{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains an example for how to use the `taxbrain` python package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from taxbrain import TaxBrain, differences_plot, distribution_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "reform_url = \"https://raw.githubusercontent.com/PSLmodels/Tax-Calculator/master/taxcalc/reforms/Larson2019.json\"\n",
    "start_year = 2021\n",
    "end_year = 2030"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Static Reform\n",
    "\n",
    "After importing the `TaxBrain` class from the `taxbrain` package, we initiate an instance of the class by specifying the start and end year of the anlaysis, which microdata to use, and a policy reform. Additional arguments can be used to specify econoimc assumptions and individual behavioral elasticites.\n",
    "\n",
    "Once the class has been initiated, the `run()` method will handle executing each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_static = TaxBrain(start_year, end_year, use_cps=True, reform=reform_url)\n",
    "tb_static.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the calculators have been run, you can produce a number of tables, including a weighted total of a given variable each year under both current law and the user reform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Tax Liability Over the Budget Window\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base</th>\n",
       "      <td>2.571828e+12</td>\n",
       "      <td>2.715400e+12</td>\n",
       "      <td>2.846491e+12</td>\n",
       "      <td>2.990706e+12</td>\n",
       "      <td>3.151490e+12</td>\n",
       "      <td>3.537569e+12</td>\n",
       "      <td>3.728943e+12</td>\n",
       "      <td>3.912164e+12</td>\n",
       "      <td>4.090795e+12</td>\n",
       "      <td>4.268168e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>2.624536e+12</td>\n",
       "      <td>2.781239e+12</td>\n",
       "      <td>2.926065e+12</td>\n",
       "      <td>3.085554e+12</td>\n",
       "      <td>3.263417e+12</td>\n",
       "      <td>3.659957e+12</td>\n",
       "      <td>3.871454e+12</td>\n",
       "      <td>4.075211e+12</td>\n",
       "      <td>4.263201e+12</td>\n",
       "      <td>4.449451e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Difference</th>\n",
       "      <td>5.270752e+10</td>\n",
       "      <td>6.583917e+10</td>\n",
       "      <td>7.957361e+10</td>\n",
       "      <td>9.484773e+10</td>\n",
       "      <td>1.119264e+11</td>\n",
       "      <td>1.223880e+11</td>\n",
       "      <td>1.425109e+11</td>\n",
       "      <td>1.630469e+11</td>\n",
       "      <td>1.724056e+11</td>\n",
       "      <td>1.812827e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2021          2022          2023          2024  \\\n",
       "Base        2.571828e+12  2.715400e+12  2.846491e+12  2.990706e+12   \n",
       "Reform      2.624536e+12  2.781239e+12  2.926065e+12  3.085554e+12   \n",
       "Difference  5.270752e+10  6.583917e+10  7.957361e+10  9.484773e+10   \n",
       "\n",
       "                    2025          2026          2027          2028  \\\n",
       "Base        3.151490e+12  3.537569e+12  3.728943e+12  3.912164e+12   \n",
       "Reform      3.263417e+12  3.659957e+12  3.871454e+12  4.075211e+12   \n",
       "Difference  1.119264e+11  1.223880e+11  1.425109e+11  1.630469e+11   \n",
       "\n",
       "                    2029          2030  \n",
       "Base        4.090795e+12  4.268168e+12  \n",
       "Reform      4.263201e+12  4.449451e+12  \n",
       "Difference  1.724056e+11  1.812827e+11  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Combined Tax Liability Over the Budget Window\")\n",
    "tb_static.weighted_totals(\"combined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are interested in a detailed look on the reform's effect, you can produce a differences table for a given year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Differences Table\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>tax_cut</th>\n",
       "      <th>perc_cut</th>\n",
       "      <th>tax_inc</th>\n",
       "      <th>perc_inc</th>\n",
       "      <th>mean</th>\n",
       "      <th>tot_change</th>\n",
       "      <th>share_of_change</th>\n",
       "      <th>ubi</th>\n",
       "      <th>benefit_cost_total</th>\n",
       "      <th>benefit_value_total</th>\n",
       "      <th>pc_aftertaxinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-10n</th>\n",
       "      <td>0.115333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028253</td>\n",
