AirBnb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Sorting #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "from statsmodels.discrete.discrete_model import Probit\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "\n",
    "df = pd.io.stata.read_stata('20160213_data/finished_do.dta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproduction of results # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For table exportation, methods as_latex() and to_latex() of statsmodels and pandas respectively have been used. For better visuality of the paper, the method calls have been deleted for the final export of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 2 - The impact of race on likelihood of acceptance ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>yes I</th>   <th>yes II</th>   <th>yes III</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black</th>       <td>-0.08***</td> <td>-0.08***</td> <td>-0.09***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_race_black</th>       <td></td>      <td>0.07***</td>  <td>0.09***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td></td>      <td>(0.02)</td>   <td>(0.02)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_gender_M</th>         <td></td>     <td>-0.05***</td> <td>-0.05***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td></td>      <td>(0.01)</td>   <td>(0.01)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multiple_listings</th>     <td></td>         <td></td>      <td>0.06***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td></td>         <td></td>      <td>(0.01)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_property</th>       <td></td>         <td></td>     <td>-0.07***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td></td>         <td></td>      <td>(0.02)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ten_reviews</th>           <td></td>         <td></td>      <td>0.12***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td></td>         <td></td>      <td>(0.01)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_price</th>             <td></td>         <td></td>     <td>-0.06***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td></td>         <td></td>      <td>(0.01)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>0.49***</td>  <td>0.50***</td>  <td>0.76***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.07)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>           <td>0.01</td>     <td>0.01</td>     <td>0.04</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                    <td>0.01</td>     <td>0.01</td>     <td>0.04</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                   <td>6235</td>     <td>6235</td>     <td>6168</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R²_adj</th>              <td>0.006</td>    <td>0.009</td>    <td>0.039</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "============================================\n",
       "                   yes I    yes II  yes III \n",
       "--------------------------------------------\n",
       "guest_black       -0.08*** -0.08*** -0.09***\n",
       "                  (0.02)   (0.02)   (0.02)  \n",
       "host_race_black            0.07***  0.09*** \n",
       "                           (0.02)   (0.02)  \n",
       "host_gender_M              -0.05*** -0.05***\n",
       "                           (0.01)   (0.01)  \n",
       "multiple_listings                   0.06*** \n",
       "                                    (0.01)  \n",
       "shared_property                     -0.07***\n",
       "                                    (0.02)  \n",
       "ten_reviews                         0.12*** \n",
       "                                    (0.01)  \n",
       "log_price                           -0.06***\n",
       "                                    (0.01)  \n",
       "Intercept         0.49***  0.50***  0.76*** \n",
       "                  (0.01)   (0.01)   (0.07)  \n",
       "R-squared         0.01     0.01     0.04    \n",
       "                  0.01     0.01     0.04    \n",
       "N                 6235     6235     6168    \n",
       "R²_adj            0.006    0.009    0.039   \n",
       "============================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model = df[['yes','guest_black','name_by_city', 'host_gender_M', 'host_race_black']].dropna()\n",
    "df_model3 = df[['yes','guest_black','name_by_city', 'host_gender_M', 'host_race_black', 'multiple_listings', 'shared_property', 'ten_reviews', 'log_price']].dropna()\n",
    "\n",
    "model = smf.ols('yes ~ guest_black', data=df_model)\n",
    "result1 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + host_race_black + host_gender_M', data=df_model)\n",
    "result2 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + host_race_black + host_gender_M + multiple_listings + shared_property + ten_reviews + log_price', data=df_model3)\n",
    "result3 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model3['name_by_city']})\n",
    "\n",
    "\n",
    "tble = summary_col([result1, result2, result3], stars=True, float_format='%.2f', regressor_order=['guest_black', 'host_race_black', 'host_gender_M', 'multiple_listings', 'shared_property', 'ten_reviews', 'log_price', 'Intercept'], info_dict={'N':lambda x: \"{0:d}\".format(int(x.nobs)), 'R²_adj':lambda x: \"{0:.3f}\".format(float(x.rsquared_adj))})\n",
