r0tfXRdsWL16sp556Stddd50cDof27dunu+66S4HAv1dCycjI0LPPPiuv19ti3Jqamg7PHTgeZGdn65ZbbtG4ceN09dVXKxJpXgzxt7/9Teeee65GjhwZnwQBAABiLByJqKKxWjXBOs3fvlTv7ljW7nnMTRW7temrecx+TfOYifZG5jERFYyEtKtmf7zTkMS4uitiHAYAAAAA8ZeRkaFvfOMb+sY3viGp6QXKX6mtrT2qmA0NDfr73/+uv//975KkgoICjRgxQsOGDZPT6Tz2pAEAOEYUhwJok8ZwUAfqK7WrpkRPr35N26v2SoZx1PFqgvV6feuHWrZvrb414mINSe2nNKdXCXaX1q5de8jxkUhEd911l954440W4y1dulR//etfddppp+nBBx9Ur169jjo3AAAAAABORJF/TXzVBuvjnEmT1NP7KyUjTTVvb9Xe4qbVTB9++GH98Y9/VFJSkkpLS5s9SHnOOefo3nvvVe/eveOVMk5QoUhIwUj4X98RU4YMWQ2L7FabLIal0/JwuVwxn0AePXq0Jk+efMiKNqZpas6cOTyUDAAATggNoYDKGyq1s3q/nl7zetM85jGoDtZp7pYlWr5vrb414hLlpPRVmitJbpuTecxuLmKaqg3UHfnATpJ6en9NGH6qlj2/UHuL90g6PsbVEdNUMBJSOBKWKVOSIYsM2SxW2SxWGcfwHEJ7MQ4DAAAAgBPb18eYDz74oKqqqlRUVKSioiLNnTv3qGLOnj1bs2fPls1m0xlnnKHvf//7sUoXAICjQnEogCOqDzXqQH2l3tu5XC+se1fBSEiWGE3I7Ks7oN8u/bsuGjRR04ecpdLSMlVVVR1yXGlpqd544w3l5+fr17/+tXJycrRx40b99Kc/1bp1/34b6meffaYrrrhChYWFcZ/UAgAAAACgK2l64FIKRcJxzuTfvHm99Oj1/601n36pJe9/oLVr1+rAgQMqLy9Xjx491KtXL5122mk699xzddJJJ8U7XZwgQpGwaoP1agwHFQgHFTFNBSJBNYQCipim7FabPDaXJMlutclhsSnB7pLL6jjiQ8q33nqrbrvttjbncs0112jp0qWSpBkzZnTI24VPOeWUQx5KltRiYQMAAMDx5qt5zHd3LFPhhvcUMmM33tlTW6Zff/Y3Tcs5U9MGnyGzJsg8ZjdnSjH9NxYLA08dptun36BVn3yhRYsWddlxdUMooLpggwKRoALhkCSpNtSgUKTps8vqlNNql8UwZLfY5LQ5lGh3yWY58mNNjMMAAAAAAG2VlJSk8ePHa/z48frWt76lkpISvfLKK3rrrbfaHSsUCjUbU86bN0+LFi2Sz+eTz+dTXl6evF5vLNMHAKBFFIcCOKyGUEAH6is1d8sSvbxxcYf0EZGp17d+qOpArSYnjmj1OKvVqscee0x9+vSRJPl8Pj322GOaOnVqszeelpSU6I477lBhYWG73ypaXFysYcOGHd2FAAAAAADQhRlqGiPbLDaFw4E4Z9PEZrHK4XTqwgsv0LSLLznmeM8991wMssKJqiEUUHWwTvXBRu2rPaAVJRu0rXKvtlXuVUndwWgBtSS5bU4NSMrUgOQsDU3N1ikZw+S02pVodyvR4Y7JiqIrVqyIPpDscDj0rW9965hjtiQxMbHF9pYKGwAAAI4nDf8qDH118weau2VJh/QRkak5mz9QVWOtJnnyWz2OeczuwZDaVKzYmayGVU6nSxdeeKEuvPDCY4733HPPKRwOq67u3yukJiQkyGq1tjtWxIyoNtigmkC9ApGgVpVu1vryHdFxWF2oodnx6e5kDUzurYFJWRqVMUTZ3l5y2RzyOhLktsWmgJNxGAAAAADg6zIyMnTzzTfr5ptvliRVVFRo7ty5evnll9t0vs/ni35evXq1Nm/erM2bN+u1115rdtzEiRM1Y8YMDRw4MGa5AwDwla511xpAlxKKhFVWX6F3tn+mVze93+H9Ld79hQ6GSlrdP3r06OiE6leys7M1atQorVixoln7qlWrtHDhQp1zzjntysFms7X6pt79+/ersbGxXfEAAAAAAOgqvipmS3J4VFrfNYpDkxweSZIRg0I7oDXhSFgHG6tVE6jXipINem/H5yo6sO2w59SHGrWufIfWle/Q29s+U7LDo7OzT9E3+p2inu5Upbm8SrC7jimvP/7xj9HPl19+uXr16nVM8VpTWlraYnuPHj06pD8AAIDO0DSPWak3t33SYYWhX7dw1+eqDO9vdT/zmN2DxbAoyZEQ7zSaSXIkxOTlNbFWH2pUeUOVDjZUa/GuL7Ro5xc60FB52HPK6itVVl+p5fvW6cWNizQkpa+m9B+jsVl5SrS7leZKks3S/iLVr2McBgAAAAA4nJSUFF177bW69tprJUm1tbV68803NXv2bAWDQTmdzmb3YPLzm14mZpqmioqKWo370Ucf6aOPPopujx49WjNnzlRubm4HXQkAoDuhOBRAqw42VGnDwZ2aveG9TutzafGaVve19gdwXl7eIZOqkvT666+3e1K1V69eWrBgQYv7Zs2apWXLlrUrHgAAAAAAXYXVsMhiWJTt7aXS+oPxTkeSlO3NlNVilbULPsiKE0NtsEEHG6q1rXKPnlw9VzurW3+g/3AqA7Wau+VDvbH1Y12Wc6YuGTxRiQ63Up1JslosuuWWW1RQUKAhQ4a0Kd7q1aujE8B2u1033XTTUeXVFu+//36L7WeeeWaH9QkAANCRTNNUeUOV1pVv10sbF3Vavx/vXt3qPuYxuwe7xaZ+3qx4p9FMv6Qs2bvQaqYRM6KKhhrVBOs1f/tSFW5cpEA4eFSxNlXs1qaK3Xpl0wf69knTNDytn1KciUr8V4Eu4zAAAAAAQEfzeDyaMWOGZsyYIUkKhULasmWL/H6/du/erfT0dEnSzp07VV1d3ea4K1asaHbPKDc3VwUFBRo1apQMw4jtRQAATnhd5w4xgC6lJlCvqkCd/uJ/XabMTuvX4mz9Z6m1t3ZmZma22P7FF1/EJCcAAAAAAE4EhmHIbrGpf1KWvihZH+90JEn9kjLl6EIPseLEUtFYo4qGar22+UPN3fKhwmbkmGOGzYhe3vS+Pt+/Qd8+aZoGJmepZ0KKxo8f3644X1+t5tJLL211Bahj9Y9//EOrVx9axDBgwABdffXVHdInAABAR6sN1qsqUKtn/POYx0Snclhtctuc6uFKPuIqmJ3BaljUJzFDDmvXGFeHI2GV1FeouLpUT62Zq/XlO2MSd39duX699FlN7T9WM4dNViAcUqrLyzgMAAAAANDpbDabhg0bpmHDhjVrj0QiGj9+vPx+f7uKRL+ybt063XfffXK5XBo7dqx8Pp98Pp/69u1LsSgA4Ii6xh1iAF1KxIyoIlCjVzYtVkld564kYrha/1lKTExssd3j8bTYXlZWpnA4LKvVGpPcAAAAAAA43jmsdg1I6piHH4/GgKTeslvs8U4DJ6CDDdU60FCpP6x4UWvKtsQ8/vaqvbrv02d05ykzNDJjiDISUtu8Ws+6deu0ePFiSU0TyN/+9rdjllcwGFR5ebmKior0yiuv6L333jvkmAkTJuh3v/tdq/faAAAAurKIGVFFY41e2rhQpfUVndo385iwGlZZDIsGJPfpEsWhfRJ7yWpYusS4OhwJa3/dQa0v36Hff/6CaoMNMY0fMU29s32pNlcU6wdj/kumTKW5ktr8gCzjMAAAAABARxo4cKB+8pOfyDRN7dq1S6tWrdJTTz3V7jgNDQ1asmSJlixZIkm64447NHnyZElSaWmpevToIYvFEtPcAQDHP4pDARyiNtigysZqvbdzeaf3bfU6JZtFCh26koPN1vJPlt3e8mSXaZqqr6+P2QTLc889F5M4AAAAAADEi8vq0JDUfkpzJam8oSquuWR5eqqfN1MumyOueeDEU9lYq/KGKv1++QtaV76jw/oJhIN66PN/6q5TC3RKxjD1SkiV1XLkh/v/9Kc/RT9fdNFFys7OPqY8iouLD3k78dcZhiGfz6exY8fq3HPP1cknn3xM/QEAAMRTbbBeBxurtWhX56+8yTwmDMOQy+bQaZk+rdi/Nt7p6LQsnxxWu6xxfig0YkZUUl+h9eU7dP+y59QYDnZYX5srduu3S/+he8ddI4thUarL26bzGIcBAAAAADqDYRjq16+f+vXrp4svvlimaeqLL77Q7NmztX79+nbHy8vLk9R0L+nuu+9WY2Oj8vLy5PP5NHDgQOXn58vhYL4dALo7ikMBHKI2WK/3d61U2Dx0YrOjGVaLHFleBXYd+qbVcDjc4jmhUKjFdqvV2urbeAEAAAAA6I6cNoccFrsm9hml17d8ENdczup7ipxWuxzW+K9wghNHYyigysYa/XHVnA4tDP1K2Izof754ST8/7TrZLTb1TEg57PGbNm3Su+++K0myWCwxWa3GZrOpd++mFYHD4bCqqqpUXV0d3W+apvx+vw4cOKDy8nJVVlbqjDPOaPMKOwAAAF2FaZqqCdZr8a4vFGEeE3HisbvlS89RD1dyXFcPdVjsGt/7ZHns7rjl8JWKhhoVV5fqweUvdGhh6Fd2VO3TH1YU6ifjrpHL5pDb5jzs8YzDAAAAAADxYhiGRo8erdGjR0fbioqKVFhYqJUrVx723LS0NGVmZkqS9u3bp/LycknS8uXLtXx58wWgHA6HZs6cqQsvvJB7TgDQDVEcCqCZhlBAjeGgPtjd+W/b/YpzYGqLk6p1dXUtHl9fX99ie2ZmJpMrAAAAAAD8B4/drYm9R+mtrR8pZLb8AHNHc1odOi1rhDz2hLj0jxNTxDRV3lClj4pXafm+dZ3WbyAc1J9XzdVvJt6khKDzsA9n//nPf5ZpmpKk888/X4MGDTrm/nv16qUFCxY0a9u/f78WL16sJ598Unv27JFpmtqzZ4/mzJmjOXPmyOfz6YEHHlBOTs4x9w8AANBZGsNBBZjHRJw5rQ7ZLTad2Xe05mxeFLc8xmb5lGBzKcHmilsOklQfalRNsF5Pr3lddaGGTut3XfkOvbXtU10yeKKyPD1kMVpfPZVxGAAAAACgK8nPz9cvf/nL6PbWrVtVWFgou92u7du3a8eOphfg+ny+6P0jv99/2JiBQEDPPfecnnvuuWjbFVdcoUsvvVTJyckdcBUAgK6k9bujALqlxnBAWyv36EBDVdxySBjdp8X2kpKSdrWPHTs2Zjm15KmnntKsWbP01FNPdWg/AAAAAADEUoLdpWRnoqYOGB+3HC4edKY8dvcRV/cA2qOqsUal9ZX6x9r5nd737poSzdn8gQ42VCscabnoetu2bXrrrbckNb0l+Oabb+6wfHr16qWCggLNmzdPPp/vkP1+v19XXXWVNm/e3GE5AAAAxFpjOKBNFbtV0VgTtxyYx4QkJToSdHb2aPV0p8alf7fNqYsGnqFEuzuuRcYRM6KDDdV6d8cyrSvf0en9v7Lxfe2uLtXBhupWj2EcBgAAAADo6gYNGqR77rlHd999tx5//HG98MILuvfee3XRRRdFjzlScWhLXn75ZV199dW6+OKLdfHFF+sf//iHamtrY5k6AKCLoDgUQDOBSEjbq/bENQd7ukeu4T0Pad+0aVOLx2/cuLHF9ksvvTSWaR1i69atWrZsmbZu3dqh/QAAAAAAEEsWw6IUp1cXDjxDfRMzOr3/wcl9NbnfWKU4vayUg5iJmBFVB+v1/Lp3O3W1mq+bt+Vj7akpU3Ww5dWhnnzySUUiEUnS1KlTNWTIkA7PKTExUQ888ECL37WKigr99Kc/7fAcAAAAYiUQDmpH1b645sA8JiQpweaS1+HRrLyLZKjzx7Uzhk5VujtFXoen0/v+utpgg8obqjR7w8K49B+IhPS3ojdVG2xQqJWX9DAOAwAAAAAcb7xer0477TTl5uZG26ZMmaIrrrhCw4cPl9VqPaq4L730kr75zW/qjjvu0F/+8hd99tln0TEzAOD4RnEogGaC4aC2V8Z3UlWSUqflynDZmrUtW7ZMlZWVzdrKy8u1bNmyQ84/66yzdNppp3VojgAAAAAAHK8S7C557G5dm3+xrEbn3SJ0WOy6Nv9ieexuuVg1FDFUE6xXeUOVlu1bG7ccwmZEC3YuV22wXqZpNtu3a9cuzZs3L7rdkavV/KecnBwNGzasxX0rV66kYAAAABw3ApGQtlXG9yW3EvOYaFqBMtWZpGGp/XV29qmd2veI9ByN732SUlxJcX/hUk2gXot3faFAOBi3HNYe2K7dNSWqaeElPYzDAAAAAAAnCp/Pp2uvvVa///3vNXv27KMe45qmqS1btmju3Ll67LHHovcWKisr9dFHH+ngwYOxTBsA0ElsRz4EQHcRNiMKmxHtqt4f71RkTXIp/ZpTdOC5lYrUN00mNTQ06Ec/+pF+97vfKSUlRQcPHtSPfvQjBQKBZufm5OTo97///VH129jYqE8++aRNx5aWlh5VHwAAAAAAdAUpTq/6e3vr+vxp+qv/NUVkHvmkY2A1LLrppOnK8qQr2ZnYoX2hezFNs+mh5J1fKGzG9+22H+5erZnDJqsu1CiP3RVtf+qppxQKhSRJkyZNavam387Qv39/rV+/vsV9mzdv1qBBgzo1HwAAgPYKRyKKMI/JPGYXYrNYlexM1BVDzlFpXbn8B7Z0eJ/9vJn6f75LlWhPkNNq7/D+DqchFFAgEtSinV/ENQ9JWrBjufp5M5Xs8DQrmGUcBgAAAAA4EblcLl1wwQW64IILJEl1dXV68803NXv27EPuRR2Oz+eLjqNXrlyphx9+WJLUp08f5efnq76+XhdeeKHy8/NjfxEAgJiiOBRA1FcrGtSGGuKcSRNn/xT1+vZY9f60USuWfi5JWrx4sSZOnKgePXrowIEDCgb//RZSi8WiSy+9VD/96U/l8XgOiffYY4/p8ccfP2yfZWVluv7662N7IQAAAAAAdEFWi0Xp7hSNycxX2Azr70XzOqxA1Gax6lu+y3RS+lClu1Nk6cTVSnHiC0RCCkXCWrwr/g8l14Ua9NmeIp07YFy0OHTv3r2aM2dO9Jhbbrml0/OyWFr/zv3nKqcAAABdUURNLwGpCzXGOZMmzv4pyvrOeGV+Usc8ZjfmsbsVjoR100lX6MnVL6uoAwtE+3kzdduobyrNlaQkx6H/hjpbXbBBq0u36EBD5ZEP7mAfF6/RVblT1RAOyG1zSmIcBgAAAADoPhISEnTllVfqyiuvlCQFAgG9++67mj17tiorWx+3+3y+6Ge/3x/9XFxcrOLiYknShx9+GG0fNmyYCgoKNHr06GYvZwIAxB/FoQCizH89AGrGeYWFr7P29OiBJ+7Xng079OH7S7R06VLt27dP5eXlslqtSk1N1aBBgzR69Ghddtllys7OjnfKAAAAAAAcN+xWm9LdKRqfdZIS7Qn6+9p5qgrUxrSPNFeSrsufpuFpA5TuTpHNwi1JxFYgHNT+unIdbKyOdyqSpA0Hd2pSv1Oi208//XS0MGDixIk66aSTOj2n3bt3t7ovKyurEzMBAAA4NuFIF5rHTHfrgSd+rb0bdmgJ85jdVpIzUZJ0y8kz9PKmBXp/14rovHusnJIxXLPyLlKaK1mpTm+XeAAzEAlqXfn2eKchSWoIB7Sjap96ulOixaGMwwAAAAAA3ZXD4dBFF12kiy66SJIUCoW0cuVK7d27V0VFRfL7/aqqqmq2IujXi0Nbs2HDBv3iF7+Ibvfr108FBQU6/fTTD/uCJABAx+NJLABRhpomkawWa5wz+Teb0ZTLyJEjNe7UsccU67bbbtNtt90Wi7QAAAAAADhhOKx29UxI1ciMYRqY3Ef/3PCOlu8riknsib1H6oqh5yjJkahUV5JsXeieA04cgXBI26v2xjuNqK2VexSORBSOhHWg7IBefvnl6L7vfve7nZ7Prl27tHbt2hb39ezZs9nELwAAQFf11TymvQuNKb568c3Jo0ZpLPOY3VqSM1FWi1UFw87TqIzh+sfaN1RWX3HMcRPtCfrm8PM0uleuvHaPvI6ELlEYapqmgpGQtld2nXHYtsq9Oik9R5JUUlLCOAwAAAAAgH+x2WwaM2aMJOmSSy6RaZravXu3+vTpI0mqqKiIrhTaHjt37tSDDz4Y3T733HN1/vnna+DAgRSLAkAnozgUQJTFaPpDLNXpVUndwThn0yTF5ZUk/kgEAAAAAKAD2Sw29XSnymVz6gbfpTot6yQt3LFU68q3tXvFE4sM5acP1pT+p2lYan8lORLlsbu7xAOsODEFI0Ft7UIPJRfXlCoYCSkQCemZZ55RY2OjJOm0007TKaeccoSzYysQCOinP/2pwuFwi/tvuukmWa1dp8ACAACgNZZ/jSdSXF6VNVTGOZsmKa6mFSOtBvOYkDx2t5xWh+wWm3522o1atHO5Piz+QuUNVe2OlWhP0ITeJ2tK/3FKdSYr1eWVw2rvgKyPTiASkmlK27rQS3q2V+5VINK0UijjMAAAAAAAWmcYhrKzs6PbycnJeuqpp6Krivr9fu3fv7/dcefPn6/58+crISFBeXl5mjJliiZMmBDL1AEAraA4FECUxTBkt9g0IClLGw7ujHc6kqQBSZkyZERXEAUAAAAAAB3DMAwlOTxyW51yZTiV32OQSurK9WHxSm0o367imhKFzUiL59osVvVJzFBu2kCd0ecU9XAny21zKsnhia6mA3SUUCSi0rqKeKcRFTFNlTdUydFgqrCwMNp+yy23dFoO5eXlWrJkiZ566ilt2bKlxWOuvPJKzZo1q9NyAgAAOBYWwyKbxaoBSVnaXLE73ulIkgYm9Y7mBUhNY+N0d4oS7C5dPOhMnTtgvNaUbdZne9doR9WewxaKJjk86peUpTG98jS6V56cVrs8drcS7V1jtdCvC0fCqgs1qDbYEO9UokrrKxSORHTgwAHGYQAAAAAAtINhGMrKylJWVpbOOeccSdL69etVWFiozz//vN3x6urq9Pnnn2vEiBHRtrfeektVVVXy+XwaOnSoHA5HzPIHAFAcCuA/2C02DUjOincaUQOTesthtXe5CS8AAAAAAE5UdqtNPdzJCkfC8to9yvL0VCgSUtiMqLimRPvryhUMhyRJDqtNmZ4e6u3JkMUwZLPY5LG7lGBzy2ph9Rx0DlOmQpFQvNNoJhAO6rn/e0719fWSpNGjR2vcuHHtjvOPf/xDzz33nCSptra2xWP279+vKVOmSJLC4bBqampUWdn6alqDBg3SzTffrEsuuaTd+QAAAMSTw2LXgKSuM485IClTdl6Gg/9gGIY8drc8drcaQwGdljlCI3sOU8SMqCZYp53V+1UdqFUoEpLVsMpjdyvb20spTq8Mw5DT6pDH7pbL6uiyc+SmpGAXG4M1hptWDf3r3/7KOAwAAAAAgGM0fPhw3XfffdHt7du3q7CwUB999FGbY+Tn50c/v/3229q+fbskyWazyeFwqK6uTtOnT9dll12mlJSUWKUOAN0SMxUAmnFY7Rqc3DfeaUQNTukjB5OqAAAAAAB0OqvFqiSnR0lOj8KRiIKRoFJdScqNhGXKlCQZMmSzWGW32OWw2mQxKAhF5+uKj0vXVtfo5cKXottHu1pNZWWldu7cedhjQqHQIcfYbDY5nU4lJCQoIyND2dnZys3N1YQJEzRixIgu+5A5AADA4TisNg1O6RPvNKJyUrLlsNrjnQa6MKfNIafNIdM0FTLDSgsnK9OTLtM0/zWuNmQxDNkttuj/Hi9/qxtdbCRmMSyqqqzSP1/4Z7SNcRgAAAAAALExYMAA/ehHP9KPfvQjSdLevXv10ksvacGCBS0e73K5NHjwYElSdXW1duzYEd0XCoUUCjW9dOrVV1/Vq6++Gt13/vnn64orrlBGRkZHXQoAnJCouALQjNvmVEZCioal9tOGg4ef8OhoGe5UDUvtL7fNGdc8AAAAAADo7qwWi6wWp1xijI6uxzAMuWyOeKfRTHpqDy357GMlOtzHFOe2227TbbfdFqOsAAAAjm9um1NZnh7KSemrzRW745pLZkIPDUntyzwm2sQwDNmNpuLPBLninc4xs6hphdOuxGW1Kyk5SStWrDjmIkzGYQAAAAAAHF5WVpZuv/123X777ZKkuro6rVu3TkVFRfL7/UpMTJTN1lSqtHbtWpmm2aa4b7/9tt5+++3o9tlnn60ZM2YoOzs79hcBACcQikMBNGOzWOWyOfWNfqfGvTj07OxT5LDa5OxiD/cBAAAAAACg67AZVvVN7Dpvj3VZHerhTpbdYo13KgAAACcUm8Uml9Whb2S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",
      "text/plain": [
       "<Figure size 3750x2700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u2713 Saved visualization: district_demographics_geography.png\n"
     ]
    }
   ],
   "source": [
    "# Publication-ready styling for Policy Studies Journal\n",
    "sns.set_theme(style=\"whitegrid\", context=\"paper\", font=\"serif\")\n",
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 300,\n",
    "    \"savefig.dpi\": 300,\n",
    "    \"axes.titlesize\": 12,\n",
    "    \"axes.titleweight\": \"bold\",\n",
    "    \"axes.labelsize\": 11,\n",
    "    \"axes.labelweight\": \"bold\",\n",
    "    \"xtick.labelsize\": 9,\n",
    "    \"ytick.labelsize\": 9,\n",
    "    \"legend.fontsize\": 9,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "})\n",
    "\n",
    "\n",
    "\n",
    "if district_chars is not None and 'pct_change' in district_chars.columns:\n",
    "    print(\"=\" * 80)\n",
    "    print(\"VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n",
    "    print(\"=\" * 80)\n",
    "\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(12.5, 9))\n",
    "    fig.suptitle('District Characteristics and Treatment Effects', fontsize=13, fontweight='bold', y=1.02)\n",
    "\n",
    "    def _scatter_with_fit(ax, x, y, xlab, title):\n",
    "        sc = ax.scatter(\n",
    "            x, y, s=85, alpha=0.85, c=y, cmap='RdYlGn_r',\n",
    "            edgecolors='white', linewidth=0.6\n",
    "        )\n",
    "        for xi, yi, lbl in zip(x, y, valid['district']):\n",
    "            ax.text(xi, yi, f\" {lbl}\", fontsize=8, fontweight='bold')\n",
    "        if pd.Series(x).nunique() > 1:\n",
    "            z = np.polyfit(x, y, 1)\n",
    "            p = np.poly1d(z)\n",
    "            ax.plot(x, p(x), color='black', linestyle='--', linewidth=1.1, alpha=0.7)\n",
    "        corr = pd.Series(x).corr(pd.Series(y))\n",
    "        ax.set_title(f'{title} (r={corr:.2f})', pad=pad)\n",
    "        ax.set_xlabel(xlab)\n",
    "        ax.set_ylabel('Treatment Effect (%)')\n",
    "        ax.axhline(0, color='gray', linestyle='--', linewidth=0.8, alpha=0.6)\n",
    "        ax.grid(True, alpha=0.25)\n",
    "        return sc\n",
    "\n",
    "    # Plot 1: Rurality (RUCA code)\n",
    "    ax1 = axes[0, 0]\n",
    "    valid = district_chars.dropna(subset=['avg_ruca_code', 'pct_change'])\n",