       "      <td>24.497135</td>\n",
       "      <td>5.471870</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10z</th>\n",
       "      <td>8.506045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10p</th>\n",
       "      <td>12.191270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.522697</td>\n",
       "      <td>45.300423</td>\n",
       "      <td>3.005029</td>\n",
       "      <td>0.036635</td>\n",
       "      <td>0.069506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.046035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-20</th>\n",
       "      <td>20.814365</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.202324</td>\n",
       "      <td>73.037656</td>\n",
       "      <td>15.208011</td>\n",
       "      <td>0.316545</td>\n",
       "      <td>0.600569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.060796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>20.813097</td>\n",
       "      <td>0.003684</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>14.892930</td>\n",
       "      <td>71.555567</td>\n",
       "      <td>28.382182</td>\n",
       "      <td>0.590721</td>\n",
       "      <td>1.120753</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.060109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>20.812646</td>\n",
       "      <td>0.107658</td>\n",
       "      <td>0.517273</td>\n",
       "      <td>12.245201</td>\n",
       "      <td>58.835387</td>\n",
       "      <td>30.982088</td>\n",
       "      <td>0.644819</td>\n",
       "      <td>1.223391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.048941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-50</th>\n",
       "      <td>20.814848</td>\n",
       "      <td>0.393846</td>\n",
       "      <td>1.892140</td>\n",
       "      <td>12.605872</td>\n",
       "      <td>60.561919</td>\n",
       "      <td>33.336457</td>\n",
       "      <td>0.693893</td>\n",
       "      <td>1.316498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.034722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-60</th>\n",
       "      <td>20.813603</td>\n",
       "      <td>1.661413</td>\n",
       "      <td>7.982344</td>\n",
       "      <td>13.266555</td>\n",
       "      <td>63.739831</td>\n",
       "      <td>15.158956</td>\n",
       "      <td>0.315512</td>\n",
       "      <td>0.598610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-70</th>\n",
       "      <td>20.812398</td>\n",
       "      <td>3.244058</td>\n",
       "      <td>15.587141</td>\n",
       "      <td>12.647319</td>\n",
       "      <td>60.768196</td>\n",
       "      <td>-82.475428</td>\n",
       "      <td>-1.716511</td>\n",
       "      <td>-3.256673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-80</th>\n",
       "      <td>20.812279</td>\n",
       "      <td>4.142464</td>\n",
       "      <td>19.903941</td>\n",
       "      <td>12.784183</td>\n",
       "      <td>61.426154</td>\n",
       "      <td>-209.261379</td>\n",
       "      <td>-4.355206</td>\n",
       "      <td>-8.262969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-90</th>\n",
       "      <td>20.815758</td>\n",
       "      <td>5.345207</td>\n",
       "      <td>25.678658</td>\n",
       "      <td>13.271583</td>\n",
       "      <td>63.757388</td>\n",
       "      <td>-455.338094</td>\n",
       "      <td>-9.478207</td>\n",
       "      <td>-17.982647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.485572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-100</th>\n",
       "      <td>20.813722</td>\n",
       "      <td>2.303335</td>\n",
       "      <td>11.066423</td>\n",
       "      <td>16.766514</td>\n",
       "      <td>80.555098</td>\n",
       "      <td>3154.586539</td>\n",
       "      <td>65.658688</td>\n",
       "      <td>124.571764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.309040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>208.135364</td>\n",
       "      <td>17.201665</td>\n",
       "      <td>8.264653</td>\n",
       "      <td>129.233431</td>\n",
       "      <td>62.091049</td>\n",
       "      <td>253.236737</td>\n",
       "      <td>52.707521</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.336668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-95</th>\n",
       "      <td>10.406899</td>\n",
       "      <td>1.978420</td>\n",
       "      <td>19.010660</td>\n",
       "      <td>7.667279</td>\n",
       "      <td>73.674963</td>\n",
       "      <td>-573.761480</td>\n",
       "      <td>-5.971078</td>\n",
       "      <td>-11.328702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99</th>\n",
