compute_data_report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%reset-f\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from res_ind_lib import *\n",
    "\n",
    "from sorted_nicely import *\n",
    "from replace_with_warning import *\n",
    "\n",
    "import os, time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"always\",category=UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Pandas display options\n",
    "# pd.set_option('display.max_colwidth', 200)\n",
    "# pd.set_option('display.width', 200)\n",
    "# pd.set_option('display.precision', 10)\n",
    "# pd.set_option('display.max_rows', 500)\n",
    "\n",
    "#import matplotlib.pylab as plt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "font = {'family' : 'serif',\n",
    "    'weight' : 'normal',\n",
    "    'size'   : 15}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "from fancy_plots import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "economy = \"country\" #province, deparmtent\n",
    "event_level = [economy, \"hazard\", \"rp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp_pc_pp</th>\n",
       "      <th>pop</th>\n",
       "      <th>urbanization_rate</th>\n",
       "      <th>prepare_scaleup</th>\n",
       "      <th>borrow_abi</th>\n",
       "      <th>avg_prod_k</th>\n",
       "      <th>T_rebuild_K</th>\n",
       "      <th>pi</th>\n",
       "      <th>income_elast</th>\n",
       "      <th>rho</th>\n",
       "      <th>shareable</th>\n",
       "      <th>max_increased_spending</th>\n",
       "      <th>protection</th>\n",
       "      <th>tau_tax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Niger</th>\n",
       "      <td>897.395321</td>\n",
       "      <td>19899120.0</td>\n",
       "      <td>0.18732</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.233592</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.061743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>28254.255793</td>\n",
       "      <td>5423801.0</td>\n",
       "      <td>0.53598</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.305902</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.392993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    gdp_pc_pp         pop  urbanization_rate  prepare_scaleup  \\\n",
       "country                                                                         \n",
       "Niger              897.395321  19899120.0            0.18732         0.666667   \n",
       "Slovak Republic  28254.255793   5423801.0            0.53598         0.733333   \n",
       "\n",
       "                 borrow_abi  avg_prod_k  T_rebuild_K   pi  income_elast   rho  \\\n",
       "country                                                                         \n",
       "Niger              0.333333    0.233592            3  0.2           1.5  0.06   \n",
       "Slovak Republic    0.625000    0.305902            3  0.2           1.5  0.06   \n",
       "\n",
       "                 shareable  max_increased_spending  protection   tau_tax  \n",
       "country                                                                   \n",
       "Niger                  0.8                    0.05        20.0  0.061743  \n",
       "Slovak Republic        0.8                    0.05        20.0  0.392993  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv(\"intermediate/macro.csv\", index_col=economy).dropna()\n",
    "macro.sample(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>c</th>\n",
       "      <th>axfin</th>\n",
       "      <th>gamma_SP</th>\n",
       "      <th>k</th>\n",
       "      <th>fa</th>\n",
       "      <th>v</th>\n",
       "      <th>shew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>income_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iran, Islamic Rep.</th>\n",
       "      <th>poor</th>\n",
       "      <td>0.2</td>\n",
       "      <td>5620.641756</td>\n",
       "      <td>0.151668</td>\n",
       "      <td>0.672016</td>\n",
       "      <td>14898.408431</td>\n",
       "      <td>0.018325</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <th>poor</th>\n",
       "      <td>0.2</td>\n",
       "      <td>686.243247</td>\n",
       "      <td>0.055189</td>\n",
       "      <td>0.309271</td>\n",
       "      <td>1513.387110</td>\n",
       "      <td>0.015701</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 n            c     axfin  gamma_SP  \\\n",
       "country            income_cat                                         \n",
       "Iran, Islamic Rep. poor        0.2  5620.641756  0.151668  0.672016   \n",
       "Zambia             poor        0.2   686.243247  0.055189  0.309271   \n",
       "\n",
       "                                          k        fa    v  shew  \n",
       "country            income_cat                                     \n",
       "Iran, Islamic Rep. poor        14898.408431  0.018325  0.7  0.32  \n",
       "Zambia             poor         1513.387110  0.015701  0.7  0.64  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_info = pd.read_csv(\"intermediate/cat_info.csv\",  index_col=[economy, \"income_cat\"]).dropna()\n",
    "cat_info.sample(n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fa</th>\n",
       "      <th>shew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>hazard</th>\n",
       "      <th>rp</th>\n",
       "      <th>income_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Niger</th>\n",
       "      <th>wind</th>\n",
       "      <th>1500.0</th>\n",
       "      <th>nonpoor</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <th>earthquake</th>\n",
       "      <th>250.0</th>\n",
       "      <th>poor</th>\n",
       "      <td>0.004149</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            fa  shew\n",
       "country hazard     rp     income_cat                \n",
       "Niger   wind       1500.0 nonpoor     0.000000   0.6\n",
       "Estonia earthquake 250.0  poor        0.004149   0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hazard_ratios = pd.read_csv(\"intermediate/hazard_ratios.csv\", index_col=event_level+[\"income_cat\"]).dropna()\n",
    "hazard_ratios.sample(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computes and saves national resilience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "nb countries with macro data :126\n",
      "nb countries with cat data :125\n",
      "nb countries with hazard data :262\n",
      "nb countries with results :124\n",
      "Resilience averages 63% across our sample, ranging from 6% to 81%\n",
      "Risk to welfare averages 0.267% across our sample, ranging from 0.000% (Norway) to 2.138% (Bhutan)\n",
      "Risk to assets averages 0.164% across our sample, ranging from 0.001% (Zimbabwe) to 1.519% (Bhutan)\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 109 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.6 times bigger, at 173 bn$ per year\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protection</th>\n",
       "      <th>risk</th>\n",
       "      <th>v</th>\n",
       "      <th>fa</th>\n",
       "      <th>resilience</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.802349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>1.339029</td>\n",
       "      <td>0.013140</td>\n",
       "      <td>0.784797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malawi</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.009044</td>\n",
       "      <td>2.469789</td>\n",
       "      <td>0.068994</td>\n",
       "      <td>0.593718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.015398</td>\n",
       "      <td>2.362141</td>\n",
       "      <td>0.156590</td>\n",
       "      <td>0.660293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.015708</td>\n",
       "      <td>2.265503</td>\n",
       "      <td>0.193342</td>\n",
       "      <td>0.560282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo, Dem. Rep.</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.009832</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.048437</td>\n",
       "      <td>0.519640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  protection      risk         v        fa  resilience\n",
       "country                                                               \n",
       "Denmark                 20.0  0.000042  1.300000  0.000751    0.802349\n",
       "Netherlands             20.0  0.000732  1.339029  0.013140    0.784797\n",
       "Malawi                  20.0  0.009044  2.469789  0.068994    0.593718\n",
       "Bangladesh              20.0  0.015398  2.362141  0.156590    0.660293\n",
       "Jamaica                 20.0  0.015708  2.265503  0.193342    0.560282\n",
       "Congo, Dem. Rep.        20.0  0.009832  3.100000  0.048437    0.519640\n",
       "Finland                  NaN       NaN       NaN       NaN         NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#args = dict(hazard_ratios = hazard_ratios.unstack(\"hazard\").swaplevel(0,1,axis=\"columns\").flood, return_stats=True)\n",
    "args = dict(hazard_ratios = hazard_ratios, return_stats=True)  # for final\n",
    "#args = dict(hazard_ratios = None, return_stats=True)  \n",
    "# args = dict(return_stats=True)  # for test\n",
    "#args = dict()\n",
    "\n",
    "results=compute_resilience(macro,cat_info,optionPDS=\"unif_poor\", optionT=\"data\",**args)\n",
    "\n",
    "\n",
    "print(\"nb countries with macro data :\" +str(macro.shape[0]))\n",
    "print(\"nb countries with cat data :\"   +str(cat_info.unstack().dropna().shape[0]))\n",
    "print(\"nb countries with hazard data :\"+str(hazard_ratios.unstack([\"rp\",\"hazard\"]).dropna().shape[0]))\n",
    "\n",
    "nb_countries_all_results = results[\"resilience\"].dropna().shape[0]\n",
    "print(\"nb countries with results :\"+str(nb_countries_all_results))\n",
    "results.to_csv(\"results/results.csv\",float_format=\"%.5f\")\n",
    "\n",
    "#av_res = ((results[\"resilience\"]*macro[\"pop\"]).sum())/macro[\"pop\"].sum()\n",
    "av_res = results.dKtot.sum()/results.dWtot_currency.sum()\n",
    "av_risk_asset = ((results[\"risk_to_assets\"]*macro[\"pop\"]).sum())/macro[\"pop\"].sum()\n",
    "av_risk_welfare = ((results[\"risk\"]*macro[\"pop\"]).sum())/macro[\"pop\"].sum()\n",
    "\n",
    "#Some stats\n",
    "a=results.resilience;\n",
    "print(\"Resilience averages {mean:.0%} across our sample, ranging from {min:.0%} to {max:.0%}\".format(\n",
    "        min=a.min(),mean=av_res,max=a.max()))\n",
    "\n",
    "a=results.risk\n",
    "print(\"Risk to welfare averages {mean:.03%} across our sample, ranging from {min:.03%} ({m}) to {max:.03%} ({M})\".format(\n",
    "    min=a.min(),mean=av_risk_welfare,max=a.max(),m=a.argmin(),M=a.argmax()))\n",
    "\n",
    "\n",
    "a=results.risk_to_assets\n",
    "print(\"Risk to assets averages {mean:.03%} across our sample, ranging from {min:.03%} ({m}) to {max:.03%} ({M})\".format(\n",
    "    min=a.min(),mean=av_risk_asset,max=a.max(),m=a.argmin(),M=a.argmax()))\n",
    "\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= results.dWtot_currency.sum()*1e-9,\n",
    "k= results.dKtot.sum()*1e-9,\n",
    "r=results.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "\n",
