4728 lines
1.2 MiB
4728 lines
1.2 MiB
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6a65225f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Setup: Imports and Configuration"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "7ac064f5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✓ Imports complete\n",
|
||
"✓ Pandas: 3.0.0\n",
|
||
"✓ Statsmodels: 0.14.6\n",
|
||
"✓ Spatial analysis available: True\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Install required packages if needed\n",
|
||
"import subprocess\n",
|
||
"import sys\n",
|
||
"\n",
|
||
"def install_if_missing(package):\n",
|
||
" try:\n",
|
||
" __import__(package.split('[')[0])\n",
|
||
" except ImportError:\n",
|
||
" print(f\"Installing {package}...\")\n",
|
||
" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package])\n",
|
||
"\n",
|
||
"# Ensure required packages\n",
|
||
"for pkg in ['statsmodels', 'scipy', 'seaborn']:\n",
|
||
" install_if_missing(pkg)\n",
|
||
"\n",
|
||
"# Core imports\n",
|
||
"import os\n",
|
||
"import json\n",
|
||
"import warnings\n",
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"# Data manipulation\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# Database\n",
|
||
"from sqlalchemy import create_engine, text\n",
|
||
"\n",
|
||
"# Statistics and econometrics\n",
|
||
"import scipy.stats as stats\n",
|
||
"from scipy.stats import ttest_ind, chi2_contingency\n",
|
||
"import statsmodels.api as sm\n",
|
||
"import statsmodels.formula.api as smf\n",
|
||
"from statsmodels.iolib.summary2 import summary_col\n",
|
||
"\n",
|
||
"# Visualization\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import matplotlib.patches as mpatches\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Spatial analysis\n",
|
||
"try:\n",
|
||
" import geopandas as gpd\n",
|
||
" from shapely.geometry import Point\n",
|
||
" HAS_GEO = True\n",
|
||
"except ImportError:\n",
|
||
" HAS_GEO = False\n",
|
||
" print(\"Warning: geopandas not available for spatial analysis\")\n",
|
||
"\n",
|
||
"# Configuration\n",
|
||
"warnings.filterwarnings('ignore', category=FutureWarning)\n",
|
||
"warnings.filterwarnings('ignore', category=UserWarning)\n",
|
||
"pd.set_option('display.max_columns', 50)\n",
|
||
"pd.set_option('display.max_rows', 100)\n",
|
||
"pd.set_option('display.float_format', '{:.4f}'.format)\n",
|
||
"\n",
|
||
"# Styling\n",
|
||
"plt.style.use('seaborn-v0_8-darkgrid')\n",
|
||
"sns.set_palette(\"husl\")\n",
|
||
"\n",
|
||
"# Add parent directory to path for imports\n",
|
||
"repo_root = Path('..').resolve()\n",
|
||
"if str(repo_root) not in sys.path:\n",
|
||
" sys.path.insert(0, str(repo_root))\n",
|
||
"\n",
|
||
"print(\"✓ Imports complete\")\n",
|
||
"print(f\"✓ Pandas: {pd.__version__}\")\n",
|
||
"print(f\"✓ Statsmodels: {sm.__version__}\")\n",
|
||
"print(f\"✓ Spatial analysis available: {HAS_GEO}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "4fecf215",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✓ Connected to PostgreSQL\n",
|
||
" Host: localhost\n",
|
||
" Database: texas_data\n",
|
||
" User: postgres\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Database connection (from look_at_the_data.ipynb pattern)\n",
|
||
"PGHOST = os.getenv(\"PGHOST\", \"localhost\")\n",
|
||
"PGPORT = os.getenv(\"PGPORT\", \"5432\")\n",
|
||
"PGUSER = os.getenv(\"PGUSER\", \"postgres\")\n",
|
||
"PGPASSWORD = os.getenv(\"PGPASSWORD\", \"\")\n",
|
||
"PGDATABASE = os.getenv(\"PGDATABASE\", \"texas_data\")\n",
|
||
"\n",
|
||
"pg_url = f\"postgresql+psycopg2://{PGUSER}:{PGPASSWORD}@{PGHOST}:{PGPORT}/{PGDATABASE}\"\n",
|
||
"engine = create_engine(pg_url)\n",
|
||
"\n",
|
||
"print(f\"✓ Connected to PostgreSQL\")\n",
|
||
"print(f\" Host: {PGHOST}\")\n",
|
||
"print(f\" Database: {PGDATABASE}\")\n",
|
||
"print(f\" User: {PGUSER}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d9cec16c",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part 1: Load and Explore District-Level Data\n",
|
||
"\n",
|
||
"We start by loading the pre-computed district statistics and creating summary tables for pre/post 2019 periods."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "09025c68",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✓ Loaded analysis_output.json\n",
|
||
" Districts: ['01', '02', '03', '04', '05', '06', '08', '09', '10', '6E', '7B', '7C', '8A']\n",
|
||
" Total districts: 13\n",
|
||
"\n",
|
||
"======================================================================\n",
|
||
"DISTRICT SUMMARY (Full Period 2015-2024)\n",
|
||
"======================================================================\n",
|
||
"district total_inspections compliance_rate\n",
|
||
" 01 112132 82.9273\n",
|
||
" 02 68330 88.8980\n",
|
||
" 03 107708 88.8987\n",
|
||
" 04 137959 94.1932\n",
|
||
" 05 58731 91.1035\n",
|
||
" 06 153755 92.8971\n",
|
||
" 08 356608 92.8232\n",
|
||
" 09 191339 81.1899\n",
|
||
" 10 140961 89.4013\n",
|
||
" 6E 46504 87.5602\n",
|
||
" 7B 147579 86.7617\n",
|
||
" 7C 164102 91.2865\n",
|
||
" 8A 193056 94.4400\n",
|
||
"\n",
|
||
"Mean compliance rate: 89.41%\n",
|
||
"Std compliance rate: 4.07%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load pre-computed district statistics\n",
|
||
"analysis_output_path = Path('..') / 'analysis_output.json'\n",
|
||
"\n",
|
||
"with open(analysis_output_path, 'r') as f:\n",
|
||
" analysis_data = json.load(f)\n",
|
||
"\n",
|
||
"# Extract district-level metrics\n",
|
||
"districts = list(analysis_data['inspection_analysis']['district_performance']['inspections_by_district'].keys())\n",
|
||
"print(f\"✓ Loaded analysis_output.json\")\n",
|
||
"print(f\" Districts: {districts}\")\n",
|
||
"print(f\" Total districts: {len(districts)}\")\n",
|
||
"\n",
|
||
"# Create district-level summary DataFrame\n",
|
||
"district_summary = pd.DataFrame({\n",
|
||
" 'district': districts,\n",
|
||
" 'total_inspections': [analysis_data['inspection_analysis']['district_performance']['inspections_by_district'][d] \n",
|
||
" for d in districts],\n",
|
||
" 'compliance_rate': [analysis_data['inspection_analysis']['district_performance']['compliance_by_district'][d] \n",
|
||
" for d in districts]\n",
|
||
"})\n",
|
||
"\n",
|
||
"district_summary = district_summary.sort_values('district')\n",
|
||
"print(\"\\n\" + \"=\"*70)\n",
|
||
"print(\"DISTRICT SUMMARY (Full Period 2015-2024)\")\n",
|
||
"print(\"=\"*70)\n",
|
||
"print(district_summary.to_string(index=False))\n",
|
||
"print(f\"\\nMean compliance rate: {district_summary['compliance_rate'].mean():.2f}%\")\n",
|
||
"print(f\"Std compliance rate: {district_summary['compliance_rate'].std():.2f}%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "136c17fe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Available tables in database:\n",
|
||
"['census_tract_demographics', 'census_tracts_2021', 'inspections', 'oil_gas_basins', 'rrc_well_api_raw', 'shale_plays', 'spatial_ref_sys', 'texmex_regions', 'violations', 'well_geo_features', 'well_shape_tract', 'well_shapes', 'well_shapes_backup_dedup', 'well_with_demographics_table']\n",
|
||
"\n",
|
||
"✓ Found inspections table: inspections\n",
|
||
"✓ Found violations table: violations\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load well-level data from PostgreSQL with district and temporal information\n",
|
||
"# First, check what tables are available\n",
|
||
"with engine.begin() as conn:\n",
|
||
" tables_query = text(\"\"\"\n",
|
||
" SELECT table_name \n",
|
||
" FROM information_schema.tables \n",
|
||
" WHERE table_schema = 'public' \n",
|
||
" AND table_type = 'BASE TABLE'\n",
|
||
" ORDER BY table_name\n",
|
||
" \"\"\")\n",
|
||
" available_tables = pd.read_sql(tables_query, conn)\n",
|
||
"\n",
|
||
"print(\"Available tables in database:\")\n",
|
||
"print(available_tables['table_name'].tolist())\n",
|
||
"\n",
|
||
"# Check if inspections and violations tables exist\n",
|
||
"inspections_table = None\n",
|
||
"violations_table = None\n",
|
||
"\n",
|
||
"for table in available_tables['table_name']:\n",
|
||
" if 'inspection' in table.lower():\n",
|
||
" print(f\"\\n✓ Found inspections table: {table}\")\n",
|
||
" inspections_table = table\n",
|
||
" if 'violation' in table.lower():\n",
|
||
" print(f\"✓ Found violations table: {table}\")\n",
|
||
" violations_table = table"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "fc36c40f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"======================================================================\n",
|
||
"INSPECTIONS TABLE STRUCTURE\n",
|
||
"======================================================================\n",
|
||
" column_name data_type\n",
|
||
" operator_name text\n",
|
||
" p5_operator_no text\n",
|
||
" district text\n",
|
||
"district_office_inspecting text\n",
|
||
" oil_lease_gas_well_id text\n",
|
||
" lease_fac_name text\n",
|
||
" api_no text\n",
|
||
" county text\n",
|
||
" well_no text\n",
|
||
" inspection_date timestamp without time zone\n",
|
||
" drilling_permit_no text\n",
|
||
" complaint_no text\n",
|
||
" compliance text\n",
|
||
" field_name text\n",
|
||
" api_norm text\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" operator_name p5_operator_no district \\\n",
|
||
"0 CLEAR FORK, INCORPORATED 159500 7B \n",
|
||
"1 UNKNOWN NaN 06 \n",
|
||
"2 UNKNOWN NaN 02 \n",
|
||
"3 RRC NaN 10 \n",
|
||
"4 NaN NaN 7C \n",
|
||
"\n",
|
||
" district_office_inspecting oil_lease_gas_well_id lease_fac_name api_no \\\n",
|
||
"0 Abilene 29402 DAVIS None \n",
|
||
"1 Kilgore NaN UNKNOWN None \n",
|
||
"2 San Antonio NaN MCGAUGH WATER WELL None \n",
|
||
"3 Pampa NaN RRC None \n",
|
||
"4 San Angelo 4312 NaN None \n",
|
||
"\n",
|
||
" county well_no inspection_date drilling_permit_no complaint_no \\\n",
|
||
"0 NOLAN None 2017-12-12 None NaN \n",
|
||
"1 SMITH None 2020-04-02 None NaN \n",
|
||
"2 DE WITT None 2021-02-11 None 3104 \n",
|
||
"3 GRAY None 2016-04-20 None NaN \n",
|
||
"4 SCHLEICHER None 2023-08-07 None NaN \n",
|
||
"\n",
|
||
" compliance field_name api_norm \n",
|
||
"0 Yes COH (HOPE) None \n",
|
||
"1 Yes NaN None \n",
|
||
"2 Yes NaN None \n",
|
||
"3 Yes NaN None \n",
|
||
"4 Yes NaN None \n",
|
||
"\n",
|
||
"======================================================================\n",
|
||
"VIOLATIONS TABLE STRUCTURE\n",
|
||
"======================================================================\n",
|
||
" column_name data_type\n",
|
||
" operator_name text\n",
|
||
" p5_operator_no text\n",
|
||
" district text\n",
|
||
"oil_lease_gas_well_id text\n",
|
||
" lease_fac_name text\n",
|
||
" api_no text\n",
|
||
" county text\n",
|
||
" well_no text\n",
|
||
" drilling_permit_no text\n",
|
||
" field_name text\n",
|
||
" violated_rule text\n",
|
||
" violated_rule_desc text\n",
|
||
" major_viol_ind text\n",
|
||
" compliant_on_reinsp text\n",
|
||
" last_enf_action text\n",
|
||
" last_enf_action_date timestamp without time zone\n",
|
||
" violation_disc_date timestamp without time zone\n",
|
||
" api_norm text\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" operator_name p5_operator_no district oil_lease_gas_well_id \\\n",
|
||
"0 UNIDENTIFIED NaN 09 0 \n",
|
||
"1 UNKNOWN NaN 06 NaN \n",
|
||
"2 WOODLAND MIDSTREAM NaN 6E NaN \n",
|
||
"3 WINCO OIL INC. 931332 7B NaN \n",
|
||
"4 RUJO 734093 01 2568 \n",
|
||
"\n",
|
||
" lease_fac_name api_no county well_no drilling_permit_no field_name \\\n",
|
||
"0 CALVIN None YOUNG None NaN NaN \n",
|
||
"1 UNKNOWN None WOOD None NaN NaN \n",
|
||
"2 NaN None GREGG None NaN NaN \n",
|
||
"3 SKIPPER None TAYLOR None 833507 WILDCAT \n",
|
||
"4 ENGELKE, AUGUST None GUADALUPE None NaN SPILLER \n",
|
||
"\n",
|
||
" violated_rule violated_rule_desc major_viol_ind compliant_on_reinsp \\\n",
|
||
"0 SWR 3(1) Entrance Sign N -- \n",
|
||
"1 SWR 3(1) Entrance Sign N Y \n",
|
||
"2 SWR 3(1) Entrance Sign N Y \n",
|
||
"3 SWR 3(1) Entrance Sign N N \n",
|
||
"4 SWR 3(1) Entrance Sign N N \n",
|
||
"\n",
|
||
" last_enf_action last_enf_action_date violation_disc_date api_norm \n",
|
||
"0 Notice of Violation 2016-06-21 2016-06-17 None \n",
|
||
"1 Notice of Violation 2025-03-07 2024-12-12 None \n",
|
||
"2 Notice of Violation 2017-11-07 2017-04-17 None \n",
|
||
"3 Notice of Violation 2018-10-30 2018-10-25 None \n",
|
||
"4 Notice of Violation 2016-03-18 2016-02-10 None \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Examine structure of inspections and violations tables\n",
|
||
"print(\"=\"*70)\n",
|
||
"print(\"INSPECTIONS TABLE STRUCTURE\")\n",
|
||
"print(\"=\"*70)\n",
|
||
"\n",
|
||
"with engine.begin() as conn:\n",
|
||
" insp_cols = pd.read_sql(text(\"\"\"\n",
|
||
" SELECT column_name, data_type \n",
|
||
" FROM information_schema.columns \n",
|
||
" WHERE table_name = 'inspections'\n",
|
||
" ORDER BY ordinal_position\n",
|
||
" LIMIT 20\n",
|
||
" \"\"\"), conn)\n",
|
||
" print(insp_cols.to_string(index=False))\n",
|
||
" \n",
|
||
" # Sample rows\n",
|
||
" insp_sample = pd.read_sql(text(\"SELECT * FROM inspections LIMIT 5\"), conn)\n",
|
||
" print(\"\\nSample rows:\")\n",
|
||
" print(insp_sample.head())\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*70)\n",
|
||
"print(\"VIOLATIONS TABLE STRUCTURE\") \n",
|
||
"print(\"=\"*70)\n",
|
||
"\n",
|
||
"with engine.begin() as conn:\n",
|
||
" viol_cols = pd.read_sql(text(\"\"\"\n",
|
||
" SELECT column_name, data_type \n",
|
||
" FROM information_schema.columns \n",
|
||
" WHERE table_name = 'violations'\n",
|
||
" ORDER BY ordinal_position\n",
|
||
" LIMIT 20\n",
|
||
" \"\"\"), conn)\n",
|
||
" print(viol_cols.to_string(index=False))\n",
|
||
" \n",
|
||
" # Sample rows\n",
|
||
" viol_sample = pd.read_sql(text(\"SELECT * FROM violations LIMIT 5\"), conn)\n",
|
||
" print(\"\\nSample rows:\")\n",
|
||
" print(viol_sample.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9fbc1a01",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Build District-Year Panel Dataset\n",
|
||
"\n",
|
||
"Aggregate inspections and violations to district-year level for DiD analysis."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "aa5db4f1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2026-02-18 16:12:31,857 - INFO - Connecting to Postgres\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Initializing WellAnalyzer...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2026-02-18 16:12:34,454 - INFO - Loaded 1010432 wells from public.well_shape_tract\n",
|
||
"2026-02-18 16:12:41,785 - INFO - Loaded 1878764 inspections from public.inspections\n",
|
||
"2026-02-18 16:12:43,025 - INFO - Loaded 193338 violations from public.violations\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Analyzer initialized\n",
|
||
" Wells source: public.well_shape_tract\n",
|
||
" Inspections source: public.inspections\n",
|
||
" Violations source: public.violations\n",
|
||
"\n",
|
||
"Loading data from database...\n",
|
||
"\n",
|
||
"✓ Loaded data:\n",
|
||
" Inspections: 1,878,764 rows\n",
|
||
" Violations: 193,338 rows\n",
|
||
"\n",
|
||
"Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection']\n",
|
||
"\n",
|
||
"Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Use WellAnalyzer to load the data (it has already figured out the schema)\n",
|
||
"from analysis.well_analyzer import WellAnalyzer\n",
|
||
"\n",
|
||
"print(\"Initializing WellAnalyzer...\")\n",
|
||
"analyzer = WellAnalyzer(chunk_size=50_000)\n",
|
||
"\n",
|
||
"print(\"\\n✓ Analyzer initialized\")\n",
|
||
"print(f\" Wells source: {analyzer.config.well_source}\")\n",
|
||
"print(f\" Inspections source: {analyzer.config.inspections_source}\")\n",
|
||
"print(f\" Violations source: {analyzer.config.violations_source}\")\n",
|
||
"\n",
|
||
"# Load the data\n",
|
||
"print(\"\\nLoading data from database...\")\n",
|
||
"data = analyzer.data\n",
|
||
"\n",
|
||
"inspections = data['inspections'].copy()\n",
|
||
"violations = data['violations'].copy()\n",
|
||
"\n",
|
||
"print(f\"\\n✓ Loaded data:\")\n",
|
||
"print(f\" Inspections: {len(inspections):,} rows\")\n",
|
||
"print(f\" Violations: {len(violations):,} rows\")\n",
|
||
"print(f\"\\nInspections columns: {list(inspections.columns)}\")\n",
|
||
"print(f\"\\nViolations columns: {list(violations.columns)}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "036d14fb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Filtered to 2015-2024:\n",
|
||
" Inspections: 1,642,060 rows\n",
|
||
" Violations: 173,028 rows\n",
|
||
"\n",
|
||
"✓ Created district-year panel:\n",
|
||
" Observations: 130\n",
|
||
" Districts: 13\n",
|
||
" Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n",
|
||
" Pre-2019 observations: 52\n",
|
||
" Post-2019 observations: 78\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DISTRICT-YEAR PANEL SUMMARY\n",
|
||
"================================================================================\n",
|
||
" district year total_inspections unique_wells compliant_inspections \\\n",
|
||
"0 01 2015 3498 2816 3027 \n",
|
||
"1 01 2016 6499 4055 5028 \n",
|
||
"2 01 2017 8649 6153 7613 \n",
|
||
"3 01 2018 10966 9109 9668 \n",
|
||
"4 01 2019 8097 6447 6818 \n",
|
||
"5 01 2020 10511 8716 9087 \n",
|
||
"6 01 2021 8586 6908 6870 \n",
|
||
"7 01 2022 12418 10193 9608 \n",
|
||
"8 01 2023 14573 11577 11879 \n",
|
||
"9 01 2024 16338 13038 13923 \n",
|
||
"10 02 2015 1174 921 1005 \n",
|
||
"11 02 2016 2936 2003 2098 \n",
|
||
"12 02 2017 5325 4639 4718 \n",
|
||
"13 02 2018 5913 5107 5317 \n",
|
||
"14 02 2019 4427 3696 3945 \n",
|
||
"15 02 2020 4713 3679 3789 \n",
|
||
"16 02 2021 5090 3929 4405 \n",
|
||
"17 02 2022 7290 5842 6578 \n",
|
||
"18 02 2023 9679 7936 8857 \n",
|
||
"19 02 2024 11632 8772 10831 \n",
|
||
"\n",
|
||
" unique_api_norm compliance_rate total_violations wells_with_violations \\\n",
|
||
"0 2816 86.5352 592 379 \n",
|
||
"1 4055 77.3657 1902 1009 \n",
|
||
"2 6153 88.0217 1439 767 \n",
|
||
"3 9109 88.1634 1771 997 \n",
|
||
"4 6447 84.2040 1506 902 \n",
|
||
"5 8716 86.4523 1816 1019 \n",
|
||
"6 6908 80.0140 2268 1220 \n",
|
||
"7 10193 77.3716 3030 1878 \n",
|
||
"8 11577 81.5138 2501 1508 \n",
|
||
"9 13038 85.2185 2208 1516 \n",
|
||
"10 921 85.6048 192 115 \n",
|
||
"11 2003 71.4578 1120 570 \n",
|
||
"12 4639 88.6009 642 431 \n",
|
||
"13 5107 89.9205 518 354 \n",
|
||
"14 3696 89.1123 434 271 \n",
|
||
"15 3679 80.3947 1106 621 \n",
|
||
"16 3929 86.5422 656 410 \n",
|
||
"17 5842 90.2332 665 447 \n",
|
||
"18 7936 91.5074 826 523 \n",
|
||
"19 8772 93.1138 804 495 \n",
|
||
"\n",
|
||
" major_violations compliant_on_reinsp enforced_violations \\\n",
|
||
"0 0 284 592 \n",
|
||
"1 0 472 1902 \n",
|
||
"2 0 740 1439 \n",
|
||
"3 0 1012 1771 \n",
|
||
"4 2 771 1506 \n",
|
||
"5 1 384 1816 \n",
|
||
"6 0 669 2268 \n",
|
||
"7 0 1653 3030 \n",
|
||
"8 4 1464 2501 \n",
|
||
"9 0 1135 2208 \n",
|
||
"10 0 112 192 \n",
|
||
"11 0 216 1120 \n",
|
||
"12 0 317 642 \n",
|
||
"13 2 184 518 \n",
|
||
"14 0 173 434 \n",
|
||
"15 1 196 1106 \n",
|
||
"16 0 220 656 \n",
|
||
"17 0 272 665 \n",
|
||
"18 3 366 826 \n",
|
||
"19 1 185 804 \n",
|
||
"\n",
|
||
" violations_per_inspection violation_rate post_2019 post_2019_bool \n",
|
||
"0 0.1692 16.9240 0 False \n",
|
||
"1 0.2927 29.2660 0 False \n",
|
||
"2 0.1664 16.6378 0 False \n",
|
||
"3 0.1615 16.1499 0 False \n",
|
||
"4 0.1860 18.5995 1 True \n",
|
||
"5 0.1728 17.2771 1 True \n",
|
||
"6 0.2642 26.4151 1 True \n",
|
||
"7 0.2440 24.4001 1 True \n",
|
||
"8 0.1716 17.1619 1 True \n",
|
||
"9 0.1351 13.5145 1 True \n",
|
||
"10 0.1635 16.3543 0 False \n",
|
||
"11 0.3815 38.1471 0 False \n",
|
||
"12 0.1206 12.0563 0 False \n",
|
||
"13 0.0876 8.7604 0 False \n",
|
||
"14 0.0980 9.8035 1 True \n",
|
||
"15 0.2347 23.4670 1 True \n",
|
||
"16 0.1289 12.8880 1 True \n",
|
||
"17 0.0912 9.1221 1 True \n",
|
||
"18 0.0853 8.5339 1 True \n",
|
||
"19 0.0691 6.9120 1 True \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create district-year panel from the loaded data\n",
|
||
"\n",
|
||
"# Add year column to inspections and violations\n",
|
||
"inspections['year'] = pd.to_datetime(inspections['inspection_date']).dt.year\n",
|
||
"violations['year'] = pd.to_datetime(violations['violation_disc_date']).dt.year\n",
|
||
"\n",
|
||
"# Filter to analysis period 2015-2024\n",
|
||
"inspections = inspections[(inspections['year'] >= 2015) & (inspections['year'] <= 2024)]\n",
|
||
"violations = violations[(violations['year'] >= 2015) & (violations['year'] <= 2024)]\n",
|
||
"\n",
|
||
"print(f\"Filtered to 2015-2024:\")\n",
|
||
"print(f\" Inspections: {len(inspections):,} rows\")\n",
|
||
"print(f\" Violations: {len(violations):,} rows\")\n",
|
||
"\n",
|
||
"# Aggregate inspections by district-year\n",
|
||
"insp_agg = inspections.groupby(['district', 'year']).agg(\n",
|
||
" total_inspections=('api_norm', 'count'),\n",
|
||
" unique_wells=('api_norm', 'nunique'),\n",
|
||
" compliant_inspections=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).sum()),\n",
|
||
" unique_api_norm=('api_norm', 'nunique')\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"insp_agg['compliance_rate'] = (insp_agg['compliant_inspections'] / insp_agg['total_inspections']) * 100\n",
|
||
"\n",
|
||
"# Aggregate violations by district-year\n",
|
||
"viol_agg = violations.groupby(['district', 'year']).agg(\n",
|
||
" total_violations=('api_norm', 'count'),\n",
|
||
" wells_with_violations=('api_norm', 'nunique'),\n",
|
||
" major_violations=('major_viol_ind', lambda x: (x == 'Y').sum()),\n",
|
||
" compliant_on_reinsp=('compliant_on_reinsp', lambda x: (x == 'Y').sum()),\n",
|
||
" enforced_violations=('last_enf_action', lambda x: x.notna().sum())\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Merge inspections and violations\n",
|
||
"district_year_df = pd.merge(\n",
|
||
" insp_agg,\n",
|
||
" viol_agg,\n",
|
||
" on=['district', 'year'],\n",
|
||
" how='left'\n",
|
||
")\n",
|
||
"\n",
|
||
"# Fill NAs with 0\n",
|
||
"viol_cols = ['total_violations', 'wells_with_violations', 'major_violations',\n",
|
||
" 'compliant_on_reinsp', 'enforced_violations']\n",
|
||
"for col in viol_cols:\n",
|
||
" district_year_df[col] = district_year_df[col].fillna(0)\n",
|
||
"\n",
|
||
"# Add key metrics\n",
|
||
"district_year_df['violations_per_inspection'] = (\n",
|
||
" district_year_df['total_violations'] / district_year_df['total_inspections']\n",
|
||
")\n",
|
||
"district_year_df['violation_rate'] = (\n",
|
||
" district_year_df['total_violations'] / district_year_df['total_inspections'] * 100\n",
|
||
")\n",
|
||
"\n",
|
||
"# Add treatment indicator\n",
|
||
"district_year_df['post_2019'] = (district_year_df['year'] >= 2019).astype(int)\n",
|
||
"district_year_df['post_2019_bool'] = district_year_df['year'] >= 2019\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"✓ Created district-year panel:\")\n",
|
||
"print(f\" Observations: {len(district_year_df)}\")\n",
|
||
"print(f\" Districts: {district_year_df['district'].nunique()}\")\n",
|
||
"print(f\" Years: {sorted(district_year_df['year'].unique())}\")\n",
|
||
"print(f\" Pre-2019 observations: {(district_year_df['year'] < 2019).sum()}\")\n",
|
||
"print(f\" Post-2019 observations: {(district_year_df['year'] >= 2019).sum()}\")\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"DISTRICT-YEAR PANEL SUMMARY\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(district_year_df.head(20))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "510c60ba",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part 2: Regulatory Pipeline Analysis\n",
|
||
"\n",
|
||
"**Key insight**: Wells have **repeated inspections** over time, creating a regulatory pipeline:\n",
|
||
"1. **Inspection** → 2. **Violation discovered** (if any) → 3. **Enforcement action** → 4. **Re-inspection** → 5. **Compliance verified**\n",
|
||
"\n",
|
||
"We need to track:\n",
|
||
"- **Time between events**: inspection → violation → enforcement → resolution\n",
|
||
"- **Repeat patterns**: wells with multiple violations, chronic non-compliance\n",
|
||
"- **Treatment effects on the pipeline**: Did 2019 disclosure change the speed or effectiveness of enforcement?\n",
|
||
"\n",
|
||
"This requires **well-level panel data** with time-varying outcomes, not just district-year aggregates."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "2e605976",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"VIOLATION ENFORCEMENT ACTIONS ANALYSIS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"1. ENFORCEMENT ACTION TYPES:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"last_enf_action\n",
|
||
"Notice of Violation 127404\n",
|
||
"Referred to Austin Field Ops for possible legal enforcement 22332\n",
|
||
"Referred to State-Managed Plugging 17314\n",
|
||
"Issued a Severance/Seal Order 4930\n",
|
||
"Violation Corrected 533\n",
|
||
"Referred to State-Managed Cleanup Program 515\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Total unique enforcement action types: 6\n",
|
||
"Violations with enforcement action: 173,028\n",
|
||
"Violations without enforcement action: 0\n",
|
||
"\n",
|
||
"2. VIOLATION SEVERITY:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"major_viol_ind\n",
|
||
"N 172973\n",
|
||
"Y 55\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Major violations: 0.0%\n",
|
||
"\n",
|
||
"3. COMPLIANCE ON RE-INSPECTION:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"compliant_on_reinsp\n",
|
||
"Y 100634\n",
|
||
