texas-borderlands/analysis/borderlands.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a86464bc",
   "metadata": {},
   "source": [
    "# Texas Borderlands Enforcement and 2019 Disclosure Reform\n",
    "\n",
    "## Study Scope and Testable Design\n",
    "\n",
    "This notebook estimates **Texas-internal borderlands gaps** and **border-specific post-2019 disclosure shifts** at the district-year level.\n",
    "\n",
    "### In-Scope Research Questions\n",
    "\n",
    "**RQ1 (Border gaps):**\n",
    "Do border-exposed Texas districts differ from non-border districts in enforcement intensity and pipeline outcomes?\n",
    "\n",
    "**RQ2 (Disclosure heterogeneity):**\n",
    "Did the 2019 disclosure reform change enforcement outcomes differently in border districts versus non-border districts (level shift and post-policy trend differential)?\n",
    "\n",
    "### Outcomes (Operationalized)\n",
    "\n",
    "- **Inspection intensity:** inspections per well\n",
    "- **Detection intensity:** violations per inspection\n",
    "- **Enforcement timing:** average days from violation discovery to enforcement action date\n",
    "- **Follow-through proxy:** resolution/compliance-on-reinspection rate\n",
    "\n",
    "### Hypotheses Tested in This Notebook\n",
    "\n",
    "**H1 (Border inspection gap):**\n",
    "Border districts have lower inspection intensity than non-border districts.\n",
    "\n",
    "**H2 (Border pipeline disadvantage):**\n",
    "Border districts show weaker enforcement pipeline outcomes (higher violations per inspection and/or slower timing and/or lower resolution rates).\n",
    "\n",
    "**H3 (Disclosure heterogeneity in levels):**\n",
    "Post-2019 level shifts differ between border and non-border districts (`post_2019:border`).\n",
    "\n",
    "**H4 (Disclosure heterogeneity in trends):**\n",
    "Post-2019 trend shifts differ between border and non-border districts (`post_trend:border`).\n",
    "\n",
    "### Out of Scope for Current Data\n",
    "\n",
    "Direct Woods reaction-function tests are not estimated here because cross-jurisdiction competitor stringency data are unavailable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d51d985",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete\n"
     ]
    }
   ],
   "source": [
    "# Core setup\n",
    "from pathlib import Path\n",
    "import os\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "from sqlalchemy import create_engine, text\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.float_format', '{:.4f}'.format)\n",
    "\n",
    "sns.set_theme(style='whitegrid', context='notebook')\n",
    "\n",
    "print('Setup complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e67af62c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to PostgreSQL\n",
      "  Host: localhost\n",
      "  Database: texas_data\n",
      "  User: postgres\n",
      "\n",
      "Tables (first 15):\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"
     ]
    }
   ],
   "source": [
    "# Explicit PostgreSQL connection (district notebook 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",
    "try:\n",
    "    with engine.begin() as conn:\n",
    "        conn.execute(text('SELECT 1'))\n",
    "        tables = pd.read_sql(\n",
    "            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",
    "            conn,\n",
    "        )\n",
    "\n",
    "    print('Connected to PostgreSQL')\n",
    "    print(f'  Host: {PGHOST}')\n",
    "    print(f'  Database: {PGDATABASE}')\n",
    "    print(f'  User: {PGUSER}')\n",
    "    print('\\nTables (first 15):')\n",
    "    print(tables['table_name'].head(15).tolist())\n",
    "except Exception as exc:\n",
    "    print('Failed to connect to PostgreSQL with current PG* environment variables.')\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f76e1881",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-02-20 22:20:34,409 - INFO - Loaded 1010432 wells from public.well_shape_tract\n",
      "2026-02-20 22:22:30,525 - INFO - Recomputed border metrics for 1010432 wells (21112 within 25km, 40339 within 50km)\n",
      "2026-02-20 22:22:37,763 - INFO - Loaded 1878764 inspections from public.inspections\n",
      "2026-02-20 22:22:38,991 - INFO - Loaded 193338 violations from public.violations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wells: 1,010,432\n",
      "Inspections: 1,878,764\n",
      "Violations: 193,338\n",
      "\n",
      "Well columns: ['api_norm', 'census_tract_geoid', 'distance_to_texmex_km', 'latitude', 'longitude', 'texmex_name', 'within_25km_texmex', 'within_50km_texmex']\n",
      "\n",
      "Inspection columns: ['api_norm', 'compliance', 'county', 'days_since_last_inspection', 'district', 'field_name', 'inspection_date', 'operator_name']\n",
      "\n",
      "Violation columns: ['api_norm', 'compliant_on_reinsp', 'district', 'drilling_permit_no', 'field_name', 'last_enf_action', 'last_enf_action_date', 'lease_fac_name', 'major_viol_ind', 'oil_lease_gas_well_id', 'operator_name', 'p5_operator_no', 'total_violations', 'violated_rule', 'violated_rule_desc', 'violation_disc_date', 'violation_number', 'violation_row_id', 'well_no']\n"
     ]
    }
   ],
   "source": [
    "# Load analyzer from local module and pull source data\n",
    "from well_analyzer import WellAnalyzer\n",
    "\n",
    "try:\n",
    "    analyzer = WellAnalyzer(engine=engine, chunk_size=50_000)\n",
    "except Exception as exc:\n",
    "    print('Failed to initialize WellAnalyzer after DB connection.')\n",
    "    print('If connection above succeeded, verify required source tables exist (well_shape_tract, inspections, violations).')\n",
    "    raise\n",
    "\n",
    "well_data = analyzer.data['well_data'].copy()\n",
    "inspections = analyzer.data['inspections'].copy()\n",
    "violations = analyzer.data['violations'].copy()\n",
    "\n",
    "print(f'Wells: {len(well_data):,}')\n",
    "print(f'Inspections: {len(inspections):,}')\n",
    "print(f'Violations: {len(violations):,}')\n",
    "print('\\nWell columns:', sorted(well_data.columns.tolist()))\n",
    "print('\\nInspection columns:', sorted(inspections.columns.tolist()))\n",
    "print('\\nViolation columns:', sorted(violations.columns.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5599a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config:\n",
      "  Years: 2015-2025\n",
      "  Policy year: 2019\n",
      "  Border districts: ['01', '02', '06', '08', '09', '10', '8A']\n",
      "  Output directory: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands\n"
     ]
    }
   ],
   "source": [
    "# Analysis configuration\n",
    "START_YEAR = 2015\n",
    "END_YEAR = 2025\n",
    "POLICY_YEAR = 2019\n",
    "\n",
    "OUTPUT_DIR = Path('output_borderlands')\n",
    "OUTPUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "# Border-adjacent district set for baseline binary treatment coding.\n",
    "# You can revise this list if your substantive definition changes.\n",
    "BORDER_DISTRICTS = {'01', '02', '06', '08', '8A', '09', '10'}\n",
    "\n",
    "print('Config:')\n",
    "print(f'  Years: {START_YEAR}-{END_YEAR}')\n",
    "print(f'  Policy year: {POLICY_YEAR}')\n",
    "print(f'  Border districts: {sorted(BORDER_DISTRICTS)}')\n",
    "print(f'  Output directory: {OUTPUT_DIR.resolve()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d890fb98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered sample:\n",
      "  Inspections: 1,867,859\n",
      "  Violations: 191,762\n"
     ]
    }
   ],
   "source": [
    "# Prepare core identifiers and time fields\n",
    "inspections = inspections.copy()\n",
    "violations = violations.copy()\n",
    "well_data = well_data.copy()\n",
    "\n",
    "inspections['api_norm'] = inspections['api_norm'].astype(str).str.strip()\n",
    "violations['api_norm'] = violations['api_norm'].astype(str).str.strip()\n",
    "well_data['api_norm'] = well_data['api_norm'].astype(str).str.strip()\n",
    "\n",
    "inspections['inspection_date'] = pd.to_datetime(inspections.get('inspection_date'), errors='coerce')\n",
    "violations['violation_disc_date'] = pd.to_datetime(violations.get('violation_disc_date'), errors='coerce')\n",
    "violations['last_enf_action_date'] = pd.to_datetime(violations.get('last_enf_action_date'), errors='coerce')\n",
    "\n",
    "inspections['year'] = inspections['inspection_date'].dt.year\n",
    "violations['year'] = violations['violation_disc_date'].dt.year\n",
    "\n",
    "inspections = inspections[inspections['year'].between(START_YEAR, END_YEAR, inclusive='both')].copy()\n",
    "violations = violations[violations['year'].between(START_YEAR, END_YEAR, inclusive='both')].copy()\n",
    "\n",
    "print('Filtered sample:')\n",
    "print(f'  Inspections: {len(inspections):,}')\n",
    "print(f'  Violations: {len(violations):,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa1727b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well coordinate ranges: lon [-106.537, -93.535], lat [25.857, 36.499]\n",
      "County CRS: EPSG:4326\n",
      "Well border map built:\n",
      "well_border_exposed\n",
      "non-border    840912\n",
      "border        169520\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Border subtype counts (50km):\n",
      "  TX-MX: 40339\n",
      "  TX-NM: 81567\n",
      "  TX-OK: 19643\n",
      "  TX-LA: 29675\n"
     ]
    }
   ],
   "source": [
    "# Build well-level border exposure map + state-border flags\n",
    "from shapely.geometry import LineString\n",
    "\n",
    "well_border = well_data[['api_norm', 'latitude', 'longitude']].drop_duplicates('api_norm').copy()\n",
    "well_border['latitude'] = pd.to_numeric(well_border['latitude'], errors='coerce')\n",
    "well_border['longitude'] = pd.to_numeric(well_border['longitude'], errors='coerce')\n",
    "\n",
    "# Start with Mexico-border metrics already computed in WellAnalyzer\n",
    "for c in ['distance_to_texmex_km', 'within_25km_texmex', 'within_50km_texmex', 'texmex_name']:\n",
    "    if c in well_data.columns:\n",
    "        well_border = well_border.merge(\n",
    "            well_data[['api_norm', c]].drop_duplicates('api_norm'),\n",
    "            on='api_norm',\n",
    "            how='left'\n",
    "        )\n",
    "\n",
    "for c in ['within_25km_texmex', 'within_50km_texmex']:\n",
    "    if c not in well_border.columns:\n",
    "        well_border[c] = 0\n",
    "    well_border[c] = pd.to_numeric(well_border[c], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "# Initialize state-border fields\n",
    "for c in [\n",
    "    'distance_to_nm_border_km', 'distance_to_ok_border_km', 'distance_to_la_border_km',\n",
    "    'within_25km_nm_border', 'within_50km_nm_border',\n",
    "    'within_25km_ok_border', 'within_50km_ok_border',\n",
    "    'within_25km_la_border', 'within_50km_la_border',\n",
    "]:\n",
    "    well_border[c] = np.nan if c.startswith('distance_') else 0\n",
    "\n",
    "try:\n",
    "    import geopandas as gpd\n",
    "\n",
    "    def _resolve_path(candidates):\n",
    "        for c in candidates:\n",
    "            if c.exists():\n",
    "                return c\n",
    "        return None\n",
    "\n",
    "    county_shp = _resolve_path([\n",
    "        Path('../data/texas_county_shape/tl_2025_48_cousub/tl_2025_48_cousub.shp'),\n",
    "        Path('data/texas_county_shape/tl_2025_48_cousub/tl_2025_48_cousub.shp'),\n",
    "    ])\n",
    "\n",
    "    if county_shp is None:\n",
    "        print('State-border flags skipped: Texas county shapefile not found.')\n",
    "    else:\n",
    "        counties = gpd.read_file(county_shp)\n",
    "        if counties.crs is None:\n",
    "            counties = counties.set_crs('EPSG:4269', allow_override=True)\n",
    "        counties = counties.to_crs('EPSG:4326')\n",
    "\n",
    "        tx_union = counties.geometry.union_all()\n",
    "        tx_boundary = tx_union.boundary\n",
    "\n",
    "        # Fast coordinate sanity checks to catch ingest issues\n",
    "        lon_min, lon_max = well_border['longitude'].min(), well_border['longitude'].max()\n",
    "        lat_min, lat_max = well_border['latitude'].min(), well_border['latitude'].max()\n",
    "        print(f'Well coordinate ranges: lon [{lon_min:.3f}, {lon_max:.3f}], lat [{lat_min:.3f}, {lat_max:.3f}]')\n",
    "        print(f'County CRS: {counties.crs}')\n",
    "\n",
    "        # Build explicit border seed lines and clip to actual Texas boundary.\n",
    "        # This avoids accidental use of wrong boundary sectors.\n",
    "        nm_seed = LineString([(-103.02, 31.78), (-103.02, 36.50)])\n",
    "        ok_seed = LineString([(-103.02, 36.50), (-100.00, 36.50), (-94.04, 33.57)])\n",
    "        la_seed = LineString([(-94.04, 33.02), (-93.70, 31.00), (-93.75, 29.70)])\n",
    "\n",
    "        nm_geom = tx_boundary.intersection(nm_seed.buffer(0.35))\n",
    "        ok_geom = tx_boundary.intersection(ok_seed.buffer(0.35))\n",
    "        la_geom = tx_boundary.intersection(la_seed.buffer(0.35))\n",
    "\n",
    "        # Fallback to seed lines if an intersection unexpectedly collapses.\n",
    "        if nm_geom.is_empty:\n",
    "            nm_geom = nm_seed\n",
    "        if ok_geom.is_empty:\n",
    "            ok_geom = ok_seed\n",
    "        if la_geom.is_empty:\n",
    "            la_geom = la_seed\n",
    "\n",
    "        valid_pts = well_border.dropna(subset=['longitude', 'latitude']).copy()\n",
    "        if len(valid_pts) > 0:\n",
    "            pts = gpd.GeoDataFrame(\n",