    "tble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 4. Proportion of Positive Responses by Race and Gender ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>guest_male_white</th>\n",
       "      <th>guest_male_black</th>\n",
       "      <th>guest_female_white</th>\n",
       "      <th>guest_female_black</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>host_female_black</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_female_white</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_male_black</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host_male_white</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   guest_male_white  guest_male_black  guest_female_white  \\\n",
       "host_female_black              0.43              0.38                0.53   \n",
       "host_female_white              0.46              0.35                0.49   \n",
       "host_male_black                0.64              0.40                0.59   \n",
       "host_male_white                0.42              0.35                0.49   \n",
       "\n",
       "                   guest_female_black  \n",
       "host_female_black                0.59  \n",
       "host_female_white                0.44  \n",
       "host_male_black                  0.43  \n",
       "host_male_white                  0.32  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format race/gender interaction columns as naming is inconsistent\n",
    "df['host_male'] = df['host_gender_M']\n",
    "df['host_female'] = df['host_gender_F']\n",
    "df['guest_race_black']= df['guest_black']\n",
    "df['guest_race_white'] = df.guest_black.apply(lambda x: 1 if x==0 else 0)\n",
    "\n",
    "for gender in ['female', 'male']:\n",
    "    for race in ['white', 'black']:\n",
    "        for side in ['guest', 'host']:\n",
    "            df[side+'_'+gender+'_'+race] = df.apply(lambda x: 1 if x[side+'_'+gender] == 1 and x[side+'_race_'+race] ==1 else 0, axis=1)\n",
    "\n",
    "df['no'] = df.yes.apply(lambda x: 0 if x==1 else 1)\n",
    "host_combinations = ['host_male_white', 'host_male_black','host_female_white', 'host_female_black']\n",
    "guest_combinations = ['guest_female_white', 'guest_female_black', 'guest_male_white', 'guest_male_black']\n",
    "\n",
    "# Sum of positive responses\n",
    "table1 = pd.pivot_table(df, values=guest_combinations, index=host_combinations,\n",
    "                   columns=['yes'], aggfunc=np.sum)\n",
    "# Sum of negative responses\n",
    "table2 = pd.pivot_table(df, values=guest_combinations, index=host_combinations,\n",
    "                   columns=['no'], aggfunc=np.sum)\n",
    "# Response rate\n",
    "table3 = table1 / (table1+table2)\n",
    "\n",
    "# Formatting\n",
    "table3.drop(columns=table3.columns[::2], inplace=True)\n",
    "table3 = table3.iloc[1:]\n",
    "table3.reset_index(inplace=True)\n",
    "table3.reindex(host_combinations)\n",
    "host_combinations.reverse()\n",
    "table3.index = host_combinations\n",
    "table3 = table3.iloc[:,4:]\n",
    "table3.columns = table3.columns.get_level_values(0)\n",
    "table3 = table3[['guest_male_white', 'guest_male_black','guest_female_white', 'guest_female_black']]\n",
    "host_combinations.reverse()\n",
    "table3.reindex(host_combinations)\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "table3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 5. Are Effects Driven by Host Characteristics? ##\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>yes I</th>   <th>yes II</th>   <th>yes III</th> <th>yes IIII</th> <th>yes IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black</th>         <td>-0.07***</td> <td>-0.08***</td> <td>-0.09***</td> <td>-0.08***</td> <td>-0.09***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_property</th>       <td>0.00</td>       <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.01)</td>      <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_guest_black</th>    <td>-0.02</td>      <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.03)</td>      <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multiple_listings</th>       <td></td>      <td>0.10***</td>     <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>      <td>(0.02)</td>      <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multiple_black</th>          <td></td>       <td>-0.00</td>      <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>      <td>(0.03)</td>      <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ten_reviews</th>             <td></td>         <td></td>      <td>0.14***</td>     <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>      <td>(0.02)</td>      <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ten_reviews_black</th>       <td></td>         <td></td>       <td>0.01</td>       <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>      <td>(0.02)</td>      <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>young</th>                   <td></td>         <td></td>         <td></td>       <td>-0.03</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>      <td>(0.02)</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>young_black</th>             <td></td>         <td></td>         <td></td>       <td>-0.01</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>      <td>(0.02)</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>any_black</th>               <td></td>         <td></td>         <td></td>         <td></td>      <td>0.10***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>         <td></td>      <td>(0.01)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>any_black_gb</th>            <td></td>         <td></td>         <td></td>         <td></td>      <td>0.06**</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>         <td></td>      <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>0.49***</td>  <td>0.46***</td>  <td>0.42***</td>  <td>0.50***</td>  <td>0.46***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>             <td>0.01</td>     <td>0.01</td>     <td>0.03</td>     <td>0.01</td>     <td>0.02</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td>0.01</td>     <td>0.01</td>     <td>0.03</td>     <td>0.01</td>     <td>0.02</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                     <td>6235</td>     <td>6235</td>     <td>6235</td>     <td>6235</td>     <td>6235</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2_adj</th>                <td>0.006</td>    <td>0.014</td>    <td>0.027</td>    <td>0.007</td>    <td>0.019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Implied coefficient</th>   <td>-0.09</td>    <td>-0.08</td>    <td>-0.08</td>    <td>-0.09</td>    <td>-0.04</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "=================================================================\n",
       "                     yes I    yes II  yes III  yes IIII yes IIIII\n",
       "-----------------------------------------------------------------\n",
       "guest_black         -0.07*** -0.08*** -0.09*** -0.08*** -0.09*** \n",
       "                    (0.02)   (0.02)   (0.02)   (0.02)   (0.02)   \n",
       "shared_property     0.00                                         \n",
       "                    (0.01)                                       \n",
       "shared_guest_black  -0.02                                        \n",
       "                    (0.03)                                       \n",
       "multiple_listings            0.10***                             \n",
       "                             (0.02)                              \n",
       "multiple_black               -0.00                               \n",
       "                             (0.03)                              \n",
       "ten_reviews                           0.14***                    \n",
       "                                      (0.02)                     \n",
       "ten_reviews_black                     0.01                       \n",
       "                                      (0.02)                     \n",
       "young                                          -0.03             \n",
       "                                               (0.02)            \n",
       "young_black                                    -0.01             \n",
       "                                               (0.02)            \n",
       "any_black                                               0.10***  \n",
       "                                                        (0.01)   \n",
       "any_black_gb                                            0.06**   \n",
       "                                                        (0.02)   \n",
       "Intercept           0.49***  0.46***  0.42***  0.50***  0.46***  \n",
       "                    (0.01)   (0.01)   (0.01)   (0.01)   (0.01)   \n",
       "R-squared           0.01     0.01     0.03     0.01     0.02     \n",
       "                    0.01     0.01     0.03     0.01     0.02     \n",
       "N                   6235     6235     6235     6235     6235     \n",
       "R2_adj              0.006    0.014    0.027    0.007    0.019    \n",
       "Implied coefficient -0.09    -0.08    -0.08    -0.09    -0.04    \n",
       "=================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['shared_guest_black'] = df['shared_property'] * df['guest_black']\n",
    "df['multiple_black'] = df['multiple_listings'] * df['guest_black']\n",
    "df['ten_reviews_black'] = df['ten_reviews'] * df['guest_black']\n",
    "df['young_black'] = df['young'] * df['guest_black']\n",
    "df['any_black_gb'] = df['any_black'] * df['guest_black']\n",
    "\n",
    "\n",
    "# This extra df is created to jointly drop NA values including 'name_by_city'. Otherwise, as \n",
    "# 'name_by_city' is only called in the .fit() function, the length of it does not match the\n",
    "# exogenous variables from the model where NA values where already dropped.\n",
    "df_model = df[['yes','guest_black','name_by_city', 'shared_property', 'shared_guest_black', \n",
    "               'multiple_listings', 'multiple_black', 'ten_reviews', 'ten_reviews_black',\n",
    "              'young', 'young_black', 'any_black', 'any_black_gb']].dropna()\n",
    "\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + shared_property + shared_guest_black', data=df_model)\n",
    "result1 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + multiple_listings + multiple_black', data=df_model)\n",
    "result2 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + ten_reviews + ten_reviews_black', data=df_model)\n",
    "result3 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + young + young_black', data=df_model)\n",