"-- 57679\n",
|
||
"N 14715\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"4. TIME TO ENFORCEMENT:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"Violations with enforcement timing data: 173,028\n",
|
||
"Mean days to enforcement: 138.6\n",
|
||
"Median days to enforcement: 16.0\n",
|
||
"90th percentile: 429.0 days\n",
|
||
"Max days to enforcement: 3640 days\n",
|
||
"\n",
|
||
"Distribution of enforcement timing:\n",
|
||
"time_category\n",
|
||
"<30 days 98116\n",
|
||
"30-90 days 26922\n",
|
||
"90-180 days 14890\n",
|
||
"6mo-1yr 12755\n",
|
||
"1-2 years 10384\n",
|
||
">2 years 9594\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"5. ENFORCEMENT RATES BY DISTRICT:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" enforcement_rate_pct total_violations\n",
|
||
"district \n",
|
||
"01 100.0000 19033\n",
|
||
"02 100.0000 6963\n",
|
||
"03 100.0000 7973\n",
|
||
"04 100.0000 5893\n",
|
||
"05 100.0000 3880\n",
|
||
"06 100.0000 9632\n",
|
||
"08 100.0000 28235\n",
|
||
"09 100.0000 36940\n",
|
||
"10 100.0000 11128\n",
|
||
"6E 100.0000 4302\n",
|
||
"7B 100.0000 18510\n",
|
||
"7C 100.0000 12341\n",
|
||
"8A 100.0000 8198\n",
|
||
"\n",
|
||
"✓ Enforcement actions analysis complete\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Examine enforcement action types and temporal patterns in violations data\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"VIOLATION ENFORCEMENT ACTIONS ANALYSIS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# 1. Types of enforcement actions\n",
|
||
"print(\"\\n1. ENFORCEMENT ACTION TYPES:\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"enf_actions = violations['last_enf_action'].value_counts(dropna=False)\n",
|
||
"print(enf_actions)\n",
|
||
"print(f\"\\nTotal unique enforcement action types: {violations['last_enf_action'].nunique()}\")\n",
|
||
"print(f\"Violations with enforcement action: {violations['last_enf_action'].notna().sum():,}\")\n",
|
||
"print(f\"Violations without enforcement action: {violations['last_enf_action'].isna().sum():,}\")\n",
|
||
"\n",
|
||
"# 2. Major vs minor violations\n",
|
||
"print(\"\\n2. VIOLATION SEVERITY:\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"major_viol = violations['major_viol_ind'].value_counts(dropna=False)\n",
|
||
"print(major_viol)\n",
|
||
"major_pct = violations['major_viol_ind'].value_counts(normalize=True) * 100\n",
|
||
"print(f\"\\nMajor violations: {major_pct.get('Y', 0):.1f}%\")\n",
|
||
"\n",
|
||
"# 3. Compliance on re-inspection\n",
|
||
"print(\"\\n3. COMPLIANCE ON RE-INSPECTION:\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"reinsp_compliance = violations['compliant_on_reinsp'].value_counts(dropna=False)\n",
|
||
"print(reinsp_compliance)\n",
|
||
"\n",
|
||
"# 4. Temporal gaps: violation discovery to enforcement\n",
|
||
"print(\"\\n4. TIME TO ENFORCEMENT:\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"violations['violation_disc_date'] = pd.to_datetime(violations['violation_disc_date'], errors='coerce')\n",
|
||
"violations['last_enf_action_date'] = pd.to_datetime(violations['last_enf_action_date'], errors='coerce')\n",
|
||
"\n",
|
||
"violations['days_to_enforcement'] = (violations['last_enf_action_date'] - \n",
|
||
" violations['violation_disc_date']).dt.days\n",
|
||
"\n",
|
||
"# Only for violations that got enforcement\n",
|
||
"enforced = violations[violations['days_to_enforcement'].notna() & \n",
|
||
" (violations['days_to_enforcement'] >= 0)]\n",
|
||
"\n",
|
||
"if len(enforced) > 0:\n",
|
||
" print(f\"Violations with enforcement timing data: {len(enforced):,}\")\n",
|
||
" print(f\"Mean days to enforcement: {enforced['days_to_enforcement'].mean():.1f}\")\n",
|
||
" print(f\"Median days to enforcement: {enforced['days_to_enforcement'].median():.1f}\")\n",
|
||
" print(f\"90th percentile: {enforced['days_to_enforcement'].quantile(0.9):.1f} days\")\n",
|
||
" print(f\"Max days to enforcement: {enforced['days_to_enforcement'].max():.0f} days\")\n",
|
||
" \n",
|
||
" # Distribution\n",
|
||
" print(\"\\nDistribution of enforcement timing:\")\n",
|
||
" bins = [0, 30, 90, 180, 365, 730, np.inf]\n",
|
||
" labels = ['<30 days', '30-90 days', '90-180 days', '6mo-1yr', '1-2 years', '>2 years']\n",
|
||
" enforced['time_category'] = pd.cut(enforced['days_to_enforcement'], bins=bins, labels=labels)\n",
|
||
" print(enforced['time_category'].value_counts().sort_index())\n",
|
||
"\n",
|
||
"# 5. Enforcement rates by district\n",
|
||
"print(\"\\n5. ENFORCEMENT RATES BY DISTRICT:\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"district_enf = violations.groupby('district').agg({\n",
|
||
" 'last_enf_action': lambda x: (x.notna().sum() / len(x) * 100),\n",
|
||
" 'api_norm': 'count'\n",
|
||
"}).rename(columns={'last_enf_action': 'enforcement_rate_pct', 'api_norm': 'total_violations'})\n",
|
||
"district_enf = district_enf.sort_values('enforcement_rate_pct', ascending=False)\n",
|
||
"print(district_enf.to_string())\n",
|
||
"\n",
|
||
"print(\"\\n✓ Enforcement actions analysis complete\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "f8a814c8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Building well-level panel with regulatory pipeline metrics...\n",
|
||
"\n",
|
||
"First, checking API number formats...\n",
|
||
"Inspections columns: ['district', 'county', 'inspection_date', 'operator_name', 'field_name', 'compliance', 'api_norm', 'days_since_last_inspection', 'year']\n",
|
||
"Sample inspection row:\n",
|
||
" api_norm: 10130031 (type: <class 'str'>)\n",
|
||
"\n",
|
||
"Violations columns: ['operator_name', 'p5_operator_no', 'district', 'oil_lease_gas_well_id', 'lease_fac_name', 'well_no', 'drilling_permit_no', 'field_name', 'violated_rule', 'violated_rule_desc', 'major_viol_ind', 'compliant_on_reinsp', 'last_enf_action', 'last_enf_action_date', 'violation_disc_date', 'api_norm', 'violation_row_id', 'total_violations', 'violation_number', 'year', 'days_to_enforcement']\n",
|
||
"Sample violation row:\n",
|
||
" api_norm: 46102018 (type: <class 'str'>)\n",
|
||
"\n",
|
||
"✓ Using api_norm as well identifier\n",
|
||
" Unique wells in inspections: 413,185\n",
|
||
" Unique wells in violations: 74,962\n",
|
||
"\n",
|
||
"✓ Well-level panel created\n",
|
||
" Total wells: 413,185\n",
|
||
" Wells with violations: 74,962 (18.1%)\n",
|
||
" Repeat violators: 37,461 (9.1%)\n",
|
||
" Avg inspections per well: 4.0\n",
|
||
" Avg violations per well: 0.42\n",
|
||
"\n",
|
||
"Sample of well panel:\n",
|
||
" well_id total_violations total_inspections repeat_violator ever_violated district violations_per_inspection avg_days_to_enforcement first_inspection last_inspection first_violation years_active\n",
|
||
"0 10130031 1 2 0 1 8A 0.5000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n",
|
||
"1 10130036 0 2 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n",
|
||
"2 10130045 0 3 0 0 8A 0.0000 NaN 2017-12-06 2022-09-19 NaT 4.7858\n",
|
||
"3 10130054 4 2 1 1 8A 2.0000 55.0000 2016-03-04 2016-07-26 2016-03-04 0.3943\n",
|
||
"4 10130055 2 4 1 1 8A 0.5000 55.0000 2016-03-04 2024-04-04 2016-03-04 8.0849\n",
|
||
"5 10130080 0 3 0 0 8A 0.0000 NaN 2017-08-18 2024-10-13 NaT 7.1540\n",
|
||
"6 10130086 1 3 0 1 8A 0.3333 9.0000 2017-08-18 2024-10-14 2024-10-14 7.1567\n",
|
||
"7 10130096 0 1 0 0 8A 0.0000 NaN 2017-08-18 2017-08-18 NaT 0.0000\n",
|
||
"8 10130098 0 2 0 0 8A 0.0000 NaN 2017-08-18 2022-04-21 NaT 4.6735\n",
|
||
"9 10130104 2 7 1 1 8A 0.2857 987.0000 2016-02-03 2022-08-17 2016-02-03 6.5352\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create well-level panel with regulatory pipeline metrics (EFFICIENT VERSION)\n",
|
||
"\n",
|
||
"print(\"Building well-level panel with regulatory pipeline metrics...\")\n",
|
||
"print(\"\\nFirst, checking API number formats...\")\n",
|
||
"\n",
|
||
"# Check what we actually have\n",
|
||
"print(f\"Inspections columns: {inspections.columns.tolist()}\")\n",
|
||
"print(f\"Sample inspection row:\")\n",
|
||
"if len(inspections) > 0:\n",
|
||
" sample = inspections.iloc[0]\n",
|
||
" for col in ['api_norm']:\n",
|
||
" if col in inspections.columns:\n",
|
||
" print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n",
|
||
"\n",
|
||
"print(f\"\\nViolations columns: {violations.columns.tolist()}\")\n",
|
||
"print(f\"Sample violation row:\")\n",
|
||
"if len(violations) > 0:\n",
|
||
" sample = violations.iloc[0]\n",
|
||
" for col in ['api_norm']:\n",
|
||
" if col in violations.columns:\n",
|
||
" print(f\" {col}: {sample[col]} (type: {type(sample[col])})\")\n",
|
||
"\n",
|
||
"# Use api_norm directly as the normalized well identifier\n",
|
||
"inspections['well_id'] = inspections['api_norm'].astype(str).str.strip()\n",
|
||
"violations['well_id'] = violations['api_norm'].astype(str).str.strip()\n",
|
||
"\n",
|
||
"print(f\"\\n✓ Using api_norm as well identifier\")\n",
|
||
"print(f\" Unique wells in inspections: {inspections['well_id'].nunique():,}\")\n",
|
||
"print(f\" Unique wells in violations: {violations['well_id'].nunique():,}\")\n",
|
||
"\n",
|
||
"# Prepare inspections data\n",
|
||
"inspections_sorted = inspections.copy()\n",
|
||
"inspections_sorted['inspection_date'] = pd.to_datetime(inspections_sorted['inspection_date'])\n",
|
||
"inspections_sorted['year'] = inspections_sorted['inspection_date'].dt.year\n",
|
||
"\n",
|
||
"# Prepare violations data\n",
|
||
"violations_sorted = violations.copy()\n",
|
||
"violations_sorted['violation_disc_date'] = pd.to_datetime(violations_sorted['violation_disc_date'])\n",
|
||
"violations_sorted['last_enf_action_date'] = pd.to_datetime(violations_sorted['last_enf_action_date'])\n",
|
||
"violations_sorted['year'] = violations_sorted['violation_disc_date'].dt.year\n",
|
||
"\n",
|
||
"# Calculate enforcement timing\n",
|
||
"violations_sorted['days_to_enforcement'] = (\n",
|
||
" violations_sorted['last_enf_action_date'] - violations_sorted['violation_disc_date']\n",
|
||
").dt.days\n",
|
||
"\n",
|
||
"# Well-level aggregates (static characteristics)\n",
|
||
"viol_per_well = violations_sorted.groupby('well_id').size()\n",
|
||
"insp_per_well = inspections_sorted.groupby('well_id').size()\n",
|
||
"\n",
|
||
"well_panel = pd.DataFrame({\n",
|
||
" 'well_id': viol_per_well.index.union(insp_per_well.index),\n",
|
||
"})\n",
|
||
"\n",
|
||
"well_panel = well_panel.merge(\n",
|
||
" viol_per_well.rename('total_violations'),\n",
|
||
" left_on='well_id', right_index=True, how='left'\n",
|
||
").merge(\n",
|
||
" insp_per_well.rename('total_inspections'),\n",
|
||
" left_on='well_id', right_index=True, how='left'\n",
|
||
")\n",
|
||
"\n",
|
||
"well_panel['total_violations'] = well_panel['total_violations'].fillna(0).astype(int)\n",
|
||
"well_panel['total_inspections'] = well_panel['total_inspections'].fillna(0).astype(int)\n",
|
||
"\n",
|
||
"# Identify repeat violators and wells with violations\n",
|
||
"well_panel['repeat_violator'] = (well_panel['total_violations'] > 1).astype(int)\n",
|
||
"well_panel['ever_violated'] = (well_panel['total_violations'] > 0).astype(int)\n",
|
||
"\n",
|
||
"# Get district for each well (from inspections - use most common district)\n",
|
||
"well_district = inspections_sorted.groupby('well_id')['district'].agg(\n",
|
||
" lambda x: x.mode()[0] if len(x.mode()) > 0 else x.iloc[0]\n",
|
||
").rename('district')\n",
|
||
"well_panel = well_panel.merge(well_district, left_on='well_id', right_index=True, how='left')\n",
|
||
"\n",
|
||
"# Violation rate per inspection\n",
|
||
"well_panel['violations_per_inspection'] = (\n",
|
||
" well_panel['total_violations'] / well_panel['total_inspections'].replace(0, np.nan)\n",
|
||
")\n",
|
||
"\n",
|
||
"# Average enforcement speed for wells with violations\n",
|
||
"avg_enf_time = violations_sorted.groupby('well_id')['days_to_enforcement'].mean().rename('avg_days_to_enforcement')\n",
|
||
"well_panel = well_panel.merge(avg_enf_time, left_on='well_id', right_index=True, how='left')\n",
|
||
"\n",
|
||
"# First and last activity dates\n",
|
||
"first_insp = inspections_sorted.groupby('well_id')['inspection_date'].min().rename('first_inspection')\n",
|
||
"last_insp = inspections_sorted.groupby('well_id')['inspection_date'].max().rename('last_inspection')\n",
|
||
"first_viol = violations_sorted.groupby('well_id')['violation_disc_date'].min().rename('first_violation')\n",
|
||
"\n",
|
||
"well_panel = well_panel.merge(first_insp, left_on='well_id', right_index=True, how='left')\n",
|
||
"well_panel = well_panel.merge(last_insp, left_on='well_id', right_index=True, how='left')\n",
|
||
"well_panel = well_panel.merge(first_viol, left_on='well_id', right_index=True, how='left')\n",
|
||
"\n",
|
||
"# Calculate well lifespan in data\n",
|
||
"well_panel['years_active'] = (\n",
|
||
" (well_panel['last_inspection'] - well_panel['first_inspection']).dt.days / 365.25\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\n✓ Well-level panel created\")\n",
|
||
"print(f\" Total wells: {len(well_panel):,}\")\n",
|
||
"print(f\" Wells with violations: {well_panel['ever_violated'].sum():,} ({well_panel['ever_violated'].mean()*100:.1f}%)\")\n",
|
||
"print(f\" Repeat violators: {well_panel['repeat_violator'].sum():,} ({well_panel['repeat_violator'].mean()*100:.1f}%)\")\n",
|
||
"print(f\" Avg inspections per well: {well_panel['total_inspections'].mean():.1f}\")\n",
|
||
"print(f\" Avg violations per well: {well_panel['total_violations'].mean():.2f}\")\n",
|
||
"\n",
|
||
"print(\"\\nSample of well panel:\")\n",
|
||
"print(well_panel.head(10).to_string())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "cfc295ce",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Creating district-year panel with pipeline metrics...\n",
|
||
"\n",
|
||
"✓ District-year panel with PIPELINE metrics:\n",
|
||
" Observations: 130\n",
|
||
" Districts: 13\n",
|
||
" Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"PIPELINE METRICS: Sample rows\n",
|
||
"================================================================================\n",
|
||
"district year total_inspections unique_wells compliance_rate avg_days_between_insp median_days_between_insp total_violations wells_with_violations share_major_violations avg_days_to_enforcement median_days_to_enforcement enforcement_rate resolution_rate violations_per_inspection violation_discovery_rate post_2019 year_from_policy offshore_jurisdiction\n",
|
||
" 01 2015 3498 2816 86.5352 38.8023 35.0000 592 379 0.0000 124.9122 19.0000 100.0000 47.9730 0.1692 13.4588 0 -4 0\n",
|
||
" 01 2016 6499 4055 77.3657 109.3848 85.0000 1902 1009 0.0000 230.0910 20.0000 100.0000 24.8160 0.2927 24.8829 0 -3 0\n",
|
||
" 01 2017 8649 6153 88.0217 220.8480 160.0000 1439 767 0.0000 326.0945 33.0000 100.0000 51.4246 0.1664 12.4655 0 -2 0\n",
|
||
" 01 2018 10966 9109 88.1634 263.2386 173.0000 1771 997 0.0000 290.2191 82.0000 100.0000 57.1429 0.1615 10.9452 0 -1 0\n",
|
||
" 01 2019 8097 6447 84.2040 358.4922 231.0000 1506 902 0.1328 261.2776 73.0000 100.0000 51.1952 0.1860 13.9910 1 0 0\n",
|
||
" 01 2020 10511 8716 86.4523 560.7332 337.0000 1816 1019 0.0551 318.0391 260.0000 100.0000 21.1454 0.1728 11.6911 1 1 0\n",
|
||
" 01 2021 8586 6908 80.0140 754.0327 595.0000 2268 1220 0.0000 164.8298 89.0000 100.0000 29.4974 0.2642 17.6607 1 2 0\n",
|
||
" 01 2022 12418 10193 77.3716 1015.8347 1211.0000 3030 1878 0.0000 142.2416 40.0000 100.0000 54.5545 0.2440 18.4244 1 3 0\n",
|
||
" 01 2023 14573 11577 81.5138 866.2592 752.0000 2501 1508 0.1599 173.2783 88.0000 100.0000 58.5366 0.1716 13.0258 1 4 0\n",
|
||
" 01 2024 16338 13038 85.2185 758.6809 570.0000 2208 1516 0.0000 93.9008 28.0000 100.0000 51.4040 0.1351 11.6276 1 5 0\n",
|
||
" 02 2015 1174 921 85.6048 34.1107 28.0000 192 115 0.0000 171.5260 41.0000 100.0000 58.3333 0.1635 12.4864 0 -4 1\n",
|
||
" 02 2016 2936 2003 71.4578 110.6093 77.0000 1120 570 0.0000 273.4232 49.0000 100.0000 19.2857 0.3815 28.4573 0 -3 1\n",
|
||
" 02 2017 5325 4639 88.6009 276.1325 251.0000 642 431 0.0000 270.1137 151.5000 100.0000 49.3769 0.1206 9.2908 0 -2 1\n",
|
||
" 02 2018 5913 5107 89.9205 292.9596 214.0000 518 354 0.3861 222.4208 49.5000 100.0000 35.5212 0.0876 6.9317 0 -1 1\n",
|
||
" 02 2019 4427 3696 89.1123 407.7089 370.0000 434 271 0.0000 275.1613 119.5000 100.0000 39.8618 0.0980 7.3323 1 0 1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create district-year panel with PIPELINE metrics (not just counts)\n",
|
||
"# Focus on: speed, effectiveness, and dynamics of the regulatory process\n",
|
||
"\n",
|
||
"print(\"Creating district-year panel with pipeline metrics...\")\n",
|
||
"\n",
|
||
"# Group by district-year and calculate pipeline performance indicators\n",
|
||
"pipeline_metrics = inspections_sorted.groupby(['district', 'year']).agg(\n",
|
||
" total_inspections=('well_id', 'count'),\n",
|
||
" unique_wells=('well_id', 'nunique'),\n",
|
||
" compliance_rate=('compliance', lambda x: (x.astype(str).str.upper().isin(['YES', 'Y'])).mean() * 100),\n",
|
||
" avg_days_between_insp=('days_since_last_inspection', 'mean'),\n",
|
||
" median_days_between_insp=('days_since_last_inspection', 'median')\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Add violation pipeline metrics\n",
|
||
"viol_pipeline = violations_sorted.groupby(['district', 'year']).agg(\n",
|
||
" total_violations=('well_id', 'count'),\n",
|
||
" wells_with_violations=('well_id', 'nunique'),\n",
|
||
" share_major_violations=('major_viol_ind', lambda x: (x == 'Y').mean() * 100),\n",
|
||
" avg_days_to_enforcement=('days_to_enforcement', 'mean'),\n",
|
||
" median_days_to_enforcement=('days_to_enforcement', 'median'),\n",
|
||
" enforcement_rate=('last_enf_action', lambda x: x.notna().mean() * 100),\n",
|
||
" resolution_rate=('compliant_on_reinsp', lambda x: (x == 'Y').mean() * 100)\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Merge\n",
|
||
"district_year_panel = pd.merge(pipeline_metrics, viol_pipeline, \n",
|
||
" on=['district', 'year'], how='left')\n",
|
||
"\n",
|
||
"# Fill NAs for districts with no violations in that year\n",
|
||
"viol_cols = ['total_violations', 'wells_with_violations', 'share_major_violations',\n",
|
||
" 'avg_days_to_enforcement', 'median_days_to_enforcement', \n",
|
||
" 'enforcement_rate', 'resolution_rate']\n",
|
||
"for col in viol_cols:\n",
|
||
" district_year_panel[col] = district_year_panel[col].fillna(0)\n",
|
||
"\n",
|
||
"# Calculate key pipeline ratios\n",
|
||
"district_year_panel['violations_per_inspection'] = (\n",
|
||
" district_year_panel['total_violations'] / district_year_panel['total_inspections']\n",
|
||
")\n",
|
||
"district_year_panel['violation_discovery_rate'] = (\n",
|
||
" district_year_panel['wells_with_violations'] / district_year_panel['unique_wells'] * 100\n",
|
||
")\n",
|
||
"\n",
|
||
"# Treatment indicator\n",
|
||
"district_year_panel['post_2019'] = (district_year_panel['year'] >= 2019).astype(int)\n",
|
||
"district_year_panel['year_from_policy'] = district_year_panel['year'] - 2019 # for event study\n",
|
||
"# Add offshore-jurisdiction indicator (districts with offshore + onshore oversight)\n",
|
||
"offshore_jurisdiction_districts = ['02', '03', '04']\n",
|
||
"district_year_panel['offshore_jurisdiction'] = district_year_panel['district'].isin(offshore_jurisdiction_districts).astype(int)\n",
|
||
"\n",
|
||
"print(f\"\\n✓ District-year panel with PIPELINE metrics:\")\n",
|
||
"print(f\" Observations: {len(district_year_panel)}\")\n",
|
||
"print(f\" Districts: {district_year_panel['district'].nunique()}\")\n",
|
||
"print(f\" Years: {sorted(district_year_panel['year'].unique())}\")\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"PIPELINE METRICS: Sample rows\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(district_year_panel.head(15).to_string(index=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5f6f45dd",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Pre/Post 2019 Comparison: Did the Disclosure Policy Change the Pipeline?\n",
|
||
"\n",
|
||
"Compare regulatory pipeline metrics before and after the January 2019 disclosure policy."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "eebc7ad8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"PRE vs POST 2019 COMPARISON: Pipeline Metrics\n",
|
||
"================================================================================\n",
|
||
" Pre-2019 Post-2019 Change Pct_Change\n",
|
||
"total_inspections 8782.5577 15197.0128 6414.4551 73.0363\n",
|
||
"unique_wells 6621.8654 10939.8974 4318.0321 65.2087\n",
|
||
"avg_days_between_insp 163.8185 577.2449 413.4264 252.3687\n",
|
||
"compliance_rate 87.1610 89.5490 2.3881 2.7398\n",
|
||
"violation_discovery_rate 12.1762 8.3792 -3.7970 -31.1840\n",
|
||
"violations_per_inspection 0.1488 0.0965 -0.0523 -35.1660\n",
|
||
"avg_days_to_enforcement 174.2728 124.5786 -49.6943 -28.5152\n",
|
||
"median_days_to_enforcement 68.4135 50.4103 -18.0032 -26.3153\n",
|
||
"enforcement_rate 100.0000 100.0000 0.0000 0.0000\n",
|
||
"resolution_rate 52.2074 61.1763 8.9688 17.1792\n",
|
||
"share_major_violations 0.0077 0.1239 0.1162 1507.8328\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"KEY FINDINGS:\n",
|
||
"================================================================================\n",
|
||
"⚠ Inspection frequency DECREASED substantially: 252.4% increase in days between inspections\n",
|
||
"→ Compliance rate INCREASED by 2.4 percentage points\n",
|
||
"→ Enforcement became FASTER by 49.7 days on average\n",
|
||
"→ Resolution rate INCREASED by 9.0 percentage points\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Compare pre vs post 2019 across all pipeline metrics\n",
|
||
"\n",
|
||
"pre_post = district_year_panel.groupby('post_2019').agg({\n",
|
||
" # Inspection intensity\n",
|
||
" 'total_inspections': 'mean',\n",
|
||
" 'unique_wells': 'mean',\n",
|
||
" 'avg_days_between_insp': 'mean',\n",
|
||
" \n",
|
||
" # Compliance outcomes\n",
|
||
" 'compliance_rate': 'mean',\n",
|
||
" 'violation_discovery_rate': 'mean',\n",
|
||
" 'violations_per_inspection': 'mean',\n",
|
||
" \n",
|
||
" # Enforcement speed and effectiveness\n",
|
||
" 'avg_days_to_enforcement': 'mean',\n",
|
||
" 'median_days_to_enforcement': 'mean',\n",
|
||
" 'enforcement_rate': 'mean',\n",
|
||
" 'resolution_rate': 'mean',\n",
|
||
" \n",
|
||
" # Violation severity\n",
|
||
" 'share_major_violations': 'mean'\n",
|
||
"}).T\n",
|
||
"\n",
|
||
"pre_post.columns = ['Pre-2019', 'Post-2019']\n",
|
||
"pre_post['Change'] = pre_post['Post-2019'] - pre_post['Pre-2019']\n",
|
||
"pre_post['Pct_Change'] = (pre_post['Change'] / pre_post['Pre-2019'].replace(0, np.nan) * 100)\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"PRE vs POST 2019 COMPARISON: Pipeline Metrics\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(pre_post.to_string())\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"KEY FINDINGS:\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# Highlight major changes\n",
|
||
"if pre_post.loc['avg_days_between_insp', 'Pct_Change'] > 50:\n",
|
||
" print(f\"⚠ Inspection frequency DECREASED substantially: {pre_post.loc['avg_days_between_insp', 'Pct_Change']:.1f}% increase in days between inspections\")\n",
|
||
" \n",
|
||
"if abs(pre_post.loc['compliance_rate', 'Change']) > 2:\n",
|
||
" direction = \"INCREASED\" if pre_post.loc['compliance_rate', 'Change'] > 0 else \"DECREASED\"\n",
|
||
" print(f\"→ Compliance rate {direction} by {abs(pre_post.loc['compliance_rate', 'Change']):.1f} percentage points\")\n",
|
||
" \n",
|
||
"if abs(pre_post.loc['avg_days_to_enforcement', 'Change']) > 10:\n",
|
||
" direction = \"SLOWER\" if pre_post.loc['avg_days_to_enforcement', 'Change'] > 0 else \"FASTER\"\n",
|
||
" print(f\"→ Enforcement became {direction} by {abs(pre_post.loc['avg_days_to_enforcement', 'Change']):.1f} days on average\")\n",
|
||
" \n",
|
||
"if abs(pre_post.loc['resolution_rate', 'Change']) > 5:\n",
|
||
" direction = \"INCREASED\" if pre_post.loc['resolution_rate', 'Change'] > 0 else \"DECREASED\"\n",
|
||
" print(f\"→ Resolution rate {direction} by {abs(pre_post.loc['resolution_rate', 'Change']):.1f} percentage points\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "c2095d89",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1800x1000 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Pipeline visualization complete\n",
|
||
" Saved as 'pipeline_trends_over_time.png'\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualize the regulatory pipeline over time\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
|
||
"fig.suptitle('Regulatory Pipeline Dynamics: 2015-2024\\n(Vertical line = 2019 disclosure policy)', \n",
|
||
" fontsize=16, fontweight='bold', y=0.995)\n",
|
||
"\n",
|
||
"# Annual averages across all districts\n",
|
||
"annual_avg = district_year_panel.groupby('year').agg({\n",
|
||
" 'compliance_rate': 'mean',\n",
|
||
" 'violations_per_inspection': 'mean',\n",
|
||
" 'avg_days_to_enforcement': 'mean',\n",
|
||
" 'resolution_rate': 'mean',\n",
|
||
" 'avg_days_between_insp': 'mean',\n",
|
||
" 'violation_discovery_rate': 'mean'\n",
|
||
"})\n",
|
||
"\n",
|
||
"# Plot 1: Compliance rate\n",
|
||
"axes[0, 0].plot(annual_avg.index, annual_avg['compliance_rate'], 'o-', linewidth=2.5, markersize=8, color='steelblue')\n",
|
||
"axes[0, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2, label='2019 Policy')\n",
|
||
"axes[0, 0].set_xlabel('Year', fontsize=11)\n",
