diff --git a/us/crfb/agi_surtax/agi_surtax_10yr_impact.csv b/us/crfb/agi_surtax/agi_surtax_10yr_impact.csv
index 1b65b42..520d307 100644
--- a/us/crfb/agi_surtax/agi_surtax_10yr_impact.csv
+++ b/us/crfb/agi_surtax/agi_surtax_10yr_impact.csv
@@ -1,12 +1,12 @@
 year,revenue_billions
-2026,64.72
-2027,68.62
-2028,72.74
-2029,77.51
-2030,82.99
-2031,88.99
-2032,95.46
-2033,102.23
-2034,109.78
-2035,117.7
-Total,880.74
+2026,65.92
+2027,69.83
+2028,74.09
+2029,78.98
+2030,84.53
+2031,90.58
+2032,97.18
+2033,104.1
+2034,111.78
+2035,119.81
+Total,896.79
diff --git a/us/crfb/agi_surtax/agi_surtax_expanded_base_10yr_impact.csv b/us/crfb/agi_surtax/agi_surtax_expanded_base_10yr_impact.csv
index 278bf6f..518ac5e 100644
--- a/us/crfb/agi_surtax/agi_surtax_expanded_base_10yr_impact.csv
+++ b/us/crfb/agi_surtax/agi_surtax_expanded_base_10yr_impact.csv
@@ -1,12 +1,12 @@
 year,revenue_billions
-2026,76.81
-2027,81.25
-2028,85.67
-2029,91.08
-2030,97.18
-2031,103.8
-2032,110.89
-2033,118.33
-2034,126.65
-2035,135.49
-Total,1027.15
+2026,77.11
+2027,81.84
+2028,86.85
+2029,92.62
+2030,98.69
+2031,105.57
+2032,112.9
+2033,120.58
+2034,128.84
+2035,137.6
+Total,1042.6
diff --git a/us/crfb/agi_surtax/agi_surtax_impact.ipynb b/us/crfb/agi_surtax/agi_surtax_impact.ipynb
deleted file mode 100644
index 8367cea..0000000
--- a/us/crfb/agi_surtax/agi_surtax_impact.ipynb
+++ /dev/null
@@ -1,184 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "imports",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from policyengine_us import Microsimulation\n",
-    "from policyengine_core.reforms import Reform\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c52529ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_10yr_impact(reform, csv_path, label=\"\"):\n",
-    "    \"\"\"Run 10-year income tax impact analysis for a reform.\"\"\"\n",
-    "    pd.DataFrame(columns=[\"year\", \"revenue_billions\"]).to_csv(csv_path, index=False)\n",
-    "    \n",
-    "    total = 0\n",
-    "    for year in range(2026, 2036):\n",
-    "        baseline = Microsimulation()\n",
-    "        reformed = Microsimulation(reform=reform)\n",
-    "        \n",
-    "        baseline_tax = baseline.calculate(\"income_tax\", period=year).sum() / 1e9\n",
-    "        reformed_tax = reformed.calculate(\"income_tax\", period=year).sum() / 1e9\n",
-    "        \n",
-    "        revenue = reformed_tax - baseline_tax\n",
-    "        total += revenue\n",
-    "        print(f\"{year}: ${revenue:.2f}B\")\n",
-    "        \n",
-    "        pd.DataFrame([{\"year\": year, \"revenue_billions\": round(revenue, 2)}]).to_csv(\n",
-    "            csv_path, mode=\"a\", header=False, index=False\n",
-    "        )\n",
-    "    \n",
-    "    pd.DataFrame([{\"year\": \"Total\", \"revenue_billions\": round(total, 2)}]).to_csv(\n",
-    "        csv_path, mode=\"a\", header=False, index=False\n",
-    "    )\n",
-    "    print(f\"\\n10-Year Total{' (' + label + ')' if label else ''}: ${total:.2f}B\")\n",
-    "    return total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "analysis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Basic AGI surtax (1% on $100k/$200k, 2% on $1M)\n",
-    "reform_basic = Reform.from_dict({\n",
-    "    \"gov.contrib.crfb.surtax.in_effect\": {\n",
-    "        \"2026-01-01.2100-12-31\": True\n",
-    "    },\n",
-    "}, country_id=\"us\")\n",
-    "\n",
-    "run_10yr_impact(reform_basic, \"agi_surtax_10yr_impact.csv\", \"Basic\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "u9d2r5sr7xh",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2026: $76.81B\n",
-      "2027: $81.25B\n",
-      "2028: $85.67B\n",
-      "2029: $91.08B\n",
-      "2030: $97.18B\n",
-      "2031: $103.80B\n",