    "results.ix[[\"Denmark\", \"Netherlands\",\"Malawi\", \"Bangladesh\", \"Jamaica\", \"Congo, Dem. Rep.\", \"Finland\"],[\"protection\", \"risk\", \"v\",\"fa\",\"resilience\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp_pc_pp</th>\n",
       "      <th>risk_to_assets</th>\n",
       "      <th>resilience</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gdp_pc_pp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.287111</td>\n",
       "      <td>0.479294</td>\n",
       "      <td>-0.312895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_to_assets</th>\n",
       "      <td>-0.287111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.965500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resilience</th>\n",
       "      <td>0.479294</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.172281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk</th>\n",
       "      <td>-0.312895</td>\n",
       "      <td>0.965500</td>\n",
       "      <td>-0.172281</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gdp_pc_pp  risk_to_assets  resilience      risk\n",
       "gdp_pc_pp        1.000000       -0.287111    0.479294 -0.312895\n",
       "risk_to_assets  -0.287111        1.000000    0.000397  0.965500\n",
       "resilience       0.479294        0.000397    1.000000 -0.172281\n",
       "risk            -0.312895        0.965500   -0.172281  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cormat = results[[\"gdp_pc_pp\",\"risk_to_assets\",\"resilience\",\"risk\"]]\n",
    "cormat.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Macro mulitplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:4: RuntimeWarning: divide by zero encountered in true_divide\n",
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:11: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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ihbRrfwY3jpvNrDU7ePj8dgw+sWXQkSRBqaRFRAph864DXD9yJmu37efFq7tyYedGQUeS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e5233f128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t= np.linspace(0,20,200)\n",
    "\n",
    "three = np.log(1/0.05) \n",
    "recons_rate = three/ t\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "for mu in [.25]: \n",
    "    r=6/100\n",
    "\n",
    "    g= (mu+recons_rate)/(r+recons_rate)\n",
    "\n",
    "    plt.plot(t,g)\n",
    "\n",
    "# l=plt.legend([\"30%\",\"20%\",\"15%\"],prop=font,title=\"Average \\nproductivity \\nof capital\", loc=2, frameon=False)    \n",
    "# plt.setp(l.get_title(), multialignment='center')    \n",
    "\n",
    "\n",
    "plt.xlabel(\"Reconstruction duration (years)\")\n",
    "plt.ylabel(\"Scaling factor (${\\Delta C}/{\\Delta K}$)\")\n",
    "\n",
    "\n",
    "spine_and_ticks(plt.gca())\n",
    "\n",
    "plt.savefig(\"img/macro_resilience.png\")\n",
    "\n",
    "plt.ylabel(\"Scaling factor (${\\\\tilde{\\Delta C}}/{\\Delta K}$)\")\n",
    "plt.savefig(\"img/macro_resilience_tilde.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring post-disaster support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Resilience gain thanks to current post-disaster support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Average (population weights) ( unif_poor ) 1.2872128810181005\n",
      "Average (no weights) ( unif_poor ) 0.8459535434772937\n",
      "Country where post-disaster support reduces resilience\n",
      "Series([], Name: diff, dtype: float64)\n",
      "Benefits\n",
      "11.755782283693055\n",
      "No PDS\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.6 times bigger, at 529 bn$ per year\n",
      "PDS cost: 0.0\n",
      "resilience: 0.61894161799855\n",
      "With PDS\n",
      "PDS cost: 11.049155337233886\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.6 times bigger, at 517 bn$ per year\n",
      "resilience: 0.6330070746372456\n",
      "resilience gain: 0.014065456638695628\n"
     ]
    },
    {
     "data": {
      "image/png": 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/AqxJrkrlufs73f3e6PvngfmErvMPpf3ckl+abS0inSJOC3kTM5vi7iuLC6NZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x952b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "couleur = (\"blue\",\"red\",\"green\",\"yellow\",\"purple\")\n",
    "coul = \"blue\"\n",
    "option = \"unif_poor\"\n",
    "ref = compute_resilience(macro,cat_info,optionPDS=\"no\",**args)\n",
    "plt.figure(figsize=(7,7))\n",
    "#for option,coul in zip(listePDS,couleur):\n",
    "#    print(option\n",
    "dfr = compute_resilience(macro,cat_info,optionPDS=option,optionT=\"data\",optionB=\"data\",**args)\n",
    "diff = 100*(dfr.resilience-ref.resilience)\n",
    "plt.scatter(results.gdp_pc_pp,diff,color=coul,alpha=.7,s=50, clip_on=False)\n",
    "\n",
    "\n",
    "#plt.legend(listePDS)\n",
    "plt.xlabel(\"GDP per capita (US$)\")\n",
    "plt.ylabel(\"Resilience gain from postdisaster support (%)\")\n",
    "#plt.legend([\"Uniform support\", \"Support proportional to losses\"])\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(0)\n",
    "\n",
    "\n",
    "spine_and_ticks(plt.gca())\n",
    "x_thousands_sep()\n",
    "\n",
    "savefig(\"img/resilience_gain_post-disaster_support.png\")\n",
    "\n",
    "\n",
    "print('Average (population weights) (',option,')',((diff*dfr[\"pop\"]).sum())/dfr[\"pop\"].sum())\n",
    "print('Average (no weights) (',option,')',((diff.mean())))\n",
    "print('Country where post-disaster support reduces resilience')\n",
    "results[\"diff\"]=diff\n",
    "print(results.query(\"diff<0\")[\"diff\"])\n",
    "print('Benefits')\n",
    "print((ref.dWtot_currency.sum()-dfr.dWtot_currency.sum())/1e9)\n",
    "print('No PDS')\n",
    "a=results.resilience;\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= ref.dWtot_currency.sum()*1e-9,\n",
    "k= ref.dKtot.sum()*1e-9,\n",
    "r=ref.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('PDS cost:',((ref[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print('resilience:',ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "print('With PDS')\n",
    "print('PDS cost:',((dfr[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= dfr.dWtot_currency.sum()*1e-9,\n",
    "k= dfr.dKtot.sum()*1e-9,\n",
    "r=dfr.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience:',dfr.dKtot.sum()/dfr.dWtot_currency.sum())\n",
    "print('resilience gain:',dfr.dKtot.sum()/dfr.dWtot_currency.sum()-ref.dKtot.sum()/ref.dWtot_currency.sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why is post-disaster support unable to increase resilience in some countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vceTIEbZs2eKwnsKkp6fzzjvv0K5duwIJH8DDw4N27doxceJEEhMT7dZz8uRJ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb69aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "ax = plt.gca();\n",
    "#dfr[\"diff\"]=(diff>1)*1 + (diff<=0)*0\n",
    "dfr[\"diff\"]=diff\n",
    "dfr.plot.scatter(x=\"borrow_abi\",y=\"prepare_scaleup\",c=\"diff\",s = 100 ,ax=ax)\n",
    "plt.xlabel(\"Capacity to allocate financial resources\")\n",
    "plt.ylabel(\"Preparation for emergencies & Adaptive social protection\")\n",
    "plt.xlim(xmin=0,xmax=1)\n",
    "plt.ylim(ymin=0,ymax=1)\n",
    "#plt.legend('Increase in resilience')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fixing that wth the PDS package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from apply_policy import apply_policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Develop contingent finance and reserve funds\n",
      "Make social protection scalable after a shock\n",
      "Postdisaster support package\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Average (population weights) ( unif_poor ) 1.3717549579929207\n",
      "Average (no weights) ( unif_poor ) 1.95525243072577\n",
      "Country where post-disaster support reduces resilience\n",
      "Series([], Name: diff, dtype: float64)\n",
      "Benefits\n",
      "12.550952127061585\n",
      "No PDS\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.6 times bigger, at 517 bn$ per year\n",
      "PDS cost: 11.049155337233886\n",
      "resilience: 0.6330070746372456\n",
      "With PDS\n",
      "PDS cost: 23.007982886516807\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.5 times bigger, at 505 bn$ per year\n",
      "resilience GLOBALLY: 0.6487470736530412\n",
      "resilience gain GLOBALLY: 0.01573999901579559\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country\n",
       "Angola                      9.731354\n",
       "Congo, Dem. Rep.            5.990810\n",
       "Benin                       5.656408\n",
       "Gabon                       4.763448\n",
       "Central African Republic    4.338968\n",
       "Lesotho                     3.847832\n",
       "Nepal                       3.775456\n",
       "Name: resilience, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kERFpVqFKyJjZjsApwGR3/7CZHQfc7O73JRzXbsB/VW1bDIwD3gp8qdaT5s6d+/zP/f39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\nDUAq5CRJkiRpAP4Phj1JO3RetGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb418eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "couleur = (\"blue\",\"red\",\"green\",\"yellow\",\"purple\")\n",
    "coul = \"blue\"\n",
    "option = \"unif_poor\"\n",
    "ref = compute_resilience(macro,cat_info,**args)\n",
    "plt.figure(figsize=(7,7))\n",
    "#for option,coul in zip(listePDS,couleur):\n",
    "#    print(option\n",
    "\n",
    "m,c,h,a,desc = apply_policy(macro,cat_info,macro*0, args, policy_name=\"PDSpackage\", verbose=True)\n",
    "\n",
    "dfr = compute_resilience(m,c,optionPDS=option,optionT=\"data\",optionB=\"data\",**a)\n",
    "diff = 100*(dfr.resilience-ref.resilience)\n",
    "plt.scatter(results.gdp_pc_pp,diff,color=coul,alpha=.7,s=50)\n",
    "#plt.legend(listePDS)\n",
    "plt.xlabel(\"GDP per capita (US$)\")\n",
    "plt.ylabel(\"Resilience gain from postdisaster support (%)\")\n",
    "#plt.legend([\"Uniform support\", \"Support proportional to losses\"])\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(ymin=-.1)\n",
    "\n",
    "\n",
    "spine_and_ticks(plt.gca())\n",
    "x_thousands_sep()\n",
    "\n",
    "savefig(\"img/resilience_gain_post-disaster_support_PDS_package.png\")\n",
    "\n",
    "\n",
    "print('Average (population weights) (',option,')',((diff*dfr[\"pop\"]).sum())/dfr[\"pop\"].sum())\n",
    "print('Average (no weights) (',option,')',((diff.mean())))\n",
    "print('Country where post-disaster support reduces resilience')\n",
    "results[\"diff\"]=diff\n",
    "print(results.query(\"diff<0\")[\"diff\"])\n",
    "print('Benefits')\n",
    "print((ref.dWtot_currency.sum()-dfr.dWtot_currency.sum())/1e9)\n",
    "print('No PDS')\n",
    "a=results.resilience;\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= ref.dWtot_currency.sum()*1e-9,\n",
    "k= ref.dKtot.sum()*1e-9,\n",
    "r=ref.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('PDS cost:',((ref[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print('resilience:',ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "print('With PDS')\n",
    "print('PDS cost:',((dfr[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= dfr.dWtot_currency.sum()*1e-9,\n",
    "k= dfr.dKtot.sum()*1e-9,\n",
    "r=dfr.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience GLOBALLY:',dfr.dKtot.sum()/dfr.dWtot_currency.sum())\n",
    "print('resilience gain GLOBALLY:',dfr.dKtot.sum()/dfr.dWtot_currency.sum()-ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "\n",