|
||
"axes[0, 0].set_ylabel('Compliance Rate (%)', fontsize=11)\n",
|
||
"axes[0, 0].set_title('Compliance Rate at Inspection', fontsize=12, fontweight='bold')\n",
|
||
"axes[0, 0].legend()\n",
|
||
"axes[0, 0].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# Plot 2: Violations per inspection\n",
|
||
"axes[0, 1].plot(annual_avg.index, annual_avg['violations_per_inspection'], 'o-', \n",
|
||
" linewidth=2.5, markersize=8, color='darkorange')\n",
|
||
"axes[0, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
|
||
"axes[0, 1].set_xlabel('Year', fontsize=11)\n",
|
||
"axes[0, 1].set_ylabel('Violations per Inspection', fontsize=11)\n",
|
||
"axes[0, 1].set_title('Violation Discovery Rate', fontsize=12, fontweight='bold')\n",
|
||
"axes[0, 1].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# Plot 3: Days to enforcement\n",
|
||
"axes[0, 2].plot(annual_avg.index, annual_avg['avg_days_to_enforcement'], 'o-', \n",
|
||
" linewidth=2.5, markersize=8, color='darkgreen')\n",
|
||
"axes[0, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
|
||
"axes[0, 2].set_xlabel('Year', fontsize=11)\n",
|
||
"axes[0, 2].set_ylabel('Days', fontsize=11)\n",
|
||
"axes[0, 2].set_title('Average Days to Enforcement', fontsize=12, fontweight='bold')\n",
|
||
"axes[0, 2].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# Plot 4: Resolution rate\n",
|
||
"axes[1, 0].plot(annual_avg.index, annual_avg['resolution_rate'], 'o-', \n",
|
||
" linewidth=2.5, markersize=8, color='purple')\n",
|
||
"axes[1, 0].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
|
||
"axes[1, 0].set_xlabel('Year', fontsize=11)\n",
|
||
"axes[1, 0].set_ylabel('Resolution Rate (%)', fontsize=11)\n",
|
||
"axes[1, 0].set_title('Violations Resolved on Re-inspection', fontsize=12, fontweight='bold')\n",
|
||
"axes[1, 0].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# Plot 5: Days between inspections (NEW - shows inspection frequency decline)\n",
|
||
"axes[1, 1].plot(annual_avg.index, annual_avg['avg_days_between_insp'], 'o-', \n",
|
||
" linewidth=2.5, markersize=8, color='brown')\n",
|
||
"axes[1, 1].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
|
||
"axes[1, 1].set_xlabel('Year', fontsize=11)\n",
|
||
"axes[1, 1].set_ylabel('Days', fontsize=11)\n",
|
||
"axes[1, 1].set_title('Average Days Between Inspections', fontsize=12, fontweight='bold')\n",
|
||
"axes[1, 1].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# Plot 6: Violation discovery rate (% of wells with violations)\n",
|
||
"axes[1, 2].plot(annual_avg.index, annual_avg['violation_discovery_rate'], 'o-', \n",
|
||
" linewidth=2.5, markersize=8, color='teal')\n",
|
||
"axes[1, 2].axvline(2019, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
|
||
"axes[1, 2].set_xlabel('Year', fontsize=11)\n",
|
||
"axes[1, 2].set_ylabel('% of Wells', fontsize=11)\n",
|
||
"axes[1, 2].set_title('Share of Inspected Wells with Violations', fontsize=12, fontweight='bold')\n",
|
||
"axes[1, 2].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('pipeline_trends_over_time.png', dpi=300, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"\\n✓ Pipeline visualization complete\")\n",
|
||
"print(\" Saved as 'pipeline_trends_over_time.png'\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "661851e9",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part 3: Corrected Panel Models (Two-Way FE + Offshore Controls)\n",
|
||
"\n",
|
||
"### Econometric Approach (Corrected)\n",
|
||
"\n",
|
||
"Because all districts are exposed to the 2019 policy, core models include district and year fixed effects to absorb statewide shocks and common annual changes.\n",
|
||
"\n",
|
||
"We estimate:\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha_d + \\gamma_t + \\beta_1(\\text{Post2019}_t \\times \\text{OffshoreJurisdiction}_d) + \\epsilon_{dt}$$\n",
|
||
"\n",
|
||
"and district-specific heterogeneity:\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha_d + \\gamma_t + \\sum_d \\delta_d(\\text{District}_d \\times \\text{Post2019}_t) + \\beta_1(\\text{Post2019}_t \\times \\text{OffshoreJurisdiction}_d) + \\epsilon_{dt}$$\n",
|
||
"\n",
|
||
"Where:\n",
|
||
"- $\\alpha_d$: district fixed effects\n",
|
||
"- $\\gamma_t$: year fixed effects\n",
|
||
"- OffshoreJurisdiction = 1 for districts 02, 03, 04\n",
|
||
"\n",
|
||
"Interpretation focus:\n",
|
||
"1. Differential post-2019 effects for offshore-jurisdiction districts\n",
|
||
"2. District-specific post effects net of statewide year shocks\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "38b165a6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"CORRECTED TWO-WAY FE MODELS: Days to Enforcement\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Regression sample: 130 observations\n",
|
||
"Districts: 13\n",
|
||
"Years: [np.int32(2015), np.int32(2016), np.int32(2017), np.int32(2018), np.int32(2019), np.int32(2020), np.int32(2021), np.int32(2022), np.int32(2023), np.int32(2024)]\n",
|
||
"Offshore-jurisdiction districts (02/03/04): 3\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"Model 1: TWFE + Offshore-Jurisdiction Differential Effect\n",
|
||
"================================================================================\n",
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: log_days_to_enf R-squared: 0.699\n",
|
||
"Model: OLS Adj. R-squared: 0.637\n",
|
||
"Method: Least Squares F-statistic: 11.41\n",
|
||
"Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.000111\n",
|
||
"Time: 16:16:22 Log-Likelihood: -77.558\n",
|
||
"No. Observations: 130 AIC: 201.1\n",
|
||
"Df Residuals: 107 BIC: 267.1\n",
|
||
"Df Model: 22 \n",
|
||
"Covariance Type: cluster \n",
|
||
"===================================================================================================\n",
|
||
" coef std err z P>|z| [0.025 0.975]\n",
|
||
"---------------------------------------------------------------------------------------------------\n",
|
||
"Intercept 5.2494 0.176 29.845 0.000 4.905 5.594\n",
|
||
"C(district)[T.02] -0.5017 0.226 -2.216 0.027 -0.946 -0.058\n",
|
||
"C(district)[T.03] -1.1813 0.226 -5.217 0.000 -1.625 -0.738\n",
|
||
"C(district)[T.04] -1.1943 0.226 -5.274 0.000 -1.638 -0.750\n",
|
||
"C(district)[T.05] 0.0513 1.45e-15 3.53e+13 0.000 0.051 0.051\n",
|
||
"C(district)[T.06] 0.1766 1.49e-15 1.18e+14 0.000 0.177 0.177\n",
|
||
"C(district)[T.08] -0.9013 1.13e-15 -7.95e+14 0.000 -0.901 -0.901\n",
|
||
"C(district)[T.09] -0.5575 1.83e-15 -3.04e+14 0.000 -0.558 -0.558\n",
|
||
"C(district)[T.10] -1.1494 1.19e-15 -9.62e+14 0.000 -1.149 -1.149\n",
|
||
"C(district)[T.6E] 0.1071 2.23e-15 4.8e+13 0.000 0.107 0.107\n",
|
||
"C(district)[T.7B] -1.8730 1.33e-15 -1.41e+15 0.000 -1.873 -1.873\n",
|
||
"C(district)[T.7C] -1.2388 1.87e-15 -6.62e+14 0.000 -1.239 -1.239\n",
|
||
"C(district)[T.8A] -1.0810 1.55e-15 -6.96e+14 0.000 -1.081 -1.081\n",
|
||
"C(year)[T.2016] 0.1233 0.171 0.720 0.471 -0.212 0.459\n",
|
||
"C(year)[T.2017] 0.4207 0.215 1.958 0.050 -0.000 0.842\n",
|
||
"C(year)[T.2018] 0.4592 0.252 1.823 0.068 -0.035 0.953\n",
|
||
"C(year)[T.2019] 0.1972 0.280 0.705 0.481 -0.351 0.745\n",
|
||
"C(year)[T.2020] 0.1455 0.254 0.572 0.567 -0.353 0.644\n",
|
||
"C(year)[T.2021] -0.1071 0.214 -0.501 0.616 -0.526 0.312\n",
|
||
"C(year)[T.2022] -0.2732 0.241 -1.135 0.256 -0.745 0.198\n",
|
||
"C(year)[T.2023] -0.1778 0.275 -0.647 0.517 -0.716 0.361\n",
|
||
"C(year)[T.2024] -0.4708 0.227 -2.076 0.038 -0.915 -0.026\n",
|
||
"post_2019:offshore_jurisdiction 0.6372 0.377 1.689 0.091 -0.102 1.377\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.086 Durbin-Watson: 1.268\n",
|
||
"Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.227\n",
|
||
"Skew: -0.039 Prob(JB): 0.893\n",
|
||
"Kurtosis: 2.811 Cond. No. 15.1\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors are robust to cluster correlation (cluster)\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"Model 2: TWFE + District-Specific Post Effects + Offshore Control\n",
|
||
"================================================================================\n",
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: log_days_to_enf R-squared: 0.776\n",
|
||
"Model: OLS Adj. R-squared: 0.700\n",
|
||
"Method: Least Squares F-statistic: 7.109\n",
|
||
"Date: Wed, 18 Feb 2026 Prob (F-statistic): 0.00145\n",
|
||
"Time: 16:16:22 Log-Likelihood: -58.250\n",
|
||
"No. Observations: 130 AIC: 184.5\n",
|
||
"Df Residuals: 96 BIC: 282.0\n",
|
||
"Df Model: 33 \n",
|
||
"Covariance Type: cluster \n",
|
||
"===================================================================================================\n",
|
||
" coef std err z P>|z| [0.025 0.975]\n",
|
||
"---------------------------------------------------------------------------------------------------\n",
|
||
"Intercept 5.1802 0.146 35.397 0.000 4.893 5.467\n",
|
||
"C(district)[T.02] 0.0088 nan nan nan nan nan\n",
|
||
"C(district)[T.03] -1.3481 nan nan nan nan nan\n",
|
||
"C(district)[T.04] -1.3304 nan nan nan nan nan\n",
|
||
"C(district)[T.05] -0.0809 1.85e-14 -4.38e+12 0.000 -0.081 -0.081\n",
|
||
"C(district)[T.06] 0.5301 2.14e-14 2.47e+13 0.000 0.530 0.530\n",
|
||
"C(district)[T.08] -0.5338 1.93e-14 -2.77e+13 0.000 -0.534 -0.534\n",
|
||
"C(district)[T.09] -0.1608 1.88e-14 -8.56e+12 0.000 -0.161 -0.161\n",
|
||
"C(district)[T.10] -1.5798 1.95e-14 -8.09e+13 0.000 -1.580 -1.580\n",
|
||
"C(district)[T.6E] 0.2268 1.82e-14 1.25e+13 0.000 0.227 0.227\n",
|
||
"C(district)[T.7B] -1.6258 1.87e-14 -8.67e+13 0.000 -1.626 -1.626\n",
|
||
"C(district)[T.7C] -1.3740 2.11e-14 -6.52e+13 0.000 -1.374 -1.374\n",
|
||
"C(district)[T.8A] -1.1756 2.03e-14 -5.79e+13 0.000 -1.176 -1.176\n",
|
||
"C(year)[T.2016] 0.1233 0.183 0.675 0.500 -0.235 0.481\n",
|
||
"C(year)[T.2017] 0.4207 0.229 1.835 0.066 -0.029 0.870\n",
|
||
"C(year)[T.2018] 0.4592 0.269 1.709 0.088 -0.068 0.986\n",
|
||
"C(year)[T.2019] 0.2667 0.242 1.101 0.271 -0.208 0.742\n",
|
||
"C(year)[T.2020] 0.2150 0.183 1.176 0.239 -0.143 0.573\n",
|
||
"C(year)[T.2021] -0.0376 0.100 -0.374 0.708 -0.234 0.159\n",
|
||
"C(year)[T.2022] -0.2037 0.106 -1.921 0.055 -0.412 0.004\n",
|
||
"C(year)[T.2023] -0.1083 0.127 -0.850 0.395 -0.358 0.141\n",
|
||
"C(year)[T.2024] -0.4013 0.122 -3.294 0.001 -0.640 -0.163\n",
|
||
"C(district)[01]:post_2019 0.0459 0.052 0.876 0.381 -0.057 0.149\n",
|
||
"C(district)[02]:post_2019 -0.5936 0.013 -45.291 0.000 -0.619 -0.568\n",
|
||
"C(district)[03]:post_2019 0.5352 0.013 40.839 0.000 0.510 0.561\n",
|
||
"C(district)[04]:post_2019 0.4842 0.013 36.942 0.000 0.458 0.510\n",
|
||
"C(district)[05]:post_2019 0.2661 0.052 5.076 0.000 0.163 0.369\n",
|
||
"C(district)[06]:post_2019 -0.5433 0.052 -10.364 0.000 -0.646 -0.441\n",
|
||
"C(district)[08]:post_2019 -0.5667 0.052 -10.810 0.000 -0.669 -0.464\n",
|
||
"C(district)[09]:post_2019 -0.6154 0.052 -11.739 0.000 -0.718 -0.513\n",
|
||
"C(district)[10]:post_2019 0.7632 0.052 14.559 0.000 0.660 0.866\n",
|
||
"C(district)[6E]:post_2019 -0.1537 0.052 -2.932 0.003 -0.256 -0.051\n",
|
||
"C(district)[7B]:post_2019 -0.3660 0.052 -6.983 0.000 -0.469 -0.263\n",
|
||
"C(district)[7C]:post_2019 0.2712 0.052 5.174 0.000 0.168 0.374\n",
|
||
"C(district)[8A]:post_2019 0.2037 0.052 3.885 0.000 0.101 0.306\n",
|
||
"post_2019:offshore_jurisdiction 0.4258 0.039 10.830 0.000 0.349 0.503\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 1.327 Durbin-Watson: 1.652\n",
|
||
"Prob(Omnibus): 0.515 Jarque-Bera (JB): 1.416\n",
|
||
"Skew: -0.215 Prob(JB): 0.493\n",
|
||
"Kurtosis: 2.724 Cond. No. 5.70e+15\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors are robust to cluster correlation (cluster)\n",
|
||
"[2] The smallest eigenvalue is 4.85e-30. This might indicate that there are\n",
|
||
"strong multicollinearity problems or that the design matrix is singular.\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DISTRICT-SPECIFIC POST-2019 EFFECTS (TWFE corrected)\n",
|
||
"================================================================================\n",
|
||
"district coefficient stderr pvalue\n",
|
||
" 09 -0.6154 0.0524 0.0000\n",
|
||
" 02 -0.5936 0.0131 0.0000\n",
|
||
" 08 -0.5667 0.0524 0.0000\n",
|
||
" 06 -0.5433 0.0524 0.0000\n",
|
||
" 7B -0.3660 0.0524 0.0000\n",
|
||
" 6E -0.1537 0.0524 0.0034\n",
|
||
" 01 0.0459 0.0524 0.3813\n",
|
||
" 8A 0.2037 0.0524 0.0001\n",
|
||
" 05 0.2661 0.0524 0.0000\n",
|
||
" 7C 0.2712 0.0524 0.0000\n",
|
||
" 04 0.4842 0.0131 0.0000\n",
|
||
" 03 0.5352 0.0131 0.0000\n",
|
||
" 10 0.7632 0.0524 0.0000\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"OFFSHORE-JURISDICTION DIFFERENTIAL EFFECT\n",
|
||
"================================================================================\n",
|
||
"Coefficient (post_2019 × offshore_jurisdiction): 0.6372\n",
|
||
"P-value: 0.0913\n",
|
||
"Percent differential: +89.1%\n",
|
||
"→ Statistically meaningful differential post-2019 effect for districts 02/03/04\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"H1b MODEL: Compliance Verification (Resolution Rate)\n",
|
||
"================================================================================\n",
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: resolution_rate R-squared: 0.674\n",
|
||
"Model: OLS Adj. R-squared: 0.607\n",
|
||
"Method: Least Squares F-statistic: 59.53\n",
|
||
"Date: Wed, 18 Feb 2026 Prob (F-statistic): 1.17e-08\n",
|
||
"Time: 16:16:22 Log-Likelihood: -497.01\n",
|
||
"No. Observations: 130 AIC: 1040.\n",
|
||
"Df Residuals: 107 BIC: 1106.\n",
|
||
"Df Model: 22 \n",
|
||
"Covariance Type: cluster \n",
|
||
"===================================================================================================\n",
|
||
" coef std err z P>|z| [0.025 0.975]\n",
|
||
"---------------------------------------------------------------------------------------------------\n",
|
||
"Intercept 37.8001 3.939 9.596 0.000 30.080 45.520\n",
|
||
"C(district)[T.02] 2.5271 3.172 0.797 0.426 -3.690 8.744\n",
|
||
"C(district)[T.03] 38.3518 3.172 12.090 0.000 32.135 44.569\n",
|
||
"C(district)[T.04] 25.6387 3.172 8.083 0.000 19.421 31.856\n",
|
||
"C(district)[T.05] 0.5035 5.43e-14 9.26e+12 0.000 0.503 0.503\n",
|
||
"C(district)[T.06] -2.8983 8.65e-14 -3.35e+13 0.000 -2.898 -2.898\n",
|
||
"C(district)[T.08] 16.9255 5.75e-14 2.94e+14 0.000 16.925 16.925\n",
|
||
"C(district)[T.09] 8.4594 5.29e-14 1.6e+14 0.000 8.459 8.459\n",
|
||
"C(district)[T.10] 41.9193 5.9e-14 7.1e+14 0.000 41.919 41.919\n",
|
||
"C(district)[T.6E] 2.7924 5.25e-14 5.32e+13 0.000 2.792 2.792\n",
|
||
"C(district)[T.7B] 26.5398 5.1e-14 5.21e+14 0.000 26.540 26.540\n",
|
||
"C(district)[T.7C] 21.5777 5.18e-14 4.16e+14 0.000 21.578 21.578\n",
|
||
"C(district)[T.8A] 17.6509 4.88e-14 3.61e+14 0.000 17.651 17.651\n",
|
||
"C(year)[T.2016] -11.7452 4.767 -2.464 0.014 -21.088 -2.402\n",
|
||
"C(year)[T.2017] 4.4288 4.869 0.910 0.363 -5.114 13.971\n",
|
||
"C(year)[T.2018] 3.4111 5.341 0.639 0.523 -7.058 13.880\n",
|
||
"C(year)[T.2019] 9.5688 5.514 1.735 0.083 -1.238 20.375\n",
|
||
"C(year)[T.2020] 5.1782 5.969 0.868 0.386 -6.520 16.876\n",
|
||
"C(year)[T.2021] 11.9941 5.886 2.038 0.042 0.457 23.531\n",
|
||
"C(year)[T.2022] 15.8437 5.512 2.875 0.004 5.041 26.646\n",
|
||
"C(year)[T.2023] 17.8129 5.836 3.052 0.002 6.375 29.251\n",
|
||
"C(year)[T.2024] 13.1960 7.214 1.829 0.067 -0.943 27.335\n",
|
||
"post_2019:offshore_jurisdiction -18.5168 5.287 -3.502 0.000 -28.879 -8.155\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: 0.769 Durbin-Watson: 1.420\n",
|
||
"Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.896\n",
|
||
"Skew: -0.150 Prob(JB): 0.639\n",
|
||
"Kurtosis: 2.725 Cond. No. 15.1\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors are robust to cluster correlation (cluster)\n",
|
||
"\n",
|
||
"H1b interpretation (offshore-jurisdiction differential):\n",
|
||
" Coefficient: -18.5168\n",
|
||
" P-value: 0.0005\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/dadams/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt\n",
|
||
" return np.sqrt(np.diag(self.cov_params()))\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"=\"*80)\n",
|
||
"print(\"CORRECTED TWO-WAY FE MODELS: Days to Enforcement\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# Prepare regression data\n",
|
||
"df_reg = district_year_panel[district_year_panel['avg_days_to_enforcement'] > 0].copy()\n",
|
||
"df_reg['log_days_to_enf'] = np.log(df_reg['avg_days_to_enforcement'])\n",
|
||
"\n",
|
||
"print(f\"\\nRegression sample: {len(df_reg)} observations\")\n",
|
||
"print(f\"Districts: {df_reg['district'].nunique()}\")\n",
|
||
"print(f\"Years: {sorted(df_reg['year'].unique())}\")\n",
|
||
"print(f\"Offshore-jurisdiction districts (02/03/04): {df_reg[df_reg['offshore_jurisdiction']==1]['district'].nunique()}\")\n",
|
||
"\n",
|
||
"# Model 1: Two-way FE with offshore-jurisdiction differential post effect\n",
|
||
"formula1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
"model1 = smf.ols(formula1, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"Model 1: TWFE + Offshore-Jurisdiction Differential Effect\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(model1.summary())\n",
|
||
"\n",
|
||
"# Model 2: Two-way FE with district-specific post effects + offshore differential control\n",
|
||
"formula2 = 'log_days_to_enf ~ C(district) + C(year) + C(district):post_2019 + post_2019:offshore_jurisdiction'\n",
|
||
"model2 = smf.ols(formula2, data=df_reg).fit(cov_type='cluster', cov_kwds={'groups': df_reg['district']})\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"Model 2: TWFE + District-Specific Post Effects + Offshore Control\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(model2.summary())\n",
|
||
"\n",
|
||
"# Extract district-specific post effects\n",
|
||
"coef_df = pd.DataFrame({\n",
|
||
" 'coefficient': model2.params,\n",
|
||
" 'stderr': model2.bse,\n",
|
||
" 'pvalue': model2.pvalues\n",
|
||
"}).reset_index()\n",
|
||
"coef_df.columns = ['term', 'coefficient', 'stderr', 'pvalue']\n",
|
||
"\n",
|
||
"district_effects = coef_df[coef_df['term'].str.contains(':post_2019', na=False)].copy()\n",
|
||
"district_effects = district_effects[~district_effects['term'].str.contains('offshore_jurisdiction', na=False)]\n",
|
||
"district_effects['district'] = district_effects['term'].str.extract(r'\\[(?:T\\.)?(\\w+)\\]')[0]\n",
|
||
"district_effects = district_effects.sort_values('coefficient')\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"DISTRICT-SPECIFIC POST-2019 EFFECTS (TWFE corrected)\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(district_effects[['district', 'coefficient', 'stderr', 'pvalue']].to_string(index=False))\n",
|
||
"\n",
|
||
"# Offshore differential interpretation\n",
|
||
"if 'post_2019:offshore_jurisdiction' in model1.params:\n",
|
||
" offshore_coef = model1.params['post_2019:offshore_jurisdiction']\n",
|
||
" offshore_p = model1.pvalues['post_2019:offshore_jurisdiction']\n",
|
||
" offshore_pct = (np.exp(offshore_coef) - 1) * 100\n",
|
||
" print(\"\\n\" + \"=\"*80)\n",
|
||
" print(\"OFFSHORE-JURISDICTION DIFFERENTIAL EFFECT\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(f\"Coefficient (post_2019 × offshore_jurisdiction): {offshore_coef:.4f}\")\n",
|
||
" print(f\"P-value: {offshore_p:.4f}\")\n",
|
||
" print(f\"Percent differential: {offshore_pct:+.1f}%\")\n",
|
||
" if offshore_p < 0.10:\n",
|
||
" print(\"→ Statistically meaningful differential post-2019 effect for districts 02/03/04\")\n",
|
||
" else:\n",
|
||
" print(\"→ No statistically clear differential post-2019 effect for districts 02/03/04\")\n",
|
||
"\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"H1b MODEL: Compliance Verification (Resolution Rate)\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"df_reg_h1b = district_year_panel.copy()\n",
|
||
"formula_h1b = 'resolution_rate ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
"model_h1b = smf.ols(formula_h1b, data=df_reg_h1b).fit(\n",
|
||
" cov_type='cluster', cov_kwds={'groups': df_reg_h1b['district']}\n",
|
||
")\n",
|
||
"\n",
|
||
"h1b_coef = model_h1b.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"h1b_p = model_h1b.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"\n",
|
||
"print(model_h1b.summary())\n",
|
||
"print()\n",
|
||
"print(\"H1b interpretation (offshore-jurisdiction differential):\")\n",
|
||
"print(f\" Coefficient: {h1b_coef:.4f}\")\n",
|
||
"print(f\" P-value: {h1b_p:.4f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "54c386fe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Starting visualization...\n",
|
||
"Checking district_effects exists: <class 'pandas.DataFrame'>\n",
|
||
"Shape: (13, 5)\n",
|
||
"Columns: ['term', 'coefficient', 'stderr', 'pvalue', 'district']\n",
|
||
"\n",
|
||
"1. Creating effects_df copy...\n",
|
||
"2. Adding confidence intervals...\n",
|
||
"3. Calculating percent change...\n",
|
||
"4. Setting district as index...\n",
|
||
" Index: ['09', '02', '08', '06', '7B', '6E', '01', '8A', '05', '7C', '04', '03', '10']\n",
|
||
"5. Creating figure...\n",
|
||
"6. Preparing plot data...\n",
|
||
"7. Creating bar colors...\n",
|
||
"8. Plotting bars...\n",
|
||
"9. Adding error bars...\n",
|
||
"10. Adding plot decorations...\n",
|
||
"11. Adding significance markers...\n",
|
||
"12. Creating second plot...\n",
|
||
"13. Adding value labels...\n",
|
||
"14. Applying tight_layout...\n",
|
||
"15. Saving figure...\n",
|
||
"16. Showing plot...\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"17. Generating summary...\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"VISUALIZATION: Heterogeneous Treatment Effects\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"✓ Saved: district_treatment_effects.png\n",
|
||
"\n",
|
||
"Key findings:\n",
|
||
" • 6 districts show FASTER enforcement post-2019 (negative coefficients)\n",
|
||
" • 7 districts show SLOWER enforcement post-2019 (positive coefficients)\n",
|
||
" • 12 districts have statistically significant effects (p < 0.05)\n",
|
||
"\n",
|
||
" Fastest improvement: District 09 (-46.0% faster)\n",
|
||
" Slowest (worst): District 10 (114.5% slower)\n",
|
||
"\n",
|
||
" Range: -46.0% to 114.5%\n",
|
||
"\n",
|
||
"✓ COMPLETE!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualize heterogeneous treatment effects across districts\n",
|
||
"\n",
|
||
"print(\"Starting visualization...\")\n",
|
||
"print(f\"Checking district_effects exists: {type(district_effects)}\")\n",
|
||
"print(f\"Shape: {district_effects.shape}\")\n",
|
||
"print(f\"Columns: {district_effects.columns.tolist()}\")\n",
|
||
"\n",
|
||
"# Prepare effects dataframe with all needed columns\n",
|
||
"print(\"\\n1. Creating effects_df copy...\")\n",
|
||
"effects_df = district_effects.copy()\n",
|
||
"\n",
|
||
"print(\"2. Adding confidence intervals...\")\n",
|
||
"effects_df['ci_lower'] = effects_df['coefficient'] - 1.96 * effects_df['stderr']\n",
|
||
"effects_df['ci_upper'] = effects_df['coefficient'] + 1.96 * effects_df['stderr']\n",
|
||
"effects_df['significant'] = effects_df['pvalue'] < 0.05\n",
|
||
"\n",
|
||
"print(\"3. Calculating percent change...\")\n",
|
||
"# Calculate percent change: negative coef = faster (reduction in days)\n",
|
||
"# Keep the sign: negative = faster enforcement, positive = slower\n",
|
||
"effects_df['pct_change'] = (np.exp(effects_df['coefficient']) - 1) * 100\n",
|
||
"\n",
|
||
"print(\"4. Setting district as index...\")\n",
|
||
"# Set district as index for plotting\n",
|
||
"effects_df = effects_df.set_index('district')\n",
|
||
"print(f\" Index: {effects_df.index.tolist()}\")\n",
|
||
"\n",
|
||
"print(\"5. Creating figure...\")\n",
|
||
"# Create visualization\n",
|
||
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
|
||
"\n",
|
||
"print(\"6. Preparing plot data...\")\n",
|
||
"# Plot 1: Coefficient plot with confidence intervals\n",
|
||
"effects_plot = effects_df.sort_values('coefficient').copy()\n",
|
||
"y_pos = np.arange(len(effects_plot))\n",
|
||
"\n",
|
||
"print(\"7. Creating bar colors...\")\n",
|
||
"colors = ['forestgreen' if coef < 0 else 'orangered' for coef in effects_plot['coefficient']]\n",
|
||
"\n",
|
||
"print(\"8. Plotting bars...\")\n",
|
||
"ax1.barh(y_pos, effects_plot['coefficient'], color=colors, alpha=0.6, edgecolor='black', linewidth=0.5)\n",
|
||
"\n",
|
||
"print(\"9. Adding error bars...\")\n",
|
||
"ax1.errorbar(effects_plot['coefficient'], y_pos, \n",
|
||
" xerr=[effects_plot['coefficient'] - effects_plot['ci_lower'],\n",
|
||
" effects_plot['ci_upper'] - effects_plot['coefficient']],\n",
|
||
" fmt='none', ecolor='black', capsize=4, alpha=0.8, linewidth=1.5)\n",
|
||
"\n",
|
||
"print(\"10. Adding plot decorations...\")\n",
|
||
"ax1.axvline(0, color='black', linestyle='--', linewidth=2, alpha=0.7)\n",
|
||
"ax1.set_yticks(y_pos)\n",
|
||
"ax1.set_yticklabels([f'District {dist}' for dist in effects_plot.index], fontsize=10)\n",
|
||
"ax1.set_xlabel('Treatment Effect (log days to enforcement)', fontsize=11, fontweight='bold')\n",
|
||
"ax1.set_title('District-Specific Treatment Effects:\\nDays to Enforcement Post-2019', \n",
|
||
" fontsize=13, fontweight='bold')\n",
|
||
"ax1.grid(True, alpha=0.3, axis='x')\n",
|
||
"\n",
|
||
"print(\"11. Adding significance markers...\")\n",
|
||
"# Add significance markers\n",
|
||
"for i, (idx, row) in enumerate(effects_plot.iterrows()):\n",
|
||
" if row['significant']:\n",
|
||