       "      <td>8.325281</td>\n",
       "      <td>0.324914</td>\n",
       "      <td>3.902744</td>\n",
       "      <td>7.192839</td>\n",
       "      <td>86.397553</td>\n",
       "      <td>142.390641</td>\n",
       "      <td>1.185442</td>\n",
       "      <td>2.249095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top 1%</th>\n",
       "      <td>2.081542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.906396</td>\n",
       "      <td>91.585763</td>\n",
       "      <td>33842.372424</td>\n",
       "      <td>70.444324</td>\n",
       "      <td>133.651371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.906652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count    tax_cut   perc_cut     tax_inc   perc_inc          mean  \\\n",
       "0-10n     0.115333   0.000000   0.000000    0.028253  24.497135      5.471870   \n",
       "0-10z     8.506045   0.000000   0.000000    0.000000   0.000000      0.000000   \n",
       "0-10p    12.191270   0.000000   0.000000    5.522697  45.300423      3.005029   \n",
       "10-20    20.814365   0.000000   0.000000   15.202324  73.037656     15.208011   \n",
       "20-30    20.813097   0.003684   0.017700   14.892930  71.555567     28.382182   \n",
       "30-40    20.812646   0.107658   0.517273   12.245201  58.835387     30.982088   \n",
       "40-50    20.814848   0.393846   1.892140   12.605872  60.561919     33.336457   \n",
       "50-60    20.813603   1.661413   7.982344   13.266555  63.739831     15.158956   \n",
       "60-70    20.812398   3.244058  15.587141   12.647319  60.768196    -82.475428   \n",
       "70-80    20.812279   4.142464  19.903941   12.784183  61.426154   -209.261379   \n",
       "80-90    20.815758   5.345207  25.678658   13.271583  63.757388   -455.338094   \n",
       "90-100   20.813722   2.303335  11.066423   16.766514  80.555098   3154.586539   \n",
       "ALL     208.135364  17.201665   8.264653  129.233431  62.091049    253.236737   \n",
       "90-95    10.406899   1.978420  19.010660    7.667279  73.674963   -573.761480   \n",
       "95-99     8.325281   0.324914   3.902744    7.192839  86.397553    142.390641   \n",
       "Top 1%    2.081542   0.000000   0.000000    1.906396  91.585763  33842.372424   \n",
       "\n",
       "        tot_change  share_of_change  ubi  benefit_cost_total  \\\n",
       "0-10n     0.000631         0.001197  0.0                 0.0   \n",
       "0-10z     0.000000         0.000000  0.0                 0.0   \n",
       "0-10p     0.036635         0.069506  0.0                 0.0   \n",
       "10-20     0.316545         0.600569  0.0                 0.0   \n",
       "20-30     0.590721         1.120753  0.0                 0.0   \n",
       "30-40     0.644819         1.223391  0.0                 0.0   \n",
       "40-50     0.693893         1.316498  0.0                 0.0   \n",
       "50-60     0.315512         0.598610  0.0                 0.0   \n",
       "60-70    -1.716511        -3.256673  0.0                 0.0   \n",
       "70-80    -4.355206        -8.262969  0.0                 0.0   \n",
       "80-90    -9.478207       -17.982647  0.0                 0.0   \n",
       "90-100   65.658688       124.571764  0.0                 0.0   \n",
       "ALL      52.707521       100.000000  0.0                 0.0   \n",
       "90-95    -5.971078       -11.328702  0.0                 0.0   \n",
       "95-99     1.185442         2.249095  0.0                 0.0   \n",
       "Top 1%   70.444324       133.651371  0.0                 0.0   \n",
       "\n",
       "        benefit_value_total  pc_aftertaxinc  \n",
       "0-10n                   0.0        0.003868  \n",
       "0-10z                   0.0        0.000000  \n",
       "0-10p                   0.0       -0.046035  \n",
       "10-20                   0.0       -0.060796  \n",
       "20-30                   0.0       -0.060109  \n",
       "30-40                   0.0       -0.048941  \n",
       "40-50                   0.0       -0.034722  \n",
       "50-60                   0.0        0.020912  \n",
       "60-70                   0.0        0.181236  \n",
       "70-80                   0.0        0.314308  \n",
       "80-90                   0.0        0.485572  \n",
       "90-100                  0.0       -1.309040  \n",
       "ALL                     0.0       -0.336668  \n",
       "90-95                   0.0        0.458587  \n",
       "95-99                   0.0       -0.003750  \n",
       "Top 1%                  0.0       -4.906652  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Differences Table\")\n",
