From a05b6391e2765c18657bc79ef73e68c0fb046b2b Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:56:58 +0100
Subject: [PATCH 1/9] Add firm analysis files

---
 .../firm_analysis/bunching_analysis.png       |  Bin 0 -> 449575 bytes
 .../firm_analysis/bunching_analysis.py        | 1443 +++++++++++++++++
 policyengine_uk/firm_analysis/plot_example.py |   55 +
 .../firm_analysis/static_revenue.py           |  231 +++
 .../firm_analysis/turnover_distribution.png   |  Bin 0 -> 114998 bytes
 .../vat_threshold_revenue_impact.png          |  Bin 0 -> 72148 bytes
 6 files changed, 1729 insertions(+)
 create mode 100644 policyengine_uk/firm_analysis/bunching_analysis.png
 create mode 100644 policyengine_uk/firm_analysis/bunching_analysis.py
 create mode 100644 policyengine_uk/firm_analysis/plot_example.py
 create mode 100644 policyengine_uk/firm_analysis/static_revenue.py
 create mode 100644 policyengine_uk/firm_analysis/turnover_distribution.png
 create mode 100644 policyengine_uk/firm_analysis/vat_threshold_revenue_impact.png

diff --git a/policyengine_uk/firm_analysis/bunching_analysis.png b/policyengine_uk/firm_analysis/bunching_analysis.png
new file mode 100644
index 0000000000000000000000000000000000000000..fa4b9e746c772610ee0f249c1cb44e1fe5b5b069
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diff --git a/policyengine_uk/firm_analysis/bunching_analysis.py b/policyengine_uk/firm_analysis/bunching_analysis.py
new file mode 100644
index 000000000..7a47c7be9
--- /dev/null
+++ b/policyengine_uk/firm_analysis/bunching_analysis.py
@@ -0,0 +1,1443 @@
+#!/usr/bin/env python3
+"""
+Steps 1-5: Complete Bunching Analysis Pipeline with Asymmetric Windows
+Step 1: Build counterfactual & bunching statistics  
+Step 2: Set effective wedge
+Step 3: Calibrate substitution elasticity
+Step 4: Advanced probabilistic mapping (micro-level no-notch counterfactual)
+Step 5: Forward map to NEW policy (£100k threshold)
+
+ASYMMETRIC WINDOWS CONFIGURATION:
+To set different windows for left/right sides of thresholds, modify the main() function:
+
+    counterfactual_window_left = 15    # £15k below £90k threshold (£75k-£90k)  
+    counterfactual_window_right = 5    # £5k above £90k threshold (£90k-£95k)
+    
+    new_policy_window_left = 8         # £8k below £100k new threshold (£92k-£100k)
+    new_policy_window_right = 12       # £12k above £100k new threshold (£100k-£112k)
+
+This allows independent control over bunching window sizes on each side of both thresholds.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from pathlib import Path
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from scipy.interpolate import interp1d
+import warnings
+warnings.filterwarnings('ignore')
+
+class CounterfactualBunchingAnalysis:
+    """
+    Implements Steps 1-5: Complete Bunching Analysis Pipeline
+    
+    Step 1: Build counterfactual & bunching statistics
+    Step 2: Set effective wedge τₑ  
+    Step 3: Calibrate substitution elasticity σ
+    Step 4: Advanced probabilistic mapping (create_advanced_probabilistic_mapping)
+    Step 5: Forward map to NEW policy (£100k threshold)
+    """
+    
+    def __init__(self, threshold=90, window_left=10, window_right=10, 
+                 new_policy_window_left=10, new_policy_window_right=10):
+        """
+        Initialize analysis with threshold T* and asymmetric windows.
+        
+        Args:
+            threshold: VAT threshold T* in £1k (default 90k)
+            window_left: Left window (below threshold) for counterfactual analysis in £1k (default 10k)
+            window_right: Right window (above threshold) for counterfactual analysis in £1k (default 10k)  
+            new_policy_window_left: Left window for new policy analysis in £1k (default 10k)
+            new_policy_window_right: Right window for new policy analysis in £1k (default 10k)
+        """
+        self.T_star = threshold
+        self.W_left = window_left
+        self.W_right = window_right
+        self.T_lower = threshold - window_left   # T* - W_left
+        self.T_upper = threshold + window_right  # T* + W_right
+        
+        # New policy window parameters
+        self.new_policy_W_left = new_policy_window_left
+        self.new_policy_W_right = new_policy_window_right
+        
+        # Storage for results
+        self.bin_centers = None
+        self.f_obs = None
+        self.f_cf = None
+        self.F_obs = None
+        self.F_cf = None
+        
+        # Bunching statistics
+        self.q_N_obs = None
+        self.q_R_obs = None
+        self.q_N_cf = None
+        self.q_R_cf = None
+        self.E = None  # Excess mass
+        self.Delta_R = None  # Missing mass
+        self.b = None  # Bunching ratio
+        
+        # Step 2: Effective wedge
+        self.tau_e = None
+        
+        # Step 3: Substitution elasticity
+        self.sigma = None
+        
+    # ========================================================================
+    # STEP 1: BUILD COUNTERFACTUAL & BUNCHING STATISTICS
+    # ========================================================================
+    
+    def load_and_prepare_data(self):
+        """Load synthetic firm data and prepare binned distributions."""
+        
+        # Load data
+        data_path = Path(__file__).parent / 'synthetic_firms_turnover.csv'
+        df = pd.read_csv(data_path)
+        
+        # Focus on wider range for counterfactual estimation
+        df_filtered = df[(df['annual_turnover_k'] >= 20) & (df['annual_turnover_k'] <= 140)].copy()
+        
+        # Create bins of £1k intervals
+        bin_edges = np.arange(19.5, 140.5, 1.0)
+        self.bin_centers = np.arange(20, 140)  # 20k to 139k
+        
+        # Calculate observed histogram (density)
+        hist_counts, _ = np.histogram(df_filtered['annual_turnover_k'], 
+                                    bins=bin_edges, weights=df_filtered['weight'])
+        
+        # Normalize to get density (f^obs)
+        bin_width = 1.0
+        self.f_obs = hist_counts / bin_width
+        
+        return df_filtered
+        
+    def fit_counterfactual(self):
+        """
+        Fit the counterfactual density f^cf(T) using the same methodology as bunching_analysis.py
+        Excludes fixed range [80, 100] around threshold and uses degree 3 polynomial.
+        """
+        
+        # Use same exclusion range as bunching_analysis.py: [80, 100]
+        y_minus = 80  # Lower bound of excluded region
+        y_plus = 100  # Upper bound of excluded region
+        exclude_mask = (self.bin_centers >= y_minus) & (self.bin_centers <= y_plus)
+        
+        # Fit on data outside the window
+        reg_mask = ~exclude_mask
+        
+        # Use distance from threshold as feature
+        y_dist = self.bin_centers - self.T_star
+        
+        # Fit polynomial (degree 3 as in bunching_analysis.py)
+        poly_features = PolynomialFeatures(degree=3, include_bias=True)
+        X_poly = poly_features.fit_transform(y_dist[reg_mask].reshape(-1, 1))
+        
+        # Fit regression
+        reg = LinearRegression(fit_intercept=False)
+        reg.fit(X_poly, self.f_obs[reg_mask])
+        
+        # Predict counterfactual for all points
+        X_all = poly_features.transform(y_dist.reshape(-1, 1))
+        self.f_cf = reg.predict(X_all)
+        
+        # Ensure positive predictions (same as bunching_analysis.py)
+        self.f_cf = np.maximum(self.f_cf, 0)
+        
+    def compute_cdfs(self):
+        """Compute CDFs F^obs and F^cf."""
+        
+        # Simple cumulative sum for CDF (normalized by total mass)
+        bin_width = 1.0
+        
+        # Observed CDF
+        cumsum_obs = np.cumsum(self.f_obs * bin_width)
+        self.F_obs = cumsum_obs / cumsum_obs[-1]
+        
+        # Counterfactual CDF
+        cumsum_cf = np.cumsum(self.f_cf * bin_width)
+        self.F_cf = cumsum_cf / cumsum_cf[-1]
+        
+    def compute_masses_in_window(self):
+        """
+        Compute masses in the window as per the formulas:
+        q_N^obs, q_R^obs, q_N^cf, q_R^cf
+        """
+        
+        # Find indices for integration bounds
+        mask_N = (self.bin_centers >= self.T_lower) & (self.bin_centers < self.T_star)
+        mask_R = (self.bin_centers >= self.T_star) & (self.bin_centers <= self.T_upper)
+        
+        bin_width = 1.0
+        
+        # Observed masses
+        self.q_N_obs = np.sum(self.f_obs[mask_N] * bin_width)
+        self.q_R_obs = np.sum(self.f_obs[mask_R] * bin_width)
+        
+        # Counterfactual masses  
+        self.q_N_cf = np.sum(self.f_cf[mask_N] * bin_width)
+        self.q_R_cf = np.sum(self.f_cf[mask_R] * bin_width)
+        
+    def compute_excess_missing_mass(self):
+        """
+        Compute excess mass E and missing mass Delta_R:
+        E = ∫[f^obs - f^cf]_+ dT  (in left window)
+        Delta_R = ∫[f^cf - f^obs]_+ dT  (in right window)
+        """
+        
+        # Masks for left and right windows
+        mask_N = (self.bin_centers >= self.T_lower) & (self.bin_centers < self.T_star)
+        mask_R = (self.bin_centers >= self.T_star) & (self.bin_centers <= self.T_upper)
+        
+        bin_width = 1.0
+        
+        # Excess mass in left window: [f^obs - f^cf]_+
+        excess_left = np.maximum(self.f_obs[mask_N] - self.f_cf[mask_N], 0)
+        self.E = np.sum(excess_left * bin_width)
+        
+        # Missing mass in right window: [f^cf - f^obs]_+
+        missing_right = np.maximum(self.f_cf[mask_R] - self.f_obs[mask_R], 0)
+        self.Delta_R = np.sum(missing_right * bin_width)
+        
+    def compute_bunching_ratio(self):
+        """
+        Compute bunching ratio: b = (q_N^obs - q_N^cf) / q_N^cf
+        """
+        
+        if self.q_N_cf > 0:
+            self.b = (self.q_N_obs - self.q_N_cf) / self.q_N_cf
+        else:
+            self.b = 0
+            
+    # ========================================================================
+    # STEP 2: SET EFFECTIVE WEDGE τₑ
+    # ========================================================================
+    
+    def set_effective_wedge(self, tau_e=None, lambda_param=None, rho=None, tau=None, s_c=None, v=None):
+        """
+        Step 2: Set the (constant) effective wedge τₑ
+        
+        Args:
+            tau_e: Direct value for effective wedge (if provided, overrides calculation)
+            lambda_param: λ parameter for accounting build
+            rho: ρ parameter for accounting build  
+            tau: τ base tax rate
+            s_c: s_c parameter
+            v: v parameter
+        """
+        
+        if tau_e is not None:
+            # Direct specification
+            self.tau_e = tau_e
+            print(f"Step 2: Using given effective wedge τₑ = {self.tau_e:.4f}")
+        elif all(param is not None for param in [lambda_param, rho, tau, s_c, v]):
+            # Accounting build: τₑ = λ(1-ρ)τ - τs_c*v
+            self.tau_e = lambda_param * (1 - rho) * tau - tau * s_c * v
+            print(f"Step 2: Computed effective wedge τₑ = {self.tau_e:.4f}")
+            print(f"  λ={lambda_param}, ρ={rho}, τ={tau}, s_c={s_c}, v={v}")
+        else:
+            # Default calibration for UK VAT
+            # Reasonable estimates for UK VAT registration costs
+            self.tau_e = 0.05  # 5% effective wedge
+            print(f"Step 2: Using default effective wedge τₑ = {self.tau_e:.4f}")
+            
+    # ========================================================================
+    # STEP 3: CALIBRATE SUBSTITUTION ELASTICITY σ
+    # ========================================================================
+    
+    def calibrate_substitution_elasticity(self):
+        """
+        Step 3: Calibrate the substitution elasticity σ
+        
+        Uses CES/logit share equation:
+        σ = ln(q_R^cf/q_N^cf / q_R^obs/q_N^obs) / ln(1 + τₑ)
+        """
+        
+        if self.tau_e is None:
+            raise ValueError("Must set effective wedge τₑ before calibrating elasticity")
+            
+        if self.q_N_obs <= 0 or self.q_R_obs <= 0 or self.q_N_cf <= 0 or self.q_R_cf <= 0:
+            raise ValueError("All masses must be positive for elasticity calibration")
+            
+        # Calculate ratio of ratios
+        observed_ratio = self.q_R_obs / self.q_N_obs
+        counterfactual_ratio = self.q_R_cf / self.q_N_cf
+        
+        ratio_of_ratios = counterfactual_ratio / observed_ratio
+        
+        # Calculate elasticity: σ = ln(ratio_of_ratios) / ln(1 + τₑ)
+        if ratio_of_ratios > 0 and (1 + self.tau_e) > 0:
+            self.sigma = np.log(ratio_of_ratios) / np.log(1 + self.tau_e)
+        else:
+            self.sigma = 0
+            print("Warning: Invalid values for elasticity calculation, setting σ = 0")
+            
+        print(f"Step 3: Calibrated substitution elasticity σ = {self.sigma:.4f}")
+        print(f"  q_R^obs/q_N^obs = {observed_ratio:.4f}")
+        print(f"  q_R^cf/q_N^cf = {counterfactual_ratio:.4f}")
+        print(f"  Ratio of ratios = {ratio_of_ratios:.4f}")
+        print(f"  ln(1 + τₑ) = {np.log(1 + self.tau_e):.4f}")
+    
+    # ========================================================================
+    # STEP 4: ADVANCED PROBABILISTIC MAPPING (MICRO-LEVEL NO-NOTCH COUNTERFACTUAL)
+    # ========================================================================
+    
+    def compute_aggregate_displaced_share(self):
+        """
+        Step 4a: Compute aggregate displaced share Π = 1 - (1 + τₑ)^(-σ)
+        """
+        if self.tau_e is None or self.sigma is None:
+            raise ValueError("Must complete Steps 2-3 before computing displaced share")
+            
+        self.Pi = 1 - (1 + self.tau_e)**(-self.sigma)
+        print(f"Step 4a: Aggregate displaced share Π = {self.Pi:.4f}")
+        
+    def compute_local_bunching_probability(self, T_obs):
+        """
+        Step 4b: Compute local bunching probability π(T^obs) for given turnover
+        π(T^obs) = Π * [f^obs(T^obs) - f^cf(T^obs)]_+ / E
+        
+        Args:
+            T_obs: Observed turnover (scalar or array)
+            
+        Returns:
+            Local bunching probability (scalar or array)
+        """
+        if not hasattr(self, 'Pi'):
+            self.compute_aggregate_displaced_share()
+            
+        # Create interpolation functions for densities
+        f_obs_interp = interp1d(self.bin_centers, self.f_obs, kind='linear', 
+                               bounds_error=False, fill_value=0)
+        f_cf_interp = interp1d(self.bin_centers, self.f_cf, kind='linear', 
+                              bounds_error=False, fill_value=0)
+        
+        # Compute excess density at T_obs
+        excess_density = np.maximum(f_obs_interp(T_obs) - f_cf_interp(T_obs), 0)
+        
+        # Local bunching probability: what fraction of firms at T_obs are bunchers?
+        # All excess density should be bunchers, so π = excess / observed
+        pi_T_obs = excess_density / f_obs_interp(T_obs) if f_obs_interp(T_obs) > 0 else 0
+        
+        return pi_T_obs
+        
+    def compute_rank_among_bunchers(self, T_obs):
+        """
+        Step 4c: Compute rank among bunchers u(T^obs)
+        u(T^obs) = ∫[T*-W to T^obs] [f^obs - f^cf]_+ dT / E
+        
+        Args:
+            T_obs: Observed turnover (must be in [T*-W, T*])
+            
+        Returns:
+            Rank among bunchers ∈ [0,1]
+        """
+        # Only valid for T_obs in [T*-W, T*]
+        T_obs = np.clip(T_obs, self.T_lower, self.T_star)
+        
+        # Find integration bounds in our bin indices
+        start_idx = np.searchsorted(self.bin_centers, self.T_lower, side='left')
+        end_idx = np.searchsorted(self.bin_centers, T_obs, side='right')
+        
+        # Compute cumulative excess mass up to T_obs
+        if end_idx <= start_idx:
+            return 0.0
+            
+        # Get the relevant range
+        bin_subset = self.bin_centers[start_idx:end_idx]
+        f_obs_subset = self.f_obs[start_idx:end_idx]
+        f_cf_subset = self.f_cf[start_idx:end_idx]
+        
+        # Compute excess mass in this range
+        bin_width = 1.0
+        excess_subset = np.maximum(f_obs_subset - f_cf_subset, 0)
+        cumulative_excess = np.sum(excess_subset * bin_width)
+        
+        # Rank among bunchers
+        u_T_obs = cumulative_excess / self.E if self.E > 0 else 0
+        
+        return np.clip(u_T_obs, 0, 1)
+        
+    def compute_counterfactual_mapping(self, T_obs):
+        """
+        Step 4d: Compute deterministic counterfactual mapping E[T^cf | T^obs]
+        
+        E[T^cf | T^obs] = (1 - π(T^obs)) * T^obs + π(T^obs) * (F^cf)^(-1)(F^cf(T*) + u(T^obs) * Δ_R)
+        
+        Args:
+            T_obs: Observed turnover (scalar or array)
+            
+        Returns:
+            Expected counterfactual turnover T^cf
+        """
+        T_obs = np.atleast_1d(T_obs)
+        T_cf = np.zeros_like(T_obs, dtype=float)
+        
+        # Create CDF interpolation function
+        # Normalize CDF to be between 0 and 1
+        F_cf_normalized = self.F_cf
+        F_cf_interp = interp1d(self.bin_centers, F_cf_normalized, kind='linear',
+                              bounds_error=False, fill_value='extrapolate')
+        
+        # Inverse CDF function (approximate)
+        cdf_values = F_cf_normalized
+        inverse_cdf = interp1d(cdf_values, self.bin_centers, kind='linear',
+                              bounds_error=False, fill_value='extrapolate')
+        
+        # Get F^cf(T*)
+        F_cf_at_threshold = F_cf_interp(self.T_star)
+        
+        for i, T_obs_val in enumerate(T_obs):
+            if self.T_lower <= T_obs_val <= self.T_star:
+                # In bunching region - apply the mapping
+                
+                # Compute local bunching probability
+                pi_val = self.compute_local_bunching_probability(T_obs_val)
+                
+                # Compute rank among bunchers
+                u_val = self.compute_rank_among_bunchers(T_obs_val)
+                
+                # Compute the CDF argument
+                cdf_arg = F_cf_at_threshold + u_val * self.Delta_R / np.sum(self.f_cf)
+                cdf_arg = np.clip(cdf_arg, 0, 1)  # Ensure valid CDF value
+                
+                # Apply the mapping formula
+                T_cf_displaced = inverse_cdf(cdf_arg)
+                T_cf[i] = (1 - pi_val) * T_obs_val + pi_val * T_cf_displaced
+                
+            else:
+                # Outside bunching region - no change
+                T_cf[i] = T_obs_val
+                
+        return T_cf if len(T_cf) > 1 else T_cf[0]
+        
+    def run_step4_analysis(self, sample_turnovers=None):
+        """
+        Step 4: Run micro-level mapping analysis
+        
+        Args:
+            sample_turnovers: Array of turnover values to analyze (optional)
+        """
+        
+        if self.tau_e is None or self.sigma is None:
+            raise ValueError("Must complete Steps 1-3 before running Step 4")
+            
+        print(f"\nStep 4: Micro-level elasticity-aware mapping")
+        print("-" * 50)
+        
+        # Compute aggregate displaced share
+        self.compute_aggregate_displaced_share()
+        
+        # Use default sample if none provided
+        if sample_turnovers is None:
+            # Sample firms in the bunching region
+            sample_turnovers = np.linspace(self.T_lower, self.T_star, 11)  # 11 points from T*-W to T*
+            
+        print(f"Analyzing {len(sample_turnovers)} sample turnover points:")
+        print(f"Sample range: £{sample_turnovers[0]:.1f}k to £{sample_turnovers[-1]:.1f}k")
+        
+        # Compute mappings for sample
+        results = []
+        for T_obs in sample_turnovers:
+            pi_val = self.compute_local_bunching_probability(T_obs)
+            u_val = self.compute_rank_among_bunchers(T_obs)
+            T_cf_val = self.compute_counterfactual_mapping(T_obs)
+            
+            results.append({
+                'T_obs': T_obs,
+                'pi': pi_val,
+                'u': u_val,
+                'T_cf': T_cf_val,
+                'displacement': T_cf_val - T_obs
+            })
+            
+        # Create results DataFrame
+        results_df = pd.DataFrame(results)
+        
+        # Print summary
+        print(f"\nStep 4 Results Summary:")
+        print(f"Average bunching probability: {results_df['pi'].mean():.4f}")
+        print(f"Average displacement: £{results_df['displacement'].mean():.2f}k")
+        print(f"Max displacement: £{results_df['displacement'].max():.2f}k")
+        
+        # Store results
+        self.step4_results = results_df
+        
+        return results_df
+    def run_analysis(self, tau_e=None, include_step4=False, **wedge_params):
+        """Run the complete Steps 1-4 analysis."""
+        
+        step_range = "Steps 1-4" if include_step4 else "Steps 1-3"
+        print(f"{step_range}: Counterfactual, Wedge, Elasticity" + 
+              (", and Micro Mapping" if include_step4 else ""))
+        print("=" * 70)
+        
+        # Step 1: Load and prepare data
+        print("\nStep 1: Building counterfactual & bunching stats")
+        print("-" * 50)
+        df = self.load_and_prepare_data()
+        print(f"Loaded {len(df):,} firm observations")
+        
+        # Fit counterfactual
+        self.fit_counterfactual()
+        print(f"Fitted counterfactual excluding window [{self.T_lower}, {self.T_upper}]")
+        
+        # Compute CDFs
+        self.compute_cdfs()
+        
+        # Compute all masses and statistics
+        self.compute_masses_in_window()
+        self.compute_excess_missing_mass()
+        self.compute_bunching_ratio()
+        
+        # Step 2: Set effective wedge
+        print(f"\nStep 2: Setting effective wedge")
+        print("-" * 50)
+        self.set_effective_wedge(tau_e=tau_e, **wedge_params)
+        
+        # Step 3: Calibrate substitution elasticity
+        print(f"\nStep 3: Calibrating substitution elasticity")
+        print("-" * 50)
+        self.calibrate_substitution_elasticity()
+        
+        # Step 4: Micro-level mapping (optional)
+        if include_step4:
+            self.run_step4_analysis()
+        
+        # Print results
+        self.print_results()
+        
+        # Apply Step 5: Forward map to new policy (£100k threshold)
+        if include_step4:  # Only do Step 5 if we're doing the full analysis
+            self.step5_forward_map_to_new_policy(T_star_new=100)
+        
+        # Create firm mappings for Steps 6-7 (sample-based for demonstration)
+        if include_step4:
+            self.create_sample_firm_mappings()
+            
+            # Apply Steps 6-7: Revenue mapping and elasticity calculations
+            if hasattr(self, 'firm_mappings'):
+                # Step 6: Revenue mapping (old → new policy)  
+                self.step6_revenue_mapping(tau_old=0.20, tau_new=0.20)  # Same rate, different threshold
+                
+                # Step 7: Elasticity calculations
+                self.step7_elasticity_calculations(tau_old=0.20)
+        
+        # Create visualization with Step 4 and optionally Step 5
+        include_step5_plot = include_step4 and hasattr(self, 'f_new_policy')
+        self.plot_results(include_step5=include_step5_plot)
+        
+        # Apply Step 4 advanced mapping to synthetic firms and save results  
+        self.apply_step4_to_synthetic_firms('synthetic_firms_step4_mapped.csv')
+        
+        return self.get_results_dict()
+    
+    # ========================================================================
+    # STEP 5: FORWARD MAP TO NEW POLICY (£100K THRESHOLD)
+    # ========================================================================
+    
+    def step5_forward_map_to_new_policy(self, T_star_new=100, tau_e_new=None):
+        """
+        Step 5: Forward map from no-notch counterfactual to new policy (£100k threshold)
+        
+        Pipeline: Real (£90k) → Counterfactual (no notch) → New Policy (£100k)
+        
+        Args:
+            T_star_new: New threshold in £k (default 100k)
+            tau_e_new: New effective wedge (default same as original)
+        """
+        
+        print(f"\nStep 5: Forward mapping to NEW policy")
+        print("=" * 50)
+        print(f"New threshold: £{T_star_new}k (was £{self.T_star}k)")
+        
+        # Use same effective wedge if not specified
+        if tau_e_new is None:
+            tau_e_new = self.tau_e
+            print(f"Using same effective wedge: τₑ' = {tau_e_new:.4f}")
+        else:
+            print(f"New effective wedge: τₑ' = {tau_e_new:.4f}")
+        
+        # Step 5a: New displaced share under new wedge
+        Pi_new = 1 - (1 + tau_e_new)**(-self.sigma)
+        print(f"New displaced share: Π' = {Pi_new:.4f}")
+        
+        # Step 5b: Recenter the no-notch density at new threshold
+        # The counterfactual f_cf becomes the base for the new policy
+        
+        # Step 5c: Apply reverse mapping from counterfactual to new observed
+        f_new_policy = self.create_new_policy_distribution(T_star_new, tau_e_new, Pi_new)
+        
+        # Store results
+        self.T_star_new = T_star_new
+        self.tau_e_new = tau_e_new
+        self.Pi_new = Pi_new
+        self.f_new_policy = f_new_policy
+        
+        print(f"Step 5 complete: Generated new policy distribution with bunching at £{T_star_new}k")
+        
+        return f_new_policy
+    
+    def create_new_policy_distribution(self, T_star_new, tau_e_new, Pi_new):
+        """
+        Create new policy distribution with bunching at new threshold.
+        Uses reverse mapping from counterfactual to new observed distribution.
+        """
+        
+        print(f"Creating new policy distribution with advanced mapping...")
+        
+        # Start with the counterfactual as base (no-notch world)
+        f_new = self.f_cf.copy()
+        
+        # Apply reverse mapping to create bunching at new threshold
+        # This is the inverse of Step 4: Counterfactual → New Policy
+        
+        # Use new policy window parameters
+        W_left_new = self.new_policy_W_left
+        W_right_new = self.new_policy_W_right  
+        T_lower_new = T_star_new - W_left_new
+        T_upper_new = T_star_new + W_right_new
+        
+        # Find bins corresponding to new threshold region
+        new_region_mask = (self.bin_centers >= T_lower_new) & (self.bin_centers <= T_upper_new)
+        above_threshold_mask = (self.bin_centers > T_star_new) & (self.bin_centers <= T_upper_new)
+        
+        if not np.any(above_threshold_mask):
+            print(f"Warning: No bins above new threshold £{T_star_new}k in analysis range")
+            return f_new
+        
+        # Calculate how much mass should move down to create bunching
+        # Mass in region above new threshold in counterfactual
+        mass_above = np.sum(self.f_cf[above_threshold_mask])
+        
+        # How much should move down (based on new displaced share)
+        mass_to_move = Pi_new * mass_above
+        
+        print(f"  Mass above new threshold: {mass_above:.0f}")
+        print(f"  Mass to move down (create bunching): {mass_to_move:.0f}")
+        
+        # Redistribute mass: move from above threshold to below (creating bunching)
+        if mass_to_move > 0:
+            # Reduce density above threshold
+            reduction_factor = (1 - Pi_new)
+            f_new[above_threshold_mask] *= reduction_factor
+            
+            # Add mass below threshold (distributed according to distance from threshold)
+            below_threshold_mask = (self.bin_centers >= T_lower_new) & (self.bin_centers <= T_star_new)
+            
+            if np.any(below_threshold_mask):
+                # Weight by inverse distance from new threshold (closer bins get more mass)
+                distances = T_star_new - self.bin_centers[below_threshold_mask]
+                distances = np.maximum(distances, 0.5)  # Minimum distance
+                weights = 1.0 / distances
+                weights = weights / np.sum(weights)  # Normalize
+                
+                # Distribute the moved mass
+                f_new[below_threshold_mask] += mass_to_move * weights
+                
+                print(f"  Redistributed mass to {np.sum(below_threshold_mask)} bins below new threshold")
+        
+        # Apply smoothing to eliminate sharp changes
+        f_new = self.apply_smoothing(f_new)
+        
+        return f_new
+    
+    def apply_smoothing(self, distribution):
+        """Apply aggressive smoothing to eliminate sharp changes in Step 5 curve."""
+        smooth_dist = distribution.copy()
+        
+        # Multiple passes of progressively wider smoothing
+        
+        # Pass 1: 3-point moving average
+        for i in range(1, len(smooth_dist) - 1):
+            smooth_dist[i] = 0.25 * smooth_dist[i-1] + 0.5 * smooth_dist[i] + 0.25 * smooth_dist[i+1]
+        
+        # Pass 2: 5-point moving average  
+        for i in range(2, len(smooth_dist) - 2):
+            smooth_dist[i] = 0.1 * smooth_dist[i-2] + 0.2 * smooth_dist[i-1] + 0.4 * smooth_dist[i] + 0.2 * smooth_dist[i+1] + 0.1 * smooth_dist[i+2]
+        
+        # # Pass 3: 7-point moving average (more aggressive)
+        # for i in range(3, len(smooth_dist) - 3):
+        #     smooth_dist[i] = (0.05 * smooth_dist[i-3] + 0.1 * smooth_dist[i-2] + 0.15 * smooth_dist[i-1] + 
+        #                     0.4 * smooth_dist[i] + 0.15 * smooth_dist[i+1] + 0.1 * smooth_dist[i+2] + 0.05 * smooth_dist[i+3])
+        
+        # Pass 4: 9-point moving average (very aggressive)
+        # for i in range(4, len(smooth_dist) - 4):
+        #     weights = [0.03, 0.06, 0.09, 0.12, 0.4, 0.12, 0.09, 0.06, 0.03]
+        #     smooth_dist[i] = sum(w * smooth_dist[i + j - 4] for j, w in enumerate(weights))
+        
+        # Pass 5: Final Gaussian-like smoothing
+        for i in range(5, len(smooth_dist) - 5):
+            # 11-point Gaussian-weighted average
+            weights = [0.02, 0.03, 0.05, 0.08, 0.12, 0.4, 0.12, 0.08, 0.05, 0.03, 0.02]
+            smooth_dist[i] = sum(w * smooth_dist[i + j - 5] for j, w in enumerate(weights))
+        
+        return smooth_dist
+    
+    # ========================================================================
+    # STEP 6: REVENUE MAPPING OLD → NEW 
+    # ========================================================================
+    
+    def step6_revenue_mapping(self, tau_old=0.20, tau_new=0.20):
+        """
+        Step 6: Revenue mapping from old to new policy
+        Calculate VAT revenue under both regimes using firm-level mapping
+        """
+        print(f"\n" + "="*70)
+        print("STEP 6: REVENUE MAPPING OLD → NEW")
+        print("="*70)
+        
+        # Reasonable assumptions for missing parameters (imputed from typical UK firms)
+        # These would ideally come from supply-use tables by sector
+        theta_default = 0.85      # Share of taxable outputs (85% for typical firms)
+        s_c_default = 0.45        # Input cost share (45% of turnover)
+        v_default = 0.70          # VAT-able share of inputs (70%)
+        
+        print(f"Using imputed parameters:")
+        print(f"  θ (taxable output share): {theta_default:.2f}")
+        print(f"  s_c (input cost share): {s_c_default:.2f}") 
+        print(f"  v (VAT-able input share): {v_default:.2f}")
+        print(f"  Old VAT rate: {tau_old:.1%}")
+        print(f"  New VAT rate: {tau_new:.1%}")
+        
+        # Load firm data and compute revenues
+        if not hasattr(self, 'firm_mappings'):
+            print("Error: Need Step 4 firm mappings. Run step4_advanced_mapping first.")
+            return
+            
+        # Calculate firm-level revenues
+        revenues_old = []
+        revenues_new = []
+        revenue_changes = []
+        
+        print(f"\nCalculating revenues for {len(self.firm_mappings)} firms...")
+        
+        for i, (T_old, T_new) in enumerate(self.firm_mappings):
+            # Firm-level VAT parameters (in practice, would vary by sector)
+            theta_i = theta_default
+            s_c_i = s_c_default  
+            v_i = v_default
+            
+            # Revenue under old regime: V_old = τ_old * (θ * T_old - v * s_c * T_old)
+            V_old = tau_old * (theta_i * T_old - v_i * s_c_i * T_old)
+            V_old = max(0, V_old)  # Non-negative revenue
+            
+            # Revenue under new regime: V_new = τ_new * (θ * T_new - v * s_c * T_new)  
+            V_new = tau_new * (theta_i * T_new - v_i * s_c_i * T_new)
+            V_new = max(0, V_new)  # Non-negative revenue
+            
+            revenues_old.append(V_old)
+            revenues_new.append(V_new)
+            revenue_changes.append(V_new - V_old)
+        
+        # Store results
+        self.revenues_old = np.array(revenues_old)
+        self.revenues_new = np.array(revenues_new) 
+        self.revenue_changes = np.array(revenue_changes)
+        
+        # Aggregate results
+        total_revenue_old = np.sum(self.revenues_old)
+        total_revenue_new = np.sum(self.revenues_new)
+        total_change = total_revenue_new - total_revenue_old
+        
+        print(f"\n" + "-"*50)
+        print("STEP 6 RESULTS:")
+        print("-"*50)
+        print(f"Total VAT revenue (old policy): £{total_revenue_old:,.0f}k")
+        print(f"Total VAT revenue (new policy): £{total_revenue_new:,.0f}k")
+        print(f"Revenue change: £{total_change:,.0f}k ({100*total_change/total_revenue_old:+.2f}%)")
+        
+        # Distribution of revenue changes
+        positive_changes = self.revenue_changes[self.revenue_changes > 0]
+        negative_changes = self.revenue_changes[self.revenue_changes < 0]
+        
+        print(f"\nFirm-level revenue impacts:")
+        print(f"  Firms with revenue increase: {len(positive_changes):,} ({100*len(positive_changes)/len(self.revenue_changes):.1f}%)")
+        print(f"  Firms with revenue decrease: {len(negative_changes):,} ({100*len(negative_changes)/len(self.revenue_changes):.1f}%)")
+        if len(positive_changes) > 0:
+            print(f"  Average increase: £{np.mean(positive_changes):.2f}k")
+        if len(negative_changes) > 0:
+            print(f"  Average decrease: £{np.mean(negative_changes):.2f}k")
+        
+        return {
+            'total_old': total_revenue_old,
+            'total_new': total_revenue_new, 
+            'total_change': total_change,
+            'change_percent': 100*total_change/total_revenue_old
+        }
+    
+    # ========================================================================
+    # STEP 7: ELASTICITY CALCULATIONS
+    # ========================================================================
+    
+    def step7_elasticity_calculations(self, tau_old=0.20, epsilon_tau=0.01, epsilon_threshold=5):
+        """
+        Step 7: Calculate elasticities (behavioural and revenue)
+        """
+        print(f"\n" + "="*70)
+        print("STEP 7: ELASTICITY CALCULATIONS")  
+        print("="*70)
+        
+        # Behavioral (odds) elasticity from Step 3 calibration
+        behavioral_elasticity = -self.sigma
+        
+        print(f"1) Behavioral (odds) elasticity w.r.t wedge:")
+        print(f"   d ln(q_R/q_N) / d ln(1+τ_e) = -σ = {behavioral_elasticity:.3f}")
+        
+        # Revenue elasticity w.r.t VAT rate (finite differences)
+        print(f"\n2) Revenue elasticity w.r.t VAT rate:")
+        print(f"   Computing finite differences with ε = {epsilon_tau:.1%}...")
+        
+        # Baseline revenue (from Step 6)
+        if not hasattr(self, 'revenues_old'):
+            print("   Error: Run step6_revenue_mapping first")
+            return
+            
+        baseline_revenue = np.sum(self.revenues_old)
+        
+        # Simulate τ + ε 
+        tau_plus = tau_old + epsilon_tau
+        self.run_policy_simulation(tau_new=tau_plus)
+        revenue_plus = self.calculate_revenue_for_rate(tau_plus)
+        
+        # Simulate τ - ε
+        tau_minus = tau_old - epsilon_tau  
+        self.run_policy_simulation(tau_new=tau_minus)
+        revenue_minus = self.calculate_revenue_for_rate(tau_minus)
+        
+        # Revenue elasticity = (dV/V) / (dτ/τ)
+        dV_dtau = (revenue_plus - revenue_minus) / (2 * epsilon_tau)
+        revenue_elasticity_rate = (dV_dtau * tau_old) / baseline_revenue
+        
+        print(f"   dV/dτ = £{dV_dtau:,.0f}k per percentage point")
+        print(f"   Revenue elasticity w.r.t rate = {revenue_elasticity_rate:.3f}")
+        
+        # Revenue elasticity w.r.t threshold (finite differences)
+        print(f"\n3) Revenue elasticity w.r.t threshold:")
+        print(f"   Computing finite differences with ε = £{epsilon_threshold}k...")
+        
+        # Simulate T* + ε
+        threshold_plus = self.T_star + epsilon_threshold
+        revenue_threshold_plus = self.calculate_revenue_for_threshold(threshold_plus)
+        
+        # Simulate T* - ε  
+        threshold_minus = self.T_star - epsilon_threshold
+        revenue_threshold_minus = self.calculate_revenue_for_threshold(threshold_minus)
+        
+        # Revenue elasticity = (dV/V) / (dT*/T*)
+        dV_dthreshold = (revenue_threshold_plus - revenue_threshold_minus) / (2 * epsilon_threshold)
+        revenue_elasticity_threshold = (dV_dthreshold * self.T_star) / baseline_revenue
+        
+        print(f"   dV/dT* = £{dV_dthreshold:,.0f}k per £1k threshold change")
+        print(f"   Revenue elasticity w.r.t threshold = {revenue_elasticity_threshold:.3f}")
+        
+        print(f"\n" + "-"*50)
+        print("STEP 7 RESULTS:")
+        print("-"*50)
+        print(f"Behavioral elasticity (σ): {-behavioral_elasticity:.3f}")
+        print(f"Revenue elasticity w.r.t rate: {revenue_elasticity_rate:.3f}")  
+        print(f"Revenue elasticity w.r.t threshold: {revenue_elasticity_threshold:.3f}")
+        
+        return {
+            'behavioral_elasticity': behavioral_elasticity,
+            'revenue_elasticity_rate': revenue_elasticity_rate,
+            'revenue_elasticity_threshold': revenue_elasticity_threshold
+        }
+    
+    def run_policy_simulation(self, tau_new):
+        """Helper: Run quick policy simulation for elasticity calculation"""
+        # Recompute effective wedge for new rate
+        lambda_param = 0.75  # B2C share assumption
+        rho = 0.50          # Pass-through assumption  
+        s_c = 0.45          # Input cost share
+        v = 0.70            # VAT-able input share
+        
+        tau_e_new = lambda_param * (1 - rho) * tau_new - tau_new * s_c * v
+        
+        # Recompute displaced share
+        Pi_new = 1 - (1 + tau_e_new)**(-self.sigma)
+        
+        # Update firm mappings (simplified)
+        self.Pi_simulation = Pi_new
+    
+    def calculate_revenue_for_rate(self, tau_rate):
+        """Helper: Calculate total revenue for given VAT rate"""
+        theta = 0.85
+        s_c = 0.45  
+        v = 0.70
+        
+        total_revenue = 0
+        for T_old, T_new in self.firm_mappings[:10000]:  # Sample for speed
+            V = tau_rate * (theta * T_new - v * s_c * T_new)
+            total_revenue += max(0, V)
+        
+        # Scale up to full population
+        scaling_factor = len(self.firm_mappings) / 10000
+        return total_revenue * scaling_factor
+    
+    def calculate_revenue_for_threshold(self, new_threshold):
+        """Helper: Calculate total revenue for given threshold"""
+        # Simplified: assume threshold change affects firms near boundary
+        threshold_effect = (new_threshold - self.T_star) / self.T_star
+        
+        total_revenue = 0
+        tau_rate = 0.20
+        theta = 0.85
+        s_c = 0.45
+        v = 0.70
+        
+        for T_old, T_new in self.firm_mappings[:10000]:  # Sample for speed
+            # Adjust turnover based on threshold change (simplified)
+            if 80 <= T_old <= 100:  # Firms near threshold
+                T_adjusted = T_new * (1 + 0.1 * threshold_effect)  # Small adjustment
+            else:
+                T_adjusted = T_new
+                
+            V = tau_rate * (theta * T_adjusted - v * s_c * T_adjusted)
+            total_revenue += max(0, V)
+        
+        # Scale up to full population  
+        scaling_factor = len(self.firm_mappings) / 10000
+        return total_revenue * scaling_factor
+    
+    def map_cf_to_new_policy(self, T_cf):
+        """Helper: Map counterfactual turnovers to new policy turnovers using Step 5 distribution"""
+        # Simplified mapping using the Step 5 new policy distribution
+        T_new = np.zeros_like(T_cf)
+        
+        for i, turnover in enumerate(T_cf):
+            # Find closest bin
+            bin_idx = np.argmin(np.abs(self.bin_centers - turnover))
+            
+            # Use ratio of new policy to counterfactual at this point
+            if self.f_cf[bin_idx] > 0:
+                ratio = self.f_new_policy[bin_idx] / self.f_cf[bin_idx]
+                # Apply small adjustment based on ratio
+                T_new[i] = turnover * (0.95 + 0.1 * ratio)  # Conservative adjustment
+            else:
+                T_new[i] = turnover
+        
+        return T_new
+    
+    def create_sample_firm_mappings(self):
+        """Create sample firm mappings for Steps 6-7 demonstration"""
+        np.random.seed(42)  # Reproducible results
+        
+        # Create a realistic sample of UK firms 
+        n_firms = 100000  # Sample size for demonstration
+        
+        # Generate realistic firm turnover distribution
+        # UK firms: many small firms, few large ones
+        turnovers_old = []
+        turnovers_new = []
+        
+        for i in range(n_firms):
+            # Generate old turnover with realistic distribution
+            if np.random.random() < 0.7:  # 70% small firms
+                T_old = np.random.lognormal(np.log(30), 0.8)  # Mean ~£50k
+            elif np.random.random() < 0.9:  # 20% medium firms
+                T_old = np.random.lognormal(np.log(80), 0.6)  # Mean ~£120k
+            else:  # 10% large firms
+                T_old = np.random.lognormal(np.log(200), 0.8)  # Mean ~£400k
+            
+            T_old = max(10, min(500, T_old))  # Constrain to £10k-£500k
+            
+            # Apply mapping based on Step 4/5 logic
+            if 65 <= T_old <= 110:  # Near thresholds - apply bunching effects
+                if hasattr(self, 'f_new_policy') and hasattr(self, 'f_cf'):
+                    # Use Step 5 mapping
+                    bin_idx = np.argmin(np.abs(self.bin_centers - T_old))
+                    if self.f_cf[bin_idx] > 0:
+                        ratio = self.f_new_policy[bin_idx] / self.f_cf[bin_idx]
+                        T_new = T_old * (0.95 + 0.1 * ratio)  # Small adjustment
+                    else:
+                        T_new = T_old
+                else:
+                    # Default mapping for threshold change (90k → 100k)
+                    if T_old < 90:
+                        T_new = T_old * 1.02  # Small increase due to threshold change
+                    else:
+                        T_new = T_old * 0.98  # Small decrease
+            else:
+                # Outside bunching region - minimal change
+                T_new = T_old * np.random.normal(1.0, 0.01)  # 1% noise
+            
+            turnovers_old.append(T_old)
+            turnovers_new.append(max(10, T_new))  # Minimum £10k
+        
+        # Store mappings
+        self.firm_mappings = list(zip(turnovers_old, turnovers_new))
+        
+        print(f"Created {len(self.firm_mappings):,} sample firm mappings for Steps 6-7")
+        print(f"  Mean old turnover: £{np.mean(turnovers_old):.1f}k")
+        print(f"  Mean new turnover: £{np.mean(turnovers_new):.1f}k")
+        
+    def print_results(self):
+        """Print the analysis results."""
+        
+        has_step4 = hasattr(self, 'step4_results')
+        step_range = "Steps 1-4" if has_step4 else "Steps 1-3"
+        
+        print(f"\n{step_range} Results Summary:")
+        print("=" * 50)
+        print(f"Threshold T*: £{self.T_star:,}k")
+        print(f"Window: -£{self.W_left:,}k/+£{self.W_right:,}k")
+        print(f"Analysis range: [{self.T_lower}, {self.T_upper}]")
+        
+        print(f"\nStep 1 - Bunching Statistics:")
+        print("-" * 30)
+        print(f"  Masses in window:")
+        print(f"    q_N^obs = {self.q_N_obs:.3f}")
+        print(f"    q_R^obs = {self.q_R_obs:.3f}")
+        print(f"    q_N^cf  = {self.q_N_cf:.3f}")
+        print(f"    q_R^cf  = {self.q_R_cf:.3f}")
+        print(f"  Excess mass (E) = {self.E:.3f}")
+        print(f"  Missing mass (Δ_R) = {self.Delta_R:.3f}")
+        print(f"  Bunching ratio (b) = {self.b:.3f}")
+        
+        print(f"\nStep 2 - Effective Wedge:")
+        print("-" * 30)
+        print(f"  τₑ = {self.tau_e:.4f}")
+        
+        print(f"\nStep 3 - Substitution Elasticity:")
+        print("-" * 30)
+        print(f"  σ = {self.sigma:.4f}")
+        
+        if has_step4:
+            print(f"\nStep 4 - Micro-level Mapping:")
+            print("-" * 30)
+            print(f"  Aggregate displaced share Π = {self.Pi:.4f}")
+            print(f"  Sample firms analyzed: {len(self.step4_results)}")
+            print(f"  Avg bunching probability: {self.step4_results['pi'].mean():.4f}")
+            print(f"  Avg displacement: £{self.step4_results['displacement'].mean():.2f}k")
+        
+    def plot_results(self, include_step5=False):
+        """Create visualization of Steps 1-4 complete analysis with advanced mapping, optionally including Step 5."""
+        
+        _, ax = plt.subplots(figsize=(12, 8))
+        
+        # Plot observed and counterfactual densities (exclude first point T=20k, x=-70)
+        y_dist = self.bin_centers - self.T_star
+        
+        # Skip first point (index 0)
+        plot_mask = slice(1, None)
+        y_dist_plot = y_dist[plot_mask]
+        
+        ax.plot(y_dist_plot, self.f_obs[plot_mask], 'o-', color='blue', markersize=3, 
+                linewidth=1, label='Original Observed f^obs(T) [£90k]', alpha=0.7)
+        ax.plot(y_dist_plot, self.f_cf[plot_mask], '-', color='red', linewidth=2, 
+                label='Counterfactual f^cf(T) [No Notch]')
+        
+        # Add Step 4 advanced mapping result (also skip first point)
+        f_step4 = create_advanced_probabilistic_mapping(self)
+        ax.plot(y_dist_plot, f_step4[plot_mask], '-', color='green', linewidth=2, 
+                label='Step 4 Advanced Mapping Result', alpha=0.8)
+        
+        # Add Step 5 new policy result if available (also skip first point)
+        if include_step5 and hasattr(self, 'f_new_policy'):
+            ax.plot(y_dist_plot, self.f_new_policy[plot_mask], '-', color='purple', linewidth=2, 
+                    label=f'Step 5 New Policy [£{self.T_star_new}k]', alpha=0.8)
+        
+        # Shade the window (also skip first point)
+        window_mask = (self.bin_centers >= self.T_lower) & (self.bin_centers <= self.T_upper)
+        window_mask_plot = window_mask[plot_mask]  # Apply same exclusion to window mask
+        ax.fill_between(y_dist_plot[window_mask_plot], 0, self.f_obs[plot_mask][window_mask_plot], 
+                       alpha=0.3, color='lightblue', label=f'Window -£{self.W_left}k/+£{self.W_right}k')
+        
+        # Add vertical lines  
+        ax.axvline(x=0, color='black', linestyle='-', linewidth=2, label='Original Threshold £90k')
+        ax.axvline(x=-self.W_left, color='gray', linestyle='--', alpha=0.5, label=f'£90k -£{self.W_left}k/+£{self.W_right}k')
+        ax.axvline(x=self.W_right, color='gray', linestyle='--', alpha=0.5)
+        
+        # Add new threshold line if Step 5 is included
+        if include_step5 and hasattr(self, 'T_star_new'):
+            new_threshold_dist = self.T_star_new - self.T_star  # Distance from original
+            ax.axvline(x=new_threshold_dist, color='purple', linestyle='-', linewidth=2, 
+                      label=f'New Threshold £{self.T_star_new}k', alpha=0.8)
+        
+        # Add annotations
+        ax.text(0.02, 0.95, f'Bunching ratio b = {self.b:.3f}', 
+                transform=ax.transAxes, fontsize=11, 
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue"))
+        ax.text(0.02, 0.89, f'Effective wedge τₑ = {self.tau_e:.3f}', 
+                transform=ax.transAxes, fontsize=11, 
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow"))
+        ax.text(0.02, 0.83, f'Substitution elasticity σ = {self.sigma:.3f}', 
+                transform=ax.transAxes, fontsize=11, 
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgreen"))
+        ax.text(0.02, 0.77, f'Excess mass E = {self.E:.0f}', 
+                transform=ax.transAxes, fontsize=11, 
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral"))
+        
+        # Add Step 4 info
+        pi_mean = np.mean([self.compute_local_bunching_probability(T) 
+                          for T in self.bin_centers[window_mask]])
+        ax.text(0.02, 0.71, f'Avg bunching prob π̄ = {pi_mean:.3f}', 
+                transform=ax.transAxes, fontsize=11, 
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightpink"))
+        
+        # Add Step 5 info if included
+        if include_step5 and hasattr(self, 'Pi_new'):
+            ax.text(0.02, 0.65, f'New displaced share Π\' = {self.Pi_new:.3f}', 
+                    transform=ax.transAxes, fontsize=11, 
+                    bbox=dict(boxstyle="round,pad=0.3", facecolor="plum"))
+        
+        # Formatting
+        ax.set_xlabel('Distance from Original Threshold £90k (£1,000)', fontsize=12)
+        ax.set_ylabel('Density f(T)', fontsize=12)
+        
+        if include_step5:
+            ax.set_title('Steps 1-5: Complete Pipeline with New Policy', fontsize=14, fontweight='bold')
+        else:
+            ax.set_title('Steps 1-4: Complete Analysis with Advanced Mapping', fontsize=14, fontweight='bold')
+            
+        ax.grid(True, alpha=0.3)
+        ax.legend(fontsize=9)
+        ax.set_xlim(-70, 50)
+        
+        # Save plot
+        output_path = Path(__file__).parent / 'bunching_analysis.png'
+        plt.savefig(output_path, dpi=300, bbox_inches='tight')
+        print(f"\nPlot saved: {output_path}")
+        
+    def apply_step4_to_synthetic_firms(self, output_file='synthetic_firms_step4_mapped.csv'):
+        """
+        Apply advanced Step 4 mapping to synthetic firms and save results.
+        """
+        
+        print("\nApplying Step 4 Advanced Mapping to Synthetic Firms")
+        print("=" * 60)
+        
+        # Load synthetic firms data
+        try:
+            df = pd.read_csv('synthetic_firms_turnover.csv')
+            print(f"Loaded {len(df):,} synthetic firms from synthetic_firms_turnover.csv")
+        except FileNotFoundError:
+            print("Error: synthetic_firms_turnover.csv not found")
+            return None
+        
+        # Apply Step 4 mapping to each firm's turnover
+        print("Applying Step 4 mapping to individual firms...")
+        
+        # Get observed turnovers
+        T_obs = df['annual_turnover_k'].values
+        
+        # Apply the advanced probabilistic mapping
+        print("Creating advanced probabilistic mapping...")
+        f_mapped = create_advanced_probabilistic_mapping(self)
+        
+        # For individual firms, we need to apply the mapping stochastically
+        print("Computing counterfactual turnovers for each firm...")
+        
+        # Initialize counterfactual turnovers
+        T_cf = np.zeros_like(T_obs)
+        
+        # Create mapping probabilities for each turnover level
+        mapping_probs = {}
+        bin_width = 1.0  # £1k bins
+        
+        for i, bin_center in enumerate(self.bin_centers):
+            if self.T_lower <= bin_center <= self.T_star:
+                # Compute bunching probability for this turnover level
+                pi_val = self.compute_local_bunching_probability(bin_center)
+                
+                # Get displacement target
+                T_cf_target = self.compute_counterfactual_mapping(bin_center)
+                
+                mapping_probs[bin_center] = {
+                    'pi': pi_val,
+                    'cf_target': T_cf_target,
+                    'stay_prob': 1 - pi_val,
+                    'displace_prob': pi_val
+                }
+            else:
+                # Outside bunching region - no displacement
+                mapping_probs[bin_center] = {
+                    'pi': 0.0,
+                    'cf_target': bin_center,
+                    'stay_prob': 1.0,
+                    'displace_prob': 0.0
+                }
+        
+        # Apply mapping to each firm
+        np.random.seed(42)  # For reproducible results
+        n_displaced = 0
+        
+        for idx, turnover in enumerate(T_obs):
+            # Find closest bin center
+            bin_center = round(turnover)  # Round to nearest £1k
+            
+            if bin_center in mapping_probs:
+                prob_info = mapping_probs[bin_center]
+                
+                # Determine if this firm gets displaced
+                if np.random.random() < prob_info['displace_prob']:
+                    # Firm gets displaced
+                    T_cf[idx] = prob_info['cf_target']
+                    n_displaced += 1
+                else:
+                    # Firm stays at original location
+                    T_cf[idx] = turnover
+            else:
+                # Turnover outside our analysis range - no change
+                T_cf[idx] = turnover
+            
+            # Progress indicator
+            if (idx + 1) % 100000 == 0:
+                print(f"  Processed {idx + 1:,}/{len(T_obs):,} firms...")
+        
+        # Add results to dataframe
+        df_mapped = df.copy()
+        df_mapped['step4_counterfactual_turnover_k'] = T_cf
+        df_mapped['step4_displacement_k'] = T_cf - T_obs
+        
+        # Store firm mappings for Steps 6-7 (old_turnover, new_turnover)
+        # For Step 5: use Step 5 new policy turnovers if available
+        if hasattr(self, 'f_new_policy'):
+            # Map counterfactual turnovers to new policy turnovers (simplified)
+            T_new_policy = self.map_cf_to_new_policy(T_cf)
+        else:
+            T_new_policy = T_cf  # If no Step 5, use counterfactual as "new"
+        
+        self.firm_mappings = list(zip(T_obs, T_new_policy))
+        
+        # Save results
+        df_mapped.to_csv(output_file, index=False)
+        
+        # Print summary statistics
+        displacement = T_cf - T_obs
+        nonzero_displacement = displacement[displacement != 0]
+        
+        print(f"\nStep 4 Advanced Mapping Complete!")
+        print("=" * 40)
+        print(f"Output file: {output_file}")
+        print(f"Total firms processed: {len(df):,}")
+        print(f"Firms displaced: {n_displaced:,} ({100*n_displaced/len(df):.2f}%)")
+        print(f"Mean displacement: £{displacement.mean():.3f}k")
+        if len(nonzero_displacement) > 0:
+            print(f"Mean nonzero displacement: £{nonzero_displacement.mean():.3f}k")
+            print(f"Max displacement: £{displacement.max():.3f}k")
+            print(f"Min displacement: £{displacement.min():.3f}k")
+        
+        # Weighted statistics
+        obs_mean = np.average(T_obs, weights=df['weight'])
+        cf_mean = np.average(T_cf, weights=df['weight'])
+        
+        print(f"\nWeighted Statistics:")
+        print(f"  Original mean turnover: £{obs_mean:.1f}k")
+        print(f"  Step 4 mapped mean turnover: £{cf_mean:.1f}k")
+        print(f"  Mean change: £{cf_mean - obs_mean:.3f}k")
+        
+        return df_mapped
+        
+    def get_results_dict(self):
+        """Return results as dictionary for use in subsequent steps."""
+        
+        return {
+            'T_star': self.T_star,
+            'W_left': self.W_left,
+            'W_right': self.W_right,
+            'bin_centers': self.bin_centers,
+            'f_obs': self.f_obs,
+            'f_cf': self.f_cf,
+            'F_obs': self.F_obs,
+            'F_cf': self.F_cf,
+            'q_N_obs': self.q_N_obs,
+            'q_R_obs': self.q_R_obs,
+            'q_N_cf': self.q_N_cf,
+            'q_R_cf': self.q_R_cf,
+            'E': self.E,
+            'Delta_R': self.Delta_R,
+            'b': self.b,
+            'tau_e': self.tau_e,
+            'sigma': self.sigma
+        }
+
+
+def main():
+    """Run Steps 1-4 analysis."""
+    
+    # ==========================================================================
+    # CONFIGURATION: Set your asymmetric windows here
+    # ==========================================================================
+    # Counterfactual analysis windows (around original £90k threshold)
+    counterfactual_window_left = 25    # £10k below threshold (£80k-£90k)
+    counterfactual_window_right = 20   # £10k above threshold (£90k-£100k)
+    
+    # New policy analysis windows (around new £100k threshold)  
+    new_policy_window_left = 25        # £10k below new threshold (£90k-£100k)
+    new_policy_window_right = 20       # £10k above new threshold (£100k-£110k)
+    # ==========================================================================
+    
+    # Initialize analysis with asymmetric windows
+    analysis = CounterfactualBunchingAnalysis(
+        threshold=90, 
+        window_left=counterfactual_window_left,
+        window_right=counterfactual_window_right,
+        new_policy_window_left=new_policy_window_left,
+        new_policy_window_right=new_policy_window_right
+    )
+    
+    # Run complete analysis with Steps 1-5
+    print("STEPS 1-5: COMPLETE BUNCHING ANALYSIS PIPELINE")
+    print("Step 1: Counterfactual & bunching statistics")
+    print("Step 2: Effective wedge τₑ")
+    print("Step 3: Substitution elasticity σ")  
+    print("Step 4: Advanced probabilistic mapping (no-notch counterfactual)")
+    print("Step 5: Forward map to NEW policy (£100k threshold)")
+    print("="*70)
+    results = analysis.run_analysis(tau_e=0.05, include_step4=True)
+    
+    # Show detailed Step 4 results
+    if hasattr(analysis, 'step4_results'):
+        print("\nDetailed Step 4 Results:")
+        print("-" * 40)
+        step4_df = analysis.step4_results
+        print(step4_df.round(4).to_string(index=False))
+        
+        # Show example calculations for a few firms
+        print(f"\nExample firm mappings:")
+        example_firms = [82, 85, 88, 90]  # Firms at different positions
+        
+        for T_obs in example_firms:
+            if analysis.T_lower <= T_obs <= analysis.T_star:
+                pi = analysis.compute_local_bunching_probability(T_obs)
+                u = analysis.compute_rank_among_bunchers(T_obs) 
+                T_cf = analysis.compute_counterfactual_mapping(T_obs)
+                
+                print(f"  Firm at £{T_obs}k:")
+                print(f"    π(T^obs) = {pi:.4f} (bunching probability)")
+                print(f"    u(T^obs) = {u:.4f} (rank among bunchers)")
+                print(f"    T^cf = £{T_cf:.2f}k (counterfactual turnover)")
+                print(f"    Displacement = £{T_cf - T_obs:.2f}k")
+                print()
+    
+    # Visualization removed as requested
+    
+    # Mapping check visualization removed as requested
+    
+    return results
+
+
+# ============================================================================
+# STEP 4: ADVANCED PROBABILISTIC MAPPING - MAIN FUNCTION
+# ============================================================================
+
+def create_advanced_probabilistic_mapping(analysis, decay_rate=0.01, spread_factor=3.0):
+    """
+    Step 4: Advanced Probabilistic Mapping - MAIN STEP 4 FUNCTION
+    
+    This is the primary Step 4 implementation that creates the smooth counterfactual
+    distribution shown as the green curve in steps1234_complete_analysis.png.
+    
+    Creates comprehensive probabilistic mapping that redistributes firms across
+    the full range to match the theoretical counterfactual distribution.
+    """
+    
+    # Start with the observed distribution as base
+    mapped_distribution = np.copy(analysis.f_obs)
+    
+    print(f"Creating advanced probabilistic mapping:")
+    print(f"- Decay rate: {decay_rate} (controls displacement spread)")
+    print(f"- Spread factor: {spread_factor} (controls overall redistribution)")
+    
+    # Skip first point (T=20k, x=-70) from all calculations
+    print(f"Skipping first point (x=-70, T=20k) from mapping calculations")
+    
+    # For each bin, calculate how much it needs to change to match counterfactual
+    deficit = analysis.f_cf - analysis.f_obs  # Positive = need more, negative = have excess
+    
+    # For each bin in the analysis (skip first point)
+    for i, T_obs in enumerate(analysis.bin_centers):
+        if i == 0:  # Skip first point (T=20k)
+            continue
+        original_density = analysis.f_obs[i]
+        target_density = analysis.f_cf[i]
+        
+        if original_density > target_density:
+            # This bin has excess firms - redistribute the excess
+            excess_firms = original_density - target_density
+            
+            # Set this bin to its target density
+            mapped_distribution[i] = target_density
+            
+            # Redistribute excess firms across range (excluding first point)
+            if excess_firms > 0:
+                # Find locations that need more firms (positive deficit, skip first point)
+                deficit_mask = (deficit > 0) & (np.arange(len(deficit)) > 0)  # Exclude index 0
+                target_locations = analysis.bin_centers[deficit_mask]
+                target_deficits = deficit[deficit_mask]
+                
+                if len(target_locations) > 0:
+                    # Create probability weights based on distance and deficit size
+                    distances = np.abs(target_locations - T_obs)
+                    distance_weights = np.exp(-decay_rate * distances)
+                    deficit_weights = target_deficits * spread_factor
+                    
+                    # Combine distance and deficit weights
+                    combined_weights = distance_weights * deficit_weights
+                    probs = combined_weights / np.sum(combined_weights)
+                    
+                    # Distribute excess firms
+                    for j, target_turnover in enumerate(target_locations):
+                        target_idx = np.where(analysis.bin_centers == target_turnover)[0][0]
+                        redistribution = excess_firms * probs[j]
+                        mapped_distribution[target_idx] += redistribution
+                        
+                        # Update deficit to track progress
+                        deficit[target_idx] -= redistribution
+        # Bins without excess keep their observed density (already set in initialization)
+    
+    # Check first point before scaling
+    print(f"Before scaling: First point mapped={mapped_distribution[0]:.1f}")
+    
+    # Final adjustment: scale to match total counterfactual mass
+    total_cf = np.sum(analysis.f_cf)
+    total_mapped = np.sum(mapped_distribution)
+    scaling_factor = total_cf / total_mapped if total_mapped > 0 else 1.0
+    print(f"Scaling factor: {scaling_factor:.4f}")
+    
+    if total_mapped > 0:
+        mapped_distribution *= scaling_factor
+    
+    # Check first point after scaling
+    print(f"After scaling: First point mapped={mapped_distribution[0]:.1f}")
+    
+    return mapped_distribution
+
+
+# ============================================================================
+# STEP 4: SUPPORTING FUNCTIONS FOR ADVANCED MAPPING
+# ============================================================================
+
+def optimize_advanced_parameters(analysis):
+    """Optimize parameters for advanced probabilistic mapping."""
+    
+    from scipy.optimize import minimize
+    
+    def mapping_error(params):
+        decay_rate, spread_factor = params
+        mapped_dist = create_advanced_probabilistic_mapping(analysis, decay_rate, spread_factor)
+        error = np.sum((mapped_dist - analysis.f_cf)**2)
+        return error
+    
+    print("Optimizing advanced probabilistic mapping parameters...")
+    
+    # Optimize decay rate (0.001 to 0.1) and spread factor (0.5 to 5.0)
+    result = minimize(mapping_error, x0=[0.01, 2.0], 
+                     bounds=[(0.001, 0.1), (0.5, 5.0)],
+                     method='L-BFGS-B')
+    
+    optimal_decay = result.x[0]
+    optimal_spread = result.x[1]
+    
+    print(f"Optimal decay rate: {optimal_decay:.4f}")
+    print(f"Optimal spread factor: {optimal_spread:.2f}")
+    
+    return optimal_decay, optimal_spread
+
+
+
+
+if __name__ == "__main__":
+    results = main()
\ No newline at end of file
diff --git a/policyengine_uk/firm_analysis/plot_example.py b/policyengine_uk/firm_analysis/plot_example.py
new file mode 100644
index 000000000..8b555aa9a
--- /dev/null
+++ b/policyengine_uk/firm_analysis/plot_example.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""
+Simple turnover distribution plot from synthetic firm data.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from pathlib import Path
+
+def create_turnover_plot():
+    """Create a simple turnover distribution chart."""
+    
+    # Load data
+    data_path = Path(__file__).parent / 'synthetic_firms_turnover.csv'
+    df = pd.read_csv(data_path)
+    
+    # Create bins from 1k to 300k
+    bin_edges = np.arange(0.5, 300.5, 1.0)  # 1k intervals
+    
+    # Calculate weighted histogram
+    hist, _ = np.histogram(df['annual_turnover_k'], bins=bin_edges, weights=df['weight'])
+    
+    # Create plot
+    plt.figure(figsize=(15, 6))
+    x_positions = np.arange(len(hist))
+    
+    plt.bar(x_positions, hist, color='lightblue', alpha=0.7, edgecolor='black', linewidth=0.1)
+    
+    plt.xlabel('Annual Turnover (£k)')
+    plt.ylabel('Number of Firms (2023-24)')
+    
+    # Set x-axis labels every 10k
+    label_positions = [i for i in range(9, len(hist), 10)]  # Start at 10k (index 9)
+    label_texts = [f'{i+1}' for i in label_positions]
+    plt.xticks(label_positions, label_texts)
+    
+    # Add vertical lines at 85k and 150k
+    plt.axvline(x=84, color='red', linestyle='--', alpha=0.7, linewidth=2)  # 85k threshold
+    plt.axvline(x=149, color='red', linestyle='--', alpha=0.7, linewidth=2)  # 150k threshold
+    
+    # Add labels for the vertical lines
+    plt.text(85, max(hist) * 0.8, 'VAT Threshold', rotation=0, color='red', fontsize=8, ha='left')
+    plt.text(150, max(hist) * 0.8, 'VAT Flat Rate Scheme', rotation=0, color='red', fontsize=8, ha='left')
+    
+    plt.grid(axis='y', alpha=0.3, linestyle='--')
+    
+    # Save chart
+    output_path = Path(__file__).parent / 'turnover_distribution.png'
+    plt.savefig(output_path, dpi=300, bbox_inches='tight')
+    
+    print(f"Chart saved: {output_path}")
+
+if __name__ == "__main__":
+    create_turnover_plot()
\ No newline at end of file
diff --git a/policyengine_uk/firm_analysis/static_revenue.py b/policyengine_uk/firm_analysis/static_revenue.py
new file mode 100644
index 000000000..67aa0031e
--- /dev/null
+++ b/policyengine_uk/firm_analysis/static_revenue.py
@@ -0,0 +1,231 @@
+"""
+Static Revenue Analysis for VAT Threshold Bunching
+Uses pre-calculated VAT liability from synthetic data generation (turnover - input)
+"""
+
+import pandas as pd
+import numpy as np
+import os
+
+# Get the current directory (analysis folder)
+current_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(current_dir)
+
+# Load the data with pre-calculated VAT liability
+print("Loading data...")
+synthetic_firms = pd.read_csv(os.path.join(current_dir, 'synthetic_firms_turnover.csv'))
+
+weighted_total_firms = synthetic_firms['weight'].sum()
+print(f"Loaded {weighted_total_firms:,.0f} firms with pre-calculated VAT liability")
+
+# Check if VAT liability already exists in the data
+if 'vat_liability_k' not in synthetic_firms.columns:
+    print("ERROR: vat_liability_k column not found. Please run generate_synthetic_data.py first.")
+    exit(1)
+
+# Convert VAT liability from thousands to pounds for analysis
+synthetic_firms['vat_liability'] = synthetic_firms['vat_liability_k'] * 1000
+
+print(f"\nVAT Liability Statistics:")
+print(f"  Total VAT liability: £{(synthetic_firms['vat_liability'] * synthetic_firms['weight']).sum() / 1_000_000:,.2f} million")
+print(f"  Firms with positive VAT: {synthetic_firms[synthetic_firms['vat_liability'] > 0]['weight'].sum():,.0f}")
+print(f"  Firms with negative VAT: {synthetic_firms[synthetic_firms['vat_liability'] < 0]['weight'].sum():,.0f}")
+print(f"  Firms with zero VAT: {synthetic_firms[synthetic_firms['vat_liability'] == 0]['weight'].sum():,.0f}")
+
+# Static revenue impact analysis
+# Convert turnover from thousands to pounds for threshold comparison
+synthetic_firms['annual_turnover'] = synthetic_firms['annual_turnover_k'] * 1000
+
+# Identify firms in the bunching region (£85k to £90k)
+affected = synthetic_firms[
+    (synthetic_firms['annual_turnover'] >= 85000) & 
+    (synthetic_firms['annual_turnover'] < 90000)
+]
+
+# Calculate total revenue loss (weighted VAT liability)
+total_revenue_loss = (affected['vat_liability'] * affected['weight']).sum()
+total_revenue_loss_millions = total_revenue_loss / 1_000_000
+
+# Summary statistics (applying weights)
+total_weighted_firms = synthetic_firms['weight'].sum()
+affected_weighted_firms = affected['weight'].sum()
+
+print("\n" + "="*60)
+print("STATIC REVENUE IMPACT ANALYSIS")
+print("="*60)
+print(f"Total firms in dataset: {total_weighted_firms:,.0f}")
+print(f"Firms in bunching region (£85k-£90k): {affected_weighted_firms:,.0f}")
+print(f"Percentage of firms affected: {affected_weighted_firms/total_weighted_firms*100:.2f}%")
+print(f"\nTotal VAT liability in bunching region: £{total_revenue_loss_millions:,.2f} million")
+print(f"Average VAT per affected firm: £{total_revenue_loss/affected_weighted_firms:,.2f}" if affected_weighted_firms > 0 else "N/A")
+
+# Additional analysis by sector
+print("\n" + "="*60)
+print("TOP 5 SECTORS BY VAT LIABILITY IN BUNCHING REGION")
+print("="*60)
+if len(affected) > 0:
+    # Calculate weighted VAT liability by sector
+    sector_impact = affected.groupby('sic_code').apply(
+        lambda x: pd.Series({
+            'weighted_vat_liability': (x['vat_liability'] * x['weight']).sum(),
+            'weighted_firm_count': x['weight'].sum()
+        })
+    )
+    sector_impact = sector_impact.sort_values('weighted_vat_liability', ascending=False).head(5)
+    
+    for idx, (sector, row) in enumerate(sector_impact.iterrows(), 1):
+        vat_millions = row['weighted_vat_liability'] / 1_000_000
+        print(f"{idx}. Sector {sector}")
+        print(f"   Firms: {row['weighted_firm_count']:,.0f}, VAT: £{vat_millions:.2f} million")
+
+# Check data quality
+print("\n" + "="*60)
+print("DATA QUALITY CHECKS")
+print("="*60)
+print(f"Firms with missing SIC codes: {synthetic_firms[synthetic_firms['sic_code'].isna()]['weight'].sum():,.0f}")
+print(f"Firms with zero/negative turnover: {synthetic_firms[synthetic_firms['annual_turnover_k'] <= 0]['weight'].sum():,.0f}")
+print(f"Raw records with zero/negative weights: {(synthetic_firms['weight'] <= 0).sum()}")
+print(f"Firms with non-zero VAT liability: {synthetic_firms[synthetic_firms['vat_liability'] != 0]['weight'].sum():,.0f}")
+
+# Calculate total weighted VAT liability
+total_weighted_vat = (synthetic_firms['vat_liability'] * synthetic_firms['weight']).sum()
+total_vat_billions = total_weighted_vat / 1_000_000_000
+print(f"Total weighted VAT liability across all firms: £{total_vat_billions:,.2f} billion")
+
+# Show input/output statistics
+print(f"\nInput/Output Statistics:")
+print(f"Average input/output ratio: {(synthetic_firms['annual_input_k'] / synthetic_firms['annual_turnover_k']).mean():.2%}")
+print(f"Firms with input > output (negative VAT): {synthetic_firms[synthetic_firms['annual_input_k'] > synthetic_firms['annual_turnover_k']]['weight'].sum():,.0f}")
+
+# Generate multi-year revenue impact table
+print("\n" + "="*100)
+print("MULTI-YEAR REVENUE IMPACT ANALYSIS")
+print("="*100)
+
+# Define the fiscal years and parameters with correct baseline thresholds
+# Based on RPI with 2-year lag: 2024-25=2.0%, 2025-26=2.5%, 2026-27=3.0%
+fiscal_years = [
+    {"year": "2024-25", "rpi": "— (frozen)", "baseline": 85000, "policy": 90000, "hmrc_impact": -150, "firm_growth": 1.031},      # 3.1%
+    {"year": "2025-26", "rpi": "— (frozen)", "baseline": 85000, "policy": 90000, "hmrc_impact": -185, "firm_growth": 1.0516},     # 1.031 * 1.020
+    {"year": "2026-27", "rpi": "2024-25: +2.0%", "baseline": 87000, "policy": 90000, "hmrc_impact": -125, "firm_growth": 1.0779},    # £85,000 * 1.020 = £87,000
+    {"year": "2027-28", "rpi": "2025-26: +2.5%", "baseline": 89000, "policy": 90000, "hmrc_impact": -50, "firm_growth": 1.1102},     # £87,000 * 1.025 = £89,175 ≈ £89,000
+    {"year": "2028-29", "rpi": "2026-27: +3.0%", "baseline": 92000, "policy": 90000, "hmrc_impact": 65, "firm_growth": 1.1424},      # £89,000 * 1.030 = £91,670 ≈ £92,000
+]
+
+# Calculate PolicyEngine estimates for each year
+results = []
+for fy in fiscal_years:
+    # Apply growth factor to firm data for this year
+    # Create a copy with adjusted turnover and VAT liability
+    adjusted_firms = synthetic_firms.copy()
+    adjusted_firms['annual_turnover'] = adjusted_firms['annual_turnover_k'] * 1000 * fy['firm_growth']
+    adjusted_firms['vat_liability'] = adjusted_firms['vat_liability_k'] * 1000 * fy['firm_growth']
+    
+    # Determine the affected range based on baseline vs policy
+    if fy['baseline'] > fy['policy']:
+        # When baseline > policy (2028-29), firms between policy and baseline
+        # are now required to register (they're above £90k policy but would have been below baseline)
+        baseline_affected = adjusted_firms[
+            (adjusted_firms['annual_turnover'] >= fy['policy']) & 
+            (adjusted_firms['annual_turnover'] < fy['baseline'])
+        ]
+        # This is a revenue gain - these firms now must pay VAT
+        pe_impact = (baseline_affected['vat_liability'] * baseline_affected['weight']).sum() / 1_000_000
+    else:
+        # When baseline < policy, firms between baseline and policy can avoid VAT
+        baseline_affected = adjusted_firms[
+            (adjusted_firms['annual_turnover'] >= fy['baseline']) & 
+            (adjusted_firms['annual_turnover'] < fy['policy'])
+        ]
+        # This is a revenue loss - these firms avoid VAT
+        pe_impact = -(baseline_affected['vat_liability'] * baseline_affected['weight']).sum() / 1_000_000
+    
+    results.append({
+        "Fiscal Year": fy['year'],
+        "Lagged RPI used": fy['rpi'],
+        "Baseline": f"£{fy['baseline']:,}",
+        "Policy": f"£{fy['policy']:,}",
+        "HMRC Revenue Impact (£m)": fy['hmrc_impact'],
+        "PolicyEngine Impact (£m)": round(pe_impact, 0),
+        "Difference": round(pe_impact - fy['hmrc_impact'], 0)
+    })
+
+# Create DataFrame for nice display
+import pandas as pd
+results_df = pd.DataFrame(results)
+
+# Display the table
+print("\n" + "-"*100)
+print(f"{'Fiscal Year':<12} {'Lagged RPI':<20} {'Baseline':<12} {'Policy':<12} {'HMRC (£m)':<12} {'PE (£m)':<12} {'Diff (£m)':<10}")
+print("-"*100)
+
+for _, row in results_df.iterrows():
+    print(f"{row['Fiscal Year']:<12} {row['Lagged RPI used']:<20} {row['Baseline']:<12} {row['Policy']:<12} "
+          f"{row['HMRC Revenue Impact (£m)']:>10} {row['PolicyEngine Impact (£m)']:>10} "
+          f"{row['Difference']:>10}")
+
+print("-"*100)
+print(f"\nNote: Negative values indicate revenue loss, positive values indicate revenue gain")
+print(f"PE = PolicyEngine estimate based on firm-level VAT liability calculations")
+
+# Create and save a plot
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(12, 6))
+
+# Extract data for plotting
+years = [fy['year'] for fy in fiscal_years]
+hmrc_impacts = [fy['hmrc_impact'] for fy in fiscal_years]
+pe_impacts = [row['PolicyEngine Impact (£m)'] for row in results]
+
+# Plot the data
+x = range(len(years))
+width = 0.35
+
+bars1 = ax.bar([i - width/2 for i in x], hmrc_impacts, width, label='HMRC', color='#2E86AB')
+bars2 = ax.bar([i + width/2 for i in x], pe_impacts, width, label='PolicyEngine', color='#A23B72')
+
+# Add value labels on bars with padding
+for bar in bars1:
+    height = bar.get_height()
+    # Add padding: 5 units above positive bars, 5 units below negative bars
+    y_pos = height + 5 if height > 0 else height - 5
+    ax.text(bar.get_x() + bar.get_width()/2., y_pos,
+            f'£{int(height)}m', ha='center', va='bottom' if height > 0 else 'top')
+
+for bar in bars2:
+    height = bar.get_height()
+    # Add padding: 5 units above positive bars, 5 units below negative bars
+    y_pos = height + 5 if height > 0 else height - 5
+    ax.text(bar.get_x() + bar.get_width()/2., y_pos,
+            f'£{int(height)}m', ha='center', va='bottom' if height > 0 else 'top')
+
+# Customize the plot
+# ax.set_xlabel('Fiscal Year', fontsize=12)
+ax.set_ylabel('£ millions', fontsize=12)
+ax.set_title('Revenue Impact of Increasing the VAT Registration Threshold (2024-29)', fontsize=14, fontweight='bold')
+ax.set_xticks(x)
+ax.set_xticklabels(years)
+ax.legend()
+ax.grid(True, alpha=0.3, linestyle='--')
+ax.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
+
+# Set y-axis from -250 to 100
+ax.set_ylim(-250, 100)
+
+# Add baseline/policy annotations
+for i, fy in enumerate(fiscal_years):
+    baseline_text = f"Baseline: £{fy['baseline']/1000:.0f}k"
+    policy_text = f"Policy: £{fy['policy']/1000:.0f}k"
+    ax.text(i, ax.get_ylim()[0] * 0.9, f"{baseline_text}\n{policy_text}", 
+            ha='center', va='top', fontsize=10, color='gray')
+
+plt.tight_layout()
+
+# Save the plot
+plot_path = os.path.join(current_dir, 'vat_threshold_revenue_impact.png')
+plt.savefig(plot_path, dpi=150, bbox_inches='tight')
+print(f"\nPlot saved to: {plot_path}")
+
+# Display the plot
+# plt.show()
diff --git a/policyengine_uk/firm_analysis/turnover_distribution.png b/policyengine_uk/firm_analysis/turnover_distribution.png
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HcmV?d00001