" ax1.text(row['coefficient'], i, ' *', va='center', fontsize=14, fontweight='bold', color='red')\n",
|
||
"\n",
|
||
"print(\"12. Creating second plot...\")\n",
|
||
"# Plot 2: Percent change interpretation\n",
|
||
"ax2.barh(y_pos, effects_plot['pct_change'], color=colors, alpha=0.6, edgecolor='black', linewidth=0.5)\n",
|
||
"ax2.axvline(0, color='black', linestyle='--', linewidth=2, alpha=0.7)\n",
|
||
"ax2.set_yticks(y_pos)\n",
|
||
"ax2.set_yticklabels([f'District {dist}' for dist in effects_plot.index], fontsize=10)\n",
|
||
"ax2.set_xlabel('% Change in Days to Enforcement', fontsize=11, fontweight='bold')\n",
|
||
"ax2.set_title('Percent Change in Enforcement Speed\\n(negative = faster enforcement)', \n",
|
||
" fontsize=13, fontweight='bold')\n",
|
||
"ax2.grid(True, alpha=0.3, axis='x')\n",
|
||
"\n",
|
||
"print(\"13. Adding value labels...\")\n",
|
||
"# Add value labels for key districts\n",
|
||
"for i, (idx, row) in enumerate(effects_plot.iterrows()):\n",
|
||
" if abs(row['pct_change']) > 30: # Label large effects\n",
|
||
" label = f\"{row['pct_change']:.0f}%\"\n",
|
||
" x_pos = row['pct_change'] + (5 if row['pct_change'] > 0 else -5)\n",
|
||
" ha = 'left' if row['pct_change'] > 0 else 'right'\n",
|
||
" ax2.text(x_pos, i, label, va='center', ha=ha, fontsize=9, fontweight='bold')\n",
|
||
"\n",
|
||
"print(\"14. Applying tight_layout...\")\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"print(\"15. Saving figure...\")\n",
|
||
"plt.savefig('district_treatment_effects.png', dpi=300, bbox_inches='tight')\n",
|
||
"\n",
|
||
"print(\"16. Showing plot...\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"17. Generating summary...\")\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"VISUALIZATION: Heterogeneous Treatment Effects\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(f\"\\n✓ Saved: district_treatment_effects.png\")\n",
|
||
"print(\"\\nKey findings:\")\n",
|
||
"print(f\" • {(effects_df['coefficient'] < 0).sum()} districts show FASTER enforcement post-2019 (negative coefficients)\")\n",
|
||
"print(f\" • {(effects_df['coefficient'] > 0).sum()} districts show SLOWER enforcement post-2019 (positive coefficients)\")\n",
|
||
"print(f\" • {effects_df['significant'].sum()} districts have statistically significant effects (p < 0.05)\")\n",
|
||
"\n",
|
||
"# Show magnitude ranges\n",
|
||
"fastest = effects_df.nsmallest(3, 'coefficient')\n",
|
||
"slowest = effects_df.nlargest(3, 'coefficient')\n",
|
||
"\n",
|
||
"print(f\"\\n Fastest improvement: District {fastest.index[0]} ({fastest.iloc[0]['pct_change']:.1f}% faster)\")\n",
|
||
"print(f\" Slowest (worst): District {slowest.index[0]} ({slowest.iloc[0]['pct_change']:.1f}% slower)\")\n",
|
||
"print(f\"\\n Range: {effects_df['pct_change'].min():.1f}% to {effects_df['pct_change'].max():.1f}%\")\n",
|
||
"\n",
|
||
"print(\"\\n✓ COMPLETE!\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dc635f28",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part 4: Differential Event Study (Offshore vs Non-Offshore Districts)\n",
|
||
"\n",
|
||
"### Testing Differential Pre-Trends and Dynamic Effects\n",
|
||
"\n",
|
||
"To make event-study identification coherent, this section estimates year interactions with offshore-jurisdiction status (districts 02/03/04) while controlling for district and year fixed effects.\n",
|
||
"\n",
|
||
"Specification:\n",
|
||
"\n",
|
||
"$$Y_{dt} = \\alpha_d + \\gamma_t + \\sum_{k \\neq -1}\\delta_k\\,[\\mathbb{1}(YearFromPolicy_t=k)\\times OffshoreJurisdiction_d] + \\epsilon_{dt}$$\n",
|
||
"\n",
|
||
"Reference year is 2018 ($k=-1$).\n",
|
||
"\n",
|
||
"This tests:\n",
|
||
"1. Differential pre-trends for offshore-jurisdiction districts\n",
|
||
"2. Differential post-2019 dynamics net of statewide year shocks\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "e4cf8c50",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"DIFFERENTIAL EVENT STUDY: Offshore-Jurisdiction vs Other Districts\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Differential coefficients (offshore-jurisdiction districts relative to others):\n",
|
||
" year year_from_policy coefficient se pvalue significant\n",
|
||
" 2015 -4 0.4454 0.3619 0.2184 False\n",
|
||
" 2016 -3 -0.0606 0.4344 0.8891 False\n",
|
||
" 2017 -2 -0.1481 0.2285 0.5169 False\n",
|
||
" 2018 -1 0.0000 0.0000 1.0000 False\n",
|
||
" 2019 0 0.3479 0.2476 0.1601 False\n",
|
||
" 2020 1 0.1796 0.4833 0.7102 False\n",
|
||
" 2021 2 0.9121 0.5726 0.1112 False\n",
|
||
" 2022 3 0.7532 0.4104 0.0665 False\n",
|
||
" 2023 4 0.9166 0.4307 0.0333 True\n",
|
||
" 2024 5 1.0693 0.4890 0.0288 True\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DIFFERENTIAL PRE-TREND TEST (Offshore vs Non-Offshore)\n",
|
||
"================================================================================\n",
|
||
"Average pre-2019 differential coefficient: 0.0789\n",
|
||
"Significant pre-trend years: 0 out of 3\n",
|
||
"✓ No evidence of differential pre-trends\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Differential event study: offshore-jurisdiction (02/03/04) vs other districts\n",
|
||
"\n",
|
||
"df_event = district_year_panel.copy()\n",
|
||
"df_event = df_event[df_event['avg_days_to_enforcement'] > 0].copy()\n",
|
||
"df_event['log_days_to_enf'] = np.log(df_event['avg_days_to_enforcement'])\n",
|
||
"df_event['year_from_policy'] = df_event['year'] - 2019\n",
|
||
"\n",
|
||
"# Use 2018 as reference\n",
|
||
"years = sorted(df_event['year'].unique())\n",
|
||
"interaction_terms = []\n",
|
||
"for y in years:\n",
|
||
" if y == 2018:\n",
|
||
" continue\n",
|
||
" col = f'offshore_year_{y}'\n",
|
||
" df_event[col] = ((df_event['year'] == y).astype(int) * df_event['offshore_jurisdiction'])\n",
|
||
" interaction_terms.append(col)\n",
|
||
"\n",
|
||
"formula_event = 'log_days_to_enf ~ C(district) + C(year)'\n",
|
||
"if interaction_terms:\n",
|
||
" formula_event += ' + ' + ' + '.join(interaction_terms)\n",
|
||
"\n",
|
||
"model_event = smf.ols(formula_event, data=df_event).fit(\n",
|
||
" cov_type='cluster', cov_kwds={'groups': df_event['district']}\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"DIFFERENTIAL EVENT STUDY: Offshore-Jurisdiction vs Other Districts\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"event_coefs = []\n",
|
||
"for y in years:\n",
|
||
" if y == 2018:\n",
|
||
" event_coefs.append({\n",
|
||
" 'year': y,\n",
|
||
" 'year_from_policy': y - 2019,\n",
|
||
" 'coefficient': 0.0,\n",
|
||
" 'se': 0.0,\n",
|
||
" 'ci_lower': 0.0,\n",
|
||
" 'ci_upper': 0.0,\n",
|
||
" 'pvalue': 1.0\n",
|
||
" })\n",
|
||
" continue\n",
|
||
"\n",
|
||
" p = f'offshore_year_{y}'\n",
|
||
" if p in model_event.params:\n",
|
||
" event_coefs.append({\n",
|
||
" 'year': y,\n",
|
||
" 'year_from_policy': y - 2019,\n",
|
||
" 'coefficient': model_event.params[p],\n",
|
||
" 'se': model_event.bse[p],\n",
|
||
" 'ci_lower': model_event.conf_int().loc[p, 0],\n",
|
||
" 'ci_upper': model_event.conf_int().loc[p, 1],\n",
|
||
" 'pvalue': model_event.pvalues[p]\n",
|
||
" })\n",
|
||
"\n",
|
||
"event_df = pd.DataFrame(event_coefs).sort_values('year')\n",
|
||
"event_df['significant'] = event_df['pvalue'] < 0.05\n",
|
||
"event_df['pre_treatment'] = event_df['year'] < 2019\n",
|
||
"\n",
|
||
"print(\"\\nDifferential coefficients (offshore-jurisdiction districts relative to others):\")\n",
|
||
"print(event_df[['year', 'year_from_policy', 'coefficient', 'se', 'pvalue', 'significant']].to_string(index=False))\n",
|
||
"\n",
|
||
"# Pre-trend test\n",
|
||
"pre_treatment_df = event_df[(event_df['pre_treatment']) & (event_df['year'] != 2018)]\n",
|
||
"if len(pre_treatment_df) > 0:\n",
|
||
" print(\"\\n\" + \"=\"*80)\n",
|
||
" print(\"DIFFERENTIAL PRE-TREND TEST (Offshore vs Non-Offshore)\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(f\"Average pre-2019 differential coefficient: {pre_treatment_df['coefficient'].mean():.4f}\")\n",
|
||
" print(f\"Significant pre-trend years: {pre_treatment_df['significant'].sum()} out of {len(pre_treatment_df)}\")\n",
|
||
" if pre_treatment_df['significant'].sum() == 0:\n",
|
||
" print(\"✓ No evidence of differential pre-trends\")\n",
|
||
" else:\n",
|
||
" print(\"⚠ Differential pre-trends detected; interpret post effects cautiously\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "7c0e3dc6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x700 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Event study plot saved as 'event_study_plot.png'\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualize event study results\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(14, 7))\n",
|
||
"\n",
|
||
"# Plot coefficients with confidence intervals\n",
|
||
"x = event_df['year_from_policy']\n",
|
||
"y = event_df['coefficient']\n",
|
||
"ci_lower = event_df['ci_lower']\n",
|
||
"ci_upper = event_df['ci_upper']\n",
|
||
"\n",
|
||
"# Separate pre and post treatment\n",
|
||
"pre_mask = event_df['year'] < 2019\n",
|
||
"post_mask = event_df['year'] >= 2019\n",
|
||
"\n",
|
||
"# Plot pre-treatment\n",
|
||
"ax.scatter(x[pre_mask], y[pre_mask], color='steelblue', s=100, label='Pre-2019', zorder=3)\n",
|
||
"ax.plot(x[pre_mask], y[pre_mask], color='steelblue', alpha=0.3, linestyle='--')\n",
|
||
"for i in event_df[pre_mask].index:\n",
|
||
" ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='steelblue', alpha=0.5, linewidth=2)\n",
|
||
"\n",
|
||
"# Plot post-treatment\n",
|
||
"ax.scatter(x[post_mask], y[post_mask], color='darkred', s=100, label='Post-2019 Differential', zorder=3)\n",
|
||
"ax.plot(x[post_mask], y[post_mask], color='darkred', alpha=0.3, linestyle='--')\n",
|
||
"for i in event_df[post_mask].index:\n",
|
||
" ax.plot([x[i], x[i]], [ci_lower[i], ci_upper[i]], color='darkred', alpha=0.5, linewidth=2)\n",
|
||
"\n",
|
||
"# Add reference lines\n",
|
||
"ax.axhline(0, color='black', linestyle='-', linewidth=0.8, alpha=0.3)\n",
|
||
"ax.axvline(-1, color='gray', linestyle=':', linewidth=2, alpha=0.5, label='Policy Year (2019)')\n",
|
||
"\n",
|
||
"# Styling\n",
|
||
"ax.set_xlabel('Years Relative to Policy (2019)', fontsize=12, fontweight='bold')\n",
|
||
"ax.set_ylabel('Differential Effect on log(Days to Enforcement)', fontsize=12, fontweight='bold')\n",
|
||
"ax.set_title('Differential Event Study: Offshore vs Non-Offshore Districts\\n(Reference Year: 2018)', \n",
|
||
" fontsize=14, fontweight='bold', pad=20)\n",
|
||
"ax.legend(loc='best', frameon=True, shadow=True)\n",
|
||
"ax.grid(True, alpha=0.2)\n",
|
||
"\n",
|
||
"# Add annotations\n",
|
||
"ax.text(-1, ax.get_ylim()[1]*0.95, 'Pre-treatment\\n(Parallel Trends Test)', \n",
|
||
" ha='center', va='top', fontsize=10, style='italic', \n",
|
||
" bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.3))\n",
|
||
"ax.text(2, ax.get_ylim()[1]*0.95, 'Post-treatment\\n(Policy Effects)', \n",
|
||
" ha='center', va='top', fontsize=10, style='italic',\n",
|
||
" bbox=dict(boxstyle='round', facecolor='lightcoral', alpha=0.3))\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('event_study_plot.png', dpi=300, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"\\n✓ Event study plot saved as 'event_study_plot.png'\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e7699a6",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part 5: Heterogeneous Treatment Effects (TWFE Moderator Tests)\n",
|
||
"\n",
|
||
"This section maps to H3 (structural moderators) and includes H5 (offshore jurisdiction).\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "9d0b54fc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"BASELINE DISTRICT CHARACTERISTICS (Pre-2019)\n",
|
||
"================================================================================\n",
|
||
"district total_inspections_baseline avg_wells baseline_compliance_rate baseline_days_to_enf\n",
|
||
" 01 29612 5533.2500 85.0215 242.8292\n",
|
||
" 02 15348 3167.5000 83.8960 234.3710\n",
|
||
" 03 32975 5568.2500 94.0868 61.9163\n",
|
||
" 04 32081 5250.2500 92.7315 62.7780\n",
|
||
" 05 16329 3246.5000 92.0623 275.6829\n",
|
||
" 06 37386 6742.0000 88.9838 474.9800\n",
|
||
" 08 60999 12675.0000 88.2270 135.3338\n",
|
||
" 09 62196 12129.5000 82.3120 238.4897\n",
|
||
" 10 39620 7018.7500 88.9370 49.3502\n",
|
||
" 6E 13326 2279.0000 78.4384 301.2178\n",
|
||
" 7B 35929 5834.0000 82.7249 48.0696\n",
|
||
" 7C 40631 8175.2500 85.1540 63.0303\n",
|
||
" 8A 40261 8465.0000 90.5173 77.4983\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DISTRICT MODERATORS FOR HETEROGENEOUS EFFECTS\n",
|
||
"================================================================================\n",
|
||
"district high_capacity low_baseline_compliance high_ej border_district offshore_jurisdiction\n",
|
||
" 01 0 1 1 0 0\n",
|
||
" 02 0 1 1 0 1\n",
|
||
" 03 0 0 0 0 1\n",
|
||
" 04 0 0 0 0 1\n",
|
||
" 05 0 0 0 0 0\n",
|
||
" 06 1 0 0 1 0\n",
|
||
" 08 1 0 0 1 0\n",
|
||
" 09 1 1 1 0 0\n",
|
||
" 10 1 0 0 0 0\n",
|
||
" 6E 0 1 1 0 0\n",
|
||
" 7B 0 1 1 0 0\n",
|
||
" 7C 1 1 1 0 0\n",
|
||
" 8A 1 0 0 1 0\n",
|
||
"\n",
|
||
"OK: District characteristics merged into panel data\n",
|
||
" Panel shape: (130, 24)\n",
|
||
" High-capacity districts: 6/13\n",
|
||
" Low baseline compliance districts: 6/13\n",
|
||
" High EJ concern districts: 6/13\n",
|
||
" Border districts: 3/13\n",
|
||
" Offshore-jurisdiction districts: 3/13\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create district characteristics for heterogeneous effects analysis\n",
|
||
"# Using data already in memory from district_year_panel\n",
|
||
"\n",
|
||
"baseline_data = district_year_panel[district_year_panel['year'] < 2019].groupby('district').agg({\n",
|
||
" 'total_inspections': 'sum',\n",
|
||
" 'unique_wells': 'mean',\n",
|
||
" 'compliance_rate': 'mean',\n",
|
||
" 'avg_days_to_enforcement': 'mean'\n",
|
||
"}).reset_index()\n",
|
||
"\n",
|
||
"baseline_data.columns = ['district', 'total_inspections_baseline', 'avg_wells',\n",
|
||
" 'baseline_compliance_rate', 'baseline_days_to_enf']\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"BASELINE DISTRICT CHARACTERISTICS (Pre-2019)\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(baseline_data.to_string(index=False))\n",
|
||
"\n",
|
||
"baseline_data['high_capacity'] = (\n",
|
||
" baseline_data['total_inspections_baseline'] > baseline_data['total_inspections_baseline'].median()\n",
|
||
").astype(int)\n",
|
||
"\n",
|
||
"baseline_data['low_baseline_compliance'] = (\n",
|
||
" baseline_data['baseline_compliance_rate'] < baseline_data['baseline_compliance_rate'].median()\n",
|
||
").astype(int)\n",
|
||
"\n",
|
||
"viol_rate = district_year_panel[district_year_panel['year'] < 2019].groupby('district')['violation_discovery_rate'].mean()\n",
|
||
"baseline_data['high_ej'] = (viol_rate > viol_rate.median()).astype(int).values\n",
|
||
"\n",
|
||
"border_districts = ['06', '08', '8A']\n",
|
||
"baseline_data['border_district'] = baseline_data['district'].isin(border_districts).astype(int)\n",
|
||
"\n",
|
||
"offshore_jurisdiction_districts = ['02', '03', '04']\n",
|
||
"baseline_data['offshore_jurisdiction'] = baseline_data['district'].isin(offshore_jurisdiction_districts).astype(int)\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"DISTRICT MODERATORS FOR HETEROGENEOUS EFFECTS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(baseline_data[['district', 'high_capacity', 'low_baseline_compliance',\n",
|
||
" 'high_ej', 'border_district', 'offshore_jurisdiction']].to_string(index=False))\n",
|
||
"\n",
|
||
"df_het = district_year_panel.merge(\n",
|
||
" baseline_data[['district', 'high_capacity', 'low_baseline_compliance', 'high_ej',\n",
|
||
" 'border_district']],\n",
|
||
" on='district', how='left'\n",
|
||
")\n",
|
||
"\n",
|
||
"# Ensure offshore_jurisdiction is present as a plain column for formula evaluation\n",
|
||
"if 'offshore_jurisdiction' not in df_het.columns:\n",
|
||
" if 'offshore_jurisdiction' in district_year_panel.columns:\n",
|
||
" df_het['offshore_jurisdiction'] = district_year_panel['offshore_jurisdiction'].values\n",
|
||
" else:\n",
|
||
" df_het['offshore_jurisdiction'] = df_het['district'].isin(offshore_jurisdiction_districts).astype(int)\n",
|
||
"print()\n",
|
||
"print(\"OK: District characteristics merged into panel data\")\n",
|
||
"print(f\" Panel shape: {df_het.shape}\")\n",
|
||
"print(f\" High-capacity districts: {baseline_data['high_capacity'].sum()}/{len(baseline_data)}\")\n",
|
||
"print(f\" Low baseline compliance districts: {baseline_data['low_baseline_compliance'].sum()}/{len(baseline_data)}\")\n",
|
||
"print(f\" High EJ concern districts: {baseline_data['high_ej'].sum()}/{len(baseline_data)}\")\n",
|
||
"print(f\" Border districts: {baseline_data['border_district'].sum()}/{len(baseline_data)}\")\n",
|
||
"print(f\" Offshore-jurisdiction districts: {baseline_data['offshore_jurisdiction'].sum()}/{len(baseline_data)}\")\n",
|
||
"\n",
|
||
"print(f\" offshore_jurisdiction in df_het columns: {'offshore_jurisdiction' in df_het.columns}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "f1c3d33b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"TRIPLE DIFFERENCE-IN-DIFFERENCES RESULTS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"H3a: Capacity (High-Capacity Districts)\n",
|
||
"--------------------------------------------------------------------------------\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "PatsyError",
|
||
"evalue": "Error evaluating factor: NameError: name 'offshore_jurisdiction' is not defined\n log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\n ^^^^^^^^^^^^^^^^^^^^^",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/compat.py:40\u001b[39m, in \u001b[36mcall_and_wrap_exc\u001b[39m\u001b[34m(msg, origin, f, *args, **kwargs)\u001b[39m\n\u001b[32m 39\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:179\u001b[39m, in \u001b[36mEvalEnvironment.eval\u001b[39m\u001b[34m(self, expr, source_name, inner_namespace)\u001b[39m\n\u001b[32m 178\u001b[39m code = \u001b[38;5;28mcompile\u001b[39m(expr, source_name, \u001b[33m\"\u001b[39m\u001b[33meval\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m.flags, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m179\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43meval\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mVarLookupDict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_namespaces\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m<string>:1\u001b[39m\n",
|
||
"\u001b[31mNameError\u001b[39m: name 'offshore_jurisdiction' is not defined",
|
||
"\nThe above exception was the direct cause of the following exception:\n",
|
||
"\u001b[31mPatsyError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 19\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m-\u001b[39m\u001b[33m\"\u001b[39m*\u001b[32m80\u001b[39m)\n\u001b[32m 18\u001b[39m formula_h1 = \u001b[33m'\u001b[39m\u001b[33mlog_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m model_h1 = \u001b[43msmf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mols\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformula_h1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdf_het\u001b[49m\u001b[43m)\u001b[49m.fit(cov_type=\u001b[33m'\u001b[39m\u001b[33mcluster\u001b[39m\u001b[33m'\u001b[39m, \n\u001b[32m 20\u001b[39m cov_kwds={\u001b[33m'\u001b[39m\u001b[33mgroups\u001b[39m\u001b[33m'\u001b[39m: df_het[\u001b[33m'\u001b[39m\u001b[33mdistrict\u001b[39m\u001b[33m'\u001b[39m]})\n\u001b[32m 22\u001b[39m interaction_coef_h1 = model_h1.params.get(\u001b[33m'\u001b[39m\u001b[33mpost_2019:high_capacity\u001b[39m\u001b[33m'\u001b[39m, np.nan)\n\u001b[32m 23\u001b[39m interaction_se_h1 = model_h1.bse.get(\u001b[33m'\u001b[39m\u001b[33mpost_2019:high_capacity\u001b[39m\u001b[33m'\u001b[39m, np.nan)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/base/model.py:203\u001b[39m, in \u001b[36mModel.from_formula\u001b[39m\u001b[34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[39m\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m missing == \u001b[33m'\u001b[39m\u001b[33mnone\u001b[39m\u001b[33m'\u001b[39m: \u001b[38;5;66;03m# with patsy it's drop or raise. let's raise.\u001b[39;00m\n\u001b[32m 201\u001b[39m missing = \u001b[33m'\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m203\u001b[39m tmp = \u001b[43mhandle_formula_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m=\u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 204\u001b[39m \u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 205\u001b[39m ((endog, exog), missing_idx, design_info) = tmp\n\u001b[32m 206\u001b[39m max_endog = \u001b[38;5;28mcls\u001b[39m._formula_max_endog\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/statsmodels/formula/formulatools.py:68\u001b[39m, in \u001b[36mhandle_formula_data\u001b[39m\u001b[34m(Y, X, formula, depth, missing)\u001b[39m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data_util._is_using_pandas(Y, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m---> \u001b[39m\u001b[32m68\u001b[39m result = \u001b[43mdmatrices\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 69\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdataframe\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m=\u001b[49m\u001b[43mna_action\u001b[49m\n\u001b[32m 70\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 71\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 72\u001b[39m result = dmatrices(\n\u001b[32m 73\u001b[39m formula, Y, depth, return_type=\u001b[33m\"\u001b[39m\u001b[33mdataframe\u001b[39m\u001b[33m\"\u001b[39m, NA_action=na_action\n\u001b[32m 74\u001b[39m )\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:317\u001b[39m, in \u001b[36mdmatrices\u001b[39m\u001b[34m(formula_like, data, eval_env, NA_action, return_type)\u001b[39m\n\u001b[32m 307\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Construct two design matrices given a formula_like and data.\u001b[39;00m\n\u001b[32m 308\u001b[39m \n\u001b[32m 309\u001b[39m \u001b[33;03mThis function is identical to :func:`dmatrix`, except that it requires\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 314\u001b[39m \u001b[33;03mSee :func:`dmatrix` for details.\u001b[39;00m\n\u001b[32m 315\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 316\u001b[39m eval_env = EvalEnvironment.capture(eval_env, reference=\u001b[32m1\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m (lhs, rhs) = \u001b[43m_do_highlevel_design\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_type\u001b[49m\n\u001b[32m 319\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 320\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m lhs.shape[\u001b[32m1\u001b[39m] == \u001b[32m0\u001b[39m:\n\u001b[32m 321\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m PatsyError(\u001b[33m\"\u001b[39m\u001b[33mmodel is missing required outcome variables\u001b[39m\u001b[33m\"\u001b[39m)\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:162\u001b[39m, in \u001b[36m_do_highlevel_design\u001b[39m\u001b[34m(formula_like, data, eval_env, NA_action, return_type)\u001b[39m\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdata_iter_maker\u001b[39m():\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m([data])\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m design_infos = \u001b[43m_try_incr_builders\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 163\u001b[39m \u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m design_infos \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m build_design_matrices(\n\u001b[32m 167\u001b[39m design_infos, data, NA_action=NA_action, return_type=return_type\n\u001b[32m 168\u001b[39m )\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/highlevel.py:56\u001b[39m, in \u001b[36m_try_incr_builders\u001b[39m\u001b[34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(formula_like, ModelDesc):\n\u001b[32m 55\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(eval_env, EvalEnvironment)\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdesign_matrix_builders\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 57\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlhs_termlist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformula_like\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrhs_termlist\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 58\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 59\u001b[39m \u001b[43m \u001b[49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 60\u001b[39m \u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 61\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/build.py:746\u001b[39m, in \u001b[36mdesign_matrix_builders\u001b[39m\u001b[34m(termlists, data_iter_maker, eval_env, NA_action)\u001b[39m\n\u001b[32m 743\u001b[39m factor_states = _factors_memorize(all_factors, data_iter_maker, eval_env)\n\u001b[32m 744\u001b[39m \u001b[38;5;66;03m# Now all the factors have working eval methods, so we can evaluate them\u001b[39;00m\n\u001b[32m 745\u001b[39m \u001b[38;5;66;03m# on some data to find out what type of data they return.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m746\u001b[39m (num_column_counts, cat_levels_contrasts) = \u001b[43m_examine_factor_types\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 747\u001b[39m \u001b[43m \u001b[49m\u001b[43mall_factors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfactor_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_iter_maker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNA_action\u001b[49m\n\u001b[32m 748\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 749\u001b[39m \u001b[38;5;66;03m# Now we need the factor infos, which encapsulate the knowledge of\u001b[39;00m\n\u001b[32m 750\u001b[39m \u001b[38;5;66;03m# how to turn any given factor into a chunk of data:\u001b[39;00m\n\u001b[32m 751\u001b[39m factor_infos = {}\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/build.py:491\u001b[39m, in \u001b[36m_examine_factor_types\u001b[39m\u001b[34m(factors, factor_states, data_iter_maker, NA_action)\u001b[39m\n\u001b[32m 489\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m data \u001b[38;5;129;01min\u001b[39;00m data_iter_maker():\n\u001b[32m 490\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m factor \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(examine_needed):\n\u001b[32m--> \u001b[39m\u001b[32m491\u001b[39m value = \u001b[43mfactor\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfactor_states\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfactor\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 492\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m factor \u001b[38;5;129;01min\u001b[39;00m cat_sniffers \u001b[38;5;129;01mor\u001b[39;00m guess_categorical(value):\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m factor \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m cat_sniffers:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:599\u001b[39m, in \u001b[36mEvalFactor.eval\u001b[39m\u001b[34m(self, memorize_state, data)\u001b[39m\n\u001b[32m 598\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34meval\u001b[39m(\u001b[38;5;28mself\u001b[39m, memorize_state, data):\n\u001b[32m--> \u001b[39m\u001b[32m599\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meval_code\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/eval.py:582\u001b[39m, in \u001b[36mEvalFactor._eval\u001b[39m\u001b[34m(self, code, memorize_state, data)\u001b[39m\n\u001b[32m 580\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_eval\u001b[39m(\u001b[38;5;28mself\u001b[39m, code, memorize_state, data):\n\u001b[32m 581\u001b[39m inner_namespace = VarLookupDict([data, memorize_state[\u001b[33m\"\u001b[39m\u001b[33mtransforms\u001b[39m\u001b[33m\"\u001b[39m]])\n\u001b[32m--> \u001b[39m\u001b[32m582\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall_and_wrap_exc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 583\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mError evaluating factor\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 584\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 585\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemorize_state\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meval_env\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[43m \u001b[49m\u001b[43mcode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 587\u001b[39m \u001b[43m \u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m=\u001b[49m\u001b[43minner_namespace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 588\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m~/Repos/texas-district-analysis/.venv/lib/python3.14/site-packages/patsy/compat.py:43\u001b[39m, in \u001b[36mcall_and_wrap_exc\u001b[39m\u001b[34m(msg, origin, f, *args, **kwargs)\u001b[39m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 42\u001b[39m new_exc = PatsyError(\u001b[33m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m % (msg, e.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m, e), origin)\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m new_exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n",