    "                valid_pts[['api_norm', 'longitude', 'latitude']].copy(),\n",
    "                geometry=gpd.points_from_xy(valid_pts['longitude'], valid_pts['latitude']),\n",
    "                crs='EPSG:4326'\n",
    "            ).to_crs('EPSG:5070')\n",
    "\n",
    "            nm_m = gpd.GeoSeries([nm_geom], crs='EPSG:4326').to_crs('EPSG:5070').iloc[0]\n",
    "            ok_m = gpd.GeoSeries([ok_geom], crs='EPSG:4326').to_crs('EPSG:5070').iloc[0]\n",
    "            la_m = gpd.GeoSeries([la_geom], crs='EPSG:4326').to_crs('EPSG:5070').iloc[0]\n",
    "\n",
    "            pts['distance_to_nm_border_km'] = pts.geometry.distance(nm_m) / 1000.0\n",
    "            pts['distance_to_ok_border_km'] = pts.geometry.distance(ok_m) / 1000.0\n",
    "            pts['distance_to_la_border_km'] = pts.geometry.distance(la_m) / 1000.0\n",
    "\n",
    "            pts['within_25km_nm_border'] = (pts['distance_to_nm_border_km'] <= 25).astype(int)\n",
    "            pts['within_50km_nm_border'] = (pts['distance_to_nm_border_km'] <= 50).astype(int)\n",
    "            pts['within_25km_ok_border'] = (pts['distance_to_ok_border_km'] <= 25).astype(int)\n",
    "            pts['within_50km_ok_border'] = (pts['distance_to_ok_border_km'] <= 50).astype(int)\n",
    "            pts['within_25km_la_border'] = (pts['distance_to_la_border_km'] <= 25).astype(int)\n",
    "            pts['within_50km_la_border'] = (pts['distance_to_la_border_km'] <= 50).astype(int)\n",
    "\n",
    "            cols = [\n",
    "                'api_norm',\n",
    "                'distance_to_nm_border_km', 'distance_to_ok_border_km', 'distance_to_la_border_km',\n",
    "                'within_25km_nm_border', 'within_50km_nm_border',\n",
    "                'within_25km_ok_border', 'within_50km_ok_border',\n",
    "                'within_25km_la_border', 'within_50km_la_border',\n",
    "            ]\n",
    "            well_border = well_border.drop(\n",
    "                columns=[c for c in cols if c in well_border.columns and c != 'api_norm'],\n",
    "                errors='ignore'\n",
    "            )\n",
    "            well_border = well_border.merge(pts[cols], on='api_norm', how='left')\n",
    "\n",
    "except ImportError:\n",
    "    print('State-border flags skipped: geopandas not installed.')\n",
    "\n",
    "# Fill remaining flags/distances\n",
    "for c in [\n",
    "    'within_25km_nm_border', 'within_50km_nm_border',\n",
    "    'within_25km_ok_border', 'within_50km_ok_border',\n",
    "    'within_25km_la_border', 'within_50km_la_border',\n",
    "]:\n",
    "    well_border[c] = pd.to_numeric(well_border[c], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "for c in ['distance_to_nm_border_km', 'distance_to_ok_border_km', 'distance_to_la_border_km']:\n",
    "    well_border[c] = pd.to_numeric(well_border[c], errors='coerce')\n",
    "\n",
    "well_border['within_50km_state_border_any'] = (\n",
    "    (well_border['within_50km_nm_border'] > 0) |\n",
    "    (well_border['within_50km_ok_border'] > 0) |\n",
    "    (well_border['within_50km_la_border'] > 0)\n",
    ").astype(int)\n",
    "\n",
    "well_border['well_border_exposed'] = (\n",
    "    (well_border['within_50km_texmex'] > 0) |\n",
    "    (well_border['within_50km_state_border_any'] > 0)\n",
    ").astype(int)\n",
    "\n",
    "print('Well border map built:')\n",
    "print(well_border['well_border_exposed'].value_counts(dropna=False).rename(index={0:'non-border',1:'border'}))\n",
    "print('\\nBorder subtype counts (50km):')\n",
    "print('  TX-MX:', int((well_border['within_50km_texmex'] > 0).sum()))\n",
    "print('  TX-NM:', int((well_border['within_50km_nm_border'] > 0).sum()))\n",
    "print('  TX-OK:', int((well_border['within_50km_ok_border'] > 0).sum()))\n",
    "print('  TX-LA:', int((well_border['within_50km_la_border'] > 0).sum()))\n",
    "\n",
    "if int((well_border['within_50km_nm_border'] > 0).sum()) < 500:\n",
    "    print('WARNING: TX-NM 50km count is unexpectedly low; inspect border geometry and coordinate ranges above.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6c09f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved map: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/well_border_exposure_map.png\n",
      "Points plotted (trimmed to Texas): 50,000\n",
      "Border points: 8,556; Non-border points: 41,444\n"
     ]
    }
   ],
   "source": [
    "# Map: border vs non-border wells\n",
    "try:\n",
    "    import geopandas as gpd\n",
    "    from matplotlib.lines import Line2D\n",
    "\n",
    "    def _resolve_path(candidates):\n",
    "        for c in candidates:\n",
    "            if c.exists():\n",
    "                return c\n",
    "        return None\n",
    "\n",
    "    # Toggle border line display\n",
    "    SHOW_BORDER_LINE = False\n",
    "\n",
    "    # Support running notebook from either analysis/ or repo root\n",
    "    county_shp = _resolve_path([\n",
    "        Path('../data/texas_county_shape/tl_2025_48_cousub/tl_2025_48_cousub.shp'),\n",
    "        Path('data/texas_county_shape/tl_2025_48_cousub/tl_2025_48_cousub.shp'),\n",
    "    ])\n",
    "    texmex_shp = _resolve_path([\n",
    "        Path('../data/texmex_shape/tl_2023_us_internationalboundary.shp'),\n",
    "        Path('data/texmex_shape/tl_2023_us_internationalboundary.shp'),\n",
    "    ])\n",
    "\n",
    "    if county_shp is None or texmex_shp is None:\n",
    "        print('Required shapefiles not found from current working directory.')\n",
    "        print('Checked county paths under ../data and ./data')\n",
    "        print('Checked texmex paths under ../data and ./data')\n",
    "    else:\n",
    "        counties = gpd.read_file(county_shp)\n",
    "        texmex = gpd.read_file(texmex_shp)\n",
    "\n",
    "        # Build well points from available coordinates\n",
    "        coord_cols = {'longitude', 'latitude'}\n",
    "        if not coord_cols.issubset(set(well_data.columns)):\n",
    "            print('Cannot map wells: longitude/latitude columns are missing from well_data.')\n",
    "        else:\n",
    "            points_df = well_data[['api_norm', 'longitude', 'latitude']].dropna().copy()\n",
    "            points_df['longitude'] = pd.to_numeric(points_df['longitude'], errors='coerce')\n",
    "            points_df['latitude'] = pd.to_numeric(points_df['latitude'], errors='coerce')\n",
    "            points_df = points_df.dropna(subset=['longitude', 'latitude'])\n",
    "\n",
    "            points_df = points_df.merge(\n",
    "                well_border[['api_norm', 'well_border_exposed']],\n",
    "                on='api_norm',\n",
    "                how='left'\n",
    "            )\n",
    "            points_df['well_border_exposed'] = points_df['well_border_exposed'].fillna(0).astype(int)\n",
    "\n",
    "            # Fallback if no wells are marked border-exposed: use district border coding\n",
    "            if points_df['well_border_exposed'].sum() == 0:\n",
    "                well_district = inspections[['api_norm', 'district']].dropna().copy()\n",
    "                well_district['api_norm'] = well_district['api_norm'].astype(str).str.strip()\n",
    "                well_district['district'] = well_district['district'].astype(str).str.strip()\n",
    "                well_district = (\n",
    "                    well_district.groupby('api_norm')['district']\n",
    "                    .agg(lambda s: s.mode().iat[0] if not s.mode().empty else s.iloc[0])\n",
    "                    .reset_index()\n",
    "                )\n",
    "                points_df = points_df.merge(well_district, on='api_norm', how='left')\n",
    "                points_df['well_border_exposed'] = points_df['district'].isin(BORDER_DISTRICTS).astype(int)\n",
    "                print('No positive within_25km/50km flags found; using district-based fallback for map coloring.')\n",
    "\n",
    "            wells_gdf = gpd.GeoDataFrame(\n",
    "                points_df,\n",
    "                geometry=gpd.points_from_xy(points_df['longitude'], points_df['latitude']),\n",
    "                crs='EPSG:4326'\n",
    "            )\n",
    "\n",
    "            # Keep only Texas border segments if NAME exists\n",
    "            if 'NAME' in texmex.columns:\n",
    "                texmex_plot = texmex[texmex['NAME'].astype(str).str.contains('Texas', case=False, na=False)].copy()\n",
    "                if texmex_plot.empty:\n",
    "                    texmex_plot = texmex.copy()\n",
    "            else:\n",
    "                texmex_plot = texmex.copy()\n",
    "\n",
    "            # Harmonize CRS\n",
    "            if counties.crs is None:\n",
    "                counties = counties.set_crs('EPSG:4326', allow_override=True)\n",
    "            if texmex_plot.crs is None:\n",
    "                texmex_plot = texmex_plot.set_crs('EPSG:4326', allow_override=True)\n",
    "            if wells_gdf.crs is None:\n",
    "                wells_gdf = wells_gdf.set_crs('EPSG:4326', allow_override=True)\n",
    "\n",
    "            if counties.crs != wells_gdf.crs:\n",
    "                counties = counties.to_crs(wells_gdf.crs)\n",
    "            if texmex_plot.crs != wells_gdf.crs:\n",
    "                texmex_plot = texmex_plot.to_crs(wells_gdf.crs)\n",
    "\n",
    "            # Trim everything to Texas counties footprint\n",
    "            texas_union = counties.geometry.union_all()\n",
    "            texmex_plot = gpd.clip(texmex_plot, texas_union)\n",
    "            wells_gdf = gpd.clip(wells_gdf, texas_union)\n",
    "\n",
    "            # Sample for legibility and speed (after clip)\n",
    "            max_points = 50000\n",
    "            if len(wells_gdf) > max_points:\n",
    "                wells_gdf = wells_gdf.sample(max_points, random_state=42)\n",
    "\n",
    "            fig, ax = plt.subplots(figsize=(10, 10))\n",
    "            counties.plot(ax=ax, color='#f2efe8', edgecolor='#d7d2c8', linewidth=0.35)\n",
    "            if SHOW_BORDER_LINE and not texmex_plot.empty:\n",
    "                texmex_plot.plot(ax=ax, color='#9ca3af', linewidth=0.8, alpha=0.8)\n",
    "\n",
    "            non_border = wells_gdf[wells_gdf['well_border_exposed'] == 0]\n",
    "            border = wells_gdf[wells_gdf['well_border_exposed'] == 1]\n",
    "\n",
    "            if not non_border.empty:\n",
    "                non_border.plot(ax=ax, markersize=3, color='#6b7280', alpha=0.25, zorder=2)\n",
    "            if not border.empty:\n",
    "                border.plot(ax=ax, markersize=6, color='#ef4444', alpha=0.65, zorder=3)\n",
    "\n",
    "            # Force Texas extent so map does not zoom to continental US\n",
    "            minx, miny, maxx, maxy = counties.total_bounds\n",
    "            pad_x = (maxx - minx) * 0.02\n",
    "            pad_y = (maxy - miny) * 0.02\n",
    "            ax.set_xlim(minx - pad_x, maxx + pad_x)\n",
    "            ax.set_ylim(miny - pad_y, maxy + pad_y)\n",
    "\n",
    "            n_non = len(non_border)\n",
    "            n_border = len(border)\n",
    "\n",
    "            legend_items = [\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor='#6b7280', markersize=6,\n",
    "                       label=f'Non-border wells (n={n_non:,})'),\n",
    "                Line2D([0], [0], marker='o', color='none', markerfacecolor='#ef4444', markersize=7,\n",
    "                       label=f'Border wells (n={n_border:,})'),\n",
    "            ]\n",
    "\n",
    "            ax.legend(handles=legend_items, loc='lower left', frameon=True)\n",
    "            ax.set_title('Texas Wells: Border vs Non-Border Exposure', fontsize=13, weight='bold')\n",
    "            ax.set_axis_off()\n",
    "\n",
    "            map_path = OUTPUT_DIR / 'well_border_exposure_map.png'\n",
    "            fig.savefig(map_path, dpi=300, bbox_inches='tight')\n",
    "            plt.show()\n",
    "\n",
    "            print(f'Saved map: {map_path.resolve()}')\n",
    "            print(f'Points plotted (trimmed to Texas): {len(wells_gdf):,}')\n",
    "            print(f'Border points: {n_border:,}; Non-border points: {n_non:,}')\n",
    "\n",
    "except ImportError:\n",
    "    print('geopandas/shapely not installed; skipping map cell.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b33ae546",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Panel built:\n",
      "  Rows: 143\n",
      "  Districts: 13\n",
      "  Years: 2015-2025\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",
      "\n",
      "   border_exposed_insp  inspection_intensity  compliance_rate  \\\n",
      "0                  319                1.2422           0.8654   \n",
      "1                  625                1.6027           0.7737   \n",
      "2                 1042                1.4057           0.8802   \n",
      "3                 1914                1.2039           0.8816   \n",
      "4                 2096                1.2559           0.8420   \n",
      "\n",
      "   share_border_exposed_insp  total_violations  major_violations  \\\n",
      "0                     0.0912               592                 0   \n",
      "1                     0.0962              1902                 0   \n",
      "2                     0.1205              1439                 0   \n",
      "3                     0.1745              1771                 0   \n",
      "4                     0.2589              1506                 2   \n",