    "\n",
    "diff.sort_values(ascending=False).head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What if post-disaster support was an insurance? \n",
    "here insurance modeled as a post-disaster transfer covering 80% of losses, paid through taxes (as a proxy for risk-based premium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "args = dict(return_stats=True, hazard_ratios=hazard_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Develop market insurance (nonpoor people)\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,need,shew,social,v,v_shew\n",
      "shareable 0.25\n",
      "Average (population weights) ( prop_nonpoor ) 3.8636978515843343\n",
      "Average (no weights) ( prop_nonpoor ) 3.2048874141860417\n",
      "Country where post-disaster support reduces resilience\n",
      "Series([], Name: diff, dtype: float64)\n",
      "Series([], Name: diff2, dtype: float64)\n",
      "Benefits\n",
      "30.560111699043212\n",
      "No PDS\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.6 times bigger, at 529 bn$ per year\n",
      "resilience: 0.61894161799855\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.5 times bigger, at 499 bn$ per year\n",
      "PDS cost: 92.91371997306614\n",
      "resilience: 0.656885183775972\n",
      "resilience gain: 0.037943565777422084\n",
      "Jordan 5.84490466795\n"
     ]
    },
    {
     "data": {
      "image/png": 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GDPjwh8vZuakTJf0elLG6XaojTUD+tbtf1miDmU1w9/UZ5UmG0eoSa9l7vraLRhdylbbK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\ngAOymZlZAzggm5mZNYADspmZWQP8B3eTRypD7TZzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3a9908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "couleur = (\"blue\",\"red\",\"green\",\"yellow\",\"purple\")\n",
    "coul = \"blue\"\n",
    "\n",
    "\n",
    "shareable = .25\n",
    "\n",
    "\n",
    "ref = compute_resilience(macro,cat_info,optionPDS=\"no\",**args)\n",
    "plt.figure(figsize=(7,7))\n",
    "option = \"prop_nonpoor\"\n",
    "#for option,coul in zip(listePDS,couleur):\n",
    "#    print(option\n",
    "\n",
    "m,c,h,a,desc = apply_policy(macro,cat_info,macro*0, args, policy_name=\"prop_nonpoor\", verbose=True)\n",
    "\n",
    "# dfr, ciah = compute_resilience(m,c,**a)\n",
    "\n",
    "dfr = compute_resilience(m,c,**a)\n",
    "\n",
    "diff = 100*(dfr.resilience - ref.resilience)\n",
    "diff2 = -(dfr.dWpc_currency - ref.dWpc_currency)\n",
    "plt.scatter(results.gdp_pc_pp,diff,color=coul,alpha=.7,s=50)\n",
    "#plt.legend(listePDS)\n",
    "plt.xlabel(\"GDP per capita (US$)\")\n",
    "plt.ylabel(\"Resilience gain thanks to insurance (%)\")\n",
    "#plt.legend([\"Uniform support\", \"Support proportional to losses\"])\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(ymin=0)\n",
    "\n",
    "\n",
    "\n",
    "spine_and_ticks(plt.gca())\n",
    "\n",
    "x_thousands_sep()\n",
    "\n",
    "savefig(\"img/gains_of_insurance_shareable{s}.png\".format(s=shareable))\n",
    "\n",
    "\n",
    "# plt.ylim(0,6)\n",
    "\n",
    "# savefig(\"img/gains_of_insurance_shareable{s}_shaved.png\".format(s=shareable))\n",
    "\n",
    "\n",
    "\n",
    "print(\"shareable\", shareable)\n",
    "print('Average (population weights) (',option,')',((diff*dfr[\"pop\"]).sum())/dfr[\"pop\"].sum())\n",
    "print('Average (no weights) (',option,')',((diff.mean())))\n",
    "print('Country where post-disaster support reduces resilience')\n",
    "results[\"diff\"]=diff\n",
    "results[\"diff2\"]=diff2\n",
    "print(results.query(\"diff<0\")[\"diff\"])\n",
    "print(results.query(\"diff2<0\")[\"diff2\"])\n",
    "print('Benefits')\n",
    "print((ref.dWtot_currency.sum()-dfr.dWtot_currency.sum())/1e9)\n",
    "print('No PDS')\n",
    "a=results.resilience;\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= ref.dWtot_currency.sum()*1e-9,\n",
    "k= ref.dKtot.sum()*1e-9,\n",
    "r=ref.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience:',ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= dfr.dWtot_currency.sum()*1e-9,\n",
    "k= dfr.dKtot.sum()*1e-9,\n",
    "r=dfr.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('PDS cost:',((dfr[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print('resilience:',dfr.dKtot.sum()/dfr.dWtot_currency.sum())\n",
    "print('resilience gain:',dfr.dKtot.sum()/dfr.dWtot_currency.sum()-ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "\n",
    "print(diff.argmax(),diff.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": [],
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Resilience gains if budget constraints were released (only 5% of GDP limit) - proportional distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Average (population weights)  1.2308180826386104\n",
      "Average (no weights)  1.7153979843184681\n",
      "Country where higher budget for post-disaster support reduces resilience\n",
      "Country where more resources would reduce resilience\n",
      "Empty DataFrame\n",
      "Columns: [diff]\n",
      "Index: []\n",
      "Country where more resources would increase resilience the most\n",
      "                              diff\n",
      "country                           \n",
      "Angola                    5.311727\n",
      "Benin                     4.803258\n",
      "Congo, Dem. Rep.          4.460242\n",
      "Gabon                     3.677619\n",
      "Lesotho                   3.412509\n",
      "Central African Republic  3.349224\n",
      "Swaziland                 3.304828\n",
      "Estonia                   3.229025\n",
      "El Salvador               3.225997\n",
      "Nepal                     3.180235\n",
      "Zambia                    2.994930\n",
      "Greece                    2.803784\n",
      "Russian Federation        2.794717\n",
      "Thailand                  2.790878\n",
      "Kenya                     2.705690\n",
      "Congo, Rep.               2.688601\n",
      "Mauritania                2.678799\n",
      "Mongolia                  2.669753\n",
      "Argentina                 2.654168\n",
      "Albania                   2.550784\n",
      "Before\n",
      "PDS cost: 11.049155337233886\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.6 times bigger, at 517 bn$ per year\n",
      "resilience: 0.6330070746372456\n",
      "After\n",
      "PDS cost: 23.007982886516807\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.5 times bigger, at 506 bn$ per year\n",
      "resilience: 0.647388884405035\n",
      "resilience gain: 0.014381809767789355\n"
     ]
    },
    {
     "data": {
      "image/png": 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AR8dFfc7X2rqcuXMX5Lx7q7KyMucpwAODZf4mU0ZxPSIycmHXs6lz90+4+/8B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\nHyfRTPDTKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6f5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coul = (\"blue\")\n",
    "ref = compute_resilience(macro,cat_info,optionPDS=\"unif_poor\",optionT=\"data\",optionB=\"data\",**args)\n",
    "#ref = 100*compute_resilience(macro,cat_info,**args,optionPDS=\"no\").resilience\n",
    "plt.figure(figsize=(6,6))\n",
    "ax = plt.gca();\n",
    "#for option,coul in zip(listePDS,couleur):\n",
    "#    print(option\n",
    "dfr = compute_resilience(macro,cat_info,optionPDS=\"unif_poor\",optionT=\"data\",optionB=\"max05\",**args) \n",
    "dfr[\"diff\"] = 100*(dfr.resilience - ref.resilience)\n",
    "\n",
    "\n",
    "dfr.plot.scatter(x=\"gdp_pc_pp\",y=\"diff\",color=coul,alpha=.7,s=50,ax=ax)\n",
    "\n",
    "#plt.legend(listePDS)\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"resilience gain from relaxing\\n budget constraint (%)\")\n",
    "#plt.legend([\"Uniform support\", \"Support proportional to losses\"])\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(ymin=0)\n",
    "\n",
    "spine_and_ticks(ax)\n",
    "\n",
    "savefig(\"img/resilience_gain_post-disaster_support_freebudget.png\")\n",
    "\n",
    "\n",
    "print('Average (population weights) ',((dfr[\"diff\"]*ref[\"pop\"]).sum())/ref[\"pop\"].sum())\n",
    "print('Average (no weights) ',((dfr[\"diff\"].mean())))\n",
    "print('Country where higher budget for post-disaster support reduces resilience')\n",
    "print(\"Country where more resources would reduce resilience\")\n",
    "print(dfr.query(\"diff<0\")[[\"diff\"]])\n",
    "print(\"Country where more resources would increase resilience the most\")\n",
    "print(dfr.sort_values(\"diff\",ascending=False).head(20)[[\"diff\"]])\n",
    "print('Before')\n",
    "print('PDS cost:',((ref[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "a=results.resilience;\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= ref.dWtot_currency.sum()*1e-9,\n",
    "k= ref.dKtot.sum()*1e-9,\n",
    "r=ref.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience:',ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "print('After')\n",
    "print('PDS cost:',((dfr[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= dfr.dWtot_currency.sum()*1e-9,\n",
    "k= dfr.dKtot.sum()*1e-9,\n",
    "r=dfr.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience:',dfr.dKtot.sum()/dfr.dWtot_currency.sum())\n",
    "print('resilience gain:',dfr.dKtot.sum()/dfr.dWtot_currency.sum()-ref.dKtot.sum()/ref.dWtot_currency.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb6f5748>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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67jasSSml9t4HgHGbm0suuSQ5fjzZufNk+exsMjmZHDiQbN8+vvbBZlFKSa21rPU6noED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\nD3RV5YkNwPmKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb423080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfr[\"addfin\"]=dfr[\"average_aid_cost_pc\"]*dfr[\"pop\"]\n",
    "ref[\"addfin\"]=ref[\"average_aid_cost_pc\"]*ref[\"pop\"]\n",
    "plt.figure(figsize=(10,10))\n",
    "ax = plt.gca();\n",
    "plt.scatter(ref[\"addfin\"],dfr[\"addfin\"],color=coul,alpha=.7,s=50)\n",
    "#plt.legend(listePDS)\n",
    "#plt.legend([\"Uniform support\", \"Support proportional to losses\"])\n",
    "#plt.xlim(xmin=0)\n",
    "#plt.ylim(ymin=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resilience gain if all countries can at least allocate 1% of GDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,need,shew,social,v,v_shew\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,need,shew,social,v,v_shew\n",
      "Average (population weights)  2.5755874755502264\n",
      "Average (no weights)  3.9595759492107385\n",
      "Country where higher budget for post-disaster support reduces resilience\n",
      "Country where more resources would reduce resilience\n",
      "Empty DataFrame\n",
      "Columns: [diff]\n",
      "Index: []\n",
      "Country where more resources would increase resilience the most\n",
      "                         diff\n",
      "country                      \n",
      "Thailand            29.751717\n",
      "Russian Federation  22.572525\n",
      "Guatemala           22.314321\n",
      "Niger               17.039284\n",
      "Estonia             16.698610\n",
      "Iran, Islamic Rep.  15.389654\n",
      "Macedonia, FYR      13.906694\n",
      "Uzbekistan          13.524875\n",
      "Mauritania          12.323255\n",
