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Adjust plots for trump-clinton ETI and add utilitarian ETI plot
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‎examples/Simulate_all_policies.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"https://raw.githubusercontent.com/jdebacker/examples/pres_proposals/psl_examples/taxcalc/Obama2015.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n",
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"https://raw.githubusercontent.com/jdebacker/examples/pres_proposals/psl_examples/taxcalc/Romney2012.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n",
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"https://raw.githubusercontent.com/jdebacker/examples/pres_proposals/psl_examples/taxcalc/Clinton2016.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n",
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"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/Trump2016.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n",
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"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/Biden2020.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json', 'https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json']\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json', 'https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json']\n",
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"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n",
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"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n",
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"https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/TCJA.json\n",
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"['https://raw.githubusercontent.com/PSLmodels/examples/main/psl_examples/taxcalc/2017_law.json']\n"
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]
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}
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],
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"source": [
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"# Create IOT objects for each candidate platform\n",
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"policies = []\n",
@@ -617,7 +641,109 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"eti_dict = {\n",
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" \"eti_values\": [0.18, 0.106, 0.567, 1.83, 1.9],\n",
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" \"knot_points\": [30000, 75000, 250000, 2000000, 10000000]\n",
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"}\n",
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"# ETI values from Gruber and Saez (2002) (Table 3) and Saez (2004) (Tables 2, 4, 5)\n",
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"# Compute MTR schedule under current law\n",
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"iot_2023 = iot_user.iot_comparison(\n",
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" policies=[{}],\n",
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" baseline_policies=[None],\n",
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" labels=[\"2023 Law\"],\n",
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" years=[2023],\n",
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" data=\"CPS\",\n",
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" eti=eti_dict\n",
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" )\n",
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"fig = px.line(\n",
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" x=iot_2023.iot[0].df().z,\n",
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" y=iot_2023.iot[0].df().mtr\n",
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" )\n",
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"fig.update_layout(\n",
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" template=template,\n",
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" xaxis_title=\"Wages and Salaries\",\n",
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" yaxis_title=r\"$T'(z)$\",\n",
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")\n",
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"fig.write_image(\n",
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" os.path.join(path, \"MTR_2023.png\"),\n",
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" scale=4\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Thought experiment: What beliefs about ETI do the candidates need to \n",
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"# justify their policies if we assume they are utilitarian? \n",
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"# If the elasticities are wildly counterfactual, we can reject this hypothesis\n",
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"\n",
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"eti_utilitarian = np.zeros((len(iot_all.iot), len(iot_all.iot[0].df().z)))\n",
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"for i in range(len(iot_all.iot)):\n",
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" eti_utilitarian[i, :] = iot.inverse_optimal_tax.find_eti(\n",
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" iot_all.iot[i], g_z = np.ones(len(iot_all.iot[i].df().z))\n",
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" )\n",
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"\n",
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"# Convert the 2D array to a DataFrame for plotting\n",
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"eti_df = pd.DataFrame(eti_utilitarian.T, columns=labels)\n",
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"\n",
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"fig_eti = px.line(\n",
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" eti_df,\n",
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" x=iot_all.iot[0].df().z,\n",
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" y=eti_df.columns,\n",
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" labels={\"x\": \"Wages and Salaries\", \"value\": r\"$\\varepsilon$\", \"variable\": \"Candidate\"},\n",
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")\n",
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"fig_eti.update_layout(\n",
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" template=template,\n",
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")\n",
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"\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"dot\", color=\"blue\"),\n",
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" selector=dict(name=\"Obama 2015\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"dot\", color=\"red\"),\n",
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" selector=dict(name=\"Romney 2012\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"dash\", color=\"blue\"),\n",
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" selector=dict(name=\"Clinton 2016\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"dash\", color=\"red\"),\n",
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" selector=dict(name=\"Trump 2016\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"dashdot\", color=\"blue\"),\n",
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" selector=dict(name=\"Biden 2020\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"dashdot\", color=\"red\"),\n",
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" selector=dict(name=\"Trump 2020\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"solid\", color=\"blue\"),\n",
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" selector=dict(name=\"Harris 2024\")\n",
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")\n",
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"fig_eti.update_traces(\n",
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" line=dict(dash=\"solid\", color=\"red\"),\n",
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" selector=dict(name=\"Trump 2024\")\n",
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")\n",
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"fig_eti.write_image(\n",