From 221e68ecc96f3b33ee2a4accad6210beb87c5138 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:57:34 +0100
Subject: [PATCH 2/9] format

---
 .../firm_analysis/bunching_analysis.py        | 1402 ++++++++++-------
 policyengine_uk/firm_analysis/plot_example.py |   87 +-
 .../firm_analysis/static_revenue.py           |  329 ++--
 3 files changed, 1146 insertions(+), 672 deletions(-)

diff --git a/policyengine_uk/firm_analysis/bunching_analysis.py b/policyengine_uk/firm_analysis/bunching_analysis.py
index 7a47c7be9..0314d0bd9 100644
--- a/policyengine_uk/firm_analysis/bunching_analysis.py
+++ b/policyengine_uk/firm_analysis/bunching_analysis.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Steps 1-5: Complete Bunching Analysis Pipeline with Asymmetric Windows
-Step 1: Build counterfactual & bunching statistics  
+Step 1: Build counterfactual & bunching statistics
 Step 2: Set effective wedge
 Step 3: Calibrate substitution elasticity
 Step 4: Advanced probabilistic mapping (micro-level no-notch counterfactual)
@@ -10,9 +10,9 @@
 ASYMMETRIC WINDOWS CONFIGURATION:
 To set different windows for left/right sides of thresholds, modify the main() function:
 
-    counterfactual_window_left = 15    # £15k below £90k threshold (£75k-£90k)  
+    counterfactual_window_left = 15    # £15k below £90k threshold (£75k-£90k)
     counterfactual_window_right = 5    # £5k above £90k threshold (£90k-£95k)
-    
+
     new_policy_window_left = 8         # £8k below £100k new threshold (£92k-£100k)
     new_policy_window_right = 12       # £12k above £100k new threshold (£100k-£112k)
 
@@ -27,48 +27,56 @@
 from sklearn.linear_model import LinearRegression
 from scipy.interpolate import interp1d
 import warnings
-warnings.filterwarnings('ignore')
+
+warnings.filterwarnings("ignore")
+
 
 class CounterfactualBunchingAnalysis:
     """
     Implements Steps 1-5: Complete Bunching Analysis Pipeline
-    
+
     Step 1: Build counterfactual & bunching statistics
-    Step 2: Set effective wedge τₑ  
+    Step 2: Set effective wedge τₑ
     Step 3: Calibrate substitution elasticity σ
     Step 4: Advanced probabilistic mapping (create_advanced_probabilistic_mapping)
     Step 5: Forward map to NEW policy (£100k threshold)
     """
-    
-    def __init__(self, threshold=90, window_left=10, window_right=10, 
-                 new_policy_window_left=10, new_policy_window_right=10):
+
+    def __init__(
+        self,
+        threshold=90,
+        window_left=10,
+        window_right=10,
+        new_policy_window_left=10,
+        new_policy_window_right=10,
+    ):
         """
         Initialize analysis with threshold T* and asymmetric windows.
-        
+
         Args:
             threshold: VAT threshold T* in £1k (default 90k)
             window_left: Left window (below threshold) for counterfactual analysis in £1k (default 10k)
-            window_right: Right window (above threshold) for counterfactual analysis in £1k (default 10k)  
+            window_right: Right window (above threshold) for counterfactual analysis in £1k (default 10k)
             new_policy_window_left: Left window for new policy analysis in £1k (default 10k)
             new_policy_window_right: Right window for new policy analysis in £1k (default 10k)
         """
         self.T_star = threshold
         self.W_left = window_left
         self.W_right = window_right
-        self.T_lower = threshold - window_left   # T* - W_left
+        self.T_lower = threshold - window_left  # T* - W_left
         self.T_upper = threshold + window_right  # T* + W_right
-        
+
         # New policy window parameters
         self.new_policy_W_left = new_policy_window_left
         self.new_policy_W_right = new_policy_window_right
-        
+
         # Storage for results
         self.bin_centers = None
         self.f_obs = None
         self.f_cf = None
         self.F_obs = None
         self.F_cf = None
-        
+
         # Bunching statistics
         self.q_N_obs = None
         self.q_R_obs = None
@@ -77,160 +85,185 @@ def __init__(self, threshold=90, window_left=10, window_right=10,
         self.E = None  # Excess mass
         self.Delta_R = None  # Missing mass
         self.b = None  # Bunching ratio
-        
+
         # Step 2: Effective wedge
         self.tau_e = None
-        
+
         # Step 3: Substitution elasticity
         self.sigma = None
-        
+
     # ========================================================================
     # STEP 1: BUILD COUNTERFACTUAL & BUNCHING STATISTICS
     # ========================================================================
-    
+
     def load_and_prepare_data(self):
         """Load synthetic firm data and prepare binned distributions."""
-        
+
         # Load data
-        data_path = Path(__file__).parent / 'synthetic_firms_turnover.csv'
+        data_path = Path(__file__).parent / "synthetic_firms_turnover.csv"
         df = pd.read_csv(data_path)
-        
+
         # Focus on wider range for counterfactual estimation
-        df_filtered = df[(df['annual_turnover_k'] >= 20) & (df['annual_turnover_k'] <= 140)].copy()
-        
+        df_filtered = df[
+            (df["annual_turnover_k"] >= 20) & (df["annual_turnover_k"] <= 140)
+        ].copy()
+
         # Create bins of £1k intervals
         bin_edges = np.arange(19.5, 140.5, 1.0)
         self.bin_centers = np.arange(20, 140)  # 20k to 139k
-        
+
         # Calculate observed histogram (density)
-        hist_counts, _ = np.histogram(df_filtered['annual_turnover_k'], 
-                                    bins=bin_edges, weights=df_filtered['weight'])
-        
+        hist_counts, _ = np.histogram(
+            df_filtered["annual_turnover_k"],
+            bins=bin_edges,
+            weights=df_filtered["weight"],
+        )
+
         # Normalize to get density (f^obs)
         bin_width = 1.0
         self.f_obs = hist_counts / bin_width
-        
+
         return df_filtered
-        
+
     def fit_counterfactual(self):
         """
         Fit the counterfactual density f^cf(T) using the same methodology as bunching_analysis.py
         Excludes fixed range [80, 100] around threshold and uses degree 3 polynomial.
         """
-        
+
         # Use same exclusion range as bunching_analysis.py: [80, 100]
         y_minus = 80  # Lower bound of excluded region
         y_plus = 100  # Upper bound of excluded region
-        exclude_mask = (self.bin_centers >= y_minus) & (self.bin_centers <= y_plus)
-        
+        exclude_mask = (self.bin_centers >= y_minus) & (
+            self.bin_centers <= y_plus
+        )
+
         # Fit on data outside the window
         reg_mask = ~exclude_mask
-        
+
         # Use distance from threshold as feature
         y_dist = self.bin_centers - self.T_star
-        
+
         # Fit polynomial (degree 3 as in bunching_analysis.py)
         poly_features = PolynomialFeatures(degree=3, include_bias=True)
         X_poly = poly_features.fit_transform(y_dist[reg_mask].reshape(-1, 1))
-        
+
         # Fit regression
         reg = LinearRegression(fit_intercept=False)
         reg.fit(X_poly, self.f_obs[reg_mask])
-        
+
         # Predict counterfactual for all points
         X_all = poly_features.transform(y_dist.reshape(-1, 1))
         self.f_cf = reg.predict(X_all)
-        
+
         # Ensure positive predictions (same as bunching_analysis.py)
         self.f_cf = np.maximum(self.f_cf, 0)
-        
+
     def compute_cdfs(self):
         """Compute CDFs F^obs and F^cf."""
-        
+
         # Simple cumulative sum for CDF (normalized by total mass)
         bin_width = 1.0
-        
+
         # Observed CDF
         cumsum_obs = np.cumsum(self.f_obs * bin_width)
         self.F_obs = cumsum_obs / cumsum_obs[-1]
-        
+
         # Counterfactual CDF
         cumsum_cf = np.cumsum(self.f_cf * bin_width)
         self.F_cf = cumsum_cf / cumsum_cf[-1]
-        
+
     def compute_masses_in_window(self):
         """
         Compute masses in the window as per the formulas:
         q_N^obs, q_R^obs, q_N^cf, q_R^cf
         """
-        
+
         # Find indices for integration bounds
-        mask_N = (self.bin_centers >= self.T_lower) & (self.bin_centers < self.T_star)
-        mask_R = (self.bin_centers >= self.T_star) & (self.bin_centers <= self.T_upper)
-        
+        mask_N = (self.bin_centers >= self.T_lower) & (
+            self.bin_centers < self.T_star
+        )
+        mask_R = (self.bin_centers >= self.T_star) & (
+            self.bin_centers <= self.T_upper
+        )
+
         bin_width = 1.0
-        
+
         # Observed masses
         self.q_N_obs = np.sum(self.f_obs[mask_N] * bin_width)
         self.q_R_obs = np.sum(self.f_obs[mask_R] * bin_width)
-        
-        # Counterfactual masses  
+
+        # Counterfactual masses
         self.q_N_cf = np.sum(self.f_cf[mask_N] * bin_width)
         self.q_R_cf = np.sum(self.f_cf[mask_R] * bin_width)
-        
+
     def compute_excess_missing_mass(self):
         """
         Compute excess mass E and missing mass Delta_R:
         E = ∫[f^obs - f^cf]_+ dT  (in left window)
         Delta_R = ∫[f^cf - f^obs]_+ dT  (in right window)
         """
-        
+
         # Masks for left and right windows
-        mask_N = (self.bin_centers >= self.T_lower) & (self.bin_centers < self.T_star)
-        mask_R = (self.bin_centers >= self.T_star) & (self.bin_centers <= self.T_upper)
-        
+        mask_N = (self.bin_centers >= self.T_lower) & (
+            self.bin_centers < self.T_star
+        )
+        mask_R = (self.bin_centers >= self.T_star) & (
+            self.bin_centers <= self.T_upper
+        )
+
         bin_width = 1.0
-        
+
         # Excess mass in left window: [f^obs - f^cf]_+
         excess_left = np.maximum(self.f_obs[mask_N] - self.f_cf[mask_N], 0)
         self.E = np.sum(excess_left * bin_width)
-        
+
         # Missing mass in right window: [f^cf - f^obs]_+
         missing_right = np.maximum(self.f_cf[mask_R] - self.f_obs[mask_R], 0)
         self.Delta_R = np.sum(missing_right * bin_width)
-        
+
     def compute_bunching_ratio(self):
         """
         Compute bunching ratio: b = (q_N^obs - q_N^cf) / q_N^cf
         """
-        
+
         if self.q_N_cf > 0:
             self.b = (self.q_N_obs - self.q_N_cf) / self.q_N_cf
         else:
             self.b = 0
-            
+
     # ========================================================================
     # STEP 2: SET EFFECTIVE WEDGE τₑ
     # ========================================================================
-    
-    def set_effective_wedge(self, tau_e=None, lambda_param=None, rho=None, tau=None, s_c=None, v=None):
+
+    def set_effective_wedge(
+        self,
+        tau_e=None,
+        lambda_param=None,
+        rho=None,
+        tau=None,
+        s_c=None,
+        v=None,
+    ):
         """
         Step 2: Set the (constant) effective wedge τₑ
-        
+
         Args:
             tau_e: Direct value for effective wedge (if provided, overrides calculation)
             lambda_param: λ parameter for accounting build
-            rho: ρ parameter for accounting build  
+            rho: ρ parameter for accounting build
             tau: τ base tax rate
             s_c: s_c parameter
             v: v parameter
         """
-        
+
         if tau_e is not None:
             # Direct specification
             self.tau_e = tau_e
             print(f"Step 2: Using given effective wedge τₑ = {self.tau_e:.4f}")
-        elif all(param is not None for param in [lambda_param, rho, tau, s_c, v]):
+        elif all(
+            param is not None for param in [lambda_param, rho, tau, s_c, v]
+        ):
             # Accounting build: τₑ = λ(1-ρ)τ - τs_c*v
             self.tau_e = lambda_param * (1 - rho) * tau - tau * s_c * v
             print(f"Step 2: Computed effective wedge τₑ = {self.tau_e:.4f}")
@@ -239,698 +272,832 @@ def set_effective_wedge(self, tau_e=None, lambda_param=None, rho=None, tau=None,
             # Default calibration for UK VAT
             # Reasonable estimates for UK VAT registration costs
             self.tau_e = 0.05  # 5% effective wedge
-            print(f"Step 2: Using default effective wedge τₑ = {self.tau_e:.4f}")
-            
+            print(
+                f"Step 2: Using default effective wedge τₑ = {self.tau_e:.4f}"
+            )
+
     # ========================================================================
     # STEP 3: CALIBRATE SUBSTITUTION ELASTICITY σ
     # ========================================================================
-    
+
     def calibrate_substitution_elasticity(self):
         """
         Step 3: Calibrate the substitution elasticity σ
-        
+
         Uses CES/logit share equation:
         σ = ln(q_R^cf/q_N^cf / q_R^obs/q_N^obs) / ln(1 + τₑ)
         """
-        
+
         if self.tau_e is None:
-            raise ValueError("Must set effective wedge τₑ before calibrating elasticity")
-            
-        if self.q_N_obs <= 0 or self.q_R_obs <= 0 or self.q_N_cf <= 0 or self.q_R_cf <= 0:
-            raise ValueError("All masses must be positive for elasticity calibration")
-            
+            raise ValueError(
+                "Must set effective wedge τₑ before calibrating elasticity"
+            )
+
+        if (
+            self.q_N_obs <= 0
+            or self.q_R_obs <= 0
+            or self.q_N_cf <= 0
+            or self.q_R_cf <= 0
+        ):
+            raise ValueError(
+                "All masses must be positive for elasticity calibration"
+            )
+
         # Calculate ratio of ratios
         observed_ratio = self.q_R_obs / self.q_N_obs
         counterfactual_ratio = self.q_R_cf / self.q_N_cf
-        
+
         ratio_of_ratios = counterfactual_ratio / observed_ratio
-        
+
         # Calculate elasticity: σ = ln(ratio_of_ratios) / ln(1 + τₑ)
         if ratio_of_ratios > 0 and (1 + self.tau_e) > 0:
             self.sigma = np.log(ratio_of_ratios) / np.log(1 + self.tau_e)
         else:
             self.sigma = 0
-            print("Warning: Invalid values for elasticity calculation, setting σ = 0")
-            
-        print(f"Step 3: Calibrated substitution elasticity σ = {self.sigma:.4f}")
+            print(
+                "Warning: Invalid values for elasticity calculation, setting σ = 0"
+            )
+
+        print(
+            f"Step 3: Calibrated substitution elasticity σ = {self.sigma:.4f}"
+        )
         print(f"  q_R^obs/q_N^obs = {observed_ratio:.4f}")
         print(f"  q_R^cf/q_N^cf = {counterfactual_ratio:.4f}")
         print(f"  Ratio of ratios = {ratio_of_ratios:.4f}")
         print(f"  ln(1 + τₑ) = {np.log(1 + self.tau_e):.4f}")
-    
+
     # ========================================================================
     # STEP 4: ADVANCED PROBABILISTIC MAPPING (MICRO-LEVEL NO-NOTCH COUNTERFACTUAL)
     # ========================================================================
-    
+
     def compute_aggregate_displaced_share(self):
         """
         Step 4a: Compute aggregate displaced share Π = 1 - (1 + τₑ)^(-σ)
         """
         if self.tau_e is None or self.sigma is None:
-            raise ValueError("Must complete Steps 2-3 before computing displaced share")
-            
-        self.Pi = 1 - (1 + self.tau_e)**(-self.sigma)
+            raise ValueError(
+                "Must complete Steps 2-3 before computing displaced share"
+            )
+
+        self.Pi = 1 - (1 + self.tau_e) ** (-self.sigma)
         print(f"Step 4a: Aggregate displaced share Π = {self.Pi:.4f}")
-        
+
     def compute_local_bunching_probability(self, T_obs):
         """
         Step 4b: Compute local bunching probability π(T^obs) for given turnover
         π(T^obs) = Π * [f^obs(T^obs) - f^cf(T^obs)]_+ / E
-        
+
         Args:
             T_obs: Observed turnover (scalar or array)
-            
+
         Returns:
             Local bunching probability (scalar or array)
         """
-        if not hasattr(self, 'Pi'):
+        if not hasattr(self, "Pi"):
             self.compute_aggregate_displaced_share()
-            
+
         # Create interpolation functions for densities
-        f_obs_interp = interp1d(self.bin_centers, self.f_obs, kind='linear', 
-                               bounds_error=False, fill_value=0)
-        f_cf_interp = interp1d(self.bin_centers, self.f_cf, kind='linear', 
-                              bounds_error=False, fill_value=0)
-        
+        f_obs_interp = interp1d(
+            self.bin_centers,
+            self.f_obs,
+            kind="linear",
+            bounds_error=False,
+            fill_value=0,
+        )
+        f_cf_interp = interp1d(
+            self.bin_centers,
+            self.f_cf,
+            kind="linear",
+            bounds_error=False,
+            fill_value=0,
+        )
+
         # Compute excess density at T_obs
-        excess_density = np.maximum(f_obs_interp(T_obs) - f_cf_interp(T_obs), 0)
-        
+        excess_density = np.maximum(
+            f_obs_interp(T_obs) - f_cf_interp(T_obs), 0
+        )
+
         # Local bunching probability: what fraction of firms at T_obs are bunchers?
         # All excess density should be bunchers, so π = excess / observed
-        pi_T_obs = excess_density / f_obs_interp(T_obs) if f_obs_interp(T_obs) > 0 else 0
-        
+        pi_T_obs = (
+            excess_density / f_obs_interp(T_obs)
+            if f_obs_interp(T_obs) > 0
+            else 0
+        )
+
         return pi_T_obs
-        
+
     def compute_rank_among_bunchers(self, T_obs):
         """
         Step 4c: Compute rank among bunchers u(T^obs)
         u(T^obs) = ∫[T*-W to T^obs] [f^obs - f^cf]_+ dT / E
-        
+
         Args:
             T_obs: Observed turnover (must be in [T*-W, T*])
-            
+
         Returns:
             Rank among bunchers ∈ [0,1]
         """
         # Only valid for T_obs in [T*-W, T*]
         T_obs = np.clip(T_obs, self.T_lower, self.T_star)
-        
+
         # Find integration bounds in our bin indices
-        start_idx = np.searchsorted(self.bin_centers, self.T_lower, side='left')
-        end_idx = np.searchsorted(self.bin_centers, T_obs, side='right')
-        
+        start_idx = np.searchsorted(
+            self.bin_centers, self.T_lower, side="left"
+        )
+        end_idx = np.searchsorted(self.bin_centers, T_obs, side="right")
+
         # Compute cumulative excess mass up to T_obs
         if end_idx <= start_idx:
             return 0.0
-            
+
         # Get the relevant range
         bin_subset = self.bin_centers[start_idx:end_idx]
         f_obs_subset = self.f_obs[start_idx:end_idx]
         f_cf_subset = self.f_cf[start_idx:end_idx]
-        
+
         # Compute excess mass in this range
         bin_width = 1.0
         excess_subset = np.maximum(f_obs_subset - f_cf_subset, 0)
         cumulative_excess = np.sum(excess_subset * bin_width)
-        
+
         # Rank among bunchers
         u_T_obs = cumulative_excess / self.E if self.E > 0 else 0
-        
+
         return np.clip(u_T_obs, 0, 1)
-        
+
     def compute_counterfactual_mapping(self, T_obs):
         """
         Step 4d: Compute deterministic counterfactual mapping E[T^cf | T^obs]
-        
+
         E[T^cf | T^obs] = (1 - π(T^obs)) * T^obs + π(T^obs) * (F^cf)^(-1)(F^cf(T*) + u(T^obs) * Δ_R)
-        
+
         Args:
             T_obs: Observed turnover (scalar or array)
-            
+
         Returns:
             Expected counterfactual turnover T^cf
         """
         T_obs = np.atleast_1d(T_obs)
         T_cf = np.zeros_like(T_obs, dtype=float)
-        
+
         # Create CDF interpolation function
         # Normalize CDF to be between 0 and 1
         F_cf_normalized = self.F_cf
-        F_cf_interp = interp1d(self.bin_centers, F_cf_normalized, kind='linear',
-                              bounds_error=False, fill_value='extrapolate')
-        
+        F_cf_interp = interp1d(
+            self.bin_centers,
+            F_cf_normalized,
+            kind="linear",
+            bounds_error=False,
+            fill_value="extrapolate",
+        )
+
         # Inverse CDF function (approximate)
         cdf_values = F_cf_normalized
-        inverse_cdf = interp1d(cdf_values, self.bin_centers, kind='linear',
-                              bounds_error=False, fill_value='extrapolate')
-        
+        inverse_cdf = interp1d(
+            cdf_values,
+            self.bin_centers,
+            kind="linear",
+            bounds_error=False,
+            fill_value="extrapolate",
+        )
+
         # Get F^cf(T*)
         F_cf_at_threshold = F_cf_interp(self.T_star)
-        
+
         for i, T_obs_val in enumerate(T_obs):
             if self.T_lower <= T_obs_val <= self.T_star:
                 # In bunching region - apply the mapping
-                
+
                 # Compute local bunching probability
                 pi_val = self.compute_local_bunching_probability(T_obs_val)
-                
+
                 # Compute rank among bunchers
                 u_val = self.compute_rank_among_bunchers(T_obs_val)
-                
+
                 # Compute the CDF argument
-                cdf_arg = F_cf_at_threshold + u_val * self.Delta_R / np.sum(self.f_cf)
+                cdf_arg = F_cf_at_threshold + u_val * self.Delta_R / np.sum(
+                    self.f_cf
+                )
                 cdf_arg = np.clip(cdf_arg, 0, 1)  # Ensure valid CDF value
-                
+
                 # Apply the mapping formula
                 T_cf_displaced = inverse_cdf(cdf_arg)
                 T_cf[i] = (1 - pi_val) * T_obs_val + pi_val * T_cf_displaced
-                
+
             else:
                 # Outside bunching region - no change
                 T_cf[i] = T_obs_val
-                
+
         return T_cf if len(T_cf) > 1 else T_cf[0]
-        
+
     def run_step4_analysis(self, sample_turnovers=None):
         """
         Step 4: Run micro-level mapping analysis
-        
+
         Args:
             sample_turnovers: Array of turnover values to analyze (optional)
         """
-        
+
         if self.tau_e is None or self.sigma is None:
             raise ValueError("Must complete Steps 1-3 before running Step 4")
-            
+
         print(f"\nStep 4: Micro-level elasticity-aware mapping")
         print("-" * 50)
-        
+
         # Compute aggregate displaced share
         self.compute_aggregate_displaced_share()
-        
+
         # Use default sample if none provided
         if sample_turnovers is None:
             # Sample firms in the bunching region
-            sample_turnovers = np.linspace(self.T_lower, self.T_star, 11)  # 11 points from T*-W to T*
-            
+            sample_turnovers = np.linspace(
+                self.T_lower, self.T_star, 11
+            )  # 11 points from T*-W to T*
+
         print(f"Analyzing {len(sample_turnovers)} sample turnover points:")
-        print(f"Sample range: £{sample_turnovers[0]:.1f}k to £{sample_turnovers[-1]:.1f}k")
-        
+        print(
+            f"Sample range: £{sample_turnovers[0]:.1f}k to £{sample_turnovers[-1]:.1f}k"
+        )
+
         # Compute mappings for sample
         results = []
         for T_obs in sample_turnovers:
             pi_val = self.compute_local_bunching_probability(T_obs)
             u_val = self.compute_rank_among_bunchers(T_obs)
             T_cf_val = self.compute_counterfactual_mapping(T_obs)
-            
-            results.append({
-                'T_obs': T_obs,
-                'pi': pi_val,
-                'u': u_val,
-                'T_cf': T_cf_val,
-                'displacement': T_cf_val - T_obs
-            })
-            
+
+            results.append(
+                {
+                    "T_obs": T_obs,
+                    "pi": pi_val,
+                    "u": u_val,
+                    "T_cf": T_cf_val,
+                    "displacement": T_cf_val - T_obs,
+                }
+            )
+
         # Create results DataFrame
         results_df = pd.DataFrame(results)
-        
+
         # Print summary
         print(f"\nStep 4 Results Summary:")
         print(f"Average bunching probability: {results_df['pi'].mean():.4f}")
-        print(f"Average displacement: £{results_df['displacement'].mean():.2f}k")
+        print(
+            f"Average displacement: £{results_df['displacement'].mean():.2f}k"
+        )
         print(f"Max displacement: £{results_df['displacement'].max():.2f}k")
-        
+
         # Store results
         self.step4_results = results_df
-        
+
         return results_df
+
     def run_analysis(self, tau_e=None, include_step4=False, **wedge_params):
         """Run the complete Steps 1-4 analysis."""
-        
+
         step_range = "Steps 1-4" if include_step4 else "Steps 1-3"
-        print(f"{step_range}: Counterfactual, Wedge, Elasticity" + 
-              (", and Micro Mapping" if include_step4 else ""))
+        print(
+            f"{step_range}: Counterfactual, Wedge, Elasticity"
+            + (", and Micro Mapping" if include_step4 else "")
+        )
         print("=" * 70)
-        
+
         # Step 1: Load and prepare data
         print("\nStep 1: Building counterfactual & bunching stats")
         print("-" * 50)
         df = self.load_and_prepare_data()
         print(f"Loaded {len(df):,} firm observations")
-        
+
         # Fit counterfactual
         self.fit_counterfactual()
-        print(f"Fitted counterfactual excluding window [{self.T_lower}, {self.T_upper}]")
-        
+        print(
+            f"Fitted counterfactual excluding window [{self.T_lower}, {self.T_upper}]"
+        )
+
         # Compute CDFs
         self.compute_cdfs()
-        
+
         # Compute all masses and statistics
         self.compute_masses_in_window()
         self.compute_excess_missing_mass()
         self.compute_bunching_ratio()
-        
+
         # Step 2: Set effective wedge
         print(f"\nStep 2: Setting effective wedge")
         print("-" * 50)
         self.set_effective_wedge(tau_e=tau_e, **wedge_params)
-        
+
         # Step 3: Calibrate substitution elasticity
         print(f"\nStep 3: Calibrating substitution elasticity")
         print("-" * 50)
         self.calibrate_substitution_elasticity()
-        
+
         # Step 4: Micro-level mapping (optional)
         if include_step4:
             self.run_step4_analysis()
-        
+
         # Print results
         self.print_results()
-        
+
         # Apply Step 5: Forward map to new policy (£100k threshold)
         if include_step4:  # Only do Step 5 if we're doing the full analysis
             self.step5_forward_map_to_new_policy(T_star_new=100)
-        
+
         # Create firm mappings for Steps 6-7 (sample-based for demonstration)
         if include_step4:
             self.create_sample_firm_mappings()
-            
+
             # Apply Steps 6-7: Revenue mapping and elasticity calculations
-            if hasattr(self, 'firm_mappings'):
-                # Step 6: Revenue mapping (old → new policy)  
-                self.step6_revenue_mapping(tau_old=0.20, tau_new=0.20)  # Same rate, different threshold
-                
+            if hasattr(self, "firm_mappings"):
+                # Step 6: Revenue mapping (old → new policy)
+                self.step6_revenue_mapping(
+                    tau_old=0.20, tau_new=0.20
+                )  # Same rate, different threshold
+
                 # Step 7: Elasticity calculations
                 self.step7_elasticity_calculations(tau_old=0.20)
-        
+
         # Create visualization with Step 4 and optionally Step 5
-        include_step5_plot = include_step4 and hasattr(self, 'f_new_policy')
+        include_step5_plot = include_step4 and hasattr(self, "f_new_policy")
         self.plot_results(include_step5=include_step5_plot)
-        
-        # Apply Step 4 advanced mapping to synthetic firms and save results  
-        self.apply_step4_to_synthetic_firms('synthetic_firms_step4_mapped.csv')
-        
+
+        # Apply Step 4 advanced mapping to synthetic firms and save results
+        self.apply_step4_to_synthetic_firms("synthetic_firms_step4_mapped.csv")
+
         return self.get_results_dict()
-    
+
     # ========================================================================
     # STEP 5: FORWARD MAP TO NEW POLICY (£100K THRESHOLD)
     # ========================================================================
-    
+
     def step5_forward_map_to_new_policy(self, T_star_new=100, tau_e_new=None):
         """
         Step 5: Forward map from no-notch counterfactual to new policy (£100k threshold)
-        
+
         Pipeline: Real (£90k) → Counterfactual (no notch) → New Policy (£100k)
-        
+
         Args:
             T_star_new: New threshold in £k (default 100k)
             tau_e_new: New effective wedge (default same as original)
         """
-        
+
         print(f"\nStep 5: Forward mapping to NEW policy")
         print("=" * 50)
         print(f"New threshold: £{T_star_new}k (was £{self.T_star}k)")
-        
+
         # Use same effective wedge if not specified
         if tau_e_new is None:
             tau_e_new = self.tau_e
             print(f"Using same effective wedge: τₑ' = {tau_e_new:.4f}")
         else:
             print(f"New effective wedge: τₑ' = {tau_e_new:.4f}")
-        
+
         # Step 5a: New displaced share under new wedge
-        Pi_new = 1 - (1 + tau_e_new)**(-self.sigma)
+        Pi_new = 1 - (1 + tau_e_new) ** (-self.sigma)
         print(f"New displaced share: Π' = {Pi_new:.4f}")
-        
+
         # Step 5b: Recenter the no-notch density at new threshold
         # The counterfactual f_cf becomes the base for the new policy
-        
+
         # Step 5c: Apply reverse mapping from counterfactual to new observed
-        f_new_policy = self.create_new_policy_distribution(T_star_new, tau_e_new, Pi_new)
-        
+        f_new_policy = self.create_new_policy_distribution(
+            T_star_new, tau_e_new, Pi_new
+        )
+
         # Store results
         self.T_star_new = T_star_new
         self.tau_e_new = tau_e_new
         self.Pi_new = Pi_new
         self.f_new_policy = f_new_policy
-        
-        print(f"Step 5 complete: Generated new policy distribution with bunching at £{T_star_new}k")
-        
+
+        print(
+            f"Step 5 complete: Generated new policy distribution with bunching at £{T_star_new}k"
+        )
+
         return f_new_policy
-    
+
     def create_new_policy_distribution(self, T_star_new, tau_e_new, Pi_new):
         """
         Create new policy distribution with bunching at new threshold.
         Uses reverse mapping from counterfactual to new observed distribution.
         """
-        
+
         print(f"Creating new policy distribution with advanced mapping...")
-        
+
         # Start with the counterfactual as base (no-notch world)
         f_new = self.f_cf.copy()
-        
+
         # Apply reverse mapping to create bunching at new threshold
         # This is the inverse of Step 4: Counterfactual → New Policy
-        
+
         # Use new policy window parameters
         W_left_new = self.new_policy_W_left
-        W_right_new = self.new_policy_W_right  
+        W_right_new = self.new_policy_W_right
         T_lower_new = T_star_new - W_left_new
         T_upper_new = T_star_new + W_right_new
-        
+
         # Find bins corresponding to new threshold region
-        new_region_mask = (self.bin_centers >= T_lower_new) & (self.bin_centers <= T_upper_new)
-        above_threshold_mask = (self.bin_centers > T_star_new) & (self.bin_centers <= T_upper_new)
-        
+        new_region_mask = (self.bin_centers >= T_lower_new) & (
+            self.bin_centers <= T_upper_new
+        )
+        above_threshold_mask = (self.bin_centers > T_star_new) & (
+            self.bin_centers <= T_upper_new
+        )
+
         if not np.any(above_threshold_mask):
-            print(f"Warning: No bins above new threshold £{T_star_new}k in analysis range")
+            print(
+                f"Warning: No bins above new threshold £{T_star_new}k in analysis range"
+            )
             return f_new
-        
+
         # Calculate how much mass should move down to create bunching
         # Mass in region above new threshold in counterfactual
         mass_above = np.sum(self.f_cf[above_threshold_mask])
-        
+
         # How much should move down (based on new displaced share)
         mass_to_move = Pi_new * mass_above
-        
+
         print(f"  Mass above new threshold: {mass_above:.0f}")
         print(f"  Mass to move down (create bunching): {mass_to_move:.0f}")
-        
+
         # Redistribute mass: move from above threshold to below (creating bunching)
         if mass_to_move > 0:
             # Reduce density above threshold
-            reduction_factor = (1 - Pi_new)
+            reduction_factor = 1 - Pi_new
             f_new[above_threshold_mask] *= reduction_factor
-            
+
             # Add mass below threshold (distributed according to distance from threshold)
-            below_threshold_mask = (self.bin_centers >= T_lower_new) & (self.bin_centers <= T_star_new)
-            
+            below_threshold_mask = (self.bin_centers >= T_lower_new) & (
+                self.bin_centers <= T_star_new
+            )
+
             if np.any(below_threshold_mask):
                 # Weight by inverse distance from new threshold (closer bins get more mass)
                 distances = T_star_new - self.bin_centers[below_threshold_mask]
                 distances = np.maximum(distances, 0.5)  # Minimum distance
                 weights = 1.0 / distances
                 weights = weights / np.sum(weights)  # Normalize
-                
+
                 # Distribute the moved mass
                 f_new[below_threshold_mask] += mass_to_move * weights
-                
-                print(f"  Redistributed mass to {np.sum(below_threshold_mask)} bins below new threshold")
-        
+
+                print(
+                    f"  Redistributed mass to {np.sum(below_threshold_mask)} bins below new threshold"
+                )
+
         # Apply smoothing to eliminate sharp changes
         f_new = self.apply_smoothing(f_new)
-        
+
         return f_new
-    
+
     def apply_smoothing(self, distribution):
         """Apply aggressive smoothing to eliminate sharp changes in Step 5 curve."""
         smooth_dist = distribution.copy()
-        
+
         # Multiple passes of progressively wider smoothing
-        
+
         # Pass 1: 3-point moving average
         for i in range(1, len(smooth_dist) - 1):
-            smooth_dist[i] = 0.25 * smooth_dist[i-1] + 0.5 * smooth_dist[i] + 0.25 * smooth_dist[i+1]
-        
-        # Pass 2: 5-point moving average  
+            smooth_dist[i] = (
+                0.25 * smooth_dist[i - 1]
+                + 0.5 * smooth_dist[i]
+                + 0.25 * smooth_dist[i + 1]
+            )
+
+        # Pass 2: 5-point moving average
         for i in range(2, len(smooth_dist) - 2):
-            smooth_dist[i] = 0.1 * smooth_dist[i-2] + 0.2 * smooth_dist[i-1] + 0.4 * smooth_dist[i] + 0.2 * smooth_dist[i+1] + 0.1 * smooth_dist[i+2]
-        
+            smooth_dist[i] = (
+                0.1 * smooth_dist[i - 2]
+                + 0.2 * smooth_dist[i - 1]
+                + 0.4 * smooth_dist[i]
+                + 0.2 * smooth_dist[i + 1]
+                + 0.1 * smooth_dist[i + 2]
+            )
+
         # # Pass 3: 7-point moving average (more aggressive)
         # for i in range(3, len(smooth_dist) - 3):
-        #     smooth_dist[i] = (0.05 * smooth_dist[i-3] + 0.1 * smooth_dist[i-2] + 0.15 * smooth_dist[i-1] + 
+        #     smooth_dist[i] = (0.05 * smooth_dist[i-3] + 0.1 * smooth_dist[i-2] + 0.15 * smooth_dist[i-1] +
         #                     0.4 * smooth_dist[i] + 0.15 * smooth_dist[i+1] + 0.1 * smooth_dist[i+2] + 0.05 * smooth_dist[i+3])
-        
+
         # Pass 4: 9-point moving average (very aggressive)
         # for i in range(4, len(smooth_dist) - 4):
         #     weights = [0.03, 0.06, 0.09, 0.12, 0.4, 0.12, 0.09, 0.06, 0.03]
         #     smooth_dist[i] = sum(w * smooth_dist[i + j - 4] for j, w in enumerate(weights))
-        
+
         # Pass 5: Final Gaussian-like smoothing
         for i in range(5, len(smooth_dist) - 5):
             # 11-point Gaussian-weighted average
-            weights = [0.02, 0.03, 0.05, 0.08, 0.12, 0.4, 0.12, 0.08, 0.05, 0.03, 0.02]
-            smooth_dist[i] = sum(w * smooth_dist[i + j - 5] for j, w in enumerate(weights))
-        
+            weights = [
+                0.02,
+                0.03,
+                0.05,
+                0.08,
+                0.12,
+                0.4,
+                0.12,
+                0.08,
+                0.05,
+                0.03,
+                0.02,
+            ]
+            smooth_dist[i] = sum(
+                w * smooth_dist[i + j - 5] for j, w in enumerate(weights)
+            )
+
         return smooth_dist
-    
+
     # ========================================================================
-    # STEP 6: REVENUE MAPPING OLD → NEW 
+    # STEP 6: REVENUE MAPPING OLD → NEW
     # ========================================================================
-    
+
     def step6_revenue_mapping(self, tau_old=0.20, tau_new=0.20):
         """
         Step 6: Revenue mapping from old to new policy
         Calculate VAT revenue under both regimes using firm-level mapping
         """
-        print(f"\n" + "="*70)
+        print(f"\n" + "=" * 70)
         print("STEP 6: REVENUE MAPPING OLD → NEW")
-        print("="*70)
-        
+        print("=" * 70)
+
         # Reasonable assumptions for missing parameters (imputed from typical UK firms)
         # These would ideally come from supply-use tables by sector
-        theta_default = 0.85      # Share of taxable outputs (85% for typical firms)
-        s_c_default = 0.45        # Input cost share (45% of turnover)
-        v_default = 0.70          # VAT-able share of inputs (70%)
-        
+        theta_default = (
+            0.85  # Share of taxable outputs (85% for typical firms)
+        )
+        s_c_default = 0.45  # Input cost share (45% of turnover)
+        v_default = 0.70  # VAT-able share of inputs (70%)
+
         print(f"Using imputed parameters:")
         print(f"  θ (taxable output share): {theta_default:.2f}")
-        print(f"  s_c (input cost share): {s_c_default:.2f}") 
+        print(f"  s_c (input cost share): {s_c_default:.2f}")
         print(f"  v (VAT-able input share): {v_default:.2f}")
         print(f"  Old VAT rate: {tau_old:.1%}")
         print(f"  New VAT rate: {tau_new:.1%}")
-        
+
         # Load firm data and compute revenues
-        if not hasattr(self, 'firm_mappings'):
-            print("Error: Need Step 4 firm mappings. Run step4_advanced_mapping first.")
+        if not hasattr(self, "firm_mappings"):
+            print(
+                "Error: Need Step 4 firm mappings. Run step4_advanced_mapping first."
+            )
             return
-            
+
         # Calculate firm-level revenues
         revenues_old = []
         revenues_new = []
         revenue_changes = []
-        
+
         print(f"\nCalculating revenues for {len(self.firm_mappings)} firms...")
-        
+
         for i, (T_old, T_new) in enumerate(self.firm_mappings):
             # Firm-level VAT parameters (in practice, would vary by sector)
             theta_i = theta_default
-            s_c_i = s_c_default  
+            s_c_i = s_c_default
             v_i = v_default
-            
+
             # Revenue under old regime: V_old = τ_old * (θ * T_old - v * s_c * T_old)
             V_old = tau_old * (theta_i * T_old - v_i * s_c_i * T_old)
             V_old = max(0, V_old)  # Non-negative revenue
-            
-            # Revenue under new regime: V_new = τ_new * (θ * T_new - v * s_c * T_new)  
+
+            # Revenue under new regime: V_new = τ_new * (θ * T_new - v * s_c * T_new)
             V_new = tau_new * (theta_i * T_new - v_i * s_c_i * T_new)
             V_new = max(0, V_new)  # Non-negative revenue
-            
+
             revenues_old.append(V_old)
             revenues_new.append(V_new)
             revenue_changes.append(V_new - V_old)
-        
+
         # Store results
         self.revenues_old = np.array(revenues_old)
-        self.revenues_new = np.array(revenues_new) 
+        self.revenues_new = np.array(revenues_new)
         self.revenue_changes = np.array(revenue_changes)
-        
+
         # Aggregate results
         total_revenue_old = np.sum(self.revenues_old)
         total_revenue_new = np.sum(self.revenues_new)
         total_change = total_revenue_new - total_revenue_old
-        
-        print(f"\n" + "-"*50)
+
+        print(f"\n" + "-" * 50)
         print("STEP 6 RESULTS:")
-        print("-"*50)
+        print("-" * 50)
         print(f"Total VAT revenue (old policy): £{total_revenue_old:,.0f}k")
         print(f"Total VAT revenue (new policy): £{total_revenue_new:,.0f}k")
-        print(f"Revenue change: £{total_change:,.0f}k ({100*total_change/total_revenue_old:+.2f}%)")
-        
+        print(
+            f"Revenue change: £{total_change:,.0f}k ({100*total_change/total_revenue_old:+.2f}%)"
+        )
+
         # Distribution of revenue changes
         positive_changes = self.revenue_changes[self.revenue_changes > 0]
         negative_changes = self.revenue_changes[self.revenue_changes < 0]
-        
+
         print(f"\nFirm-level revenue impacts:")
-        print(f"  Firms with revenue increase: {len(positive_changes):,} ({100*len(positive_changes)/len(self.revenue_changes):.1f}%)")
-        print(f"  Firms with revenue decrease: {len(negative_changes):,} ({100*len(negative_changes)/len(self.revenue_changes):.1f}%)")
+        print(
+            f"  Firms with revenue increase: {len(positive_changes):,} ({100*len(positive_changes)/len(self.revenue_changes):.1f}%)"
+        )
+        print(
+            f"  Firms with revenue decrease: {len(negative_changes):,} ({100*len(negative_changes)/len(self.revenue_changes):.1f}%)"
+        )
         if len(positive_changes) > 0:
             print(f"  Average increase: £{np.mean(positive_changes):.2f}k")
         if len(negative_changes) > 0:
             print(f"  Average decrease: £{np.mean(negative_changes):.2f}k")
-        
+
         return {
-            'total_old': total_revenue_old,
-            'total_new': total_revenue_new, 
-            'total_change': total_change,
-            'change_percent': 100*total_change/total_revenue_old
+            "total_old": total_revenue_old,
+            "total_new": total_revenue_new,
+            "total_change": total_change,
+            "change_percent": 100 * total_change / total_revenue_old,
         }
-    
+
     # ========================================================================
     # STEP 7: ELASTICITY CALCULATIONS
     # ========================================================================
-    
-    def step7_elasticity_calculations(self, tau_old=0.20, epsilon_tau=0.01, epsilon_threshold=5):
+
+    def step7_elasticity_calculations(
+        self, tau_old=0.20, epsilon_tau=0.01, epsilon_threshold=5
+    ):
         """
         Step 7: Calculate elasticities (behavioural and revenue)
         """
-        print(f"\n" + "="*70)
-        print("STEP 7: ELASTICITY CALCULATIONS")  
-        print("="*70)
-        
+        print(f"\n" + "=" * 70)
+        print("STEP 7: ELASTICITY CALCULATIONS")
+        print("=" * 70)
+
         # Behavioral (odds) elasticity from Step 3 calibration
         behavioral_elasticity = -self.sigma
-        
+
         print(f"1) Behavioral (odds) elasticity w.r.t wedge:")
-        print(f"   d ln(q_R/q_N) / d ln(1+τ_e) = -σ = {behavioral_elasticity:.3f}")
-        
+        print(
+            f"   d ln(q_R/q_N) / d ln(1+τ_e) = -σ = {behavioral_elasticity:.3f}"
+        )
+
         # Revenue elasticity w.r.t VAT rate (finite differences)
         print(f"\n2) Revenue elasticity w.r.t VAT rate:")
         print(f"   Computing finite differences with ε = {epsilon_tau:.1%}...")
-        
+
         # Baseline revenue (from Step 6)
-        if not hasattr(self, 'revenues_old'):
+        if not hasattr(self, "revenues_old"):
             print("   Error: Run step6_revenue_mapping first")
             return
-            
+
         baseline_revenue = np.sum(self.revenues_old)
-        
-        # Simulate τ + ε 
+
+        # Simulate τ + ε
         tau_plus = tau_old + epsilon_tau
         self.run_policy_simulation(tau_new=tau_plus)
         revenue_plus = self.calculate_revenue_for_rate(tau_plus)
-        
+
         # Simulate τ - ε
-        tau_minus = tau_old - epsilon_tau  
+        tau_minus = tau_old - epsilon_tau
         self.run_policy_simulation(tau_new=tau_minus)
         revenue_minus = self.calculate_revenue_for_rate(tau_minus)
-        
+
         # Revenue elasticity = (dV/V) / (dτ/τ)
         dV_dtau = (revenue_plus - revenue_minus) / (2 * epsilon_tau)
         revenue_elasticity_rate = (dV_dtau * tau_old) / baseline_revenue
-        
+
         print(f"   dV/dτ = £{dV_dtau:,.0f}k per percentage point")
-        print(f"   Revenue elasticity w.r.t rate = {revenue_elasticity_rate:.3f}")
-        
+        print(
+            f"   Revenue elasticity w.r.t rate = {revenue_elasticity_rate:.3f}"
+        )
+
         # Revenue elasticity w.r.t threshold (finite differences)
         print(f"\n3) Revenue elasticity w.r.t threshold:")
-        print(f"   Computing finite differences with ε = £{epsilon_threshold}k...")
-        
+        print(
+            f"   Computing finite differences with ε = £{epsilon_threshold}k..."
+        )
+
         # Simulate T* + ε
         threshold_plus = self.T_star + epsilon_threshold
-        revenue_threshold_plus = self.calculate_revenue_for_threshold(threshold_plus)
-        
-        # Simulate T* - ε  
+        revenue_threshold_plus = self.calculate_revenue_for_threshold(
+            threshold_plus
+        )
+
+        # Simulate T* - ε
         threshold_minus = self.T_star - epsilon_threshold
-        revenue_threshold_minus = self.calculate_revenue_for_threshold(threshold_minus)
-        
+        revenue_threshold_minus = self.calculate_revenue_for_threshold(
+            threshold_minus
+        )
+
         # Revenue elasticity = (dV/V) / (dT*/T*)
-        dV_dthreshold = (revenue_threshold_plus - revenue_threshold_minus) / (2 * epsilon_threshold)
-        revenue_elasticity_threshold = (dV_dthreshold * self.T_star) / baseline_revenue
-        
+        dV_dthreshold = (revenue_threshold_plus - revenue_threshold_minus) / (
+            2 * epsilon_threshold
+        )
+        revenue_elasticity_threshold = (
+            dV_dthreshold * self.T_star
+        ) / baseline_revenue
+
         print(f"   dV/dT* = £{dV_dthreshold:,.0f}k per £1k threshold change")
-        print(f"   Revenue elasticity w.r.t threshold = {revenue_elasticity_threshold:.3f}")
-        
-        print(f"\n" + "-"*50)
+        print(
+            f"   Revenue elasticity w.r.t threshold = {revenue_elasticity_threshold:.3f}"
+        )
+
+        print(f"\n" + "-" * 50)
         print("STEP 7 RESULTS:")
-        print("-"*50)
+        print("-" * 50)
         print(f"Behavioral elasticity (σ): {-behavioral_elasticity:.3f}")
-        print(f"Revenue elasticity w.r.t rate: {revenue_elasticity_rate:.3f}")  
-        print(f"Revenue elasticity w.r.t threshold: {revenue_elasticity_threshold:.3f}")
-        
+        print(f"Revenue elasticity w.r.t rate: {revenue_elasticity_rate:.3f}")
+        print(
+            f"Revenue elasticity w.r.t threshold: {revenue_elasticity_threshold:.3f}"
+        )
+
         return {
-            'behavioral_elasticity': behavioral_elasticity,
-            'revenue_elasticity_rate': revenue_elasticity_rate,
-            'revenue_elasticity_threshold': revenue_elasticity_threshold
+            "behavioral_elasticity": behavioral_elasticity,
+            "revenue_elasticity_rate": revenue_elasticity_rate,
+            "revenue_elasticity_threshold": revenue_elasticity_threshold,
         }
-    
+
     def run_policy_simulation(self, tau_new):
         """Helper: Run quick policy simulation for elasticity calculation"""
         # Recompute effective wedge for new rate
         lambda_param = 0.75  # B2C share assumption
-        rho = 0.50          # Pass-through assumption  
-        s_c = 0.45          # Input cost share
-        v = 0.70            # VAT-able input share
-        
+        rho = 0.50  # Pass-through assumption
+        s_c = 0.45  # Input cost share
+        v = 0.70  # VAT-able input share
+
         tau_e_new = lambda_param * (1 - rho) * tau_new - tau_new * s_c * v
-        
+
         # Recompute displaced share
-        Pi_new = 1 - (1 + tau_e_new)**(-self.sigma)
-        
+        Pi_new = 1 - (1 + tau_e_new) ** (-self.sigma)
+
         # Update firm mappings (simplified)
         self.Pi_simulation = Pi_new
-    
+
     def calculate_revenue_for_rate(self, tau_rate):
         """Helper: Calculate total revenue for given VAT rate"""
         theta = 0.85
-        s_c = 0.45  
+        s_c = 0.45
         v = 0.70
-        
+
         total_revenue = 0
         for T_old, T_new in self.firm_mappings[:10000]:  # Sample for speed
             V = tau_rate * (theta * T_new - v * s_c * T_new)
             total_revenue += max(0, V)
-        
+
         # Scale up to full population
         scaling_factor = len(self.firm_mappings) / 10000
         return total_revenue * scaling_factor
-    
+
     def calculate_revenue_for_threshold(self, new_threshold):
         """Helper: Calculate total revenue for given threshold"""
         # Simplified: assume threshold change affects firms near boundary
         threshold_effect = (new_threshold - self.T_star) / self.T_star
-        
+
         total_revenue = 0
         tau_rate = 0.20
         theta = 0.85
         s_c = 0.45
         v = 0.70
-        
+
         for T_old, T_new in self.firm_mappings[:10000]:  # Sample for speed
             # Adjust turnover based on threshold change (simplified)
             if 80 <= T_old <= 100:  # Firms near threshold
-                T_adjusted = T_new * (1 + 0.1 * threshold_effect)  # Small adjustment
+                T_adjusted = T_new * (
+                    1 + 0.1 * threshold_effect
+                )  # Small adjustment
             else:
                 T_adjusted = T_new
-                
+
             V = tau_rate * (theta * T_adjusted - v * s_c * T_adjusted)
             total_revenue += max(0, V)
-        
-        # Scale up to full population  
+
+        # Scale up to full population
         scaling_factor = len(self.firm_mappings) / 10000
         return total_revenue * scaling_factor
-    
+
     def map_cf_to_new_policy(self, T_cf):
         """Helper: Map counterfactual turnovers to new policy turnovers using Step 5 distribution"""
         # Simplified mapping using the Step 5 new policy distribution
         T_new = np.zeros_like(T_cf)
-        
+
         for i, turnover in enumerate(T_cf):
             # Find closest bin
             bin_idx = np.argmin(np.abs(self.bin_centers - turnover))
-            
+
             # Use ratio of new policy to counterfactual at this point
             if self.f_cf[bin_idx] > 0:
                 ratio = self.f_new_policy[bin_idx] / self.f_cf[bin_idx]
                 # Apply small adjustment based on ratio
-                T_new[i] = turnover * (0.95 + 0.1 * ratio)  # Conservative adjustment
+                T_new[i] = turnover * (
+                    0.95 + 0.1 * ratio
+                )  # Conservative adjustment
             else:
                 T_new[i] = turnover
-        
+
         return T_new
-    
+
     def create_sample_firm_mappings(self):
         """Create sample firm mappings for Steps 6-7 demonstration"""
         np.random.seed(42)  # Reproducible results
-        
-        # Create a realistic sample of UK firms 
+
+        # Create a realistic sample of UK firms
         n_firms = 100000  # Sample size for demonstration
-        
+
         # Generate realistic firm turnover distribution
         # UK firms: many small firms, few large ones
         turnovers_old = []
         turnovers_new = []
-        
+
         for i in range(n_firms):
             # Generate old turnover with realistic distribution
             if np.random.random() < 0.7:  # 70% small firms
@@ -939,51 +1106,57 @@ def create_sample_firm_mappings(self):
                 T_old = np.random.lognormal(np.log(80), 0.6)  # Mean ~£120k
             else:  # 10% large firms
                 T_old = np.random.lognormal(np.log(200), 0.8)  # Mean ~£400k
-            
+
             T_old = max(10, min(500, T_old))  # Constrain to £10k-£500k
-            
+
             # Apply mapping based on Step 4/5 logic
             if 65 <= T_old <= 110:  # Near thresholds - apply bunching effects
-                if hasattr(self, 'f_new_policy') and hasattr(self, 'f_cf'):
+                if hasattr(self, "f_new_policy") and hasattr(self, "f_cf"):
                     # Use Step 5 mapping
                     bin_idx = np.argmin(np.abs(self.bin_centers - T_old))
                     if self.f_cf[bin_idx] > 0:
                         ratio = self.f_new_policy[bin_idx] / self.f_cf[bin_idx]
-                        T_new = T_old * (0.95 + 0.1 * ratio)  # Small adjustment
+                        T_new = T_old * (
+                            0.95 + 0.1 * ratio
+                        )  # Small adjustment
                     else:
                         T_new = T_old
                 else:
                     # Default mapping for threshold change (90k → 100k)
                     if T_old < 90:
-                        T_new = T_old * 1.02  # Small increase due to threshold change
+                        T_new = (
+                            T_old * 1.02
+                        )  # Small increase due to threshold change
                     else:
                         T_new = T_old * 0.98  # Small decrease
             else:
                 # Outside bunching region - minimal change
                 T_new = T_old * np.random.normal(1.0, 0.01)  # 1% noise
-            
+
             turnovers_old.append(T_old)
             turnovers_new.append(max(10, T_new))  # Minimum £10k
-        
+
         # Store mappings
         self.firm_mappings = list(zip(turnovers_old, turnovers_new))
-        
-        print(f"Created {len(self.firm_mappings):,} sample firm mappings for Steps 6-7")
+
+        print(
+            f"Created {len(self.firm_mappings):,} sample firm mappings for Steps 6-7"
+        )
         print(f"  Mean old turnover: £{np.mean(turnovers_old):.1f}k")
         print(f"  Mean new turnover: £{np.mean(turnovers_new):.1f}k")
-        
+
     def print_results(self):
         """Print the analysis results."""
-        
-        has_step4 = hasattr(self, 'step4_results')
+
+        has_step4 = hasattr(self, "step4_results")
         step_range = "Steps 1-4" if has_step4 else "Steps 1-3"
-        
+
         print(f"\n{step_range} Results Summary:")
         print("=" * 50)
         print(f"Threshold T*: £{self.T_star:,}k")
         print(f"Window: -£{self.W_left:,}k/+£{self.W_right:,}k")
         print(f"Analysis range: [{self.T_lower}, {self.T_upper}]")
-        
+
         print(f"\nStep 1 - Bunching Statistics:")
         print("-" * 30)
         print(f"  Masses in window:")
@@ -994,186 +1167,296 @@ def print_results(self):
         print(f"  Excess mass (E) = {self.E:.3f}")
         print(f"  Missing mass (Δ_R) = {self.Delta_R:.3f}")
         print(f"  Bunching ratio (b) = {self.b:.3f}")
-        
+
         print(f"\nStep 2 - Effective Wedge:")
         print("-" * 30)
         print(f"  τₑ = {self.tau_e:.4f}")
-        
+
         print(f"\nStep 3 - Substitution Elasticity:")
         print("-" * 30)
         print(f"  σ = {self.sigma:.4f}")
-        
+
         if has_step4:
             print(f"\nStep 4 - Micro-level Mapping:")
             print("-" * 30)
             print(f"  Aggregate displaced share Π = {self.Pi:.4f}")
             print(f"  Sample firms analyzed: {len(self.step4_results)}")
-            print(f"  Avg bunching probability: {self.step4_results['pi'].mean():.4f}")
-            print(f"  Avg displacement: £{self.step4_results['displacement'].mean():.2f}k")
-        
+            print(
+                f"  Avg bunching probability: {self.step4_results['pi'].mean():.4f}"
+            )
+            print(
+                f"  Avg displacement: £{self.step4_results['displacement'].mean():.2f}k"
+            )
+
     def plot_results(self, include_step5=False):
         """Create visualization of Steps 1-4 complete analysis with advanced mapping, optionally including Step 5."""
-        
+
         _, ax = plt.subplots(figsize=(12, 8))
-        
+
         # Plot observed and counterfactual densities (exclude first point T=20k, x=-70)
         y_dist = self.bin_centers - self.T_star
-        
+
         # Skip first point (index 0)
         plot_mask = slice(1, None)
         y_dist_plot = y_dist[plot_mask]
-        
-        ax.plot(y_dist_plot, self.f_obs[plot_mask], 'o-', color='blue', markersize=3, 
-                linewidth=1, label='Original Observed f^obs(T) [£90k]', alpha=0.7)
-        ax.plot(y_dist_plot, self.f_cf[plot_mask], '-', color='red', linewidth=2, 
-                label='Counterfactual f^cf(T) [No Notch]')
-        
+
+        ax.plot(
+            y_dist_plot,
+            self.f_obs[plot_mask],
+            "o-",
+            color="blue",
+            markersize=3,
+            linewidth=1,
+            label="Original Observed f^obs(T) [£90k]",
+            alpha=0.7,
+        )
+        ax.plot(
+            y_dist_plot,
+            self.f_cf[plot_mask],
+            "-",
+            color="red",
+            linewidth=2,
+            label="Counterfactual f^cf(T) [No Notch]",
+        )
+
         # Add Step 4 advanced mapping result (also skip first point)
         f_step4 = create_advanced_probabilistic_mapping(self)
-        ax.plot(y_dist_plot, f_step4[plot_mask], '-', color='green', linewidth=2, 
-                label='Step 4 Advanced Mapping Result', alpha=0.8)
-        
+        ax.plot(
+            y_dist_plot,
+            f_step4[plot_mask],
+            "-",
+            color="green",
+            linewidth=2,
+            label="Step 4 Advanced Mapping Result",
+            alpha=0.8,
+        )
+
         # Add Step 5 new policy result if available (also skip first point)
-        if include_step5 and hasattr(self, 'f_new_policy'):
-            ax.plot(y_dist_plot, self.f_new_policy[plot_mask], '-', color='purple', linewidth=2, 
-                    label=f'Step 5 New Policy [£{self.T_star_new}k]', alpha=0.8)
-        
+        if include_step5 and hasattr(self, "f_new_policy"):
+            ax.plot(
+                y_dist_plot,
+                self.f_new_policy[plot_mask],
+                "-",
+                color="purple",
+                linewidth=2,
+                label=f"Step 5 New Policy [£{self.T_star_new}k]",
+                alpha=0.8,
+            )
+
         # Shade the window (also skip first point)
-        window_mask = (self.bin_centers >= self.T_lower) & (self.bin_centers <= self.T_upper)
-        window_mask_plot = window_mask[plot_mask]  # Apply same exclusion to window mask
-        ax.fill_between(y_dist_plot[window_mask_plot], 0, self.f_obs[plot_mask][window_mask_plot], 
-                       alpha=0.3, color='lightblue', label=f'Window -£{self.W_left}k/+£{self.W_right}k')
-        
-        # Add vertical lines  
-        ax.axvline(x=0, color='black', linestyle='-', linewidth=2, label='Original Threshold £90k')
-        ax.axvline(x=-self.W_left, color='gray', linestyle='--', alpha=0.5, label=f'£90k -£{self.W_left}k/+£{self.W_right}k')
-        ax.axvline(x=self.W_right, color='gray', linestyle='--', alpha=0.5)
-        
+        window_mask = (self.bin_centers >= self.T_lower) & (
+            self.bin_centers <= self.T_upper
+        )
+        window_mask_plot = window_mask[
+            plot_mask
+        ]  # Apply same exclusion to window mask
+        ax.fill_between(
+            y_dist_plot[window_mask_plot],
+            0,
+            self.f_obs[plot_mask][window_mask_plot],
+            alpha=0.3,
+            color="lightblue",
+            label=f"Window -£{self.W_left}k/+£{self.W_right}k",
+        )
+
+        # Add vertical lines
+        ax.axvline(
+            x=0,
+            color="black",
+            linestyle="-",
+            linewidth=2,
+            label="Original Threshold £90k",
+        )
+        ax.axvline(
+            x=-self.W_left,
+            color="gray",
+            linestyle="--",
+            alpha=0.5,
+            label=f"£90k -£{self.W_left}k/+£{self.W_right}k",
+        )
+        ax.axvline(x=self.W_right, color="gray", linestyle="--", alpha=0.5)
+
         # Add new threshold line if Step 5 is included
-        if include_step5 and hasattr(self, 'T_star_new'):
-            new_threshold_dist = self.T_star_new - self.T_star  # Distance from original
-            ax.axvline(x=new_threshold_dist, color='purple', linestyle='-', linewidth=2, 
-                      label=f'New Threshold £{self.T_star_new}k', alpha=0.8)
-        
+        if include_step5 and hasattr(self, "T_star_new"):
+            new_threshold_dist = (
+                self.T_star_new - self.T_star
+            )  # Distance from original
+            ax.axvline(
+                x=new_threshold_dist,
+                color="purple",
+                linestyle="-",
+                linewidth=2,
+                label=f"New Threshold £{self.T_star_new}k",
+                alpha=0.8,
+            )
+
         # Add annotations
-        ax.text(0.02, 0.95, f'Bunching ratio b = {self.b:.3f}', 
-                transform=ax.transAxes, fontsize=11, 
-                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue"))
-        ax.text(0.02, 0.89, f'Effective wedge τₑ = {self.tau_e:.3f}', 
-                transform=ax.transAxes, fontsize=11, 
-                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow"))
-        ax.text(0.02, 0.83, f'Substitution elasticity σ = {self.sigma:.3f}', 
-                transform=ax.transAxes, fontsize=11, 
-                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgreen"))
-        ax.text(0.02, 0.77, f'Excess mass E = {self.E:.0f}', 
-                transform=ax.transAxes, fontsize=11, 
-                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral"))
-        
+        ax.text(
+            0.02,
+            0.95,
+            f"Bunching ratio b = {self.b:.3f}",
+            transform=ax.transAxes,
+            fontsize=11,
+            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue"),
+        )
+        ax.text(
+            0.02,
+            0.89,
+            f"Effective wedge τₑ = {self.tau_e:.3f}",
+            transform=ax.transAxes,
+            fontsize=11,
+            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow"),
+        )
+        ax.text(
+            0.02,
+            0.83,
+            f"Substitution elasticity σ = {self.sigma:.3f}",
+            transform=ax.transAxes,
+            fontsize=11,
+            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgreen"),
+        )
+        ax.text(
+            0.02,
+            0.77,
+            f"Excess mass E = {self.E:.0f}",
+            transform=ax.transAxes,
+            fontsize=11,
+            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral"),
+        )
+
         # Add Step 4 info
-        pi_mean = np.mean([self.compute_local_bunching_probability(T) 
-                          for T in self.bin_centers[window_mask]])
-        ax.text(0.02, 0.71, f'Avg bunching prob π̄ = {pi_mean:.3f}', 
-                transform=ax.transAxes, fontsize=11, 
-                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightpink"))
-        
+        pi_mean = np.mean(
+            [
+                self.compute_local_bunching_probability(T)
+                for T in self.bin_centers[window_mask]
+            ]
+        )
+        ax.text(
+            0.02,
+            0.71,
+            f"Avg bunching prob π̄ = {pi_mean:.3f}",
+            transform=ax.transAxes,
+            fontsize=11,
+            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightpink"),
+        )
+
         # Add Step 5 info if included
-        if include_step5 and hasattr(self, 'Pi_new'):
-            ax.text(0.02, 0.65, f'New displaced share Π\' = {self.Pi_new:.3f}', 
-                    transform=ax.transAxes, fontsize=11, 
-                    bbox=dict(boxstyle="round,pad=0.3", facecolor="plum"))
-        
+        if include_step5 and hasattr(self, "Pi_new"):
+            ax.text(
+                0.02,
+                0.65,
+                f"New displaced share Π' = {self.Pi_new:.3f}",
+                transform=ax.transAxes,
+                fontsize=11,
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="plum"),
+            )
+
         # Formatting
-        ax.set_xlabel('Distance from Original Threshold £90k (£1,000)', fontsize=12)
-        ax.set_ylabel('Density f(T)', fontsize=12)
-        
+        ax.set_xlabel(
+            "Distance from Original Threshold £90k (£1,000)", fontsize=12
+        )
+        ax.set_ylabel("Density f(T)", fontsize=12)
+
         if include_step5:
-            ax.set_title('Steps 1-5: Complete Pipeline with New Policy', fontsize=14, fontweight='bold')
+            ax.set_title(
+                "Steps 1-5: Complete Pipeline with New Policy",
+                fontsize=14,
+                fontweight="bold",
+            )
         else:
-            ax.set_title('Steps 1-4: Complete Analysis with Advanced Mapping', fontsize=14, fontweight='bold')
-            
+            ax.set_title(
+                "Steps 1-4: Complete Analysis with Advanced Mapping",
+                fontsize=14,
+                fontweight="bold",
+            )
+
         ax.grid(True, alpha=0.3)
         ax.legend(fontsize=9)
         ax.set_xlim(-70, 50)
-        
+
         # Save plot
-        output_path = Path(__file__).parent / 'bunching_analysis.png'
-        plt.savefig(output_path, dpi=300, bbox_inches='tight')
+        output_path = Path(__file__).parent / "bunching_analysis.png"
+        plt.savefig(output_path, dpi=300, bbox_inches="tight")
         print(f"\nPlot saved: {output_path}")
-        
-    def apply_step4_to_synthetic_firms(self, output_file='synthetic_firms_step4_mapped.csv'):
+
+    def apply_step4_to_synthetic_firms(
+        self, output_file="synthetic_firms_step4_mapped.csv"
+    ):
         """
         Apply advanced Step 4 mapping to synthetic firms and save results.
         """
-        
+
         print("\nApplying Step 4 Advanced Mapping to Synthetic Firms")
         print("=" * 60)
-        
+
         # Load synthetic firms data
         try:
-            df = pd.read_csv('synthetic_firms_turnover.csv')
-            print(f"Loaded {len(df):,} synthetic firms from synthetic_firms_turnover.csv")
+            df = pd.read_csv("synthetic_firms_turnover.csv")
+            print(
+                f"Loaded {len(df):,} synthetic firms from synthetic_firms_turnover.csv"
+            )
         except FileNotFoundError:
             print("Error: synthetic_firms_turnover.csv not found")
             return None
-        
+
         # Apply Step 4 mapping to each firm's turnover
         print("Applying Step 4 mapping to individual firms...")
-        
+
         # Get observed turnovers
-        T_obs = df['annual_turnover_k'].values
-        
+        T_obs = df["annual_turnover_k"].values
+
         # Apply the advanced probabilistic mapping
         print("Creating advanced probabilistic mapping...")
         f_mapped = create_advanced_probabilistic_mapping(self)
-        
+
         # For individual firms, we need to apply the mapping stochastically
         print("Computing counterfactual turnovers for each firm...")
-        
+
         # Initialize counterfactual turnovers
         T_cf = np.zeros_like(T_obs)
-        
+
         # Create mapping probabilities for each turnover level
         mapping_probs = {}
         bin_width = 1.0  # £1k bins
-        
+
         for i, bin_center in enumerate(self.bin_centers):
             if self.T_lower <= bin_center <= self.T_star:
                 # Compute bunching probability for this turnover level
                 pi_val = self.compute_local_bunching_probability(bin_center)
-                
+
                 # Get displacement target
                 T_cf_target = self.compute_counterfactual_mapping(bin_center)
-                
+
                 mapping_probs[bin_center] = {
-                    'pi': pi_val,
-                    'cf_target': T_cf_target,
-                    'stay_prob': 1 - pi_val,
-                    'displace_prob': pi_val
+                    "pi": pi_val,
+                    "cf_target": T_cf_target,
+                    "stay_prob": 1 - pi_val,
+                    "displace_prob": pi_val,
                 }
             else:
                 # Outside bunching region - no displacement
                 mapping_probs[bin_center] = {
-                    'pi': 0.0,
-                    'cf_target': bin_center,
-                    'stay_prob': 1.0,
-                    'displace_prob': 0.0
+                    "pi": 0.0,
+                    "cf_target": bin_center,
+                    "stay_prob": 1.0,
+                    "displace_prob": 0.0,
                 }
-        
+
         # Apply mapping to each firm
         np.random.seed(42)  # For reproducible results
         n_displaced = 0
-        
+
         for idx, turnover in enumerate(T_obs):
             # Find closest bin center
             bin_center = round(turnover)  # Round to nearest £1k
-            
+
             if bin_center in mapping_probs:
                 prob_info = mapping_probs[bin_center]
-                
+
                 # Determine if this firm gets displaced
-                if np.random.random() < prob_info['displace_prob']:
+                if np.random.random() < prob_info["displace_prob"]:
                     # Firm gets displaced
-                    T_cf[idx] = prob_info['cf_target']
+                    T_cf[idx] = prob_info["cf_target"]
                     n_displaced += 1
                 else:
                     # Firm stays at original location
@@ -1181,141 +1464,145 @@ def apply_step4_to_synthetic_firms(self, output_file='synthetic_firms_step4_mapp
             else:
                 # Turnover outside our analysis range - no change
                 T_cf[idx] = turnover
-            
+
             # Progress indicator
             if (idx + 1) % 100000 == 0:
                 print(f"  Processed {idx + 1:,}/{len(T_obs):,} firms...")
-        
+
         # Add results to dataframe
         df_mapped = df.copy()
-        df_mapped['step4_counterfactual_turnover_k'] = T_cf
-        df_mapped['step4_displacement_k'] = T_cf - T_obs
-        
+        df_mapped["step4_counterfactual_turnover_k"] = T_cf
+        df_mapped["step4_displacement_k"] = T_cf - T_obs
+
         # Store firm mappings for Steps 6-7 (old_turnover, new_turnover)
         # For Step 5: use Step 5 new policy turnovers if available
-        if hasattr(self, 'f_new_policy'):
+        if hasattr(self, "f_new_policy"):
             # Map counterfactual turnovers to new policy turnovers (simplified)
             T_new_policy = self.map_cf_to_new_policy(T_cf)
         else:
             T_new_policy = T_cf  # If no Step 5, use counterfactual as "new"
-        
+
         self.firm_mappings = list(zip(T_obs, T_new_policy))
-        
+
         # Save results
         df_mapped.to_csv(output_file, index=False)
-        
+
         # Print summary statistics
         displacement = T_cf - T_obs
         nonzero_displacement = displacement[displacement != 0]
-        
+
         print(f"\nStep 4 Advanced Mapping Complete!")
         print("=" * 40)
         print(f"Output file: {output_file}")
         print(f"Total firms processed: {len(df):,}")
-        print(f"Firms displaced: {n_displaced:,} ({100*n_displaced/len(df):.2f}%)")
+        print(
+            f"Firms displaced: {n_displaced:,} ({100*n_displaced/len(df):.2f}%)"
+        )
         print(f"Mean displacement: £{displacement.mean():.3f}k")
         if len(nonzero_displacement) > 0:
-            print(f"Mean nonzero displacement: £{nonzero_displacement.mean():.3f}k")
+            print(
+                f"Mean nonzero displacement: £{nonzero_displacement.mean():.3f}k"
+            )
             print(f"Max displacement: £{displacement.max():.3f}k")
             print(f"Min displacement: £{displacement.min():.3f}k")
-        
+
         # Weighted statistics
-        obs_mean = np.average(T_obs, weights=df['weight'])
-        cf_mean = np.average(T_cf, weights=df['weight'])
-        
+        obs_mean = np.average(T_obs, weights=df["weight"])
+        cf_mean = np.average(T_cf, weights=df["weight"])
+
         print(f"\nWeighted Statistics:")
         print(f"  Original mean turnover: £{obs_mean:.1f}k")
         print(f"  Step 4 mapped mean turnover: £{cf_mean:.1f}k")
         print(f"  Mean change: £{cf_mean - obs_mean:.3f}k")
-        
+
         return df_mapped
-        
+
     def get_results_dict(self):
         """Return results as dictionary for use in subsequent steps."""
-        
+
         return {
-            'T_star': self.T_star,
-            'W_left': self.W_left,
-            'W_right': self.W_right,
-            'bin_centers': self.bin_centers,
-            'f_obs': self.f_obs,
-            'f_cf': self.f_cf,
-            'F_obs': self.F_obs,
-            'F_cf': self.F_cf,
-            'q_N_obs': self.q_N_obs,
-            'q_R_obs': self.q_R_obs,
-            'q_N_cf': self.q_N_cf,
-            'q_R_cf': self.q_R_cf,
-            'E': self.E,
-            'Delta_R': self.Delta_R,
-            'b': self.b,
-            'tau_e': self.tau_e,
-            'sigma': self.sigma
+            "T_star": self.T_star,
+            "W_left": self.W_left,
+            "W_right": self.W_right,
+            "bin_centers": self.bin_centers,
+            "f_obs": self.f_obs,
+            "f_cf": self.f_cf,
+            "F_obs": self.F_obs,
+            "F_cf": self.F_cf,
+            "q_N_obs": self.q_N_obs,
+            "q_R_obs": self.q_R_obs,
+            "q_N_cf": self.q_N_cf,
+            "q_R_cf": self.q_R_cf,
+            "E": self.E,
+            "Delta_R": self.Delta_R,
+            "b": self.b,
+            "tau_e": self.tau_e,
+            "sigma": self.sigma,
         }
 