    "result4 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + any_black + any_black_gb', data=df_model)\n",
    "result5 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "tble5 = summary_col([result1, result2, result3, result4, result5], stars=True, float_format='%.2f', \n",
    "                   regressor_order=['guest_black','shared_property', 'shared_guest_black', \n",
    "                        'multiple_listings', 'multiple_black', 'ten_reviews', 'ten_reviews_black',\n",
    "                        'young', 'young_black', 'any_black', 'any_black_gb'],  \n",
    "                   info_dict={'N':lambda x: \"{0:d}\".format(int(x.nobs)),'R2_adj':lambda x: \"{:.3f}\".format(x.rsquared_adj),\n",
    "                             'Implied coefficient':lambda x: \"{0:.2f}\".format(float(x.params[1]+x.params[3]))})\n",
    "tble5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 6. Are Effects Driven by Location Characteristics? ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "               <td></td>                <th>yes I</th>   <th>yes II</th>   <th>yes III</th> <th>yes IIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black</th>                <td>-0.08***</td> <td>-0.08***</td> <td>-0.09***</td>  <td>-0.12**</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.06)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_median</th>               <td>-0.06***</td>     <td></td>         <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>(0.02)</td>      <td></td>         <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black_price_median</th>     <td>0.01</td>       <td></td>         <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>(0.03)</td>      <td></td>         <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>black_proportion</th>               <td></td>       <td>0.05</td>       <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td></td>      <td>(0.05)</td>      <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black_pop_black</th>          <td></td>       <td>0.02</td>       <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td></td>      <td>(0.07)</td>      <td></td>         <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tract_listings</th>                 <td></td>         <td></td>       <td>-0.00</td>      <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td></td>         <td></td>      <td>(0.00)</td>      <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black_tract_listings</th>     <td></td>         <td></td>       <td>0.00</td>       <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td></td>         <td></td>      <td>(0.00)</td>      <td></td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pr_filled</th>                      <td></td>         <td></td>         <td></td>      <td>0.56***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td></td>         <td></td>         <td></td>      <td>(0.08)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black_pr_filled</th>          <td></td>         <td></td>         <td></td>       <td>0.09</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td></td>         <td></td>         <td></td>      <td>(0.12)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                   <td>0.52***</td>  <td>0.48***</td>  <td>0.49***</td>  <td>0.24***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>(0.02)</td>   <td>(0.01)</td>   <td>(0.02)</td>   <td>(0.03)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                    <td>0.01</td>     <td>0.01</td>     <td>0.01</td>     <td>0.03</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                             <td>0.01</td>     <td>0.01</td>     <td>0.01</td>     <td>0.03</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                            <td>6235</td>     <td>6223</td>     <td>6235</td>     <td>6101</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2_adj</th>                       <td>0.009</td>    <td>0.006</td>    <td>0.006</td>    <td>0.030</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "==============================================================\n",
       "                            yes I    yes II  yes III  yes IIII\n",
       "--------------------------------------------------------------\n",
       "guest_black                -0.08*** -0.08*** -0.09*** -0.12** \n",
       "                           (0.02)   (0.02)   (0.02)   (0.06)  \n",
       "price_median               -0.06***                           \n",
       "                           (0.02)                             \n",
       "guest_black_price_median   0.01                               \n",
       "                           (0.03)                             \n",
       "black_proportion                    0.05                      \n",
       "                                    (0.05)                    \n",
       "guest_black_pop_black               0.02                      \n",
       "                                    (0.07)                    \n",
       "tract_listings                               -0.00            \n",
       "                                             (0.00)           \n",
       "guest_black_tract_listings                   0.00             \n",
       "                                             (0.00)           \n",