    "tb_static.differences_table(start_year, \"weighted_deciles\", \"combined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TaxBrain comes with two (and counting) built in plots as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "differences_plot(tb_static, 'combined', figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(tb_static, 2021, figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run a partial-equlibrium dynamic simulation by initiating the TaxBrian instance exactly as you would for the static reform, but with your behavioral assumptions specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_dynamic = TaxBrain(start_year, end_year, use_cps=True, reform=reform_url,\n",
    "                      behavior={\"sub\": 0.25})\n",
    "tb_dynamic.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once that finishes running, we can produce the same weighted total table as we did with the static run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partial Equilibrium - Combined Tax Liability\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base</th>\n",
       "      <td>2.571828e+12</td>\n",
       "      <td>2.715400e+12</td>\n",
       "      <td>2.846491e+12</td>\n",
       "      <td>2.990706e+12</td>\n",
       "      <td>3.151490e+12</td>\n",
       "      <td>3.537569e+12</td>\n",
       "      <td>3.728943e+12</td>\n",
       "      <td>3.912164e+12</td>\n",
       "      <td>4.090795e+12</td>\n",
       "      <td>4.268168e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>2.601925e+12</td>\n",
       "      <td>2.756096e+12</td>\n",
       "      <td>2.898242e+12</td>\n",
       "      <td>3.054837e+12</td>\n",
       "      <td>3.229669e+12</td>\n",
       "      <td>3.618923e+12</td>\n",
       "      <td>3.828904e+12</td>\n",
       "      <td>4.026075e+12</td>\n",
       "      <td>4.211117e+12</td>\n",
       "      <td>4.395952e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Difference</th>\n",
       "      <td>3.009671e+10</td>\n",
       "      <td>4.069630e+10</td>\n",
       "      <td>5.175027e+10</td>\n",
       "      <td>6.413062e+10</td>\n",
       "      <td>7.817885e+10</td>\n",
       "      <td>8.135412e+10</td>\n",
       "      <td>9.996079e+10</td>\n",
       "      <td>1.139105e+11</td>\n",
       "      <td>1.203217e+11</td>\n",
       "      <td>1.277839e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2021          2022          2023          2024  \\\n",
       "Base        2.571828e+12  2.715400e+12  2.846491e+12  2.990706e+12   \n",
       "Reform      2.601925e+12  2.756096e+12  2.898242e+12  3.054837e+12   \n",
       "Difference  3.009671e+10  4.069630e+10  5.175027e+10  6.413062e+10   \n",
       "\n",
       "                    2025          2026          2027          2028  \\\n",
       "Base        3.151490e+12  3.537569e+12  3.728943e+12  3.912164e+12   \n",
       "Reform      3.229669e+12  3.618923e+12  3.828904e+12  4.026075e+12   \n",
       "Difference  7.817885e+10  8.135412e+10  9.996079e+10  1.139105e+11   \n",
       "\n",
       "                    2029          2030  \n",
       "Base        4.090795e+12  4.268168e+12  \n",
       "Reform      4.211117e+12  4.395952e+12  \n",
       "Difference  1.203217e+11  1.277839e+11  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Partial Equilibrium - Combined Tax Liability\")\n",
    "tb_dynamic.weighted_totals(\"combined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can produce a distribution table to see details on the effects of the reform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution Table\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>c00100</th>\n",
       "      <th>count_StandardDed</th>\n",
       "      <th>standard</th>\n",
       "      <th>count_ItemDed</th>\n",
       "      <th>c04470</th>\n",
       "      <th>c04600</th>\n",
       "      <th>c04800</th>\n",
       "      <th>taxbc</th>\n",
       "      <th>c62100</th>\n",
       "      <th>...</th>\n",
       "      <th>othertaxes</th>\n",
       "      <th>refund</th>\n",
       "      <th>iitax</th>\n",
       "      <th>payrolltax</th>\n",
       "      <th>combined</th>\n",
       "      <th>ubi</th>\n",
       "      <th>benefit_cost_total</th>\n",
       "      <th>benefit_value_total</th>\n",
       "      <th>expanded_income</th>\n",
       "      <th>aftertax_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-10n</th>\n",
       "      <td>0.115333</td>\n",
       "      <td>-9.047595</td>\n",
       "      <td>0.115333</td>\n",