 
 def main():
     """Run Steps 1-4 analysis."""
-    
+
     # ==========================================================================
     # CONFIGURATION: Set your asymmetric windows here
     # ==========================================================================
     # Counterfactual analysis windows (around original £90k threshold)
-    counterfactual_window_left = 25    # £10k below threshold (£80k-£90k)
-    counterfactual_window_right = 20   # £10k above threshold (£90k-£100k)
-    
-    # New policy analysis windows (around new £100k threshold)  
-    new_policy_window_left = 25        # £10k below new threshold (£90k-£100k)
-    new_policy_window_right = 20       # £10k above new threshold (£100k-£110k)
+    counterfactual_window_left = 25  # £10k below threshold (£80k-£90k)
+    counterfactual_window_right = 20  # £10k above threshold (£90k-£100k)
+
+    # New policy analysis windows (around new £100k threshold)
+    new_policy_window_left = 25  # £10k below new threshold (£90k-£100k)
+    new_policy_window_right = 20  # £10k above new threshold (£100k-£110k)
     # ==========================================================================
-    
+
     # Initialize analysis with asymmetric windows
     analysis = CounterfactualBunchingAnalysis(
-        threshold=90, 
+        threshold=90,
         window_left=counterfactual_window_left,
         window_right=counterfactual_window_right,
         new_policy_window_left=new_policy_window_left,
-        new_policy_window_right=new_policy_window_right
+        new_policy_window_right=new_policy_window_right,
     )
-    
+
     # Run complete analysis with Steps 1-5
     print("STEPS 1-5: COMPLETE BUNCHING ANALYSIS PIPELINE")
     print("Step 1: Counterfactual & bunching statistics")
     print("Step 2: Effective wedge τₑ")
-    print("Step 3: Substitution elasticity σ")  
+    print("Step 3: Substitution elasticity σ")
     print("Step 4: Advanced probabilistic mapping (no-notch counterfactual)")
     print("Step 5: Forward map to NEW policy (£100k threshold)")
-    print("="*70)
+    print("=" * 70)
     results = analysis.run_analysis(tau_e=0.05, include_step4=True)
-    
+
     # Show detailed Step 4 results
-    if hasattr(analysis, 'step4_results'):
+    if hasattr(analysis, "step4_results"):
         print("\nDetailed Step 4 Results:")
         print("-" * 40)
         step4_df = analysis.step4_results
         print(step4_df.round(4).to_string(index=False))
-        
+
         # Show example calculations for a few firms
         print(f"\nExample firm mappings:")
         example_firms = [82, 85, 88, 90]  # Firms at different positions
-        
+
         for T_obs in example_firms:
             if analysis.T_lower <= T_obs <= analysis.T_star:
                 pi = analysis.compute_local_bunching_probability(T_obs)
-                u = analysis.compute_rank_among_bunchers(T_obs) 
+                u = analysis.compute_rank_among_bunchers(T_obs)
                 T_cf = analysis.compute_counterfactual_mapping(T_obs)
-                
+
                 print(f"  Firm at £{T_obs}k:")
                 print(f"    π(T^obs) = {pi:.4f} (bunching probability)")
                 print(f"    u(T^obs) = {u:.4f} (rank among bunchers)")
                 print(f"    T^cf = £{T_cf:.2f}k (counterfactual turnover)")
                 print(f"    Displacement = £{T_cf - T_obs:.2f}k")
                 print()
-    
+
     # Visualization removed as requested
-    
+
     # Mapping check visualization removed as requested
-    
+
     return results
 
 
@@ -1323,86 +1610,97 @@ def main():
 # STEP 4: ADVANCED PROBABILISTIC MAPPING - MAIN FUNCTION
 # ============================================================================
 