-      "2032: $110.89B\n",
-      "2033: $118.33B\n",
-      "2034: $126.65B\n",
-      "2035: $135.49B\n",
-      "\n",
-      "10-Year Total (Expanded Base): $1027.15B\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(1027.1456010329425)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Surtax with EXPANDED BASE (adds retirement contributions, HSA, tax-exempt income, etc.)\n",
-    "reform_expanded = Reform.from_dict({\n",
-    "    \"gov.contrib.crfb.surtax.in_effect\": {\n",
-    "        \"2026-01-01.2100-12-31\": True\n",
-    "    },\n",
-    "    \"gov.contrib.crfb.surtax.increased_base.in_effect\": {\n",
-    "        \"2026-01-01.2100-12-31\": True\n",
-    "    },\n",
-    "}, country_id=\"us\")\n",
-    "\n",
-    "run_10yr_impact(reform_expanded, \"agi_surtax_expanded_base_10yr_impact.csv\", \"Expanded Base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "104a0qlmca5",
-   "source": "# CBO Option 46b comparison: 2% flat surtax on AGI above $100k/$200k\nreform_cbo_2pct = Reform.from_dict({\n    \"gov.contrib.crfb.surtax.in_effect\": {\n        \"2026-01-01.2100-12-31\": True\n    },\n    # Override to flat 2% (no tiered structure)\n    \"gov.contrib.crfb.surtax.rate.single[1].rate\": {\n        \"2026-01-01.2100-12-31\": 0.02\n    },\n    \"gov.contrib.crfb.surtax.rate.single[2].rate\": {\n        \"2026-01-01.2100-12-31\": 0.02\n    },\n    \"gov.contrib.crfb.surtax.rate.joint[1].rate\": {\n        \"2026-01-01.2100-12-31\": 0.02\n    },\n    \"gov.contrib.crfb.surtax.rate.joint[2].rate\": {\n        \"2026-01-01.2100-12-31\": 0.02\n    },\n}, country_id=\"us\")\n\nrun_10yr_impact(reform_cbo_2pct, \"cbo_comparison_2pct_10yr.csv\", \"CBO 2% Flat\")",
-   "metadata": {},
-   "execution_count": null,
-   "outputs": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5f2c0e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "baseline = Microsimulation()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "5fd22e28",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.0)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "baseline.calculate(\"traditional_403b_contributions\", 2026).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ebe1e92",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/us/crfb/agi_surtax/agi_surtax_impact.py b/us/crfb/agi_surtax/agi_surtax_impact.py
new file mode 100644
index 0000000..9580c4e
--- /dev/null
+++ b/us/crfb/agi_surtax/agi_surtax_impact.py
@@ -0,0 +1,189 @@
+from policyengine_us import Microsimulation
+from policyengine_core.reforms import Reform
+import pandas as pd
+
+
+def run_10yr_impact(reform, csv_path, label=""):
+    """Run 10-year income tax impact analysis for a reform."""
+    pd.DataFrame(columns=["year", "revenue_billions"]).to_csv(csv_path, index=False)
+
+    total = 0
+    for year in range(2026, 2036):
+        baseline = Microsimulation()
+        reformed = Microsimulation(reform=reform)
+
+        baseline_tax = baseline.calculate("income_tax", period=year).sum() / 1e9
+        reformed_tax = reformed.calculate("income_tax", period=year).sum() / 1e9
+
+        revenue = reformed_tax - baseline_tax
+        total += revenue
+        print(f"{year}: ${revenue:.2f}B")
+
+        pd.DataFrame([{"year": year, "revenue_billions": round(revenue, 2)}]).to_csv(
+            csv_path, mode="a", header=False, index=False
+        )
+
+    pd.DataFrame([{"year": "Total", "revenue_billions": round(total, 2)}]).to_csv(
+        csv_path, mode="a", header=False, index=False
+    )
+    print(f"\n10-Year Total{' (' + label + ')' if label else ''}: ${total:.2f}B")
+    return total
+
+
+def validate_income_sources(year=2026):
+    """Validate that key income sources have data in the microsimulation."""