|
||
"\u001b[31mPatsyError\u001b[39m: Error evaluating factor: NameError: name 'offshore_jurisdiction' is not defined\n log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction\n ^^^^^^^^^^^^^^^^^^^^^"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Triple-DiD Models: Test each hypothesis\n",
|
||
"\n",
|
||
"# Prepare outcome variable\n",
|
||
"df_het['log_days_to_enf'] = np.log(df_het['avg_days_to_enforcement'] + 1)\n",
|
||
"df_het['log_viol_per_insp'] = np.log(df_het['violations_per_inspection'] + 0.01)\n",
|
||
"\n",
|
||
"hypotheses_results = {}\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"TRIPLE DIFFERENCE-IN-DIFFERENCES RESULTS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# H3a: Capacity moderator\n",
|
||
"print(\"\\n\" + \"-\"*80) \n",
|
||
"print(\"H3a: Capacity (High-Capacity Districts)\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"\n",
|
||
"formula_h1 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:high_capacity + post_2019:offshore_jurisdiction'\n",
|
||
"model_h1 = smf.ols(formula_h1, data=df_het).fit(cov_type='cluster', \n",
|
||
" cov_kwds={'groups': df_het['district']})\n",
|
||
"\n",
|
||
"interaction_coef_h1 = model_h1.params.get('post_2019:high_capacity', np.nan)\n",
|
||
"interaction_se_h1 = model_h1.bse.get('post_2019:high_capacity', np.nan)\n",
|
||
"interaction_p_h1 = model_h1.pvalues.get('post_2019:high_capacity', np.nan)\n",
|
||
"\n",
|
||
"print(f\"Triple-DiD coefficient (post_2019 × high_capacity): {interaction_coef_h1:.4f}\")\n",
|
||
"print(f\"Standard error: {interaction_se_h1:.4f}\")\n",
|
||
"print(f\"P-value: {interaction_p_h1:.4f}\")\n",
|
||
"print(f\"Significant at 5%: {'Yes' if interaction_p_h1 < 0.05 else 'No'}\")\n",
|
||
"\n",
|
||
"if interaction_coef_h1 < 0:\n",
|
||
" print(\"→ High-capacity districts show SMALLER increases in enforcement delays\")\n",
|
||
"elif interaction_coef_h1 > 0:\n",
|
||
" print(\"→ High-capacity districts show LARGER increases in enforcement delays\")\n",
|
||
" \n",
|
||
"hypotheses_results['H3a'] = {\n",
|
||
" 'coefficient': interaction_coef_h1,\n",
|
||
" 'se': interaction_se_h1,\n",
|
||
" 'pvalue': interaction_p_h1,\n",
|
||
" 'hypothesis': 'Capacity moderates responsiveness',\n",
|
||
" 'supported': (interaction_coef_h1 < 0) and (interaction_p_h1 < 0.05)\n",
|
||
"}\n",
|
||
"\n",
|
||
"# H3b: Baseline performance moderator\n",
|
||
"print(\"\\n\" + \"-\"*80)\n",
|
||
"print(\"H3b: Baseline Performance (Low Baseline Compliance)\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"\n",
|
||
"formula_h2 = 'log_viol_per_insp ~ C(district) + C(year) + post_2019:low_baseline_compliance + post_2019:offshore_jurisdiction'\n",
|
||
"model_h2 = smf.ols(formula_h2, data=df_het).fit(cov_type='cluster', \n",
|
||
" cov_kwds={'groups': df_het['district']})\n",
|
||
"\n",
|
||
"interaction_coef_h2 = model_h2.params.get('post_2019:low_baseline_compliance', np.nan)\n",
|
||
"interaction_se_h2 = model_h2.bse.get('post_2019:low_baseline_compliance', np.nan)\n",
|
||
"interaction_p_h2 = model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan)\n",
|
||
"\n",
|
||
"print(f\"Triple-DiD coefficient (post_2019 × low_baseline_compliance): {interaction_coef_h2:.4f}\")\n",
|
||
"print(f\"Standard error: {interaction_se_h2:.4f}\")\n",
|
||
"print(f\"P-value: {interaction_p_h2:.4f}\")\n",
|
||
"print(f\"Significant at 5%: {'Yes' if interaction_p_h2 < 0.05 else 'No'}\")\n",
|
||
"\n",
|
||
"if interaction_coef_h2 < 0:\n",
|
||
" print(\"→ Low compliance districts show LARGER reductions in violations\")\n",
|
||
"elif interaction_coef_h2 > 0:\n",
|
||
" print(\"→ Low compliance districts show SMALLER reductions in violations\")\n",
|
||
" \n",
|
||
"hypotheses_results['H3b'] = {\n",
|
||
" 'coefficient': interaction_coef_h2,\n",
|
||
" 'se': interaction_se_h2,\n",
|
||
" 'pvalue': interaction_p_h2,\n",
|
||
" 'hypothesis': 'Low compliance districts show larger improvements',\n",
|
||
" 'supported': (interaction_coef_h2 < 0) and (interaction_p_h2 < 0.05)\n",
|
||
"}\n",
|
||
"\n",
|
||
"# H3e: Border proximity moderator\n",
|
||
"print(\"\\n\" + \"-\"*80)\n",
|
||
"print(\"H3e: Border Proximity\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"\n",
|
||
"formula_h4 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:border_district + post_2019:offshore_jurisdiction'\n",
|
||
"model_h4 = smf.ols(formula_h4, data=df_het).fit(cov_type='cluster', \n",
|
||
" cov_kwds={'groups': df_het['district']})\n",
|
||
"\n",
|
||
"interaction_coef_h4 = model_h4.params.get('post_2019:border_district', np.nan)\n",
|
||
"interaction_se_h4 = model_h4.bse.get('post_2019:border_district', np.nan)\n",
|
||
"interaction_p_h4 = model_h4.pvalues.get('post_2019:border_district', np.nan)\n",
|
||
"\n",
|
||
"print(f\"Triple-DiD coefficient (post_2019 × border_district): {interaction_coef_h4:.4f}\")\n",
|
||
"print(f\"Standard error: {interaction_se_h4:.4f}\")\n",
|
||
"print(f\"P-value: {interaction_p_h4:.4f}\")\n",
|
||
"print(f\"Significant at 5%: {'Yes' if interaction_p_h4 < 0.05 else 'No'}\")\n",
|
||
"\n",
|
||
"if abs(interaction_coef_h4) > 0 and interaction_p_h4 < 0.05:\n",
|
||
" print(\"→ Border districts show DIFFERENT response to disclosure policy\")\n",
|
||
"else:\n",
|
||
" print(\"→ No significant difference for border districts\")\n",
|
||
" \n",
|
||
"hypotheses_results['H3e'] = {\n",
|
||
" 'coefficient': interaction_coef_h4,\n",
|
||
" 'se': interaction_se_h4,\n",
|
||
" 'pvalue': interaction_p_h4,\n",
|
||
" 'hypothesis': 'Border districts behave differently',\n",
|
||
" 'supported': (abs(interaction_coef_h4) > 0) and (interaction_p_h4 < 0.05)\n",
|
||
"}\n",
|
||
"\n",
|
||
"# H5: Offshore jurisdiction moderator\n",
|
||
"print(\"\\n\" + \"-\"*80)\n",
|
||
"print(\"H5: Offshore-Jurisdiction Districts (02/03/04)\")\n",
|
||
"print(\"-\"*80)\n",
|
||
"\n",
|
||
"formula_h5 = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
"model_h5 = smf.ols(formula_h5, data=df_het).fit(cov_type='cluster', \n",
|
||
" cov_kwds={'groups': df_het['district']})\n",
|
||
"\n",
|
||
"interaction_coef_h5 = model_h5.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"interaction_se_h5 = model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"interaction_p_h5 = model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"\n",
|
||
"print(f\"Differential coefficient (post_2019 × offshore_jurisdiction): {interaction_coef_h5:.4f}\")\n",
|
||
"print(f\"Standard error: {interaction_se_h5:.4f}\")\n",
|
||
"print(f\"P-value: {interaction_p_h5:.4f}\")\n",
|
||
"print(f\"Significant at 5%: {'Yes' if interaction_p_h5 < 0.05 else 'No'}\")\n",
|
||
"\n",
|
||
"hypotheses_results['H5'] = {\n",
|
||
" 'coefficient': interaction_coef_h5,\n",
|
||
" 'se': interaction_se_h5,\n",
|
||
" 'pvalue': interaction_p_h5,\n",
|
||
" 'hypothesis': 'Offshore-jurisdiction districts behave differently',\n",
|
||
" 'supported': (abs(interaction_coef_h5) > 0) and (interaction_p_h5 < 0.05)\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Summary\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"HYPOTHESIS TESTING SUMMARY\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"for h_name, h_result in hypotheses_results.items():\n",
|
||
" status = \"✓ SUPPORTED\" if h_result['supported'] else \"✗ NOT SUPPORTED\"\n",
|
||
" print(f\"{h_name}: {status}\")\n",
|
||
" print(f\" {h_result['hypothesis']}\")\n",
|
||
" print(f\" Coefficient: {h_result['coefficient']:.4f} (p={h_result['pvalue']:.4f})\")\n",
|
||
" print()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"id": "90104c18",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1800x600 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Heterogeneous effects plot saved as 'heterogeneous_effects.png'\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualize differential interaction effects (TWFE-consistent)\n",
|
||
"\n",
|
||
"interaction_rows = [\n",
|
||
" {\n",
|
||
" 'Hypothesis': 'H3a: High Capacity',\n",
|
||
" 'coef': model_h1.params.get('post_2019:high_capacity', np.nan),\n",
|
||
" 'se': model_h1.bse.get('post_2019:high_capacity', np.nan),\n",
|
||
" 'pvalue': model_h1.pvalues.get('post_2019:high_capacity', np.nan),\n",
|
||
" },\n",
|
||
" {\n",
|
||
" 'Hypothesis': 'H3b: Low Baseline Compliance',\n",
|
||
" 'coef': model_h2.params.get('post_2019:low_baseline_compliance', np.nan),\n",
|
||
" 'se': model_h2.bse.get('post_2019:low_baseline_compliance', np.nan),\n",
|
||
" 'pvalue': model_h2.pvalues.get('post_2019:low_baseline_compliance', np.nan),\n",
|
||
" },\n",
|
||
" {\n",
|
||
" 'Hypothesis': 'H3e: Border District',\n",
|
||
" 'coef': model_h4.params.get('post_2019:border_district', np.nan),\n",
|
||
" 'se': model_h4.bse.get('post_2019:border_district', np.nan),\n",
|
||
" 'pvalue': model_h4.pvalues.get('post_2019:border_district', np.nan),\n",
|
||
" },\n",
|
||
" {\n",
|
||
" 'Hypothesis': 'H5: Offshore Jurisdiction',\n",
|
||
" 'coef': model_h5.params.get('post_2019:offshore_jurisdiction', np.nan),\n",
|
||
" 'se': model_h5.bse.get('post_2019:offshore_jurisdiction', np.nan),\n",
|
||
" 'pvalue': model_h5.pvalues.get('post_2019:offshore_jurisdiction', np.nan),\n",
|
||
" }\n",
|
||
"]\n",
|
||
"\n",
|
||
"int_df = pd.DataFrame(interaction_rows)\n",
|
||
"int_df['ci_low'] = int_df['coef'] - 1.96 * int_df['se']\n",
|
||
"int_df['ci_high'] = int_df['coef'] + 1.96 * int_df['se']\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(12, 6))\n",
|
||
"y = np.arange(len(int_df))\n",
|
||
"colors = ['darkred' if (c > 0) else 'steelblue' for c in int_df['coef']]\n",
|
||
"\n",
|
||
"ax.barh(y, int_df['coef'], color=colors, alpha=0.75)\n",
|
||
"ax.errorbar(\n",
|
||
" int_df['coef'], y,\n",
|
||
" xerr=[int_df['coef'] - int_df['ci_low'], int_df['ci_high'] - int_df['coef']],\n",
|
||
" fmt='none', ecolor='black', capsize=4\n",
|
||
")\n",
|
||
"ax.axvline(0, color='black', linestyle='--', linewidth=1)\n",
|
||
"ax.set_yticks(y)\n",
|
||
"ax.set_yticklabels(int_df['Hypothesis'])\n",
|
||
"ax.set_xlabel('Differential effect on log(days to enforcement)')\n",
|
||
"ax.set_title('TWFE Interaction Effects (95% CI)')\n",
|
||
"ax.grid(True, axis='x', alpha=0.3)\n",
|
||
"\n",
|
||
"for i, p in enumerate(int_df['pvalue']):\n",
|
||
" if pd.notna(p) and p < 0.05:\n",
|
||
" ax.text(int_df.loc[i, 'coef'], i, ' *', va='center', fontsize=14, color='black')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('heterogeneous_effects.png', dpi=300, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"OK: Heterogeneous effects plot saved as 'heterogeneous_effects.png'\")\n",
|
||
"print(\"Interaction estimates:\")\n",
|
||
"print(int_df[['Hypothesis', 'coef', 'se', 'pvalue']].to_string(index=False))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"id": "fabc134e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"DISTRICT PERFORMANCE ANALYSIS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"High Performers (enforcement improved >40%): ['09', '08', '06', '7B', '6E', '02']\n",
|
||
"Low Performers (enforcement worsened >30%): ['10', '04', '03']\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"COMPARING HIGH vs LOW PERFORMERS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"--- HIGH PERFORMERS ---\n",
|
||
"district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n",
|
||
" 09 -0.9239 -60.3014 79906 80.5994 211.0839\n",
|
||
" 08 -0.8820 -58.6034 73122 89.0789 130.9169\n",
|
||
" 06 -0.7906 -54.6442 42132 89.0666 451.1938\n",
|
||
" 7B -0.6348 -46.9978 46919 80.6755 51.0413\n",
|
||
" 6E -0.4534 -36.4538 13327 78.4406 301.2178\n",
|
||
" 02 -0.4513 -36.3227 17288 83.0064 228.0713\n",
|
||
"\n",
|
||
"--- LOW PERFORMERS ---\n",
|
||
"district coefficient pct_change total_inspections_baseline baseline_compliance_rate baseline_days_to_enf\n",
|
||
" 10 0.3948 48.4118 43570 88.9002 48.6891\n",
|
||
" 04 0.6408 89.7959 34783 92.9634 61.5493\n",
|
||
" 03 0.6638 94.2124 43477 94.3861 59.0730\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"AVERAGE CHARACTERISTICS BY PERFORMANCE\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"High Performers (n=6):\n",
|
||
" Avg baseline inspections: 45,449\n",
|
||
" Avg baseline compliance: 83.5%\n",
|
||
" Avg baseline days to enforcement: 228.9\n",
|
||
" Avg wells: 8,519\n",
|
||
"\n",
|
||
"Low Performers (n=3):\n",
|
||
" Avg baseline inspections: 40,610\n",
|
||
" Avg baseline compliance: 92.1%\n",
|
||
" Avg baseline days to enforcement: 56.4\n",
|
||
" Avg wells: 6,913\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"PRE/POST COMPARISON BY PERFORMANCE GROUP\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"High Performers:\n",
|
||
" Days to enforcement: 228.9 → 122.8 (-46.3%)\n",
|
||
" Compliance rate: 83.5% → 88.0% (+4.6pp)\n",
|
||
" Violations per inspection: 0.196 → 0.116 (-41.1%)\n",
|
||
" Violation discovery rate: 15.7% → 10.1% (-5.6pp)\n",
|
||
" Total inspections: 272,694 → 754,109 (+176.5%)\n",
|
||
"\n",
|
||
"Low Performers:\n",
|
||
" Days to enforcement: 56.4 → 103.7 (+83.7%)\n",
|
||
" Compliance rate: 92.1% → 91.1% (-1.0pp)\n",
|
||
" Violations per inspection: 0.077 → 0.073 (-6.0%)\n",
|
||
" Violation discovery rate: 7.7% → 7.2% (-0.5pp)\n",
|
||
" Total inspections: 121,830 → 271,373 (+122.7%)\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"KEY INSIGHTS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"High performers improved enforcement speed dramatically, while:\n",
|
||
" • Maintaining or improving compliance\n",
|
||
" • Potentially reducing inspection intensity (may be more targeted)\n",
|
||
" • Finding fewer violations (better deterrence?)\n",
|
||
"\n",
|
||
"Low performers got slower at enforcement, while:\n",
|
||
" • May have experienced increased workload\n",
|
||
" • Different regulatory priorities\n",
|
||
" • Resource constraints\n",
|
||
"\n",
|
||
"✓ District performance comparison complete\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Deep dive: What distinguishes high vs low performing districts?\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"DISTRICT PERFORMANCE ANALYSIS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# Get treatment effects from model 2\n",
|
||
"district_treatment_effects = effects_df.copy()\n",
|
||
"district_treatment_effects = district_treatment_effects.reset_index()\n",
|
||
"\n",
|
||
"# Classify districts by performance\n",
|
||
"district_treatment_effects['performance'] = 'Middle'\n",
|
||
"district_treatment_effects.loc[district_treatment_effects['coefficient'] < -0.4, 'performance'] = 'High Performers'\n",
|
||
"district_treatment_effects.loc[district_treatment_effects['coefficient'] > 0.3, 'performance'] = 'Low Performers'\n",
|
||
"\n",
|
||
"high_performers = district_treatment_effects[district_treatment_effects['performance'] == 'High Performers']['district'].tolist()\n",
|
||
"low_performers = district_treatment_effects[district_treatment_effects['performance'] == 'Low Performers']['district'].tolist()\n",
|
||
"\n",
|
||
"print(f\"\\nHigh Performers (enforcement improved >40%): {high_performers}\")\n",
|
||
"print(f\"Low Performers (enforcement worsened >30%): {low_performers}\")\n",
|
||
"\n",
|
||
"# Compare characteristics\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"COMPARING HIGH vs LOW PERFORMERS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# Merge treatment effects with baseline characteristics\n",
|
||
"comparison_df = district_treatment_effects.merge(baseline_data, on='district', how='left')\n",
|
||
"\n",
|
||
"# Compare high vs low performers\n",
|
||
"high_perf_stats = comparison_df[comparison_df['performance'] == 'High Performers']\n",
|
||
"low_perf_stats = comparison_df[comparison_df['performance'] == 'Low Performers']\n",
|
||
"\n",
|
||
"print(\"\\n--- HIGH PERFORMERS ---\")\n",
|
||
"print(high_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n",
|
||
" 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n",
|
||
"\n",
|
||
"print(\"\\n--- LOW PERFORMERS ---\")\n",
|
||
"print(low_perf_stats[['district', 'coefficient', 'pct_change', 'total_inspections_baseline', \n",
|
||
" 'baseline_compliance_rate', 'baseline_days_to_enf']].to_string(index=False))\n",
|
||
"\n",
|
||
"# Statistical comparison\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"AVERAGE CHARACTERISTICS BY PERFORMANCE\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"for group_name, group_data in [('High Performers', high_perf_stats), \n",
|
||
" ('Low Performers', low_perf_stats)]:\n",
|
||
" print(f\"\\n{group_name} (n={len(group_data)}):\")\n",
|
||
" print(f\" Avg baseline inspections: {group_data['total_inspections_baseline'].mean():,.0f}\")\n",
|
||
" print(f\" Avg baseline compliance: {group_data['baseline_compliance_rate'].mean():.1f}%\")\n",
|
||
" print(f\" Avg baseline days to enforcement: {group_data['baseline_days_to_enf'].mean():.1f}\")\n",
|
||
" print(f\" Avg wells: {group_data['avg_wells'].mean():,.0f}\")\n",
|
||
"\n",
|
||
"# Look at pre/post trends for these districts\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"PRE/POST COMPARISON BY PERFORMANCE GROUP\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"for group_name, districts in [('High Performers', high_performers), \n",
|
||
" ('Low Performers', low_performers)]:\n",
|
||
" group_data = district_year_panel[district_year_panel['district'].isin(districts)]\n",
|
||
" \n",
|
||
" pre = group_data[group_data['year'] < 2019].agg({\n",
|
||
" 'avg_days_to_enforcement': 'mean',\n",
|
||
" 'compliance_rate': 'mean',\n",
|
||
" 'violations_per_inspection': 'mean',\n",
|
||
" 'total_inspections': 'sum',\n",
|
||
" 'violation_discovery_rate': 'mean'\n",
|
||
" })\n",
|
||
" \n",
|
||
" post = group_data[group_data['year'] >= 2019].agg({\n",
|
||
" 'avg_days_to_enforcement': 'mean',\n",
|
||
" 'compliance_rate': 'mean',\n",
|
||
" 'violations_per_inspection': 'mean',\n",
|
||
" 'total_inspections': 'sum',\n",
|
||
" 'violation_discovery_rate': 'mean'\n",
|
||
" })\n",
|
||
" \n",
|
||
" print(f\"\\n{group_name}:\")\n",
|
||
" print(f\" Days to enforcement: {pre['avg_days_to_enforcement']:.1f} → {post['avg_days_to_enforcement']:.1f} \"\n",
|
||
" f\"({((post['avg_days_to_enforcement']/pre['avg_days_to_enforcement'])-1)*100:+.1f}%)\")\n",
|
||
" print(f\" Compliance rate: {pre['compliance_rate']:.1f}% → {post['compliance_rate']:.1f}% \"\n",
|
||
" f\"({post['compliance_rate']-pre['compliance_rate']:+.1f}pp)\")\n",
|
||
" print(f\" Violations per inspection: {pre['violations_per_inspection']:.3f} → {post['violations_per_inspection']:.3f} \"\n",
|
||
" f\"({((post['violations_per_inspection']/pre['violations_per_inspection'])-1)*100:+.1f}%)\")\n",
|
||
" print(f\" Violation discovery rate: {pre['violation_discovery_rate']:.1f}% → {post['violation_discovery_rate']:.1f}% \"\n",
|
||
" f\"({post['violation_discovery_rate']-pre['violation_discovery_rate']:+.1f}pp)\")\n",
|
||
" print(f\" Total inspections: {pre['total_inspections']:,.0f} → {post['total_inspections']:,.0f} \"\n",
|
||
" f\"({((post['total_inspections']/pre['total_inspections'])-1)*100:+.1f}%)\")\n",
|
||
"\n",
|
||
"# Key differences summary\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"KEY INSIGHTS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"print(\"\\nHigh performers improved enforcement speed dramatically, while:\")\n",
|
||
"print(\" • Maintaining or improving compliance\")\n",
|
||
"print(\" • Potentially reducing inspection intensity (may be more targeted)\")\n",
|
||
"print(\" • Finding fewer violations (better deterrence?)\")\n",
|
||
"\n",
|
||
"print(\"\\nLow performers got slower at enforcement, while:\")\n",
|
||
"print(\" • May have experienced increased workload\")\n",
|
||
"print(\" • Different regulatory priorities\")\n",
|
||
"print(\" • Resource constraints\")\n",
|
||
"\n",
|
||
"print(\"\\n✓ District performance comparison complete\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "33e621d0",
|
||
"metadata": {},
|
||
"source": [
|
||
"# District-Level Analysis: Heterogeneous Effects of 2019 RRC Disclosure Policy\n",
|
||
"\n",
|
||
"## Research Question\n",
|
||
"How did the January 2019 policy change (making well-specific violation data publicly searchable) affect enforcement patterns across RRC districts with different characteristics?\n",
|
||
"\n",
|
||
"## Key Hypotheses\n",
|
||
"- **H1**: High-capacity districts show smaller disclosure effects\n",
|
||
"- **H2**: Districts with poor baseline compliance show larger improvements \n",
|
||
"- **H3**: Environmental justice concerns moderate disclosure impacts\n",
|
||
"- **H4**: Border districts behave differently due to competition\n",
|
||
"\n",
|
||
"## Analytical Approach\n",
|
||
"1. **Descriptive Analysis**: District-level summary statistics pre/post 2019\n",
|
||
"2. **Difference-in-Differences**: Basic DiD with district×post2019 interactions\n",
|
||
"3. **Event Study**: Test parallel trends and estimate dynamic treatment effects\n",
|
||
"4. **Triple Difference**: Test moderators (EJ, capacity, baseline compliance, border proximity)\n",
|
||
"5. **Spatial Analysis**: Map heterogeneous treatment effects and test for spillovers\n",
|
||
"6. **Robustness**: Alternative specifications, placebo tests, sensitivity checks\n",
|
||
"\n",
|
||
"## Data Sources\n",
|
||
"- PostgreSQL database: well locations, violations (2015-2024), inspections, enforcement actions\n",
|
||
"- analysis_output.json: pre-computed district-level statistics\n",
|
||
"- Spatial layers: district boundaries, demographics, EJ indicators"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c7b729db",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Part 6: Spatial Analysis\n",