-def create_advanced_probabilistic_mapping(analysis, decay_rate=0.01, spread_factor=3.0):
+
+def create_advanced_probabilistic_mapping(
+    analysis, decay_rate=0.01, spread_factor=3.0
+):
     """
     Step 4: Advanced Probabilistic Mapping - MAIN STEP 4 FUNCTION
-    
+
     This is the primary Step 4 implementation that creates the smooth counterfactual
     distribution shown as the green curve in steps1234_complete_analysis.png.
-    
+
     Creates comprehensive probabilistic mapping that redistributes firms across
     the full range to match the theoretical counterfactual distribution.
     """
-    
+
     # Start with the observed distribution as base
     mapped_distribution = np.copy(analysis.f_obs)
-    
+
     print(f"Creating advanced probabilistic mapping:")
     print(f"- Decay rate: {decay_rate} (controls displacement spread)")
-    print(f"- Spread factor: {spread_factor} (controls overall redistribution)")
-    
+    print(
+        f"- Spread factor: {spread_factor} (controls overall redistribution)"
+    )
+
     # Skip first point (T=20k, x=-70) from all calculations
     print(f"Skipping first point (x=-70, T=20k) from mapping calculations")
-    
+
     # For each bin, calculate how much it needs to change to match counterfactual
-    deficit = analysis.f_cf - analysis.f_obs  # Positive = need more, negative = have excess
-    
+    deficit = (
+        analysis.f_cf - analysis.f_obs
+    )  # Positive = need more, negative = have excess
+
     # For each bin in the analysis (skip first point)
     for i, T_obs in enumerate(analysis.bin_centers):
         if i == 0:  # Skip first point (T=20k)
             continue
         original_density = analysis.f_obs[i]
         target_density = analysis.f_cf[i]
-        
+
         if original_density > target_density:
             # This bin has excess firms - redistribute the excess
             excess_firms = original_density - target_density
-            
+
             # Set this bin to its target density
             mapped_distribution[i] = target_density
-            
+
             # Redistribute excess firms across range (excluding first point)
             if excess_firms > 0:
                 # Find locations that need more firms (positive deficit, skip first point)
-                deficit_mask = (deficit > 0) & (np.arange(len(deficit)) > 0)  # Exclude index 0
+                deficit_mask = (deficit > 0) & (
+                    np.arange(len(deficit)) > 0
+                )  # Exclude index 0
                 target_locations = analysis.bin_centers[deficit_mask]
                 target_deficits = deficit[deficit_mask]
-                
+
                 if len(target_locations) > 0:
                     # Create probability weights based on distance and deficit size
                     distances = np.abs(target_locations - T_obs)
                     distance_weights = np.exp(-decay_rate * distances)
                     deficit_weights = target_deficits * spread_factor
-                    
+
                     # Combine distance and deficit weights
                     combined_weights = distance_weights * deficit_weights
                     probs = combined_weights / np.sum(combined_weights)
-                    
+
                     # Distribute excess firms
                     for j, target_turnover in enumerate(target_locations):
-                        target_idx = np.where(analysis.bin_centers == target_turnover)[0][0]
+                        target_idx = np.where(
+                            analysis.bin_centers == target_turnover
+                        )[0][0]
                         redistribution = excess_firms * probs[j]
                         mapped_distribution[target_idx] += redistribution
-                        
+
                         # Update deficit to track progress
                         deficit[target_idx] -= redistribution
         # Bins without excess keep their observed density (already set in initialization)
-    
+
     # Check first point before scaling
     print(f"Before scaling: First point mapped={mapped_distribution[0]:.1f}")
-    
+
     # Final adjustment: scale to match total counterfactual mass
     total_cf = np.sum(analysis.f_cf)
     total_mapped = np.sum(mapped_distribution)
     scaling_factor = total_cf / total_mapped if total_mapped > 0 else 1.0
     print(f"Scaling factor: {scaling_factor:.4f}")
-    
+
     if total_mapped > 0:
         mapped_distribution *= scaling_factor
-    
+
     # Check first point after scaling
     print(f"After scaling: First point mapped={mapped_distribution[0]:.1f}")
-    
+
     return mapped_distribution
 
 
@@ -1410,34 +1708,38 @@ def create_advanced_probabilistic_mapping(analysis, decay_rate=0.01, spread_fact
 # STEP 4: SUPPORTING FUNCTIONS FOR ADVANCED MAPPING
 # ============================================================================
 
+
 def optimize_advanced_parameters(analysis):
     """Optimize parameters for advanced probabilistic mapping."""
-    
+
     from scipy.optimize import minimize
-    
+
     def mapping_error(params):
         decay_rate, spread_factor = params
-        mapped_dist = create_advanced_probabilistic_mapping(analysis, decay_rate, spread_factor)
-        error = np.sum((mapped_dist - analysis.f_cf)**2)
+        mapped_dist = create_advanced_probabilistic_mapping(
+            analysis, decay_rate, spread_factor
+        )
+        error = np.sum((mapped_dist - analysis.f_cf) ** 2)
         return error
-    
+
     print("Optimizing advanced probabilistic mapping parameters...")
-    
+
     # Optimize decay rate (0.001 to 0.1) and spread factor (0.5 to 5.0)
-    result = minimize(mapping_error, x0=[0.01, 2.0], 
-                     bounds=[(0.001, 0.1), (0.5, 5.0)],
-                     method='L-BFGS-B')
-    
+    result = minimize(
+        mapping_error,
+        x0=[0.01, 2.0],
+        bounds=[(0.001, 0.1), (0.5, 5.0)],
+        method="L-BFGS-B",
+    )
+
     optimal_decay = result.x[0]
     optimal_spread = result.x[1]
-    
+
     print(f"Optimal decay rate: {optimal_decay:.4f}")
     print(f"Optimal spread factor: {optimal_spread:.2f}")
-    
-    return optimal_decay, optimal_spread
-
 
+    return optimal_decay, optimal_spread
 
 
 if __name__ == "__main__":
-    results = main()
\ No newline at end of file
+    results = main()
diff --git a/policyengine_uk/firm_analysis/plot_example.py b/policyengine_uk/firm_analysis/plot_example.py
index 8b555aa9a..bbe903725 100644
--- a/policyengine_uk/firm_analysis/plot_example.py
+++ b/policyengine_uk/firm_analysis/plot_example.py
@@ -8,48 +8,81 @@
 import numpy as np
 from pathlib import Path
 
+
 def create_turnover_plot():
     """Create a simple turnover distribution chart."""
-    
+
     # Load data
-    data_path = Path(__file__).parent / 'synthetic_firms_turnover.csv'
+    data_path = Path(__file__).parent / "synthetic_firms_turnover.csv"
     df = pd.read_csv(data_path)
-    
+
     # Create bins from 1k to 300k
     bin_edges = np.arange(0.5, 300.5, 1.0)  # 1k intervals
-    
+
     # Calculate weighted histogram
-    hist, _ = np.histogram(df['annual_turnover_k'], bins=bin_edges, weights=df['weight'])
-    
+    hist, _ = np.histogram(
+        df["annual_turnover_k"], bins=bin_edges, weights=df["weight"]
+    )
+
     # Create plot
     plt.figure(figsize=(15, 6))
     x_positions = np.arange(len(hist))
-    
-    plt.bar(x_positions, hist, color='lightblue', alpha=0.7, edgecolor='black', linewidth=0.1)
-    
-    plt.xlabel('Annual Turnover (£k)')
-    plt.ylabel('Number of Firms (2023-24)')
-    
+
+    plt.bar(
+        x_positions,
+        hist,
+        color="lightblue",
+        alpha=0.7,
+        edgecolor="black",
+        linewidth=0.1,
+    )
+
+    plt.xlabel("Annual Turnover (£k)")
+    plt.ylabel("Number of Firms (2023-24)")
+
     # Set x-axis labels every 10k
-    label_positions = [i for i in range(9, len(hist), 10)]  # Start at 10k (index 9)
-    label_texts = [f'{i+1}' for i in label_positions]
+    label_positions = [
+        i for i in range(9, len(hist), 10)
+    ]  # Start at 10k (index 9)
+    label_texts = [f"{i+1}" for i in label_positions]
     plt.xticks(label_positions, label_texts)
-    
+
     # Add vertical lines at 85k and 150k
-    plt.axvline(x=84, color='red', linestyle='--', alpha=0.7, linewidth=2)  # 85k threshold
-    plt.axvline(x=149, color='red', linestyle='--', alpha=0.7, linewidth=2)  # 150k threshold
-    
+    plt.axvline(
+        x=84, color="red", linestyle="--", alpha=0.7, linewidth=2
+    )  # 85k threshold
+    plt.axvline(
+        x=149, color="red", linestyle="--", alpha=0.7, linewidth=2
+    )  # 150k threshold
+
     # Add labels for the vertical lines
-    plt.text(85, max(hist) * 0.8, 'VAT Threshold', rotation=0, color='red', fontsize=8, ha='left')
-    plt.text(150, max(hist) * 0.8, 'VAT Flat Rate Scheme', rotation=0, color='red', fontsize=8, ha='left')
-    
-    plt.grid(axis='y', alpha=0.3, linestyle='--')
-    
+    plt.text(
+        85,
+        max(hist) * 0.8,
+        "VAT Threshold",
+        rotation=0,
+        color="red",
+        fontsize=8,
+        ha="left",
+    )
+    plt.text(
+        150,
+        max(hist) * 0.8,
+        "VAT Flat Rate Scheme",
+        rotation=0,
+        color="red",
+        fontsize=8,
+        ha="left",
+    )
+
+    plt.grid(axis="y", alpha=0.3, linestyle="--")
+
     # Save chart
-    output_path = Path(__file__).parent / 'turnover_distribution.png'
-    plt.savefig(output_path, dpi=300, bbox_inches='tight')
-    
+    output_path = Path(__file__).parent / "turnover_distribution.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+
     print(f"Chart saved: {output_path}")
 
+
 if __name__ == "__main__":
-    create_turnover_plot()
\ No newline at end of file
+    create_turnover_plot()
diff --git a/policyengine_uk/firm_analysis/static_revenue.py b/policyengine_uk/firm_analysis/static_revenue.py
index 67aa0031e..db1e4bf4f 100644
--- a/policyengine_uk/firm_analysis/static_revenue.py
+++ b/policyengine_uk/firm_analysis/static_revenue.py
@@ -13,103 +13,186 @@
 
 # Load the data with pre-calculated VAT liability
 print("Loading data...")
-synthetic_firms = pd.read_csv(os.path.join(current_dir, 'synthetic_firms_turnover.csv'))
+synthetic_firms = pd.read_csv(
+    os.path.join(current_dir, "synthetic_firms_turnover.csv")
+)
 
-weighted_total_firms = synthetic_firms['weight'].sum()
-print(f"Loaded {weighted_total_firms:,.0f} firms with pre-calculated VAT liability")
+weighted_total_firms = synthetic_firms["weight"].sum()
+print(
+    f"Loaded {weighted_total_firms:,.0f} firms with pre-calculated VAT liability"
+)
 
 # Check if VAT liability already exists in the data
-if 'vat_liability_k' not in synthetic_firms.columns:
-    print("ERROR: vat_liability_k column not found. Please run generate_synthetic_data.py first.")
+if "vat_liability_k" not in synthetic_firms.columns:
+    print(
+        "ERROR: vat_liability_k column not found. Please run generate_synthetic_data.py first."
+    )
     exit(1)
 
 # Convert VAT liability from thousands to pounds for analysis
-synthetic_firms['vat_liability'] = synthetic_firms['vat_liability_k'] * 1000
+synthetic_firms["vat_liability"] = synthetic_firms["vat_liability_k"] * 1000
 
 print(f"\nVAT Liability Statistics:")
-print(f"  Total VAT liability: £{(synthetic_firms['vat_liability'] * synthetic_firms['weight']).sum() / 1_000_000:,.2f} million")
-print(f"  Firms with positive VAT: {synthetic_firms[synthetic_firms['vat_liability'] > 0]['weight'].sum():,.0f}")
-print(f"  Firms with negative VAT: {synthetic_firms[synthetic_firms['vat_liability'] < 0]['weight'].sum():,.0f}")
-print(f"  Firms with zero VAT: {synthetic_firms[synthetic_firms['vat_liability'] == 0]['weight'].sum():,.0f}")
+print(
+    f"  Total VAT liability: £{(synthetic_firms['vat_liability'] * synthetic_firms['weight']).sum() / 1_000_000:,.2f} million"
+)
+print(
+    f"  Firms with positive VAT: {synthetic_firms[synthetic_firms['vat_liability'] > 0]['weight'].sum():,.0f}"
+)
+print(
+    f"  Firms with negative VAT: {synthetic_firms[synthetic_firms['vat_liability'] < 0]['weight'].sum():,.0f}"
+)
+print(
+    f"  Firms with zero VAT: {synthetic_firms[synthetic_firms['vat_liability'] == 0]['weight'].sum():,.0f}"
+)
 
 # Static revenue impact analysis
 # Convert turnover from thousands to pounds for threshold comparison
-synthetic_firms['annual_turnover'] = synthetic_firms['annual_turnover_k'] * 1000
+synthetic_firms["annual_turnover"] = (
+    synthetic_firms["annual_turnover_k"] * 1000
+)
 
 # Identify firms in the bunching region (£85k to £90k)
 affected = synthetic_firms[
-    (synthetic_firms['annual_turnover'] >= 85000) & 
-    (synthetic_firms['annual_turnover'] < 90000)
+    (synthetic_firms["annual_turnover"] >= 85000)
+    & (synthetic_firms["annual_turnover"] < 90000)
 ]
 
 # Calculate total revenue loss (weighted VAT liability)
-total_revenue_loss = (affected['vat_liability'] * affected['weight']).sum()
+total_revenue_loss = (affected["vat_liability"] * affected["weight"]).sum()
 total_revenue_loss_millions = total_revenue_loss / 1_000_000
 
 # Summary statistics (applying weights)
-total_weighted_firms = synthetic_firms['weight'].sum()
-affected_weighted_firms = affected['weight'].sum()
+total_weighted_firms = synthetic_firms["weight"].sum()
+affected_weighted_firms = affected["weight"].sum()
 
-print("\n" + "="*60)
+print("\n" + "=" * 60)
 print("STATIC REVENUE IMPACT ANALYSIS")
-print("="*60)
+print("=" * 60)
 print(f"Total firms in dataset: {total_weighted_firms:,.0f}")
 print(f"Firms in bunching region (£85k-£90k): {affected_weighted_firms:,.0f}")
-print(f"Percentage of firms affected: {affected_weighted_firms/total_weighted_firms*100:.2f}%")
-print(f"\nTotal VAT liability in bunching region: £{total_revenue_loss_millions:,.2f} million")
-print(f"Average VAT per affected firm: £{total_revenue_loss/affected_weighted_firms:,.2f}" if affected_weighted_firms > 0 else "N/A")
+print(
+    f"Percentage of firms affected: {affected_weighted_firms/total_weighted_firms*100:.2f}%"
+)
+print(
+    f"\nTotal VAT liability in bunching region: £{total_revenue_loss_millions:,.2f} million"
+)
+print(
+    f"Average VAT per affected firm: £{total_revenue_loss/affected_weighted_firms:,.2f}"
+    if affected_weighted_firms > 0
+    else "N/A"
+)
 
 # Additional analysis by sector
-print("\n" + "="*60)
+print("\n" + "=" * 60)
 print("TOP 5 SECTORS BY VAT LIABILITY IN BUNCHING REGION")
-print("="*60)
+print("=" * 60)
 if len(affected) > 0:
     # Calculate weighted VAT liability by sector
-    sector_impact = affected.groupby('sic_code').apply(
-        lambda x: pd.Series({
-            'weighted_vat_liability': (x['vat_liability'] * x['weight']).sum(),
-            'weighted_firm_count': x['weight'].sum()
-        })
+    sector_impact = affected.groupby("sic_code").apply(
+        lambda x: pd.Series(
+            {
+                "weighted_vat_liability": (
+                    x["vat_liability"] * x["weight"]
+                ).sum(),
+                "weighted_firm_count": x["weight"].sum(),
+            }
+        )
     )
-    sector_impact = sector_impact.sort_values('weighted_vat_liability', ascending=False).head(5)
-    
+    sector_impact = sector_impact.sort_values(
+        "weighted_vat_liability", ascending=False
+    ).head(5)
+
     for idx, (sector, row) in enumerate(sector_impact.iterrows(), 1):
-        vat_millions = row['weighted_vat_liability'] / 1_000_000
+        vat_millions = row["weighted_vat_liability"] / 1_000_000
         print(f"{idx}. Sector {sector}")
-        print(f"   Firms: {row['weighted_firm_count']:,.0f}, VAT: £{vat_millions:.2f} million")
+        print(
+            f"   Firms: {row['weighted_firm_count']:,.0f}, VAT: £{vat_millions:.2f} million"
+        )
 
 # Check data quality
-print("\n" + "="*60)
+print("\n" + "=" * 60)
 print("DATA QUALITY CHECKS")
-print("="*60)
-print(f"Firms with missing SIC codes: {synthetic_firms[synthetic_firms['sic_code'].isna()]['weight'].sum():,.0f}")
-print(f"Firms with zero/negative turnover: {synthetic_firms[synthetic_firms['annual_turnover_k'] <= 0]['weight'].sum():,.0f}")
-print(f"Raw records with zero/negative weights: {(synthetic_firms['weight'] <= 0).sum()}")
-print(f"Firms with non-zero VAT liability: {synthetic_firms[synthetic_firms['vat_liability'] != 0]['weight'].sum():,.0f}")
+print("=" * 60)
+print(
+    f"Firms with missing SIC codes: {synthetic_firms[synthetic_firms['sic_code'].isna()]['weight'].sum():,.0f}"
+)
+print(
+    f"Firms with zero/negative turnover: {synthetic_firms[synthetic_firms['annual_turnover_k'] <= 0]['weight'].sum():,.0f}"
+)
+print(
+    f"Raw records with zero/negative weights: {(synthetic_firms['weight'] <= 0).sum()}"
+)
+print(
+    f"Firms with non-zero VAT liability: {synthetic_firms[synthetic_firms['vat_liability'] != 0]['weight'].sum():,.0f}"
+)
 
 # Calculate total weighted VAT liability
-total_weighted_vat = (synthetic_firms['vat_liability'] * synthetic_firms['weight']).sum()
+total_weighted_vat = (
+    synthetic_firms["vat_liability"] * synthetic_firms["weight"]
+).sum()
 total_vat_billions = total_weighted_vat / 1_000_000_000
-print(f"Total weighted VAT liability across all firms: £{total_vat_billions:,.2f} billion")
+print(
+    f"Total weighted VAT liability across all firms: £{total_vat_billions:,.2f} billion"
+)
 
 # Show input/output statistics
 print(f"\nInput/Output Statistics:")
-print(f"Average input/output ratio: {(synthetic_firms['annual_input_k'] / synthetic_firms['annual_turnover_k']).mean():.2%}")
-print(f"Firms with input > output (negative VAT): {synthetic_firms[synthetic_firms['annual_input_k'] > synthetic_firms['annual_turnover_k']]['weight'].sum():,.0f}")
+print(
+    f"Average input/output ratio: {(synthetic_firms['annual_input_k'] / synthetic_firms['annual_turnover_k']).mean():.2%}"
+)
+print(
+    f"Firms with input > output (negative VAT): {synthetic_firms[synthetic_firms['annual_input_k'] > synthetic_firms['annual_turnover_k']]['weight'].sum():,.0f}"
+)
 
 # Generate multi-year revenue impact table
-print("\n" + "="*100)
+print("\n" + "=" * 100)
 print("MULTI-YEAR REVENUE IMPACT ANALYSIS")
-print("="*100)
+print("=" * 100)
 
 # Define the fiscal years and parameters with correct baseline thresholds
 # Based on RPI with 2-year lag: 2024-25=2.0%, 2025-26=2.5%, 2026-27=3.0%
 fiscal_years = [
-    {"year": "2024-25", "rpi": "— (frozen)", "baseline": 85000, "policy": 90000, "hmrc_impact": -150, "firm_growth": 1.031},      # 3.1%
-    {"year": "2025-26", "rpi": "— (frozen)", "baseline": 85000, "policy": 90000, "hmrc_impact": -185, "firm_growth": 1.0516},     # 1.031 * 1.020
-    {"year": "2026-27", "rpi": "2024-25: +2.0%", "baseline": 87000, "policy": 90000, "hmrc_impact": -125, "firm_growth": 1.0779},    # £85,000 * 1.020 = £87,000
-    {"year": "2027-28", "rpi": "2025-26: +2.5%", "baseline": 89000, "policy": 90000, "hmrc_impact": -50, "firm_growth": 1.1102},     # £87,000 * 1.025 = £89,175 ≈ £89,000
-    {"year": "2028-29", "rpi": "2026-27: +3.0%", "baseline": 92000, "policy": 90000, "hmrc_impact": 65, "firm_growth": 1.1424},      # £89,000 * 1.030 = £91,670 ≈ £92,000
+    {
+        "year": "2024-25",
+        "rpi": "— (frozen)",
+        "baseline": 85000,
+        "policy": 90000,
+        "hmrc_impact": -150,
+        "firm_growth": 1.031,
+    },  # 3.1%
+    {
+        "year": "2025-26",
+        "rpi": "— (frozen)",
+        "baseline": 85000,
+        "policy": 90000,
+        "hmrc_impact": -185,
+        "firm_growth": 1.0516,
+    },  # 1.031 * 1.020
+    {
+        "year": "2026-27",
+        "rpi": "2024-25: +2.0%",
+        "baseline": 87000,
+        "policy": 90000,
+        "hmrc_impact": -125,
+        "firm_growth": 1.0779,
+    },  # £85,000 * 1.020 = £87,000
+    {
+        "year": "2027-28",
+        "rpi": "2025-26: +2.5%",
+        "baseline": 89000,
+        "policy": 90000,
+        "hmrc_impact": -50,
+        "firm_growth": 1.1102,
+    },  # £87,000 * 1.025 = £89,175 ≈ £89,000
+    {
+        "year": "2028-29",
+        "rpi": "2026-27: +3.0%",
+        "baseline": 92000,
+        "policy": 90000,
+        "hmrc_impact": 65,
+        "firm_growth": 1.1424,
+    },  # £89,000 * 1.030 = £91,670 ≈ £92,000
 ]
 
 # Calculate PolicyEngine estimates for each year
@@ -118,55 +201,78 @@
     # Apply growth factor to firm data for this year
     # Create a copy with adjusted turnover and VAT liability
     adjusted_firms = synthetic_firms.copy()
-    adjusted_firms['annual_turnover'] = adjusted_firms['annual_turnover_k'] * 1000 * fy['firm_growth']
-    adjusted_firms['vat_liability'] = adjusted_firms['vat_liability_k'] * 1000 * fy['firm_growth']
-    
+    adjusted_firms["annual_turnover"] = (
+        adjusted_firms["annual_turnover_k"] * 1000 * fy["firm_growth"]
+    )
+    adjusted_firms["vat_liability"] = (
+        adjusted_firms["vat_liability_k"] * 1000 * fy["firm_growth"]
+    )
+
     # Determine the affected range based on baseline vs policy
-    if fy['baseline'] > fy['policy']:
+    if fy["baseline"] > fy["policy"]:
         # When baseline > policy (2028-29), firms between policy and baseline
         # are now required to register (they're above £90k policy but would have been below baseline)
         baseline_affected = adjusted_firms[
-            (adjusted_firms['annual_turnover'] >= fy['policy']) & 
-            (adjusted_firms['annual_turnover'] < fy['baseline'])
+            (adjusted_firms["annual_turnover"] >= fy["policy"])
+            & (adjusted_firms["annual_turnover"] < fy["baseline"])
         ]
         # This is a revenue gain - these firms now must pay VAT
-        pe_impact = (baseline_affected['vat_liability'] * baseline_affected['weight']).sum() / 1_000_000
+        pe_impact = (
+            baseline_affected["vat_liability"] * baseline_affected["weight"]
+        ).sum() / 1_000_000
     else:
         # When baseline < policy, firms between baseline and policy can avoid VAT
         baseline_affected = adjusted_firms[
-            (adjusted_firms['annual_turnover'] >= fy['baseline']) & 
-            (adjusted_firms['annual_turnover'] < fy['policy'])
+            (adjusted_firms["annual_turnover"] >= fy["baseline"])
+            & (adjusted_firms["annual_turnover"] < fy["policy"])
         ]
         # This is a revenue loss - these firms avoid VAT
-        pe_impact = -(baseline_affected['vat_liability'] * baseline_affected['weight']).sum() / 1_000_000
-    
-    results.append({
-        "Fiscal Year": fy['year'],
-        "Lagged RPI used": fy['rpi'],
-        "Baseline": f"£{fy['baseline']:,}",
-        "Policy": f"£{fy['policy']:,}",
-        "HMRC Revenue Impact (£m)": fy['hmrc_impact'],
-        "PolicyEngine Impact (£m)": round(pe_impact, 0),
-        "Difference": round(pe_impact - fy['hmrc_impact'], 0)
-    })
+        pe_impact = (
+            -(
+                baseline_affected["vat_liability"]
+                * baseline_affected["weight"]
+            ).sum()
+            / 1_000_000
+        )
+
+    results.append(
+        {
+            "Fiscal Year": fy["year"],
+            "Lagged RPI used": fy["rpi"],
+            "Baseline": f"£{fy['baseline']:,}",
+            "Policy": f"£{fy['policy']:,}",
+            "HMRC Revenue Impact (£m)": fy["hmrc_impact"],
+            "PolicyEngine Impact (£m)": round(pe_impact, 0),
+            "Difference": round(pe_impact - fy["hmrc_impact"], 0),
+        }
+    )
 
 # Create DataFrame for nice display
 import pandas as pd
+
 results_df = pd.DataFrame(results)
 
 # Display the table
-print("\n" + "-"*100)
-print(f"{'Fiscal Year':<12} {'Lagged RPI':<20} {'Baseline':<12} {'Policy':<12} {'HMRC (£m)':<12} {'PE (£m)':<12} {'Diff (£m)':<10}")
-print("-"*100)
+print("\n" + "-" * 100)
+print(
+    f"{'Fiscal Year':<12} {'Lagged RPI':<20} {'Baseline':<12} {'Policy':<12} {'HMRC (£m)':<12} {'PE (£m)':<12} {'Diff (£m)':<10}"
+)
+print("-" * 100)
 
 for _, row in results_df.iterrows():
-    print(f"{row['Fiscal Year']:<12} {row['Lagged RPI used']:<20} {row['Baseline']:<12} {row['Policy']:<12} "
-          f"{row['HMRC Revenue Impact (£m)']:>10} {row['PolicyEngine Impact (£m)']:>10} "
-          f"{row['Difference']:>10}")
+    print(
+        f"{row['Fiscal Year']:<12} {row['Lagged RPI used']:<20} {row['Baseline']:<12} {row['Policy']:<12} "
+        f"{row['HMRC Revenue Impact (£m)']:>10} {row['PolicyEngine Impact (£m)']:>10} "
+        f"{row['Difference']:>10}"
+    )
 
-print("-"*100)
-print(f"\nNote: Negative values indicate revenue loss, positive values indicate revenue gain")
-print(f"PE = PolicyEngine estimate based on firm-level VAT liability calculations")
+print("-" * 100)
+print(
+    f"\nNote: Negative values indicate revenue loss, positive values indicate revenue gain"
+)
+print(
+    f"PE = PolicyEngine estimate based on firm-level VAT liability calculations"
+)
 
 # Create and save a plot
 import matplotlib.pyplot as plt
@@ -174,41 +280,67 @@
 fig, ax = plt.subplots(figsize=(12, 6))
 
 # Extract data for plotting
-years = [fy['year'] for fy in fiscal_years]
-hmrc_impacts = [fy['hmrc_impact'] for fy in fiscal_years]
-pe_impacts = [row['PolicyEngine Impact (£m)'] for row in results]
+years = [fy["year"] for fy in fiscal_years]
+hmrc_impacts = [fy["hmrc_impact"] for fy in fiscal_years]
+pe_impacts = [row["PolicyEngine Impact (£m)"] for row in results]
 
 # Plot the data
 x = range(len(years))
 width = 0.35
 
-bars1 = ax.bar([i - width/2 for i in x], hmrc_impacts, width, label='HMRC', color='#2E86AB')
-bars2 = ax.bar([i + width/2 for i in x], pe_impacts, width, label='PolicyEngine', color='#A23B72')
+bars1 = ax.bar(
+    [i - width / 2 for i in x],
+    hmrc_impacts,
+    width,
+    label="HMRC",
+    color="#2E86AB",
+)
+bars2 = ax.bar(
+    [i + width / 2 for i in x],
+    pe_impacts,
+    width,
+    label="PolicyEngine",
+    color="#A23B72",
+)
 
 # Add value labels on bars with padding
 for bar in bars1:
     height = bar.get_height()
     # Add padding: 5 units above positive bars, 5 units below negative bars
     y_pos = height + 5 if height > 0 else height - 5
-    ax.text(bar.get_x() + bar.get_width()/2., y_pos,
-            f'£{int(height)}m', ha='center', va='bottom' if height > 0 else 'top')
+    ax.text(
+        bar.get_x() + bar.get_width() / 2.0,
+        y_pos,
+        f"£{int(height)}m",
+        ha="center",
+        va="bottom" if height > 0 else "top",
+    )
 
 for bar in bars2:
     height = bar.get_height()
     # Add padding: 5 units above positive bars, 5 units below negative bars
     y_pos = height + 5 if height > 0 else height - 5
-    ax.text(bar.get_x() + bar.get_width()/2., y_pos,
-            f'£{int(height)}m', ha='center', va='bottom' if height > 0 else 'top')
+    ax.text(
+        bar.get_x() + bar.get_width() / 2.0,
+        y_pos,
+        f"£{int(height)}m",
+        ha="center",
+        va="bottom" if height > 0 else "top",
+    )
 
 # Customize the plot
 # ax.set_xlabel('Fiscal Year', fontsize=12)
-ax.set_ylabel('£ millions', fontsize=12)
-ax.set_title('Revenue Impact of Increasing the VAT Registration Threshold (2024-29)', fontsize=14, fontweight='bold')
+ax.set_ylabel("£ millions", fontsize=12)
+ax.set_title(
+    "Revenue Impact of Increasing the VAT Registration Threshold (2024-29)",
+    fontsize=14,
+    fontweight="bold",
+)
 ax.set_xticks(x)
 ax.set_xticklabels(years)
 ax.legend()
-ax.grid(True, alpha=0.3, linestyle='--')
-ax.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
+ax.grid(True, alpha=0.3, linestyle="--")
+ax.axhline(y=0, color="black", linestyle="-", linewidth=0.5)
 
 # Set y-axis from -250 to 100
 ax.set_ylim(-250, 100)
@@ -217,14 +349,21 @@
 for i, fy in enumerate(fiscal_years):
     baseline_text = f"Baseline: £{fy['baseline']/1000:.0f}k"
     policy_text = f"Policy: £{fy['policy']/1000:.0f}k"
-    ax.text(i, ax.get_ylim()[0] * 0.9, f"{baseline_text}\n{policy_text}", 
-            ha='center', va='top', fontsize=10, color='gray')
+    ax.text(
+        i,
+        ax.get_ylim()[0] * 0.9,
+        f"{baseline_text}\n{policy_text}",
+        ha="center",
+        va="top",
+        fontsize=10,
+        color="gray",
+    )
 
 plt.tight_layout()
 
 # Save the plot
-plot_path = os.path.join(current_dir, 'vat_threshold_revenue_impact.png')
-plt.savefig(plot_path, dpi=150, bbox_inches='tight')
+plot_path = os.path.join(current_dir, "vat_threshold_revenue_impact.png")
+plt.savefig(plot_path, dpi=150, bbox_inches="tight")
 print(f"\nPlot saved to: {plot_path}")
 
 # Display the plot

From 37d0c3ce3140169ebe72ba06cc505980b2cbd093 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 15:58:44 +0100
Subject: [PATCH 3/9] changelog

---
 changelog_entry.yaml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index e69de29bb..41840b381 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -0,0 +1,4 @@
+- bump: patch
+  changes:
+    added:
+    - add firm analysis files
\ No newline at end of file

From 291e68a0dda1c2c844e4c5d68a37cdc7ac1a14f0 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 20:57:00 +0100
Subject: [PATCH 4/9] sync to pe uk data

---
 changelog_entry.yaml                          |   7 +-
 policyengine_uk/data/__init__.py              |   3 +
 policyengine_uk/data/firm_dataset_schema.py   | 138 ++++++++++++++++++
 policyengine_uk/entities.py                   |  40 ++++-
 policyengine_uk/simulation.py                 |  75 ++++++++++
 policyengine_uk/tax_benefit_system.py         |  26 +++-
 .../variables/input/business_group.py         |  13 ++
 policyengine_uk/variables/input/firm.py       | 133 +++++++++++++++++
 policyengine_uk/variables/input/sector.py     |  13 ++
 9 files changed, 441 insertions(+), 7 deletions(-)
 create mode 100644 policyengine_uk/data/firm_dataset_schema.py
 create mode 100644 policyengine_uk/variables/input/business_group.py
 create mode 100644 policyengine_uk/variables/input/firm.py
 create mode 100644 policyengine_uk/variables/input/sector.py

diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index 41840b381..8515e2ff7 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -1,4 +1,7 @@
-- bump: patch
+- bump: minor
   changes:
     added:
-    - add firm analysis files
\ No newline at end of file
+    - Firm dataset support with 3 new entities (firm, sector, business_group)
+    - 16 firm variables for business microdata analysis
+    - UKFirmSingleYearDataset class for firm data handling
+    - FIRM_2023_24 dataset constant for firm_2023_24.h5 integration
\ No newline at end of file
diff --git a/policyengine_uk/data/__init__.py b/policyengine_uk/data/__init__.py
index 905fb1306..9ee80a30a 100644
--- a/policyengine_uk/data/__init__.py
+++ b/policyengine_uk/data/__init__.py
@@ -2,3 +2,6 @@
     UKMultiYearDataset,
     UKSingleYearDataset,
 )
+from policyengine_uk.data.firm_dataset_schema import (
+    UKFirmSingleYearDataset,
+)
diff --git a/policyengine_uk/data/firm_dataset_schema.py b/policyengine_uk/data/firm_dataset_schema.py
new file mode 100644
index 000000000..e3684707f
--- /dev/null
+++ b/policyengine_uk/data/firm_dataset_schema.py
@@ -0,0 +1,138 @@
+import pandas as pd
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from policyengine_uk import Microsimulation
+
+from pathlib import Path
+import h5py
+
+
+class UKFirmSingleYearDataset:
+    firm: pd.DataFrame
+    sector: pd.DataFrame
+    business_group: pd.DataFrame
+
+    @staticmethod
+    def validate_file_path(file_path: str, raise_exception: bool = True):
+        if not file_path.endswith(".h5"):
+            if raise_exception:
+                raise ValueError(
+                    "File path must end with '.h5' for UKFirmDataset."
+                )
+            return False
+        if not Path(file_path).exists():
+            if raise_exception:
+                raise FileNotFoundError(f"File not found: {file_path}")
+            return False
+
+        # Check if the file contains time_period, firm, sector, and business_group datasets
+        with h5py.File(file_path, "r") as f:
+            required_datasets = [
+                "time_period",
+                "firm",
+                "sector",
+                "business_group",
+            ]
+            for dataset in required_datasets:
+                if dataset not in f:
+                    if raise_exception:
+                        raise ValueError(
+                            f"Dataset '{dataset}' not found in the file: {file_path}"
+                        )
+                    else:
+                        return False
+
+        return True
+
+    def __init__(
+        self,
+        file_path: str = None,
+        firm: pd.DataFrame = None,
+        sector: pd.DataFrame = None,
+        business_group: pd.DataFrame = None,
+        fiscal_year: int = 2025,
+    ):
+        file_path = str(file_path) if file_path else None
+        if file_path is not None:
+            self.validate_file_path(file_path)
+            with pd.HDFStore(file_path) as f:
+                self.firm = f["firm"]
+                self.sector = f["sector"]
+                self.business_group = f["business_group"]
+                self.time_period = str(f["time_period"].iloc[0])
+        else:
+            if firm is None or sector is None or business_group is None:
+                raise ValueError(
+                    "Must provide either a file path or all three DataFrames (firm, sector, business_group)."
+                )
+            self.firm = firm
+            self.sector = sector
+            self.business_group = business_group
+            self.time_period = str(fiscal_year)
+
+        self.data_format = "arrays"
+        self.tables = (self.firm, self.sector, self.business_group)
+        self.table_names = ("firm", "sector", "business_group")
+
+    def save(self, file_path: str):
+        with pd.HDFStore(file_path) as f:
+            f.put("firm", self.firm, format="table", data_columns=True)
+            f.put("sector", self.sector, format="table", data_columns=True)
+            f.put(
+                "business_group",
+                self.business_group,
+                format="table",
+                data_columns=True,
+            )
+            f.put("time_period", pd.Series([self.time_period]), format="table")
+
+    def load(self):
+        data = {}
+        for df in (self.firm, self.sector, self.business_group):
+            for col in df.columns:
+                data[col] = df[col].values
+
+        return data
+
+    def copy(self):
+        return UKFirmSingleYearDataset(
+            firm=self.firm.copy(),
+            sector=self.sector.copy(),
+            business_group=self.business_group.copy(),
+            fiscal_year=self.time_period,
+        )
+
+    def validate(self):
+        # Check for NaNs in the tables
+        for df in self.tables:
+            for col in df.columns:
+                if df[col].isna().any():
+                    raise ValueError(f"Column '{col}' contains NaN values.")
+
+    @staticmethod
+    def from_simulation(
+        simulation: "Microsimulation", fiscal_year: int = 2025
+    ):
+        entity_dfs = {}
+
+        for entity in ["firm", "sector", "business_group"]:
+            input_variables = [
+                variable
+                for variable in simulation.input_variables
+                if simulation.tax_benefit_system.variables[variable].entity.key
+                == entity
+            ]
+            if len(input_variables) == 0:
+                entity_dfs[entity] = pd.DataFrame()
+            else:
+                entity_dfs[entity] = simulation.calculate_dataframe(
+                    input_variables, period=fiscal_year
+                )
+
+        return UKFirmSingleYearDataset(
+            firm=entity_dfs["firm"],
+            sector=entity_dfs["sector"],
+            business_group=entity_dfs["business_group"],
+            fiscal_year=fiscal_year,
+        )
diff --git a/policyengine_uk/entities.py b/policyengine_uk/entities.py
index d83ee53b2..3130fa68e 100644
--- a/policyengine_uk/entities.py
+++ b/policyengine_uk/entities.py
@@ -43,4 +43,42 @@
     is_person=True,
 )
 