    "    if len(valid) > 0:\n",
    "        _scatter_with_fit(\n",
    "            ax1,\n",
    "            valid['avg_ruca_code'].values,\n",
    "            valid['pct_change'].values,\n",
    "            'Average RUCA Code (1=Metro, 10=Rural)',\n",
    "            'Rurality vs Treatment Effect'\n",
    "        )\n",
    "\n",
    "    # Plot 2: Number of wells\n",
    "    ax2 = axes[0, 1]\n",
    "    valid = district_chars.dropna(subset=['n_wells', 'pct_change'])\n",
    "    if len(valid) > 0:\n",
    "        _scatter_with_fit(\n",
    "            ax2,\n",
    "            valid['n_wells'].values,\n",
    "            valid['pct_change'].values,\n",
    "            'Number of Wells Inspected',\n",
    "            'District Size vs Treatment Effect'\n",
    "        )\n",
    "\n",
    "    # Plot 3: Number of basins\n",
    "    ax3 = axes[1, 0]\n",
    "    valid = district_chars.dropna(subset=['n_basins', 'pct_change'])\n",
    "    if len(valid) > 0:\n",
    "        _scatter_with_fit(\n",
    "            ax3,\n",
    "            valid['n_basins'].values,\n",
    "            valid['pct_change'].values,\n",
    "            'Number of Unique Basins',\n",
    "            'Basin Diversity vs Treatment Effect'\n",
    "        )\n",
    "\n",
    "    # Plot 4: High EJ Score\n",
    "    ax4 = axes[1, 1]\n",
    "    valid = district_chars.dropna(subset=['avg_ej_score', 'pct_change'])\n",
    "    if len(valid) > 0:\n",
    "        _scatter_with_fit(\n",
    "            ax4,\n",
    "            valid['avg_ej_score'].values,\n",
    "            valid['pct_change'].values,\n",
    "            'EnviroJustice Score',\n",
    "            'EJ Score vs Treatment Effect'\n",
    "        )\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.savefig('district_demographics_geography.png', bbox_inches='tight')\n",
    "    plt.show()\n",
    "    print(\"\\n\u2713 Saved visualization: district_demographics_geography.png\")\n",
    "else:\n",
    "    print(\"\\n\u2717 Cannot create visualizations - district_chars not available\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "019e6591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\n",
      "================================================================================\n",
      "H3c Environmental Justice: coef=0.1818, p=0.4866\n",
      "H3f Rurality: coef=0.2213, p=0.4649\n",
      "H3e Border Proximity: coef=-0.3626, p=0.1669\n",
      "H3d Geology terms:\n",
      "                         term  coefficient  pvalue\n",
      "C(primary_basin)[0]:post_2019       0.5322  0.0000\n",
      "C(primary_basin)[3]:post_2019      -0.5707  0.0000\n",
      "C(primary_basin)[2]:post_2019      -0.2929  0.1735\n",
      "C(primary_basin)[8]:post_2019      -0.0931  0.2983\n",
      "C(primary_basin)[5]:post_2019      -0.1062  0.6661\n",
      "               hypothesis                         term  coefficient  pvalue\n",
      "H3c Environmental Justice           post_2019:high_eji       0.1818  0.4866\n",
      "             H3f Rurality         post_2019:high_rural       0.2213  0.4649\n",
      "     H3e Border Proximity post_2019:border_competition      -0.3626  0.1669\n",