|
||
"\n",
|
||
"### Geographic Patterns in Treatment Effects\n",
|
||
"\n",
|
||
"Now that we've identified massive heterogeneity in how districts responded to the 2019 policy, let's examine **spatial patterns**:\n",
|
||
"\n",
|
||
"1. **Spatial autocorrelation**: Do neighboring districts have similar treatment effects?\n",
|
||
"2. **Geographic clusters**: Are high/low performers geographically clustered?\n",
|
||
"3. **Spillover effects**: Does one district's response affect its neighbors?\n",
|
||
"\n",
|
||
"This helps answer: Is the heterogeneity random or geographically structured?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"id": "510085f2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"SPATIAL MAPPING OF TREATMENT EFFECTS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"District Treatment Effects Summary:\n",
|
||
"district coefficient pct_change effect_category\n",
|
||
" 09 -0.9239 -60.3014 Strong Improvement\n",
|
||
" 08 -0.8820 -58.6034 Strong Improvement\n",
|
||
" 06 -0.7906 -54.6442 Strong Improvement\n",
|
||
" 7B -0.6348 -46.9978 Strong Improvement\n",
|
||
" 6E -0.4534 -36.4538 Moderate Improvement\n",
|
||
" 02 -0.4513 -36.3227 Moderate Improvement\n",
|
||
" 7C -0.1121 -10.6071 Moderate Improvement\n",
|
||
" 8A -0.0547 -5.3278 No Change\n",
|
||
" 05 -0.0520 -5.0693 No Change\n",
|
||
" 01 0.0335 3.4088 No Change\n",
|
||
" 10 0.3948 48.4118 Strong Decline\n",
|
||
" 04 0.6408 89.7959 Strong Decline\n",
|
||
" 03 0.6638 94.2124 Strong Decline\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Spatial map saved as 'district_spatial_map.png'\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"GEOGRAPHIC PATTERNS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"West Texas / Permian Basin (Districts 7B, 7C, 6E):\n",
|
||
" Average treatment effect: -31.4%\n",
|
||
"\n",
|
||
"South Texas / Border (Districts 03, 04, 08, 8A):\n",
|
||
" Average treatment effect: 30.0%\n",
|
||
"\n",
|
||
"Panhandle (Districts 09, 10):\n",
|
||
" Average treatment effect: -5.9%\n",
|
||
"\n",
|
||
"East Texas (Districts 01, 02, 05):\n",
|
||
" Average treatment effect: -12.7%\n",
|
||
"\n",
|
||
"✓ Spatial analysis complete\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Spatial map of district-specific treatment effects\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"SPATIAL MAPPING OF TREATMENT EFFECTS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"# Prepare data for mapping\n",
|
||
"map_data = effects_df.reset_index()\n",
|
||
"map_data['treatment_effect_pct'] = map_data['pct_change']\n",
|
||
"map_data['effect_category'] = pd.cut(map_data['pct_change'], \n",
|
||
" bins=[-100, -40, -10, 10, 40, 200],\n",
|
||
" labels=['Strong Improvement', 'Moderate Improvement', \n",
|
||
" 'No Change', 'Moderate Decline', 'Strong Decline'])\n",
|
||
"\n",
|
||
"print(\"\\nDistrict Treatment Effects Summary:\")\n",
|
||
"print(map_data[['district', 'coefficient', 'pct_change', 'effect_category']].sort_values('pct_change').to_string(index=False))\n",
|
||
"\n",
|
||
"# Create a simple visualization showing district locations and effects\n",
|
||
"# Texas RRC district rough geographic positions (approximate)\n",
|
||
"district_positions = {\n",
|
||
" '01': (32.7, -96.8), # East Texas (Tyler area)\n",
|
||
" '02': (30.2, -94.0), # Southeast (Beaumont/Houston area)\n",
|
||
" '03': (29.5, -99.0), # South Texas (San Antonio area)\n",
|
||
" '04': (29.3, -100.9), # South Texas (Eagle Pass area)\n",
|
||
" '05': (31.5, -99.5), # Central Texas (Brady area)\n",
|
||
" '06': (31.8, -106.4), # West Texas (El Paso area)\n",
|
||
" '7B': (31.8, -102.4), # Permian Basin (Midland area)\n",
|
||
" '7C': (32.0, -101.5), # Permian Basin (Big Spring area)\n",
|
||
" '08': (28.0, -99.0), # South Texas (Laredo area)\n",
|
||
" '8A': (26.2, -98.2), # South Texas (McAllen area)\n",
|
||
" '09': (33.5, -101.9), # Panhandle (Lubbock area)\n",
|
||
" '10': (35.2, -101.8), # Panhandle (Amarillo area)\n",
|
||
" '6E': (31.0, -103.5), # West Texas (Pecos area)\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Add coordinates to map data\n",
|
||
"map_data['lat'] = map_data['district'].map(lambda x: district_positions.get(x, (0, 0))[0])\n",
|
||
"map_data['lon'] = map_data['district'].map(lambda x: district_positions.get(x, (0, 0))[1])\n",
|
||
"\n",
|
||
"# Create spatial visualization\n",
|
||
"fig, ax = plt.subplots(figsize=(16, 12))\n",
|
||
"\n",
|
||
"# Color by performance\n",
|
||
"colors = []\n",
|
||
"for pct in map_data['pct_change']:\n",
|
||
" if pct < -40:\n",
|
||
" colors.append('darkgreen')\n",
|
||
" elif pct < -10:\n",
|
||
" colors.append('lightgreen')\n",
|
||
" elif pct < 10:\n",
|
||
" colors.append('gray')\n",
|
||
" elif pct < 40:\n",
|
||
" colors.append('orange')\n",
|
||
" else:\n",
|
||
" colors.append('darkred')\n",
|
||
"\n",
|
||
"# Plot districts\n",
|
||
"scatter = ax.scatter(map_data['lon'], map_data['lat'], \n",
|
||
" c=colors, s=1000, alpha=0.7, edgecolors='black', linewidth=2, zorder=3)\n",
|
||
"\n",
|
||
"# Add district labels and values\n",
|
||
"for _, row in map_data.iterrows():\n",
|
||
" ax.text(row['lon'], row['lat'], f\"{row['district']}\\n{row['pct_change']:.0f}%\", \n",
|
||
" ha='center', va='center', fontsize=11, fontweight='bold', zorder=4)\n",
|
||
"\n",
|
||
"# Styling\n",
|
||
"ax.set_xlabel('Longitude', fontsize=13, fontweight='bold')\n",
|
||
"ax.set_ylabel('Latitude', fontsize=13, fontweight='bold')\n",
|
||
"ax.set_title('Geographic Distribution of District Treatment Effects\\n2019 Disclosure Policy Impact on Enforcement Speed', \n",
|
||
" fontsize=15, fontweight='bold', pad=20)\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
"# Add legend\n",
|
||
"from matplotlib.patches import Patch\n",
|
||
"legend_elements = [\n",
|
||
" Patch(facecolor='darkgreen', label='Strong Improvement (< -40%)'),\n",
|
||
" Patch(facecolor='lightgreen', label='Moderate Improvement (-40% to -10%)'),\n",
|
||
" Patch(facecolor='gray', label='No Change (-10% to +10%)'),\n",
|
||
" Patch(facecolor='orange', label='Moderate Decline (+10% to +40%)'),\n",
|
||
" Patch(facecolor='darkred', label='Strong Decline (> +40%)')\n",
|
||
"]\n",
|
||
"ax.legend(handles=legend_elements, loc='upper left', fontsize=11, frameon=True, shadow=True)\n",
|
||
"\n",
|
||
"# Add Texas outline note\n",
|
||
"ax.text(0.02, 0.02, 'Note: District positions are approximate center points', \n",
|
||
" transform=ax.transAxes, fontsize=9, style='italic', alpha=0.7)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('district_spatial_map.png', dpi=300, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"\\n✓ Spatial map saved as 'district_spatial_map.png'\")\n",
|
||
"\n",
|
||
"# Geographic patterns analysis\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"print(\"GEOGRAPHIC PATTERNS\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"print(\"\\nWest Texas / Permian Basin (Districts 7B, 7C, 6E):\")\n",
|
||
"west_districts = map_data[map_data['district'].isin(['7B', '7C', '6E'])]\n",
|
||
"print(f\" Average treatment effect: {west_districts['pct_change'].mean():.1f}%\")\n",
|
||
"\n",
|
||
"print(\"\\nSouth Texas / Border (Districts 03, 04, 08, 8A):\")\n",
|
||
"south_districts = map_data[map_data['district'].isin(['03', '04', '08', '8A'])]\n",
|
||
"print(f\" Average treatment effect: {south_districts['pct_change'].mean():.1f}%\")\n",
|
||
"\n",
|
||
"print(\"\\nPanhandle (Districts 09, 10):\")\n",
|
||
"panhandle_districts = map_data[map_data['district'].isin(['09', '10'])]\n",
|
||
"print(f\" Average treatment effect: {panhandle_districts['pct_change'].mean():.1f}%\")\n",
|
||
"\n",
|
||
"print(\"\\nEast Texas (Districts 01, 02, 05):\")\n",
|
||
"east_districts = map_data[map_data['district'].isin(['01', '02', '05'])]\n",
|
||
"print(f\" Average treatment effect: {east_districts['pct_change'].mean():.1f}%\")\n",
|
||
"\n",
|
||
"print(\"\\n✓ Spatial analysis complete\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"id": "fa8946f2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"SPATIAL SPILLOVER ANALYSIS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"District own effects vs. neighbor averages:\n",
|
||
"district own_effect neighbor_avg n_neighbors\n",
|
||
" 09 -60.3014 -3.0643 3\n",
|
||
" 08 -58.6034 42.2340 2\n",
|
||
" 06 -54.6442 26.6710 2\n",
|
||
" 7B -46.9978 -35.7874 3\n",
|
||
" 6E -36.4538 -29.3296 4\n",
|
||
" 02 -36.3227 -0.8303 2\n",
|
||
" 7C -10.6071 -47.9177 4\n",
|
||
" 8A -5.3278 17.8045 2\n",
|
||
" 05 -5.0693 2.8475 5\n",
|
||
" 01 3.4088 -20.6960 2\n",
|
||
" 10 48.4118 -60.3014 1\n",
|
||
" 04 89.7959 -6.3450 3\n",
|
||
" 03 94.2124 26.4663 3\n",
|
||
"\n",
|
||
"✓ Correlation between own and neighbor effects: -0.110\n",
|
||
" → Weak spatial autocorrelation: effects appear geographically random\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Spatial spillover analysis complete\n",
|
||
"✓ Saved: spatial_spillovers.png\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Spatial dynamics: Moran's I for district treatment effects\n",
|
||
"\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"SPATIAL DYNAMICS: MORAN'S I\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"\n",
|
||
"neighbors = {\n",
|
||
" '01': ['02', '05'],\n",
|
||
" '02': ['01', '05'],\n",
|
||
" '03': ['04', '05', '8A'],\n",
|
||
" '04': ['03', '06', '08'],\n",
|
||
" '05': ['01', '02', '03', '6E', '7C'],\n",
|
||
" '06': ['04', '6E'],\n",
|
||
" '6E': ['05', '06', '7B', '7C'],\n",
|
||
" '7B': ['6E', '7C', '09'],\n",
|
||
" '7C': ['05', '6E', '7B', '09'],\n",
|
||
" '08': ['04', '8A'],\n",
|
||
" '8A': ['03', '08'],\n",
|
||
" '09': ['7B', '7C', '10'],\n",
|
||
" '10': ['09']\n",
|
||
"}\n",
|
||
"\n",
|
||
"valid = map_data[['district', 'pct_change']].dropna().copy().sort_values('district')\n",
|
||
"districts = valid['district'].tolist()\n",
|
||
"y = valid['pct_change'].astype(float).to_numpy()\n",
|
||
"n = len(districts)\n",
|
||
"\n",
|
||
"d_idx = {d: i for i, d in enumerate(districts)}\n",
|
||
"W = np.zeros((n, n), dtype=float)\n",
|
||
"for d in districts:\n",
|
||
" i = d_idx[d]\n",
|
||
" nbrs = [k for k in neighbors.get(d, []) if k in d_idx]\n",
|
||
" if len(nbrs) == 0:\n",
|
||
" continue\n",
|
||
" w = 1.0 / len(nbrs)\n",
|
||
" for k in nbrs:\n",
|
||
" W[i, d_idx[k]] = w\n",
|
||
"\n",
|
||
"S0 = W.sum()\n",
|
||
"z = y - y.mean()\n",
|
||
"\n",
|
||
"if S0 > 0 and np.dot(z, z) > 0:\n",
|
||
" moran_i = (n / S0) * ((z @ W @ z) / (z @ z))\n",
|
||
"else:\n",
|
||
" moran_i = np.nan\n",
|
||
"\n",
|
||
"rng = np.random.default_rng(42)\n",
|
||
"B = 5000\n",
|
||
"if pd.notna(moran_i):\n",
|
||
" perm_stats = []\n",
|
||
" for _ in range(B):\n",
|
||
" zp = rng.permutation(z)\n",
|
||
" ip = (n / S0) * ((zp @ W @ zp) / (zp @ zp))\n",
|
||
" perm_stats.append(ip)\n",
|
||
" perm_stats = np.array(perm_stats)\n",
|
||
" p_two_sided = (np.sum(np.abs(perm_stats) >= abs(moran_i)) + 1) / (B + 1)\n",
|
||
"else:\n",
|
||
" p_two_sided = np.nan\n",
|
||
"\n",
|
||
"print(f\"N districts: {n}\")\n",
|
||
"print(f\"Moran's I: {moran_i:.4f}\")\n",
|
||
"print(f\"Permutation p-value (two-sided, B={B}): {p_two_sided:.4f}\")\n",
|
||
"\n",
|
||
"if pd.notna(p_two_sided):\n",
|
||
" if p_two_sided < 0.05 and moran_i > 0:\n",
|
||
" print(\"Interpretation: positive spatial autocorrelation (supports H4)\")\n",
|
||
" elif p_two_sided < 0.05 and moran_i < 0:\n",
|
||
" print(\"Interpretation: significant negative spatial autocorrelation\")\n",
|
||
" else:\n",
|
||
" print(\"Interpretation: no statistically significant spatial autocorrelation\")\n",
|
||
"\n",
|
||
"try:\n",
|
||
" import libpysal\n",
|
||
" from esda.moran import Moran\n",
|
||
"\n",
|
||
" w_obj = {d: [k for k in neighbors.get(d, []) if k in d_idx] for d in districts}\n",
|
||
" w_ps = libpysal.weights.W(w_obj)\n",
|
||
" w_ps.transform = 'r'\n",
|
||
" moran_pkg = Moran(y, w_ps, permutations=999)\n",
|
||
"\n",
|
||
" print()\n",
|
||
" print(\"Package check (esda/libpysal):\")\n",
|
||
" print(f\" Moran's I: {moran_pkg.I:.4f}\")\n",
|
||
" print(f\" Permutation p-value (two-sided): {moran_pkg.p_sim:.4f}\")\n",
|
||
"except Exception:\n",
|
||
" print()\n",
|
||
" print(\"Package check skipped (esda/libpysal unavailable).\")\n",
|
||
"\n",
|
||
"lag_y = W @ y\n",
|
||
"fig, ax = plt.subplots(figsize=(9, 7))\n",
|
||
"ax.scatter(y, lag_y, s=140, edgecolor='black', alpha=0.75)\n",
|
||
"for d, xv, yv in zip(districts, y, lag_y):\n",
|
||
" ax.text(xv, yv, f\" {d}\", fontsize=9)\n",
|
||
"\n",
|
||
"coef = np.polyfit(y, lag_y, 1)\n",
|
||
"poly = np.poly1d(coef)\n",
|
||
"xs = np.linspace(y.min(), y.max(), 100)\n",
|
||
"ax.plot(xs, poly(xs), '--', linewidth=2)\n",
|
||
"\n",
|
||
"ax.axhline(0, color='gray', linestyle='--', linewidth=1)\n",
|
||
"ax.axvline(0, color='gray', linestyle='--', linewidth=1)\n",
|
||
"ax.set_xlabel('District treatment effect (%)')\n",
|
||
"ax.set_ylabel('Spatial lag of treatment effect')\n",
|
||
"ax.set_title(\"Moran Scatterplot for District Treatment Effects\")\n",
|
||
"ax.grid(True, alpha=0.3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('spatial_spillovers.png', dpi=300, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"Saved: spatial_spillovers.png\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cf9c7ade",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Part 7: Robustness Checks and Sensitivity Analysis\n",
|
||
"\n",
|
||
"Now that we've established the heterogeneous treatment effects, let's validate our findings through several robustness tests:\n",
|
||
"\n",
|
||
"1. **Placebo tests**: Run DiD with fake policy dates (should show no effect)\n",
|
||
"2. **Alternative outcomes**: Test with other measures (compliance rate, violations per inspection)\n",
|
||
"3. **Sample restrictions**: Exclude outlier districts and re-run\n",
|
||
"4. **Time sensitivity**: Test different time windows"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "ce410119",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"ROBUSTNESS TEST 1: PLACEBO TESTS\n",
|
||
"================================================================================\n",
|
||
"Testing fake policy years for offshore-jurisdiction differential effects\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Placebo Test: Fake policy in 2017\n",
|
||
"============================================================\n",
|
||
"Differential placebo effect (offshore-jurisdiction districts):\n",
|
||
" Coefficient: 0.3114\n",
|
||
" Std Error: 0.3428\n",
|
||
" P-value: 0.3637\n",
|
||
" 95% CI: [-0.3605, 0.9832]\n",
|
||
" Percent change: +36.5%\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Placebo Test: Fake policy in 2021\n",
|
||
"============================================================\n",
|
||
"Differential placebo effect (offshore-jurisdiction districts):\n",
|
||
" Coefficient: 0.7854\n",
|
||
" Std Error: 0.3533\n",
|
||
" P-value: 0.0262\n",
|
||
" 95% CI: [0.0930, 1.4778]\n",
|
||
" Percent change: +119.3%\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"PLACEBO TEST SUMMARY\n",
|
||
"================================================================================\n",
|
||
" placebo_year coefficient std_error pvalue pct_change significant\n",
|
||
" 2017 0.3114 0.3428 0.3637 36.5311 False\n",
|
||
" 2021 0.7854 0.3533 0.0262 119.3319 True\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Robustness Test 1: Placebo Tests with Fake Policy Dates\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"ROBUSTNESS TEST 1: PLACEBO TESTS\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"Testing fake policy years for offshore-jurisdiction differential effects\")\n",
|
||
"\n",
|
||
"placebo_years = [2017, 2021]\n",
|
||
"placebo_results = []\n",
|
||
"\n",
|
||
"for placebo_year in placebo_years:\n",
|
||
" print()\n",
|
||
" print(\"=\"*60)\n",
|
||
" print(f\"Placebo Test: Fake policy in {placebo_year}\")\n",
|
||
" print(\"=\"*60)\n",
|
||
"\n",
|
||
" df_placebo = df_reg.copy()\n",
|
||
" df_placebo['post_placebo'] = (df_placebo['year'] >= placebo_year).astype(int)\n",
|
||
"\n",
|
||
" formula = 'log_days_to_enf ~ C(district) + C(year) + post_placebo:offshore_jurisdiction'\n",
|
||
" model_placebo = smf.ols(formula, data=df_placebo).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_placebo['district']}\n",
|
||
" )\n",
|
||
"\n",
|
||
" term = 'post_placebo:offshore_jurisdiction'\n",
|
||
" coef = model_placebo.params.get(term, np.nan)\n",
|
||
" se = model_placebo.bse.get(term, np.nan)\n",
|
||
" pval = model_placebo.pvalues.get(term, np.nan)\n",
|
||
"\n",
|
||
" if term in model_placebo.params.index:\n",
|
||
" ci_lower = model_placebo.conf_int().loc[term, 0]\n",
|
||
" ci_upper = model_placebo.conf_int().loc[term, 1]\n",
|
||
" else:\n",
|
||
" ci_lower, ci_upper = np.nan, np.nan\n",
|
||
"\n",
|
||
" pct_change = (np.exp(coef) - 1) * 100 if pd.notna(coef) else np.nan\n",
|
||
"\n",
|
||
" print(\"Differential placebo effect (offshore-jurisdiction districts):\")\n",
|
||
" print(f\" Coefficient: {coef:.4f}\")\n",
|
||
" print(f\" Std Error: {se:.4f}\")\n",
|
||
" print(f\" P-value: {pval:.4f}\")\n",
|
||
" print(f\" 95% CI: [{ci_lower:.4f}, {ci_upper:.4f}]\")\n",
|
||
" print(f\" Percent change: {pct_change:+.1f}%\")\n",
|
||
"\n",
|
||
" placebo_results.append({\n",
|
||
" 'placebo_year': placebo_year,\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'std_error': se,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'pct_change': pct_change,\n",
|
||
" 'significant': (pval < 0.05) if pd.notna(pval) else False\n",
|
||
" })\n",
|
||
"\n",
|
||
"placebo_df = pd.DataFrame(placebo_results)\n",
|
||
"print()\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(\"PLACEBO TEST SUMMARY\")\n",
|
||
"print(\"=\"*80)\n",
|
||
"print(placebo_df.to_string(index=False))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "5c9294c4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"ROBUSTNESS TEST 2: ALTERNATIVE OUTCOME MEASURES\n",
|
||
"================================================================================\n",
|
||
"Testing offshore-jurisdiction differential effects across outcomes\n",
|
||
"\n",
|
||
"Outcome: Compliance Rate (pp)\n",
|
||
" Coefficient: -2.7970\n",
|
||
" P-value: 0.4370\n",
|
||
"\n",
|
||
"Outcome: Violations per Inspection\n",
|
||
" Coefficient: 0.0401\n",
|
||
" P-value: 0.2865\n",
|
||
"\n",
|
||
"Outcome: Log(Total Violations)\n",
|
||
" Coefficient: 0.3713\n",
|
||
" P-value: 0.2843\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"ALTERNATIVE OUTCOMES SUMMARY\n",
|
||
"================================================================================\n",
|
||
" outcome coefficient pvalue significant\n",
|
||
" Compliance Rate (pp) -2.7970 0.4370 False\n",
|
||
"Violations per Inspection 0.0401 0.2865 False\n",
|
||
" Log(Total Violations) 0.3713 0.2843 False\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Robustness Test 2: Alternative Outcome Measures\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"ROBUSTNESS TEST 2: ALTERNATIVE OUTCOME MEASURES\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"Testing offshore-jurisdiction differential effects across outcomes\")\n",
|
||
"\n",
|
||
"# Prepare alternative outcomes\n",
|
||
"df_alt = district_year_panel.copy()\n",
|
||
"df_alt['post_2019'] = (df_alt['year'] >= 2019).astype(int)\n",
|
||
"\n",
|
||
"alternative_outcomes = []\n",
|
||
"\n",
|
||
"# Outcome 1: Measured compliance rate (percentage points)\n",
|
||
"if 'compliance_rate' in df_alt.columns:\n",
|
||
" df_alt['compliance_rate_pp'] = df_alt['compliance_rate']\n",
|
||
"\n",
|
||
" formula = 'compliance_rate_pp ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
" model_alt1 = smf.ols(formula, data=df_alt).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_alt['district']}\n",
|
||
" )\n",
|
||
"\n",
|
||
" coef = model_alt1.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" pval = model_alt1.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"\n",
|
||
" print()\n",
|
||
" print(\"Outcome: Compliance Rate (pp)\")\n",
|
||
" print(f\" Coefficient: {coef:.4f}\")\n",
|
||
" print(f\" P-value: {pval:.4f}\")\n",
|
||
"\n",
|
||
" alternative_outcomes.append({\n",
|
||
" 'outcome': 'Compliance Rate (pp)',\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'significant': pval < 0.05\n",
|
||
" })\n",
|
||
"\n",
|
||
"# Outcome 2: Violations per inspection\n",
|
||
"if 'total_violations' in df_alt.columns and 'total_inspections' in df_alt.columns:\n",
|
||
" df_alt['violations_per_inspection'] = (\n",
|
||
" df_alt['total_violations'] / df_alt['total_inspections']\n",
|
||
" ).replace([np.inf, -np.inf], np.nan)\n",
|
||
"\n",
|
||
" df_vpi = df_alt.dropna(subset=['violations_per_inspection'])\n",
|
||
"\n",
|
||
" formula = 'violations_per_inspection ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
" model_alt2 = smf.ols(formula, data=df_vpi).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_vpi['district']}\n",
|
||
" )\n",
|
||
"\n",
|
||
" coef = model_alt2.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" pval = model_alt2.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"\n",
|
||
" print()\n",
|
||
" print(\"Outcome: Violations per Inspection\")\n",
|
||
" print(f\" Coefficient: {coef:.4f}\")\n",
|
||
" print(f\" P-value: {pval:.4f}\")\n",
|
||
"\n",
|
||
" alternative_outcomes.append({\n",
|
||
" 'outcome': 'Violations per Inspection',\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'significant': pval < 0.05\n",
|
||
" })\n",
|
||
"\n",
|
||
"# Outcome 3: Log of total violations\n",
|
||
"if 'total_violations' in df_alt.columns:\n",
|
||
" df_alt['log_violations'] = np.log(df_alt['total_violations'].replace(0, 0.1))\n",
|
||
"\n",
|
||
" formula = 'log_violations ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
" model_alt3 = smf.ols(formula, data=df_alt).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_alt['district']}\n",
|
||
" )\n",
|
||
"\n",
|
||
" coef = model_alt3.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" pval = model_alt3.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"\n",
|
||
" print()\n",
|
||
" print(\"Outcome: Log(Total Violations)\")\n",
|
||
" print(f\" Coefficient: {coef:.4f}\")\n",
|
||
" print(f\" P-value: {pval:.4f}\")\n",
|
||
"\n",
|
||
" alternative_outcomes.append({\n",
|
||
" 'outcome': 'Log(Total Violations)',\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'significant': pval < 0.05\n",
|
||
" })\n",
|
||
"\n",
|
||
"if alternative_outcomes:\n",
|
||
" alt_df = pd.DataFrame(alternative_outcomes)\n",
|
||
" print()\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(\"ALTERNATIVE OUTCOMES SUMMARY\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(alt_df.to_string(index=False))\n",
|
||
"else:\n",
|
||
" print(\"WARN: Could not compute alternative outcomes (missing data)\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "63a2ea9a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS\n",
|
||
"================================================================================\n",
|
||
"Testing offshore-jurisdiction differential effect sensitivity\n",
|
||
"\n",
|
||
"Restriction: Exclude Extreme Outliers (['09', '02', '03', '10'])\n",
|
||
" Coefficient: 1.0153; p-value: 0.0000\n",
|
||
"\n",
|
||
"Restriction: Exclude 2015-2016\n",
|
||
" Coefficient: 0.7705; p-value: 0.0545\n",
|
||
"\n",
|
||
"Restriction: Exclude Pandemic Years (2020-2021)\n",
|
||
" Coefficient: 0.7126; p-value: 0.0249\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"SAMPLE RESTRICTIONS SUMMARY\n",
|
||
"================================================================================\n",
|
||
" restriction n_districts coefficient pvalue\n",
|
||
"Exclude Extreme Outliers 9 1.0153 0.0000\n",
|
||
" Exclude 2015-2016 13 0.7705 0.0545\n",
|
||
" Exclude Pandemic Years 13 0.7126 0.0249\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Robustness Test 3: Sample Restrictions\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"ROBUSTNESS TEST 3: SAMPLE RESTRICTIONS\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"Testing offshore-jurisdiction differential effect sensitivity\")\n",
|
||
"\n",
|
||
"sample_restrictions = []\n",
|
||
"\n",
|
||
"def fit_offshore_model(df_in):\n",
|
||
" formula = 'log_days_to_enf ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
" m = smf.ols(formula, data=df_in).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_in['district']}\n",
|
||
" )\n",
|
||
" coef = m.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" pval = m.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" return m, coef, pval\n",
|
||
"\n",
|
||
"# Restriction 1: Exclude extreme districts\n",
|
||
"extreme_districts = []\n",
|
||
"if 'district_effects' in locals() and len(district_effects) >= 4:\n",
|
||
" sorted_effects = district_effects.sort_values('coefficient')\n",
|
||
" extreme_districts = sorted_effects.head(2)['district'].tolist() + sorted_effects.tail(2)['district'].tolist()\n",
|
||
"\n",
|
||
"if extreme_districts:\n",
|
||
" df_restricted = df_reg[~df_reg['district'].isin(extreme_districts)].copy()\n",
|
||
" _, coef, pval = fit_offshore_model(df_restricted)\n",
|
||
" print()\n",
|
||
" print(f\"Restriction: Exclude Extreme Outliers ({extreme_districts})\")\n",
|
||
" print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n",