-entities = [Household, BenUnit, Person]
+Firm = build_entity(
+    key="firm",
+    plural="firms",
+    label="Firm",
+    doc="A business entity with employees and operations.",
+    is_person=True,
+)
+
+Sector = build_entity(
+    key="sector",
+    plural="sectors",
+    label="Sector",
+    doc="An economic sector containing multiple firms.",
+    roles=[
+        {
+            "key": "member",
+            "plural": "members",
+            "label": "Member",
+            "doc": "A sector in the business classification.",
+        }
+    ],
+)
+
+BusinessGroup = build_entity(
+    key="business_group",
+    plural="business_groups",
+    label="Business Group",
+    doc="A business group containing multiple firms.",
+    roles=[
+        {
+            "key": "member",
+            "plural": "members",
+            "label": "Member",
+            "doc": "A business group in the firm dataset.",
+        }
+    ],
+)
+
+entities = [Household, BenUnit, Person, Firm, Sector, BusinessGroup]
diff --git a/policyengine_uk/simulation.py b/policyengine_uk/simulation.py
index e2537d54b..473b206af 100644
--- a/policyengine_uk/simulation.py
+++ b/policyengine_uk/simulation.py
@@ -19,6 +19,9 @@
     UKMultiYearDataset,
     UKSingleYearDataset,
 )
+from policyengine_uk.data.firm_dataset_schema import (
+    UKFirmSingleYearDataset,
+)
 from policyengine_uk.utils.scenario import Scenario
 from policyengine_uk.data.economic_assumptions import (
     extend_single_year_dataset,
@@ -97,6 +100,8 @@ def __init__(
             self.build_from_single_year_dataset(dataset)
         elif isinstance(dataset, UKMultiYearDataset):
             self.build_from_multi_year_dataset(dataset)
+        elif isinstance(dataset, UKFirmSingleYearDataset):
+            self.build_from_firm_dataset(dataset)
         elif dataset is None:
             self.build_from_url(
                 "hf://policyengine/policyengine-uk-data/enhanced_frs_2023_24.h5"
@@ -224,6 +229,8 @@ def build_from_url(self, url: str) -> None:
             self.build_from_multi_year_dataset(UKMultiYearDataset(dataset))
         elif UKSingleYearDataset.validate_file_path(dataset, False):
             self.build_from_single_year_dataset(UKSingleYearDataset(dataset))
+        elif UKFirmSingleYearDataset.validate_file_path(dataset, False):
+            self.build_from_firm_dataset(UKFirmSingleYearDataset(dataset))
         else:
             dataset = Dataset.from_file(dataset, self.default_input_period)
             self.build_from_dataset(dataset)
@@ -367,6 +374,74 @@ def build_from_multi_year_dataset(
 
         self.dataset = dataset
 
+    def build_from_firm_dataset(
+        self, dataset: UKFirmSingleYearDataset
+    ) -> None:
+        """Build simulation from a firm dataset.
+
+        Args:
+            dataset: UKFirmSingleYearDataset containing firm data
+        """
+        self.build_from_firm_ids(
+            dataset.firm.firm_id,
+            dataset.firm.firm_sector_id,
+            dataset.firm.firm_business_group_id,
+            dataset.sector.sector_id,
+            dataset.business_group.business_group_id,
+        )
+
+        # Load variable values
+        for table in dataset.tables:
+            for variable in table.columns:
+                if variable not in self.tax_benefit_system.variables:
+                    continue
+                self.set_input(variable, dataset.time_period, table[variable])
+
+        self.dataset = dataset
+
+    def build_from_firm_ids(
+        self,
+        firm_id: np.ndarray,
+        firm_sector_id: np.ndarray,
+        firm_business_group_id: np.ndarray,
+        sector_id: np.ndarray,
+        business_group_id: np.ndarray,
+    ) -> None:
+        """Build simulation entities from firm ID arrays.
+
+        Args:
+            firm_id: Array of firm IDs
+            firm_sector_id: Array mapping firms to sectors
+            firm_business_group_id: Array mapping firms to business groups
+            sector_id: Array of sector IDs
+            business_group_id: Array of business group IDs
+        """
+        from policyengine_core.simulations.simulation_builder import (
+            SimulationBuilder,
+        )
+
+        builder = SimulationBuilder()
+        builder.populations = self.tax_benefit_system.instantiate_entities()
+
+        # Declare entities
+        builder.declare_person_entity("firm", firm_id)
+        builder.declare_entity("sector", np.unique(sector_id))
+        builder.declare_entity("business_group", np.unique(business_group_id))
+
+        # Link firms to sectors and business groups
+        builder.join_with_persons(
+            builder.populations["sector"],
+            firm_sector_id,
+            np.array(["member"] * len(firm_sector_id)),
+        )
+        builder.join_with_persons(
+            builder.populations["business_group"],
+            firm_business_group_id,
+            np.array(["member"] * len(firm_business_group_id)),
+        )
+
+        self.build_from_populations(builder.populations)
+
     def build_from_ids(
         self,
         person_id: np.ndarray,
diff --git a/policyengine_uk/tax_benefit_system.py b/policyengine_uk/tax_benefit_system.py
index e76cddbd9..d1b12404e 100644
--- a/policyengine_uk/tax_benefit_system.py
+++ b/policyengine_uk/tax_benefit_system.py
@@ -14,7 +14,14 @@
 from policyengine_core.variables import Variable
 
 # PolicyEngine UK imports
-from policyengine_uk.entities import BenUnit, Household, Person
+from policyengine_uk.entities import (
+    BenUnit,
+    Household,
+    Person,
+    Firm,
+    Sector,
+    BusinessGroup,
+)
 from policyengine_uk.parameters.gov.contrib.create_private_pension_uprating import (
     add_private_pension_uprating_factor,
 )
@@ -38,6 +45,7 @@
 # Module constants
 COUNTRY_DIR = Path(__file__).parent
 ENHANCED_FRS = "hf://policyengine/policyengine-uk-data/enhanced_frs_2023_24.h5"
+FIRM_2023_24 = "hf://policyengine/policyengine-uk-data/firm_2023_24.h5"
 
 
 class CountryTaxBenefitSystem(TaxBenefitSystem):
@@ -108,16 +116,26 @@ def __init__(self):
         self.variables = {}
 
         # Create copies of entity classes to avoid modifying originals
-        person, benunit, household = (
+        person, benunit, household, firm, sector, business_group = (
             copy.copy(Person),
             copy.copy(BenUnit),
             copy.copy(Household),
+            copy.copy(Firm),
+            copy.copy(Sector),
+            copy.copy(BusinessGroup),
         )
 
         # Set up entities
-        self.entities = [person, benunit, household]
+        self.entities = [
+            person,
+            benunit,
+            household,
+            firm,
+            sector,
+            business_group,
+        ]
         self.person_entity = person
-        self.group_entities = [benunit, household]
+        self.group_entities = [benunit, household, sector, business_group]
         self.group_entity_keys = [entity.key for entity in self.group_entities]
 
         # Link entities to this tax-benefit system
diff --git a/policyengine_uk/variables/input/business_group.py b/policyengine_uk/variables/input/business_group.py
new file mode 100644
index 000000000..3da3d77ed
--- /dev/null
+++ b/policyengine_uk/variables/input/business_group.py
@@ -0,0 +1,13 @@
+from policyengine_uk.model_api import *
+
+label = "Business Group"
+description = "Business group variables."
+
+
+class business_group_id(Variable):
+    value_type = int
+    entity = BusinessGroup
+    label = "business group ID"
+    documentation = "Unique identifier for each business group"
+    definition_period = YEAR
+    quantity_type = STOCK
diff --git a/policyengine_uk/variables/input/firm.py b/policyengine_uk/variables/input/firm.py
new file mode 100644
index 000000000..221de3a7a
--- /dev/null
+++ b/policyengine_uk/variables/input/firm.py
@@ -0,0 +1,133 @@
+from policyengine_uk.model_api import *
+
+label = "Firm"
+description = "Business firm variables from firm dataset."
+
+
+class firm_id(Variable):
+    value_type = int
+    entity = Firm
+    label = "firm ID"
+    documentation = "Unique identifier for each firm"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class firm_sector_id(Variable):
+    value_type = int
+    entity = Firm
+    label = "firm sector ID"
+    documentation = "Identifier linking firm to its sector"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class firm_business_group_id(Variable):
+    value_type = int
+    entity = Firm
+    label = "firm business group ID"
+    documentation = "Identifier linking firm to its business group"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class year(Variable):
+    value_type = int
+    entity = Firm
+    label = "year"
+    documentation = "Year of the data"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class firm_weight(Variable):
+    value_type = float
+    entity = Firm
+    label = "firm weight"
+    documentation = "Statistical weight for firm in population"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class sic_code(Variable):
+    value_type = str
+    entity = Firm
+    label = "SIC code"
+    documentation = "Standard Industrial Classification code"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class annual_turnover_k(Variable):
+    value_type = float
+    entity = Firm
+    label = "annual turnover (thousands)"
+    documentation = "Annual turnover in thousands of pounds"
+    definition_period = YEAR
+    unit = GBP
+    quantity_type = FLOW
+
+
+class annual_input_k(Variable):
+    value_type = float
+    entity = Firm
+    label = "annual input (thousands)"
+    documentation = "Annual input costs in thousands of pounds"
+    definition_period = YEAR
+    unit = GBP
+    quantity_type = FLOW
+
+
+class vat_liability_k(Variable):
+    value_type = float
+    entity = Firm
+    label = "VAT liability (thousands)"
+    documentation = "VAT liability in thousands of pounds"
+    definition_period = YEAR
+    unit = GBP
+    quantity_type = FLOW
+
+
+class employment(Variable):
+    value_type = int
+    entity = Firm
+    label = "employment"
+    documentation = "Number of employees"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class vat_registered(Variable):
+    value_type = bool
+    entity = Firm
+    label = "VAT registered"
+    documentation = "Whether the firm is registered for VAT"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class hmrc_band(Variable):
+    value_type = str
+    entity = Firm
+    label = "HMRC band"
+    documentation = "HMRC size band classification"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class employment_band(Variable):
+    value_type = str
+    entity = Firm
+    label = "employment band"
+    documentation = "Employment size band classification"
+    definition_period = YEAR
+    quantity_type = STOCK
+
+
+class sic_numeric(Variable):
+    value_type = int
+    entity = Firm
+    label = "SIC numeric"
+    documentation = "Numeric version of SIC code"
+    definition_period = YEAR
+    quantity_type = STOCK
diff --git a/policyengine_uk/variables/input/sector.py b/policyengine_uk/variables/input/sector.py
new file mode 100644
index 000000000..e33a7678a
--- /dev/null
+++ b/policyengine_uk/variables/input/sector.py
@@ -0,0 +1,13 @@
+from policyengine_uk.model_api import *
+
+label = "Sector"
+description = "Economic sector variables."
+
+
+class sector_id(Variable):
+    value_type = int
+    entity = Sector
+    label = "sector ID"
+    documentation = "Unique identifier for each sector"
+    definition_period = YEAR
+    quantity_type = STOCK

From 7fab8ebd26b5010ea3c165f73427282c19438223 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 21:01:10 +0100
Subject: [PATCH 5/9] delete files

---
 .../firm_analysis/bunching_analysis.png       |  Bin 449575 -> 0 bytes
 .../firm_analysis/bunching_analysis.py        | 1745 -----------------
 policyengine_uk/firm_analysis/plot_example.py |   88 -
 .../firm_analysis/static_revenue.py           |  370 ----
 .../firm_analysis/turnover_distribution.png   |  Bin 114998 -> 0 bytes
 .../vat_threshold_revenue_impact.png          |  Bin 72148 -> 0 bytes
 6 files changed, 2203 deletions(-)
 delete mode 100644 policyengine_uk/firm_analysis/bunching_analysis.png
 delete mode 100644 policyengine_uk/firm_analysis/bunching_analysis.py
 delete mode 100644 policyengine_uk/firm_analysis/plot_example.py
 delete mode 100644 policyengine_uk/firm_analysis/static_revenue.py
 delete mode 100644 policyengine_uk/firm_analysis/turnover_distribution.png
 delete mode 100644 policyengine_uk/firm_analysis/vat_threshold_revenue_impact.png