      "              H3d Geology   C(primary_basin):post_2019          NaN  0.0000\n"
     ]
    }
   ],
   "source": [
    "## Step 5: TWFE moderator tests for H3c/H3d/H3e/H3f\n",
    "\n",
    "if district_chars is not None and 'pct_change' in district_chars.columns:\n",
    "    print(\"=\"*80)\n",
    "    print(\"TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\")\n",
    "    print(\"=\"*80)\n",
    "\n",
    "    district_chars['high_eji'] = (district_chars['avg_ej_score'] > district_chars['avg_ej_score'].median()).astype(int)\n",
    "    district_chars['high_rural'] = (district_chars['avg_ruca_code'] > district_chars['avg_ruca_code'].median()).astype(int)\n",
    "    border_competition_districts = ['01','02','06','08','8A','09','10']\n",
    "    district_chars['border_competition'] = district_chars['district'].isin(border_competition_districts).astype(int)\n",
    "    district_chars['primary_basin'] = district_chars['primary_basin'].fillna('Unknown')\n",
    "\n",
    "    df_struct = district_year_panel.merge(\n",
    "        district_chars[['district','high_eji','high_rural','border_competition','primary_basin']],\n",
    "        on='district', how='left'\n",
    "    )\n",
    "    df_struct = df_struct[df_struct['avg_days_to_enforcement'] > 0].copy()\n",
    "    df_struct['log_days_to_enf'] = np.log(df_struct['avg_days_to_enforcement'])\n",
    "\n",
    "    results=[]\n",
    "    def run_binary(name, term):\n",
    "        m=smf.ols(f'log_days_to_enf ~ C(district) + C(year) + post_2019:{term} + post_2019:offshore_jurisdiction', data=df_struct).fit(cov_type='cluster', cov_kwds={'groups':df_struct['district']})\n",
    "        key=f'post_2019:{term}'; coef=m.params.get(key,np.nan); p=m.pvalues.get(key,np.nan)\n",
    "        print(f\"{name}: coef={coef:.4f}, p={p:.4f}\")\n",
    "        results.append({'hypothesis':name,'term':key,'coefficient':coef,'pvalue':p})\n",
    "\n",
    "    run_binary('H3c Environmental Justice','high_eji')\n",
    "    run_binary('H3f Rurality','high_rural')\n",
    "    run_binary('H3e Border Proximity','border_competition')\n",
    "\n",
    "    m=smf.ols('log_days_to_enf ~ C(district) + C(year) + C(primary_basin):post_2019 + post_2019:offshore_jurisdiction', data=df_struct).fit(cov_type='cluster', cov_kwds={'groups':df_struct['district']})\n",
    "    terms=[t for t in m.params.index if 'C(primary_basin)' in t and ':post_2019' in t]\n",
    "    if terms:\n",
    "        bdf=pd.DataFrame({'term':terms,'coefficient':[m.params[t] for t in terms],'pvalue':[m.pvalues[t] for t in terms]}).sort_values('pvalue')\n",
    "        print('H3d Geology terms:')\n",
    "        print(bdf.to_string(index=False))\n",
    "        minp=bdf['pvalue'].min()\n",
    "    else:\n",
    "        minp=np.nan\n",
    "    results.append({'hypothesis':'H3d Geology','term':'C(primary_basin):post_2019','coefficient':np.nan,'pvalue':minp})\n",
    "\n",
    "    h3_results_df=pd.DataFrame(results)\n",
    "    print(h3_results_df.to_string(index=False))\n",
    "else:\n",