+    print(f"\n{'=' * 80}")
+    print(f"Income Source Validation ({year})")
+    print("=" * 80)
+
+    baseline = Microsimulation()
+
+    # Sources with external benchmarks for validation
+    # Format: (variable_name, label, external_low, external_high, source_note)
+    sources = [
+        (
+            "traditional_401k_contributions",
+            "Traditional 401(k) contributions",
+            250,
+            300,
+            "JCT JCX-48-24: $251B tax expenditure implies ~$300-400B contributions",
+        ),
+        (
+            "tax_exempt_social_security",
+            "Tax-exempt Social Security",
+            580,
+            690,
+            "SSA: $1.38T total benefits (2023), ~42-50% tax-exempt",
+        ),
+        (
+            "health_insurance_premiums",
+            "Employer health insurance premiums",
+            900,
+            1000,
+            "CMS/KFF: $1.6T private insurance, ~75% employer-paid",
+        ),
+        (
+            "tax_exempt_interest_income",
+            "Tax-exempt interest income",
+            120,
+            150,
+            "CRS IF12969: $4.2T muni bonds at 3-4% yield",
+        ),
+        (
+            "health_savings_account_ald",
+            "Health Savings Account (ALD)",
+            50,
+            60,
+            "Devenir 2024: $56B total HSA contributions",
+        ),
+        (
+            "traditional_ira_contributions",
+            "Traditional IRA contributions",
+            20,
+            25,
+            "ICI: $22B traditional IRA contributions (2020)",
+        ),
+        (
+            "traditional_403b_contributions",
+            "Traditional 403(b) contributions",
+            50,
+            70,
+            "Expected: similar to 401k scale for nonprofits/education",
+        ),
+    ]
+
+    results = []
+    print(
+        f"{'Income Source':<35} {'PE 2026':>12} {'External':>15} {'Status':>10}"
+    )
+    print("-" * 80)
+
+    for var_name, label, ext_low, ext_high, note in sources:
+        try:
+            total = baseline.calculate(var_name, period=year).sum() / 1e9
+            external_range = f"${ext_low}-{ext_high}B"
+
+            if total == 0:
+                status = "⚠️ No data"
+            elif ext_low <= total <= ext_high:
+                status = "✅ Match"
+            elif total < ext_low * 0.5:
+                status = "❌ Low"
+            elif total > ext_high * 1.5:
+                status = "❌ High"
+            else:
+                status = "⚠️ Close"
+
+            print(f"{label:<35} {f'${total:.1f}B':>12} {external_range:>15} {status:>10}")
+            results.append((label, total, ext_low, ext_high, status, note))
+        except Exception as e:
+            print(f"{label:<35} {'N/A':>12} {f'${ext_low}-{ext_high}B':>15} {'❌ Error':>10}")
+            results.append((label, None, ext_low, ext_high, f"Error: {e}", note))
+
+    print("-" * 80)
+    print("\nNotes on external benchmarks:")
+    for label, total, ext_low, ext_high, status, note in results:
+        if "❌" in status or "⚠️" in status:
+            print(f"  • {label}: {note}")
+
+    return results
+
+
+if __name__ == "__main__":
+    # Validate income sources first
+    validate_income_sources(2026)
+
+    # Basic AGI surtax (1% on $100k/$200k, 2% on $1M)
+    reform_basic = Reform.from_dict(
+        {
+            "gov.contrib.crfb.surtax.in_effect": {"2026-01-01.2100-12-31": True},
+        },
+        country_id="us",
+    )
+
+    print("=" * 50)
+    print("Basic AGI Surtax")
+    print("=" * 50)
+    run_10yr_impact(reform_basic, "agi_surtax_10yr_impact.csv", "Basic")
+
+    # Surtax with EXPANDED BASE (adds retirement contributions, HSA, tax-exempt income, etc.)
+    reform_expanded = Reform.from_dict(
+        {
+            "gov.contrib.crfb.surtax.in_effect": {"2026-01-01.2100-12-31": True},
+            "gov.contrib.crfb.surtax.increased_base.in_effect": {
+                "2026-01-01.2100-12-31": True
+            },
+        },
+        country_id="us",
+    )
+
+    print("\n" + "=" * 50)
+    print("Expanded Base AGI Surtax")
+    print("=" * 50)
+    run_10yr_impact(reform_expanded, "agi_surtax_expanded_base_10yr_impact.csv", "Expanded Base")
+
+    # CBO Option 46b comparison: 2% flat surtax on AGI above $100k/$200k
+    reform_cbo_2pct = Reform.from_dict(
+        {
+            "gov.contrib.crfb.surtax.in_effect": {"2026-01-01.2100-12-31": True},
+            # Override to flat 2% (no tiered structure)
+            "gov.contrib.crfb.surtax.rate.single[1].rate": {
+                "2026-01-01.2100-12-31": 0.02
+            },
+            "gov.contrib.crfb.surtax.rate.single[2].rate": {
+                "2026-01-01.2100-12-31": 0.02
+            },
+            "gov.contrib.crfb.surtax.rate.joint[1].rate": {
+                "2026-01-01.2100-12-31": 0.02
+            },
+            "gov.contrib.crfb.surtax.rate.joint[2].rate": {
+                "2026-01-01.2100-12-31": 0.02
+            },
+        },
+        country_id="us",
+    )
+
+    print("\n" + "=" * 50)
+    print("CBO 2% Flat Surtax Comparison")
+    print("=" * 50)
+    run_10yr_impact(reform_cbo_2pct, "cbo_comparison_2pct_10yr.csv", "CBO 2% Flat")
diff --git a/us/crfb/agi_surtax/cbo_comparison_2pct_10yr.csv b/us/crfb/agi_surtax/cbo_comparison_2pct_10yr.csv
index 4667862..0826c62 100644
--- a/us/crfb/agi_surtax/cbo_comparison_2pct_10yr.csv
+++ b/us/crfb/agi_surtax/cbo_comparison_2pct_10yr.csv
@@ -1,12 +1,12 @@
 year,revenue_billions
-2026,104.6
-2027,111.28
-2028,118.21
-2029,125.98
-2030,134.67
-2031,144.06
-2032,154.16
-2033,164.76
-2034,176.55
-2035,188.89
-Total,1423.14
+2026,105.0
+2027,111.42
+2028,118.25
+2029,125.92
+2030,134.44
+2031,143.67
+2032,153.82
+2033,164.5
+2034,176.32
+2035,188.69
+Total,1422.03