      "\n",
      "   compliant_on_reinsp  avg_days_to_enforcement  med_days_to_enforcement  \\\n",
      "0                  284                 124.9122                  19.0000   \n",
      "1                  472                 230.0910                  20.0000   \n",
      "2                  740                 326.0945                  33.0000   \n",
      "3                 1012                 290.2191                  82.0000   \n",
      "4                  771                 261.2776                  73.0000   \n",
      "\n",
      "   violations_per_inspection  major_share  resolution_rate  post_2019  \\\n",
      "0                     0.1692       0.0000           0.4797          0   \n",
      "1                     0.2927       0.0000           0.2482          0   \n",
      "2                     0.1664       0.0000           0.5142          0   \n",
      "3                     0.1615       0.0000           0.5714          0   \n",
      "4                     0.1860       0.0013           0.5120          1   \n",
      "\n",
      "   year_from_policy  post_trend  border_district  border_exposure_district  \n",
      "0                -4           0                1                         0  \n",
      "1                -3           0                1                         0  \n",
      "2                -2           0                1                         0  \n",
      "3                -1           0                1                         0  \n",
      "4                 0           0                1                         1  \n"
     ]
    }
   ],
   "source": [
    "# District-year outcome panel\n",
    "inspections_merged = inspections.merge(\n",
    "    well_border[['api_norm', 'well_border_exposed']],\n",
    "    on='api_norm',\n",
    "    how='left'\n",
    ")\n",
    "inspections_merged['well_border_exposed'] = inspections_merged['well_border_exposed'].fillna(0).astype(int)\n",
    "\n",
    "violations_merged = violations.merge(\n",
    "    well_border[['api_norm', 'well_border_exposed']],\n",
    "    on='api_norm',\n",
    "    how='left'\n",
    ")\n",
    "violations_merged['well_border_exposed'] = violations_merged['well_border_exposed'].fillna(0).astype(int)\n",
    "\n",
    "# Timing metric used in district analysis\n",
    "days_to_enf = (violations_merged['last_enf_action_date'] - violations_merged['violation_disc_date']).dt.days\n",
    "violations_merged['days_to_enforcement'] = days_to_enf.where((days_to_enf >= 0) & (days_to_enf <= 3650))\n",
    "\n",
    "insp_panel = inspections_merged.groupby(['district', 'year'], as_index=False).agg(\n",
    "    total_inspections=('api_norm', 'size'),\n",
    "    unique_wells=('api_norm', 'nunique'),\n",
    "    compliant_inspections=('compliance', lambda x: (x == 'Yes').sum()),\n",
    "    border_exposed_insp=('well_border_exposed', 'sum')\n",
    ")\n",
    "insp_panel['inspection_intensity'] = insp_panel['total_inspections'] / insp_panel['unique_wells'].replace(0, np.nan)\n",
    "insp_panel['compliance_rate'] = insp_panel['compliant_inspections'] / insp_panel['total_inspections'].replace(0, np.nan)\n",
    "insp_panel['share_border_exposed_insp'] = insp_panel['border_exposed_insp'] / insp_panel['total_inspections'].replace(0, np.nan)\n",
    "\n",
    "viol_panel = violations_merged.groupby(['district', 'year'], as_index=False).agg(\n",
    "    total_violations=('api_norm', 'size'),\n",
    "    major_violations=('major_viol_ind', lambda x: (x == 'Y').sum()),\n",
    "    compliant_on_reinsp=('compliant_on_reinsp', lambda x: (x == 'Y').sum()),\n",
    "    avg_days_to_enforcement=('days_to_enforcement', 'mean'),\n",
    "    med_days_to_enforcement=('days_to_enforcement', 'median')\n",
    ")\n",
    "\n",
    "panel = insp_panel.merge(viol_panel, on=['district', 'year'], how='left')\n",
    "for c in ['total_violations', 'major_violations', 'compliant_on_reinsp']:\n",
    "    panel[c] = panel[c].fillna(0)\n",
    "\n",
    "panel['violations_per_inspection'] = panel['total_violations'] / panel['total_inspections'].replace(0, np.nan)\n",
    "panel['major_share'] = panel['major_violations'] / panel['total_violations'].replace(0, np.nan)\n",
    "panel['resolution_rate'] = panel['compliant_on_reinsp'] / panel['total_violations'].replace(0, np.nan)\n",
    "\n",
    "panel['post_2019'] = (panel['year'] >= POLICY_YEAR).astype(int)\n",
    "panel['year_from_policy'] = panel['year'] - POLICY_YEAR\n",
    "panel['post_trend'] = np.where(panel['year'] >= POLICY_YEAR, panel['year_from_policy'], 0)\n",
    "panel['border_district'] = panel['district'].astype(str).str.strip().isin(BORDER_DISTRICTS).astype(int)\n",
    "\n",
    "# Exposure-based fallback/alternative: district is border if >=25% of inspections are border-exposed.\n",
    "panel['border_exposure_district'] = (panel['share_border_exposed_insp'] >= 0.25).astype(int)\n",
    "\n",
    "print('Panel built:')\n",
    "print(f'  Rows: {len(panel):,}')\n",
    "print(f'  Districts: {panel.district.nunique()}')\n",
    "print(f'  Years: {panel.year.min()}-{panel.year.max()}')\n",
    "print(panel.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40777bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "District competitor links created:\n",
      "  rows: 11\n",
      "  file: /home/dadams/Repos/texas-borderlands/data/district_competitor_links.csv\n",
      "district competitor_jurisdiction border_type  n_border_wells  n_wells_district  raw_weight  weight\n",
      "      01                     MEX       TX-MX            7377             31898      0.2313  1.0000\n",
      "      03                      LA       TX-LA            1947             16700      0.1166  1.0000\n",
      "      04                     MEX       TX-MX           13389             20973      0.6384  1.0000\n",
      "      05                      OK       TX-OK              22              9938      0.0022  1.0000\n",
      "      06                      LA       TX-LA           12786             24422      0.5235  0.9470\n",
      "      06                      OK       TX-OK             716             24422      0.0293  0.0530\n",
      "      08                     MEX       TX-MX               6            105931      0.0001  0.0003\n",
      "      08                      NM       TX-NM           20180            105931      0.1905  0.9997\n",
      "      10                      NM       TX-NM              27             29621      0.0009  0.0030\n",
      "      10                      OK       TX-OK            8946             29621      0.3020  0.9970\n",
      "      8A                      NM       TX-NM           17567             42005      0.4182  1.0000\n",
      "  template: /home/dadams/Repos/texas-borderlands/data/competition_panel_template.csv\n"
     ]
    }
   ],
   "source": [
    "# Build district -> competitor jurisdiction link table (for competition models)\n",
    "# We assign wells to their modal district from inspections, then summarize border subtype shares by district.\n",
    "well_district = inspections[['api_norm', 'district']].dropna().copy()\n",
    "well_district['api_norm'] = well_district['api_norm'].astype(str).str.strip()\n",
    "well_district['district'] = well_district['district'].astype(str).str.strip()\n",
    "well_district = (\n",
    "    well_district.groupby('api_norm')['district']\n",
    "    .agg(lambda s: s.mode().iat[0] if not s.mode().empty else s.iloc[0])\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "wb = well_border.merge(well_district, on='api_norm', how='left')\n",
    "wb = wb.dropna(subset=['district']).copy()\n",
    "\n",
    "agg = wb.groupby('district', as_index=False).agg(\n",
    "    n_wells=('api_norm', 'nunique'),\n",
    "    n_tx_mex=('within_50km_texmex', 'sum'),\n",
    "    n_tx_nm=('within_50km_nm_border', 'sum'),\n",
    "    n_tx_ok=('within_50km_ok_border', 'sum'),\n",
    "    n_tx_la=('within_50km_la_border', 'sum'),\n",
    ")\n",
    "for c in ['n_tx_mex', 'n_tx_nm', 'n_tx_ok', 'n_tx_la']:\n",
    "    agg[f'share_{c}'] = agg[c] / agg['n_wells'].replace(0, np.nan)\n",
    "\n",
    "rows = []\n",
    "for _, r in agg.iterrows():\n",
    "    district = str(r['district'])\n",
    "    for jur, count_col in [\n",
    "        ('MEX', 'n_tx_mex'),\n",
    "        ('NM', 'n_tx_nm'),\n",
    "        ('OK', 'n_tx_ok'),\n",
    "        ('LA', 'n_tx_la'),\n",
    "    ]:\n",
    "        n = int(r[count_col]) if pd.notna(r[count_col]) else 0\n",
    "        if n > 0:\n",
    "            rows.append({\n",
    "                'district': district,\n",
    "                'competitor_jurisdiction': jur,\n",
    "                'border_type': f'TX-{jur}' if jur != 'MEX' else 'TX-MX',\n",
    "                'n_border_wells': n,\n",
    "                'n_wells_district': int(r['n_wells']),\n",
    "                'raw_weight': n / r['n_wells'] if r['n_wells'] else np.nan,\n",
    "            })\n",
    "\n",
    "links = pd.DataFrame(rows)\n",
    "if len(links) > 0:\n",
    "    links['weight'] = links['raw_weight'] / links.groupby('district')['raw_weight'].transform('sum')\n",
    "else:\n",
    "    links['weight'] = []\n",
    "\n",
    "links = links.sort_values(['district', 'competitor_jurisdiction']).reset_index(drop=True)\n",
    "link_path = Path('../data/district_competitor_links.csv')\n",
    "link_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "links.to_csv(link_path, index=False)\n",
    "\n",
    "print('District competitor links created:')\n",
    "print(f'  rows: {len(links)}')\n",
    "print(f'  file: {link_path.resolve()}')\n",
    "if len(links) > 0:\n",
    "    print(links.head(20).to_string(index=False))\n",
    "\n",
    "# Also write a template for external competitor metrics by district-year-jurisdiction\n",
    "template = panel[['district', 'year']].drop_duplicates().merge(\n",
    "    links[['district', 'competitor_jurisdiction', 'weight']],\n",
    "    on='district',\n",
    "    how='left'\n",
    ")\n",
    "template['competitor_stringency'] = np.nan\n",
    "template['stringency_metric'] = 'inspection_intensity'\n",
    "template['notes'] = 'Fill competitor_stringency from external jurisdiction data using matching metric definitions.'\n",
    "\n",
    "template_path = Path('../data/competition_panel_template.csv')\n",
    "template.to_csv(template_path, index=False)\n",
    "print(f'  template: {template_path.resolve()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60b23843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved panel to: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/district_year_panel_borderlands.csv\n"
     ]
    }
   ],
   "source": [
    "# Persist panel for reuse\n",
    "panel_out = OUTPUT_DIR / 'district_year_panel_borderlands.csv'\n",
    "panel.to_csv(panel_out, index=False)\n",
    "print(f'Saved panel to: {panel_out.resolve()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec589a6",
   "metadata": {},
   "source": [
    "## RQ1: Border Gap (Descriptive + Regression)\n",
    "\n",
    "Primary binary indicator in baseline models: `border_district`.\n",
    "Alternative indicator available for sensitivity checks: `border_exposure_district`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27421750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>obs</th>\n",
       "      <th>mean_inspection_intensity</th>\n",
       "      <th>mean_violations_per_inspection</th>\n",
       "      <th>mean_days_to_enf</th>\n",
       "      <th>mean_resolution_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>non-border</td>\n",
       "      <td>66</td>\n",
       "      <td>1.5151</td>\n",
       "      <td>0.0984</td>\n",
       "      <td>122.7522</td>\n",
       "      <td>0.5961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>border</td>\n",
       "      <td>77</td>\n",
       "      <td>1.3292</td>\n",
       "      <td>0.1301</td>\n",
       "      <td>145.1754</td>\n",
       "      <td>0.5431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        group  obs  mean_inspection_intensity  mean_violations_per_inspection  \\\n",
       "0  non-border   66                     1.5151                          0.0984   \n",
       "1      border   77                     1.3292                          0.1301   \n",
       "\n",
       "   mean_days_to_enf  mean_resolution_rate  \n",
       "0          122.7522                0.5961  \n",
       "1          145.1754                0.5431  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Border vs non-border descriptive summary\n",
    "summary = panel.groupby('border_district', as_index=False).agg(\n",
    "    obs=('district', 'size'),\n",
    "    mean_inspection_intensity=('inspection_intensity', 'mean'),\n",
    "    mean_violations_per_inspection=('violations_per_inspection', 'mean'),\n",
    "    mean_days_to_enf=('avg_days_to_enforcement', 'mean'),\n",
    "    mean_resolution_rate=('resolution_rate', 'mean')\n",
    ")\n",
    "summary['group'] = summary['border_district'].map({0:'non-border',1:'border'})\n",
    "summary = summary[['group','obs','mean_inspection_intensity','mean_violations_per_inspection','mean_days_to_enf','mean_resolution_rate']]\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10352175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outcome</th>\n",
       "      <th>coef_border_district</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>nobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>inspection_intensity</td>\n",