diff --git a/policyengine_uk/firm_analysis/bunching_analysis.png b/policyengine_uk/firm_analysis/bunching_analysis.png
deleted file mode 100644
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GIT binary patch
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diff --git a/policyengine_uk/firm_analysis/bunching_analysis.py b/policyengine_uk/firm_analysis/bunching_analysis.py
deleted file mode 100644
index 0314d0bd9..000000000
--- a/policyengine_uk/firm_analysis/bunching_analysis.py
+++ /dev/null
@@ -1,1745 +0,0 @@
-#!/usr/bin/env python3
-"""
-Steps 1-5: Complete Bunching Analysis Pipeline with Asymmetric Windows
-Step 1: Build counterfactual & bunching statistics
-Step 2: Set effective wedge
-Step 3: Calibrate substitution elasticity
-Step 4: Advanced probabilistic mapping (micro-level no-notch counterfactual)
-Step 5: Forward map to NEW policy (£100k threshold)
-
-ASYMMETRIC WINDOWS CONFIGURATION:
-To set different windows for left/right sides of thresholds, modify the main() function:
-
-    counterfactual_window_left = 15    # £15k below £90k threshold (£75k-£90k)
-    counterfactual_window_right = 5    # £5k above £90k threshold (£90k-£95k)
-
-    new_policy_window_left = 8         # £8k below £100k new threshold (£92k-£100k)
-    new_policy_window_right = 12       # £12k above £100k new threshold (£100k-£112k)
-
-This allows independent control over bunching window sizes on each side of both thresholds.
-"""
-
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from pathlib import Path
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression
-from scipy.interpolate import interp1d
-import warnings
-
-warnings.filterwarnings("ignore")
-
-
-class CounterfactualBunchingAnalysis:
-    """
-    Implements Steps 1-5: Complete Bunching Analysis Pipeline
-
-    Step 1: Build counterfactual & bunching statistics
-    Step 2: Set effective wedge τₑ
-    Step 3: Calibrate substitution elasticity σ
-    Step 4: Advanced probabilistic mapping (create_advanced_probabilistic_mapping)
-    Step 5: Forward map to NEW policy (£100k threshold)
-    """
-
-    def __init__(
-        self,
-        threshold=90,
-        window_left=10,
-        window_right=10,
-        new_policy_window_left=10,
-        new_policy_window_right=10,
-    ):
-        """
-        Initialize analysis with threshold T* and asymmetric windows.
-
-        Args:
-            threshold: VAT threshold T* in £1k (default 90k)
-            window_left: Left window (below threshold) for counterfactual analysis in £1k (default 10k)
-            window_right: Right window (above threshold) for counterfactual analysis in £1k (default 10k)
-            new_policy_window_left: Left window for new policy analysis in £1k (default 10k)
-            new_policy_window_right: Right window for new policy analysis in £1k (default 10k)
-        """
-        self.T_star = threshold
-        self.W_left = window_left
-        self.W_right = window_right
-        self.T_lower = threshold - window_left  # T* - W_left
-        self.T_upper = threshold + window_right  # T* + W_right
-
-        # New policy window parameters
-        self.new_policy_W_left = new_policy_window_left
-        self.new_policy_W_right = new_policy_window_right
-
-        # Storage for results
-        self.bin_centers = None
-        self.f_obs = None
-        self.f_cf = None
-        self.F_obs = None
-        self.F_cf = None
-
-        # Bunching statistics
-        self.q_N_obs = None
-        self.q_R_obs = None
-        self.q_N_cf = None
-        self.q_R_cf = None
-        self.E = None  # Excess mass
-        self.Delta_R = None  # Missing mass
-        self.b = None  # Bunching ratio
-
-        # Step 2: Effective wedge
-        self.tau_e = None
-
-        # Step 3: Substitution elasticity
-        self.sigma = None
-
-    # ========================================================================
-    # STEP 1: BUILD COUNTERFACTUAL & BUNCHING STATISTICS
-    # ========================================================================
-
-    def load_and_prepare_data(self):
-        """Load synthetic firm data and prepare binned distributions."""
-
-        # Load data
-        data_path = Path(__file__).parent / "synthetic_firms_turnover.csv"
-        df = pd.read_csv(data_path)
-
-        # Focus on wider range for counterfactual estimation
-        df_filtered = df[
-            (df["annual_turnover_k"] >= 20) & (df["annual_turnover_k"] <= 140)
-        ].copy()
-
-        # Create bins of £1k intervals
-        bin_edges = np.arange(19.5, 140.5, 1.0)
-        self.bin_centers = np.arange(20, 140)  # 20k to 139k
-
-        # Calculate observed histogram (density)
-        hist_counts, _ = np.histogram(
-            df_filtered["annual_turnover_k"],
-            bins=bin_edges,
-            weights=df_filtered["weight"],
-        )
-
-        # Normalize to get density (f^obs)
-        bin_width = 1.0
-        self.f_obs = hist_counts / bin_width
-
-        return df_filtered
-
-    def fit_counterfactual(self):
-        """
-        Fit the counterfactual density f^cf(T) using the same methodology as bunching_analysis.py
-        Excludes fixed range [80, 100] around threshold and uses degree 3 polynomial.
-        """
-
-        # Use same exclusion range as bunching_analysis.py: [80, 100]
-        y_minus = 80  # Lower bound of excluded region
-        y_plus = 100  # Upper bound of excluded region
-        exclude_mask = (self.bin_centers >= y_minus) & (
-            self.bin_centers <= y_plus
-        )
-
-        # Fit on data outside the window
-        reg_mask = ~exclude_mask
-
-        # Use distance from threshold as feature
-        y_dist = self.bin_centers - self.T_star
-
-        # Fit polynomial (degree 3 as in bunching_analysis.py)
-        poly_features = PolynomialFeatures(degree=3, include_bias=True)
-        X_poly = poly_features.fit_transform(y_dist[reg_mask].reshape(-1, 1))
-
-        # Fit regression
-        reg = LinearRegression(fit_intercept=False)
-        reg.fit(X_poly, self.f_obs[reg_mask])
-
-        # Predict counterfactual for all points
-        X_all = poly_features.transform(y_dist.reshape(-1, 1))
-        self.f_cf = reg.predict(X_all)
-
-        # Ensure positive predictions (same as bunching_analysis.py)
-        self.f_cf = np.maximum(self.f_cf, 0)
-
-    def compute_cdfs(self):
-        """Compute CDFs F^obs and F^cf."""
-
-        # Simple cumulative sum for CDF (normalized by total mass)
-        bin_width = 1.0
-
-        # Observed CDF
-        cumsum_obs = np.cumsum(self.f_obs * bin_width)
-        self.F_obs = cumsum_obs / cumsum_obs[-1]
-
-        # Counterfactual CDF
-        cumsum_cf = np.cumsum(self.f_cf * bin_width)
-        self.F_cf = cumsum_cf / cumsum_cf[-1]
-
-    def compute_masses_in_window(self):
-        """
-        Compute masses in the window as per the formulas:
-        q_N^obs, q_R^obs, q_N^cf, q_R^cf
-        """
-
-        # Find indices for integration bounds
-        mask_N = (self.bin_centers >= self.T_lower) & (
-            self.bin_centers < self.T_star
-        )
-        mask_R = (self.bin_centers >= self.T_star) & (
-            self.bin_centers <= self.T_upper
-        )
-
-        bin_width = 1.0
-
-        # Observed masses
-        self.q_N_obs = np.sum(self.f_obs[mask_N] * bin_width)
-        self.q_R_obs = np.sum(self.f_obs[mask_R] * bin_width)
-
-        # Counterfactual masses
-        self.q_N_cf = np.sum(self.f_cf[mask_N] * bin_width)
-        self.q_R_cf = np.sum(self.f_cf[mask_R] * bin_width)
-
-    def compute_excess_missing_mass(self):
-        """
-        Compute excess mass E and missing mass Delta_R:
-        E = ∫[f^obs - f^cf]_+ dT  (in left window)
-        Delta_R = ∫[f^cf - f^obs]_+ dT  (in right window)
-        """
-
-        # Masks for left and right windows
-        mask_N = (self.bin_centers >= self.T_lower) & (
-            self.bin_centers < self.T_star
-        )
-        mask_R = (self.bin_centers >= self.T_star) & (
-            self.bin_centers <= self.T_upper
-        )
-
-        bin_width = 1.0
-
-        # Excess mass in left window: [f^obs - f^cf]_+
-        excess_left = np.maximum(self.f_obs[mask_N] - self.f_cf[mask_N], 0)
-        self.E = np.sum(excess_left * bin_width)
-
-        # Missing mass in right window: [f^cf - f^obs]_+
-        missing_right = np.maximum(self.f_cf[mask_R] - self.f_obs[mask_R], 0)
-        self.Delta_R = np.sum(missing_right * bin_width)
-
-    def compute_bunching_ratio(self):
-        """
-        Compute bunching ratio: b = (q_N^obs - q_N^cf) / q_N^cf
-        """
-
-        if self.q_N_cf > 0:
-            self.b = (self.q_N_obs - self.q_N_cf) / self.q_N_cf
-        else:
-            self.b = 0
-
-    # ========================================================================
-    # STEP 2: SET EFFECTIVE WEDGE τₑ
-    # ========================================================================
-
-    def set_effective_wedge(
-        self,
-        tau_e=None,
-        lambda_param=None,
-        rho=None,
-        tau=None,
-        s_c=None,
-        v=None,
-    ):
-        """
-        Step 2: Set the (constant) effective wedge τₑ
-
-        Args:
-            tau_e: Direct value for effective wedge (if provided, overrides calculation)
-            lambda_param: λ parameter for accounting build
-            rho: ρ parameter for accounting build
-            tau: τ base tax rate
-            s_c: s_c parameter
-            v: v parameter
-        """
-
-        if tau_e is not None:
-            # Direct specification
-            self.tau_e = tau_e
-            print(f"Step 2: Using given effective wedge τₑ = {self.tau_e:.4f}")
-        elif all(
-            param is not None for param in [lambda_param, rho, tau, s_c, v]
-        ):
-            # Accounting build: τₑ = λ(1-ρ)τ - τs_c*v
-            self.tau_e = lambda_param * (1 - rho) * tau - tau * s_c * v
-            print(f"Step 2: Computed effective wedge τₑ = {self.tau_e:.4f}")
-            print(f"  λ={lambda_param}, ρ={rho}, τ={tau}, s_c={s_c}, v={v}")
-        else:
-            # Default calibration for UK VAT
-            # Reasonable estimates for UK VAT registration costs
-            self.tau_e = 0.05  # 5% effective wedge
-            print(
-                f"Step 2: Using default effective wedge τₑ = {self.tau_e:.4f}"
-            )
-
-    # ========================================================================
-    # STEP 3: CALIBRATE SUBSTITUTION ELASTICITY σ
-    # ========================================================================
-
-    def calibrate_substitution_elasticity(self):
-        """
-        Step 3: Calibrate the substitution elasticity σ
-
-        Uses CES/logit share equation:
-        σ = ln(q_R^cf/q_N^cf / q_R^obs/q_N^obs) / ln(1 + τₑ)
-        """
-
-        if self.tau_e is None:
-            raise ValueError(
-                "Must set effective wedge τₑ before calibrating elasticity"
-            )
-
-        if (
-            self.q_N_obs <= 0
-            or self.q_R_obs <= 0
-            or self.q_N_cf <= 0
-            or self.q_R_cf <= 0
-        ):
-            raise ValueError(
-                "All masses must be positive for elasticity calibration"
-            )
-
-        # Calculate ratio of ratios
-        observed_ratio = self.q_R_obs / self.q_N_obs
-        counterfactual_ratio = self.q_R_cf / self.q_N_cf
-
-        ratio_of_ratios = counterfactual_ratio / observed_ratio
-
-        # Calculate elasticity: σ = ln(ratio_of_ratios) / ln(1 + τₑ)
-        if ratio_of_ratios > 0 and (1 + self.tau_e) > 0:
-            self.sigma = np.log(ratio_of_ratios) / np.log(1 + self.tau_e)
-        else:
-            self.sigma = 0
-            print(
-                "Warning: Invalid values for elasticity calculation, setting σ = 0"
-            )
-
-        print(
-            f"Step 3: Calibrated substitution elasticity σ = {self.sigma:.4f}"
-        )
-        print(f"  q_R^obs/q_N^obs = {observed_ratio:.4f}")
-        print(f"  q_R^cf/q_N^cf = {counterfactual_ratio:.4f}")
-        print(f"  Ratio of ratios = {ratio_of_ratios:.4f}")
-        print(f"  ln(1 + τₑ) = {np.log(1 + self.tau_e):.4f}")
-
-    # ========================================================================
-    # STEP 4: ADVANCED PROBABILISTIC MAPPING (MICRO-LEVEL NO-NOTCH COUNTERFACTUAL)
-    # ========================================================================
-
-    def compute_aggregate_displaced_share(self):
-        """
-        Step 4a: Compute aggregate displaced share Π = 1 - (1 + τₑ)^(-σ)
-        """
-        if self.tau_e is None or self.sigma is None:
-            raise ValueError(
-                "Must complete Steps 2-3 before computing displaced share"
-            )
-
-        self.Pi = 1 - (1 + self.tau_e) ** (-self.sigma)
-        print(f"Step 4a: Aggregate displaced share Π = {self.Pi:.4f}")
-
-    def compute_local_bunching_probability(self, T_obs):
-        """
-        Step 4b: Compute local bunching probability π(T^obs) for given turnover
-        π(T^obs) = Π * [f^obs(T^obs) - f^cf(T^obs)]_+ / E
-
-        Args:
-            T_obs: Observed turnover (scalar or array)
-
-        Returns:
-            Local bunching probability (scalar or array)
-        """
-        if not hasattr(self, "Pi"):
-            self.compute_aggregate_displaced_share()
-
-        # Create interpolation functions for densities
-        f_obs_interp = interp1d(
-            self.bin_centers,
-            self.f_obs,
-            kind="linear",
-            bounds_error=False,
-            fill_value=0,
-        )
-        f_cf_interp = interp1d(
-            self.bin_centers,
-            self.f_cf,
-            kind="linear",
-            bounds_error=False,
-            fill_value=0,
-        )
-
-        # Compute excess density at T_obs
-        excess_density = np.maximum(
-            f_obs_interp(T_obs) - f_cf_interp(T_obs), 0
-        )
-
-        # Local bunching probability: what fraction of firms at T_obs are bunchers?
-        # All excess density should be bunchers, so π = excess / observed
-        pi_T_obs = (
-            excess_density / f_obs_interp(T_obs)
-            if f_obs_interp(T_obs) > 0
-            else 0
-        )
-
-        return pi_T_obs
-
-    def compute_rank_among_bunchers(self, T_obs):
-        """
-        Step 4c: Compute rank among bunchers u(T^obs)
-        u(T^obs) = ∫[T*-W to T^obs] [f^obs - f^cf]_+ dT / E
-
-        Args:
-            T_obs: Observed turnover (must be in [T*-W, T*])
-
-        Returns:
-            Rank among bunchers ∈ [0,1]
-        """
-        # Only valid for T_obs in [T*-W, T*]
-        T_obs = np.clip(T_obs, self.T_lower, self.T_star)
-
-        # Find integration bounds in our bin indices
-        start_idx = np.searchsorted(
-            self.bin_centers, self.T_lower, side="left"
-        )
-        end_idx = np.searchsorted(self.bin_centers, T_obs, side="right")
-
-        # Compute cumulative excess mass up to T_obs
-        if end_idx <= start_idx:
-            return 0.0
-
-        # Get the relevant range
-        bin_subset = self.bin_centers[start_idx:end_idx]
-        f_obs_subset = self.f_obs[start_idx:end_idx]
-        f_cf_subset = self.f_cf[start_idx:end_idx]
-
-        # Compute excess mass in this range
-        bin_width = 1.0
-        excess_subset = np.maximum(f_obs_subset - f_cf_subset, 0)
-        cumulative_excess = np.sum(excess_subset * bin_width)
-
-        # Rank among bunchers
-        u_T_obs = cumulative_excess / self.E if self.E > 0 else 0
-
-        return np.clip(u_T_obs, 0, 1)
-
-    def compute_counterfactual_mapping(self, T_obs):
-        """
-        Step 4d: Compute deterministic counterfactual mapping E[T^cf | T^obs]
-
-        E[T^cf | T^obs] = (1 - π(T^obs)) * T^obs + π(T^obs) * (F^cf)^(-1)(F^cf(T*) + u(T^obs) * Δ_R)
-
-        Args:
-            T_obs: Observed turnover (scalar or array)
-
-        Returns:
-            Expected counterfactual turnover T^cf
-        """
-        T_obs = np.atleast_1d(T_obs)
-        T_cf = np.zeros_like(T_obs, dtype=float)
-
-        # Create CDF interpolation function
-        # Normalize CDF to be between 0 and 1
-        F_cf_normalized = self.F_cf
-        F_cf_interp = interp1d(
-            self.bin_centers,
-            F_cf_normalized,
-            kind="linear",
-            bounds_error=False,
-            fill_value="extrapolate",
-        )
-
-        # Inverse CDF function (approximate)
-        cdf_values = F_cf_normalized
-        inverse_cdf = interp1d(
-            cdf_values,
-            self.bin_centers,
-            kind="linear",
-            bounds_error=False,
-            fill_value="extrapolate",
-        )
-
-        # Get F^cf(T*)
-        F_cf_at_threshold = F_cf_interp(self.T_star)
-
-        for i, T_obs_val in enumerate(T_obs):
-            if self.T_lower <= T_obs_val <= self.T_star:
-                # In bunching region - apply the mapping
-
-                # Compute local bunching probability
-                pi_val = self.compute_local_bunching_probability(T_obs_val)
-
-                # Compute rank among bunchers
-                u_val = self.compute_rank_among_bunchers(T_obs_val)
-
-                # Compute the CDF argument
-                cdf_arg = F_cf_at_threshold + u_val * self.Delta_R / np.sum(
-                    self.f_cf
-                )
-                cdf_arg = np.clip(cdf_arg, 0, 1)  # Ensure valid CDF value
-
-                # Apply the mapping formula
-                T_cf_displaced = inverse_cdf(cdf_arg)
-                T_cf[i] = (1 - pi_val) * T_obs_val + pi_val * T_cf_displaced
-
-            else:
-                # Outside bunching region - no change
-                T_cf[i] = T_obs_val
-
-        return T_cf if len(T_cf) > 1 else T_cf[0]
-
-    def run_step4_analysis(self, sample_turnovers=None):
-        """
-        Step 4: Run micro-level mapping analysis
-
-        Args:
-            sample_turnovers: Array of turnover values to analyze (optional)
-        """
-
-        if self.tau_e is None or self.sigma is None:
-            raise ValueError("Must complete Steps 1-3 before running Step 4")
-
-        print(f"\nStep 4: Micro-level elasticity-aware mapping")
-        print("-" * 50)
-
-        # Compute aggregate displaced share
-        self.compute_aggregate_displaced_share()
-
-        # Use default sample if none provided
-        if sample_turnovers is None:
-            # Sample firms in the bunching region
-            sample_turnovers = np.linspace(
-                self.T_lower, self.T_star, 11
-            )  # 11 points from T*-W to T*
-
-        print(f"Analyzing {len(sample_turnovers)} sample turnover points:")
-        print(
-            f"Sample range: £{sample_turnovers[0]:.1f}k to £{sample_turnovers[-1]:.1f}k"
-        )
-
-        # Compute mappings for sample
-        results = []
-        for T_obs in sample_turnovers:
-            pi_val = self.compute_local_bunching_probability(T_obs)
-            u_val = self.compute_rank_among_bunchers(T_obs)
-            T_cf_val = self.compute_counterfactual_mapping(T_obs)
-
-            results.append(
-                {
-                    "T_obs": T_obs,
-                    "pi": pi_val,
-                    "u": u_val,
-                    "T_cf": T_cf_val,
-                    "displacement": T_cf_val - T_obs,
-                }
-            )
-
-        # Create results DataFrame
-        results_df = pd.DataFrame(results)
-
-        # Print summary
-        print(f"\nStep 4 Results Summary:")
-        print(f"Average bunching probability: {results_df['pi'].mean():.4f}")
-        print(
-            f"Average displacement: £{results_df['displacement'].mean():.2f}k"
-        )
-        print(f"Max displacement: £{results_df['displacement'].max():.2f}k")
-
-        # Store results
-        self.step4_results = results_df
-
-        return results_df
-
-    def run_analysis(self, tau_e=None, include_step4=False, **wedge_params):
-        """Run the complete Steps 1-4 analysis."""
-
-        step_range = "Steps 1-4" if include_step4 else "Steps 1-3"
-        print(
-            f"{step_range}: Counterfactual, Wedge, Elasticity"
-            + (", and Micro Mapping" if include_step4 else "")
-        )
-        print("=" * 70)
-
-        # Step 1: Load and prepare data
-        print("\nStep 1: Building counterfactual & bunching stats")
-        print("-" * 50)
-        df = self.load_and_prepare_data()
-        print(f"Loaded {len(df):,} firm observations")
-
-        # Fit counterfactual
-        self.fit_counterfactual()
-        print(
-            f"Fitted counterfactual excluding window [{self.T_lower}, {self.T_upper}]"
-        )
-
-        # Compute CDFs
-        self.compute_cdfs()
-
-        # Compute all masses and statistics
-        self.compute_masses_in_window()
-        self.compute_excess_missing_mass()
-        self.compute_bunching_ratio()
-
-        # Step 2: Set effective wedge
-        print(f"\nStep 2: Setting effective wedge")
-        print("-" * 50)
-        self.set_effective_wedge(tau_e=tau_e, **wedge_params)
-
-        # Step 3: Calibrate substitution elasticity
-        print(f"\nStep 3: Calibrating substitution elasticity")
-        print("-" * 50)
-        self.calibrate_substitution_elasticity()
-
-        # Step 4: Micro-level mapping (optional)
-        if include_step4:
-            self.run_step4_analysis()
-
-        # Print results
-        self.print_results()
-
-        # Apply Step 5: Forward map to new policy (£100k threshold)
-        if include_step4:  # Only do Step 5 if we're doing the full analysis
-            self.step5_forward_map_to_new_policy(T_star_new=100)
-
-        # Create firm mappings for Steps 6-7 (sample-based for demonstration)
-        if include_step4:
-            self.create_sample_firm_mappings()
-
-            # Apply Steps 6-7: Revenue mapping and elasticity calculations
-            if hasattr(self, "firm_mappings"):
-                # Step 6: Revenue mapping (old → new policy)
-                self.step6_revenue_mapping(
-                    tau_old=0.20, tau_new=0.20
-                )  # Same rate, different threshold
-
-                # Step 7: Elasticity calculations
-                self.step7_elasticity_calculations(tau_old=0.20)
-
-        # Create visualization with Step 4 and optionally Step 5
-        include_step5_plot = include_step4 and hasattr(self, "f_new_policy")
-        self.plot_results(include_step5=include_step5_plot)
-
-        # Apply Step 4 advanced mapping to synthetic firms and save results
-        self.apply_step4_to_synthetic_firms("synthetic_firms_step4_mapped.csv")
-
-        return self.get_results_dict()
-
-    # ========================================================================
-    # STEP 5: FORWARD MAP TO NEW POLICY (£100K THRESHOLD)
-    # ========================================================================
-
-    def step5_forward_map_to_new_policy(self, T_star_new=100, tau_e_new=None):
-        """
-        Step 5: Forward map from no-notch counterfactual to new policy (£100k threshold)
-
-        Pipeline: Real (£90k) → Counterfactual (no notch) → New Policy (£100k)
-
-        Args:
-            T_star_new: New threshold in £k (default 100k)
-            tau_e_new: New effective wedge (default same as original)
-        """
-
-        print(f"\nStep 5: Forward mapping to NEW policy")
-        print("=" * 50)
-        print(f"New threshold: £{T_star_new}k (was £{self.T_star}k)")
-
-        # Use same effective wedge if not specified
-        if tau_e_new is None:
-            tau_e_new = self.tau_e
-            print(f"Using same effective wedge: τₑ' = {tau_e_new:.4f}")
-        else:
-            print(f"New effective wedge: τₑ' = {tau_e_new:.4f}")
-
-        # Step 5a: New displaced share under new wedge
-        Pi_new = 1 - (1 + tau_e_new) ** (-self.sigma)
-        print(f"New displaced share: Π' = {Pi_new:.4f}")
-
-        # Step 5b: Recenter the no-notch density at new threshold
-        # The counterfactual f_cf becomes the base for the new policy
-
-        # Step 5c: Apply reverse mapping from counterfactual to new observed
-        f_new_policy = self.create_new_policy_distribution(
-            T_star_new, tau_e_new, Pi_new
-        )
-
-        # Store results
-        self.T_star_new = T_star_new
-        self.tau_e_new = tau_e_new
-        self.Pi_new = Pi_new
-        self.f_new_policy = f_new_policy
-
-        print(
-            f"Step 5 complete: Generated new policy distribution with bunching at £{T_star_new}k"
-        )
-
-        return f_new_policy
-
-    def create_new_policy_distribution(self, T_star_new, tau_e_new, Pi_new):
-        """
-        Create new policy distribution with bunching at new threshold.
-        Uses reverse mapping from counterfactual to new observed distribution.
-        """
-
-        print(f"Creating new policy distribution with advanced mapping...")
-
-        # Start with the counterfactual as base (no-notch world)
-        f_new = self.f_cf.copy()
-
-        # Apply reverse mapping to create bunching at new threshold
-        # This is the inverse of Step 4: Counterfactual → New Policy
-
-        # Use new policy window parameters
-        W_left_new = self.new_policy_W_left
-        W_right_new = self.new_policy_W_right
-        T_lower_new = T_star_new - W_left_new
-        T_upper_new = T_star_new + W_right_new
-
-        # Find bins corresponding to new threshold region
-        new_region_mask = (self.bin_centers >= T_lower_new) & (
-            self.bin_centers <= T_upper_new
-        )
-        above_threshold_mask = (self.bin_centers > T_star_new) & (
-            self.bin_centers <= T_upper_new
-        )
-
-        if not np.any(above_threshold_mask):
-            print(
-                f"Warning: No bins above new threshold £{T_star_new}k in analysis range"
-            )
-            return f_new
-
-        # Calculate how much mass should move down to create bunching
-        # Mass in region above new threshold in counterfactual
-        mass_above = np.sum(self.f_cf[above_threshold_mask])
-
-        # How much should move down (based on new displaced share)
-        mass_to_move = Pi_new * mass_above
-
-        print(f"  Mass above new threshold: {mass_above:.0f}")
-        print(f"  Mass to move down (create bunching): {mass_to_move:.0f}")
-
-        # Redistribute mass: move from above threshold to below (creating bunching)
-        if mass_to_move > 0:
-            # Reduce density above threshold
-            reduction_factor = 1 - Pi_new
-            f_new[above_threshold_mask] *= reduction_factor
-
-            # Add mass below threshold (distributed according to distance from threshold)
-            below_threshold_mask = (self.bin_centers >= T_lower_new) & (
-                self.bin_centers <= T_star_new
-            )
-
-            if np.any(below_threshold_mask):
-                # Weight by inverse distance from new threshold (closer bins get more mass)
-                distances = T_star_new - self.bin_centers[below_threshold_mask]
-                distances = np.maximum(distances, 0.5)  # Minimum distance
-                weights = 1.0 / distances
-                weights = weights / np.sum(weights)  # Normalize
-
-                # Distribute the moved mass
-                f_new[below_threshold_mask] += mass_to_move * weights
-
-                print(
-                    f"  Redistributed mass to {np.sum(below_threshold_mask)} bins below new threshold"
-                )
-
-        # Apply smoothing to eliminate sharp changes
-        f_new = self.apply_smoothing(f_new)
-
-        return f_new
-
-    def apply_smoothing(self, distribution):
-        """Apply aggressive smoothing to eliminate sharp changes in Step 5 curve."""
-        smooth_dist = distribution.copy()
-
-        # Multiple passes of progressively wider smoothing
-
-        # Pass 1: 3-point moving average
-        for i in range(1, len(smooth_dist) - 1):
-            smooth_dist[i] = (
-                0.25 * smooth_dist[i - 1]
-                + 0.5 * smooth_dist[i]
-                + 0.25 * smooth_dist[i + 1]
-            )
-
-        # Pass 2: 5-point moving average
-        for i in range(2, len(smooth_dist) - 2):
-            smooth_dist[i] = (
-                0.1 * smooth_dist[i - 2]
-                + 0.2 * smooth_dist[i - 1]
-                + 0.4 * smooth_dist[i]
-                + 0.2 * smooth_dist[i + 1]
-                + 0.1 * smooth_dist[i + 2]
-            )
-
-        # # Pass 3: 7-point moving average (more aggressive)
-        # for i in range(3, len(smooth_dist) - 3):
-        #     smooth_dist[i] = (0.05 * smooth_dist[i-3] + 0.1 * smooth_dist[i-2] + 0.15 * smooth_dist[i-1] +
-        #                     0.4 * smooth_dist[i] + 0.15 * smooth_dist[i+1] + 0.1 * smooth_dist[i+2] + 0.05 * smooth_dist[i+3])
-
-        # Pass 4: 9-point moving average (very aggressive)
-        # for i in range(4, len(smooth_dist) - 4):
-        #     weights = [0.03, 0.06, 0.09, 0.12, 0.4, 0.12, 0.09, 0.06, 0.03]
-        #     smooth_dist[i] = sum(w * smooth_dist[i + j - 4] for j, w in enumerate(weights))
-
-        # Pass 5: Final Gaussian-like smoothing
-        for i in range(5, len(smooth_dist) - 5):
-            # 11-point Gaussian-weighted average
-            weights = [
-                0.02,
-                0.03,
-                0.05,
-                0.08,
-                0.12,
-                0.4,
-                0.12,
-                0.08,
-                0.05,
-                0.03,
-                0.02,
-            ]
-            smooth_dist[i] = sum(
-                w * smooth_dist[i + j - 5] for j, w in enumerate(weights)
-            )
-
-        return smooth_dist
-
-    # ========================================================================
-    # STEP 6: REVENUE MAPPING OLD → NEW
-    # ========================================================================
-
-    def step6_revenue_mapping(self, tau_old=0.20, tau_new=0.20):
-        """
-        Step 6: Revenue mapping from old to new policy
-        Calculate VAT revenue under both regimes using firm-level mapping
-        """
-        print(f"\n" + "=" * 70)
-        print("STEP 6: REVENUE MAPPING OLD → NEW")
-        print("=" * 70)
-
-        # Reasonable assumptions for missing parameters (imputed from typical UK firms)
-        # These would ideally come from supply-use tables by sector
-        theta_default = (
-            0.85  # Share of taxable outputs (85% for typical firms)
-        )
-        s_c_default = 0.45  # Input cost share (45% of turnover)
-        v_default = 0.70  # VAT-able share of inputs (70%)
-
-        print(f"Using imputed parameters:")
-        print(f"  θ (taxable output share): {theta_default:.2f}")
-        print(f"  s_c (input cost share): {s_c_default:.2f}")
-        print(f"  v (VAT-able input share): {v_default:.2f}")
-        print(f"  Old VAT rate: {tau_old:.1%}")
-        print(f"  New VAT rate: {tau_new:.1%}")
-
-        # Load firm data and compute revenues
-        if not hasattr(self, "firm_mappings"):
-            print(
-                "Error: Need Step 4 firm mappings. Run step4_advanced_mapping first."
-            )
-            return
-
-        # Calculate firm-level revenues
-        revenues_old = []
-        revenues_new = []
-        revenue_changes = []
-
-        print(f"\nCalculating revenues for {len(self.firm_mappings)} firms...")
-
-        for i, (T_old, T_new) in enumerate(self.firm_mappings):
-            # Firm-level VAT parameters (in practice, would vary by sector)
-            theta_i = theta_default
-            s_c_i = s_c_default
-            v_i = v_default
-
-            # Revenue under old regime: V_old = τ_old * (θ * T_old - v * s_c * T_old)
-            V_old = tau_old * (theta_i * T_old - v_i * s_c_i * T_old)
-            V_old = max(0, V_old)  # Non-negative revenue
-
-            # Revenue under new regime: V_new = τ_new * (θ * T_new - v * s_c * T_new)
-            V_new = tau_new * (theta_i * T_new - v_i * s_c_i * T_new)
-            V_new = max(0, V_new)  # Non-negative revenue
-
-            revenues_old.append(V_old)
-            revenues_new.append(V_new)
-            revenue_changes.append(V_new - V_old)
-
-        # Store results
-        self.revenues_old = np.array(revenues_old)
-        self.revenues_new = np.array(revenues_new)
-        self.revenue_changes = np.array(revenue_changes)
-
-        # Aggregate results
-        total_revenue_old = np.sum(self.revenues_old)
-        total_revenue_new = np.sum(self.revenues_new)
-        total_change = total_revenue_new - total_revenue_old
-
-        print(f"\n" + "-" * 50)
-        print("STEP 6 RESULTS:")
-        print("-" * 50)
-        print(f"Total VAT revenue (old policy): £{total_revenue_old:,.0f}k")
-        print(f"Total VAT revenue (new policy): £{total_revenue_new:,.0f}k")
-        print(
-            f"Revenue change: £{total_change:,.0f}k ({100*total_change/total_revenue_old:+.2f}%)"
-        )
-
-        # Distribution of revenue changes
-        positive_changes = self.revenue_changes[self.revenue_changes > 0]
-        negative_changes = self.revenue_changes[self.revenue_changes < 0]
-
-        print(f"\nFirm-level revenue impacts:")
-        print(
-            f"  Firms with revenue increase: {len(positive_changes):,} ({100*len(positive_changes)/len(self.revenue_changes):.1f}%)"
-        )
-        print(
-            f"  Firms with revenue decrease: {len(negative_changes):,} ({100*len(negative_changes)/len(self.revenue_changes):.1f}%)"
-        )
-        if len(positive_changes) > 0:
-            print(f"  Average increase: £{np.mean(positive_changes):.2f}k")
-        if len(negative_changes) > 0:
-            print(f"  Average decrease: £{np.mean(negative_changes):.2f}k")
-
-        return {
-            "total_old": total_revenue_old,
-            "total_new": total_revenue_new,
-            "total_change": total_change,
-            "change_percent": 100 * total_change / total_revenue_old,
-        }
-
-    # ========================================================================
-    # STEP 7: ELASTICITY CALCULATIONS
-    # ========================================================================
-
-    def step7_elasticity_calculations(
-        self, tau_old=0.20, epsilon_tau=0.01, epsilon_threshold=5
-    ):
-        """
-        Step 7: Calculate elasticities (behavioural and revenue)
-        """
-        print(f"\n" + "=" * 70)
-        print("STEP 7: ELASTICITY CALCULATIONS")
-        print("=" * 70)
-
-        # Behavioral (odds) elasticity from Step 3 calibration
-        behavioral_elasticity = -self.sigma
-
-        print(f"1) Behavioral (odds) elasticity w.r.t wedge:")
-        print(
-            f"   d ln(q_R/q_N) / d ln(1+τ_e) = -σ = {behavioral_elasticity:.3f}"
-        )
-
-        # Revenue elasticity w.r.t VAT rate (finite differences)
-        print(f"\n2) Revenue elasticity w.r.t VAT rate:")
-        print(f"   Computing finite differences with ε = {epsilon_tau:.1%}...")
-
-        # Baseline revenue (from Step 6)
-        if not hasattr(self, "revenues_old"):
-            print("   Error: Run step6_revenue_mapping first")
-            return
-
-        baseline_revenue = np.sum(self.revenues_old)
-
-        # Simulate τ + ε
-        tau_plus = tau_old + epsilon_tau
-        self.run_policy_simulation(tau_new=tau_plus)
-        revenue_plus = self.calculate_revenue_for_rate(tau_plus)
-
-        # Simulate τ - ε
-        tau_minus = tau_old - epsilon_tau
-        self.run_policy_simulation(tau_new=tau_minus)
-        revenue_minus = self.calculate_revenue_for_rate(tau_minus)
-
-        # Revenue elasticity = (dV/V) / (dτ/τ)
-        dV_dtau = (revenue_plus - revenue_minus) / (2 * epsilon_tau)
-        revenue_elasticity_rate = (dV_dtau * tau_old) / baseline_revenue
-
-        print(f"   dV/dτ = £{dV_dtau:,.0f}k per percentage point")
-        print(
-            f"   Revenue elasticity w.r.t rate = {revenue_elasticity_rate:.3f}"
-        )
-
-        # Revenue elasticity w.r.t threshold (finite differences)
-        print(f"\n3) Revenue elasticity w.r.t threshold:")
-        print(
-            f"   Computing finite differences with ε = £{epsilon_threshold}k..."
-        )
-
-        # Simulate T* + ε
-        threshold_plus = self.T_star + epsilon_threshold
-        revenue_threshold_plus = self.calculate_revenue_for_threshold(
-            threshold_plus
-        )
-
-        # Simulate T* - ε
-        threshold_minus = self.T_star - epsilon_threshold
-        revenue_threshold_minus = self.calculate_revenue_for_threshold(
-            threshold_minus
-        )
-
-        # Revenue elasticity = (dV/V) / (dT*/T*)
-        dV_dthreshold = (revenue_threshold_plus - revenue_threshold_minus) / (
-            2 * epsilon_threshold
-        )
-        revenue_elasticity_threshold = (
-            dV_dthreshold * self.T_star
-        ) / baseline_revenue
-
-        print(f"   dV/dT* = £{dV_dthreshold:,.0f}k per £1k threshold change")
-        print(
-            f"   Revenue elasticity w.r.t threshold = {revenue_elasticity_threshold:.3f}"
-        )
-
-        print(f"\n" + "-" * 50)
-        print("STEP 7 RESULTS:")
-        print("-" * 50)
-        print(f"Behavioral elasticity (σ): {-behavioral_elasticity:.3f}")
-        print(f"Revenue elasticity w.r.t rate: {revenue_elasticity_rate:.3f}")
-        print(
-            f"Revenue elasticity w.r.t threshold: {revenue_elasticity_threshold:.3f}"
-        )
-
-        return {
-            "behavioral_elasticity": behavioral_elasticity,
-            "revenue_elasticity_rate": revenue_elasticity_rate,
-            "revenue_elasticity_threshold": revenue_elasticity_threshold,
-        }
-
-    def run_policy_simulation(self, tau_new):
-        """Helper: Run quick policy simulation for elasticity calculation"""
-        # Recompute effective wedge for new rate
-        lambda_param = 0.75  # B2C share assumption
-        rho = 0.50  # Pass-through assumption
-        s_c = 0.45  # Input cost share
-        v = 0.70  # VAT-able input share
-
-        tau_e_new = lambda_param * (1 - rho) * tau_new - tau_new * s_c * v
-
-        # Recompute displaced share
-        Pi_new = 1 - (1 + tau_e_new) ** (-self.sigma)
-
-        # Update firm mappings (simplified)
-        self.Pi_simulation = Pi_new
-
-    def calculate_revenue_for_rate(self, tau_rate):
-        """Helper: Calculate total revenue for given VAT rate"""
-        theta = 0.85
-        s_c = 0.45
-        v = 0.70
-
-        total_revenue = 0
-        for T_old, T_new in self.firm_mappings[:10000]:  # Sample for speed
-            V = tau_rate * (theta * T_new - v * s_c * T_new)
-            total_revenue += max(0, V)
-
-        # Scale up to full population
-        scaling_factor = len(self.firm_mappings) / 10000
-        return total_revenue * scaling_factor
-
-    def calculate_revenue_for_threshold(self, new_threshold):
-        """Helper: Calculate total revenue for given threshold"""
-        # Simplified: assume threshold change affects firms near boundary
-        threshold_effect = (new_threshold - self.T_star) / self.T_star
-
-        total_revenue = 0
-        tau_rate = 0.20
-        theta = 0.85
-        s_c = 0.45
-        v = 0.70
-
-        for T_old, T_new in self.firm_mappings[:10000]:  # Sample for speed
-            # Adjust turnover based on threshold change (simplified)
-            if 80 <= T_old <= 100:  # Firms near threshold
-                T_adjusted = T_new * (
-                    1 + 0.1 * threshold_effect
-                )  # Small adjustment
-            else:
-                T_adjusted = T_new
-
-            V = tau_rate * (theta * T_adjusted - v * s_c * T_adjusted)
-            total_revenue += max(0, V)
-
-        # Scale up to full population
-        scaling_factor = len(self.firm_mappings) / 10000
-        return total_revenue * scaling_factor
-
-    def map_cf_to_new_policy(self, T_cf):
-        """Helper: Map counterfactual turnovers to new policy turnovers using Step 5 distribution"""
-        # Simplified mapping using the Step 5 new policy distribution
-        T_new = np.zeros_like(T_cf)
-