|
||
" sample_restrictions.append({'restriction': 'Exclude Extreme Outliers', 'n_districts': df_restricted['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n",
|
||
"\n",
|
||
"# Restriction 2: Exclude 2015-2016\n",
|
||
"df_recent = df_reg[df_reg['year'] >= 2017].copy()\n",
|
||
"_, coef, pval = fit_offshore_model(df_recent)\n",
|
||
"print()\n",
|
||
"print(\"Restriction: Exclude 2015-2016\")\n",
|
||
"print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n",
|
||
"sample_restrictions.append({'restriction': 'Exclude 2015-2016', 'n_districts': df_recent['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n",
|
||
"\n",
|
||
"# Restriction 3: Exclude pandemic years\n",
|
||
"df_no_pandemic = df_reg[~df_reg['year'].isin([2020, 2021])].copy()\n",
|
||
"_, coef, pval = fit_offshore_model(df_no_pandemic)\n",
|
||
"print()\n",
|
||
"print(\"Restriction: Exclude Pandemic Years (2020-2021)\")\n",
|
||
"print(f\" Coefficient: {coef:.4f}; p-value: {pval:.4f}\")\n",
|
||
"sample_restrictions.append({'restriction': 'Exclude Pandemic Years', 'n_districts': df_no_pandemic['district'].nunique(), 'coefficient': coef, 'pvalue': pval})\n",
|
||
"\n",
|
||
"if sample_restrictions:\n",
|
||
" restrict_df = pd.DataFrame(sample_restrictions)\n",
|
||
" print()\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(\"SAMPLE RESTRICTIONS SUMMARY\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(restrict_df.to_string(index=False))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "1bb308ea",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Testing alternative functional forms and control strategies...\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Specification 1: Linear Days (no log transformation)\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"Linear effect on days to enforcement:\n",
|
||
" Coefficient: 80.14 days\n",
|
||
" P-value: 0.1227\n",
|
||
" Interpretation: 80.1 days slower enforcement\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Specification 2: Year Fixed Effects (not just post dummy)\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"Average effect across post-2019 years:\n",
|
||
" Mean coefficient: -0.1144\n",
|
||
" Percent change: -10.8%\n",
|
||
" Years included: 6\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Specification 3: District-Specific Time Trends\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"Pooled effect with district-specific trends:\n",
|
||
" Coefficient: 0.1667\n",
|
||
" P-value: 0.6063\n",
|
||
" Percent change: +18.1%\n",
|
||
" Controls for: pre-existing district-specific trends\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Specification 4: Winsorized Outcome (top/bottom 5%)\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"Pooled effect with winsorized outcome:\n",
|
||
" Coefficient: 0.5889\n",
|
||
" P-value: 0.1137\n",
|
||
" Percent change: +80.2%\n",
|
||
" Robust to: extreme outliers\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"SPECIFICATION SENSITIVITY SUMMARY\n",
|
||
"================================================================================\n",
|
||
" specification coefficient pvalue interpretation\n",
|
||
" Linear (no log) 80.1362 0.1227 80.1 days\n",
|
||
"Year FE (average) -0.1144 NaN -10.8%\n",
|
||
" District Trends 0.1667 0.6063 +18.1%\n",
|
||
" Winsorized 0.5889 0.1137 +80.2%\n",
|
||
"\n",
|
||
"Baseline specification: +89.1%\n",
|
||
"⚠️ Some specification sensitivity detected (range: 0.422)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Robustness Test 4: Sensitivity to Specification\n",
|
||
"\n",
|
||
"# Test different model specifications to ensure results aren't driven by \n",
|
||
"# specific functional form choices\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\" * 80)\n",
|
||
"print(\"ROBUSTNESS TEST 4: SPECIFICATION SENSITIVITY\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"\\nTesting alternative functional forms and control strategies...\\n\")\n",
|
||
"\n",
|
||
"specification_tests = []\n",
|
||
"\n",
|
||
"# Specification 1: Linear (non-log) outcome\n",
|
||
"print(\"=\"*60)\n",
|
||
"print(\"Specification 1: Linear Days (no log transformation)\")\n",
|
||
"print(\"=\"*60)\n",
|
||
"\n",
|
||
"df_linear = df_reg.copy()\n",
|
||
"\n",
|
||
"formula = 'avg_days_to_enforcement ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
"model_linear = smf.ols(formula, data=df_linear).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_linear['district']}\n",
|
||
")\n",
|
||
"\n",
|
||
"coef = model_linear.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"pval = model_linear.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"\n",
|
||
"print(f\"\\nLinear effect on days to enforcement:\")\n",
|
||
"print(f\" Coefficient: {coef:.2f} days\")\n",
|
||
"print(f\" P-value: {pval:.4f}\")\n",
|
||
"print(f\" Interpretation: {abs(coef):.1f} days {'faster' if coef < 0 else 'slower'} enforcement\")\n",
|
||
"\n",
|
||
"specification_tests.append({\n",
|
||
" 'specification': 'Linear (no log)',\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'interpretation': f\"{coef:.1f} days\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# Specification 2: Add year fixed effects (instead of just post dummy)\n",
|
||
"print(\"\\n\" + \"=\"*60)\n",
|
||
"print(\"Specification 2: Year Fixed Effects (not just post dummy)\")\n",
|
||
"print(\"=\"*60)\n",
|
||
"\n",
|
||
"df_year_fe = df_reg.copy()\n",
|
||
"\n",
|
||
"formula = 'log_days_to_enf ~ C(year) + C(district) + post_2019:offshore_jurisdiction'\n",
|
||
"model_year_fe = smf.ols(formula, data=df_year_fe).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_year_fe['district']}\n",
|
||
")\n",
|
||
"\n",
|
||
"# Calculate average post-2019 effect\n",
|
||
"post_years = [2019, 2020, 2021, 2022, 2023, 2024]\n",
|
||
"post_coefs = []\n",
|
||
"for year in post_years:\n",
|
||
" param_name = f'C(year)[T.{year}]'\n",
|
||
" if param_name in model_year_fe.params.index:\n",
|
||
" post_coefs.append(model_year_fe.params[param_name])\n",
|
||
"\n",
|
||
"if post_coefs:\n",
|
||
" avg_post_effect = np.mean(post_coefs)\n",
|
||
" pct_change = (np.exp(avg_post_effect) - 1) * 100\n",
|
||
" \n",
|
||
" print(f\"\\nAverage effect across post-2019 years:\")\n",
|
||
" print(f\" Mean coefficient: {avg_post_effect:.4f}\")\n",
|
||
" print(f\" Percent change: {pct_change:+.1f}%\")\n",
|
||
" print(f\" Years included: {len(post_coefs)}\")\n",
|
||
" \n",
|
||
" specification_tests.append({\n",
|
||
" 'specification': 'Year FE (average)',\n",
|
||
" 'coefficient': avg_post_effect,\n",
|
||
" 'pvalue': np.nan,\n",
|
||
" 'interpretation': f\"{pct_change:+.1f}%\"\n",
|
||
" })\n",
|
||
"\n",
|
||
"# Specification 3: Add district-specific time trends\n",
|
||
"print(\"\\n\" + \"=\"*60)\n",
|
||
"print(\"Specification 3: District-Specific Time Trends\")\n",
|
||
"print(\"=\"*60)\n",
|
||
"\n",
|
||
"df_trends = df_reg.copy()\n",
|
||
"df_trends['year_numeric'] = df_trends['year'].astype(int)\n",
|
||
"\n",
|
||
"# Create district-year interaction for trends\n",
|
||
"df_trends['district_trend'] = df_trends.groupby('district')['year_numeric'].transform(\n",
|
||
" lambda x: x - x.min()\n",
|
||
")\n",
|
||
"\n",
|
||
"formula = 'log_days_to_enf ~ C(district) + C(year) + C(district):district_trend + post_2019:offshore_jurisdiction'\n",
|
||
"model_trends = smf.ols(formula, data=df_trends).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_trends['district']}\n",
|
||
")\n",
|
||
"\n",
|
||
"coef = model_trends.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"pval = model_trends.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"pct_change = (np.exp(coef) - 1) * 100\n",
|
||
"\n",
|
||
"print(f\"\\nPooled effect with district-specific trends:\")\n",
|
||
"print(f\" Coefficient: {coef:.4f}\")\n",
|
||
"print(f\" P-value: {pval:.4f}\")\n",
|
||
"print(f\" Percent change: {pct_change:+.1f}%\")\n",
|
||
"print(f\" Controls for: pre-existing district-specific trends\")\n",
|
||
"\n",
|
||
"specification_tests.append({\n",
|
||
" 'specification': 'District Trends',\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'interpretation': f\"{pct_change:+.1f}%\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# Specification 4: Winsorize extreme values (top/bottom 5%)\n",
|
||
"print(\"\\n\" + \"=\"*60)\n",
|
||
"print(\"Specification 4: Winsorized Outcome (top/bottom 5%)\")\n",
|
||
"print(\"=\"*60)\n",
|
||
"\n",
|
||
"from scipy.stats import mstats\n",
|
||
"\n",
|
||
"df_winsor = df_reg.copy()\n",
|
||
"df_winsor['log_days_winsor'] = mstats.winsorize(\n",
|
||
" df_winsor['log_days_to_enf'], \n",
|
||
" limits=[0.05, 0.05]\n",
|
||
")\n",
|
||
"\n",
|
||
"formula = 'log_days_winsor ~ C(district) + C(year) + post_2019:offshore_jurisdiction'\n",
|
||
"model_winsor = smf.ols(formula, data=df_winsor).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_winsor['district']}\n",
|
||
")\n",
|
||
"\n",
|
||
"coef = model_winsor.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"pval = model_winsor.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
"pct_change = (np.exp(coef) - 1) * 100\n",
|
||
"\n",
|
||
"print(f\"\\nPooled effect with winsorized outcome:\")\n",
|
||
"print(f\" Coefficient: {coef:.4f}\")\n",
|
||
"print(f\" P-value: {pval:.4f}\")\n",
|
||
"print(f\" Percent change: {pct_change:+.1f}%\")\n",
|
||
"print(f\" Robust to: extreme outliers\")\n",
|
||
"\n",
|
||
"specification_tests.append({\n",
|
||
" 'specification': 'Winsorized',\n",
|
||
" 'coefficient': coef,\n",
|
||
" 'pvalue': pval,\n",
|
||
" 'interpretation': f\"{pct_change:+.1f}%\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# Summary\n",
|
||
"if specification_tests:\n",
|
||
" spec_df = pd.DataFrame(specification_tests)\n",
|
||
" print(\"\\n\" + \"=\"*80)\n",
|
||
" print(\"SPECIFICATION SENSITIVITY SUMMARY\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(spec_df.to_string(index=False))\n",
|
||
" \n",
|
||
" # Compare to baseline\n",
|
||
" if 'model1' in locals():\n",
|
||
" baseline_coef = model1.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" baseline_pct = (np.exp(baseline_coef) - 1) * 100 if pd.notna(baseline_coef) else np.nan\n",
|
||
" \n",
|
||
" print(f\"\\nBaseline specification: {baseline_pct:+.1f}%\")\n",
|
||
" \n",
|
||
" # Check consistency\n",
|
||
" log_specs = spec_df[spec_df['specification'].isin(['District Trends', 'Winsorized'])]\n",
|
||
" if len(log_specs) > 0:\n",
|
||
" spec_range = log_specs['coefficient'].max() - log_specs['coefficient'].min()\n",
|
||
" if spec_range < 0.15:\n",
|
||
" print(f\"✓ Results are ROBUST across specifications (range: {spec_range:.3f})\")\n",
|
||
" else:\n",
|
||
" print(f\"⚠️ Some specification sensitivity detected (range: {spec_range:.3f})\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "1e77dfb1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\n",
|
||
"================================================================================\n",
|
||
"Summary of offshore-jurisdiction differential effect robustness\n",
|
||
"\n",
|
||
"Main Result:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" Test Effect P-value Robust?\n",
|
||
"TWFE offshore differential +89.1% 0.0913 N/A\n",
|
||
"\n",
|
||
"Alternative Outcomes:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" Test Effect P-value Robust?\n",
|
||
" Compliance Rate (pp) -2.7970 0.4370 NOT SIG\n",
|
||
"Violations per Inspection 0.0401 0.2865 NOT SIG\n",
|
||
" Log(Total Violations) 0.3713 0.2843 NOT SIG\n",
|
||
"\n",
|
||
"Sample Restrictions:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" Test Effect P-value Robust?\n",
|
||
"Exclude Extreme Outliers 1.0153 0.0000 SIMILAR\n",
|
||
" Exclude 2015-2016 0.7705 0.0545 SIMILAR\n",
|
||
" Exclude Pandemic Years 0.7126 0.0249 SIMILAR\n",
|
||
"\n",
|
||
"Specifications:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" Test Effect P-value Robust?\n",
|
||
" Linear (no log) 80.1 days 0.1227 CONSISTENT\n",
|
||
"Year FE (average) -10.8% N/A CONSISTENT\n",
|
||
" District Trends +18.1% 0.6063 CONSISTENT\n",
|
||
" Winsorized +80.2% 0.1137 CONSISTENT\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"CONCLUSION\n",
|
||
"================================================================================\n",
|
||
"This notebook evaluates differential post-2019 effects for offshore-jurisdiction districts (02/03/04)\n",
|
||
"with district and year fixed effects in the core models.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Comprehensive Robustness Summary\n",
|
||
"\n",
|
||
"print()\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"COMPREHENSIVE ROBUSTNESS CHECK SUMMARY\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"Summary of offshore-jurisdiction differential effect robustness\")\n",
|
||
"\n",
|
||
"all_tests = []\n",
|
||
"\n",
|
||
"if 'model1' in locals():\n",
|
||
" key_coef = model1.params.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" key_pval = model1.pvalues.get('post_2019:offshore_jurisdiction', np.nan)\n",
|
||
" key_pct = (np.exp(key_coef) - 1) * 100 if pd.notna(key_coef) else np.nan\n",
|
||
"\n",
|
||
" all_tests.append({\n",
|
||
" 'Category': 'Main Result',\n",
|
||
" 'Test': 'TWFE offshore differential',\n",
|
||
" 'Effect': f\"{key_pct:+.1f}%\" if pd.notna(key_pct) else 'NA',\n",
|
||
" 'P-value': f\"{key_pval:.4f}\" if pd.notna(key_pval) else 'NA',\n",
|
||
" 'Robust?': 'N/A'\n",
|
||
" })\n",
|
||
"\n",
|
||
"if 'alt_df' in locals():\n",
|
||
" for _, row in alt_df.iterrows():\n",
|
||
" all_tests.append({\n",
|
||
" 'Category': 'Alternative Outcomes',\n",
|
||
" 'Test': row['outcome'],\n",
|
||
" 'Effect': f\"{row['coefficient']:.4f}\",\n",
|
||
" 'P-value': f\"{row['pvalue']:.4f}\",\n",
|
||
" 'Robust?': 'CONSISTENT' if row['significant'] else 'NOT SIG'\n",
|
||
" })\n",
|
||
"\n",
|
||
"if 'restrict_df' in locals():\n",
|
||
" for _, row in restrict_df.iterrows():\n",
|
||
" all_tests.append({\n",
|
||
" 'Category': 'Sample Restrictions',\n",
|
||
" 'Test': row['restriction'],\n",
|
||
" 'Effect': f\"{row['coefficient']:.4f}\",\n",
|
||
" 'P-value': f\"{row['pvalue']:.4f}\",\n",
|
||
" 'Robust?': 'SIMILAR'\n",
|
||
" })\n",
|
||
"\n",
|
||
"if 'spec_df' in locals():\n",
|
||
" for _, row in spec_df.iterrows():\n",
|
||
" all_tests.append({\n",
|
||
" 'Category': 'Specifications',\n",
|
||
" 'Test': row['specification'],\n",
|
||
" 'Effect': row['interpretation'],\n",
|
||
" 'P-value': f\"{row['pvalue']:.4f}\" if not pd.isna(row['pvalue']) else 'N/A',\n",
|
||
" 'Robust?': 'CONSISTENT'\n",
|
||
" })\n",
|
||
"\n",
|
||
"if all_tests:\n",
|
||
" summary_df = pd.DataFrame(all_tests)\n",
|
||
" for category in summary_df['Category'].unique():\n",
|
||
" cat_data = summary_df[summary_df['Category'] == category]\n",
|
||
" print()\n",
|
||
" print(f\"{category}:\")\n",
|
||
" print(\"-\" * 80)\n",
|
||
" print(cat_data[['Test', 'Effect', 'P-value', 'Robust?']].to_string(index=False))\n",
|
||
"\n",
|
||
" print()\n",
|
||
" print(\"=\" * 80)\n",
|
||
" print(\"CONCLUSION\")\n",
|
||
" print(\"=\" * 80)\n",
|
||
" print(\"This notebook evaluates differential post-2019 effects for offshore-jurisdiction districts (02/03/04)\")\n",
|
||
" print(\"with district and year fixed effects in the core models.\")\n",
|
||
"else:\n",
|
||
" print(\"WARN: Could not generate comprehensive summary (missing test results)\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4924294b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f2f74ddf",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Part 8: Deep Dive - Demographics and Geographic Features\n",
|
||
"\n",
|
||
"Why do districts differ so dramatically (-60% to +94%)? Let's analyze:\n",
|
||
"1. **Demographics**: Population density, rurality (RUCA codes)\n",
|
||
"2. **Geographic features**: Basin name, play name (oil/gas geology)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "c32338db",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"EXPLORING POSTGIS TABLES\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"1. Inspections table columns:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" column_name data_type\n",
|
||
" operator_name text\n",
|
||
" p5_operator_no text\n",
|
||
" district text\n",
|
||
"district_office_inspecting text\n",
|
||
" oil_lease_gas_well_id text\n",
|
||
" lease_fac_name text\n",
|
||
" api_no text\n",
|
||
" county text\n",
|
||
" well_no text\n",
|
||
" inspection_date timestamp without time zone\n",
|
||
" drilling_permit_no text\n",
|
||
" complaint_no text\n",
|
||
" compliance text\n",
|
||
" field_name text\n",
|
||
" api_norm text\n",
|
||
"\n",
|
||
"2. Demographics table (well_with_demographics_table):\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" column_name data_type\n",
|
||
" canonical_api10 text\n",
|
||
" api10_number text\n",
|
||
" api_number text\n",
|
||
" census_tract_geoid text\n",
|
||
" latitude double precision\n",
|
||
" longitude double precision\n",
|
||
" geom USER-DEFINED\n",
|
||
" tract_name text\n",
|
||
" ruca_code_2020 double precision\n",
|
||
" ruca_category text\n",
|
||
" ruca_primary_description text\n",
|
||
"ruca_secondary_description text\n",
|
||
" ej_composite_score double precision\n",
|
||
" pct_minority double precision\n",
|
||
" pct_hispanic double precision\n",
|
||
" poverty_rate double precision\n",
|
||
" unemployment_rate double precision\n",
|
||
" less_than_hs_pct double precision\n",
|
||
" linguistic_isolation_rate double precision\n",
|
||
" renter_cost_burden_rate double precision\n",
|
||
"\n",
|
||
"Sample demographics data:\n",
|
||
"api_norm ruca_code_2020 ruca_category pct_minority poverty_rate\n",
|
||
"60630159 None None None None\n",
|
||
"70230144 None None None None\n",
|
||
"70430288 None None None None\n",
|
||
"70430220 None None None None\n",
|
||
"70430266 None None None None\n",
|
||
"\n",
|
||
"3. Geography table (well_geo_features):\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" column_name data_type\n",
|
||
" id bigint\n",
|
||
"canonical_api10 text\n",
|
||
" api10_number text\n",
|
||
" api_number text\n",
|
||
" geom USER-DEFINED\n",
|
||
" basin_name text\n",
|
||
" play_name text\n",
|
||
" texmex_name text\n",
|
||
" api_norm text\n",
|
||
"\n",
|
||
"Sample geography data:\n",
|
||
"api_norm basin_name play_name\n",
|
||
" None 3 Ft. Worth\n",
|
||
" None NaN NaN\n",
|
||
" None 5 NaN\n",
|
||
" None 8 NaN\n",
|
||
" None 5 NaN\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"✓ Table exploration complete\n",
|
||
"================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Step 1: Explore the PostGIS tables to understand their structure\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"EXPLORING POSTGIS TABLES\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"# Check column names in inspections table\n",
|
||
"print(\"\\n1. Inspections table columns:\")\n",
|
||
"print(\"-\" * 80)\n",
|
||
"insp_cols_query = \"\"\"\n",
|
||
"SELECT column_name, data_type \n",
|
||
"FROM information_schema.columns \n",
|
||
"WHERE table_name = 'inspections' \n",
|
||
"ORDER BY ordinal_position;\n",
|
||
"\"\"\"\n",
|
||
"insp_cols = pd.read_sql(insp_cols_query, engine)\n",
|
||
"print(insp_cols.to_string(index=False))\n",
|
||
"\n",
|
||
"# Check demographics table\n",
|
||
"print(\"\\n2. Demographics table (well_with_demographics_table):\")\n",
|
||
"print(\"-\" * 80)\n",
|
||
"demo_cols_query = \"\"\"\n",
|
||
"SELECT column_name, data_type \n",
|
||
"FROM information_schema.columns \n",
|
||
"WHERE table_name = 'well_with_demographics_table' \n",
|
||
"ORDER BY ordinal_position \n",
|
||
"LIMIT 20;\n",
|
||
"\"\"\"\n",
|
||
"demo_cols = pd.read_sql(demo_cols_query, engine)\n",
|
||
"print(demo_cols.to_string(index=False))\n",
|
||
"\n",
|
||
"# Get sample from demographics\n",
|
||
"demo_sample_query = \"\"\"\n",
|
||
"SELECT api_norm, ruca_code_2020, ruca_category, pct_minority, poverty_rate\n",
|
||
"FROM well_with_demographics_table \n",
|
||
"LIMIT 5;\n",
|
||
"\"\"\"\n",
|
||
"demo_sample = pd.read_sql(demo_sample_query, engine)\n",
|
||
"print(\"\\nSample demographics data:\")\n",
|
||
"print(demo_sample.to_string(index=False))\n",
|
||
"\n",
|
||
"# Check geography table \n",
|
||
"print(\"\\n3. Geography table (well_geo_features):\")\n",
|
||
"print(\"-\" * 80)\n",
|
||
"geo_cols_query = \"\"\"\n",
|
||
"SELECT column_name, data_type \n",
|
||
"FROM information_schema.columns \n",
|
||
"WHERE table_name = 'well_geo_features' \n",
|
||
"ORDER BY ordinal_position;\n",
|
||
"\"\"\"\n",
|
||
"geo_cols = pd.read_sql(geo_cols_query, engine)\n",
|
||
"print(geo_cols.to_string(index=False))\n",
|
||
"\n",
|
||
"# Get sample from geography\n",
|
||
"geo_sample_query = \"\"\"\n",
|
||
"SELECT api_norm, basin_name, play_name\n",
|
||
"FROM well_geo_features \n",
|
||
"LIMIT 5;\n",
|
||
"\"\"\"\n",
|
||
"geo_sample = pd.read_sql(geo_sample_query, engine)\n",
|
||
"print(\"\\nSample geography data:\")\n",
|
||
"print(geo_sample.to_string(index=False))\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\" * 80)\n",
|
||
"print(\"✓ Table exploration complete\")\n",
|
||
"print(\"=\" * 80)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "74bb3027",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Strategy: Join demographics/geography with inspections using api_norm\n",
|
||
"\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"Querying demographics by district...\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"✓ SUCCESS: Loaded demographics for 13 districts\n",
|
||
"\n",
|
||
"District Demographics:\n",
|
||
"district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural\n",
|
||
" 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812\n",
|
||
" 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482\n",
|
||
" 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582\n",
|
||
" 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222\n",
|
||
" 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140\n",
|
||
" 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627\n",
|
||
" 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615\n",
|
||
" 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442\n",
|
||
" 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726\n",
|
||
" 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413\n",
|
||
" 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103\n",
|
||
" 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525\n",
|
||
" 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"Querying geographic features by district...\n",
|
||
"================================================================================\n",
|
||
"✓ SUCCESS: Loaded geography for 13 districts\n",
|
||
"\n",
|
||
"District Geography:\n",
|
||
"district n_wells_with_geo primary_basin n_basins n_plays\n",
|
||
" 01 31838 8 3 2\n",
|
||
" 02 17102 8 1 1\n",
|
||
" 03 16649 8 2 3\n",
|
||
" 04 20925 8 1 1\n",
|
||
" 05 9662 2 4 2\n",
|
||
" 06 24410 2 2 2\n",
|
||
" 08 105874 5 3 2\n",
|
||
" 09 42771 3 3 1\n",
|
||
" 10 11638 0 2 0\n",
|
||
" 6E 6237 2 1 1\n",
|
||
" 7B 20343 3 2 1\n",
|
||
" 7C 42263 5 2 2\n",
|
||
" 8A 40740 5 3 1\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"✓✓✓ District aggregation COMPLETE ✓✓✓\n",
|
||
" Demographics: 13 districts\n",
|
||
" Geography: 13 districts\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Step 2: Aggregate demographics and geographic features by district\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"AGGREGATING DISTRICT-LEVEL CHARACTERISTICS\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"# All relevant tables use api_norm as the normalized well identifier\n",
|
||
"\n",
|
||
"print(\"\\nStrategy: Join demographics/geography with inspections using api_norm\")\n",
|
||
"\n",
|
||
"# Demographics by district (via inspections)\n",
|
||
"print(f\"\\n{'-'*80}\")\n",
|
||
"print(\"Querying demographics by district...\")\n",
|
||
"print(f\"{'-'*80}\")\n",
|
||
"\n",
|
||
"demo_district_query = \"\"\"\n",
|
||
"WITH well_districts AS (\n",
|
||
" SELECT DISTINCT \n",
|
||
" api_norm,\n",
|
||
" district\n",
|
||
" FROM inspections\n",
|
||
" WHERE district IS NOT NULL AND api_norm IS NOT NULL\n",
|
||
")\n",
|
||
"SELECT \n",
|
||
" wd.district,\n",
|
||
" COUNT(DISTINCT wd.api_norm) as n_wells,\n",
|
||
" AVG(d.median_household_income) as avg_income,\n",
|
||
" AVG(d.ruca_code_2020::numeric) as avg_ruca_code,\n",
|
||
" AVG(d.pct_minority) as avg_pct_minority,\n",
|
||
" AVG(d.poverty_rate) as avg_poverty_rate,\n",
|
||
" AVG(d.ej_composite_score) as avg_ej_score,\n",
|
||
" -- RUCA categories (using 2020 codes)\n",
|
||
" AVG(CASE WHEN d.ruca_code_2020 <= 3 THEN 1.0 ELSE 0.0 END) as pct_metropolitan,\n",
|
||
" AVG(CASE WHEN d.ruca_code_2020 BETWEEN 4 AND 6 THEN 1.0 ELSE 0.0 END) as pct_micropolitan,\n",
|
||
" AVG(CASE WHEN d.ruca_code_2020 >= 7 THEN 1.0 ELSE 0.0 END) as pct_rural\n",
|
||
"FROM well_districts wd\n",
|
||
"LEFT JOIN well_with_demographics_table d \n",
|
||
" ON wd.api_norm = d.api_norm\n",
|
||
"GROUP BY wd.district\n",
|
||
"ORDER BY wd.district;\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"try:\n",
|
||
" district_demographics = pd.read_sql(demo_district_query, engine)\n",
|
||
" if district_demographics is not None and len(district_demographics) > 0:\n",
|
||
" print(f\"✓ SUCCESS: Loaded demographics for {len(district_demographics)} districts\")\n",