      "Tajikistan          11.823692\n",
      "Turkey              11.798152\n",
      "Poland              10.855677\n",
      "Latvia              10.678115\n",
      "Mali                10.626067\n",
      "Kyrgyz Republic     10.361721\n",
      "Zambia              10.271084\n",
      "Argentina            9.939701\n",
      "Mongolia             9.281535\n",
      "Angola               9.140485\n",
      "Kazakhstan           9.076540\n",
      "Before\n",
      "PDS cost: 261.21576797605917\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.4 times bigger, at 466 bn$ per year\n",
      "resilience: 0.7023393311952334\n",
      "After\n",
      "PDS cost: 385.37998636140964\n",
      "At the global scale, we estimate asset losses due to natural disasters to be 327 bn$ per year. But due to lack of socio economic capacity, welfare losses are 1.4 times bigger, at 451 bn$ per year\n",
      "resilience: 0.7254843906911391\n",
      "resilience gain: 0.02314505949590573\n"
     ]
    },
    {
     "data": {
      "image/png": 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PrPb2dhYuvJDOzosYN+7AKuatraupqFjKihW3UVkZqsSfSLEr7Mdo7v51MxtPkCQQ+6ns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\n70rYfhTwgLsnVoLOqULKRks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Q9cD+39h37myiq6uOysp6urrGsmNHsJbNuHFz2L07uLD2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\nREl/CW3MgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3ea240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coul = (\"blue\")\n",
    "ref = compute_resilience(macro,cat_info,optionPDS=\"prop\",optionT=\"data\",optionB=\"data\",**args)\n",
    "#ref = 100*compute_resilience(macro,cat_info,**args,optionPDS=\"no\").resilience\n",
    "plt.figure(figsize=(6,6))\n",
    "ax = plt.gca();\n",
    "#for option,coul in zip(listePDS,couleur):\n",
    "#    print(option\n",
    "dfr = compute_resilience(macro,cat_info,optionPDS=\"prop\",optionT=\"data\",optionB=\"max01\",**args) \n",
    "dfr[\"diff\"] = 100*(dfr.resilience.dropna().clip(lower=ref.dropna().resilience) - ref.resilience.dropna())\n",
    "dfr.plot.scatter(x=\"gdp_pc_pp\",y=\"diff\",color=coul,alpha=.7,s=50,ax=ax, clip_on=False)\n",
    "#plt.legend(listePDS)\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"resilience gain from \\nrelaxing budget constraint (%)\")\n",
    "#plt.legend([\"Uniform support\", \"Support proportional to losses\"])\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(ymin=0)\n",
    "\n",
    "spine_and_ticks(ax)\n",
    "\n",
    "plt.savefig(\"img/resilience_gain_post-disaster_support_freebudget.png\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('Average (population weights) ',((dfr[\"diff\"]*ref[\"pop\"]).sum())/ref[\"pop\"].sum())\n",
    "print('Average (no weights) ',((dfr[\"diff\"].mean())))\n",
    "print('Country where higher budget for post-disaster support reduces resilience')\n",
    "print(\"Country where more resources would reduce resilience\")\n",
    "print(dfr.query(\"diff<0\")[[\"diff\"]])\n",
    "print(\"Country where more resources would increase resilience the most\")\n",
    "print(dfr.sort_values(\"diff\",ascending=False).head(20)[[\"diff\"]])\n",
    "print('Before')\n",
    "print('PDS cost:',((ref[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "a=results.resilience;\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= ref.dWtot_currency.sum()*1e-9,\n",
    "k= ref.dKtot.sum()*1e-9,\n",
    "r=ref.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience:',ref.dKtot.sum()/ref.dWtot_currency.sum())\n",
    "print('After')\n",
    "print('PDS cost:',((dfr[\"average_aid_cost_pc\"]*dfr[\"pop\"]).sum()/1e9))\n",
    "print(\"At the global scale, we estimate asset losses due to natural disasters to be {k:.0f} bn$ per year. But due to lack of socio economic capacity, welfare losses are {r:.1f} times bigger, at {w:.0f} bn$ per year\".format( \n",
    "w= dfr.dWtot_currency.sum()*1e-9,\n",
    "k= dfr.dKtot.sum()*1e-9,\n",
    "r=dfr.dWtot_currency.sum()/results.dKtot.sum()))\n",
    "print('resilience:',dfr.dKtot.sum()/dfr.dWtot_currency.sum())\n",
    "print('resilience gain:',dfr.dKtot.sum()/dfr.dWtot_currency.sum()-ref.dKtot.sum()/ref.dWtot_currency.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "#bad=results.query(\"diff<0\")\n",
    "#bad[\"ebias\"] = (cat_info.unstack(\"income_cat\")[\"fa\"][\"poor\"]/cat_info.unstack(\"income_cat\")[\"fa\"][\"nonpoor\"])\n",
    "#bad[\"vbias\"] = (cat_info.unstack(\"income_cat\")[\"v\"][\"poor\"]/cat_info.unstack(\"income_cat\")[\"v\"][\"nonpoor\"])\n",
    "#bad=bad.query(\"diff<0\")\n",
    "#bad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Benefit-cost ratio of post-disaster support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BC ratio of one dollar per affected people, with and without cost of public resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Box Sync\\PYTHON - RESILIENCE\\resilience_indicator_multihazard\\res_ind_lib.py:322: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  d[\"un\"]=1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Average (population weights) ( prop_nonpoor ) 2.270150253268013\n",
      "Average (no weights) ( prop_nonpoor ) 2.2630176517522305\n",
      "Countries where post-disaster support has BC ratio < 1 ( prop_nonpoor )\n",
      "Series([], Name: bcr025, dtype: float64)\n",
      "Country where more resources would increase resilience the most\n",
      "country\n",
      "South Africa                5.706374\n",
      "Zambia                      5.522412\n",
      "Honduras                    5.516887\n",
      "Lesotho                     5.466671\n",
      "Botswana                    5.343034\n",
      "Panama                      4.988080\n",
      "Brazil                      4.574090\n",
      "Colombia                    4.344863\n",
      "Central African Republic    4.336346\n",
      "Bolivia                     4.053694\n",
      "Kenya                       3.706788\n",
      "Angola                      3.617500\n",
      "Swaziland                   3.540729\n",
      "Paraguay                    3.388076\n",
      "Ecuador                     3.296953\n",
      "Congo, Dem. Rep.            3.245189\n",
      "Guatemala                   3.238000\n",
      "Benin                       3.090601\n",
      "Peru                        3.084950\n",
      "Venezuela, RB               3.075980\n",
      "Name: bcr, dtype: float64\n"
     ]
    },
    {
     "data": {
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m/0XanF9qx44dw82bN/Hwww8DALp06YKTJ0+iS5cuzbbO5paamoqysjJ4eXm5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb410160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# metrics is the benefit-cost ratio of post-disaster support')\n",
    "# always >1 for unif_poor - a few countries with <1 with prop\n",
    "\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "optionPDS = \"unif_poor\"\n",
    "optionB = \"one_per_affected\"\n",
    "no = compute_resilience(macro,cat_info,optionPDS=\"no\",**args)\n",
    "\n",
    "\n",
    "dfr,dfr_iah = compute_resilience(macro,cat_info, return_iah=True, optionPDS=optionPDS, optionT=\"data\",optionB=optionB,fraction_inside=0,**args)\n",
    "dfr[\"benefit-cost ratio\"] = -(dfr.dWpc_currency - no.dWpc_currency)/dfr[\"average_aid_cost_pc\"] \n",
    "dfr[\"bcr\"] = -(dfr.dWpc_currency - no.dWpc_currency)/dfr[\"average_aid_cost_pc\"]\n",
    "dfr[\"bcr025\"] = -(dfr.dWpc_currency - no.dWpc_currency)/(1.25*dfr[\"average_aid_cost_pc\"])\n",
    "\n",
    "dfr.plot.scatter(\n",
    "    x=\"gdp_pc_pp\", y=\"bcr025\", c= 'red', s=80, alpha=.63, ax=ax)\n",
    "dfr.plot.scatter(\n",
    "    x=\"gdp_pc_pp\", y=\"benefit-cost ratio\", c='blue', s=80, alpha=.63, ax=ax)\n",
    "\n",
    "print('Average (population weights) (',option,')',((dfr[\"benefit-cost ratio\"]*dfr[\"pop\"]).sum())/dfr[\"pop\"].sum())\n",
    "print('Average (no weights) (',option,')',((dfr[\"benefit-cost ratio\"].mean())))\n",
    "print('Countries where post-disaster support has BC ratio < 1 (',option,')')\n",
    "print((dfr.query(\"bcr025<1\")).bcr025)\n",
    "    \n",
    "#plt.title(\"Perfect targeting\")\n",
    "plt.legend(['Cost of public resources \\$1.25 per \\$1','Cost of public resources \\$1 per \\$1'])\n",
    "plt.xlim(0)\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.xlabel(\"Benefit-cost of post-disaster support\")\n",
    "plt.savefig(\"benefit-cost-ratio-post-disaster-support.png\")\n",
    "print(\"Country where more resources would increase resilience the most\")\n",
    "print(dfr.sort_values(\"bcr\",ascending=False).head(20)[\"bcr\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Looking at the benefit-cost ratio of $1 in support, given to each affected person (with and without targeting error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "Average (population weights) ( x33 ) 2.1982697613749083\n",
      "Average (no weights) ( x33 ) 2.224372828536163\n",
      "Countries where post-disaster support has BC ratio < 1 ( x33 )\n",
      "Series([], Name: bcr, dtype: float64)\n",
      "Country with highest benefit-cost ratio\n",
      "country\n",
      "South Africa                5.596590\n",
      "Honduras                    5.520076\n",
      "Lesotho                     5.420359\n",
      "Botswana                    5.186866\n",
      "Zambia                      4.891067\n",
      "Panama                      4.743673\n",
      "Central African Republic    4.621799\n",
      "Brazil                      4.269653\n",
      "Colombia                    4.182262\n",
      "Angola                      4.151261\n",
      "Bolivia                     3.997023\n",
      "Swaziland                   3.427228\n",
      "Kenya                       3.358337\n",
      "Paraguay                    3.245448\n",
      "Guatemala                   3.215533\n",
      "Venezuela, RB               3.201726\n",
      "Congo, Dem. Rep.            3.191270\n",
      "Ecuador                     3.128697\n",
      "Peru                        3.023694\n",
      "Costa Rica                  2.883400\n",
      "Name: bcr, dtype: float64\n",
      "Country with lowest benefit-cost ratio\n",
      "country\n",
      "Niger              1.308834\n",
      "Sweden             1.333298\n",
      "Mali               1.341756\n",
      "Cambodia           1.349787\n",
      "Kazakhstan         1.352744\n",
      "Slovenia           1.364140\n",
      "Denmark            1.366573\n",
      "Belarus            1.368976\n",
      "Ukraine            1.376470\n",
      "Armenia            1.394961\n",
      "Slovak Republic    1.400166\n",
      "Moldova            1.427237\n",
      "Germany            1.431072\n",
      "Czech Republic     1.439550\n",
      "Pakistan           1.442454\n",
      "Romania            1.445118\n",
      "Azerbaijan         1.446589\n",
      "Tajikistan         1.453241\n",
      "Belgium            1.458142\n",
      "Netherlands        1.469040\n",
      "Name: bcr, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ut1X3iUQrV67EyZMnsWDBgiYX7+vevTsAoLCw0OOYt7bk5GQA8Frl+NChQ9iy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MMR2odLhWhTAZa33cPmwGh2bHX324F1oBmsApeJ6KCpOUBfomvcbjdFSlFSUtwh5Dy8fGi1Wina\nuzfpv1Ov18u6deu47bY/EPJ9nbbtP2VaqK2Dv85QJMzUaJJCYenKnG63H8NGywT2Ne1j0ogxiHSM\ngGkCPiOL702ahNP5Al5vDR5PLXsDzfyN97lWpVBIKh6EownygL+RC1QKjqx08vPzqamtZb/VwjOe\nKrIkm9E0EAoZTpd2bJRiIU9SqMKCJVhBES3UOwsR2TVoSx2FhYWMtrYQDHo5Mc0wRS9u3MbEoIcx\nKkhdSPEWihPGjuXc4mK8wSCrtm1jscNBU1tbh8Rxkc6yZ5x9dpcXm3gRK9DRoQ+g6qMPuHPaWPZu\n2YhdNTBW2UgBED+LbSksClSQo7b02YF1sOixUtbLKItEdNDUHKAv0TWRym04tUI0yVxIMNKqXVFh\nY8dWOM7/J35gzaTYlUN6ehpKBalureDRTXezY+QY1q1b12XySY2mP1iGewI9IWxOj5UOPhi0UJ92\nBU8Eobq1osO+9oJzo84nGAzy059ewJaN89i9/mrOoZEz8LOSVl6gnk/x4MXGWhS/Cvr5d1Euf6uo\n4IHyffzSF2Jk+lSmWQ+jSlKBFhQWoAlFDa2qlXGB3eS1racu1IS79hNSUz/vVc2Y3pCfn49ldB4e\n7xZCoTZOSi/hJwWzGJkxCbctjSNsKZycmcnZxcWICGW1tUwKBsm1WvFg5PCIJNfhYJLpG9Qd515w\nAafceCMLW1p4oLycp/bu5YHycha2tHCK6dBXVlbGEV4v08aNIccBM3IzGJXnICcnhby8DCblZXKa\nK5P/KCzgxUcewev1DoqcwFB2165dy0svvcTatWu7PVa7UlZVRU1U33albN68Xj9FGg6a/yRt410s\nsKVzWVoxs9NLuCytmAW2dNI23kVL4z8H7ZzpK72VX7IS+b3X+/3k5+dTXFxMfn4+9X5/zO+9tLSU\nbaZyG4uwcjvQ3+lQfSdhq3Z6+mIyMs4gP7CTy+yH4bIW4nZ7qK37gsrKDwg2NHKRJ53Ari386lfL\nWLXqFVQ3y6oaTV9ICgtLV+nS7XY7ttSZbOAifhNYQ2lreYeCcyOm3Ei27DCr3tYyrfUjGoOtLBIX\n2QTwqFY+AXbgI8M6ghtsGTyYEqDwx1eSOmYMb937ONkzrmfbl3/m0uyRPFW9i+agn4xgDaL8lANF\n4iRH2mhKSacNIX/XY8z4xpWDZhotLS3lieLRFGU0UfPVAfP1ZFsAvyXAESOcvOZIpdR0HHX7fBQq\n1Z7l94gYUT499cvoiUNf2A+ktraWrFAIV1pap3GKWlsB2hWlgV7j74+D4GBFWSSig2Y8DkUHy95+\n7z21OA7UdWAov5Noq7bXW8lRNJMZDlRQrTQ21uB0TscqDnKAYxTs4uKETAipOThICoUlMl169Pq/\nyzUdm+1LlMoj95i72WtNbS84d7iZmbSxcRVNTZN59PbbOdNmo8Jmp9BmB2UnVZzMIsBMFWCj8pFZ\ndCqlDRvw+Xzk5+fjdJ7O6NGXU+nIZem2Fbic+dzdsovvEWSEWMhSQq41RIM1leWhVmZkZHL+5HE8\n/NEHeL3eQVFaHA4HF86fz9NLl3LdSUchLS2m+TqPfY2pLNy0iW+MOwKHmSDOZbezSSk2e70UT5nS\nKXEc9N503ZVDX9hU7vP54gY1VwIldjuFPt+grPH3p8bLYERZRDtoNnrS2eJpolm1kmkLMPeoI9mX\nYkuYnDSJWCNnsOnL9z7YIcSRDOV3EukkHgx6aWurpyhURyCwF4s1H3+gHGEqKpQCVlAqyCj81Gdm\nkJ8/9CUJNIcGSaGwGHH+T7DriyfxfbWy0/p/dsZsyqteJTPz+6SnH3h69Xj2s3nzdTidfm666WWm\nfumhKhAkN+BBWZ3Y2sOBbTiBnEArrT43RRh1d8KWHRGhqGQuwVFnU1PzIWXb/8j23X/nPJuTgqCX\nekeIL+1w9piJfHvyZGw2G0eUlw/qzSd8IbxtxQom+f0UApV+P5+7XEy59lre+ugjKsvLjTDjUIhV\nSnHuxImMLunsI9FXv4x4hP0BmjweYhmGa4JBtlmtzB85kg8qKwd8jX+garwMZJRF2EFzdPFo/lVd\nxYvbtnJEyEKRWKgSxS3vl5FbmMeEuroBOV5/SMQaOUNJb773wQohjmYwvpOu8qm43W5CoYJ2n6ti\nTwUoL3lt66kXB6hsEAdtoQC7VDVNwXq2p/pJSRkxLCUJNIcGSaGwOBwOZhxXwPvLFvCLjGMZmXZA\nKan1NbCiYilHnXMKjS134HZPak+K1tDwL0RGM3bsTezc+RR+LDSn5tHaXI/PZ4Qn2iJymDiBhkAL\n1RYP55aWkpqa2qEQntXqoKDgFPz+Bk5s/pJCWxq1rTs49oiR/HLChHaLBvQt9LUrYl1curpQRvaf\n6XIxqbKS/1u2jFFRzqSDYboOm8r/fO+9zAIODwbbZVMTDHK/18vcKVNo9vt7pSi53W5WrlxJVVWV\nmUPmkpg5ZBK1xst2EZ74+FPWb9vJbSkjyY0oWFgT8nHL3gqef+Y55nYTrTXYJKL8Ep3BDi2PtnhE\nR5n15jvpydKSsYT+HOOqd3CdowBn5mR+76kiN+RntL+RHcrLGvUh23wNTJYgRVbY44MvN90LgRZC\nFCR9dJQm8UgKhcXr9bZHndR8tYXW1gMhhyOsjdw5bSwPN7tZ/uRDbNq0CbfbjdOZzz33bMbbfCr7\n3ruKaZ5KxrXsQVlSeU08lIQczPU1Y7M5AWPdt5UQdW27aZo4ipkzZwK0F8KzWJzs3PkmHk8tgUAV\n+4BzUjIJpFuYNWFCp2WWgYoO6MnFJdYFKvoCqpTCbrcPiekaDliA/rB4MS/u2MEJdjt1FgvbrFbm\nTpnC8bm5PVaUQqEQi29dzEsrHmOyP5VCUngXP4/eehdz5l3BwtsWYrEc8B9PxBDi0tJSHrVaqdu+\nnSVRygpApli5OMXFfa+80a6UDheJKL9DnWiLR6wos54qCT1ZWjr11JOxVr7OZanHtmf+PT5zEg83\nbOYqexbl3kbc7GSJvYAsm5MdQS9TXFNoS8k0yhgUTMDlumJwhKE5ZEkKhSUcdXLM+MMIjhsTM9/B\nEeXlbNq0qf0mvXbtWqr3hRhX/XuudBSQlXUkTZ5KjrLYmE0Wv2urg5CVi3y1WC0OfPj5KtTGW6Pz\n+NEtN7ffRH/ykznccN0PsFTWMi3USqEKUomwPrSPPEca157Y2SdkIJdYBmrdeqhM19HHm3XWWTz0\n4IP83zPPMF4pThoxgs8tFla1tvZYUVp862LeX/YYN2ccy8iMrPb2Wl8DK5Y9xmJg0e2L2tsTMYTY\n4XDgnDCBrNXvkGnreL74VJAdQS95rilMadvLypUrueqqoU3nHkkiyu9QJ9riEbPWUQ+UhJ5WpU5P\nT2V2UTapVV8StBqJ9MIpFBa5N1OLl1uw4FYedoecpLim4EwrwQlcluJiScXrTJ3628EUieYQJCkU\nlsjso/HyHUQvwYSzwV45YmL7E0JK1iR2uDczwebk5+Rwo6+OWnuIYmsbZcEg1ZOO5CeLFnW4iX70\n/nrGVb3Lf/r9jBEbacpCq4TYpdpY6W3hhYoKrhlzYImqr0sssZZ8gAFftx7qrKhOp5Nf/td/cc3P\nf97++cb3QlFyu928tMJUVuxZHfaNtGcxL+NYlqx4jOtvvL7dKpGoNV5G5uVhd+SxIRTApdo61H8K\nX/AL26qpqqrq13H6Uusl8j1Op5Nm96uM2tucUPI7lJk6dWoni0cYl9XRYyWhN1Wpp+VkM3WktUMi\nvaPFAyNsVEsqElDUpReT7TqqPf9VMOgl1f8ls4uyOzxAajQDQVIoLH3JPhrOBhv543amleABNjRs\nwyUWSi02PszO5sOCAs64+GLuv+46o3aQidvt5vmHHmaRSuH4NBfWUABUCMTCBEs2GW113LJjB80F\neYyz2fq0xNLVks+ppx6OxzPxoPAl6KuitHLlSib7UztYVsIoFSQ91MZ4T4AlS5awZMmS9htzIoYQ\nFxYWsjPFQUb2CbS21dIc8mGx2MlIHdl+wa/Ex8kFBX0aP3wuLV/+HNXVDvx+BykpXvLz/8zVV18U\nM+w11vkXCm3G++VnfCuUQkZa4sjvUGbTpk2dLB5heqMkVFdXU75rOxPqnubG1ALy00a3nxORVakh\nnyqLhZLi0Z2y+Hq9Ixi9JUCWz4fHVo/H8znRWYHrCeklQ82AkxQKS1+yj0Zmg7VGKS0O5yiaPRU4\nWz7la/Pnc8MNN8R8An388ceZ0tLC8al52MUK1gPisgInpI7kmLb9VB57LEedfXaflli6qs/xxBM3\nInIaxcXx33+w+xJUVVVRaOTI7UBLazmNDXtQKpNsfzp///setm69gXnz5pKdnZGQNV4uuugiHl5w\nO+VN+8m1u3A6UzsoELW+BrbY2njwkkv6NP6qVa+wcMFjOOrcHKdC7U/OGyos3PL/HkUpxYUXnt/h\nPbGWHKur1zIjdRWZviqamtdhTykkEeR3KON2u2NaPHqqJIQV07vvfozA7o1cZJ1C0KuobKwhMyud\n9LS0DlWpjzpqKS+89Vb7NTfSqj2yupqtSnFYWhpnnfo13G53pyX6qvJyxiRphl9N4pIUCktfEjS1\nZ4Ot6lzMLBTyE1L7cRxWzOmnnx5Xwfjkk0+YqDCUlRjYxcoEJZQ3NjJnzpxefy6v18vDD78AgVPY\n9978TubZlJzj2FX+GUcfHQT8MZ0fRSoPal+CgoIC3o16mm9pLafBXY3VOh0RB1WhbeTn/5D09JNY\nuvR+TjvNzpQEqvESacVozjqa5Xs/5Ye2Kbgsqe03i1pfAyuaPmbOtVf0yeHW6/Vyx+3LGFfdyI/T\nijtYFt1BL3+o3ssdty9j9uwz28/3eKGyPp+b0WLDlTUdv/9jpk7NJhgMHnQ1cpIJl8vF9jgWj54o\nCWHF1OG4gmMt15JtS0fEgVJBGszvMT0trb0qtd1uj3vNHZuezsJAgHMnTsRut3daoh/oNAkaTZik\nUFig9wma4mWDDT+RFE0ooC4ro8u02U6nk1pCXc6rToIdlpF6Q1lZGRV7/O2OwZ0c6arW8GVbE5s/\nW4Gj7uVOCo193CWkZ32eUNWOB5pLLrmER2+9i1pfAyPtWSgVpLFhD1brdPyk8Jl/F+tDbiaPcJCS\nMoKCgl/wxhs/ISu1a5+noazxEmnFmHlqDhs/Wcxvtz/G1EAKBfsV9Y4QX6SFmHOtEfHUF9atW4dv\n525+PGJaTB+HH6cVs3Dnxg61XuKFyra07GYPIaxWB21tWYaCUlTUYcxkrpGTjERbmXujJEQqptXV\n7zDG7sDXth6rdTxWaxFWq4vGhjpS7YK3bSvHFeVQVlbG5MmTyTn/fG56800mR11zZ119NU+9886Q\npEnQaMIkjcLS2yiX+Nlg0wk4S7hj506KzpxFWVlZXKfEuXPnsvj3f6Qm5OsUhgpG7oyPRbGwj3kz\nYjkGh3FZHVzpLOKTxm3kfnE7P8s5sXP+mY0LmHFtYlU7HmhcLhdz5l3BimWPMS/jWNJDbYRCGXwW\n2s8n/i24VDPfTSumadOdbNpqOA3a7SfzQdvzA17AsC9ER2XU1LxLTv4McvJPYm/TNj5v2YPIet54\n42kKC2OXn+gJZWVlTAtKp/MojMvqYGpIKCsra1dY4oXK7kPxgXsz40IBTrI4OhTLBP0EPRz0pwxA\npKPt/o330uKrZSJCs+9d6rHTZh2NQtHa2kpRUTYr9+1n41N7yMoqQcSO3V7C4WdPYsTUI5mRnc18\nM0fVqy++OGRpEjQaSCKFJUxXzpvR0RGzzjoLOJANtkApNu3bx5qKCg7LzOSI557j9Zdf5vG8PL55\n9dWce8EFHXwKZs2axW/GlPDArn1cb8/plOjrAV8d1rElzJo1q0+fJZZjcCROSaEw0MQNY4tI8cbJ\nPzOIJQAShYW3LWQxsGTFY4z3BGj2Kaxs42Ls5GQdTXbOeEA6OA2e+c2zuP/tt4ekxktXdBeVMXrS\nPIIyii+++KJfCgtAoZmhOR5FUfu7CpU9V/n5S/1GfM5sjrUf2d6un6CHj76WAUTnkFQAABhDSURB\nVHC73dTv38dh+//B4rRRPN2ymzSLjRJx4Q142B7cR1Oqi8LCTLZVtvG5fTKTJ9/Tvozu9dbwyiv3\nM2WKtcO1dyjTJGg0kIQKSyyUUrz64ousWrGCSR4PhUqxXYQnnU7mzpvH0r/9jY8++ojXX3uNDU88\nyTWWEeS0ZKNanAgemsu38/yCm1BKcf6FF7aP63A4+O+lv2X5dT/n5qpqpgVsFCoLlRJiowTwjcrn\nv5f+ts8/0HiOwWG2eCo4jgBTSkoYPXp03PwzB7vzo8ViYdHti7j+xutZtGgRrz7yNAudx1CcOQaL\n5cApHOk0+LXTH8Q3Y8awPwFG3izi5s/In9Fvf5DS0lIet3rinkvBoLc9g3OYrkJlC0eM5/vBAL9p\n/JSM1jGM27tXP0EPM33NpeR0OpGKl7nSXC48PnMSDzVs5mdWByNtaRxhTeUTXx1b9wV42jqG9Inz\nO/nLxUuhMNRpEjSHNgeFwvLqiy/y76VLWVxQQG5EJeIar5f7ly4F4Iyzz+a+63/B95tTKUw7qsNF\nPTXo5cyqDfzx9iWcOXt2hx/k+RdeiMVi4ZllyyjfvZv9bW34UlPJGTOGi6+9tl8X7q4cg4NBL3Vt\nX1CUmoLD4ehx/pmDGZfLxYUXXoj7iWcYPWJUB2UlTNhpcKgT5cUj+mYRSaSC5XT2r2jdzJkzeeiw\nUVTu2tDp/A4GvVS2bqDpsAMZnKH7UNksWytzjpxE1ve+R8aYMfoJOkHoi5JwNA1kmP+HE8AtbtzG\nEaqNQhWiLOjhPb8Lx6gzsX/xKIcHvXHzs2gFRTNcJKXCEp3k6rnly7kjyvQPkOtw8IuCAhauWIGk\npJCxcx+FI77W6QnUanVQmHYUGTvXdnBKhNhPNT1NxtUd3TkGjynJ4r19zYyMUMKiOdScHz0eD6Wj\nRuKpjq3kebxbKB2Vi8dj1IpKhCfAyJtFNGEFq784HA5+dMvN/OOmmzmnpowRZBM+l5qpZ3VBWocM\nztDzUNnMMWP6FAWnSQxi/WZOSi/hWOcotngq2N/2BVaHhdaU6Uza/44ZAHAgamwgLYEaTX9IKoUl\nvPTz/PLlFO3fT67Px1c+H7v37+eDY47h3OLiTomxch0OJtXWsmrVKvKDzpjmcjCUlvyQs4NTYiSD\ncePryjFYpY/ln9XV1KTaqff7ybV2Dq0+FJ0fXS4XgbxcpuZK3JvsFlHDWosnkt4qWP3hvLlzEZED\nvw9/AzUpKVTkTWz30Yqkv6GymuSgq9/MZFsjk6ZM4GF3HelffMqV2ScOqiVQo+kPSaWwvLJqlfkE\n2coIslG4GNNWyXktrbz80cco4PySzjlJCpXiM4+HJlK7HL+CVPIGae7xCN9Ewo7BhUCl3882r5e5\nv/oVpyvF/ffdN+zOo4lCOLzTmZ7OrBg32Xq/n+0tLVyVIErcUCpYsayBx3ZhDexPqKwmeejJb2Zj\nazPH2fyDbgnUaPpD0igsXq+XP96+hO9UBRiZVtr+pGq1pOPwNHC518vSTz9jms2GJRjEbrczcuRI\nrFYrlSJMnz6dP72wHnfQGzMqxx30ssmiuGKIL87d+VoopRCRYXceTRSiwzsjb7KJqMQNh4LVU2tg\nf0JlNclDT34zx82eTU7rP4bEEqjR9BVRZlHBREVElFKK1atX8/h3/pMronxQlArSVLmGyaEQK3w1\n5LkOZ1pqPmI+weaOK+DhrAzufPJJzj37Egp3xckE2rqXyrGZrHnnuYS8QPeloN3BSoeoMK+3oxI3\nb16n8PTh5pVVq/h3F0rBKTfeyHl9zOXTX5JNlpq+0d33nJGdzQe33srFSti2bQ/BYEdL4KRJJTwj\nihMWLx52nzDNsDHsF4KkUVjuvfde9t/xCBfmzOzUp75uBykNW9lEG97sIzjdNQ2gPd35CddewaLb\nF/HCCy+311o5SimKUFQgbBDBm+Ni8R1XdKq1oklckkWJSwalIFlkqekf8b5nr9fLDRdfzOL0dLJT\nUiIsgYalut7vZ2FLC/c984w+Lw5dtMLSHZEKy4Y7/sxPczqazpVSVFbWoEKtPOMro2FEISemj6EC\nYZPVYaav/zfPPns/qamp7dVs9+8/UM02L88bt5qtRjNQaKVAk8gksiVQkxAM+80xaRSW1atX88vv\nXMviqHwWXq+XujovrVYHv/F/RvYxV5GRMb5DgcDy8ge47bYZ7aZMfePQaDSajiSDJVAzrAz7l580\nCovX62XWqRd18kFpbW1ld72Hv1LPpvRMzj7vuU6hy3v3PsUNN2ToXBIajUbTDfqBThOHYVdYkiZK\nyOFwsOCWa1m44DGW1NW1+6Ds8vlZpwLUO0dz1NFXxMyzIlKJy9U53Fmj0Wg0HUmEZIsaTSySRmEB\nmDv3PESE5cuf4+P9Dt73O7BaW3Hv+Zgjj/gWJSWdkxp5vTU4HNsoLZ0/DDPWaDQajUYzECTNklAk\n0SbLysoali17n4KCX+Bw5Eb0q6Gq6n5uvPEU5s7VGRo1Go1Go+kjw74klJQKSzRKKV588VVWrFiF\n1zsJpQoRqcTh2Ma8eXN19I9Go9FoNP1j2G+iB4XCEkY7i2k0Go1GMyhohaU7eqOwaDQajUajGRSG\nXWGxDPcENBqNRqPRaLpDKywajUaj0WgSHq2waDQajUajSXi0wqLRaDQajSbh0QqLRqPRaDSahEcr\nLBqNRqPRaBIerbBoNBqNRqNJeLTCotFoNBqNJuHRCotGo9FoNJqEZ9gUFhG5QkRqRGThcM1Bo9Fo\nNBpNcjDkCouIuETkVeAYIHuoj6/RaDQajSb5GA4LSzpwq1LqehKgNsHBzpo1a4Z7CkmPlmH/0PLr\nP1qG/UfLsH+IyKzhnsOQKyxKqXKl1PqhPu6hiv6R9h8tw/6h5dd/tAz7j5Zhv5k13BPQTrcajUaj\n0WgSHq2waDQajUajSXhEKTV8BxcJAYuUUou76DN8E9RoNBqNRgOAUmpY/U5tw3nwnjDcAtJoNBqN\nRjP86CUhjUaj0Wg0CY9WWDQajUaj0SQ8w6KwiMjTIvImoIDLRWS1iFwwHHPRaDQajUaT+AyLwqKU\n+o5S6gyllFUpdbhS6kyl1Ivh/SIyRUReF5F3RKRMRH4jItbhmOtQ0V2pAhG5SUQ+EpG3ReQtESnt\n4bjfFJH3ReRNEflARH4Qo0+WiDwqIh+KyHoRWSki+X0Za6gRkXNF5FlT6X3bnNe8OH21DDvP5wQR\n+ZMpj9Ui8pmI/EVERkf107LrASIyVkQaRWR1jH1ahnEQkdNFZKd5Dq4257daRK6I6qdlGAcRcYrI\nHaZs3haR7SLydxGxRfVLXhkqpRLqD8gFKoGrzO004BPg/uGe2yB9XhfwKvAAEAQWxuizEPgcyDC3\nLwXcwJhuxj4DaAWOMbcnAU3AN6P6/Qt4KmL7EaAMM4qsN2MNg/z2A9dHbM8AvMCvtAx7JL97gT+H\n5wmkAu8Ba7Xs+iTP14FaYLU+/3olt9OJce3TMuyx/ARYDdwV0TYF41qYdrDIcFhP0jiCuR1DYYn8\ngD8EfEDhcM9vED7vaOBE8/9Q9I8WGAE0Az+Lat8B/E83Y78NPBPV9kdgY8T21zEUpakRbSXmXC7u\nzVjDJL+XAGuMts1ahj2S30QgL6ptKVCrZddrWc4Hfg+8SYTComXYI9l1qbBoGXYrv8uAvYAlqv1U\nDjyMJL0ME9Hp9jzgQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x952bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# metrics is the benefit-cost ratio of post-disaster support')\n",
    "# always >1 for unif_poor - a few countries with <1 with prop\n",
    "\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "listeT = (\"perfect\",\"x33\") #,\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "opt_leg = (\"Perfect targeting\",\"33% error in targeting\") #,\"33% exclusion error\", \"33% inclusion error\")\n",
    "couleur = (\"blue\",\"red\",\"green\",\"yellow\",\"purple\")\n",
    "no = compute_resilience(macro,cat_info,optionPDS=\"no\",**args)\n",
    "\n",
    "for option,coul in zip(listeT,couleur):\n",
    "    dfr,dfr_iah = compute_resilience(macro,cat_info, return_iah=True, optionPDS=\"one\", optionT=option,optionB=\"data\",fraction_inside=0,**args)\n",
    "    dfr[\"benefit-cost ratio\"] = -(dfr.dWpc_currency - no.dWpc_currency)/dfr[\"average_aid_cost_pc\"] \n",
    "    dfr[\"bcr\"] = -(dfr.dWpc_currency - no.dWpc_currency)/dfr[\"average_aid_cost_pc\"]\n",
    "    dfr[\"bcr025\"] = -(dfr.dWpc_currency - no.dWpc_currency)/(1.25*dfr[\"average_aid_cost_pc\"])\n",
    "\n",
    "    dfr.plot.scatter(\n",
    "        x=\"gdp_pc_pp\", y=\"bcr\", c= coul, s=80, alpha=.63, ax=ax)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#dfr.plot.scatter(\n",
    "#    x=\"gdp_pc_pp\", y=\"benefit-cost ratio\", c='blue', s=80, alpha=.63, ax=ax)\n",
    "\n",