       "      <td>1.885216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.047595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049084</td>\n",
       "      <td>0.049084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611626</td>\n",
       "      <td>0.611626</td>\n",
       "      <td>-8.430574e+00</td>\n",
       "      <td>-8.479658e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10z</th>\n",
       "      <td>8.506734</td>\n",
       "      <td>-0.086591</td>\n",
       "      <td>8.506734</td>\n",
       "      <td>121.959361</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.086591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.441833e-08</td>\n",
       "      <td>-6.441833e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-10p</th>\n",
       "      <td>12.190350</td>\n",
       "      <td>23.835225</td>\n",
       "      <td>12.144855</td>\n",
       "      <td>178.016958</td>\n",
       "      <td>0.045495</td>\n",
       "      <td>0.116874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156897</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>23.734567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.690110</td>\n",
       "      <td>-0.669324</td>\n",
       "      <td>2.839255</td>\n",
       "      <td>2.169931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.591287</td>\n",
       "      <td>17.591287</td>\n",
       "      <td>4.252581e+01</td>\n",
       "      <td>4.035588e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-20</th>\n",
       "      <td>20.814011</td>\n",
       "      <td>177.819214</td>\n",
       "      <td>20.491224</td>\n",
       "      <td>289.192224</td>\n",
       "      <td>0.322101</td>\n",
       "      <td>4.482440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.335962</td>\n",
       "      <td>1.207506</td>\n",
       "      <td>173.742984</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.353213</td>\n",
       "      <td>-8.131132</td>\n",
       "      <td>24.561933</td>\n",
       "      <td>16.430801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.166538</td>\n",
       "      <td>89.166538</td>\n",
       "      <td>2.804744e+02</td>\n",
       "      <td>2.640436e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>20.813074</td>\n",
       "      <td>318.530254</td>\n",
       "      <td>19.727546</td>\n",
       "      <td>305.886750</td>\n",
       "      <td>1.085527</td>\n",
       "      <td>17.711842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.303495</td>\n",
       "      <td>10.628916</td>\n",
       "      <td>302.825447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.086544</td>\n",
       "      <td>-6.282383</td>\n",
       "      <td>45.921631</td>\n",
       "      <td>39.639249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.784931</td>\n",
       "      <td>196.784931</td>\n",
       "      <td>5.424965e+02</td>\n",
       "      <td>5.028573e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>20.813979</td>\n",
       "      <td>369.826021</td>\n",
       "      <td>19.639097</td>\n",
       "      <td>345.024423</td>\n",
       "      <td>1.174882</td>\n",
       "      <td>20.363631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>170.540148</td>\n",
       "      <td>17.835818</td>\n",
       "      <td>352.426171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.746650</td>\n",
       "      <td>1.639923</td>\n",
       "      <td>52.621174</td>\n",
       "      <td>54.261097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337.505102</td>\n",
       "      <td>337.505102</td>\n",
       "      <td>7.416037e+02</td>\n",
       "      <td>6.873426e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-50</th>\n",
       "      <td>20.811791</td>\n",
       "      <td>482.550223</td>\n",
       "      <td>19.397962</td>\n",
       "      <td>349.136484</td>\n",
       "      <td>1.413829</td>\n",
       "      <td>26.956624</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.348918</td>\n",
       "      <td>26.328386</td>\n",
       "      <td>459.980551</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.507941</td>\n",
       "      <td>8.096689</td>\n",
       "      <td>65.973792</td>\n",
       "      <td>74.070482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>402.260964</td>\n",
       "      <td>402.260964</td>\n",
       "      <td>9.321310e+02</td>\n",
       "      <td>8.580605e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-60</th>\n",
       "      <td>20.814918</td>\n",
       "      <td>661.630936</td>\n",
       "      <td>18.599439</td>\n",
       "      <td>347.051639</td>\n",
       "      <td>2.215478</td>\n",