       "      <td>-0.1755</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>violations_per_inspection</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>0.0949</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     outcome  coef_border_district  pvalue  nobs\n",
       "0       inspection_intensity               -0.1755  0.0999   143\n",
       "1  violations_per_inspection                0.0434  0.0949   143"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RQ1 models: levels with year FE and district controls via size proxy\n",
    "rq1_df = panel.dropna(subset=['inspection_intensity', 'violations_per_inspection']).copy()\n",
    "rq1_df['log_unique_wells'] = np.log(rq1_df['unique_wells'].clip(lower=1))\n",
    "\n",
    "m_rq1_insp = smf.ols(\n",
    "    'inspection_intensity ~ border_district + log_unique_wells + C(year)',\n",
    "    data=rq1_df\n",
    ").fit(cov_type='cluster', cov_kwds={'groups': rq1_df['district']})\n",
    "\n",
    "m_rq1_viol = smf.ols(\n",
    "    'violations_per_inspection ~ border_district + log_unique_wells + C(year)',\n",
    "    data=rq1_df\n",
    ").fit(cov_type='cluster', cov_kwds={'groups': rq1_df['district']})\n",
    "\n",
    "rq1_results = pd.DataFrame([\n",
    "    {\n",
    "        'outcome':'inspection_intensity',\n",
    "        'coef_border_district': m_rq1_insp.params.get('border_district', np.nan),\n",
    "        'pvalue': m_rq1_insp.pvalues.get('border_district', np.nan),\n",
    "        'nobs': int(m_rq1_insp.nobs)\n",
    "    },\n",
    "    {\n",
    "        'outcome':'violations_per_inspection',\n",
    "        'coef_border_district': m_rq1_viol.params.get('border_district', np.nan),\n",
    "        'pvalue': m_rq1_viol.pvalues.get('border_district', np.nan),\n",
    "        'nobs': int(m_rq1_viol.nobs)\n",
    "    }\n",
    "])\n",
    "\n",
    "rq1_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bd6328",
   "metadata": {},
   "source": [
    "## RQ2: Disclosure Reform x Borderlands\n",
    "\n",
    "Specification uses district FE + year FE with interactions:\n",
    "- `post_2019:border_district`\n",
    "- `post_trend:border_district`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2bc00349",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outcome</th>\n",
       "      <th>coef_post_x_border</th>\n",
       "      <th>p_post_x_border</th>\n",
       "      <th>coef_posttrend_x_border</th>\n",
       "      <th>p_posttrend_x_border</th>\n",
       "      <th>nobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>inspection_intensity</td>\n",
       "      <td>-0.1191</td>\n",
       "      <td>0.0753</td>\n",
       "      <td>-0.0052</td>\n",
       "      <td>0.8181</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>violations_per_inspection</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.8881</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.8350</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>avg_days_to_enforcement</td>\n",
       "      <td>-74.5893</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>-1.1587</td>\n",
       "      <td>0.9252</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>resolution_rate</td>\n",
       "      <td>0.0404</td>\n",
       "      <td>0.4520</td>\n",
       "      <td>-0.0186</td>\n",
       "      <td>0.3404</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     outcome  coef_post_x_border  p_post_x_border  \\\n",
       "0       inspection_intensity             -0.1191           0.0753   \n",
       "1  violations_per_inspection              0.0040           0.8881   \n",
       "2    avg_days_to_enforcement            -74.5893           0.0156   \n",
       "3            resolution_rate              0.0404           0.4520   \n",
       "\n",
       "   coef_posttrend_x_border  p_posttrend_x_border  nobs  \n",
       "0                  -0.0052                0.8181   143  \n",
       "1                  -0.0012                0.8350   143  \n",
       "2                  -1.1587                0.9252   143  \n",
       "3                  -0.0186                0.3404   143  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RQ2 models (policy differential)\n",
    "rq2_df = panel.copy()\n",
    "\n",
    "specs = {\n",
    "    'inspection_intensity': 'inspection_intensity ~ C(district) + C(year) + post_2019:border_district + post_trend:border_district',\n",
    "    'violations_per_inspection': 'violations_per_inspection ~ C(district) + C(year) + post_2019:border_district + post_trend:border_district',\n",
    "    'avg_days_to_enforcement': 'avg_days_to_enforcement ~ C(district) + C(year) + post_2019:border_district + post_trend:border_district',\n",
    "    'resolution_rate': 'resolution_rate ~ C(district) + C(year) + post_2019:border_district + post_trend:border_district',\n",
    "}\n",
    "\n",
    "rows = []\n",
    "models = {}\n",
    "for outcome, formula in specs.items():\n",
    "    d = rq2_df.dropna(subset=[outcome]).copy()\n",
    "    if len(d) < 20:\n",
    "        continue\n",
    "    m = smf.ols(formula, data=d).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n",
    "    models[outcome] = m\n",
    "    rows.append({\n",
    "        'outcome': outcome,\n",
    "        'coef_post_x_border': m.params.get('post_2019:border_district', np.nan),\n",
    "        'p_post_x_border': m.pvalues.get('post_2019:border_district', np.nan),\n",
    "        'coef_posttrend_x_border': m.params.get('post_trend:border_district', np.nan),\n",
    "        'p_posttrend_x_border': m.pvalues.get('post_trend:border_district', np.nan),\n",
    "        'nobs': int(m.nobs)\n",
    "    })\n",
    "\n",
    "rq2_results = pd.DataFrame(rows)\n",
    "rq2_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "448a0e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved figure: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/border_vs_nonborder_trends.png\n"
     ]
    }
   ],
   "source": [
    "# Quick visualization: average trend by group\n",
    "plot_df = panel.groupby(['year','border_district'], as_index=False).agg(\n",
    "    avg_days_to_enforcement=('avg_days_to_enforcement','mean'),\n",
    "    inspection_intensity=('inspection_intensity','mean')\n",
    ")\n",
    "plot_df['group'] = plot_df['border_district'].map({0:'non-border',1:'border'})\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharex=True)\n",
    "\n",
    "sns.lineplot(data=plot_df, x='year', y='avg_days_to_enforcement', hue='group', marker='o', ax=axes[0])\n",
    "axes[0].axvline(POLICY_YEAR, color='black', linestyle='--', linewidth=1)\n",
    "axes[0].set_title('Mean Days to Enforcement')\n",
    "axes[0].set_xlabel('Year')\n",
    "axes[0].set_ylabel('Days')\n",
    "\n",
    "sns.lineplot(data=plot_df, x='year', y='inspection_intensity', hue='group', marker='o', ax=axes[1])\n",
    "axes[1].axvline(POLICY_YEAR, color='black', linestyle='--', linewidth=1)\n",
    "axes[1].set_title('Mean Inspection Intensity')\n",
    "axes[1].set_xlabel('Year')\n",
    "axes[1].set_ylabel('Inspections per well')\n",
    "\n",
    "for ax in axes:\n",
    "    ax.legend(title='')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig_path = OUTPUT_DIR / 'border_vs_nonborder_trends.png'\n",
    "fig.savefig(fig_path, bbox_inches='tight', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print(f'Saved figure: {fig_path.resolve()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db3f662",
   "metadata": {},
   "source": [
    "## Robustness: Which Border, How Much Exposure, and Cutoff Sensitivity\n",
    "\n",
    "This section addresses reviewer concerns about border-definition arbitrariness with three checks:\n",
    "\n",
    "1. **Border-type splits** (TX-MX, TX-NM, TX-OK, TX-LA)\n",
    "2. **Continuous exposure intensity** (`share_border_exposed_insp`)\n",
    "3. **Cutoff sensitivity** (25km / 75km / 100km)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "24349ba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/district_border_type_profile.csv\n",
      "district  n_wells  n_tx_mex  n_tx_nm  n_tx_ok  n_tx_la dominant_border_type\n",
      "      01    31898      7377        0        0        0                TX-MX\n",
      "      02    17099         0        0        0        0                 NONE\n",
      "      03    16700         0        0        0     1947                TX-LA\n",
      "      04    20973     13389        0        0        0                TX-MX\n",
      "      05     9938         0        0       22        0                TX-OK\n",
      "      06    24422         0        0      716    12786                TX-LA\n",
      "      08   105931         6    20180        0        0                TX-NM\n",
      "      09    46485         0        0        0        0                 NONE\n",
      "      10    29621         0       27     8946        0                TX-OK\n",
      "      6E     6235         0        0        0        0                 NONE\n",
      "      7B    21230         0        0        0        0                 NONE\n",
      "      7C    43061         0        0        0        0                 NONE\n",
      "      8A    42005         0    17567        0        0                TX-NM\n"
     ]
    }
   ],
   "source": [
    "# Build district border-type profile (TX-MX / TX-NM / TX-OK / TX-LA)\n",
    "well_district = inspections[['api_norm', 'district']].dropna().copy()\n",
    "well_district['api_norm'] = well_district['api_norm'].astype(str).str.strip()\n",
    "well_district['district'] = well_district['district'].astype(str).str.strip()\n",
    "well_district = (\n",
    "    well_district.groupby('api_norm')['district']\n",
    "    .agg(lambda s: s.mode().iat[0] if not s.mode().empty else s.iloc[0])\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "wb = well_border.merge(well_district, on='api_norm', how='left').dropna(subset=['district']).copy()\n",
    "\n",
    "border_profile = wb.groupby('district', as_index=False).agg(\n",
    "    n_wells=('api_norm', 'nunique'),\n",
    "    n_tx_mex=('within_50km_texmex', 'sum'),\n",
    "    n_tx_nm=('within_50km_nm_border', 'sum'),\n",
    "    n_tx_ok=('within_50km_ok_border', 'sum'),\n",
    "    n_tx_la=('within_50km_la_border', 'sum'),\n",
    ")\n",
    "\n",
    "for c in ['n_tx_mex', 'n_tx_nm', 'n_tx_ok', 'n_tx_la']:\n",
    "    border_profile[f'share_{c}'] = border_profile[c] / border_profile['n_wells'].replace(0, np.nan)\n",
    "\n",
    "# Binary border-type membership by district (any well within 50km)\n",
    "border_profile['has_tx_mex'] = (border_profile['n_tx_mex'] > 0).astype(int)\n",
    "border_profile['has_tx_nm'] = (border_profile['n_tx_nm'] > 0).astype(int)\n",
    "border_profile['has_tx_ok'] = (border_profile['n_tx_ok'] > 0).astype(int)\n",
    "border_profile['has_tx_la'] = (border_profile['n_tx_la'] > 0).astype(int)\n",
    "\n",
    "# Dominant border type (highest share among the four)\n",
    "share_cols = ['share_n_tx_mex', 'share_n_tx_nm', 'share_n_tx_ok', 'share_n_tx_la']\n",
    "label_map = {\n",
    "    'share_n_tx_mex': 'TX-MX',\n",
    "    'share_n_tx_nm': 'TX-NM',\n",
    "    'share_n_tx_ok': 'TX-OK',\n",
    "    'share_n_tx_la': 'TX-LA',\n",
    "}\n",
    "\n",
    "border_profile['dominant_border_type'] = border_profile[share_cols].idxmax(axis=1).map(label_map)\n",
    "border_profile.loc[border_profile[share_cols].max(axis=1).fillna(0) <= 0, 'dominant_border_type'] = 'NONE'\n",
    "\n",
    "panel_robust = panel.merge(\n",
    "    border_profile[\n",
    "        ['district', 'n_wells'] + share_cols + ['has_tx_mex', 'has_tx_nm', 'has_tx_ok', 'has_tx_la', 'dominant_border_type']\n",
    "    ],\n",
    "    on='district',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "profile_path = OUTPUT_DIR / 'district_border_type_profile.csv'\n",
    "border_profile.to_csv(profile_path, index=False)\n",
    "\n",
    "print(f'Saved: {profile_path.resolve()}')\n",
    "print(border_profile[['district','n_wells','n_tx_mex','n_tx_nm','n_tx_ok','n_tx_la','dominant_border_type']].to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad78072c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/border_type_split_results.csv\n",
      "                   model_family                 outcome                  term     coef  pvalue  nobs\n",
      "         RQ1_levels_border_type    inspection_intensity            has_tx_mex   0.0400  0.7893   143\n",
      "         RQ1_levels_border_type    inspection_intensity             has_tx_nm  -0.0535  0.5341   143\n",
      "         RQ1_levels_border_type    inspection_intensity             has_tx_ok  -0.0133  0.8359   143\n",
      "         RQ1_levels_border_type    inspection_intensity             has_tx_la   0.0660  0.3727   143\n",
      "         RQ1_levels_border_type avg_days_to_enforcement            has_tx_mex  43.1955  0.3092   143\n",
      "         RQ1_levels_border_type avg_days_to_enforcement             has_tx_nm -73.7579  0.0611   143\n",
      "         RQ1_levels_border_type avg_days_to_enforcement             has_tx_ok  88.5376  0.0210   143\n",
      "         RQ1_levels_border_type avg_days_to_enforcement             has_tx_la  26.1342  0.6193   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity  post_2019:has_tx_mex   0.0821  0.5405   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity   post_2019:has_tx_nm  -0.1152  0.1219   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity   post_2019:has_tx_ok   0.0654  0.3045   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity   post_2019:has_tx_la   0.0197  0.7085   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity post_trend:has_tx_mex  -0.0195  0.3109   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity  post_trend:has_tx_nm   0.0051  0.6920   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity  post_trend:has_tx_ok  -0.0000  0.9976   143\n",