       "pr_filled                                             0.56*** \n",
       "                                                      (0.08)  \n",
       "guest_black_pr_filled                                 0.09    \n",
       "                                                      (0.12)  \n",
       "Intercept                  0.52***  0.48***  0.49***  0.24*** \n",
       "                           (0.02)   (0.01)   (0.02)   (0.03)  \n",
       "R-squared                  0.01     0.01     0.01     0.03    \n",
       "                           0.01     0.01     0.01     0.03    \n",
       "N                          6235     6223     6235     6101    \n",
       "R2_adj                     0.009    0.006    0.006    0.030   \n",
       "==============================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['guest_black_price_median'] = df['guest_black'] * df['price_median']\n",
    "df['guest_black_pop_black'] = df['guest_black'] * df['black_proportion']\n",
    "df['guest_black_tract_listings'] = df['guest_black'] * df['tract_listings']\n",
    "df['guest_black_pr_filled'] = df['guest_black'] * df['pr_filled']\n",
    "\n",
    "df_model = df[['yes', 'name_by_city', 'guest_black','price_median', 'guest_black_price_median']].dropna()\n",
    "model = smf.ols('yes ~ guest_black + price_median + guest_black_price_median', data=df_model)\n",
    "result1 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "df_model = df[['yes', 'name_by_city', 'guest_black', 'black_proportion', 'guest_black_pop_black']].dropna()\n",
    "model = smf.ols('yes ~ guest_black + black_proportion + guest_black_pop_black', data=df_model)\n",
    "result2 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "df_model = df[['yes', 'name_by_city', 'guest_black','tract_listings', 'guest_black_tract_listings']].dropna()\n",
    "model = smf.ols('yes ~ guest_black + tract_listings + guest_black_tract_listings', data=df_model)\n",
    "result3 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "df_model = df[['yes', 'name_by_city', 'guest_black','pr_filled', 'guest_black_pr_filled']].dropna()\n",
    "model = smf.ols('yes ~ guest_black + pr_filled + guest_black_pr_filled', data=df_model)\n",
    "result4 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "tble6 = summary_col([result1, result2, result3, result4], stars=True, float_format='%.2f', \n",
    "                   regressor_order=['guest_black','price_median', 'guest_black_price_median', 'black_proportion', \n",
    "                        'guest_black_pop_black', 'tract_listings', 'guest_black_tract_listings', 'pr_filled',\n",
    "                        'guest_black_pr_filled'],  \n",
    "                   info_dict={'N':lambda x: \"{0:d}\".format(int(x.nobs)),'R2_adj':lambda x: \"{:.3f}\".format(x.rsquared_adj)})\n",
    "tble6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "End of reproduction of main results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Own empirical analysis # "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomization controls ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regress each control on treatment variable ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Control Variable     P-Value Coeff\n",
      "host_gender_M:        0.896  0.001\n",
      "host_race_black:      0.972 -0.000\n",
      "multiple_listings:    0.451  0.009\n",
      "shared_property:      0.929  0.001\n",
      "log_price:            0.792  0.005\n",
      "host_race_black:      0.972 -0.000\n",
      "host_gender_M:        0.896  0.001\n",
      "host_gender_F:        0.439 -0.009\n",
      "shared_property:      0.929  0.001\n",
      "multiple_listings:    0.451  0.009\n",
      "ten_reviews:          0.041  0.026\n",
      "young:                0.799  0.003\n",
      "price_median:         0.772 -0.004\n",
      "black_proportion:     0.919 -0.001\n",
      "guest_male:           0.408 -0.010\n",
      "tract_listings:       0.848  0.045\n",
      "pr_filled:            0.899 -0.000\n",
      "baltimore:            0.906 -0.001\n",
      "dallas:               0.311 -0.006\n",
      "los_angeles:          0.743  0.004\n",
      "sl:                   0.382 -0.003\n",
      "dc:                   0.505  0.007\n"
     ]
    }
   ],
   "source": [
    "used_controls = ['host_gender_M', 'host_race_black', 'multiple_listings', 'shared_property', 'log_price', \n",
    "                     'host_race_black', 'host_gender_M', 'host_gender_F', 'shared_property', 'multiple_listings', \n",
    "                     'ten_reviews', 'young', 'price_median', 'black_proportion', 'guest_male',\n",
    "                     'tract_listings', 'pr_filled', 'baltimore', 'dallas', 'los_angeles', 'sl', 'dc']\n",
    "used_control_models = {}\n",
    "\n",
    "print('{0:20} {1:>1} {2:>1}'.format('Control Variable', 'P-Value', 'Coeff'))\n",
    "for control in used_controls:\n",
    "    model = smf.ols(control + ' ~ guest_black', data=df)\n",
    "    result = model.fit()\n",
    "    used_control_models[control] = result\n",