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" os.path.join(path, \"eti_utilitarian.png\"),\n",
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" scale=4\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# one plot with epsilon(z) for each candidate\n",
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"# Will pick Trump and Clinton (2016) for example\n",
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"\n",
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"\n",
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"# First, plot just their g(z)\n",
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"fig = px.line(\n",
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" x=iot_all.iot[2].df().z[10:],\n",
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" os.path.join(path, \"trump_clinton_g_z_numerical.png\"),\n",
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" scale=4\n",
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" )\n",
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"# Now find the epsilon(z) that would give Trump's policies the same g(z) as Clinton\n",
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"eti_beliefs_lw, eti_beliefs_jjz = iot.inverse_optimal_tax.find_eti(iot_all.iot[2], iot_all.iot[3], g_z_type=\"g_z\")\n",
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"idx = np.where(np.absolute(eti_beliefs_jjz[1:]) < 10)[0]\n",
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"fig2 = px.line(\n",
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" x=iot_all.iot[2].df().z[idx],\n",
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" y=eti_beliefs_jjz[idx],\n",
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" labels={\"x\": \"Wages and Salaries\", \"y\": r\"$\\text{Implied } \\varepsilon$\"},\n",
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"\n",
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"\n",
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"# What ETI does Clinton need to justify Trump's SWW given her policy?\n",
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"eti_clinton = iot.inverse_optimal_tax.find_eti(\n",
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" iot_all.iot[2], \n",
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" g_z=iot_all.iot[3].df().g_z)\n",
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"\n",
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"fig_eti_clinton = px.line(\n",
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" x=iot_all.iot[2].df().z,\n",
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" y=eti_clinton,\n",
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" labels={\"x\": \"Wages and Salaries\", \"y\": r\"$\\varepsilon$\"},\n",
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" )\n",
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"fig2.update_layout(\n",
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"fig_eti_clinton.update_layout(\n",
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" template=template,\n",
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")\n",
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"fig2.write_image(\n",
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" os.path.join(path, \"trump_eti.png\"),\n",
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"fig_eti_clinton.update_traces(\n",
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" line=dict(dash=\"dash\", color=\"blue\"),\n",
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" selector=dict(name=\"Clinton 2016\")\n",
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")\n",
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"fig_eti_clinton.write_image(\n",
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" os.path.join(path, \"eti_clinton.png\"),\n",
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" scale=4\n",
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" )"
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" )\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"eti_dict = {\n",
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" \"eti_values\": [0.18, 0.106, 0.567, 1.83, 1.9],\n",
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" \"knot_points\": [30000, 75000, 250000, 2000000, 10000000]\n",
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"}\n",
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"# ETI values from Gruber and Saez (2002) (Table 3) and Saez (2004) (Tables 2, 4, 5)\n",
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"# Compute MTR schedule under current law\n",
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"iot_2023 = iot_user.iot_comparison(\n",
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" policies=[{}],\n",
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" baseline_policies=[None],\n",
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" labels=[\"2023 Law\"],\n",
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" years=[2023],\n",
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" data=\"CPS\",\n",
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" eti=eti_dict\n",
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" )\n",
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"fig = px.line(\n",
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" x=iot_2023.iot[0].df().z,\n",
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" y=iot_2023.iot[0].df().mtr\n",
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"\n",
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"# Converse: What elasticty does Trump need to justify Clinton's weights?\n",
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"eti_trump = iot.inverse_optimal_tax.find_eti(\n",
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" iot_all.iot[3], \n",
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" g_z=iot_all.iot[2].df().g_z)\n",
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"\n",
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"fig_eti_trump = px.line(\n",
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" x=iot_all.iot[2].df().z,\n",
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" y=eti_trump,\n",
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" labels={\"x\": \"Wages and Salaries\", \"y\": r\"$\\varepsilon$\"},\n",
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" )\n",
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"fig.update_layout(\n",
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"fig_eti_trump.update_layout(\n",
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" template=template,\n",
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" xaxis_title=\"Wages and Salaries\",\n",
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" yaxis_title=r\"$T'(z)$\",\n",
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")\n",
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"fig.write_image(\n",
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" os.path.join(path, \"MTR_2023.png\"),\n",
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"fig_eti_trump.update_traces(\n",
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" line=dict(dash=\"dash\", color=\"red\"),\n",
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" selector=dict(name=\"Trump 2016\")\n",
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")\n",
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"fig_eti_trump.write_image(\n",
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" os.path.join(path, \"eti_trump.png\"),\n",
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" scale=4\n",
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" )"
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" )\n"
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]
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}
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],
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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"version": "3.11.7"
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}
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},
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"nbformat": 4,

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