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OBBB Notebook #54

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711da57
OBBB Notebook
PavelMakarchuk May 14, 2025
8463bac
updated reforms
PavelMakarchuk May 14, 2025
5042795
newest values
PavelMakarchuk May 14, 2025
2647002
rename
PavelMakarchuk May 14, 2025
1dbe9a5
many more charts
PavelMakarchuk May 14, 2025
863fc63
more notebooks
PavelMakarchuk May 14, 2025
39f5fb1
salt uncapped
PavelMakarchuk May 15, 2025
481a5a5
minor
PavelMakarchuk May 16, 2025
626096b
minor
PavelMakarchuk May 16, 2025
50c7c0f
Taxable income under obbb analysis
PavelMakarchuk May 16, 2025
677c8b0
more tables
PavelMakarchuk May 20, 2025
3c0f8ac
Merge branch 'main' of https://github.com/PolicyEngine/analysis-noteb…
PavelMakarchuk May 21, 2025
1a67c3f
kids percentage
PavelMakarchuk May 21, 2025
9578cb9
more salt
PavelMakarchuk May 22, 2025
4fd496f
black and other
PavelMakarchuk May 23, 2025
4bd7eba
managers amendment
PavelMakarchuk May 23, 2025
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375 changes: 187 additions & 188 deletions household_net_income_breakdown.ipynb
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1,427 changes: 1,427 additions & 0 deletions test.ipynb
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48 changes: 28 additions & 20 deletions us/congress/fisc_10_year.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@
" },\n",
" \"gov.contrib.congress.golden.fisc_act.in_effect\": {\n",
" \"2026-01-01.2100-12-31\": True\n",
" }\n",
" },\n",
" },\n",
" country_id=\"us\",\n",
")"
Expand All @@ -86,14 +86,23 @@
"outputs": [],
"source": [
"def calculate_budgetary_impact(reform, year):\n",
" baseline = Microsimulation(dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n",
" baseline_income = baseline.calculate(\"income_tax\", map_to=\"household\", period=year).sum()\n",
" baseline = Microsimulation(\n",
" dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\"\n",
" )\n",
" baseline_income = baseline.calculate(\n",
" \"income_tax\", map_to=\"household\", period=year\n",
" ).sum()\n",
"\n",
" reformed = Microsimulation(reform=reform, dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\")\n",
" reformed_income = reformed.calculate(\"income_tax\", map_to=\"household\", period=year).sum()\n",
" reformed = Microsimulation(\n",
" reform=reform,\n",
" dataset=\"hf://policyengine/policyengine-us-data/enhanced_cps_2024.h5\",\n",
" )\n",
" reformed_income = reformed.calculate(\n",
" \"income_tax\", map_to=\"household\", period=year\n",
" ).sum()\n",
"\n",
" impact = reformed_income - baseline_income\n",
" return impact "
" return impact"
]
},
{
Expand Down Expand Up @@ -342,19 +351,20 @@
" static_impact = calculate_budgetary_impact(reform_static, year)\n",
" df.loc[year, \"Static Impact\"] = static_impact\n",
" print(f\" Done: ${static_impact:,.0f}\")\n",
" \n",
"\n",
" # Calculate dynamic impact\n",
" print(f\"Calculating dynamic impact for {year}...\", end=\"\")\n",
" dynamic_impact = calculate_budgetary_impact(reform_dynamic, year)\n",
" df.loc[year, \"Dynamic Impact\"] = dynamic_impact\n",
" print(f\" Done: ${dynamic_impact:,.0f}\")\n",
" \n",
"\n",
" # Print the results for this year\n",
" print(f\"{year} | ${static_impact:,.0f} | ${dynamic_impact:,.0f}\")\n",
" print(\"-\" * 45)\n",
" \n",
"\n",
" # Optional: Clear memory after each year\n",
" import gc\n",
"\n",
" gc.collect()"
]
},
Expand All @@ -381,23 +391,21 @@
"# Visualize the 10-year impact\n",
"fig = go.Figure()\n",
"\n",
"fig.add_trace(go.Bar(\n",
" x=df.index,\n",
" y=df[\"Static Impact\"],\n",
" name=\"Static Impact\"\n",
"))\n",
"fig.add_trace(go.Bar(x=df.index, y=df[\"Static Impact\"], name=\"Static Impact\"))\n",
"\n",
"fig.add_trace(go.Bar(\n",