    "    print('{0:20} {1:>6.3f} {2:6.3f}'.format(control + ':', result.pvalues[1], result.params[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ten_reviews is correlated at a 5% significance level. However, the coefficient is \"only\" 0.026 (both variables are binary). Therefore the effect is not too large. This is not good, but does not greatly damage the interpretation and validity of the whole paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regress treatment variable on all controls ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>guest_black</td>   <th>  R-squared:         </th> <td>   0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.5590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 Aug 2020</td> <th>  Prob (F-statistic):</th>  <td> 0.923</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:58:45</td>     <th>  Log-Likelihood:    </th> <td> -4520.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6235</td>      <th>  AIC:               </th> <td>   9077.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6217</td>      <th>  BIC:               </th> <td>   9198.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    17</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    0.5556</td> <td>    0.099</td> <td>    5.617</td> <td> 0.000</td> <td>    0.362</td> <td>    0.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_gender_M</th>     <td>   -0.0133</td> <td>    0.017</td> <td>   -0.801</td> <td> 0.423</td> <td>   -0.046</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_race_black</th>   <td>   -0.0011</td> <td>    0.026</td> <td>   -0.040</td> <td> 0.968</td> <td>   -0.052</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multiple_listings</th> <td>    0.0037</td> <td>    0.015</td> <td>    0.257</td> <td> 0.797</td> <td>   -0.025</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_property</th>   <td>   -0.0148</td> <td>    0.022</td> <td>   -0.683</td> <td> 0.495</td> <td>   -0.057</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_price</th>         <td>    0.0014</td> <td>    0.015</td> <td>    0.092</td> <td> 0.927</td> <td>   -0.029</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>host_gender_F</th>     <td>   -0.0169</td> <td>    0.016</td> <td>   -1.087</td> <td> 0.277</td> <td>   -0.047</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ten_reviews</th>       <td>    0.0304</td> <td>    0.015</td> <td>    2.044</td> <td> 0.041</td> <td>    0.001</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>young</th>             <td>    0.0059</td> <td>    0.013</td> <td>    0.438</td> <td> 0.661</td> <td>   -0.020</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_median</th>      <td>   -0.0115</td> <td>    0.021</td> <td>   -0.548</td> <td> 0.584</td> <td>   -0.053</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>black_proportion</th>  <td>   -0.0029</td> <td>    0.039</td> <td>   -0.076</td> <td> 0.940</td> <td>   -0.079</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_male</th>        <td>   -0.0119</td> <td>    0.013</td> <td>   -0.934</td> <td> 0.350</td> <td>   -0.037</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tract_listings</th>    <td>    0.0004</td> <td>    0.001</td> <td>    0.446</td> <td> 0.656</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pr_filled</th>         <td>   -0.0971</td> <td>    0.084</td> <td>   -1.159</td> <td> 0.247</td> <td>   -0.261</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>baltimore</th>         <td>   -0.0085</td> <td>    0.030</td> <td>   -0.281</td> <td> 0.778</td> <td>   -0.068</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dallas</th>            <td>   -0.0336</td> <td>    0.029</td> <td>   -1.175</td> <td> 0.240</td> <td>   -0.090</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>los_angeles</th>       <td>   -0.0122</td> <td>    0.018</td> <td>   -0.685</td> <td> 0.493</td> <td>   -0.047</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sl</th>                <td>   -0.0390</td> <td>    0.044</td> <td>   -0.892</td> <td> 0.373</td> <td>   -0.125</td> <td>    0.047</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>21655.531</td> <th>  Durbin-Watson:     </th> <td>   1.977</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1032.828</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.004</td>   <th>  Prob(JB):          </th> <td>5.30e-225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 1.006</td>   <th>  Cond. No.          </th> <td>    269.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            guest_black   R-squared:                       0.002\n",
       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
       "Method:                 Least Squares   F-statistic:                    0.5590\n",
       "Date:                Tue, 18 Aug 2020   Prob (F-statistic):              0.923\n",
       "Time:                        11:58:45   Log-Likelihood:                -4520.5\n",
       "No. Observations:                6235   AIC:                             9077.\n",
       "Df Residuals:                    6217   BIC:                             9198.\n",
       "Df Model:                          17                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             0.5556      0.099      5.617      0.000       0.362       0.750\n",
       "host_gender_M        -0.0133      0.017     -0.801      0.423      -0.046       0.019\n",
       "host_race_black      -0.0011      0.026     -0.040      0.968      -0.052       0.050\n",
       "multiple_listings     0.0037      0.015      0.257      0.797      -0.025       0.032\n",
       "shared_property      -0.0148      0.022     -0.683      0.495      -0.057       0.028\n",