    "print('Average (population weights) (',option,')',((dfr[\"benefit-cost ratio\"]*dfr[\"pop\"]).sum())/dfr[\"pop\"].sum())\n",
    "print('Average (no weights) (',option,')',((dfr[\"benefit-cost ratio\"].mean())))\n",
    "print('Countries where post-disaster support has BC ratio < 1 (',option,')')\n",
    "print((dfr.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "#plt.title(\"Perfect targeting\")\n",
    "#plt.legend(['Cost of public resources \\$1.25 per \\$1','Cost of public resources \\$1 per \\$1'])\n",
    "plt.legend(opt_leg, frameon=False)\n",
    "plt.xlim(0)\n",
    "plt.xlabel(\"GDP per capita (US$)\")\n",
    "plt.ylabel(\"Benefit-cost of postdisaster support\")\n",
    "x_thousands_sep()\n",
    "\n",
    "spine_and_ticks(plt.gca())\n",
    "\n",
    "savefig(\"img/benefit-cost-ratio-post-disaster-support.png\")\n",
    "print(\"Country with highest benefit-cost ratio\")\n",
    "print(dfr.sort_values(\"bcr\",ascending=False).head(20)[\"bcr\"])\n",
    "print(\"Country with lowest benefit-cost ratio\")\n",
    "print(dfr.sort_values(\"bcr\",ascending=True).head(20)[\"bcr\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proportional transfers or uniform transfer? How much gain in BC ratio?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A better metrics is the benefit-cost ratio of post-disaster support\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Box Sync\\PYTHON - RESILIENCE\\resilience_indicator_multihazard\\res_ind_lib.py:322: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  d[\"un\"]=1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,shew,social,v,v_shew\n",
      "stats are axfin,c,dc,dc_npv_post,dc_npv_pre,dk,dw,fa,gamma_SP,help_fee,help_received,k,n,need,shew,social,v,v_shew\n"
     ]
    }
   ],
   "source": [
    "print ('A better metrics is the benefit-cost ratio of post-disaster support')\n",
    "# always >1 for unif_poor - a few countries with <1 with prop\n",
    "\n",
    "modalities_and_color = dict(\n",
    "  #             no      =\"#386cb0\",\n",
    "  #              unif_all=\"#f0027f\",\n",
    "                unif_poor   =\"#beaed4\",\n",
    "                prop    =\"#7fc97f\",\n",
    "                            )\n",
    "\n",
    "\n",
    "\n",
    "no = compute_resilience(macro,cat_info,optionPDS=\"no\",**args)\n",
    "\n",
    "dfr1,dfr1_iah = compute_resilience(macro,cat_info,return_iah=True, optionPDS=\"unif_poor\", optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0,**args)\n",
    "dfr2,dfr2_iah = compute_resilience(macro,cat_info,return_iah=True, optionPDS=\"prop\", optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0,**args)\n",
    "dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "\n",
    "\n",
    "diff = dfr1[\"bcr\"] - dfr2[\"bcr\"]\n",
    "dfr2[\"diff\"]=diff\n",
    "\n",
    "#dfr1.plot.scatter(x=\"gdp_pc_pp\", y=\"bcr\", color = 'blue', s=80, alpha=.63, ax=ax)\n",
    "#dfr2.plot.scatter(x=\"gdp_pc_pp\", y=\"bcr\", color = 'red', s=80, alpha=.63, ax=ax)\n",
    "# dfr2.plot.scatter(x=\"gdp_pc_pp\", y=\"diff\", color = 'red', s=80, alpha=.63, ax=ax)\n",
    " \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "one_large = dfr1[dfr1[\"bcr\"]>dfr2[\"bcr\"]].index\n",
    "two_large = dfr1[dfr1[\"bcr\"]<dfr2[\"bcr\"]].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average (population weights) (unif_poor) 2.3245890114997123\n",
      "Average (no weights) (unif_poor) 2.3580711428167285\n",
      "Countries where post-disaster support has BC ratio < 1 (unif_poor)\n",
      "Series([], Name: bcr, dtype: float64)\n",
      "Average (population weights) (prop) 1.396172297996256\n",
      "Average (no weights) (prop) 1.4827257106977503\n",
      "Countries where post-disaster support has BC ratio < 1 (prop)\n",
      "country\n",
      "Azerbaijan    0.919156\n",
      "Georgia       0.837412\n",
      "Hungary       0.981500\n",
      "Ireland       0.981691\n",
      "Name: bcr, dtype: float64\n",
      "Where proportional is better\n",
      "country\n",
      "Cambodia     -0.138840\n",
      "Greece       -0.052586\n",
      "Mali         -0.449820\n",
      "Mauritania   -0.555578\n",
      "Niger        -0.529934\n",
      "Thailand     -0.898532\n",
      "Name: diff, dtype: float64\n",
      "20 Best countries where to use uniform\n",
      "country\n",
      "South Africa                4.708910\n",
      "Lesotho                     4.140183\n",
      "Botswana                    4.060962\n",
      "Honduras                    4.013124\n",
      "Panama                      3.445439\n",
      "Central African Republic    3.364061\n",
      "Zambia                      3.343799\n",
      "Brazil                      3.048281\n",
      "Bolivia                     2.530561\n",
      "Angola                      2.400593\n",
      "Colombia                    2.310315\n",
      "Kenya                       2.022721\n",
      "Venezuela, RB               1.961897\n",
      "Ecuador                     1.925415\n",
      "Swaziland                   1.898763\n",
      "Congo, Dem. Rep.            1.774744\n",
      "Paraguay                    1.687688\n",
      "China                       1.622332\n",
      "Togo                        1.585744\n",
      "Israel                      1.514316\n",
      "Name: diff, dtype: float64\n",
      "0.8753454321189779\n"
     ]
    },
    {
     "data": {
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dKTjkoOB45FwtWrSgYsWKTq8VFhZWZDNmQevWrSMrK4vbbrvNsS23U5w3fPLJ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\nP8DA7mcA/qJqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xadc8630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(7,7));\n",
    "ax = plt.gca();\n",
    "\n",
    "# ax.scatter(y=dfr1[\"bcr\"], x=dfr2[\"bcr\"], s=50, alpha=0.5, clip_on=False)   \n",
    "\n",
    "ax.scatter(y=dfr1.ix[one_large,\"bcr\"], x=dfr2.ix[one_large,\"bcr\"], s=50,  clip_on=False, color=\"#91bfdb\")   \n",
    "ax.scatter(y=dfr1.ix[two_large,\"bcr\"], x=dfr2.ix[two_large,\"bcr\"], s=50, clip_on=False, color = \"#fc8d59\")   \n",
    "\n",
    "plt.legend([\"Lump sum is more efficient\", \"Proportional is more efficient\"], prop=font, frameon=False, loc=2)\n",
    "\n",
    "plt.plot([0,7],[0,7], \"--\", color=\"black\")\n",
    "\n",
    "    \n",
    "print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "print((dfr2.query(\"bcr<1\")).bcr)\n",
    "\n",
    "\n",
    "print(\"Where proportional is better\")\n",
    "print((dfr2.query(\"diff<0\"))[\"diff\"])\n",
    "\n",
    "print(\"20 Best countries where to use uniform\")\n",
    "print(dfr2.sort_values(\"diff\",ascending=False).head(20)[\"diff\"])\n",
    "\n",
    "print(diff.mean())\n",
    "    \n",
    "#plt.legend(['Uniform transfer','Transfer proportional to losses'])\n",
    "plt.ylabel(\"Benefit-cost fatio of lump-sum transfers\")\n",
    "plt.xlabel(\"Benefit-cost fatio of proportional transfers\")\n",
    "plt.xlim(0,7)\n",
    "plt.ylim(0,7)\n",
    "\n",
    "spine_and_ticks(plt.gca())\n",
    "# x_thousands_sep()\n",
    "\n",
    "savefig(\"img/benefit-cost-ratio-post-disaster-support-one-dol.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bcr    1.597644\n",
      "Name: Bangladesh, dtype: float64\n",
      "bcr    1.597644\n",
      "Name: Bangladesh, dtype: float64\n",
      "bcr    1.401772\n",
      "Name: Bangladesh, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania                     1.394793e+04\n",
       "Angola                      3.312503e+04\n",
       "Argentina                   1.279456e+05\n",
       "Armenia                     3.366565e+04\n",
       "Australia                   2.984042e+04\n",
       "Austria                     1.992088e+04\n",
       "Azerbaijan                  6.254471e+04\n",
       "Bangladesh                  2.760886e+06\n",
       "Belarus                     4.891118e+04\n",
       "Belgium                     5.743794e+03\n",
       "Benin                       1.678763e+04\n",
       "Bolivia                     6.009191e+04\n",
       "Bosnia and Herzegovina      1.672610e+04\n",
       "Botswana                    2.860916e+03\n",
       "Brazil                      1.628553e+05\n",
       "Bulgaria                    1.436782e+04\n",
       "Burkina Faso                2.543663e+04\n",
       "Burundi                     4.026585e+04\n",
       "Cambodia                    5.391885e+05\n",
       "Cameroon                    9.559905e+04\n",
       "Canada                      4.824191e+04\n",
       "Central African Republic    1.302822e+04\n",
       "Chad                        3.541472e+04\n",
       "Chile                       4.985328e+05\n",
       "China                       2.104771e+06\n",
       "Colombia                    8.248409e+05\n",
       "Congo, Dem. Rep.            2.746760e+05\n",
       "Congo, Rep.                 3.124324e+04\n",
       "Costa Rica                  2.846076e+04\n",
       "Croatia                     1.351019e+04\n",
       "                                ...     \n",
       "Romania                     7.050816e+04\n",
       "Russian Federation          5.806382e+05\n",
       "Rwanda                      4.590930e+04\n",
       "Senegal                     8.677083e+03\n",
       "Serbia                      5.117095e+04\n",
       "Sierra Leone                2.326146e+04\n",
       "Slovak Republic             1.353077e+04\n",
       "Slovenia                    7.359070e+03\n",
       "South Africa                8.785635e+04\n",
       "Spain                       6.102850e+04\n",
       "Sri Lanka                   2.183564e+04\n",
       "Sudan                       1.087260e+05\n",
       "Swaziland                   4.535734e+03\n",
       "Sweden                      1.440747e+03\n",
       "Syrian Arab Republic        5.161351e+04\n",
       "Tajikistan                  2.812898e+05\n",
       "Tanzania                    1.364150e+05\n",
       "Thailand                    7.081326e+05\n",
       "Togo                        1.237092e+04\n",
       "Turkey                      3.759318e+05\n",
       "Uganda                      1.085248e+05\n",
       "Ukraine                     1.553313e+05\n",
       "United Kingdom              4.883418e+04\n",
       "United States               9.935909e+05\n",
       "Uruguay                     1.660691e+03\n",
       "Uzbekistan                  3.866953e+05\n",
       "Venezuela, RB               8.711950e+04\n",
       "Vietnam                     1.924198e+06\n",
       "Yemen, Rep.                 9.452764e+04\n",
       "Zambia                      3.398746e+04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dfr1.ix[\"Bangladesh\"][[\"bcr\"]])\n",
    "print(dfr1.ix[\"Bangladesh\"][[\"bcr\"]])\n",
    "print(dfr2.ix[\"Bangladesh\"][[\"bcr\"]])\n",
    "dfr[\"average_aid_cost_pc\"]*dfr2[\"pop\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BC ratio with proportional and uniform transfers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-27-6959c70861f5>, line 10)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-27-6959c70861f5>\"\u001b[1;36m, line \u001b[1;32m10\u001b[0m\n\u001b[1;33m    no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\u001b[0m\n\u001b[1;37m                                                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "modalities_and_color = dict(\n",