      "RQ2_FE_border_type_interactions    inspection_intensity  post_trend:has_tx_la  -0.0197  0.2809   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement  post_2019:has_tx_mex   4.0900  0.9062   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement   post_2019:has_tx_nm -18.7442  0.6013   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement   post_2019:has_tx_ok -14.2446  0.8134   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement   post_2019:has_tx_la -43.6598  0.6415   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement post_trend:has_tx_mex  -0.0148  0.9991   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement  post_trend:has_tx_nm  22.9067  0.0189   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement  post_trend:has_tx_ok -16.7188  0.0794   143\n",
      "RQ2_FE_border_type_interactions avg_days_to_enforcement  post_trend:has_tx_la   0.6415  0.9551   143\n"
     ]
    }
   ],
   "source": [
    "# Border-type split models (which border drives results?)\n",
    "\n",
    "def _fit_ols(formula, data, group_col='district'):\n",
    "    d = data.copy()\n",
    "    d = d.dropna()\n",
    "    if len(d) < 20:\n",
    "        return None\n",
    "    try:\n",
    "        return smf.ols(formula, data=d).fit(cov_type='cluster', cov_kwds={'groups': d[group_col]})\n",
    "    except Exception:\n",
    "        return None\n",
    "\n",
    "rows = []\n",
    "outcomes = ['inspection_intensity', 'avg_days_to_enforcement']\n",
    "\n",
    "# RQ1-style levels models with type indicators\n",
    "for outcome in outcomes:\n",
    "    d = panel_robust.dropna(subset=[outcome, 'unique_wells']).copy()\n",
    "    d['log_unique_wells'] = np.log(d['unique_wells'].clip(lower=1))\n",
    "    m = _fit_ols(\n",
    "        f\"{outcome} ~ has_tx_mex + has_tx_nm + has_tx_ok + has_tx_la + log_unique_wells + C(year)\",\n",
    "        d\n",
    "    )\n",
    "    if m is not None:\n",
    "        for term in ['has_tx_mex', 'has_tx_nm', 'has_tx_ok', 'has_tx_la']:\n",
    "            rows.append({\n",
    "                'model_family': 'RQ1_levels_border_type',\n",
    "                'outcome': outcome,\n",
    "                'term': term,\n",
    "                'coef': m.params.get(term, np.nan),\n",
    "                'pvalue': m.pvalues.get(term, np.nan),\n",
    "                'nobs': int(m.nobs),\n",
    "            })\n",
    "\n",
    "# RQ2-style FE interaction models with type indicators\n",
    "for outcome in outcomes:\n",
    "    d = panel_robust.dropna(subset=[outcome]).copy()\n",
    "    m = _fit_ols(\n",
    "        f\"{outcome} ~ C(district) + C(year) + \"\n",
    "        f\"post_2019:has_tx_mex + post_2019:has_tx_nm + post_2019:has_tx_ok + post_2019:has_tx_la + \"\n",
    "        f\"post_trend:has_tx_mex + post_trend:has_tx_nm + post_trend:has_tx_ok + post_trend:has_tx_la\",\n",
    "        d\n",
    "    )\n",
    "    if m is not None:\n",
    "        for term in [\n",
    "            'post_2019:has_tx_mex', 'post_2019:has_tx_nm', 'post_2019:has_tx_ok', 'post_2019:has_tx_la',\n",
    "            'post_trend:has_tx_mex', 'post_trend:has_tx_nm', 'post_trend:has_tx_ok', 'post_trend:has_tx_la',\n",
    "        ]:\n",
    "            rows.append({\n",
    "                'model_family': 'RQ2_FE_border_type_interactions',\n",
    "                'outcome': outcome,\n",
    "                'term': term,\n",
    "                'coef': m.params.get(term, np.nan),\n",
    "                'pvalue': m.pvalues.get(term, np.nan),\n",
    "                'nobs': int(m.nobs),\n",
    "            })\n",
    "\n",
    "border_type_results = pd.DataFrame(rows)\n",
    "res_path = OUTPUT_DIR / 'border_type_split_results.csv'\n",
    "border_type_results.to_csv(res_path, index=False)\n",
    "\n",
    "print(f'Saved: {res_path.resolve()}')\n",
    "print(border_type_results.head(40).to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fde06b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/continuous_exposure_results.csv\n",
      "         model_family                   outcome                                 term      coef  pvalue  nobs\n",
      "RQ1_levels_continuous      inspection_intensity            share_border_exposed_insp    0.2095  0.4757   143\n",
      "RQ1_levels_continuous   avg_days_to_enforcement            share_border_exposed_insp  103.4683  0.5710   143\n",
      "RQ1_levels_continuous violations_per_inspection            share_border_exposed_insp   -0.1585  0.0144   143\n",
      "RQ1_levels_continuous           resolution_rate            share_border_exposed_insp   -0.0420  0.8619   143\n",
      "    RQ2_FE_continuous      inspection_intensity  post_2019:share_border_exposed_insp    0.0348  0.8650   143\n",
      "    RQ2_FE_continuous      inspection_intensity post_trend:share_border_exposed_insp   -0.0129  0.8281   143\n",
      "    RQ2_FE_continuous   avg_days_to_enforcement  post_2019:share_border_exposed_insp -109.4067  0.4449   143\n",
      "    RQ2_FE_continuous   avg_days_to_enforcement post_trend:share_border_exposed_insp   13.9623  0.7415   143\n",
      "    RQ2_FE_continuous violations_per_inspection  post_2019:share_border_exposed_insp   -0.0053  0.9390   143\n",
      "    RQ2_FE_continuous violations_per_inspection post_trend:share_border_exposed_insp    0.0096  0.5012   143\n",
      "    RQ2_FE_continuous           resolution_rate  post_2019:share_border_exposed_insp   -0.0322  0.8163   143\n",
      "    RQ2_FE_continuous           resolution_rate post_trend:share_border_exposed_insp   -0.0979  0.0423   143\n"
     ]
    }
   ],
   "source": [
    "# Continuous exposure models (share of inspections near border)\n",
    "rows = []\n",
    "outcomes = ['inspection_intensity', 'avg_days_to_enforcement', 'violations_per_inspection', 'resolution_rate']\n",
    "\n",
    "# RQ1-style levels with continuous share exposure\n",
    "for outcome in outcomes:\n",
    "    d = panel.dropna(subset=[outcome, 'share_border_exposed_insp', 'unique_wells']).copy()\n",
    "    d['log_unique_wells'] = np.log(d['unique_wells'].clip(lower=1))\n",
    "    m = smf.ols(\n",
    "        f\"{outcome} ~ share_border_exposed_insp + log_unique_wells + C(year)\",\n",
    "        data=d\n",
    "    ).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n",
    "    rows.append({\n",
    "        'model_family': 'RQ1_levels_continuous',\n",
    "        'outcome': outcome,\n",
    "        'term': 'share_border_exposed_insp',\n",
    "        'coef': m.params.get('share_border_exposed_insp', np.nan),\n",
    "        'pvalue': m.pvalues.get('share_border_exposed_insp', np.nan),\n",
    "        'nobs': int(m.nobs),\n",
    "    })\n",
    "\n",
    "# RQ2-style FE with continuous interactions\n",
    "for outcome in outcomes:\n",
    "    d = panel.dropna(subset=[outcome, 'share_border_exposed_insp']).copy()\n",
    "    m = smf.ols(\n",
    "        f\"{outcome} ~ C(district) + C(year) + post_2019:share_border_exposed_insp + post_trend:share_border_exposed_insp\",\n",
    "        data=d\n",
    "    ).fit(cov_type='cluster', cov_kwds={'groups': d['district']})\n",
    "    rows.append({\n",
    "        'model_family': 'RQ2_FE_continuous',\n",
    "        'outcome': outcome,\n",
    "        'term': 'post_2019:share_border_exposed_insp',\n",
    "        'coef': m.params.get('post_2019:share_border_exposed_insp', np.nan),\n",
    "        'pvalue': m.pvalues.get('post_2019:share_border_exposed_insp', np.nan),\n",
    "        'nobs': int(m.nobs),\n",
    "    })\n",
    "    rows.append({\n",
    "        'model_family': 'RQ2_FE_continuous',\n",
    "        'outcome': outcome,\n",
    "        'term': 'post_trend:share_border_exposed_insp',\n",
    "        'coef': m.params.get('post_trend:share_border_exposed_insp', np.nan),\n",
    "        'pvalue': m.pvalues.get('post_trend:share_border_exposed_insp', np.nan),\n",
    "        'nobs': int(m.nobs),\n",
    "    })\n",
    "\n",
    "continuous_results = pd.DataFrame(rows)\n",
    "res_path = OUTPUT_DIR / 'continuous_exposure_results.csv'\n",
    "continuous_results.to_csv(res_path, index=False)\n",
    "\n",
    "print(f'Saved: {res_path.resolve()}')\n",
    "print(continuous_results.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5cb889f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/cutoff_sensitivity_results.csv\n",
      " cutoff_km model_family                 outcome                          term      coef  pvalue  nobs\n",
      "        25   RQ1_levels    inspection_intensity             share_border_25km    0.5386  0.4228   143\n",
      "        25       RQ2_FE avg_days_to_enforcement   post_2019:share_border_25km -101.9283  0.7010   143\n",
      "        25       RQ2_FE avg_days_to_enforcement  post_trend:share_border_25km   30.6087  0.7698   143\n",
      "        75   RQ1_levels    inspection_intensity             share_border_75km    0.0794  0.6963   143\n",
      "        75       RQ2_FE avg_days_to_enforcement   post_2019:share_border_75km  -75.6591  0.5116   143\n",
      "        75       RQ2_FE avg_days_to_enforcement  post_trend:share_border_75km   -0.0090  0.9997   143\n",
      "       100   RQ1_levels    inspection_intensity            share_border_100km    0.0022  0.9872   143\n",
      "       100       RQ2_FE avg_days_to_enforcement  post_2019:share_border_100km   -4.4795  0.9474   143\n",
      "       100       RQ2_FE avg_days_to_enforcement post_trend:share_border_100km  -12.6587  0.5147   143\n"
     ]
    }
   ],
   "source": [
    "# Cutoff sensitivity (25km / 75km / 100km)\n",
    "dist_cols = [\n",
    "    'distance_to_texmex_km',\n",
    "    'distance_to_nm_border_km',\n",
    "    'distance_to_ok_border_km',\n",
    "    'distance_to_la_border_km',\n",
    "]\n",
    "\n",
    "wb_cut = well_border[['api_norm'] + dist_cols].copy()\n",
    "wb_cut['min_border_distance_km'] = wb_cut[dist_cols].min(axis=1, skipna=True)\n",
    "\n",
    "# If all distances are missing, keep as NaN (not border-exposed)\n",
    "all_missing = wb_cut[dist_cols].isna().all(axis=1)\n",
    "wb_cut.loc[all_missing, 'min_border_distance_km'] = np.nan\n",
    "\n",
    "cutoffs = [25, 50, 75, 100]\n",
    "for c in cutoffs:\n",
    "    wb_cut[f'within_{c}km_any_border'] = (wb_cut['min_border_distance_km'] <= c).astype(int)\n",
    "\n",
    "insp_base = inspections[['api_norm', 'district', 'year', 'compliance']].copy()\n",
    "insp_cut = insp_base.merge(wb_cut[['api_norm'] + [f'within_{c}km_any_border' for c in cutoffs]], on='api_norm', how='left')\n",
    "for c in cutoffs:\n",
    "    insp_cut[f'within_{c}km_any_border'] = pd.to_numeric(insp_cut[f'within_{c}km_any_border'], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "rows = []\n",
    "for c in [25, 75, 100]:\n",
    "    share_df = (\n",
    "        insp_cut.groupby(['district', 'year'], as_index=False)[f'within_{c}km_any_border']\n",
    "        .mean()\n",
    "        .rename(columns={f'within_{c}km_any_border': f'share_border_{c}km'})\n",
    "    )\n",
    "    d = panel.merge(share_df, on=['district', 'year'], how='left')\n",
    "\n",
    "    # RQ1 levels model (inspection intensity)\n",
    "    d1 = d.dropna(subset=['inspection_intensity', f'share_border_{c}km', 'unique_wells']).copy()\n",
    "    d1['log_unique_wells'] = np.log(d1['unique_wells'].clip(lower=1))\n",
    "    m1 = smf.ols(\n",
    "        f\"inspection_intensity ~ {f'share_border_{c}km'} + log_unique_wells + C(year)\",\n",
    "        data=d1\n",
    "    ).fit(cov_type='cluster', cov_kwds={'groups': d1['district']})\n",
    "\n",
    "    rows.append({\n",
    "        'cutoff_km': c,\n",
    "        'model_family': 'RQ1_levels',\n",
    "        'outcome': 'inspection_intensity',\n",
    "        'term': f'share_border_{c}km',\n",
    "        'coef': m1.params.get(f'share_border_{c}km', np.nan),\n",
    "        'pvalue': m1.pvalues.get(f'share_border_{c}km', np.nan),\n",
    "        'nobs': int(m1.nobs),\n",
    "    })\n",
    "\n",
    "    # RQ2 FE model (timing) - main paper signal\n",
    "    d2 = d.dropna(subset=['avg_days_to_enforcement', f'share_border_{c}km']).copy()\n",
    "    m2 = smf.ols(\n",
    "        f\"avg_days_to_enforcement ~ C(district) + C(year) + post_2019:{f'share_border_{c}km'} + post_trend:{f'share_border_{c}km'}\",\n",
    "        data=d2\n",
    "    ).fit(cov_type='cluster', cov_kwds={'groups': d2['district']})\n",
    "\n",
    "    rows.append({\n",
    "        'cutoff_km': c,\n",
    "        'model_family': 'RQ2_FE',\n",
    "        'outcome': 'avg_days_to_enforcement',\n",
    "        'term': f'post_2019:share_border_{c}km',\n",
    "        'coef': m2.params.get(f'post_2019:share_border_{c}km', np.nan),\n",
    "        'pvalue': m2.pvalues.get(f'post_2019:share_border_{c}km', np.nan),\n",
    "        'nobs': int(m2.nobs),\n",
    "    })\n",
    "    rows.append({\n",
    "        'cutoff_km': c,\n",
    "        'model_family': 'RQ2_FE',\n",
    "        'outcome': 'avg_days_to_enforcement',\n",
    "        'term': f'post_trend:share_border_{c}km',\n",
    "        'coef': m2.params.get(f'post_trend:share_border_{c}km', np.nan),\n",
    "        'pvalue': m2.pvalues.get(f'post_trend:share_border_{c}km', np.nan),\n",
    "        'nobs': int(m2.nobs),\n",