-        for i, turnover in enumerate(T_cf):
-            # Find closest bin
-            bin_idx = np.argmin(np.abs(self.bin_centers - turnover))
-
-            # Use ratio of new policy to counterfactual at this point
-            if self.f_cf[bin_idx] > 0:
-                ratio = self.f_new_policy[bin_idx] / self.f_cf[bin_idx]
-                # Apply small adjustment based on ratio
-                T_new[i] = turnover * (
-                    0.95 + 0.1 * ratio
-                )  # Conservative adjustment
-            else:
-                T_new[i] = turnover
-
-        return T_new
-
-    def create_sample_firm_mappings(self):
-        """Create sample firm mappings for Steps 6-7 demonstration"""
-        np.random.seed(42)  # Reproducible results
-
-        # Create a realistic sample of UK firms
-        n_firms = 100000  # Sample size for demonstration
-
-        # Generate realistic firm turnover distribution
-        # UK firms: many small firms, few large ones
-        turnovers_old = []
-        turnovers_new = []
-
-        for i in range(n_firms):
-            # Generate old turnover with realistic distribution
-            if np.random.random() < 0.7:  # 70% small firms
-                T_old = np.random.lognormal(np.log(30), 0.8)  # Mean ~£50k
-            elif np.random.random() < 0.9:  # 20% medium firms
-                T_old = np.random.lognormal(np.log(80), 0.6)  # Mean ~£120k
-            else:  # 10% large firms
-                T_old = np.random.lognormal(np.log(200), 0.8)  # Mean ~£400k
-
-            T_old = max(10, min(500, T_old))  # Constrain to £10k-£500k
-
-            # Apply mapping based on Step 4/5 logic
-            if 65 <= T_old <= 110:  # Near thresholds - apply bunching effects
-                if hasattr(self, "f_new_policy") and hasattr(self, "f_cf"):
-                    # Use Step 5 mapping
-                    bin_idx = np.argmin(np.abs(self.bin_centers - T_old))
-                    if self.f_cf[bin_idx] > 0:
-                        ratio = self.f_new_policy[bin_idx] / self.f_cf[bin_idx]
-                        T_new = T_old * (
-                            0.95 + 0.1 * ratio
-                        )  # Small adjustment
-                    else:
-                        T_new = T_old
-                else:
-                    # Default mapping for threshold change (90k → 100k)
-                    if T_old < 90:
-                        T_new = (
-                            T_old * 1.02
-                        )  # Small increase due to threshold change
-                    else:
-                        T_new = T_old * 0.98  # Small decrease
-            else:
-                # Outside bunching region - minimal change
-                T_new = T_old * np.random.normal(1.0, 0.01)  # 1% noise
-
-            turnovers_old.append(T_old)
-            turnovers_new.append(max(10, T_new))  # Minimum £10k
-
-        # Store mappings
-        self.firm_mappings = list(zip(turnovers_old, turnovers_new))
-
-        print(
-            f"Created {len(self.firm_mappings):,} sample firm mappings for Steps 6-7"
-        )
-        print(f"  Mean old turnover: £{np.mean(turnovers_old):.1f}k")
-        print(f"  Mean new turnover: £{np.mean(turnovers_new):.1f}k")
-
-    def print_results(self):
-        """Print the analysis results."""
-
-        has_step4 = hasattr(self, "step4_results")
-        step_range = "Steps 1-4" if has_step4 else "Steps 1-3"
-
-        print(f"\n{step_range} Results Summary:")
-        print("=" * 50)
-        print(f"Threshold T*: £{self.T_star:,}k")
-        print(f"Window: -£{self.W_left:,}k/+£{self.W_right:,}k")
-        print(f"Analysis range: [{self.T_lower}, {self.T_upper}]")
-
-        print(f"\nStep 1 - Bunching Statistics:")
-        print("-" * 30)
-        print(f"  Masses in window:")
-        print(f"    q_N^obs = {self.q_N_obs:.3f}")
-        print(f"    q_R^obs = {self.q_R_obs:.3f}")
-        print(f"    q_N^cf  = {self.q_N_cf:.3f}")
-        print(f"    q_R^cf  = {self.q_R_cf:.3f}")
-        print(f"  Excess mass (E) = {self.E:.3f}")
-        print(f"  Missing mass (Δ_R) = {self.Delta_R:.3f}")
-        print(f"  Bunching ratio (b) = {self.b:.3f}")
-
-        print(f"\nStep 2 - Effective Wedge:")
-        print("-" * 30)
-        print(f"  τₑ = {self.tau_e:.4f}")
-
-        print(f"\nStep 3 - Substitution Elasticity:")
-        print("-" * 30)
-        print(f"  σ = {self.sigma:.4f}")
-
-        if has_step4:
-            print(f"\nStep 4 - Micro-level Mapping:")
-            print("-" * 30)
-            print(f"  Aggregate displaced share Π = {self.Pi:.4f}")
-            print(f"  Sample firms analyzed: {len(self.step4_results)}")
-            print(
-                f"  Avg bunching probability: {self.step4_results['pi'].mean():.4f}"
-            )
-            print(
-                f"  Avg displacement: £{self.step4_results['displacement'].mean():.2f}k"
-            )
-
-    def plot_results(self, include_step5=False):
-        """Create visualization of Steps 1-4 complete analysis with advanced mapping, optionally including Step 5."""
-
-        _, ax = plt.subplots(figsize=(12, 8))
-
-        # Plot observed and counterfactual densities (exclude first point T=20k, x=-70)
-        y_dist = self.bin_centers - self.T_star
-
-        # Skip first point (index 0)
-        plot_mask = slice(1, None)
-        y_dist_plot = y_dist[plot_mask]
-
-        ax.plot(
-            y_dist_plot,
-            self.f_obs[plot_mask],
-            "o-",
-            color="blue",
-            markersize=3,
-            linewidth=1,
-            label="Original Observed f^obs(T) [£90k]",
-            alpha=0.7,
-        )
-        ax.plot(
-            y_dist_plot,
-            self.f_cf[plot_mask],
-            "-",
-            color="red",
-            linewidth=2,
-            label="Counterfactual f^cf(T) [No Notch]",
-        )
-
-        # Add Step 4 advanced mapping result (also skip first point)
-        f_step4 = create_advanced_probabilistic_mapping(self)
-        ax.plot(
-            y_dist_plot,
-            f_step4[plot_mask],
-            "-",
-            color="green",
-            linewidth=2,
-            label="Step 4 Advanced Mapping Result",
-            alpha=0.8,
-        )
-
-        # Add Step 5 new policy result if available (also skip first point)
-        if include_step5 and hasattr(self, "f_new_policy"):
-            ax.plot(
-                y_dist_plot,
-                self.f_new_policy[plot_mask],
-                "-",
-                color="purple",
-                linewidth=2,
-                label=f"Step 5 New Policy [£{self.T_star_new}k]",
-                alpha=0.8,
-            )
-
-        # Shade the window (also skip first point)
-        window_mask = (self.bin_centers >= self.T_lower) & (
-            self.bin_centers <= self.T_upper
-        )
-        window_mask_plot = window_mask[
-            plot_mask
-        ]  # Apply same exclusion to window mask
-        ax.fill_between(
-            y_dist_plot[window_mask_plot],
-            0,
-            self.f_obs[plot_mask][window_mask_plot],
-            alpha=0.3,
-            color="lightblue",
-            label=f"Window -£{self.W_left}k/+£{self.W_right}k",
-        )
-
-        # Add vertical lines
-        ax.axvline(
-            x=0,
-            color="black",
-            linestyle="-",
-            linewidth=2,
-            label="Original Threshold £90k",
-        )
-        ax.axvline(
-            x=-self.W_left,
-            color="gray",
-            linestyle="--",
-            alpha=0.5,
-            label=f"£90k -£{self.W_left}k/+£{self.W_right}k",
-        )
-        ax.axvline(x=self.W_right, color="gray", linestyle="--", alpha=0.5)
-
-        # Add new threshold line if Step 5 is included
-        if include_step5 and hasattr(self, "T_star_new"):
-            new_threshold_dist = (
-                self.T_star_new - self.T_star
-            )  # Distance from original
-            ax.axvline(
-                x=new_threshold_dist,
-                color="purple",
-                linestyle="-",
-                linewidth=2,
-                label=f"New Threshold £{self.T_star_new}k",
-                alpha=0.8,
-            )
-
-        # Add annotations
-        ax.text(
-            0.02,
-            0.95,
-            f"Bunching ratio b = {self.b:.3f}",
-            transform=ax.transAxes,
-            fontsize=11,
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue"),
-        )
-        ax.text(
-            0.02,
-            0.89,
-            f"Effective wedge τₑ = {self.tau_e:.3f}",
-            transform=ax.transAxes,
-            fontsize=11,
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow"),
-        )
-        ax.text(
-            0.02,
-            0.83,
-            f"Substitution elasticity σ = {self.sigma:.3f}",
-            transform=ax.transAxes,
-            fontsize=11,
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgreen"),
-        )
-        ax.text(
-            0.02,
-            0.77,
-            f"Excess mass E = {self.E:.0f}",
-            transform=ax.transAxes,
-            fontsize=11,
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral"),
-        )
-
-        # Add Step 4 info
-        pi_mean = np.mean(
-            [
-                self.compute_local_bunching_probability(T)
-                for T in self.bin_centers[window_mask]
-            ]
-        )
-        ax.text(
-            0.02,
-            0.71,
-            f"Avg bunching prob π̄ = {pi_mean:.3f}",
-            transform=ax.transAxes,
-            fontsize=11,
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="lightpink"),
-        )
-
-        # Add Step 5 info if included
-        if include_step5 and hasattr(self, "Pi_new"):
-            ax.text(
-                0.02,
-                0.65,
-                f"New displaced share Π' = {self.Pi_new:.3f}",
-                transform=ax.transAxes,
-                fontsize=11,
-                bbox=dict(boxstyle="round,pad=0.3", facecolor="plum"),
-            )
-
-        # Formatting
-        ax.set_xlabel(
-            "Distance from Original Threshold £90k (£1,000)", fontsize=12
-        )
-        ax.set_ylabel("Density f(T)", fontsize=12)
-
-        if include_step5:
-            ax.set_title(
-                "Steps 1-5: Complete Pipeline with New Policy",
-                fontsize=14,
-                fontweight="bold",
-            )
-        else:
-            ax.set_title(
-                "Steps 1-4: Complete Analysis with Advanced Mapping",
-                fontsize=14,
-                fontweight="bold",
-            )
-
-        ax.grid(True, alpha=0.3)
-        ax.legend(fontsize=9)
-        ax.set_xlim(-70, 50)
-
-        # Save plot
-        output_path = Path(__file__).parent / "bunching_analysis.png"
-        plt.savefig(output_path, dpi=300, bbox_inches="tight")
-        print(f"\nPlot saved: {output_path}")
-
-    def apply_step4_to_synthetic_firms(
-        self, output_file="synthetic_firms_step4_mapped.csv"
-    ):
-        """
-        Apply advanced Step 4 mapping to synthetic firms and save results.
-        """
-
-        print("\nApplying Step 4 Advanced Mapping to Synthetic Firms")
-        print("=" * 60)
-
-        # Load synthetic firms data
-        try:
-            df = pd.read_csv("synthetic_firms_turnover.csv")
-            print(
-                f"Loaded {len(df):,} synthetic firms from synthetic_firms_turnover.csv"
-            )
-        except FileNotFoundError:
-            print("Error: synthetic_firms_turnover.csv not found")
-            return None
-
-        # Apply Step 4 mapping to each firm's turnover
-        print("Applying Step 4 mapping to individual firms...")
-
-        # Get observed turnovers
-        T_obs = df["annual_turnover_k"].values
-
-        # Apply the advanced probabilistic mapping
-        print("Creating advanced probabilistic mapping...")
-        f_mapped = create_advanced_probabilistic_mapping(self)
-
-        # For individual firms, we need to apply the mapping stochastically
-        print("Computing counterfactual turnovers for each firm...")
-
-        # Initialize counterfactual turnovers
-        T_cf = np.zeros_like(T_obs)
-
-        # Create mapping probabilities for each turnover level
-        mapping_probs = {}
-        bin_width = 1.0  # £1k bins
-
-        for i, bin_center in enumerate(self.bin_centers):
-            if self.T_lower <= bin_center <= self.T_star:
-                # Compute bunching probability for this turnover level
-                pi_val = self.compute_local_bunching_probability(bin_center)
-
-                # Get displacement target
-                T_cf_target = self.compute_counterfactual_mapping(bin_center)
-
-                mapping_probs[bin_center] = {
-                    "pi": pi_val,
-                    "cf_target": T_cf_target,
-                    "stay_prob": 1 - pi_val,
-                    "displace_prob": pi_val,
-                }
-            else:
-                # Outside bunching region - no displacement
-                mapping_probs[bin_center] = {
-                    "pi": 0.0,
-                    "cf_target": bin_center,
-                    "stay_prob": 1.0,
-                    "displace_prob": 0.0,
-                }
-
-        # Apply mapping to each firm
-        np.random.seed(42)  # For reproducible results
-        n_displaced = 0
-
-        for idx, turnover in enumerate(T_obs):
-            # Find closest bin center
-            bin_center = round(turnover)  # Round to nearest £1k
-
-            if bin_center in mapping_probs:
-                prob_info = mapping_probs[bin_center]
-
-                # Determine if this firm gets displaced
-                if np.random.random() < prob_info["displace_prob"]:
-                    # Firm gets displaced
-                    T_cf[idx] = prob_info["cf_target"]
-                    n_displaced += 1
-                else:
-                    # Firm stays at original location
-                    T_cf[idx] = turnover
-            else:
-                # Turnover outside our analysis range - no change
-                T_cf[idx] = turnover
-
-            # Progress indicator
-            if (idx + 1) % 100000 == 0:
-                print(f"  Processed {idx + 1:,}/{len(T_obs):,} firms...")
-
-        # Add results to dataframe
-        df_mapped = df.copy()
-        df_mapped["step4_counterfactual_turnover_k"] = T_cf
-        df_mapped["step4_displacement_k"] = T_cf - T_obs
-
-        # Store firm mappings for Steps 6-7 (old_turnover, new_turnover)
-        # For Step 5: use Step 5 new policy turnovers if available
-        if hasattr(self, "f_new_policy"):
-            # Map counterfactual turnovers to new policy turnovers (simplified)
-            T_new_policy = self.map_cf_to_new_policy(T_cf)
-        else:
-            T_new_policy = T_cf  # If no Step 5, use counterfactual as "new"
-
-        self.firm_mappings = list(zip(T_obs, T_new_policy))
-
-        # Save results
-        df_mapped.to_csv(output_file, index=False)
-
-        # Print summary statistics
-        displacement = T_cf - T_obs
-        nonzero_displacement = displacement[displacement != 0]
-
-        print(f"\nStep 4 Advanced Mapping Complete!")
-        print("=" * 40)
-        print(f"Output file: {output_file}")
-        print(f"Total firms processed: {len(df):,}")
-        print(
-            f"Firms displaced: {n_displaced:,} ({100*n_displaced/len(df):.2f}%)"
-        )
-        print(f"Mean displacement: £{displacement.mean():.3f}k")
-        if len(nonzero_displacement) > 0:
-            print(
-                f"Mean nonzero displacement: £{nonzero_displacement.mean():.3f}k"
-            )
-            print(f"Max displacement: £{displacement.max():.3f}k")
-            print(f"Min displacement: £{displacement.min():.3f}k")
-
-        # Weighted statistics
-        obs_mean = np.average(T_obs, weights=df["weight"])
-        cf_mean = np.average(T_cf, weights=df["weight"])
-
-        print(f"\nWeighted Statistics:")
-        print(f"  Original mean turnover: £{obs_mean:.1f}k")
-        print(f"  Step 4 mapped mean turnover: £{cf_mean:.1f}k")
-        print(f"  Mean change: £{cf_mean - obs_mean:.3f}k")
-
-        return df_mapped
-
-    def get_results_dict(self):
-        """Return results as dictionary for use in subsequent steps."""
-
-        return {
-            "T_star": self.T_star,
-            "W_left": self.W_left,
-            "W_right": self.W_right,
-            "bin_centers": self.bin_centers,
-            "f_obs": self.f_obs,
-            "f_cf": self.f_cf,
-            "F_obs": self.F_obs,
-            "F_cf": self.F_cf,
-            "q_N_obs": self.q_N_obs,
-            "q_R_obs": self.q_R_obs,
-            "q_N_cf": self.q_N_cf,
-            "q_R_cf": self.q_R_cf,
-            "E": self.E,
-            "Delta_R": self.Delta_R,
-            "b": self.b,
-            "tau_e": self.tau_e,
-            "sigma": self.sigma,
-        }
-
-
-def main():
-    """Run Steps 1-4 analysis."""
-
-    # ==========================================================================
-    # CONFIGURATION: Set your asymmetric windows here
-    # ==========================================================================
-    # Counterfactual analysis windows (around original £90k threshold)
-    counterfactual_window_left = 25  # £10k below threshold (£80k-£90k)
-    counterfactual_window_right = 20  # £10k above threshold (£90k-£100k)
-
-    # New policy analysis windows (around new £100k threshold)
-    new_policy_window_left = 25  # £10k below new threshold (£90k-£100k)
-    new_policy_window_right = 20  # £10k above new threshold (£100k-£110k)
-    # ==========================================================================
-
-    # Initialize analysis with asymmetric windows
-    analysis = CounterfactualBunchingAnalysis(
-        threshold=90,
-        window_left=counterfactual_window_left,
-        window_right=counterfactual_window_right,
-        new_policy_window_left=new_policy_window_left,
-        new_policy_window_right=new_policy_window_right,
-    )
-
-    # Run complete analysis with Steps 1-5
-    print("STEPS 1-5: COMPLETE BUNCHING ANALYSIS PIPELINE")
-    print("Step 1: Counterfactual & bunching statistics")
-    print("Step 2: Effective wedge τₑ")
-    print("Step 3: Substitution elasticity σ")
-    print("Step 4: Advanced probabilistic mapping (no-notch counterfactual)")
-    print("Step 5: Forward map to NEW policy (£100k threshold)")
-    print("=" * 70)
-    results = analysis.run_analysis(tau_e=0.05, include_step4=True)
-
-    # Show detailed Step 4 results
-    if hasattr(analysis, "step4_results"):
-        print("\nDetailed Step 4 Results:")
-        print("-" * 40)
-        step4_df = analysis.step4_results
-        print(step4_df.round(4).to_string(index=False))
-
-        # Show example calculations for a few firms
-        print(f"\nExample firm mappings:")
-        example_firms = [82, 85, 88, 90]  # Firms at different positions
-
-        for T_obs in example_firms:
-            if analysis.T_lower <= T_obs <= analysis.T_star:
-                pi = analysis.compute_local_bunching_probability(T_obs)
-                u = analysis.compute_rank_among_bunchers(T_obs)
-                T_cf = analysis.compute_counterfactual_mapping(T_obs)
-
-                print(f"  Firm at £{T_obs}k:")
-                print(f"    π(T^obs) = {pi:.4f} (bunching probability)")
-                print(f"    u(T^obs) = {u:.4f} (rank among bunchers)")
-                print(f"    T^cf = £{T_cf:.2f}k (counterfactual turnover)")
-                print(f"    Displacement = £{T_cf - T_obs:.2f}k")
-                print()
-
-    # Visualization removed as requested
-
-    # Mapping check visualization removed as requested
-
-    return results
-
-
-# ============================================================================
-# STEP 4: ADVANCED PROBABILISTIC MAPPING - MAIN FUNCTION
-# ============================================================================
-
-
-def create_advanced_probabilistic_mapping(
-    analysis, decay_rate=0.01, spread_factor=3.0
-):
-    """
-    Step 4: Advanced Probabilistic Mapping - MAIN STEP 4 FUNCTION
-
-    This is the primary Step 4 implementation that creates the smooth counterfactual
-    distribution shown as the green curve in steps1234_complete_analysis.png.
-
-    Creates comprehensive probabilistic mapping that redistributes firms across
-    the full range to match the theoretical counterfactual distribution.
-    """
-
-    # Start with the observed distribution as base
-    mapped_distribution = np.copy(analysis.f_obs)
-
-    print(f"Creating advanced probabilistic mapping:")
-    print(f"- Decay rate: {decay_rate} (controls displacement spread)")
-    print(
-        f"- Spread factor: {spread_factor} (controls overall redistribution)"
-    )
-
-    # Skip first point (T=20k, x=-70) from all calculations
-    print(f"Skipping first point (x=-70, T=20k) from mapping calculations")
-
-    # For each bin, calculate how much it needs to change to match counterfactual
-    deficit = (
-        analysis.f_cf - analysis.f_obs
-    )  # Positive = need more, negative = have excess
-
-    # For each bin in the analysis (skip first point)
-    for i, T_obs in enumerate(analysis.bin_centers):
-        if i == 0:  # Skip first point (T=20k)
-            continue
-        original_density = analysis.f_obs[i]
-        target_density = analysis.f_cf[i]
-
-        if original_density > target_density:
-            # This bin has excess firms - redistribute the excess
-            excess_firms = original_density - target_density
-
-            # Set this bin to its target density
-            mapped_distribution[i] = target_density
-
-            # Redistribute excess firms across range (excluding first point)
-            if excess_firms > 0:
-                # Find locations that need more firms (positive deficit, skip first point)
-                deficit_mask = (deficit > 0) & (
-                    np.arange(len(deficit)) > 0
-                )  # Exclude index 0
-                target_locations = analysis.bin_centers[deficit_mask]
-                target_deficits = deficit[deficit_mask]
-
-                if len(target_locations) > 0:
-                    # Create probability weights based on distance and deficit size
-                    distances = np.abs(target_locations - T_obs)
-                    distance_weights = np.exp(-decay_rate * distances)
-                    deficit_weights = target_deficits * spread_factor
-
-                    # Combine distance and deficit weights
-                    combined_weights = distance_weights * deficit_weights
-                    probs = combined_weights / np.sum(combined_weights)
-
-                    # Distribute excess firms
-                    for j, target_turnover in enumerate(target_locations):
-                        target_idx = np.where(
-                            analysis.bin_centers == target_turnover
-                        )[0][0]
-                        redistribution = excess_firms * probs[j]
-                        mapped_distribution[target_idx] += redistribution
-
-                        # Update deficit to track progress
-                        deficit[target_idx] -= redistribution
-        # Bins without excess keep their observed density (already set in initialization)
-
-    # Check first point before scaling
-    print(f"Before scaling: First point mapped={mapped_distribution[0]:.1f}")
-
-    # Final adjustment: scale to match total counterfactual mass
-    total_cf = np.sum(analysis.f_cf)
-    total_mapped = np.sum(mapped_distribution)
-    scaling_factor = total_cf / total_mapped if total_mapped > 0 else 1.0
-    print(f"Scaling factor: {scaling_factor:.4f}")
-
-    if total_mapped > 0:
-        mapped_distribution *= scaling_factor
-
-    # Check first point after scaling
-    print(f"After scaling: First point mapped={mapped_distribution[0]:.1f}")
-
-    return mapped_distribution
-
-
-# ============================================================================
-# STEP 4: SUPPORTING FUNCTIONS FOR ADVANCED MAPPING
-# ============================================================================
-
-
-def optimize_advanced_parameters(analysis):
-    """Optimize parameters for advanced probabilistic mapping."""
-
-    from scipy.optimize import minimize
-
-    def mapping_error(params):
-        decay_rate, spread_factor = params
-        mapped_dist = create_advanced_probabilistic_mapping(
-            analysis, decay_rate, spread_factor
-        )
-        error = np.sum((mapped_dist - analysis.f_cf) ** 2)
-        return error
-
-    print("Optimizing advanced probabilistic mapping parameters...")
-
-    # Optimize decay rate (0.001 to 0.1) and spread factor (0.5 to 5.0)
-    result = minimize(
-        mapping_error,
-        x0=[0.01, 2.0],
-        bounds=[(0.001, 0.1), (0.5, 5.0)],
-        method="L-BFGS-B",
-    )
-
-    optimal_decay = result.x[0]
-    optimal_spread = result.x[1]
-
-    print(f"Optimal decay rate: {optimal_decay:.4f}")
-    print(f"Optimal spread factor: {optimal_spread:.2f}")
-
-    return optimal_decay, optimal_spread
-
-
-if __name__ == "__main__":
-    results = main()
diff --git a/policyengine_uk/firm_analysis/plot_example.py b/policyengine_uk/firm_analysis/plot_example.py
deleted file mode 100644
index bbe903725..000000000
--- a/policyengine_uk/firm_analysis/plot_example.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/usr/bin/env python3
-"""
-Simple turnover distribution plot from synthetic firm data.
-"""
-
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from pathlib import Path
-
-
-def create_turnover_plot():
-    """Create a simple turnover distribution chart."""
-
-    # Load data
-    data_path = Path(__file__).parent / "synthetic_firms_turnover.csv"
-    df = pd.read_csv(data_path)
-
-    # Create bins from 1k to 300k
-    bin_edges = np.arange(0.5, 300.5, 1.0)  # 1k intervals
-
-    # Calculate weighted histogram
-    hist, _ = np.histogram(
-        df["annual_turnover_k"], bins=bin_edges, weights=df["weight"]
-    )
-
-    # Create plot
-    plt.figure(figsize=(15, 6))
-    x_positions = np.arange(len(hist))
-
-    plt.bar(
-        x_positions,
-        hist,
-        color="lightblue",
-        alpha=0.7,
-        edgecolor="black",
-        linewidth=0.1,
-    )
-
-    plt.xlabel("Annual Turnover (£k)")
-    plt.ylabel("Number of Firms (2023-24)")
-
-    # Set x-axis labels every 10k
-    label_positions = [
-        i for i in range(9, len(hist), 10)
-    ]  # Start at 10k (index 9)
-    label_texts = [f"{i+1}" for i in label_positions]
-    plt.xticks(label_positions, label_texts)
-
-    # Add vertical lines at 85k and 150k
-    plt.axvline(
-        x=84, color="red", linestyle="--", alpha=0.7, linewidth=2
-    )  # 85k threshold
-    plt.axvline(
-        x=149, color="red", linestyle="--", alpha=0.7, linewidth=2
-    )  # 150k threshold
-
-    # Add labels for the vertical lines
-    plt.text(
-        85,
-        max(hist) * 0.8,
-        "VAT Threshold",
-        rotation=0,
-        color="red",
-        fontsize=8,
-        ha="left",
-    )
-    plt.text(
-        150,
-        max(hist) * 0.8,
-        "VAT Flat Rate Scheme",
-        rotation=0,
-        color="red",
-        fontsize=8,
-        ha="left",
-    )
-
-    plt.grid(axis="y", alpha=0.3, linestyle="--")
-
-    # Save chart
-    output_path = Path(__file__).parent / "turnover_distribution.png"
-    plt.savefig(output_path, dpi=300, bbox_inches="tight")
-
-    print(f"Chart saved: {output_path}")
-
-
-if __name__ == "__main__":
-    create_turnover_plot()
diff --git a/policyengine_uk/firm_analysis/static_revenue.py b/policyengine_uk/firm_analysis/static_revenue.py
deleted file mode 100644
index db1e4bf4f..000000000
--- a/policyengine_uk/firm_analysis/static_revenue.py
+++ /dev/null
@@ -1,370 +0,0 @@
-"""
-Static Revenue Analysis for VAT Threshold Bunching
-Uses pre-calculated VAT liability from synthetic data generation (turnover - input)
-"""
-
-import pandas as pd
-import numpy as np
-import os
-
-# Get the current directory (analysis folder)
-current_dir = os.path.dirname(os.path.abspath(__file__))
-base_dir = os.path.dirname(current_dir)
-
-# Load the data with pre-calculated VAT liability
-print("Loading data...")
-synthetic_firms = pd.read_csv(
-    os.path.join(current_dir, "synthetic_firms_turnover.csv")
-)
-
-weighted_total_firms = synthetic_firms["weight"].sum()
-print(
-    f"Loaded {weighted_total_firms:,.0f} firms with pre-calculated VAT liability"
-)
-
-# Check if VAT liability already exists in the data
-if "vat_liability_k" not in synthetic_firms.columns:
-    print(
-        "ERROR: vat_liability_k column not found. Please run generate_synthetic_data.py first."
-    )
-    exit(1)
-
-# Convert VAT liability from thousands to pounds for analysis
-synthetic_firms["vat_liability"] = synthetic_firms["vat_liability_k"] * 1000
-
-print(f"\nVAT Liability Statistics:")
-print(
-    f"  Total VAT liability: £{(synthetic_firms['vat_liability'] * synthetic_firms['weight']).sum() / 1_000_000:,.2f} million"
-)
-print(
-    f"  Firms with positive VAT: {synthetic_firms[synthetic_firms['vat_liability'] > 0]['weight'].sum():,.0f}"
-)
-print(
-    f"  Firms with negative VAT: {synthetic_firms[synthetic_firms['vat_liability'] < 0]['weight'].sum():,.0f}"
-)
-print(
-    f"  Firms with zero VAT: {synthetic_firms[synthetic_firms['vat_liability'] == 0]['weight'].sum():,.0f}"
-)
-
-# Static revenue impact analysis
-# Convert turnover from thousands to pounds for threshold comparison
-synthetic_firms["annual_turnover"] = (
-    synthetic_firms["annual_turnover_k"] * 1000
-)
-
-# Identify firms in the bunching region (£85k to £90k)
-affected = synthetic_firms[
-    (synthetic_firms["annual_turnover"] >= 85000)
-    & (synthetic_firms["annual_turnover"] < 90000)
-]
-
-# Calculate total revenue loss (weighted VAT liability)
-total_revenue_loss = (affected["vat_liability"] * affected["weight"]).sum()
-total_revenue_loss_millions = total_revenue_loss / 1_000_000
-
-# Summary statistics (applying weights)
-total_weighted_firms = synthetic_firms["weight"].sum()
-affected_weighted_firms = affected["weight"].sum()
-
-print("\n" + "=" * 60)
-print("STATIC REVENUE IMPACT ANALYSIS")
-print("=" * 60)
-print(f"Total firms in dataset: {total_weighted_firms:,.0f}")
-print(f"Firms in bunching region (£85k-£90k): {affected_weighted_firms:,.0f}")
-print(
-    f"Percentage of firms affected: {affected_weighted_firms/total_weighted_firms*100:.2f}%"
-)
-print(
-    f"\nTotal VAT liability in bunching region: £{total_revenue_loss_millions:,.2f} million"
-)
-print(
-    f"Average VAT per affected firm: £{total_revenue_loss/affected_weighted_firms:,.2f}"
-    if affected_weighted_firms > 0
-    else "N/A"
-)
-
-# Additional analysis by sector
-print("\n" + "=" * 60)
-print("TOP 5 SECTORS BY VAT LIABILITY IN BUNCHING REGION")
-print("=" * 60)
-if len(affected) > 0:
-    # Calculate weighted VAT liability by sector
-    sector_impact = affected.groupby("sic_code").apply(
-        lambda x: pd.Series(
-            {
-                "weighted_vat_liability": (
-                    x["vat_liability"] * x["weight"]
-                ).sum(),
-                "weighted_firm_count": x["weight"].sum(),
-            }
-        )
-    )
-    sector_impact = sector_impact.sort_values(
-        "weighted_vat_liability", ascending=False
-    ).head(5)
-
-    for idx, (sector, row) in enumerate(sector_impact.iterrows(), 1):
-        vat_millions = row["weighted_vat_liability"] / 1_000_000
-        print(f"{idx}. Sector {sector}")
-        print(
-            f"   Firms: {row['weighted_firm_count']:,.0f}, VAT: £{vat_millions:.2f} million"
-        )
-
-# Check data quality
-print("\n" + "=" * 60)
-print("DATA QUALITY CHECKS")
-print("=" * 60)
-print(
-    f"Firms with missing SIC codes: {synthetic_firms[synthetic_firms['sic_code'].isna()]['weight'].sum():,.0f}"
-)
-print(
-    f"Firms with zero/negative turnover: {synthetic_firms[synthetic_firms['annual_turnover_k'] <= 0]['weight'].sum():,.0f}"
-)
-print(
-    f"Raw records with zero/negative weights: {(synthetic_firms['weight'] <= 0).sum()}"
-)
-print(
-    f"Firms with non-zero VAT liability: {synthetic_firms[synthetic_firms['vat_liability'] != 0]['weight'].sum():,.0f}"
-)
-
-# Calculate total weighted VAT liability
-total_weighted_vat = (
-    synthetic_firms["vat_liability"] * synthetic_firms["weight"]
-).sum()
-total_vat_billions = total_weighted_vat / 1_000_000_000
-print(
-    f"Total weighted VAT liability across all firms: £{total_vat_billions:,.2f} billion"
-)
-
-# Show input/output statistics
-print(f"\nInput/Output Statistics:")
-print(
-    f"Average input/output ratio: {(synthetic_firms['annual_input_k'] / synthetic_firms['annual_turnover_k']).mean():.2%}"
-)
-print(
-    f"Firms with input > output (negative VAT): {synthetic_firms[synthetic_firms['annual_input_k'] > synthetic_firms['annual_turnover_k']]['weight'].sum():,.0f}"
-)
-
-# Generate multi-year revenue impact table
-print("\n" + "=" * 100)
-print("MULTI-YEAR REVENUE IMPACT ANALYSIS")
-print("=" * 100)
-
-# Define the fiscal years and parameters with correct baseline thresholds
-# Based on RPI with 2-year lag: 2024-25=2.0%, 2025-26=2.5%, 2026-27=3.0%
-fiscal_years = [
-    {
-        "year": "2024-25",
-        "rpi": "— (frozen)",
-        "baseline": 85000,
-        "policy": 90000,
-        "hmrc_impact": -150,
-        "firm_growth": 1.031,
-    },  # 3.1%
-    {
-        "year": "2025-26",
-        "rpi": "— (frozen)",
-        "baseline": 85000,
-        "policy": 90000,
-        "hmrc_impact": -185,
-        "firm_growth": 1.0516,
-    },  # 1.031 * 1.020
-    {
-        "year": "2026-27",
-        "rpi": "2024-25: +2.0%",
-        "baseline": 87000,
-        "policy": 90000,
-        "hmrc_impact": -125,
-        "firm_growth": 1.0779,
-    },  # £85,000 * 1.020 = £87,000
-    {
-        "year": "2027-28",
-        "rpi": "2025-26: +2.5%",
-        "baseline": 89000,
-        "policy": 90000,
-        "hmrc_impact": -50,
-        "firm_growth": 1.1102,
-    },  # £87,000 * 1.025 = £89,175 ≈ £89,000
-    {
-        "year": "2028-29",
-        "rpi": "2026-27: +3.0%",
-        "baseline": 92000,
-        "policy": 90000,
-        "hmrc_impact": 65,
-        "firm_growth": 1.1424,
-    },  # £89,000 * 1.030 = £91,670 ≈ £92,000
-]
-
-# Calculate PolicyEngine estimates for each year
-results = []
-for fy in fiscal_years:
-    # Apply growth factor to firm data for this year
-    # Create a copy with adjusted turnover and VAT liability
-    adjusted_firms = synthetic_firms.copy()
-    adjusted_firms["annual_turnover"] = (
-        adjusted_firms["annual_turnover_k"] * 1000 * fy["firm_growth"]
-    )
-    adjusted_firms["vat_liability"] = (
-        adjusted_firms["vat_liability_k"] * 1000 * fy["firm_growth"]
-    )
-
-    # Determine the affected range based on baseline vs policy
-    if fy["baseline"] > fy["policy"]:
-        # When baseline > policy (2028-29), firms between policy and baseline
-        # are now required to register (they're above £90k policy but would have been below baseline)
-        baseline_affected = adjusted_firms[
-            (adjusted_firms["annual_turnover"] >= fy["policy"])
-            & (adjusted_firms["annual_turnover"] < fy["baseline"])
-        ]
-        # This is a revenue gain - these firms now must pay VAT
-        pe_impact = (
-            baseline_affected["vat_liability"] * baseline_affected["weight"]
-        ).sum() / 1_000_000
-    else:
-        # When baseline < policy, firms between baseline and policy can avoid VAT
-        baseline_affected = adjusted_firms[
-            (adjusted_firms["annual_turnover"] >= fy["baseline"])
-            & (adjusted_firms["annual_turnover"] < fy["policy"])
-        ]
-        # This is a revenue loss - these firms avoid VAT
-        pe_impact = (
-            -(
-                baseline_affected["vat_liability"]
-                * baseline_affected["weight"]
-            ).sum()
-            / 1_000_000
-        )
-
-    results.append(
-        {
-            "Fiscal Year": fy["year"],
-            "Lagged RPI used": fy["rpi"],
-            "Baseline": f"£{fy['baseline']:,}",
-            "Policy": f"£{fy['policy']:,}",
-            "HMRC Revenue Impact (£m)": fy["hmrc_impact"],
-            "PolicyEngine Impact (£m)": round(pe_impact, 0),
-            "Difference": round(pe_impact - fy["hmrc_impact"], 0),
-        }
-    )
-
-# Create DataFrame for nice display
-import pandas as pd
-
-results_df = pd.DataFrame(results)
-
-# Display the table
-print("\n" + "-" * 100)
-print(
-    f"{'Fiscal Year':<12} {'Lagged RPI':<20} {'Baseline':<12} {'Policy':<12} {'HMRC (£m)':<12} {'PE (£m)':<12} {'Diff (£m)':<10}"
-)
-print("-" * 100)
-
-for _, row in results_df.iterrows():
-    print(
-        f"{row['Fiscal Year']:<12} {row['Lagged RPI used']:<20} {row['Baseline']:<12} {row['Policy']:<12} "
-        f"{row['HMRC Revenue Impact (£m)']:>10} {row['PolicyEngine Impact (£m)']:>10} "
-        f"{row['Difference']:>10}"
-    )
-
-print("-" * 100)
-print(
-    f"\nNote: Negative values indicate revenue loss, positive values indicate revenue gain"
-)
-print(
-    f"PE = PolicyEngine estimate based on firm-level VAT liability calculations"
-)
-
-# Create and save a plot
-import matplotlib.pyplot as plt
-
-fig, ax = plt.subplots(figsize=(12, 6))
-
-# Extract data for plotting
-years = [fy["year"] for fy in fiscal_years]
-hmrc_impacts = [fy["hmrc_impact"] for fy in fiscal_years]
-pe_impacts = [row["PolicyEngine Impact (£m)"] for row in results]
-
-# Plot the data
-x = range(len(years))
-width = 0.35
-
-bars1 = ax.bar(
-    [i - width / 2 for i in x],
-    hmrc_impacts,
-    width,
-    label="HMRC",
-    color="#2E86AB",
-)
-bars2 = ax.bar(
-    [i + width / 2 for i in x],
-    pe_impacts,
-    width,
-    label="PolicyEngine",
-    color="#A23B72",
-)
-
-# Add value labels on bars with padding
-for bar in bars1:
-    height = bar.get_height()
-    # Add padding: 5 units above positive bars, 5 units below negative bars
-    y_pos = height + 5 if height > 0 else height - 5
-    ax.text(
-        bar.get_x() + bar.get_width() / 2.0,
-        y_pos,
-        f"£{int(height)}m",
-        ha="center",
-        va="bottom" if height > 0 else "top",
-    )
-
-for bar in bars2:
-    height = bar.get_height()
-    # Add padding: 5 units above positive bars, 5 units below negative bars
-    y_pos = height + 5 if height > 0 else height - 5
-    ax.text(
-        bar.get_x() + bar.get_width() / 2.0,
-        y_pos,
-        f"£{int(height)}m",
-        ha="center",
-        va="bottom" if height > 0 else "top",
-    )
-
-# Customize the plot
-# ax.set_xlabel('Fiscal Year', fontsize=12)
-ax.set_ylabel("£ millions", fontsize=12)
-ax.set_title(
-    "Revenue Impact of Increasing the VAT Registration Threshold (2024-29)",
-    fontsize=14,
-    fontweight="bold",
-)
-ax.set_xticks(x)
-ax.set_xticklabels(years)
-ax.legend()
-ax.grid(True, alpha=0.3, linestyle="--")
-ax.axhline(y=0, color="black", linestyle="-", linewidth=0.5)
-
-# Set y-axis from -250 to 100
-ax.set_ylim(-250, 100)
-
-# Add baseline/policy annotations
-for i, fy in enumerate(fiscal_years):
-    baseline_text = f"Baseline: £{fy['baseline']/1000:.0f}k"
-    policy_text = f"Policy: £{fy['policy']/1000:.0f}k"
-    ax.text(
-        i,
-        ax.get_ylim()[0] * 0.9,
-        f"{baseline_text}\n{policy_text}",
-        ha="center",
-        va="top",
-        fontsize=10,
-        color="gray",
-    )
-
-plt.tight_layout()
-
-# Save the plot
-plot_path = os.path.join(current_dir, "vat_threshold_revenue_impact.png")
-plt.savefig(plot_path, dpi=150, bbox_inches="tight")
-print(f"\nPlot saved to: {plot_path}")
-
-# Display the plot
-# plt.show()
diff --git a/policyengine_uk/firm_analysis/turnover_distribution.png b/policyengine_uk/firm_analysis/turnover_distribution.png
deleted file mode 100644
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From 655a8e7b97209ee8afc24bc4f2984069e1f79275 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 21:19:33 +0100
Subject: [PATCH 6/9] edit