    "    print('Cannot run H3 tests: district_chars unavailable')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6631216",
   "metadata": {},
   "source": [
    "## Hypotheses\n",
    "\n",
    "**H1: Regulatory Pipeline Acceleration**\n",
    "- *H1a*: Disclosure reduces time from violation discovery to enforcement action.\n",
    "- *H1b*: Disclosure increases compliance verification (resolution on re-inspection).\n",
    "\n",
    "**H2: Bureaucratic Heterogeneity**\n",
    "- Policy effects vary across RRC districts.\n",
    "\n",
    "**H3: Structural Moderators**\n",
    "- *H3a*: Capacity moderates responsiveness.\n",
    "- *H3b*: Baseline performance moderates responsiveness.\n",
    "- *H3c*: Environmental justice context moderates responsiveness.\n",
    "- *H3d*: Dominant basin geology moderates responsiveness.\n",
    "- *H3e*: Border proximity moderates responsiveness.\n",
    "- *H3f*: Rurality moderates responsiveness.\n",
    "\n",
    "**H4: Spatial Dynamics**\n",
    "- District treatment effects are spatially autocorrelated (Moran's I).\n",
    "\n",
    "**H5: Offshore Jurisdiction Moderator**\n",
    "- Districts with offshore + onshore jurisdiction (02,03,04) have different post-2019 effects.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a19a82",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "**Overall pattern (2015-2025)**\n",
    "- The analysis is now structured around all districts first, then district heterogeneity, then offshore as a moderator.\n",
    "- Descriptively, average days to enforcement declined from **174.3** pre-2019 to **112.3** post-2019 (-35.6%).\n",
    "\n",
    "**H1 (all-district policy-year shift)**\n",
    "- Interrupted panel timing model:\n",
    "  - `post_2019` level shift: **0.1514**, `p=0.3294` (not significant)\n",
    "  - `post_trend` slope shift: **-0.3603**, `p=0.0010` (significant acceleration over post years)\n",
    "- Event-study decomposition (all districts, ref=2018): significant negative deviations in **2022**, **2024**, and **2025**; no significant pre-trend years.\n",
    "\n",
    "**H2 (district heterogeneity)**\n",
    "- Strong heterogeneity remains after adding year fixed effects and district-specific post terms.\n",
    "- Joint test of district post effects is highly significant.\n",
    "- Estimated district effects range from large improvements (e.g., District 09) to substantial slowdowns (e.g., Districts 03/04).\n",
    "\n",
    "**H5 (offshore moderator)**\n",
    "- Offshore differential in moderator model: **0.3819**, `p<0.001`.\n",
    "- Direction indicates relatively slower post-2019 enforcement timing in offshore-jurisdiction districts, conditional on district heterogeneity.\n",
    "\n",
    "**H3 moderators and H4 spatial dynamics**\n",
    "- H3a/H3b/H3c/H3e/H3f are not statistically significant in current models.\n",
    "- H3d (geology) shows partial support via significant basin interaction terms.\n",
    "- Moran's I: **-0.0493**, `p=0.8550` -> no significant spatial autocorrelation.\n",
    "\n",
    "**Robustness highlights**\n",
    "- Placebo: 2017 is significant in the all-district interrupted model (`p=0.0020`), while 2021 is null (`p=0.9191`), suggesting caution in attributing all level shifts to 2019 timing alone.\n",