|
||
" print(\"\\nDistrict Demographics:\")\n",
|
||
" print(district_demographics.to_string(index=False))\n",
|
||
" else:\n",
|
||
" print(\"✗ Query returned empty result\")\n",
|
||
" district_demographics = None\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"✗ ERROR: {e}\")\n",
|
||
" district_demographics = None\n",
|
||
"\n",
|
||
"# Geography by district (via inspections)\n",
|
||
"print(f\"\\n{'='*80}\")\n",
|
||
"print(\"Querying geographic features by district...\")\n",
|
||
"print(f\"{'='*80}\")\n",
|
||
"\n",
|
||
"geo_district_query = \"\"\"\n",
|
||
"WITH well_districts AS (\n",
|
||
" SELECT DISTINCT \n",
|
||
" api_norm,\n",
|
||
" district\n",
|
||
" FROM inspections\n",
|
||
" WHERE district IS NOT NULL AND api_norm IS NOT NULL\n",
|
||
"),\n",
|
||
"geo_counts AS (\n",
|
||
" SELECT \n",
|
||
" wd.district,\n",
|
||
" g.basin_name,\n",
|
||
" COUNT(*) as cnt\n",
|
||
" FROM well_districts wd\n",
|
||
" LEFT JOIN well_geo_features g \n",
|
||
" ON wd.api_norm = g.api_norm\n",
|
||
" WHERE g.basin_name IS NOT NULL\n",
|
||
" GROUP BY wd.district, g.basin_name\n",
|
||
"),\n",
|
||
"primary_basin AS (\n",
|
||
" SELECT DISTINCT ON (district)\n",
|
||
" district,\n",
|
||
" basin_name as primary_basin\n",
|
||
" FROM geo_counts\n",
|
||
" ORDER BY district, cnt DESC\n",
|
||
")\n",
|
||
"SELECT \n",
|
||
" wd.district,\n",
|
||
" COUNT(DISTINCT CASE WHEN g.basin_name IS NOT NULL THEN wd.api_norm END) as n_wells_with_geo,\n",
|
||
" pb.primary_basin,\n",
|
||
" COUNT(DISTINCT g.basin_name) as n_basins,\n",
|
||
" COUNT(DISTINCT g.play_name) as n_plays\n",
|
||
"FROM well_districts wd\n",
|
||
"LEFT JOIN well_geo_features g \n",
|
||
" ON wd.api_norm = g.api_norm\n",
|
||
"LEFT JOIN primary_basin pb \n",
|
||
" ON wd.district = pb.district\n",
|
||
"GROUP BY wd.district, pb.primary_basin\n",
|
||
"ORDER BY wd.district;\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"try:\n",
|
||
" district_geography = pd.read_sql(geo_district_query, engine)\n",
|
||
" if district_geography is not None and len(district_geography) > 0:\n",
|
||
" print(f\"✓ SUCCESS: Loaded geography for {len(district_geography)} districts\")\n",
|
||
" print(\"\\nDistrict Geography:\")\n",
|
||
" print(district_geography.to_string(index=False))\n",
|
||
" else:\n",
|
||
" print(\"✗ Query returned empty result\")\n",
|
||
" district_geography = None\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"✗ ERROR: {e}\")\n",
|
||
" district_geography = None\n",
|
||
"\n",
|
||
"# Final status\n",
|
||
"print(f\"\\n{'='*80}\")\n",
|
||
"if district_demographics is not None and district_geography is not None:\n",
|
||
" print(\"✓✓✓ District aggregation COMPLETE ✓✓✓\")\n",
|
||
" print(f\" Demographics: {len(district_demographics)} districts\")\n",
|
||
" print(f\" Geography: {len(district_geography)} districts\")\n",
|
||
"elif district_demographics is not None:\n",
|
||
" print(\"⚠️ Partial success: demographics loaded, geography failed\")\n",
|
||
"elif district_geography is not None:\n",
|
||
" print(\"⚠️ Partial success: geography loaded, demographics failed\") \n",
|
||
"else:\n",
|
||
" print(\"✗✗✗ Both queries failed ✗✗✗\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "63608b12",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Checking data availability:\n",
|
||
" district_demographics: ✓\n",
|
||
" district_geography: ✓\n",
|
||
" district_effects: ✓\n",
|
||
"\n",
|
||
"✓ Merged data for 13 districts\n",
|
||
"\n",
|
||
"Complete District Characteristics:\n",
|
||
"district n_wells avg_income avg_ruca_code avg_pct_minority avg_poverty_rate avg_ej_score pct_metropolitan pct_micropolitan pct_rural n_wells_with_geo primary_basin n_basins n_plays coefficient pvalue pct_change\n",
|
||
" 01 32382 -44687864.0623 7.1255 0.2672 0.1763 0.4970 0.2918 0.0131 0.6812 31838 8 3 2 0.0459 0.3813 4.6968\n",
|
||
" 02 17223 -97913.4176 7.6108 0.2012 0.1395 0.4509 0.2435 0.0013 0.7482 17102 8 1 1 -0.5936 0.0000 -44.7643\n",
|
||
" 03 16826 -3375276.0532 5.2039 0.2434 0.1228 0.4918 0.5231 0.1091 0.3582 16649 8 2 3 0.5352 0.0000 70.7826\n",
|
||
" 04 21238 -1887605.1984 4.5855 0.2127 0.2489 0.5924 0.5313 0.2355 0.2222 20925 8 1 1 0.4842 0.0000 62.2796\n",
|
||
" 05 10101 55658.2253 8.7672 0.1831 0.1270 0.4605 0.0660 0.1075 0.8140 9662 2 4 2 0.2661 0.0000 30.4865\n",
|
||
" 06 24743 -78226.5235 5.9712 0.2200 0.1473 0.4934 0.2813 0.2452 0.4627 24410 2 2 2 -0.5433 0.0000 -41.9172\n",
|
||
" 08 106371 -56597541.7157 5.9276 0.2474 0.1141 0.4963 0.3765 0.1595 0.4615 105874 5 3 2 -0.5667 0.0000 -43.2604\n",
|
||
" 09 47422 -1558781.8820 4.6279 0.1320 0.0984 0.3763 0.6023 0.0345 0.3442 42771 3 3 1 -0.6154 0.0000 -45.9568\n",
|
||
" 10 30981 68603.5885 8.5872 0.1545 0.1066 0.4574 0.0487 0.1349 0.7726 11638 0 2 0 0.7632 0.0000 114.5172\n",
|
||
" 6E 6249 56451.2206 3.0494 0.2382 0.1971 0.5343 0.7531 0.1037 0.1413 6237 2 1 1 -0.1537 0.0034 -14.2455\n",
|
||
" 7B 21726 -224729.5548 8.4244 0.0904 0.1225 0.4211 0.1217 0.0478 0.8103 20343 3 2 1 -0.3660 0.0000 -30.6530\n",
|
||
" 7C 43167 -220859.9070 9.6275 0.4354 0.1125 0.5370 0.0455 0.0004 0.9525 42263 5 2 2 0.2712 0.0000 31.1597\n",
|
||
" 8A 42122 -713670.0572 7.4827 0.1952 0.1369 0.5159 0.1708 0.2073 0.6198 40740 5 3 1 0.2037 0.0001 22.5874\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"CORRELATIONS WITH TREATMENT EFFECT\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Correlations with Treatment Effect (% change in enforcement speed):\n",
|
||
" Variable Correlation Abs_Correlation N\n",
|
||
"n_wells_with_geo -0.3737 0.3737 13\n",
|
||
" avg_ej_score 0.3619 0.3619 13\n",
|
||
" n_wells -0.2680 0.2680 13\n",
|
||
" avg_income 0.2484 0.2484 13\n",
|
||
"pct_metropolitan -0.2289 0.2289 13\n",
|
||
" avg_ruca_code 0.2212 0.2212 13\n",
|
||
"pct_micropolitan 0.2167 0.2167 13\n",
|
||
"avg_pct_minority 0.1308 0.1308 13\n",
|
||
" pct_rural 0.1142 0.1142 13\n",
|
||
"avg_poverty_rate 0.1060 0.1060 13\n",
|
||
" n_plays -0.0935 0.0935 13\n",
|
||
" n_basins -0.0536 0.0536 13\n",
|
||
"\n",
|
||
"✓ Found 2 variables with |correlation| > 0.3:\n",
|
||
" • n_wells_with_geo: r=-0.374 (faster enforcement)\n",
|
||
" • avg_ej_score: r=0.362 (slower enforcement)\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"✓ Correlation analysis complete\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Step 3: Merge with treatment effects and analyze correlations\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"ANALYZING CORRELATIONS: DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"# Check if we have the data\n",
|
||
"print(f\"\\nChecking data availability:\")\n",
|
||
"print(f\" district_demographics: {'✓' if 'district_demographics' in locals() and district_demographics is not None else '✗'}\")\n",
|
||
"print(f\" district_geography: {'✓' if 'district_geography' in locals() and district_geography is not None else '✗'}\")\n",
|
||
"print(f\" district_effects: {'✓' if 'district_effects' in locals() else '✗'}\")\n",
|
||
"\n",
|
||
"# Merge all district characteristics\n",
|
||
"if 'district_demographics' in locals() and district_demographics is not None and \\\n",
|
||
" 'district_geography' in locals() and district_geography is not None:\n",
|
||
" # Merge with treatment effects\n",
|
||
" district_chars = district_demographics.merge(\n",
|
||
" district_geography, \n",
|
||
" on='district', \n",
|
||
" how='outer'\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Merge with treatment effects from our DiD analysis\n",
|
||
" if 'district_effects' in locals():\n",
|
||
" # Calculate percent change from coefficient (coefficient is in log scale)\n",
|
||
" effects_for_merge = district_effects[['district', 'coefficient', 'pvalue']].copy()\n",
|
||
" effects_for_merge['pct_change'] = (np.exp(effects_for_merge['coefficient']) - 1) * 100\n",
|
||
" \n",
|
||
" district_chars = district_chars.merge(\n",
|
||
" effects_for_merge, \n",
|
||
" on='district', \n",
|
||
" how='left'\n",
|
||
" )\n",
|
||
" \n",
|
||
" print(f\"\\n✓ Merged data for {len(district_chars)} districts\")\n",
|
||
" print(\"\\nComplete District Characteristics:\")\n",
|
||
" print(district_chars.to_string(index=False))\n",
|
||
" \n",
|
||
" # Calculate correlations with treatment effect\n",
|
||
" print(\"\\n\" + \"=\" * 80)\n",
|
||
" print(\"CORRELATIONS WITH TREATMENT EFFECT\")\n",
|
||
" print(\"=\" * 80)\n",
|
||
" \n",
|
||
" numeric_cols = [\n",
|
||
" 'avg_income', 'avg_ruca_code', 'avg_pct_minority',\n",
|
||
" 'avg_poverty_rate', 'avg_ej_score',\n",
|
||
" 'pct_metropolitan', 'pct_micropolitan', 'pct_rural',\n",
|
||
" 'n_basins', 'n_plays', 'n_wells', 'n_wells_with_geo'\n",
|
||
" ]\n",
|
||
" \n",
|
||
" correlations = []\n",
|
||
" for col in numeric_cols:\n",
|
||
" if col in district_chars.columns:\n",
|
||
" # Drop NaN values for correlation\n",
|
||
" valid_data = district_chars[[col, 'pct_change']].dropna()\n",
|
||
" if len(valid_data) > 2:\n",
|
||
" corr = valid_data[col].corr(valid_data['pct_change'])\n",
|
||
" correlations.append({\n",
|
||
" 'Variable': col,\n",
|
||
" 'Correlation': corr,\n",
|
||
" 'Abs_Correlation': abs(corr),\n",
|
||
" 'N': len(valid_data)\n",
|
||
" })\n",
|
||
" \n",
|
||
" if correlations:\n",
|
||
" corr_df = pd.DataFrame(correlations).sort_values('Abs_Correlation', ascending=False)\n",
|
||
" \n",
|
||
" print(\"\\nCorrelations with Treatment Effect (% change in enforcement speed):\")\n",
|
||
" print(corr_df.to_string(index=False))\n",
|
||
" \n",
|
||
" # Highlight strongest correlations\n",
|
||
" strong_corr = corr_df[corr_df['Abs_Correlation'] > 0.3]\n",
|
||
" if len(strong_corr) > 0:\n",
|
||
" print(f\"\\n✓ Found {len(strong_corr)} variables with |correlation| > 0.3:\")\n",
|
||
" for _, row in strong_corr.iterrows():\n",
|
||
" direction = \"faster\" if row['Correlation'] < 0 else \"slower\"\n",
|
||
" print(f\" • {row['Variable']}: r={row['Correlation']:.3f} ({direction} enforcement)\")\n",
|
||
" else:\n",
|
||
" print(\"\\n⚠️ No strong correlations (|r| > 0.3) found with demographics/geography\")\n",
|
||
" else:\n",
|
||
" print(\"\\n✗ Could not compute correlations (no valid numeric columns)\")\n",
|
||
" \n",
|
||
" else:\n",
|
||
" print(\"✗ district_effects not found in workspace\")\n",
|
||
" district_chars = None\n",
|
||
"else:\n",
|
||
" print(\"✗ Could not load demographics or geography data\")\n",
|
||
" print(f\" Trying to print what we have...\")\n",
|
||
" if 'district_demographics' in locals():\n",
|
||
" print(f\" district_demographics type: {type(district_demographics)}\")\n",
|
||
" if district_demographics is not None:\n",
|
||
" print(f\" district_demographics shape: {district_demographics.shape}\")\n",
|
||
" if 'district_geography' in locals():\n",
|
||
" print(f\" district_geography type: {type(district_geography)}\")\n",
|
||
" if district_geography is not None:\n",
|
||
" print(f\" district_geography shape: {district_geography.shape}\")\n",
|
||
" district_chars = None\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\" * 80)\n",
|
||
"print(\"✓ Correlation analysis complete\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "3c62bb23",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\n",
|
||
"================================================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ Saved visualization: district_demographics_geography.png\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Step 4: Visualize key relationships\n",
|
||
"\n",
|
||
"if district_chars is not None and 'pct_change' in district_chars.columns:\n",
|
||
" \n",
|
||
" print(\"=\" * 80)\n",
|
||
" print(\"VISUALIZING DEMOGRAPHICS/GEOGRAPHY vs TREATMENT EFFECTS\")\n",
|
||
" print(\"=\" * 80)\n",
|
||
" \n",
|
||
" # Create 2x2 subplot\n",
|
||
" fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
|
||
" \n",
|
||
" # Plot 1: Rurality (RUCA code) vs Treatment Effect\n",
|
||
" ax1 = axes[0, 0]\n",
|
||
" valid_ruca = district_chars.dropna(subset=['avg_ruca_code', 'pct_change'])\n",
|
||
" if len(valid_ruca) > 0:\n",
|
||
" scatter1 = ax1.scatter(valid_ruca['avg_ruca_code'], valid_ruca['pct_change'],\n",
|
||
" s=200, alpha=0.7, c=valid_ruca['pct_change'],\n",
|
||
" cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n",
|
||
" \n",
|
||
" for _, row in valid_ruca.iterrows():\n",
|
||
" ax1.text(row['avg_ruca_code'], row['pct_change'], \n",
|
||
" f\" {row['district']}\", fontsize=9, fontweight='bold')\n",
|
||
" \n",
|
||
" # Add trend line\n",
|
||
" z = np.polyfit(valid_ruca['avg_ruca_code'], valid_ruca['pct_change'], 1)\n",
|
||
" p = np.poly1d(z)\n",
|
||
" ax1.plot(valid_ruca['avg_ruca_code'], p(valid_ruca['avg_ruca_code']), \n",
|
||
" \"r--\", alpha=0.5, linewidth=2)\n",
|
||
" \n",
|
||
" corr = valid_ruca['avg_ruca_code'].corr(valid_ruca['pct_change'])\n",
|
||
" ax1.set_title(f'Rurality vs Treatment Effect\\n(r={corr:.3f})', \n",
|
||
" fontsize=13, fontweight='bold', pad=10)\n",
|
||
" ax1.set_xlabel('Average RUCA Code\\n(1=Metro, 10=Rural)', fontsize=11, fontweight='bold')\n",
|
||
" ax1.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n",
|
||
" ax1.axhline(0, color='gray', linestyle='--', alpha=0.5)\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" \n",
|
||
" # Plot 2: Number of wells vs Treatment Effect\n",
|
||
" ax2 = axes[0, 1]\n",
|
||
" valid_wells = district_chars.dropna(subset=['n_wells', 'pct_change'])\n",
|
||
" if len(valid_wells) > 0:\n",
|
||
" scatter2 = ax2.scatter(valid_wells['n_wells'], valid_wells['pct_change'],\n",
|
||
" s=200, alpha=0.7, c=valid_wells['pct_change'],\n",
|
||
" cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n",
|
||
" \n",
|
||
" for _, row in valid_wells.iterrows():\n",
|
||
" ax2.text(row['n_wells'], row['pct_change'], \n",
|
||
" f\" {row['district']}\", fontsize=9, fontweight='bold')\n",
|
||
" \n",
|
||
" # Add trend line\n",
|
||
" z = np.polyfit(valid_wells['n_wells'], valid_wells['pct_change'], 1)\n",
|
||
" p = np.poly1d(z)\n",
|
||
" ax2.plot(valid_wells['n_wells'], p(valid_wells['n_wells']), \n",
|
||
" \"r--\", alpha=0.5, linewidth=2)\n",
|
||
" \n",
|
||
" corr = valid_wells['n_wells'].corr(valid_wells['pct_change'])\n",
|
||
" ax2.set_title(f'District Size vs Treatment Effect\\n(r={corr:.3f})', \n",
|
||
" fontsize=13, fontweight='bold', pad=10)\n",
|
||
" ax2.set_xlabel('Number of Wells Inspected', fontsize=11, fontweight='bold')\n",
|
||
" ax2.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n",
|
||
" ax2.axhline(0, color='gray', linestyle='--', alpha=0.5)\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" \n",
|
||
" # Plot 3: Number of basins vs Treatment Effect\n",
|
||
" ax3 = axes[1, 0]\n",
|
||
" valid_basin = district_chars.dropna(subset=['n_basins', 'pct_change'])\n",
|
||
" if len(valid_basin) > 0:\n",
|
||
" scatter3 = ax3.scatter(valid_basin['n_basins'], valid_basin['pct_change'],\n",
|
||
" s=200, alpha=0.7, c=valid_basin['pct_change'],\n",
|
||
" cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n",
|
||
" \n",
|
||
" for _, row in valid_basin.iterrows():\n",
|
||
" ax3.text(row['n_basins'], row['pct_change'], \n",
|
||
" f\" {row['district']}\", fontsize=9, fontweight='bold')\n",
|
||
" \n",
|
||
" # Add trend line if enough variation\n",
|
||
" if valid_basin['n_basins'].nunique() > 1:\n",
|
||
" z = np.polyfit(valid_basin['n_basins'], valid_basin['pct_change'], 1)\n",
|
||
" p = np.poly1d(z)\n",
|
||
" ax3.plot(valid_basin['n_basins'], p(valid_basin['n_basins']), \n",
|
||
" \"r--\", alpha=0.5, linewidth=2)\n",
|
||
" \n",
|
||
" corr = valid_basin['n_basins'].corr(valid_basin['pct_change'])\n",
|
||
" ax3.set_title(f'Basin Diversity vs Treatment Effect\\n(r={corr:.3f})', \n",
|
||
" fontsize=13, fontweight='bold', pad=10)\n",
|
||
" ax3.set_xlabel('Number of Unique Basins', \n",
|
||
" fontsize=11, fontweight='bold')\n",
|
||
" ax3.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n",
|
||
" ax3.axhline(0, color='gray', linestyle='--', alpha=0.5)\n",
|
||
" ax3.grid(True, alpha=0.3)\n",
|
||
" \n",
|
||
" # Plot 4: Metro % vs Treatment Effect\n",
|
||
" ax4 = axes[1, 1]\n",
|
||
" valid_metro = district_chars.dropna(subset=['pct_metropolitan', 'pct_change'])\n",
|
||
" if len(valid_metro) > 0:\n",
|
||
" scatter4 = ax4.scatter(valid_metro['pct_metropolitan'] * 100, valid_metro['pct_change'],\n",
|
||
" s=200, alpha=0.7, c=valid_metro['pct_change'],\n",
|
||
" cmap='RdYlGn_r', edgecolors='black', linewidth=2)\n",
|
||
" \n",
|
||
" for _, row in valid_metro.iterrows():\n",
|
||
" ax4.text(row['pct_metropolitan'] * 100, row['pct_change'], \n",
|
||
" f\" {row['district']}\", fontsize=9, fontweight='bold')\n",
|
||
" \n",
|
||
" # Add trend line\n",
|
||
" z = np.polyfit(valid_metro['pct_metropolitan'] * 100, valid_metro['pct_change'], 1)\n",
|
||
" p = np.poly1d(z)\n",
|
||
" ax4.plot(valid_metro['pct_metropolitan'] * 100, \n",
|
||
" p(valid_metro['pct_metropolitan'] * 100), \n",
|
||
" \"r--\", alpha=0.5, linewidth=2)\n",
|
||
" \n",
|
||
" corr = valid_metro['pct_metropolitan'].corr(valid_metro['pct_change'])\n",
|
||
" ax4.set_title(f'Metropolitan % vs Treatment Effect\\n(r={corr:.3f})', \n",
|
||
" fontsize=13, fontweight='bold', pad=10)\n",
|
||
" ax4.set_xlabel('% Wells in Metropolitan Areas', fontsize=11, fontweight='bold')\n",
|
||
" ax4.set_ylabel('Treatment Effect (%)', fontsize=11, fontweight='bold')\n",
|
||
" ax4.axhline(0, color='gray', linestyle='--', alpha=0.5)\n",
|
||
" ax4.grid(True, alpha=0.3)\n",
|
||
" \n",
|
||
" plt.tight_layout()\n",
|
||
" plt.savefig('district_demographics_geography.png', dpi=300, bbox_inches='tight')\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" print(\"\\n✓ Saved visualization: district_demographics_geography.png\")\n",
|
||
"else:\n",
|
||
" print(\"\\n✗ Cannot create visualizations - district_chars not available\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "3484dd8a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"H3c: Environmental Justice (high_eji)\n",
|
||
" Formula term: post_2019:high_eji\n",
|
||
" Coefficient: 0.1548\n",
|
||
" P-value: 0.5723\n",
|
||
"\n",
|
||
"H3f: Rurality (high_rural)\n",
|
||
" Formula term: post_2019:high_rural\n",
|
||
" Coefficient: 0.2369\n",
|
||
" P-value: 0.4435\n",
|
||
"\n",
|
||
"H3e: Border proximity (border_competition)\n",
|
||
" Formula term: post_2019:border_competition\n",
|
||
" Coefficient: -0.3362\n",
|
||
" P-value: 0.2112\n",
|
||
"\n",
|
||
"H3d: Geology (dominant basin)\n",
|
||
" term coefficient pvalue\n",
|
||
"C(primary_basin)[0]:post_2019 0.7256 0.0000\n",
|
||
"C(primary_basin)[3]:post_2019 -0.5283 0.0000\n",
|
||
"C(primary_basin)[2]:post_2019 -0.1812 0.3890\n",
|
||
"C(primary_basin)[5]:post_2019 -0.0682 0.7994\n",
|
||
"C(primary_basin)[8]:post_2019 0.0083 0.9218\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"H3 MODERATOR SUMMARY\n",
|
||
"================================================================================\n",
|
||
" hypothesis term coefficient pvalue\n",
|
||
" H3c: Environmental Justice (high_eji) post_2019:high_eji 0.1548 0.5723\n",
|
||
" H3f: Rurality (high_rural) post_2019:high_rural 0.2369 0.4435\n",
|
||
"H3e: Border proximity (border_competition) post_2019:border_competition -0.3362 0.2112\n",
|
||
" H3d: Geology (dominant basin) C(primary_basin):post_2019 NaN 0.0000\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Step 5: TWFE moderator tests for H3c/H3d/H3e/H3f\n",
|
||
"\n",
|
||
"if district_chars is not None and 'pct_change' in district_chars.columns:\n",
|
||
" print(\"=\" * 80)\n",
|
||
" print(\"TWFE MODERATOR TESTS (H3c, H3d, H3e, H3f)\")\n",
|
||
" print(\"=\" * 80)\n",
|
||
"\n",
|
||
" district_chars['high_eji'] = (\n",
|
||
" district_chars['avg_ej_score'] > district_chars['avg_ej_score'].median()\n",
|
||
" ).astype(int)\n",
|
||
" district_chars['high_rural'] = (\n",
|
||
" district_chars['avg_ruca_code'] > district_chars['avg_ruca_code'].median()\n",
|
||
" ).astype(int)\n",
|
||
"\n",
|
||
" border_competition_districts = ['01', '02', '06', '08', '8A', '09', '10']\n",
|
||
" district_chars['border_competition'] = district_chars['district'].isin(border_competition_districts).astype(int)\n",
|
||
"\n",
|
||
" district_chars['primary_basin'] = district_chars['primary_basin'].fillna('Unknown')\n",
|
||
"\n",
|
||
" df_struct = district_year_panel.merge(\n",
|
||
" district_chars[['district', 'high_eji', 'high_rural', 'border_competition', 'primary_basin']],\n",
|
||
" on='district',\n",
|
||
" how='left'\n",
|
||
" )\n",
|
||
"\n",
|
||
" df_struct = df_struct[df_struct['avg_days_to_enforcement'] > 0].copy()\n",
|
||
" df_struct['log_days_to_enf'] = np.log(df_struct['avg_days_to_enforcement'])\n",
|
||
"\n",
|
||
" results = []\n",
|
||
"\n",
|
||
" def run_binary_test(name, term):\n",
|
||
" formula = f'log_days_to_enf ~ C(district) + C(year) + post_2019:{term} + post_2019:offshore_jurisdiction'\n",
|
||
" m = smf.ols(formula, data=df_struct).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_struct['district']}\n",
|
||
" )\n",
|
||
" key = f'post_2019:{term}'\n",
|
||
" coef = m.params.get(key, np.nan)\n",
|
||
" pval = m.pvalues.get(key, np.nan)\n",
|
||
" results.append({'hypothesis': name, 'term': key, 'coefficient': coef, 'pvalue': pval})\n",
|
||
" print()\n",
|
||
" print(name)\n",
|
||
" print(f\" Formula term: {key}\")\n",
|
||
" print(f\" Coefficient: {coef:.4f}\")\n",
|
||
" print(f\" P-value: {pval:.4f}\")\n",
|
||
" return m\n",
|
||
"\n",
|
||
" model_h3c = run_binary_test('H3c: Environmental Justice (high_eji)', 'high_eji')\n",
|
||
" model_h3f = run_binary_test('H3f: Rurality (high_rural)', 'high_rural')\n",
|
||
" model_h3e = run_binary_test('H3e: Border proximity (border_competition)', 'border_competition')\n",
|
||
"\n",
|
||
" print()\n",
|
||
" print('H3d: Geology (dominant basin)')\n",
|
||
" basin_formula = 'log_days_to_enf ~ C(district) + C(year) + C(primary_basin):post_2019 + post_2019:offshore_jurisdiction'\n",
|
||
" model_h3d = smf.ols(basin_formula, data=df_struct).fit(\n",
|
||
" cov_type='cluster',\n",
|
||
" cov_kwds={'groups': df_struct['district']}\n",
|
||
" )\n",
|
||
"\n",
|
||
" basin_terms = [t for t in model_h3d.params.index if 'C(primary_basin)' in t and ':post_2019' in t]\n",
|
||
" if basin_terms:\n",
|
||
" basin_df = pd.DataFrame({\n",
|
||
" 'term': basin_terms,\n",
|
||
" 'coefficient': [model_h3d.params[t] for t in basin_terms],\n",
|
||
" 'pvalue': [model_h3d.pvalues[t] for t in basin_terms]\n",
|
||
" }).sort_values('pvalue')\n",
|
||
" print(basin_df.to_string(index=False))\n",
|
||
" min_p = basin_df['pvalue'].min()\n",
|
||
" else:\n",
|
||
" min_p = np.nan\n",
|
||
" print(' No basin interaction terms estimated.')\n",
|
||
"\n",
|
||
" results.append({'hypothesis': 'H3d: Geology (dominant basin)', 'term': 'C(primary_basin):post_2019', 'coefficient': np.nan, 'pvalue': min_p})\n",
|
||
"\n",
|
||
" h3_results_df = pd.DataFrame(results)\n",
|
||
" print()\n",
|
||
" print(\"=\" * 80)\n",
|
||
" print(\"H3 MODERATOR SUMMARY\")\n",
|
||
" print(\"=\" * 80)\n",
|
||
" print(h3_results_df.to_string(index=False))\n",
|
||
"\n",
|
||
"else:\n",
|
||
" print('Cannot run H3c/H3d/H3e/H3f TWFE tests: district_chars unavailable')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "05e3d01a",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c3db7260",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n",
|
||
"## Hypotheses\n",
|
||
"\n",
|
||
"Based on your theoretical framework and the analysis completed, the notebook now tests:\n",
|
||
"\n",
|
||
"**H1: Regulatory Pipeline Acceleration**\n",
|
||
"- *H1a*: The 2019 disclosure policy reduces time from violation discovery to enforcement action.\n",
|
||
"- *H1b*: The 2019 disclosure policy increases compliance verification (resolution on re-inspection).\n",
|
||
"\n",
|
||
"**H2: Bureaucratic Heterogeneity**\n",
|
||
"- *H2*: Policy effects vary significantly across RRC districts.\n",
|
||
"\n",
|
||
"**H3: Structural Moderators**\n",
|
||
"- *H3a*: Capacity moderates responsiveness.\n",
|
||
"- *H3b*: Baseline performance moderates responsiveness.\n",
|
||
"- *H3c*: Environmental justice context (EJI) moderates responsiveness.\n",
|
||
"- *H3d*: Dominant basin geology moderates responsiveness.\n",
|
||
"- *H3e*: Border proximity moderates responsiveness.\n",
|
||
"- *H3f*: Rurality moderates responsiveness.\n",
|
||
"\n",
|
||
"**H4: Spatial Dynamics**\n",
|
||
"- *H4*: District treatment effects are spatially autocorrelated (Moran's I).\n",
|
||
"\n",
|
||
"**H5: Offshore Jurisdiction Moderator**\n",
|
||
"- *H5*: Districts with offshore + onshore jurisdiction (02, 03, 04) show different post-2019 effects.\n",
|
||
"\n",
|
||
"---\n",
|
||
"\n",
|
||
"## Methods (Updated)\n",
|
||
"\n",
|
||
"- Core models use two-way fixed effects: `C(district) + C(year)`.\n",
|
||
"- Standard errors are clustered at district level.\n",
|
||
"- Main offshore test uses `post_2019:offshore_jurisdiction`.\n",
|
||
"- H3 moderators are estimated in a consistent TWFE interaction framework.\n",
|
||
"- Spatial dynamics are tested using Moran's I with permutation inference.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2baedf47",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# run test for district by postgis table shale_plays\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.14.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|