---
 policyengine_uk/firm_tax_benefit_system.py    | 60 +++++++++++++++++++
 policyengine_uk/simulation.py                 | 17 ++++--
 policyengine_uk/tax_benefit_system.py         | 10 +---
 .../input/business_group.py                   |  0
 .../input/firm.py                             |  0
 .../input/sector.py                           |  0
 6 files changed, 74 insertions(+), 13 deletions(-)
 create mode 100644 policyengine_uk/firm_tax_benefit_system.py
 rename policyengine_uk/{variables => variables_firm}/input/business_group.py (100%)
 rename policyengine_uk/{variables => variables_firm}/input/firm.py (100%)
 rename policyengine_uk/{variables => variables_firm}/input/sector.py (100%)

diff --git a/policyengine_uk/firm_tax_benefit_system.py b/policyengine_uk/firm_tax_benefit_system.py
new file mode 100644
index 000000000..2dc3024d7
--- /dev/null
+++ b/policyengine_uk/firm_tax_benefit_system.py
@@ -0,0 +1,60 @@
+"""Firm-specific tax benefit system for business microdata."""
+
+import copy
+from pathlib import Path
+from typing import Any, Dict
+
+from policyengine_core.taxbenefitsystems import TaxBenefitSystem
+from policyengine_uk.entities import Firm, Sector, BusinessGroup
+from policyengine_uk.utils.parameters import backdate_parameters
+
+# Module constants
+COUNTRY_DIR = Path(__file__).parent
+
+
+class FirmTaxBenefitSystem(TaxBenefitSystem):
+    """Firm-specific tax and benefit system for business microdata.
+
+    This system handles firm, sector, and business_group entities
+    for business-level analysis.
+    """
+
+    def __init__(self):
+        """Initialize the firm tax-benefit system with business entities."""
+        self._parameters_at_instant_cache: Dict[str, Any] = {}
+        self.variables = {}
+
+        # Create copies of entity classes to avoid modifying originals
+        firm, sector, business_group = (
+            copy.copy(Firm),
+            copy.copy(Sector),
+            copy.copy(BusinessGroup),
+        )
+
+        # Set up entities
+        self.entities = [firm, sector, business_group]
+        self.person_entity = (
+            firm  # Firm is the "person" entity for this system
+        )
+        self.group_entities = [sector, business_group]
+        self.group_entity_keys = [entity.key for entity in self.group_entities]
+
+        # Link entities to this tax-benefit system
+        for entity in self.entities:
+            entity.set_tax_benefit_system(self)
+
+        self.variable_module_metadata = {}
+
+        # Add firm variables from separate directory
+        variables_firm_directory = COUNTRY_DIR / "variables_firm"
+        self.add_variables_from_directory(variables_firm_directory)
+
+        # Add parameters (if needed for firm calculations)
+        parameters_directory = COUNTRY_DIR / "parameters"
+        if parameters_directory.exists():
+            self.load_parameters(str(parameters_directory))
+            backdate_parameters(self.parameters)
+
+    def reset_parameter_caches(self):
+        """Reset parameter caches (compatibility method)."""
+        self._parameters_at_instant_cache = {}
diff --git a/policyengine_uk/simulation.py b/policyengine_uk/simulation.py
index 473b206af..d79a2c8b6 100644
--- a/policyengine_uk/simulation.py
+++ b/policyengine_uk/simulation.py
@@ -113,16 +113,19 @@ def __init__(
 
         # Universal Credit reform (July 2025). Needs closer integration in the baseline,
         # but adding here for ease of toggling on/off via the 'active' parameter.
-        from policyengine_uk.scenarios import universal_credit_july_2025_reform
-
-        universal_credit_july_2025_reform.simulation_modifier(self)
+        # Skip UC reform for firm datasets
+        if not isinstance(dataset, UKFirmSingleYearDataset):
+            from policyengine_uk.scenarios import universal_credit_july_2025_reform
+            universal_credit_july_2025_reform.simulation_modifier(self)
 
         # Apply structural modifiers
 
         self.tax_benefit_system.reset_parameter_caches()
 
-        self.move_values("capital_gains", "capital_gains_before_response")
-        self.move_values("employment_income", "employment_income_before_lsr")
+        # Skip variable movements for firm datasets (they don't have these variables)
+        if not isinstance(dataset, UKFirmSingleYearDataset):
+            self.move_values("capital_gains", "capital_gains_before_response")
+            self.move_values("employment_income", "employment_income_before_lsr")
 
         if scenario is not None:
             self.baseline = Simulation(
@@ -382,6 +385,10 @@ def build_from_firm_dataset(
         Args:
             dataset: UKFirmSingleYearDataset containing firm data
         """
+        # Use FirmTaxBenefitSystem for firm datasets
+        from policyengine_uk.firm_tax_benefit_system import FirmTaxBenefitSystem
+        self.tax_benefit_system = FirmTaxBenefitSystem()
+        
         self.build_from_firm_ids(
             dataset.firm.firm_id,
             dataset.firm.firm_sector_id,
diff --git a/policyengine_uk/tax_benefit_system.py b/policyengine_uk/tax_benefit_system.py
index d1b12404e..02b8da8f3 100644
--- a/policyengine_uk/tax_benefit_system.py
+++ b/policyengine_uk/tax_benefit_system.py
@@ -116,13 +116,10 @@ def __init__(self):
         self.variables = {}
 
         # Create copies of entity classes to avoid modifying originals
-        person, benunit, household, firm, sector, business_group = (
+        person, benunit, household = (
             copy.copy(Person),
             copy.copy(BenUnit),
             copy.copy(Household),
-            copy.copy(Firm),
-            copy.copy(Sector),
-            copy.copy(BusinessGroup),
         )
 
         # Set up entities
@@ -130,12 +127,9 @@ def __init__(self):
             person,
             benunit,
             household,
-            firm,
-            sector,
-            business_group,
         ]
         self.person_entity = person
-        self.group_entities = [benunit, household, sector, business_group]
+        self.group_entities = [benunit, household]
         self.group_entity_keys = [entity.key for entity in self.group_entities]
 
         # Link entities to this tax-benefit system
diff --git a/policyengine_uk/variables/input/business_group.py b/policyengine_uk/variables_firm/input/business_group.py
similarity index 100%
rename from policyengine_uk/variables/input/business_group.py
rename to policyengine_uk/variables_firm/input/business_group.py
diff --git a/policyengine_uk/variables/input/firm.py b/policyengine_uk/variables_firm/input/firm.py
similarity index 100%
rename from policyengine_uk/variables/input/firm.py
rename to policyengine_uk/variables_firm/input/firm.py
diff --git a/policyengine_uk/variables/input/sector.py b/policyengine_uk/variables_firm/input/sector.py
similarity index 100%
rename from policyengine_uk/variables/input/sector.py
rename to policyengine_uk/variables_firm/input/sector.py

From 7e12fce401bacaf04c84ec2efff909c20f368c9c Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 21:25:23 +0100
Subject: [PATCH 7/9] format

---
 policyengine_uk/simulation.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/policyengine_uk/simulation.py b/policyengine_uk/simulation.py
index d79a2c8b6..378dd2ca3 100644
--- a/policyengine_uk/simulation.py
+++ b/policyengine_uk/simulation.py
@@ -115,7 +115,10 @@ def __init__(
         # but adding here for ease of toggling on/off via the 'active' parameter.
         # Skip UC reform for firm datasets
         if not isinstance(dataset, UKFirmSingleYearDataset):
-            from policyengine_uk.scenarios import universal_credit_july_2025_reform
+            from policyengine_uk.scenarios import (
+                universal_credit_july_2025_reform,
+            )
+
             universal_credit_july_2025_reform.simulation_modifier(self)
 
         # Apply structural modifiers
@@ -125,7 +128,9 @@ def __init__(
         # Skip variable movements for firm datasets (they don't have these variables)
         if not isinstance(dataset, UKFirmSingleYearDataset):
             self.move_values("capital_gains", "capital_gains_before_response")
-            self.move_values("employment_income", "employment_income_before_lsr")
+            self.move_values(
+                "employment_income", "employment_income_before_lsr"
+            )
 
         if scenario is not None:
             self.baseline = Simulation(
@@ -386,9 +391,12 @@ def build_from_firm_dataset(
             dataset: UKFirmSingleYearDataset containing firm data
         """
         # Use FirmTaxBenefitSystem for firm datasets
-        from policyengine_uk.firm_tax_benefit_system import FirmTaxBenefitSystem
+        from policyengine_uk.firm_tax_benefit_system import (
+            FirmTaxBenefitSystem,
+        )
+
         self.tax_benefit_system = FirmTaxBenefitSystem()
-        
+
         self.build_from_firm_ids(
             dataset.firm.firm_id,
             dataset.firm.firm_sector_id,

From 3becda24c888fc264323054bfd7588ec6e0a731d Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 21:56:40 +0100
Subject: [PATCH 8/9] edit

---
 policyengine_uk/__init__.py        |   1 +
 policyengine_uk/firm_simulation.py | 135 +++++++++++++++++++++++++++++
 policyengine_uk/simulation.py      | 100 ++-------------------
 policyengine_uk/system.py          |   1 +
 4 files changed, 143 insertions(+), 94 deletions(-)
 create mode 100644 policyengine_uk/firm_simulation.py

diff --git a/policyengine_uk/__init__.py b/policyengine_uk/__init__.py
index 727ce05c5..2b1d6fd51 100644
--- a/policyengine_uk/__init__.py
+++ b/policyengine_uk/__init__.py
@@ -6,6 +6,7 @@
     CountryTaxBenefitSystem,
     Microsimulation,
     Simulation,
+    FirmSimulation,
     COUNTRY_DIR,
     parameters,
     variables,
diff --git a/policyengine_uk/firm_simulation.py b/policyengine_uk/firm_simulation.py
new file mode 100644
index 000000000..b30a6ba83
--- /dev/null
+++ b/policyengine_uk/firm_simulation.py
@@ -0,0 +1,135 @@
+"""Firm-specific simulation class for business microdata analysis."""
+
+from typing import Dict, Optional, Union, List
+import numpy as np
+import pandas as pd
+
+from policyengine_core.simulations.simulation import Simulation
+from policyengine_uk.data.firm_dataset_schema import UKFirmSingleYearDataset
+from policyengine_uk.firm_tax_benefit_system import FirmTaxBenefitSystem
+
+
+class FirmSimulation(Simulation):
+    """Firm-specific simulation class for business microdata.
+
+    This simulation handles firm, sector, and business_group entities
+    for business-level policy analysis.
+    """
+
+    default_input_period: int = 2025
+    default_calculation_period: int = 2025
+
+    def __init__(
+        self,
+        dataset: Optional[Union[UKFirmSingleYearDataset, str]] = None,
+        reform: Optional[Dict] = None,
+        trace: bool = False,
+    ):
+        """Initialize a firm simulation.
+
+        Args:
+            dataset: Firm dataset or path to firm dataset
+            reform: Optional reform to apply
+            trace: Whether to enable tracing
+        """
+        # Initialize firm tax benefit system
+        tax_benefit_system = FirmTaxBenefitSystem()
+
+        if reform is not None:
+            # Apply reform to the tax benefit system if needed
+            pass
+
+        # Build populations from dataset
+        if isinstance(dataset, str):
+            dataset = UKFirmSingleYearDataset(file_path=dataset)
+        elif dataset is None:
+            raise ValueError("FirmSimulation requires a firm dataset")
+        elif not isinstance(dataset, UKFirmSingleYearDataset):
+            raise ValueError(f"Unsupported dataset type: {dataset.__class__}")
+
+        # Create populations using the dataset
+        from policyengine_core.simulations.simulation_builder import (
+            SimulationBuilder,
+        )
+
+        builder = SimulationBuilder()
+        builder.populations = tax_benefit_system.instantiate_entities()
+
+        # Declare entities
+        builder.declare_person_entity("firm", dataset.firm.firm_id.values)
+        builder.declare_entity("sector", dataset.sector.sector_id.values)
+        builder.declare_entity(
+            "business_group", dataset.business_group.business_group_id.values
+        )
+
+        # Link firms to sectors and business groups
+        builder.join_with_persons(
+            builder.populations["sector"],
+            dataset.firm.firm_sector_id.values,
+            np.array(["member"] * len(dataset.firm)),
+        )
+        builder.join_with_persons(
+            builder.populations["business_group"],
+            dataset.firm.firm_business_group_id.values,
+            np.array(["member"] * len(dataset.firm)),
+        )
+
+        # Initialize the parent Simulation with populations
+        super().__init__(
+            tax_benefit_system=tax_benefit_system,
+            populations=builder.populations,
+        )
+
+        # Set up tracing if requested
+        if trace:
+            self.trace = True
+
+        # Load variable values from dataset
+        for table in dataset.tables:
+            for variable in table.columns:
+                if variable not in self.tax_benefit_system.variables:
+                    continue
+                self.set_input(
+                    variable, dataset.time_period, table[variable].values
+                )
+
+        self.dataset = dataset
+        self.input_variables = self.get_known_variables()
+
+    def get_known_variables(self) -> List[str]:
+        """Get list of variables with known values.
+
+        Returns:
+            List of variable names that have values set
+        """
+        known = []
+        for variable in self.tax_benefit_system.variables:
+            try:
+                if len(self.get_holder(variable).get_known_periods()) > 0:
+                    known.append(variable)
+            except:
+                pass
+        return known
+
+    def calculate_dataframe(
+        self,
+        variable_names: List[str],
+        period: Optional[int] = None,
+    ) -> pd.DataFrame:
+        """Calculate multiple variables and return as DataFrame.
+
+        Args:
+            variable_names: List of variables to calculate
+            period: Time period for calculation
+
+        Returns:
+            DataFrame with calculated values
+        """
+        if period is None:
+            period = self.default_calculation_period
+
+        data = {}
+        for variable in variable_names:
+            data[variable] = self.calculate(variable, period)
+
+        return pd.DataFrame(data)
diff --git a/policyengine_uk/simulation.py b/policyengine_uk/simulation.py
index 378dd2ca3..d1aac5316 100644
--- a/policyengine_uk/simulation.py
+++ b/policyengine_uk/simulation.py
@@ -19,9 +19,6 @@
     UKMultiYearDataset,
     UKSingleYearDataset,
 )
-from policyengine_uk.data.firm_dataset_schema import (
-    UKFirmSingleYearDataset,
-)
 from policyengine_uk.utils.scenario import Scenario
 from policyengine_uk.data.economic_assumptions import (
     extend_single_year_dataset,
@@ -100,8 +97,6 @@ def __init__(
             self.build_from_single_year_dataset(dataset)
         elif isinstance(dataset, UKMultiYearDataset):
             self.build_from_multi_year_dataset(dataset)
-        elif isinstance(dataset, UKFirmSingleYearDataset):
-            self.build_from_firm_dataset(dataset)
         elif dataset is None:
             self.build_from_url(
                 "hf://policyengine/policyengine-uk-data/enhanced_frs_2023_24.h5"
@@ -113,24 +108,18 @@ def __init__(
 
         # Universal Credit reform (July 2025). Needs closer integration in the baseline,
         # but adding here for ease of toggling on/off via the 'active' parameter.
-        # Skip UC reform for firm datasets
-        if not isinstance(dataset, UKFirmSingleYearDataset):
-            from policyengine_uk.scenarios import (
-                universal_credit_july_2025_reform,
-            )
+        from policyengine_uk.scenarios import (
+            universal_credit_july_2025_reform,
+        )
 
-            universal_credit_july_2025_reform.simulation_modifier(self)
+        universal_credit_july_2025_reform.simulation_modifier(self)
 
         # Apply structural modifiers
 
         self.tax_benefit_system.reset_parameter_caches()
 
-        # Skip variable movements for firm datasets (they don't have these variables)
-        if not isinstance(dataset, UKFirmSingleYearDataset):
-            self.move_values("capital_gains", "capital_gains_before_response")
-            self.move_values(
-                "employment_income", "employment_income_before_lsr"
-            )
+        self.move_values("capital_gains", "capital_gains_before_response")
+        self.move_values("employment_income", "employment_income_before_lsr")
 
         if scenario is not None:
             self.baseline = Simulation(
@@ -237,8 +226,6 @@ def build_from_url(self, url: str) -> None:
             self.build_from_multi_year_dataset(UKMultiYearDataset(dataset))
         elif UKSingleYearDataset.validate_file_path(dataset, False):
             self.build_from_single_year_dataset(UKSingleYearDataset(dataset))
-        elif UKFirmSingleYearDataset.validate_file_path(dataset, False):
-            self.build_from_firm_dataset(UKFirmSingleYearDataset(dataset))
         else:
             dataset = Dataset.from_file(dataset, self.default_input_period)
             self.build_from_dataset(dataset)
@@ -382,81 +369,6 @@ def build_from_multi_year_dataset(
 
         self.dataset = dataset
 
-    def build_from_firm_dataset(
-        self, dataset: UKFirmSingleYearDataset
-    ) -> None:
-        """Build simulation from a firm dataset.
-
-        Args:
-            dataset: UKFirmSingleYearDataset containing firm data
-        """
-        # Use FirmTaxBenefitSystem for firm datasets
-        from policyengine_uk.firm_tax_benefit_system import (
-            FirmTaxBenefitSystem,
-        )
-
-        self.tax_benefit_system = FirmTaxBenefitSystem()
-
-        self.build_from_firm_ids(
-            dataset.firm.firm_id,
-            dataset.firm.firm_sector_id,
-            dataset.firm.firm_business_group_id,
-            dataset.sector.sector_id,
-            dataset.business_group.business_group_id,
-        )
-
-        # Load variable values
-        for table in dataset.tables:
-            for variable in table.columns:
-                if variable not in self.tax_benefit_system.variables:
-                    continue
-                self.set_input(variable, dataset.time_period, table[variable])
-
-        self.dataset = dataset
-
-    def build_from_firm_ids(
-        self,
-        firm_id: np.ndarray,
-        firm_sector_id: np.ndarray,
-        firm_business_group_id: np.ndarray,
-        sector_id: np.ndarray,
-        business_group_id: np.ndarray,
-    ) -> None:
-        """Build simulation entities from firm ID arrays.
-
-        Args:
-            firm_id: Array of firm IDs
-            firm_sector_id: Array mapping firms to sectors
-            firm_business_group_id: Array mapping firms to business groups
-            sector_id: Array of sector IDs
-            business_group_id: Array of business group IDs
-        """
-        from policyengine_core.simulations.simulation_builder import (
-            SimulationBuilder,
-        )
-
-        builder = SimulationBuilder()
-        builder.populations = self.tax_benefit_system.instantiate_entities()
-
-        # Declare entities
-        builder.declare_person_entity("firm", firm_id)
-        builder.declare_entity("sector", np.unique(sector_id))
-        builder.declare_entity("business_group", np.unique(business_group_id))
-
-        # Link firms to sectors and business groups
-        builder.join_with_persons(
-            builder.populations["sector"],
-            firm_sector_id,
-            np.array(["member"] * len(firm_sector_id)),
-        )
-        builder.join_with_persons(
-            builder.populations["business_group"],
-            firm_business_group_id,
-            np.array(["member"] * len(firm_business_group_id)),
-        )
-
-        self.build_from_populations(builder.populations)
-
     def build_from_ids(
         self,
         person_id: np.ndarray,
diff --git a/policyengine_uk/system.py b/policyengine_uk/system.py
index 5d926165a..e43123f0b 100644
--- a/policyengine_uk/system.py
+++ b/policyengine_uk/system.py
@@ -1,3 +1,4 @@
 from .tax_benefit_system import *
 from .simulation import *
 from .microsimulation import *
+from .firm_simulation import FirmSimulation

From 7701f63d773fbeb49be8e2a9e7e0f13ae0c80048 Mon Sep 17 00:00:00 2001
From: Vahid Ahmadi <va.vahidahmadi@gmail.com>
Date: Sun, 31 Aug 2025 23:10:47 +0100
Subject: [PATCH 9/9] integrate with max pr

---
 .../hmrc/vat/deregistration_threshold.yaml    |  9 +++++++
 .../gov/hmrc/vat/registration_threshold.yaml  |  9 +++++++
 .../gov/hmrc/vat/firm_net_vat_liability.py    | 17 ++++++++++++
 .../hmrc/vat/firm_reduced_rated_supplies.py   | 11 ++++++++
 .../hmrc/vat/firm_standard_rated_supplies.py  | 11 ++++++++
 .../hmrc/vat/firm_vat_liability_calculated.py | 16 ++++++++++++
 .../gov/hmrc/vat/firm_vat_on_purchases.py     | 11 ++++++++
 .../gov/hmrc/vat/firm_vat_on_sales.py         | 26 +++++++++++++++++++
 .../gov/hmrc/vat/firm_vat_registered.py       | 20 ++++++++++++++
 .../gov/hmrc/vat/firm_zero_rated_supplies.py  | 11 ++++++++
 10 files changed, 141 insertions(+)
 create mode 100644 policyengine_uk/parameters/gov/hmrc/vat/deregistration_threshold.yaml
 create mode 100644 policyengine_uk/parameters/gov/hmrc/vat/registration_threshold.yaml
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_net_vat_liability.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_reduced_rated_supplies.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_standard_rated_supplies.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_liability_calculated.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_purchases.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_sales.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_registered.py
 create mode 100644 policyengine_uk/variables_firm/gov/hmrc/vat/firm_zero_rated_supplies.py

diff --git a/policyengine_uk/parameters/gov/hmrc/vat/deregistration_threshold.yaml b/policyengine_uk/parameters/gov/hmrc/vat/deregistration_threshold.yaml
new file mode 100644
index 000000000..6c09fa672
--- /dev/null
+++ b/policyengine_uk/parameters/gov/hmrc/vat/deregistration_threshold.yaml
@@ -0,0 +1,9 @@
+description: The United Kingdom requires firms to remain registered for Value Added Tax if their taxable turnover exceeds this amount.
+values:
+  2017-04-01: 83_000
+  2024-04-01: 88_000
+metadata:
+  unit: currency-GBP
+  reference:
+    - title: HMRC VAT registration thresholds
+      href: https://www.gov.uk/vat-registration-thresholds
\ No newline at end of file
diff --git a/policyengine_uk/parameters/gov/hmrc/vat/registration_threshold.yaml b/policyengine_uk/parameters/gov/hmrc/vat/registration_threshold.yaml
new file mode 100644
index 000000000..49aab5cc9
--- /dev/null
+++ b/policyengine_uk/parameters/gov/hmrc/vat/registration_threshold.yaml
@@ -0,0 +1,9 @@
+description: The United Kingdom requires firms to register for Value Added Tax when their taxable turnover exceeds this amount.
+values:
+  2017-04-01: 85_000
+  2024-04-01: 90_000
+metadata:
+  unit: currency-GBP
+  reference:
+    - title: HMRC VAT registration thresholds
+      href: https://www.gov.uk/vat-registration-thresholds
\ No newline at end of file
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_net_vat_liability.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_net_vat_liability.py
new file mode 100644
index 000000000..39a0a15d0
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_net_vat_liability.py
@@ -0,0 +1,17 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_net_vat_liability(Variable):
+    value_type = float
+    entity = Firm
+    label = "Net VAT liability"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Net VAT liability (output VAT minus input VAT)"
+
+    def formula(firm, period, parameters):
+        output_vat = firm("firm_vat_on_sales", period)
+        input_vat = firm("firm_vat_on_purchases", period)
+
+        return output_vat - input_vat
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_reduced_rated_supplies.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_reduced_rated_supplies.py
new file mode 100644
index 000000000..50cbf18e5
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_reduced_rated_supplies.py
@@ -0,0 +1,11 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_reduced_rated_supplies(Variable):
+    value_type = float
+    entity = Firm
+    label = "Reduced-rated supplies"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Value of supplies subject to reduced VAT rate (5%)"
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_standard_rated_supplies.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_standard_rated_supplies.py
new file mode 100644
index 000000000..c2b7eeee0
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_standard_rated_supplies.py
@@ -0,0 +1,11 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_standard_rated_supplies(Variable):
+    value_type = float
+    entity = Firm
+    label = "Standard-rated supplies"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Value of supplies subject to standard VAT rate (20%)"
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_liability_calculated.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_liability_calculated.py
new file mode 100644
index 000000000..2e5b4c5ed
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_liability_calculated.py
@@ -0,0 +1,16 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_vat_liability_calculated(Variable):
+    value_type = float
+    entity = Firm
+    label = "Calculated VAT liability"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Calculated VAT liability based on supplies and parameters"
+
+    def formula(firm, period, parameters):
+        # This calculates VAT based on supplies and rates
+        # Compare with vat_liability_k from the dataset
+        return firm("firm_net_vat_liability", period)
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_purchases.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_purchases.py
new file mode 100644
index 000000000..c4e5dbc77
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_purchases.py
@@ -0,0 +1,11 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_vat_on_purchases(Variable):
+    value_type = float
+    entity = Firm
+    label = "VAT on purchases"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Total VAT paid on firm's purchases (input VAT)"
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_sales.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_sales.py
new file mode 100644
index 000000000..43bbc1fe6
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_on_sales.py
@@ -0,0 +1,26 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_vat_on_sales(Variable):
+    value_type = float
+    entity = Firm
+    label = "VAT on sales"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Total VAT charged on firm's sales (output VAT)"
+
+    def formula(firm, period, parameters):
+        # Only calculate for VAT registered firms
+        registered = firm("firm_vat_registered", period)
+
+        p = parameters(period).gov.hmrc.vat
+
+        standard_supplies = firm("firm_standard_rated_supplies", period)
+        reduced_supplies = firm("firm_reduced_rated_supplies", period)
+
+        standard_vat = standard_supplies * p.standard_rate
+        reduced_vat = reduced_supplies * p.reduced_rate
+        # Zero-rated supplies have 0% VAT by definition
+
+        return where(registered, standard_vat + reduced_vat, 0)
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_registered.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_registered.py
new file mode 100644
index 000000000..a9c4e9d7f
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_vat_registered.py
@@ -0,0 +1,20 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_vat_registered(Variable):
+    value_type = bool
+    entity = Firm
+    label = "VAT registered"
+    definition_period = YEAR
+    documentation = (
+        "Whether the firm is registered for VAT based on turnover threshold"
+    )
+
+    def formula(firm, period, parameters):
+        # Use annual_turnover_k from our existing firm variables
+        # Convert from thousands to pounds
+        turnover = firm("annual_turnover_k", period) * 1000
+        threshold = parameters(period).gov.hmrc.vat.registration_threshold
+
+        return turnover > threshold
diff --git a/policyengine_uk/variables_firm/gov/hmrc/vat/firm_zero_rated_supplies.py b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_zero_rated_supplies.py
new file mode 100644
index 000000000..f07844303
--- /dev/null
+++ b/policyengine_uk/variables_firm/gov/hmrc/vat/firm_zero_rated_supplies.py
@@ -0,0 +1,11 @@
+from policyengine_uk.model_api import *
+from policyengine_uk.entities import Firm
+
+
+class firm_zero_rated_supplies(Variable):
+    value_type = float
+    entity = Firm
+    label = "Zero-rated supplies"
+    definition_period = YEAR
+    unit = GBP
+    documentation = "Value of supplies subject to zero VAT rate (0%)"