" x=df.index,\n",
" y=df[\"Dynamic Impact\"],\n",
" name=\"Dynamic Impact with Behavioral Responses\"\n",
"))\n",
"fig.add_trace(\n",
" go.Bar(\n",
" x=df.index,\n",
" y=df[\"Dynamic Impact\"],\n",
" name=\"Dynamic Impact with Behavioral Responses\",\n",
" )\n",
")\n",
"\n",
"fig.update_layout(\n",
" title=\"10-Year Budgetary Impact of FISC Act\",\n",
" xaxis_title=\"Year\",\n",
" yaxis_title=\"Impact ($)\",\n",
" barmode=\"group\"\n",
" barmode=\"group\",\n",
")\n",
"\n",
"fig.show()"
Expand Down
11 changes: 7 additions & 4 deletions us/congress/fisc_debug.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,9 @@
"metadata": {},
"outputs": [],
"source": [
"adjusted_gross_income = baseline.calculate(\"adjusted_gross_income\", map_to=\"household\", period=2026)"
"adjusted_gross_income = baseline.calculate(\n",
" \"adjusted_gross_income\", map_to=\"household\", period=2026\n",
")"
]
},
{
Expand All @@ -115,7 +117,9 @@
"metadata": {},
"outputs": [],
"source": [
"fisc_credit = reformed.calculate(\"family_income_supplement_credit\", map_to=\"household\", period=2026)"
"fisc_credit = reformed.calculate(\n",
" \"family_income_supplement_credit\", map_to=\"household\", period=2026\n",
")"
]
},
{
Expand Down Expand Up @@ -748,8 +752,7 @@
"with pd.option_context(\n",
" \"display.max_rows\", None, \"display.max_columns\", None\n",
"): # more options can be specified also\n",
" print(diff_pd)\n",
" "
" print(diff_pd)"
]
},
{
Expand Down
119 changes: 62 additions & 57 deletions us/harris/niit_medicare_10_year.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,44 +18,48 @@
"metadata": {},
"outputs": [],
"source": [
"niit_reform = Reform.from_dict({\n",
" \"gov.contrib.biden.budget_2025.net_investment_income.rate\": {\n",
" \"2024-01-01.2100-12-31\": 0.012\n",
"niit_reform = Reform.from_dict(\n",
" {\n",
" \"gov.contrib.biden.budget_2025.net_investment_income.rate\": {\n",
" \"2024-01-01.2100-12-31\": 0.012\n",
" },\n",
" \"gov.contrib.biden.budget_2025.net_investment_income.threshold\": {\n",
" \"2025-01-01.2025-12-31\": 411000,\n",
" \"2026-01-01.2026-12-31\": 418450,\n",
" \"2027-01-01.2027-12-31\": 427500,\n",
" \"2028-01-01.2028-12-31\": 435950,\n",
" \"2029-01-01.2029-12-31\": 444275,\n",
" \"2030-01-01.2030-12-31\": 453025,\n",
" \"2031-01-01.2031-12-31\": 461975,\n",
" \"2032-01-01.2032-12-31\": 471175,\n",
" \"2033-01-01.2033-12-31\": 480625,\n",
" \"2034-01-01.2034-12-31\": 490300,\n",
" \"2035-01-01.2100-12-31\": 500200,\n",
" },\n",
" },\n",
" \"gov.contrib.biden.budget_2025.net_investment_income.threshold\": {\n",
" \"2025-01-01.2025-12-31\": 411000,\n",
" \"2026-01-01.2026-12-31\": 418450,\n",
" \"2027-01-01.2027-12-31\": 427500,\n",
" \"2028-01-01.2028-12-31\": 435950,\n",
" \"2029-01-01.2029-12-31\": 444275,\n",
" \"2030-01-01.2030-12-31\": 453025,\n",
" \"2031-01-01.2031-12-31\": 461975,\n",
" \"2032-01-01.2032-12-31\": 471175,\n",
" \"2033-01-01.2033-12-31\": 480625,\n",
" \"2034-01-01.2034-12-31\": 490300,\n",
" \"2035-01-01.2100-12-31\": 500200\n",
" }\n",
"}, country_id=\"us\")\n",
" country_id=\"us\",\n",
")\n",
"\n",
"# Medicare Reform\n",
"medicare_reform = Reform.from_dict({\n",
" \"gov.contrib.biden.budget_2025.medicare.rate\": {\n",
" \"2024-01-01.2100-12-31\": 0.012\n",
"medicare_reform = Reform.from_dict(\n",
" {\n",
" \"gov.contrib.biden.budget_2025.medicare.rate\": {\"2024-01-01.2100-12-31\": 0.012},\n",
" \"gov.contrib.biden.budget_2025.medicare.threshold\": {\n",
" \"2025-01-01.2025-12-31\": 411000,\n",
" \"2026-01-01.2026-12-31\": 418450,\n",
" \"2027-01-01.2027-12-31\": 427500,\n",
" \"2028-01-01.2028-12-31\": 435950,\n",
" \"2029-01-01.2029-12-31\": 444275,\n",
" \"2030-01-01.2030-12-31\": 453025,\n",
" \"2031-01-01.2031-12-31\": 461975,\n",
