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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `microdf` demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import taxcalc as tc\n",
    "import microdf as mdf\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mghenis/anaconda3/lib/python3.7/site-packages/microdf/style.py:24: MatplotlibDeprecationWarning: \n",
      "The createFontList function was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use FontManager.addfont instead.\n",
      "  fm.fontManager.ttflist += fm.createFontList([\"Roboto-Regular.ttf\"])\n"
     ]
    }
   ],
   "source": [
    "mdf.set_plot_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = mdf.calc_df(group_vars=['expanded_income', 'MARS'],\n",
    "                   metric_vars=['aftertax_income', 'XTOT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['e02400', 'mcare_ben', 'aftertax_income', 'ssi_ben', 'expanded_income',\n",
       "       'snap_ben', 'vet_ben', 'housing_ben', 's006', 'other_ben', 'e02300',\n",
       "       'mcaid_ben', 'XTOT', 'tanf_ben', 'MARS', 'wic_ben', 'market_income',\n",
       "       'bens', 'tax', 's006_m', 'aftertax_income_m', 'XTOT_m'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a reform that treats capital gains as ordinary income and sets the top marginal rate to 70%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CG_REFORM = {\n",
    "    'CG_nodiff': {2019: True},\n",
    "    'II_rt7': {2019: 0.7}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "reform = mdf.calc_df(reform=CG_REFORM, group_vars=['MARS'], group_n65=True, \n",
    "                     metric_vars=['aftertax_income', 'XTOT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['vet_ben', 's006', 'e02300', 'MARS', 'e02400', 'mcare_ben', 'ssi_ben',\n",
       "       'snap_ben', 'housing_ben', 'other_ben', 'aftertax_income',\n",
       "       'expanded_income', 'mcaid_ben', 'XTOT', 'tanf_ben', 'wic_ben',\n",
       "       'market_income', 'bens', 'tax', 'n65', 's006_m', 'aftertax_income_m',\n",
       "       'XTOT_m'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate senior UBI.\n",
    "\n",
    "Start with total revenue ($ billions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326.110945495585"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rev_m = base.aftertax_income_m.sum() - reform.aftertax_income_m.sum()\n",
    "new_rev_m / 1e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many seniors are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.21619976999999"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.add_weighted_metrics(reform, 'n65')\n",
    "\n",
    "n65_total_m = reform.n65_m.sum()\n",
    "n65_total_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Divide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5507.123840473106"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senior_ubi = new_rev_m / reform.n65_m.sum()\n",
    "senior_ubi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add senior UBI to `aftertax_income` and recalculate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reform['ubi'] = senior_ubi * reform.n65\n",
    "reform['aftertax_income'] = reform.aftertax_income + reform.ubi\n",
    "mdf.add_weighted_metrics(reform, 'aftertax_income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(base.aftertax_income_m.sum(), reform.aftertax_income_m.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze\n",
    "\n",
    "Gini, FPL, distributional impact chart\n",
    "\n",
    "### Change to Gini index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5032911973267852"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.gini(base, 'aftertax_income', 's006')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48752755152259336"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.gini(reform, 'aftertax_income', 's006')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change to poverty rate\n",
    "\n",
    "Add federal poverty line with `mdf.fpl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "base['fpl'] = mdf.fpl(base.XTOT)\n",
    "reform['fpl'] = mdf.fpl(reform.XTOT)\n",
    "\n",
    "base['fpl_XTOT_m'] = np.where(base.aftertax_income < base.fpl,\n",
    "                              base.XTOT_m, 0)\n",
    "reform['fpl_XTOT_m'] = np.where(reform.aftertax_income < reform.fpl,\n",
    "                                reform.XTOT_m, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.022307196800575246"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform.fpl_XTOT_m.sum() / base.fpl_XTOT_m.sum() - 1"
   ]
  }
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