    "- Sample restrictions keep a negative post-trend term (faster post-policy trend) across specifications.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f5cbf5",
   "metadata": {},
   "source": [
    "## Results by Hypothesis\n",
    "\n",
    "**H1 (Regulatory Pipeline Acceleration)**\n",
    "- **H1a (faster enforcement): Partially supported.**\n",
    "  - No significant immediate level break at 2019 (`post_2019 p=0.3294`).\n",
    "  - Significant negative post-policy slope (`post_trend p=0.0010`) indicates cumulative acceleration over time.\n",
    "  - Event-study year effects show significant negative deviations in 2022, 2024, and 2025, with no significant pre-trend years.\n",
    "- **H1b (higher compliance verification): Not supported in core interrupted model.**\n",
    "  - Resolution-rate level and slope shifts are not statistically significant in robustness model (`post p=0.2104`, `post_trend p=0.1424`).\n",
    "\n",
    "**H2 (Bureaucratic Heterogeneity)**\n",
    "- **Supported.**\n",
    "  - District-specific post-2019 effects are jointly significant and substantively large.\n",
    "  - Effects span strong improvement to strong worsening across districts.\n",
    "\n",
    "**H3 (Structural Moderators)**\n",
    "- **H3a Capacity:** Not supported (`p=0.9415`).\n",
    "- **H3b Baseline performance:** Not supported (`p=0.7144`).\n",
    "- **H3c Environmental justice:** Not supported (`p=0.4866`).\n",
    "- **H3d Geology:** Partially supported (some basin interactions significant).\n",
    "- **H3e Border proximity:** Not supported (`p=0.3082` main moderator block; `p=0.1669` deep-dive block).\n",
    "- **H3f Rurality:** Not supported (`p=0.4649`).\n",
    "\n",
    "**H4 (Spatial Dynamics)**\n",
    "- **Not supported.**\n",
    "  - Moran's I is near zero and not significant (`-0.0493`, `p=0.8550`).\n",
    "\n",
    "**H5 (Offshore Jurisdiction Moderator)**\n",
    "- **Supported in conditional heterogeneity model.**\n",
    "  - Offshore differential is positive and statistically significant (`coef=0.3819`, `p<0.001`) once district-specific post effects are modeled.\n",
    "  - Interpretation: offshore-jurisdiction districts experienced relatively slower post-2019 enforcement timing compared with other districts, net of district heterogeneity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f3b467",
   "metadata": {},
   "source": [
    "## Methods\n",
    "\n",
    "Core inference is organized in three layers:\n",
    "1. All-district policy-year shift (interrupted panel): `C(district) + year trend + post_2019 + post_trend`.\n",
    "2. District heterogeneity: `C(district):post_2019` with year fixed effects.\n",
    "3. Offshore moderator: `post_2019:offshore_jurisdiction` added after modeling all districts.\n",
    "\n",
    "Moderator tests (H3c/H3d/H3e/H3f) are estimated with TWFE interactions.\n",
    "Spatial dynamics are tested with Moran's I (permutation inference).\n",
    "All well-level joins and identifiers use `api_norm`.\n"
   ]
  }
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