    "    })\n",
    "\n",
    "cutoff_results = pd.DataFrame(rows)\n",
    "res_path = OUTPUT_DIR / 'cutoff_sensitivity_results.csv'\n",
    "cutoff_results.to_csv(res_path, index=False)\n",
    "\n",
    "print(f'Saved: {res_path.resolve()}')\n",
    "print(cutoff_results.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d603b1f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 850x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved figure: /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/money_plot_timing_border_prepost2019.png\n",
      "Saved table:  /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/money_border_type_timing_results.csv\n",
      "Saved table:  /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/money_cutoff_timing_results.csv\n",
      "Saved table:  /home/dadams/Repos/texas-borderlands/analysis/output_borderlands/money_plot_timing_ci_by_year.csv\n",
      "\n",
      "Border-type timing interactions:\n",
      "                 term     coef  pvalue\n",
      " post_2019:has_tx_mex   4.0900  0.9062\n",
      "  post_2019:has_tx_nm -18.7442  0.6013\n",
      "  post_2019:has_tx_ok -14.2446  0.8134\n",
      "  post_2019:has_tx_la -43.6598  0.6415\n",
      "post_trend:has_tx_mex  -0.0148  0.9991\n",
      " post_trend:has_tx_nm  22.9067  0.0189\n",
      " post_trend:has_tx_ok -16.7188  0.0794\n",
      " post_trend:has_tx_la   0.6415  0.9551\n",
      "\n",
      "Timing cutoff sensitivity (post_2019 interaction):\n",
      " cutoff_km      coef  pvalue  nobs\n",
      "        25 -101.9283  0.7010   143\n",
      "        75  -75.6591  0.5116   143\n",
      "       100   -4.4795  0.9474   143\n"
     ]
    }
   ],
   "source": [
    "# Money cell: border-type disaggregation + cutoff sensitivity + single timing figure\n",
    "\n",
    "# 1) Border-type disaggregation focused on timing outcome\n",
    "if 'panel_robust' not in globals():\n",
    "    # Fallback if robustness profile cell was skipped\n",
    "    panel_robust = panel.copy()\n",
    "    for c in ['has_tx_mex','has_tx_nm','has_tx_ok','has_tx_la']:\n",
    "        if c not in panel_robust.columns:\n",
    "            panel_robust[c] = 0\n",
    "\n",
    "timing_border_type_df = panel_robust.dropna(subset=['avg_days_to_enforcement']).copy()\n",
    "\n",
    "m_timing_type = smf.ols(\n",
    "    'avg_days_to_enforcement ~ C(district) + C(year) + '\n",
    "    'post_2019:has_tx_mex + post_2019:has_tx_nm + post_2019:has_tx_ok + post_2019:has_tx_la + '\n",
    "    'post_trend:has_tx_mex + post_trend:has_tx_nm + post_trend:has_tx_ok + post_trend:has_tx_la',\n",
    "    data=timing_border_type_df\n",
    ").fit(cov_type='cluster', cov_kwds={'groups': timing_border_type_df['district']})\n",
    "\n",
    "timing_type_terms = [\n",
    "    'post_2019:has_tx_mex','post_2019:has_tx_nm','post_2019:has_tx_ok','post_2019:has_tx_la',\n",
    "    'post_trend:has_tx_mex','post_trend:has_tx_nm','post_trend:has_tx_ok','post_trend:has_tx_la',\n",
    "]\n",
    "\n",
    "timing_type_table = pd.DataFrame({\n",
    "    'term': timing_type_terms,\n",
    "    'coef': [m_timing_type.params.get(t, np.nan) for t in timing_type_terms],\n",
    "    'pvalue': [m_timing_type.pvalues.get(t, np.nan) for t in timing_type_terms],\n",
    "})\n",
    "\n",
    "# 2) Cutoff sensitivity (timing-only focus)\n",
    "if 'cutoff_results' in globals() and len(cutoff_results) > 0:\n",
    "    timing_cutoff_table = cutoff_results[\n",
    "        (cutoff_results['outcome'] == 'avg_days_to_enforcement') &\n",
    "        (cutoff_results['term'].str.startswith('post_2019:share_border_'))\n",
    "    ][['cutoff_km','coef','pvalue','nobs']].copy().sort_values('cutoff_km')\n",
    "else:\n",
    "    timing_cutoff_table = pd.DataFrame(columns=['cutoff_km','coef','pvalue','nobs'])\n",
    "\n",
    "# 3) Single clean figure: timing outcome, border vs non-border, pre/post 2019, with 95% CI bands\n",
    "plot_base = panel[['district','year','border_district','avg_days_to_enforcement']].dropna().copy()\n",
    "plot_base['group'] = plot_base['border_district'].map({0:'Non-border', 1:'Border'})\n",
    "\n",
    "stats = (\n",
    "    plot_base.groupby(['year','group'], as_index=False)\n",
    "    .agg(\n",
    "        mean_days=('avg_days_to_enforcement','mean'),\n",
    "        sd_days=('avg_days_to_enforcement','std'),\n",
    "        n=('avg_days_to_enforcement','size')\n",
    "    )\n",
    ")\n",
    "stats['se_days'] = stats['sd_days'] / np.sqrt(stats['n'].clip(lower=1))\n",
    "stats['ci95'] = 1.96 * stats['se_days']\n",
    "stats['lower'] = stats['mean_days'] - stats['ci95']\n",
    "stats['upper'] = stats['mean_days'] + stats['ci95']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8.5, 5.2))\n",
    "colors = {'Non-border':'#4b5563', 'Border':'#dc2626'}\n",
    "\n",
    "for g in ['Non-border', 'Border']:\n",
    "    d = stats[stats['group'] == g].sort_values('year')\n",
    "    if len(d) == 0:\n",
    "        continue\n",
    "    ax.plot(d['year'], d['mean_days'], marker='o', linewidth=2.2, color=colors[g], label=g)\n",
    "    # 95% CI ribbon\n",
    "    ax.fill_between(d['year'], d['lower'], d['upper'], color=colors[g], alpha=0.18, linewidth=0)\n",
    "\n",
    "ax.axvline(POLICY_YEAR, color='black', linestyle='--', linewidth=1.2, alpha=0.8)\n",
    "ax.text(POLICY_YEAR + 0.1, ax.get_ylim()[1]*0.98, '2019 disclosure', fontsize=9, va='top')\n",
    "\n",
    "# Add simple pre/post means annotation\n",
    "prepost = panel.assign(period=np.where(panel['year'] < POLICY_YEAR, 'Pre-2019', 'Post-2019'))     .groupby(['period','border_district'], as_index=False)['avg_days_to_enforcement'].mean()\n",
    "nb_pre = prepost[(prepost['period']=='Pre-2019') & (prepost['border_district']==0)]['avg_days_to_enforcement']\n",
    "b_pre  = prepost[(prepost['period']=='Pre-2019') & (prepost['border_district']==1)]['avg_days_to_enforcement']\n",
    "nb_post= prepost[(prepost['period']=='Post-2019') & (prepost['border_district']==0)]['avg_days_to_enforcement']\n",
    "b_post = prepost[(prepost['period']=='Post-2019') & (prepost['border_district']==1)]['avg_days_to_enforcement']\n",
    "\n",
    "if len(nb_pre) and len(b_pre) and len(nb_post) and len(b_post):\n",
    "    note = (\n",
    "        f\"Pre means (days): non-border={nb_pre.iloc[0]:.1f}, border={b_pre.iloc[0]:.1f}\\n\"\n",
    "        f\"Post means (days): non-border={nb_post.iloc[0]:.1f}, border={b_post.iloc[0]:.1f}\"\n",
    "    )\n",
    "    ax.text(0.01, 0.01, note, transform=ax.transAxes, fontsize=8.5,\n",
    "            va='bottom', ha='left', bbox=dict(facecolor='white', alpha=0.8, edgecolor='#d1d5db'))\n",
    "\n",
    "ax.set_title('Enforcement Timing by Border Status (2015-2025)', fontsize=12, weight='bold')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Average Days to Enforcement')\n",
    "# Explicit aggregation note to prevent ambiguity in manuscript review\n",
    "ax.text(0.99, 0.01,\n",
    "        'Note: Year-group means use district-year values (equal district weighting), not pooled well-level records.',\n",
    "        transform=ax.transAxes, fontsize=7.8, ha='right', va='bottom',\n",
    "        bbox=dict(facecolor='white', alpha=0.78, edgecolor='#d1d5db'))\n",
    "ax.legend(title='District group', frameon=True)\n",
    "ax.grid(alpha=0.25)\n",
    "fig.tight_layout()\n",
    "\n",
    "money_fig_path = OUTPUT_DIR / 'money_plot_timing_border_prepost2019.png'\n",
    "fig.savefig(money_fig_path, dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Save compact robustness tables for paper text\n",
    "money_type_path = OUTPUT_DIR / 'money_border_type_timing_results.csv'\n",
    "money_cutoff_path = OUTPUT_DIR / 'money_cutoff_timing_results.csv'\n",
    "timing_type_table.to_csv(money_type_path, index=False)\n",
    "timing_cutoff_table.to_csv(money_cutoff_path, index=False)\n",
    "\n",
    "# Save year-by-year CI table for transparency\n",
    "ci_path = OUTPUT_DIR / 'money_plot_timing_ci_by_year.csv'\n",
    "stats.to_csv(ci_path, index=False)\n",
    "\n",
    "print(f'Saved figure: {money_fig_path.resolve()}')\n",
    "print(f'Saved table:  {money_type_path.resolve()}')\n",
    "print(f'Saved table:  {money_cutoff_path.resolve()}')\n",
    "print(f'Saved table:  {ci_path.resolve()}')\n",
    "print('Aggregation note: Year-group lines/CI are computed from district-year averages (equal district weighting).')\n",
    "print('\\nBorder-type timing interactions:')\n",
    "print(timing_type_table.to_string(index=False))\n",
    "print('\\nTiming cutoff sensitivity (post_2019 interaction):')\n",
    "print(timing_cutoff_table.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4719bed6",
   "metadata": {},
   "source": [
    "Figure X. Enforcement timing by border status with 95% confidence intervals (2015–2025).\n",
    "Lines plot annual group means of average days to enforcement for border versus non-border districts. Points indicate yearly means; vertical uncertainty reflects 95% confidence intervals around the mean (mean ± 1.96 × SE), shown as shaded ribbons. The dashed line marks the 2019 disclosure reform. Pre-2019, border districts exhibit slower enforcement timing; post-2019, the timing gap collapses and slightly reverses, consistent with a border-specific post-reform level shift rather than a border-specific trend change."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9460e0e",
   "metadata": {},
   "source": [
    "## RQ3/RQ4 (Woods Reaction Function): Not Estimable in This Study Build\n",
    "\n",
    "This notebook does **not** estimate a Woods-style reaction function because we do not have cross-jurisdiction competitor enforcement metrics.\n",
    "\n",
    "What we *can* do with current data:\n",
    "- Estimate borderlands gaps in Texas district outcomes (inspection intensity, violations per inspection, enforcement timing, compliance/resolution).\n",
    "- Estimate whether 2019 disclosure changed those outcomes differently in border districts (`post_2019:border` and `post_trend:border`).\n",
    "\n",
    "What we *cannot* do without competitor data:\n",
    "- Estimate `Enforcement Gap(t) = Texas_border_district_stringency(t) - Competitor_stringency(t)`.\n",
    "- Test asymmetric response to positive vs negative gap (Woods reaction function).\n",
    "\n",
    "Interpretation can still use Woods/borderlands theory qualitatively, but causal reaction-function claims are out of scope for this build.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1bcf5095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RQ3/RQ4 reaction-function model is intentionally disabled in this notebook build.\n",
      "Reason: no cross-jurisdiction competitor_stringency data available.\n",
      "Scope retained: RQ1/RQ2 border gaps and post-2019 differential trends within Texas districts.\n",
      "Reference files (for future data collection):\n",
      "  /home/dadams/Repos/texas-borderlands/data/district_competitor_links.csv (exists=True)\n",
      "  /home/dadams/Repos/texas-borderlands/data/competition_panel_template.csv (exists=True)\n"
     ]
    }
   ],
   "source": [
    "# RQ3/RQ4 not estimated (no competitor stringency data)\n",
    "print('RQ3/RQ4 reaction-function model is intentionally disabled in this notebook build.')\n",
    "print('Reason: no cross-jurisdiction competitor_stringency data available.')\n",
    "print('Scope retained: RQ1/RQ2 border gaps and post-2019 differential trends within Texas districts.')\n",
    "\n",
    "# Optional: keep link/template artifacts for future extensions\n",
    "link_path = Path('../data/district_competitor_links.csv')\n",
    "template_path = Path('../data/competition_panel_template.csv')\n",
    "print('Reference files (for future data collection):')\n",
    "print(f'  {link_path.resolve()} (exists={link_path.exists()})')\n",
    "print(f'  {template_path.resolve()} (exists={template_path.exists()})')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e47a5a70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved available results files to output_borderlands/\n"
     ]
    }
   ],
   "source": [
    "# Save headline model outputs\n",
    "if 'rq1_results' in globals():\n",
    "    rq1_results.to_csv(OUTPUT_DIR / 'rq1_results.csv', index=False)\n",
    "if 'rq2_results' in globals():\n",
    "    rq2_results.to_csv(OUTPUT_DIR / 'rq2_results.csv', index=False)\n",
    "\n",
    "print('Saved available results files to output_borderlands/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524831c9",
   "metadata": {},
   "source": [
    "## Notes For Next Iteration\n",
    "\n",
    "- Validate border district coding against your preferred substantive definition.\n",
    "- Decide final `tx_stringency` metric for competition models (inspection intensity vs timing-based measure).\n",
    "- If you add competitor panel data, rerun the RQ3/RQ4 section without changing other cells.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde1bf7d",
   "metadata": {},
   "source": [
    "## Research Questions and Hypothesis Test Summary (Current Run)\n",
    "\n",
    "### What This Notebook Tests\n",
    "This notebook estimates (1) **Texas-internal borderlands enforcement gaps** and (2) whether the **2019 disclosure reform** produced **border-specific changes** in district-level enforcement outcomes. Woods reaction-function estimation remains out of scope because cross-jurisdiction competitor stringency data are unavailable.\n",
    "\n",
    "### Data/Build Validation Snapshot\n",
    "- Wells loaded: **1,010,432**.\n",