       "      <td>42.562276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>367.465071</td>\n",
       "      <td>40.422590</td>\n",
       "      <td>627.504319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.123708</td>\n",
       "      <td>18.526753</td>\n",
       "      <td>89.173047</td>\n",
       "      <td>107.699799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>443.722857</td>\n",
       "      <td>443.722857</td>\n",
       "      <td>1.170630e+03</td>\n",
       "      <td>1.062930e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-70</th>\n",
       "      <td>20.813189</td>\n",
       "      <td>857.110982</td>\n",
       "      <td>18.099519</td>\n",
       "      <td>366.018828</td>\n",
       "      <td>2.713669</td>\n",
       "      <td>53.607318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>523.426343</td>\n",
       "      <td>62.638891</td>\n",
       "      <td>815.836175</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.958205</td>\n",
       "      <td>36.053421</td>\n",
       "      <td>113.998771</td>\n",
       "      <td>150.052193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>543.781063</td>\n",
       "      <td>543.781063</td>\n",
       "      <td>1.484207e+03</td>\n",
       "      <td>1.334155e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-80</th>\n",
       "      <td>20.814527</td>\n",
       "      <td>1185.103833</td>\n",
       "      <td>17.122154</td>\n",
       "      <td>377.917252</td>\n",
       "      <td>3.692372</td>\n",
       "      <td>81.886507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>795.203166</td>\n",
       "      <td>101.814527</td>\n",
       "      <td>1124.371659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.562504</td>\n",
       "      <td>72.157058</td>\n",
       "      <td>154.279338</td>\n",
       "      <td>226.436396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>633.042501</td>\n",
       "      <td>633.042501</td>\n",
       "      <td>1.925422e+03</td>\n",
       "      <td>1.698985e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-90</th>\n",
       "      <td>20.812826</td>\n",
       "      <td>1809.338784</td>\n",
       "      <td>15.228245</td>\n",
       "      <td>361.947418</td>\n",
       "      <td>5.584581</td>\n",
       "      <td>149.704843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1328.404025</td>\n",
       "      <td>181.367474</td>\n",
       "      <td>1699.916792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.436081</td>\n",
       "      <td>149.579066</td>\n",
       "      <td>229.308045</td>\n",
       "      <td>378.887111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>659.928223</td>\n",
       "      <td>659.928223</td>\n",
       "      <td>2.634744e+03</td>\n",
       "      <td>2.255857e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-100</th>\n",
       "      <td>20.814634</td>\n",
       "      <td>5502.176794</td>\n",
       "      <td>9.999064</td>\n",
       "      <td>247.617682</td>\n",
       "      <td>10.815570</td>\n",
       "      <td>359.618346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4880.673404</td>\n",
       "      <td>1053.462076</td>\n",
       "      <td>5240.960913</td>\n",
       "      <td>...</td>\n",
       "      <td>12.083528</td>\n",
       "      <td>1.731135</td>\n",
       "      <td>1037.285697</td>\n",
       "      <td>514.942876</td>\n",
       "      <td>1552.228574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>547.216215</td>\n",
       "      <td>547.216215</td>\n",
       "      <td>6.298330e+03</td>\n",
       "      <td>4.746101e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>208.135364</td>\n",
       "      <td>11378.788078</td>\n",
       "      <td>179.071173</td>\n",
       "      <td>3291.654237</td>\n",
       "      <td>29.063506</td>\n",
       "      <td>757.010701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8431.857428</td>\n",
       "      <td>1495.715662</td>\n",
       "      <td>10812.165391</td>\n",
       "      <td>...</td>\n",
       "      <td>12.083528</td>\n",
       "      <td>104.196090</td>\n",
       "      <td>1308.255769</td>\n",
       "      <td>1293.668946</td>\n",
       "      <td>2601.924715</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3871.611307</td>\n",
       "      <td>3871.611307</td>\n",
       "      <td>1.604413e+04</td>\n",
       "      <td>1.344221e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-95</th>\n",
       "      <td>10.407523</td>\n",
       "      <td>1441.345651</td>\n",
       "      <td>6.160445</td>\n",