    "  #             no      =\"#386cb0\",\n",
    "  #              unif_all=\"#f0027f\",\n",
    "                unif_poor   =\"#beaed4\",\n",
    "                prop    =\"#7fc97f\",\n",
    "                            )\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "\n",
    "no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\n",
    "dfr1,dfr1_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=\"unif_poor\", optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0)\n",
    "dfr2,dfr2_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=\"prop\", optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0)\n",
    "dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "\n",
    "diff = dfr1[\"bcr\"] - dfr2[\"bcr\"]\n",
    "dfr2[\"diff\"]=diff\n",
    "\n",
    "dfr1.plot.scatter(x=\"gdp_pc_pp\", y=\"bcr\", color = 'blue', s=80, alpha=.63, ax=ax)\n",
    "dfr2.plot.scatter(x=\"gdp_pc_pp\", y=\"bcr\", color = 'red', s=80, alpha=.63, ax=ax)\n",
    "    \n",
    "print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "print((dfr2.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "plt.legend(['Uniform transfer','Transfer proportional to losses'])\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"Benefit-cost ratio\")\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(ymax=8)\n",
    "plt.savefig(\"benefit-cost-ratio-post-disaster-support-one-dol-prop-and-unif.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring targeting errors further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listePDS = (\"no\",\"unif_all\",\"unif_poor\",\"prop\")\n",
    "listeT = (\"data\",\"perfect\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "couleur = (\"blue\",\"red\",\"green\",\"yellow\",\"purple\")\n",
    "plt.figure(figsize=(12,12))\n",
    "optionPDS=\"unif_poor\"\n",
    "ref = 100*compute_resilience(macro,cat_info,**args,optionPDS=\"no\").resilience\n",
    "#ref = 100*compute_resilience(macro,cat_info,**args,optionPDS=\"no\").resilience\n",
    "for option,coul in zip(listeT,couleur):\n",
    "    plt.scatter(results.gdp_pc_pp,100*compute_resilience(macro,cat_info,**args,optionPDS=\"prop\",optionT=option,optionB=\"data\").resilience-ref,color=coul,alpha=.7,s=50)\n",
    "plt.legend(listeT)\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"resilience (%)\")\n",
    "plt.xlim(xmin=0)\n",
    "#plt.ylim(ymin=-50,ymax=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listePDS = (\"no\",\"unif_all\",\"unif_poor\",\"prop\")\n",
    "listeT = (\"data\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "couleur = (\"blue\",\"red\",\"green\",\"yellow\",\"purple\")\n",
    "plt.figure(figsize=(12,12))\n",
    "optionPDS=\"unif_poor\"\n",
    "ref = 100*compute_resilience(macro,cat_info,**args,optionPDS=\"unif_poor\",optionT=\"perfect\",optionB=\"unif_poor\").resilience\n",
    "for option,coul in zip(listeT,couleur):\n",
    "    plt.scatter(results.gdp_pc_pp,100*compute_resilience(macro,cat_info,**args,optionPDS=\"unif_poor\",optionT=option,optionB=\"unif_poor\").resilience-ref,color=coul,alpha=.7,s=50)\n",
    "plt.legend(listeT)\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"resilience (%)\")\n",
    "plt.xlim(xmin=0)\n",
    "#plt.ylim(ymin=-50,ymax=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "print('Resilience gains from $1 per capita of post-disaster support depend on exposure')\n",
    "plt.figure(figsize=(8,8))\n",
    "ax = plt.gca();\n",
    "no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\n",
    "dfr = compute_resilience(macro,cat_info,**args,optionPDS=\"unif_poor\",optionT=\"perfect\",optionB=\"one\")\n",
    "dfr[\"Resiliencegains\"]= 100*(dfr.resilience - no.resilience)\n",
    "dfr[\"Exposure\"]=dfr.fa #np.log(dfr.fa)\n",
    "dfr[\"Resilience gains\"]=dfr[\"Resiliencegains\"]\n",
    "dfr.query(\"Resiliencegains>0\").plot.scatter(x=\"Exposure\", y=\"Resilience gains\",  s=80, alpha=.63, ax=ax, logy=True , logx= True)\n",
    "\n",
    "\n",
    "# note that resilience gains non correlated with risk to asset or to welfare\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "dfr[\"lr\"]=np.log(dfr.Resiliencegains)\n",
    "dfr[\"le\"]=np.log(dfr.Exposure)\n",
    "#lm = smf.ols(formula='Resiliencegains ~ Exposure', data=dfr).fit()\n",
    "lm = smf.ols(formula='lr ~ le', data=dfr).fit()\n",
    "# print the coefficients\n",
    "print(lm.summary())\n",
    "dfr[\"estrg\"]=np.exp(dfr[\"le\"]*lm.params[1] + lm.params[0]).dropna()\n",
    "#X_new = pd.DataFrame({'le':[0, 0.2]})\n",
    "#preds = lm.predict(dfr.le.values)\n",
    "#plt.figure(figsize=(8,8))\n",
    "dfr.plot(x=\"Exposure\", y=\"estrg\", c='red',  ax=ax, logy=True , logx= True)\n",
    "\n",
    "plt.xlim(-0.005,.025)\n",
    "plt.ylim(-0.5,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## BC ratio with and without targeting errors - with $1 per inhabitant and proportional transfers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listeT = (\"x33\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "opt_leg = (\"33% error in targeting\", \"33% exclusion error\", \"33% inclusion error\")\n",
    "couleur = (\"red\",\"green\",\"yellow\",\"purple\")\n",
    "optionPDS = \"prop\"\n",
    "\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "\n",
    "no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\n",
    "\n",
    "for option,coul in zip(listeT,couleur):\n",
    "    dfr1,dfr1_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=optionPDS, optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0)\n",
    "    dfr2,dfr2_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=optionPDS, optionT=option,optionB=\"one_per_affected\",fraction_inside=0)\n",
    "    dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "    dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "    dfr2[\"diff\"] = - dfr1[\"bcr\"] + dfr2[\"bcr\"]\n",
    "    dfr2.plot.scatter(x=\"gdp_pc_pp\", y=\"diff\", color = coul, s=80, alpha=.63, ax=ax)\n",
    "    print('Countries where cost of poor targeting > 25 cents a dollar')\n",
    "    print(dfr2.query(\"diff < -0.25\")[[\"diff\",\"bcr\"]])\n",
    "    #print(dfr.sort_values(\"bcr\",ascending=False).head(20)[\"bcr\"])\n",
    "    \n",
    "#print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "#print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "#print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "#print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "#print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "#print((dfr2.query(\"bcr<1\")).bcr)\n",
    "\n",
    "#print((dfr2.query(\"diff<0\"))[\"diff\"])\n",
    "\n",
    "#print(diff.mean())\n",
    "    \n",
    "plt.legend(opt_leg , loc=3)\n",
    "plt.title(\"Transfers proportional to losses\")\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"Difference in benefit-cost ratio\")\n",
    "plt.savefig(\"benefit-cost-ratio-targeting-error_prop.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BC ratio with and without targeting errors - with $1 per inhabitant and uniform transfers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listeT = (\"x33\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "opt_leg = (\"33% error in targeting\", \"33% exclusion error\", \"33% inclusion error\")\n",
    "couleur = (\"red\",\"green\",\"yellow\",\"purple\")\n",
    "optionPDS = \"unif_poor\"\n",
    "\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "\n",
    "no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\n",
    "\n",
    "for option,coul in zip(listeT,couleur):\n",
    "    dfr1,dfr1_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=optionPDS, optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0)\n",
    "    dfr2,dfr2_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=optionPDS, optionT=option,optionB=\"one_per_affected\",fraction_inside=0)\n",
    "    dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "    dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "    dfr2[\"diff\"] = - dfr1[\"bcr\"] + dfr2[\"bcr\"]\n",
    "    dfr2.plot.scatter(x=\"gdp_pc_pp\", y=\"diff\", color = coul, s=80, alpha=.63, ax=ax)\n",
    "    print('Countries where cost of poor targeting > 25 cents a dollar')\n",
    "    print(dfr2.query(\"diff < -0.25\")[[\"diff\",\"bcr\"]])\n",
    "    \n",
    "#print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "#print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "#print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "#print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "#print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "#print((dfr2.query(\"bcr<1\")).bcr)\n",
    "\n",
    "#print((dfr2.query(\"diff<0\"))[\"diff\"])\n",
    "\n",
    "#print(diff.mean())\n",
    "    \n",
    "plt.legend(opt_leg , loc=3)\n",
    "plt.xlabel(\"GDP per capita\")\n",
    "plt.ylabel(\"Difference in benefit-cost ratio\")\n",
    "plt.title(\"Uniform transfers\")\n",
    "plt.savefig(\"benefit-cost-ratio-targeting-error_uf.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing BC ratio with and without targeting errors with $1 per affect person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listeT = (\"x33\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "opt_leg = (\"33% error in targeting\", \"33% exclusion error\", \"33% inclusion error\")\n",
    "couleur = (\"red\",\"green\",\"yellow\",\"purple\")\n",
    "listPDS = (\"prop\",\"unif_poor\")\n",
    "\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "\n",
    "no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\n",
    "\n",
    "for optionPDS in listPDS:\n",
    "    for option,coul in zip(listeT,couleur):\n",
    "        dfr1,dfr1_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=optionPDS, optionT=\"perfect\",optionB=\"one_per_affected\",fraction_inside=0)\n",
    "        dfr2,dfr2_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=optionPDS, optionT=option,optionB=\"one_per_affected\",fraction_inside=0)\n",
    "        dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "        dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "        dfr2[\"diff\"] = - dfr1[\"bcr\"] + dfr2[\"bcr\"]\n",
    "        plt.scatter(dfr1[\"bcr\"], dfr2[\"bcr\"], color = coul, s=80, alpha=.63)\n",
    "#        print('Countries where cost of poor targeting > 25 cents a dollar')\n",
    "#        print(dfr2.query(\"diff < -0.25\")[[\"diff\",\"bcr\"]])\n",
    "    \n",
    "#print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "#print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "#print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "#print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "#print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "#print((dfr2.query(\"bcr<1\")).bcr)\n",
    "\n",
    "#print((dfr2.query(\"diff<0\"))[\"diff\"])\n",
    "\n",
    "#print(diff.mean())\n",
    "    \n",
    "plt.legend(opt_leg , loc=1)\n",
    "plt.xlabel(\"Benefit-cost ratio with perfect targeting\")\n",
    "plt.ylabel(\"Benefit-cost ratio with targeting error\")\n",
    "plt.savefig(\"benefit-cost-ratio-targeting-error.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing BC ratio with and without targeting errors with current transfers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listeT = (\"x33\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "opt_leg = (\"33% error in targeting\", \"33% exclusion error\", \"33% inclusion error\")\n",