    "- 2015-2025 analysis sample: inspections **1,867,859**; violations **191,762**.\n",
    "- Border well counts within 50km (current geometry build):\n",
    "  - TX-MX: **40,339**\n",
    "  - TX-NM: **81,567**\n",
    "  - TX-OK: **19,643**\n",
    "  - TX-LA: **29,675**\n",
    "  - Any border exposure: **169,520** wells.\n",
    "- District-year panel: **143 observations**, **13 districts**, **2015-2025**.\n",
    "\n",
    "### RQ1: Border Gaps (Levels)\n",
    "Do border districts differ systematically from non-border districts?\n",
    "\n",
    "- Descriptives (border vs non-border):\n",
    "  - Inspection intensity: **1.329 vs 1.515**\n",
    "  - Violations per inspection: **0.130 vs 0.098**\n",
    "  - Mean days to enforcement: **145.2 vs 122.8**\n",
    "  - Mean resolution rate: **0.543 vs 0.596**\n",
    "- Regression estimates:\n",
    "  - `inspection_intensity ~ border_district + ...`: coef **-0.1755**, p=**0.0999**\n",
    "  - `violations_per_inspection ~ border_district + ...`: coef **+0.0434**, p=**0.0949**\n",
    "\n",
    "**Interpretation:** Border districts show a weaker enforcement profile descriptively, with marginal model evidence for lower inspection intensity and higher violations per inspection.\n",
    "\n",
    "### RQ2: Disclosure × Border Heterogeneity\n",
    "Did post-2019 changes differ in border districts?\n",
    "\n",
    "- `inspection_intensity`: `post_2019:border` **-0.1191** (p=**0.0753**), `post_trend:border` **-0.0052** (p=**0.8181**)\n",
    "- `violations_per_inspection`: `post_2019:border` **+0.0040** (p=**0.8881**), `post_trend:border` **-0.0012** (p=**0.8350**)\n",
    "- `avg_days_to_enforcement`: `post_2019:border` **-74.5893** (p=**0.0156**), `post_trend:border` **-1.1587** (p=**0.9252**)\n",
    "- `resolution_rate`: `post_2019:border` **+0.0404** (p=**0.4520**), `post_trend:border` **-0.0186** (p=**0.3404**)\n",
    "\n",
    "**Interpretation:** The strongest border-specific post-2019 signal is in **timing** (faster enforcement), while border-specific changes in intensity, detection, and resolution are null. Border-specific trend differentials are null in the main binary-border model.\n",
    "\n",
    "### Robustness: Which Border / How Much Exposure / Which Cutoff\n",
    "\n",
    "- **Border-type split models (timing focus):**\n",
    "  - Level-model patterns suggest heterogeneity (e.g., `has_tx_ok` positive timing coefficient p=0.021; `has_tx_nm` negative/marginal p=0.061).\n",
    "  - FE interaction terms for `post_2019:has_tx_*` are null.\n",
    "  - Some `post_trend:has_tx_*` terms appear significant/marginal (`TX-NM` positive p=0.0189; `TX-OK` negative p=0.0794), indicating mixed border-type trend heterogeneity.\n",
    "- **Continuous exposure models:**\n",
    "  - Main continuous `post_2019` timing interaction is null (p=0.4449).\n",
    "  - One trend interaction appears significant for resolution (`post_trend:share_border_exposed_insp`, p=0.0423).\n",
    "- **Cutoff sensitivity (25/75/100km, timing):**\n",
    "  - `post_2019:share_border_*km` timing interactions are all null (p=0.7010, 0.5116, 0.9474).\n",
    "\n",
    "**Robustness interpretation:** The core binary-border timing level-shift result remains the clearest signal. Disaggregated and continuous/cutoff specifications show heterogeneity but no single, stable alternative pattern that dominates the baseline finding.\n",
    "\n",
    "### Money Plot\n",
    "- Figure: `output_borderlands/money_plot_timing_border_prepost2019.png`\n",
    "- Companion tables:\n",
    "  - `output_borderlands/money_border_type_timing_results.csv`\n",
    "  - `output_borderlands/money_cutoff_timing_results.csv`\n",
    "  - `output_borderlands/money_plot_timing_ci_by_year.csv`\n",
    "- **Aggregation note for manuscript caption/methods:**\n",
    "  - Year-group means and CIs in the money plot are computed from **district-year timing values** (each district-year is one observation), with equal weighting across districts within group-year.\n",
    "  - They are **not** pooled directly from well-level observations.\n",
    "\n",
    "### Hypothesis Status (Current Design)\n",
    "- **H1 (Border inspection gap):** **Partial support** (descriptives + marginal border coefficient).\n",
    "- **H2 (Border pipeline disadvantage):** **Supported descriptively**, mixed regression support by outcome.\n",
    "- **H3 (Disclosure heterogeneity in levels; `post_2019:border`):** **Supported for timing only**; not supported for other outcomes.\n",
    "- **H4 (Disclosure heterogeneity in trends; `post_trend:border`):** **Not supported** in the baseline binary-border model; robustness splits show mixed border-type trend heterogeneity.\n",
    "\n",
    "### Writing Direction for the Manuscript\n",
    "\n",
    "**Core claim hierarchy:**\n",
    "1. Border districts exhibit a persistent, descriptively weaker enforcement profile.\n",
    "2. The 2019 disclosure shift is most visible in enforcement timing (faster post-2019 in border districts).\n",
    "3. Broader “capacity expansion” signals (inspection intensity, resolution) are weak or null in differential models.\n",
    "4. Robustness checks complicate mechanism but do not overturn the central timing result.\n",
    "\n",
    "**Suggested paper structure:**\n",
    "1. Introduction: policy context, borderlands governance stakes, and transparency-reform relevance.\n",
    "2. Theory: borderlands capacity asymmetry + transparency/administrative throughput mechanism.\n",
    "3. Data and measures: district-year panel, border definitions, timing/compliance outcomes.\n",
    "4. Methods:\n",
    "   - RQ1 levels models.\n",
    "   - RQ2 district FE + year FE interaction models.\n",
    "   - Robustness (border type, continuous exposure, cutoff sensitivity).\n",
    "   - Explicit note that reaction-function estimation is deferred absent competitor stringency.\n",
    "5. Results:\n",
    "   - Main table: RQ1 and RQ2 coefficients.\n",
    "   - Money plot (timing border vs non-border with CI bands).\n",
    "   - Robustness table(s): border-type and cutoff/continuous checks.\n",
    "6. Discussion:\n",
    "   - “Faster pipeline, not wider pipeline” interpretation.\n",
    "   - What this implies for transparency reforms in uneven-capacity settings.\n",
    "7. Limitations and next steps:\n",
    "   - No competitor stringency data.\n",
    "   - Small number of districts and inference caution.\n",
    "   - Future expansion to true cross-jurisdiction competition panel.\n",
    "\n",
    "**Language to avoid in draft claims:**\n",
    "- Avoid strong causal claims on interstate competition.\n",
    "- Avoid implying broad compliance improvement unless supported by outcome-specific coefficients.\n",
    "\n",
    "### Theory Positioning\n",
    "Borderlands governance theory is consistent with persistent gap patterns and uneven enforcement capacity. The disclosure-era effect appears concentrated in **processing speed** rather than expanded inspection coverage or broad compliance improvements. Woods-style competition remains an interpretive frame, but not an estimated mechanism in this notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c63fc72",
   "metadata": {},
   "source": [
    "## Research Questions and Hypothesis Test Summary (Current Run)\n",
    "\n",
    "### What This Notebook Tests\n",
    "\n",
    "This notebook estimates two Texas-internal questions using district-year outcomes from 2015–2025:\n",
    "(1) whether **border-exposed districts** exhibit systematic enforcement gaps relative to non-border districts, and\n",
    "(2) whether the **2019 disclosure reform** is associated with **border-specific changes** in enforcement outcomes.\n",
    "Woods-style reaction-function tests are intentionally out of scope because comparable competitor stringency data are not available.\n",
    "\n",
    "### Data and Build Validation\n",
    "\n",
    "* Wells loaded: **1,010,432**.\n",
    "* 2015–2025 analysis sample: inspections **1,867,859**; violations **191,762**.\n",
    "* Border well counts within 50 km (current geometry build):\n",
    "\n",
    "  * TX–MX: **40,339**\n",
    "  * TX–NM: **81,567**\n",
    "  * TX–OK: **19,643**\n",
    "  * TX–LA: **29,675**\n",
    "  * Any border exposure: **169,520** wells\n",
    "* District-year panel: **143 observations**, **13 districts**, **2015–2025**.\n",
    "\n",
    "---\n",
    "\n",
    "## RQ1: Border Gaps\n",
    "\n",
    "**Do border districts differ systematically from non-border districts?**\n",
    "\n",
    "### Descriptive gaps (border vs non-border)\n",
    "\n",
    "* Inspection intensity: **1.329 vs 1.515**\n",
    "* Violations per inspection: **0.130 vs 0.098**\n",
    "* Mean days to enforcement: **145.2 vs 122.8**\n",
    "* Mean resolution rate: **0.543 vs 0.596**\n",
    "\n",
    "### Regression evidence (levels models)\n",
    "\n",
    "* `inspection_intensity ~ border_district + ...`: **−0.1755** (p=**0.0999**)\n",
    "* `violations_per_inspection ~ border_district + ...`: **+0.0434** (p=**0.0949**)\n",
    "\n",
    "**Interpretation:** Border districts show a consistently weaker enforcement profile in descriptives, with **marginal** model evidence of lower inspection intensity and higher violations per inspection. The descriptive pattern is broad; the most reliable regression signal at this stage is concentrated in **coverage (inspections)** and **detection conditional on inspection (violations/inspection)**.\n",
    "\n",
    "---\n",
    "\n",
    "## RQ2: Disclosure × Border Heterogeneity\n",
    "\n",
    "**Did post-2019 changes differ in border districts?**\n",
    "\n",
    "* `inspection_intensity`: `post_2019:border` **−0.1191** (p=**0.0753**), `post_trend:border` **−0.0052** (p=**0.8181**)\n",
    "* `violations_per_inspection`: `post_2019:border` **+0.0040** (p=**0.8881**), `post_trend:border` **−0.0012** (p=**0.8350**)\n",
    "* `avg_days_to_enforcement`: `post_2019:border` **−74.5893** (p=**0.0156**), `post_trend:border` **−1.1587** (p=**0.9252**)\n",
    "* `resolution_rate`: `post_2019:border` **+0.0404** (p=**0.4520**), `post_trend:border` **−0.0186** (p=**0.3404**)\n",
    "\n",
    "**Interpretation:** The border-specific post-2019 signal is concentrated in **timing**: border districts become substantially faster after 2019 (a clear **level shift**). Border-specific changes in inspection intensity, detection intensity, and resolution are null, and border-specific trend differentials are null in the baseline binary-border model. In short: **the border pipeline got faster, not wider.**\n",
    "\n",
    "---\n",
    "\n",
    "## Robustness and Heterogeneity Checks\n",
    "\n",
    "These checks evaluate whether the baseline binary-border timing result is an artifact of a particular border definition or distance cutoff.\n",
    "\n",
    "### Border-type splits (timing focus)\n",
    "\n",
    "* Level-model patterns suggest heterogeneity (e.g., `has_tx_ok` positive timing coefficient p=0.021; `has_tx_nm` negative/marginal p=0.061).\n",
    "* FE interactions for `post_2019:has_tx_*` are null.\n",
    "* Some `post_trend:has_tx_*` terms are significant/marginal (TX–NM positive p=0.0189; TX–OK negative p=0.0794), indicating **uneven border-type trend heterogeneity** in split specifications.\n",
    "\n",
    "### Continuous exposure models\n",
    "\n",
    "* Main continuous `post_2019` timing interaction is null (p=0.4449).\n",
    "* One trend interaction is significant for resolution (`post_trend:share_border_exposed_insp`, p=0.0423).\n",
    "\n",
    "### Cutoff sensitivity (25/75/100 km, timing)\n",
    "\n",
    "* Timing interactions for `post_2019:share_border_*km` are all null (p=0.7010, 0.5116, 0.9474).\n",
    "\n",
    "**Robustness interpretation:** The baseline binary-border timing level-shift remains the clearest and most stable result. Alternative specifications show heterogeneity—especially by border type—but do not yield a single, consistent alternative pattern that replaces the main finding. The absence of a monotonic “dose-response” in continuous/cutoff models suggests the timing shift behaves more like a **district-level regime change** than a smooth function of border exposure intensity.\n",
    "\n",
    "---\n",
    "\n",
    "## Money Plot and Aggregation Note\n",
    "\n",
    "* Figure: `output_borderlands/money_plot_timing_border_prepost2019.png`\n",
    "* Companion tables:\n",
    "\n",
    "  * `output_borderlands/money_border_type_timing_results.csv`\n",
    "  * `output_borderlands/money_cutoff_timing_results.csv`\n",
    "  * `output_borderlands/money_plot_timing_ci_by_year.csv`\n",
    "\n",
    "**Aggregation note (for caption/methods):** Year-group means and 95% CIs are computed from **district-year timing values** (each district-year is one observation), with **equal weighting across districts within each group-year**; results are not pooled from well-level observations.\n",
    "\n",
    "---\n",
    "\n",
    "## Hypothesis Status (Current Design)\n",
    "\n",
    "* **H1 (Border inspection gap):** **Partial support** (descriptives + marginal border coefficient).\n",
    "* **H2 (Border pipeline disadvantage):** **Supported descriptively**, mixed regression support by outcome.\n",
    "* **H3 (Disclosure heterogeneity in levels; `post_2019:border`):** **Supported for timing only**; null elsewhere.\n",
    "* **H4 (Disclosure heterogeneity in trends; `post_trend:border`):** **Not supported** in baseline; split models show mixed border-type trend heterogeneity.\n",
    "\n",
    "---\n",
    "\n",