       "      <td>152.514360</td>\n",
       "      <td>4.247078</td>\n",
       "      <td>126.279227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1162.124476</td>\n",
       "      <td>181.444739</td>\n",
       "      <td>1352.133823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000927</td>\n",
       "      <td>1.477666</td>\n",
       "      <td>165.520731</td>\n",
       "      <td>164.708564</td>\n",
       "      <td>330.229295</td>\n",
       "      <td>0.0</td>\n",
       "      <td>325.198036</td>\n",
       "      <td>325.198036</td>\n",
       "      <td>1.864336e+03</td>\n",
       "      <td>1.534107e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99</th>\n",
       "      <td>8.325552</td>\n",
       "      <td>2118.503539</td>\n",
       "      <td>3.361479</td>\n",
       "      <td>83.555458</td>\n",
       "      <td>4.964072</td>\n",
       "      <td>165.197368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1856.210381</td>\n",
       "      <td>352.157306</td>\n",
       "      <td>1999.242338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.831962</td>\n",
       "      <td>0.253469</td>\n",
       "      <td>340.517692</td>\n",
       "      <td>197.080748</td>\n",
       "      <td>537.598441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>191.109698</td>\n",
       "      <td>191.109698</td>\n",
       "      <td>2.409318e+03</td>\n",
       "      <td>1.871719e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Top 1%</th>\n",
       "      <td>2.081559</td>\n",
       "      <td>1942.327604</td>\n",
       "      <td>0.477139</td>\n",
       "      <td>11.547865</td>\n",
       "      <td>1.604420</td>\n",
       "      <td>68.141752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1862.338548</td>\n",
       "      <td>519.860031</td>\n",
       "      <td>1889.584752</td>\n",
       "      <td>...</td>\n",
       "      <td>11.250639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>531.247274</td>\n",
       "      <td>153.153564</td>\n",
       "      <td>684.400838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.908482</td>\n",
       "      <td>30.908482</td>\n",
       "      <td>2.024676e+03</td>\n",
       "      <td>1.340275e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        c00100  count_StandardDed     standard  \\\n",
       "0-10n     0.115333     -9.047595           0.115333     1.885216   \n",
       "0-10z     8.506734     -0.086591           8.506734   121.959361   \n",
       "0-10p    12.190350     23.835225          12.144855   178.016958   \n",
       "10-20    20.814011    177.819214          20.491224   289.192224   \n",
       "20-30    20.813074    318.530254          19.727546   305.886750   \n",
       "30-40    20.813979    369.826021          19.639097   345.024423   \n",
       "40-50    20.811791    482.550223          19.397962   349.136484   \n",
       "50-60    20.814918    661.630936          18.599439   347.051639   \n",
       "60-70    20.813189    857.110982          18.099519   366.018828   \n",
       "70-80    20.814527   1185.103833          17.122154   377.917252   \n",
       "80-90    20.812826   1809.338784          15.228245   361.947418   \n",
       "90-100   20.814634   5502.176794           9.999064   247.617682   \n",
       "ALL     208.135364  11378.788078         179.071173  3291.654237   \n",
       "90-95    10.407523   1441.345651           6.160445   152.514360   \n",
       "95-99     8.325552   2118.503539           3.361479    83.555458   \n",
       "Top 1%    2.081559   1942.327604           0.477139    11.547865   \n",
       "\n",
       "        count_ItemDed      c04470  c04600       c04800        taxbc  \\\n",
       "0-10n        0.000000    0.000000     0.0     0.000000     0.000000   \n",
       "0-10z        0.000000    0.000000     0.0     0.000000     0.000000   \n",
       "0-10p        0.045495    0.116874     0.0     0.156897     0.009477   \n",
       "10-20        0.322101    4.482440     0.0    13.335962     1.207506   \n",
       "20-30        1.085527   17.711842     0.0   107.303495    10.628916   \n",
       "30-40        1.174882   20.363631     0.0   170.540148    17.835818   \n",
       "40-50        1.413829   26.956624     0.0   245.348918    26.328386   \n",
       "50-60        2.215478   42.562276     0.0   367.465071    40.422590   \n",
       "60-70        2.713669   53.607318     0.0   523.426343    62.638891   \n",
       "70-80        3.692372   81.886507     0.0   795.203166   101.814527   \n",
       "80-90        5.584581  149.704843     0.0  1328.404025   181.367474   \n",