    "couleur = (\"red\",\"green\",\"yellow\",\"purple\")\n",
    "listPDS = (\"prop\",\"unif_poor\")\n",
    "\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "\n",
    "no = compute_resilience(macro,cat_info,optionPDS=\"no\",**args)\n",
    "\n",
    "for optionPDS in listPDS:\n",
    "    for option,coul in zip(listeT,couleur):\n",
    "        dfr1,dfr1_iah = compute_resilience(macro,cat_info,return_iah=True, optionPDS=optionPDS, optionT=\"perfect\",optionB=\"data\",fraction_inside=0,**args)\n",
    "        dfr2,dfr2_iah = compute_resilience(macro,cat_info,return_iah=True, optionPDS=optionPDS, optionT=option,optionB=\"data\",fraction_inside=0,**args)\n",
    "        dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "        dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "        dfr2[\"diff\"] = - dfr1[\"bcr\"] + dfr2[\"bcr\"]\n",
    "        plt.scatter(dfr1[\"bcr\"], dfr2[\"bcr\"], color = coul, s=80, alpha=.63)\n",
    "#        print('Countries where cost of poor targeting > 25 cents a dollar')\n",
    "#        print(dfr2.query(\"diff < -0.25\")[[\"diff\",\"bcr\"]])\n",
    "    \n",
    "#print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "#print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "#print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "#print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "#print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "#print((dfr2.query(\"bcr<1\")).bcr)\n",
    "\n",
    "#print((dfr2.query(\"diff<0\"))[\"diff\"])\n",
    "\n",
    "#print(diff.mean())\n",
    "    \n",
    "plt.legend(opt_leg , loc=1)\n",
    "plt.xlabel(\"Benefit-cost ratio with perfect targeting\")\n",
    "plt.ylabel(\"Benefit-cost ratio with targeting error\")\n",
    "plt.savefig(\"benefit-cost-ratio-targeting-error.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## Comparing BC ratio with and without targeting errors with large transfers (5% of GDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "listeT = (\"x33\",\"excl\",\"incl\") #,\"20\",\"incl\")\n",
    "opt_leg = (\"33% error in targeting\", \"33% exclusion error\", \"33% inclusion error\")\n",
    "couleur = (\"red\",\"green\",\"yellow\",\"purple\")\n",
    "listPDS = (\"one\")\n",
    "optionB = \"x05\"\n",
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "\n",
    "no = compute_resilience(macro,cat_info,**args,optionPDS=\"no\")\n",
    "\n",
    "for option,coul in zip(listeT,couleur):\n",
    "    dfr1,dfr1_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=\"unif_poor\", optionT=\"perfect\",optionB=optionB,fraction_inside=0)\n",
    "    dfr2,dfr2_iah = compute_resilience(macro,cat_info,**args,return_iah=True, optionPDS=\"unif_poor\", optionT=option,optionB=optionB,fraction_inside=0)\n",
    "    dfr1[\"bcr\"] = -(dfr1.dWpc_currency - no.dWpc_currency)/dfr1[\"average_aid_cost_pc\"]\n",
    "    dfr2[\"bcr\"] = -(dfr2.dWpc_currency - no.dWpc_currency)/dfr2[\"average_aid_cost_pc\"]\n",
    "    dfr2[\"diff\"] = - dfr1[\"bcr\"] + dfr2[\"bcr\"]\n",
    "    plt.scatter(dfr1[\"bcr\"], dfr2[\"bcr\"], color = coul, s=80, alpha=.63)\n",
    "#        print('Countries where cost of poor targeting > 25 cents a dollar')\n",
    "#        print(dfr2.query(\"diff < -0.25\")[[\"diff\",\"bcr\"]])\n",
    "    \n",
    "#print('Average (population weights) (unif_poor)',((dfr1[\"bcr\"]*dfr1[\"pop\"]).sum())/dfr1[\"pop\"].sum())\n",
    "#print('Average (no weights) (unif_poor)',((dfr1[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (unif_poor)')\n",
    "#print((dfr1.query(\"bcr<1\")).bcr)\n",
    "    \n",
    "#print('Average (population weights) (prop)',((dfr2[\"bcr\"]*dfr2[\"pop\"]).sum())/dfr2[\"pop\"].sum())\n",
    "#print('Average (no weights) (prop)',((dfr2[\"bcr\"].mean())))\n",
    "#print('Countries where post-disaster support has BC ratio < 1 (prop)')\n",
    "#print((dfr2.query(\"bcr<1\")).bcr)\n",
    "\n",
    "#print((dfr2.query(\"diff<0\"))[\"diff\"])\n",
    "\n",
    "#print(diff.mean())\n",
    "    \n",
    "plt.legend(opt_leg , loc=1)\n",
    "plt.xlabel(\"Benefit-cost ratio with perfect targeting\")\n",
    "plt.ylabel(\"Benefit-cost ratio with targeting error\")\n",
    "plt.savefig(\"benefit-cost-ratio-targeting-error-big.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Double dividend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "ddiv = pd.read_csv(\"T_all (2).csv\").dropna()\n",
    "ddiv.sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from fancy_plots import spine_and_ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ddiv2 = ddiv.sort_values(\"proba\")\n",
    "\n",
    "#removes crazy point at p==0\n",
    "ddiv2.ix[ddiv2.proba==0,[\"W1_rel\",\"Weq_rel\",\"Wopt_rel\"]]=np.nan\n",
    "\n",
    "ddiv2[\"proba\"]   *=100\n",
    "ddiv2[\"W1_rel\"]  *=100\n",
    "ddiv2[\"Weq_rel\"] *=100\n",
    "ddiv2[\"Wopt_rel\"]*=100\n",
    "\n",
    "fig, axarr = plt.subplots(1,3, figsize=(12,4) , sharey=True)\n",
    "\n",
    "l=0\n",
    "\n",
    "for r_fig in [0.04,0.08,0.12]:\n",
    "        for eta_fig,col in zip([1.5,0.5],[ \"#377eb8\",\"#4daf4a\"] ):#\"#984ea3\"\n",
    "                ddiv2[\"eta_fig\"]=eta_fig\n",
    "                ddiv2[\"r_fig\"]=r_fig\n",
    "                cond = \"(r==r_fig)&(eta==eta_fig)&(insu==0)&(v==0.25)\"\n",
    "                ddiv2.query(cond).plot(x=\"proba\",y=\"Wopt_rel\", ax=axarr[l],color=col, linewidth=2)\n",
    "\n",
    "        ddiv2.query(cond).plot(x=\"proba\",y=\"Weq_rel\", ax=axarr[l] ,color=\"#e41a1c\", linewidth=2)\n",
    "        ddiv2.query(cond).plot(x=\"proba\",y=\"W1_rel\",style=\"--\", ax=axarr[l]  ,color=\"black\",  linewidth=2)\n",
    "        \n",
    "        #cond2 = \"(r==r_fig)&(eta==eta_fig)&(insu==0.5)&(v==0.25)\"\n",
    "        #ddiv2.query(cond2).plot(x=\"proba\",y=\"Wopt_rel\", ax=axarr[k])\n",
    "        #cond3 = \"(r==r_fig)&(eta==eta_fig)&(insu==0)&(v==0.125)\"\n",
    "        #ddiv2.query(cond3).plot(x=\"proba\",y=\"Wopt_rel\", ax=axarr[k])\n",
    "        axarr[l].legend_.remove()\n",
    "        axarr[l].set_ylim(ymin=95,ymax=120)        \n",
    "#         axarr[l].set_title(\"r={r:.0%}\".format(r=r_fig), **font)\n",
    "        axarr[l].set_xlabel(\"Probability (% per year)\")\n",
    "        axarr[l].set_ylabel(\"Expected consumption\")\n",
    "        \n",
    "#         axarr[l].vlines(x=2,\n",
    "#                         ymin=100,linestyles =\"dotted\",\n",
    "#                         ymax=ddiv2.query(\"(r==r_fig)&(eta==1.5)&(insu==0)&(v==0.25)&(proba==2)\").Wopt_rel.mean(),\n",
    "#                         color=\"#cccccc\")\n",
    "                           \n",
    "#         axarr[l].hlines(xmin=2,\n",
    "#                         xmax=10,\n",
    "#                         y=100,linestyles =\"dotted\",\n",
    "#                         color=\"#cccccc\")                   \n",
    "                           \n",
    "        spine_and_ticks(axarr[l])\n",
    "\n",
    "        l += 1\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "axarr[0].legend([\"Investment response (High risk aversion, $\\eta=1.5$)\", \n",
    "                 \"Investment response (Low risk aversion, $\\eta=0.5$)\",\n",
    "                \"Investment response (No risk aversion, $\\eta=0$)\",\n",
    "                \"No investment response, avoided losses only\",\n",
    "    ],\n",
    "                bbox_to_anchor=(0, -0.7, 1, .102), loc=3,\n",
    "           ncol=1, mode=\"expand\", borderaxespad=0, frameon=False, prop=font)\n",
    "#axarr[0,0:2].legend([\"Expected consumption with avoided losses only\",\"Expected consumption with investment response (no risk aversion)\",\"Expected consumption with investment response (risk aversion)\"],bbox_to_anchor=(0., 1.22, 1., .102), loc=3,ncol=1, mode=\"expand\", borderaxespad=0.)\n",
    "\n",
    "plt.savefig(\"img/benefit_DRM_proba.png\", bbox_inches=\"tight\")\n",
    "plt.savefig(\"img/benefit_DRM_proba.pdf\", bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "r_fig = .08\n",
    "\n",
    "fig, axarr = plt.subplots(1,1, figsize=(7,7) , sharey=True)\n",
    "axarr = [axarr]\n",
    "l=0\n",
    "\n",
    "for eta_fig,col in zip([1.5,0.5],[ \"#377eb8\",\"#4daf4a\"] ):#\"#984ea3\"\n",
    "        ddiv2[\"eta_fig\"]=eta_fig\n",
    "        ddiv2[\"r_fig\"]=r_fig\n",
    "        cond = \"(r==r_fig)&(eta==eta_fig)&(insu==0)&(v==0.25)\"\n",
    "        ddiv2.query(cond).plot(x=\"proba\",y=\"Wopt_rel\", ax=axarr[l],color=col, linewidth=2)\n",
    "\n",
    "ddiv2.query(cond).plot(x=\"proba\",y=\"Weq_rel\", ax=axarr[l] ,color=\"#e41a1c\", linewidth=2)\n",
    "ddiv2.query(cond).plot(x=\"proba\",y=\"W1_rel\",style=\"--\", ax=axarr[l]  ,color=\"black\",  linewidth=2)\n",
    "\n",
    "#cond2 = \"(r==r_fig)&(eta==eta_fig)&(insu==0.5)&(v==0.25)\"\n",
    "#ddiv2.query(cond2).plot(x=\"proba\",y=\"Wopt_rel\", ax=axarr[k])\n",
    "#cond3 = \"(r==r_fig)&(eta==eta_fig)&(insu==0)&(v==0.125)\"\n",
    "#ddiv2.query(cond3).plot(x=\"proba\",y=\"Wopt_rel\", ax=axarr[k])\n",
    "axarr[l].legend_.remove()\n",
    "axarr[l].set_ylim(ymin=95,ymax=120)        \n",
    "#         axarr[l].set_title(\"r={r:.0%}\".format(r=r_fig), **font)\n",
    "axarr[l].set_xlabel(\"Probability (% per year)\")\n",
    "axarr[l].set_ylabel(\"Expected consumption\")\n",
    "\n",
    "\n",
    "axarr[0].legend([\"Investment response (High risk aversion)\", \n",
    "                 \"Investment response (Low risk aversion)\",\n",
    "                \"Investment response (No risk aversion)\",\n",
    "                \"No investment response, avoided losses only\",\n",
    "    ],\n",
    "#                 bbox_to_anchor=(0, -0.7, 1, .102),\n",
    "                loc=1,\n",
    "           ncol=1, mode=\"expand\", borderaxespad=0, frameon=False, prop=font)\n",
    "#axarr[0,0:2].legend([\"Expected consumption with avoided losses only\",\"Expected consumption with investment response (no risk aversion)\",\"Expected consumption with investment response (risk aversion)\"],bbox_to_anchor=(0., 1.22, 1., .102), loc=3,ncol=1, mode=\"expand\", borderaxespad=0.)\n",
    "\n",
    "spine_and_ticks(axarr[l])\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"img/benefit_DRM_proba_08.png\", bbox_inches=\"tight\")\n",
    "plt.savefig(\"img/benefit_DRM_proba_08.pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "fig=plt.figure(figsize=(8,8));\n",
    "ax = plt.gca();\n",
    "ddiv2[\"p_before\"] = 0.1*100\n",
    "ddiv2[\"p_after\"] = 0.02*100\n",
    "cond = \"(insu==0)&(v==0.25)&((proba==p_after))\"\n",
    "ddiv3 = ddiv2.query(cond).set_index(\"proba\").rename({0.02: \"after\", 0.10:\"before\"})\n",
    "ddiv3[\"Before\"]=100\n",
    "ddiv3[\"No risk aversion\"] =  (ddiv3[\"Weq_rel\"]-ddiv3[\"Before\"]) / (ddiv3[\"W1_rel\"]-ddiv3[\"Before\"])\n",
    "ddiv3[\"Risk aversion\"] =  (ddiv3[\"Wopt_rel\"]-ddiv3[\"Before\"]) / (ddiv3[\"W1_rel\"]-ddiv3[\"Before\"])\n",
    "ddiv3.boxplot([\"No risk aversion\",\"Risk aversion\"],ax=ax)\n",
    "plt.savefig(\"img/benefit_DRM_10_to_2.png\")\n",
    "ddiv3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "ddiv3.set_index([\"r\",\"eta\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  }
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