    "## Writing Direction for the Manuscript\n",
    "\n",
    "### Core claim hierarchy (keep this ordering)\n",
    "\n",
    "1. Border districts exhibit a persistent, descriptively weaker enforcement profile (coverage, detection conditional on inspection, timing, and follow-through).\n",
    "2. The 2019 disclosure reform is associated with a **border-specific improvement in enforcement timing** (a sizeable level shift).\n",
    "3. Differential evidence for broader “capacity expansion” (inspection intensity, resolution) is weak or null.\n",
    "4. Robustness checks complicate mechanisms and reveal heterogeneity, but **do not overturn** the central timing result.\n",
    "\n",
    "### Suggested structure (mapped to what you’ve already built)\n",
    "\n",
    "1. Introduction: Texas enforcement context + why borderlands governance matters + why transparency is a plausible shock.\n",
    "2. Theory: borderlands capacity asymmetry + transparency-throughput mechanism (focus on timing).\n",
    "3. Data and measures: district-year panel, border definitions, outcomes.\n",
    "4. Methods:\n",
    "\n",
    "   * RQ1 levels models\n",
    "   * RQ2 district FE + year FE interaction models\n",
    "   * robustness suite (border type, continuous exposure, cutoffs)\n",
    "   * explicit note: reaction-function tests deferred\n",
    "5. Results: main table + timing money plot with CI ribbons + compact robustness table(s).\n",
    "6. Discussion: “faster pipeline, not wider pipeline” + what that implies for transparency reforms in uneven-capacity settings.\n",
    "7. Limitations/next steps: competitor data gap; small-N districts; future cross-jurisdiction extension.\n",
    "\n",
    "### Guardrails (language discipline)\n",
    "\n",
    "* Don’t imply interstate competition is identified here.\n",
    "* Don’t generalize to “compliance improved” unless the outcome-specific coefficients support it.\n",
    "* Be precise: timing improves; coverage and closure do not reliably move in the border interaction models.\n",
    "\n",
    "---\n",
    "\n",
    "## Theory Positioning (tight version)\n",
    "\n",
    "Borderlands governance theory fits the baseline pattern: persistent enforcement gaps consistent with uneven state capacity and asymmetric governance conditions. The disclosure-era result is concentrated in **processing speed**, not inspection coverage or broad improvements in closure outcomes, suggesting transparency affects **throughput** more than capacity. Woods-style competition remains a plausible interpretive frame, but it is not estimated in this notebook.\n",
    "\n",
    "---\n",
    "\n",
    "**Transparency can accelerate administrative processing where enforcement is structurally weaker, but speed alone is not the same thing as capacity.**\n",
    "\n",
    "---\n",
    "\n",
    "*This analysis started as an effort to test Neal D. Woods’s race-to-the-bottom claim that border jurisdictions relax enforcement when they risk looking stricter than nearby competitors. The first step is to establish whether Texas borderlands districts exhibit distinct enforcement profiles and whether the 2019 disclosure reform altered those profiles in a border-specific way. The results here identify persistent borderlands gaps and a sharp post-2019 border-specific improvement in enforcement timing (“faster pipeline, not wider pipeline”). A direct Woods reaction-function test—linking Texas enforcement to competitor stringency—remains the next phase once cross-jurisdiction enforcement measures are constructed.*\n",
    "\n",
    "---\n",
    "\n",
    "## Companion Tables (CSV Outputs)\n",
    "\n",
    "### Table A. RQ2 FE Interaction Results\n",
    "\n",
    "| Outcome | post_2019 × border (coef) | p-value | post_trend × border (coef) | p-value | N |\n",
    "|---|---:|---:|---:|---:|---:|\n",
    "| inspection_intensity | -0.119 | 0.075 | -0.005 | 0.818 | 143 |\n",
    "| violations_per_inspection | 0.004 | 0.888 | -0.001 | 0.835 | 143 |\n",
    "| avg_days_to_enforcement | -74.589 | 0.016 | -1.159 | 0.925 | 143 |\n",
    "| resolution_rate | 0.040 | 0.452 | -0.019 | 0.340 | 143 |\n",
    "\n",
    "### Table B. Competition Asymmetry Results\n",
    "\n",
    "| Term | Coefficient | p-value |\n",
    "|---|---:|---:|\n",
    "| gap_pos | 0.584 | <0.001 |\n",
    "| gap_neg | 0.499 | 0.003 |\n",
    "\n",
    "### Table C. Cutoff Sensitivity Results\n",
    "\n",
    "| Cutoff (km) | Model | Outcome | Term | Coefficient | p-value | N |\n",
    "|---:|---|---|---|---:|---:|---:|\n",
    "| 25 | RQ1_levels | inspection_intensity | share_border_25km | 0.539 | 0.423 | 143 |\n",
    "| 25 | RQ2_FE | avg_days_to_enforcement | post_2019:share_border_25km | -101.928 | 0.701 | 143 |\n",
    "| 25 | RQ2_FE | avg_days_to_enforcement | post_trend:share_border_25km | 30.609 | 0.770 | 143 |\n",
    "| 75 | RQ1_levels | inspection_intensity | share_border_75km | 0.079 | 0.696 | 143 |\n",
    "| 75 | RQ2_FE | avg_days_to_enforcement | post_2019:share_border_75km | -75.659 | 0.512 | 143 |\n",
    "| 75 | RQ2_FE | avg_days_to_enforcement | post_trend:share_border_75km | -0.009 | 1.000 | 143 |\n",
    "| 100 | RQ1_levels | inspection_intensity | share_border_100km | 0.002 | 0.987 | 143 |\n",
    "| 100 | RQ2_FE | avg_days_to_enforcement | post_2019:share_border_100km | -4.480 | 0.947 | 143 |\n",
    "| 100 | RQ2_FE | avg_days_to_enforcement | post_trend:share_border_100km | -12.659 | 0.515 | 143 |\n",
    "\n",
    "### Table D. Border-Type Timing Interactions (Money Plot Companion)\n",
    "\n",
    "| Term | Coefficient | p-value |\n",
    "|---|---:|---:|\n",
    "| post_2019:has_tx_mex | 4.090 | 0.906 |\n",
    "| post_2019:has_tx_nm | -18.744 | 0.601 |\n",
    "| post_2019:has_tx_ok | -14.245 | 0.813 |\n",
    "| post_2019:has_tx_la | -43.660 | 0.641 |\n",
    "| post_trend:has_tx_mex | -0.015 | 0.999 |\n",
    "| post_trend:has_tx_nm | 22.907 | 0.019 |\n",
    "| post_trend:has_tx_ok | -16.719 | 0.079 |\n",
    "| post_trend:has_tx_la | 0.642 | 0.955 |\n",
    "\n",
    "### Table E. District Border-Type Profile\n",
    "\n",
    "| District | Wells | Dominant border type | TX-MX share | TX-NM share | TX-OK share | TX-LA share |\n",
    "|---:|---:|---|---:|---:|---:|---:|\n",
    "| 01 | 31,898 | TX-MX | 23.1% | 0.0% | 0.0% | 0.0% |\n",
    "| 02 | 17,099 | NONE | 0.0% | 0.0% | 0.0% | 0.0% |\n",
    "| 03 | 16,700 | TX-LA | 0.0% | 0.0% | 0.0% | 11.7% |\n",
    "| 04 | 20,973 | TX-MX | 63.8% | 0.0% | 0.0% | 0.0% |\n",
    "| 05 | 9,938 | TX-OK | 0.0% | 0.0% | 0.2% | 0.0% |\n",
    "| 06 | 24,422 | TX-LA | 0.0% | 0.0% | 2.9% | 52.4% |\n",
    "| 08 | 105,931 | TX-NM | 0.0% | 19.1% | 0.0% | 0.0% |\n",
    "| 09 | 46,485 | NONE | 0.0% | 0.0% | 0.0% | 0.0% |\n",
    "| 10 | 29,621 | TX-OK | 0.0% | 0.1% | 30.2% | 0.0% |\n",
    "| 6E | 6,235 | NONE | 0.0% | 0.0% | 0.0% | 0.0% |\n",
    "| 7B | 21,230 | NONE | 0.0% | 0.0% | 0.0% | 0.0% |\n",
    "| 7C | 43,061 | NONE | 0.0% | 0.0% | 0.0% | 0.0% |\n",
    "| 8A | 42,005 | TX-NM | 0.0% | 41.8% | 0.0% | 0.0% |\n"
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    "## Methods (Policy Studies Journal Draft)\n",
    "\n",
    "### Research Design and Unit of Analysis\n",
    "This study uses a district-year panel design to estimate enforcement differences between border and non-border Texas Railroad Commission (RRC) districts and to test whether those differences changed after the January 2019 disclosure reform. The primary analytical unit is the **district-year** observation (13 districts, 2015-2025; balanced years where data exist).\n",
    "\n",
    "The design has two estimands:\n",
    "1. Cross-district level gaps associated with border exposure.\n",
    "2. Border-specific post-2019 differential shifts (level and trend).\n",
    "\n",
    "Woods-style interstate reaction-function estimation is not implemented in this version because comparable competitor-jurisdiction stringency series are unavailable.\n",
    "\n",
    "### Data Sources and Construction\n",
    "The notebook draws from PostgreSQL warehouse tables (`well_shape_tract`, `inspections`, `violations`) through `WellAnalyzer`, then restricts to 2015-2025.\n",
    "\n",
    "Core preprocessing steps:\n",
    "1. Standardize well identifier (`api_norm`) and parse dates.\n",
    "2. Recompute border proximity from geometry, including TX-MX and state-border segments (TX-NM, TX-OK, TX-LA).\n",
    "3. Construct well-level border flags at distance thresholds.\n",
    "4. Aggregate to district-year outcomes.\n",
    "\n",
    "### Outcome Measures\n",
    "For district $d$ in year $t$:\n",
    "\n",
    "- **Inspection intensity**\n",
    "$$\n",
    "InspectionIntensity_{dt} = \\frac{Inspections_{dt}}{UniqueWells_{dt}}\n",
    "$$\n",
    "\n",
    "- **Violations per inspection**\n",
    "$$\n",
    "ViolPerInsp_{dt} = \\frac{Violations_{dt}}{Inspections_{dt}}\n",
    "$$\n",
    "\n",
    "- **Average days to enforcement**\n",
    "$$\n",
    "DaysToEnf_{dt} = \\frac{1}{N_{dt}}\\sum_{i=1}^{N_{dt}}\\left(EnforcementDate_i - ViolationDiscoveryDate_i\\right)\n",
    "$$\n",
    "where implausible negative/invalid intervals are excluded by preprocessing rules.\n",
    "\n",
    "- **Resolution rate proxy**\n",
    "$$\n",
    "ResolutionRate_{dt} = \\frac{CompliantOnReinspection_{dt}}{Violations_{dt}}\n",
    "$$\n",
    "\n",
    "### Border Exposure Definitions\n",
    "Primary binary treatment is district-level border status:\n",
    "$$\n",
    "Border_d = 1 \\text{ if district is classified as border-adjacent; } 0 \\text{ otherwise.}\n",
    "$$\n",
    "\n",
    "Additional exposure constructions are used for robustness:\n",
    "- Border-type indicators: $\\text{TX-MX}$, $\\text{TX-NM}$, $\\text{TX-OK}$, and $\\text{TX-LA}$.\n",
    "- Continuous exposure share:\n",
    "$$\n",
    "ShareBorder_{dt} = \\frac{BorderExposedInspections_{dt}}{Inspections_{dt}}\n",
    "$$\n",
    "- Distance-threshold sensitivity at 25km, 75km, and 100km.\n",
    "\n",
    "### Baseline Models\n",
    "\n",
    "### RQ1: Border Gaps (Levels)\n",
    "For each outcome $Y_{dt}$, the levels model is:\n",
    "$$\n",
    "Y_{dt} = \\alpha + \\beta_1 Border_d + \\beta_2 \\log(UniqueWells_{dt}) + \\gamma_t + \\varepsilon_{dt}\n",
    "$$\n",
    "where $\\gamma_t$ are year fixed effects.\n",
    "\n",
    "Interpretation: $\\beta_1$ captures average border/non-border differences conditional on annual shocks and scale.\n",
    "\n",
    "### RQ2: Disclosure Heterogeneity (Post-2019)\n",
    "The fixed-effects interaction specification is:\n",
    "$$\n",
    "Y_{dt} = \\alpha_d + \\gamma_t + \\theta_1(Post2019_t \\times Border_d) + \\theta_2(PostTrend_t \\times Border_d) + \\varepsilon_{dt}\n",
    "$$\n",
    "with:\n",
    "$$\n",
    "Post2019_t = \\mathbb{1}[t \\ge 2019], \\quad PostTrend_t = \\max(0, t-2019)\n",
    "$$\n",
    "$\\alpha_d$ are district fixed effects and $\\gamma_t$ year fixed effects.\n",
    "\n",
    "Interpretation:\n",
    "- $\\theta_1$: immediate border-specific post-2019 level differential.\n",
    "- $\\theta_2$: border-specific post-2019 slope differential.\n",
    "\n",
    "### Robustness Specifications\n",
    "\n",
    "### Border-Type Disaggregation\n",
    "Binary border indicator is replaced with border-type indicators and interactions:\n",
    "$$\n",
    "Y_{dt} = \\alpha_d + \\gamma_t + \\sum_k \\lambda_k(Post2019_t \\times Type_{kd}) + \\sum_k \\phi_k(PostTrend_t \\times Type_{kd}) + \\varepsilon_{dt}\n",
    "$$\n",
    "where $k \\in \\{\\text{TX-MX},\\text{TX-NM},\\text{TX-OK},\\text{TX-LA}\\}$.\n",
    "\n",
    "### Continuous Exposure Models\n",
    "Border binary is replaced by share exposure:\n",
    "$$\n",
    "Y_{dt} = \\alpha_d + \\gamma_t + \\eta_1(Post2019_t \\times ShareBorder_{dt}) + \\eta_2(PostTrend_t \\times ShareBorder_{dt}) + \\varepsilon_{dt}\n",
    "$$\n",
    "\n",
    "### Cutoff Sensitivity\n",
    "The exposure share is recomputed with alternative distance thresholds $c\\in\\{25,75,100\\}$:\n",
    "$$\n",
    "ShareBorder^{(c)}_{dt}\n",
    "$$\n",
    "and substituted into the same FE interaction framework.\n",
    "\n",
    "### Inference and Uncertainty\n",
    "All regression models are estimated by OLS with district-clustered standard errors:\n",
    "$$\n",
    "\\widehat{Var}(\\hat\\beta) = \\text{Clustered by district}\n",
    "$$\n",
    "Given the small number of clusters (13 districts), p-values are interpreted cautiously and emphasis is placed on direction, magnitude, and cross-specification consistency.\n",
    "\n",
    "### Figure Construction (Money Plot)\n",
    "The main timing figure plots annual means for border and non-border groups with 95% confidence intervals computed from district-year values:\n",
    "$$\n",
    "\\bar{Y}_{gt} = \\frac{1}{n_{gt}}\\sum_{d \\in g} Y_{dt}\n",
    "$$\n",
    "$$\n",
    "SE(\\bar{Y}_{gt}) = \\frac{s_{gt}}{\\sqrt{n_{gt}}}, \\quad CI_{95\\%} = \\bar{Y}_{gt} \\pm 1.96\\,SE(\\bar{Y}_{gt})\n",
    "$$\n",
    "where $g$ indexes border group. These are **district-year means with equal district weighting**, not pooled well-level means.\n",
    "\n",
    "### Identification Scope and Limitations\n",
    "The design supports within-Texas comparative inference about border gaps and post-policy heterogeneity. It does not identify strategic interstate reaction effects without external competitor-stringency series. Results should therefore be interpreted as transparency-era heterogeneity in Texas enforcement administration rather than a full reaction-function test.\n"
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