       "90-100      10.815570  359.618346     0.0  4880.673404  1053.462076   \n",
       "ALL         29.063506  757.010701     0.0  8431.857428  1495.715662   \n",
       "90-95        4.247078  126.279227     0.0  1162.124476   181.444739   \n",
       "95-99        4.964072  165.197368     0.0  1856.210381   352.157306   \n",
       "Top 1%       1.604420   68.141752     0.0  1862.338548   519.860031   \n",
       "\n",
       "              c62100  ...  othertaxes      refund        iitax   payrolltax  \\\n",
       "0-10n      -9.047595  ...    0.000000    0.000000     0.000000     0.049084   \n",
       "0-10z      -0.086591  ...    0.000000    0.000000     0.000000     0.000000   \n",
       "0-10p      23.734567  ...    0.000000    0.690110    -0.669324     2.839255   \n",
       "10-20     173.742984  ...    0.000000    9.353213    -8.131132    24.561933   \n",
       "20-30     302.825447  ...    0.000000   16.086544    -6.282383    45.921631   \n",
       "30-40     352.426171  ...    0.000000   13.746650     1.639923    52.621174   \n",
       "40-50     459.980551  ...    0.000000   13.507941     8.096689    65.973792   \n",
       "50-60     627.504319  ...    0.000000   14.123708    18.526753    89.173047   \n",
       "60-70     815.836175  ...    0.000000   14.958205    36.053421   113.998771   \n",
       "70-80    1124.371659  ...    0.000000   12.562504    72.157058   154.279338   \n",
       "80-90    1699.916792  ...    0.000000    7.436081   149.579066   229.308045   \n",
       "90-100   5240.960913  ...   12.083528    1.731135  1037.285697   514.942876   \n",
       "ALL     10812.165391  ...   12.083528  104.196090  1308.255769  1293.668946   \n",
       "90-95    1352.133823  ...    0.000927    1.477666   165.520731   164.708564   \n",
       "95-99    1999.242338  ...    0.831962    0.253469   340.517692   197.080748   \n",
       "Top 1%   1889.584752  ...   11.250639    0.000000   531.247274   153.153564   \n",
       "\n",
       "           combined  ubi  benefit_cost_total  benefit_value_total  \\\n",
       "0-10n      0.049084  0.0            0.611626             0.611626   \n",
       "0-10z      0.000000  0.0            0.000000             0.000000   \n",
       "0-10p      2.169931  0.0           17.591287            17.591287   \n",
       "10-20     16.430801  0.0           89.166538            89.166538   \n",
       "20-30     39.639249  0.0          196.784931           196.784931   \n",
       "30-40     54.261097  0.0          337.505102           337.505102   \n",
       "40-50     74.070482  0.0          402.260964           402.260964   \n",
       "50-60    107.699799  0.0          443.722857           443.722857   \n",
       "60-70    150.052193  0.0          543.781063           543.781063   \n",
       "70-80    226.436396  0.0          633.042501           633.042501   \n",
       "80-90    378.887111  0.0          659.928223           659.928223   \n",
       "90-100  1552.228574  0.0          547.216215           547.216215   \n",
       "ALL     2601.924715  0.0         3871.611307          3871.611307   \n",
       "90-95    330.229295  0.0          325.198036           325.198036   \n",
       "95-99    537.598441  0.0          191.109698           191.109698   \n",
       "Top 1%   684.400838  0.0           30.908482            30.908482   \n",
       "\n",
       "        expanded_income  aftertax_income  \n",
       "0-10n     -8.430574e+00    -8.479658e+00  \n",
       "0-10z     -6.441833e-08    -6.441833e-08  \n",
       "0-10p      4.252581e+01     4.035588e+01  \n",
       "10-20      2.804744e+02     2.640436e+02  \n",
       "20-30      5.424965e+02     5.028573e+02  \n",
       "30-40      7.416037e+02     6.873426e+02  \n",
       "40-50      9.321310e+02     8.580605e+02  \n",
       "50-60      1.170630e+03     1.062930e+03  \n",
       "60-70      1.484207e+03     1.334155e+03  \n",
       "70-80      1.925422e+03     1.698985e+03  \n",
       "80-90      2.634744e+03     2.255857e+03  \n",
       "90-100     6.298330e+03     4.746101e+03  \n",
       "ALL        1.604413e+04     1.344221e+04  \n",
       "90-95      1.864336e+03     1.534107e+03  \n",
       "95-99      2.409318e+03     1.871719e+03  \n",
       "Top 1%     2.024676e+03     1.340275e+03  \n",
       "\n",
       "[16 rows x 24 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Distribution Table\")\n",
    "tb_dynamic.distribution_table(start_year, \"weighted_deciles\", \"expanded_income\", \"reform\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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