" \"2032-01-01.2032-12-31\": 471175,\n",
" \"2033-01-01.2033-12-31\": 480625,\n",
" \"2034-01-01.2034-12-31\": 490300,\n",
" \"2035-01-01.2100-12-31\": 500200,\n",
" },\n",
" },\n",
" \"gov.contrib.biden.budget_2025.medicare.threshold\": {\n",
" \"2025-01-01.2025-12-31\": 411000,\n",
" \"2026-01-01.2026-12-31\": 418450,\n",
" \"2027-01-01.2027-12-31\": 427500,\n",
" \"2028-01-01.2028-12-31\": 435950,\n",
" \"2029-01-01.2029-12-31\": 444275,\n",
" \"2030-01-01.2030-12-31\": 453025,\n",
" \"2031-01-01.2031-12-31\": 461975,\n",
" \"2032-01-01.2032-12-31\": 471175,\n",
" \"2033-01-01.2033-12-31\": 480625,\n",
" \"2034-01-01.2034-12-31\": 490300,\n",
" \"2035-01-01.2100-12-31\": 500200\n",
" }\n",
"}, country_id=\"us\")"
" country_id=\"us\",\n",
")"
]
},
{
Expand All @@ -67,65 +71,66 @@
"def analyze_reforms():\n",
" # Set up years to analyze\n",
" years = range(2025, 2035)\n",
" \n",
"\n",
" # Define reforms dictionary\n",
" reforms = {\n",
" 'NIIT': niit_reform,\n",
" 'Medicare': medicare_reform\n",
" }\n",
" \n",
" reforms = {\"NIIT\": niit_reform, \"Medicare\": medicare_reform}\n",
"\n",
" # Create or load results dataframe\n",
" csv_path = 'tax_reform_impacts.csv'\n",
" csv_path = \"tax_reform_impacts.csv\"\n",
" if os.path.exists(csv_path):\n",
" results = pd.read_csv(csv_path, index_col=0)\n",
" print(\"Loaded existing results from CSV\")\n",
" else:\n",
" results = pd.DataFrame(index=list(years), columns=reforms.keys())\n",
" print(\"Created new results DataFrame\")\n",
" \n",
"\n",
" # Initialize baseline simulation\n",
" baseline = Microsimulation(dataset='enhanced_cps_2024')\n",
" \n",
" baseline = Microsimulation(dataset=\"enhanced_cps_2024\")\n",
"\n",
" # Process each reform\n",
" for reform_name, reform_policy in reforms.items():\n",
" print(f\"\\nProcessing {reform_name} reform...\")\n",
" \n",
"\n",
" # Initialize reform simulation\n",
" reform_sim = Microsimulation(reform=reform_policy, dataset='enhanced_cps_2024')\n",
" \n",
" reform_sim = Microsimulation(reform=reform_policy, dataset=\"enhanced_cps_2024\")\n",
"\n",
" # Calculate impacts for each year\n",
" for year in years:\n",
" # Skip if already calculated\n",
" if year in results.index and pd.notna(results.at[year, reform_name]):\n",
" print(f\"Skipping {reform_name} year {year} - already calculated\")\n",
" continue\n",
" \n",
"\n",
" print(f\"Processing {reform_name} year {year}\")\n",
" try:\n",
" baseline_income = baseline.calculate(\"household_net_income\", period=year).sum()\n",
" reformed_income = reform_sim.calculate(\"household_net_income\", period=year).sum()\n",
" \n",
" baseline_income = baseline.calculate(\n",
" \"household_net_income\", period=year\n",
" ).sum()\n",
" reformed_income = reform_sim.calculate(\n",
" \"household_net_income\", period=year\n",
" ).sum()\n",
"\n",
" # Calculate impact in billions\n",
" impact = (reformed_income - baseline_income) / 1e9\n",
" d.at[year, reform_name] = round(impact, 1)\n",
" \n",
"\n",
" # Save after each calculation\n",
" results.to_csv(csv_path)\n",
" print(f\"Saved results for {reform_name} year {year}\")\n",
" \n",
"\n",
" except Exception as e:\n",
" print(f\"Error processing {reform_name} year {year}: {str(e)}\")\n",
" # Save progress even if there's an error\n",
" results.to_csv(csv_path)\n",
" continue\n",
" \n",
"\n",
" # Calculate 2025-34 total if we have all years for both reforms\n",
" if all(pd.notna(results.loc[2025:2034].values.flatten())):\n",
" results.loc['2025-34'] = results.loc[2025:2034].sum()\n",
" results.loc[\"2025-34\"] = results.loc[2025:2034].sum()\n",
" results.to_csv(csv_path)\n",
" print(\"Added 2025-34 total\")\n",
" \n",
" return results\n"
"\n",
" return results"
]
},
{
Expand Down
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