       "log_price             0.0014      0.015      0.092      0.927      -0.029       0.032\n",
       "host_gender_F        -0.0169      0.016     -1.087      0.277      -0.047       0.014\n",
       "ten_reviews           0.0304      0.015      2.044      0.041       0.001       0.060\n",
       "young                 0.0059      0.013      0.438      0.661      -0.020       0.032\n",
       "price_median         -0.0115      0.021     -0.548      0.584      -0.053       0.030\n",
       "black_proportion     -0.0029      0.039     -0.076      0.940      -0.079       0.073\n",
       "guest_male           -0.0119      0.013     -0.934      0.350      -0.037       0.013\n",
       "tract_listings        0.0004      0.001      0.446      0.656      -0.001       0.002\n",
       "pr_filled            -0.0971      0.084     -1.159      0.247      -0.261       0.067\n",
       "baltimore            -0.0085      0.030     -0.281      0.778      -0.068       0.051\n",
       "dallas               -0.0336      0.029     -1.175      0.240      -0.090       0.022\n",
       "los_angeles          -0.0122      0.018     -0.685      0.493      -0.047       0.023\n",
       "sl                   -0.0390      0.044     -0.892      0.373      -0.125       0.047\n",
       "==============================================================================\n",
       "Omnibus:                    21655.531   Durbin-Watson:                   1.977\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1032.828\n",
       "Skew:                           0.004   Prob(JB):                    5.30e-225\n",
       "Kurtosis:                       1.006   Cond. No.                         269.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'guest_black ~ '\n",
    "for control in used_controls:\n",
    "    \n",
    "    formula += str(control + ' + ')\n",
    "# exclude one arbitrarily chosen city (here dc), otherwise dummy variable trap/fallacy\n",
    "# this is not the case with host female/male as some could not be categorized\n",
    "formula = formula[:-8] \n",
    "\n",
    "model = smf.ols(formula, data=df)\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only ten_reviews is significant, mirroring previous findings. Prob F-Stat very high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joint table for visualization ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>coef1</th>\n",
       "      <th>p-value1</th>\n",
       "      <th>coef2</th>\n",
       "      <th>p-value2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>host_gender_M</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.896</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>host_race_black</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.972</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiple_listings</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shared_property</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.929</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log_price</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>host_gender_F</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten_reviews</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>young</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>price_median</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.772</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>black_proportion</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.919</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>guest_male</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tract_listings</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pr_filled</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.899</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>baltimore</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.906</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dallas</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.311</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>los_angeles</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.743</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sl</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.382</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>dc</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             variable  coef1 p-value1  coef2 p-value2\n",
       "0       host_gender_M  0.001    0.896 -0.013    0.423\n",
       "1     host_race_black -0.000    0.972 -0.001    0.968\n",
       "2   multiple_listings  0.009    0.451  0.004    0.797\n",
       "3     shared_property  0.001    0.929 -0.015    0.495\n",
       "4           log_price  0.005    0.792  0.001    0.927\n",
       "5       host_gender_F -0.009    0.439 -0.017    0.277\n",
       "6         ten_reviews  0.026    0.041  0.030    0.041\n",
       "7               young  0.003    0.799  0.006    0.661\n",
       "8        price_median -0.004    0.772 -0.011    0.584\n",
       "9    black_proportion -0.001    0.919 -0.003    0.940\n",
       "10         guest_male -0.010    0.408 -0.012    0.350\n",
       "11     tract_listings  0.045    0.848  0.000    0.656\n",
       "12          pr_filled -0.000    0.899 -0.097    0.247\n",
       "13          baltimore -0.001    0.906 -0.008    0.778\n",
       "14             dallas -0.006    0.311 -0.034    0.240\n",
       "15        los_angeles  0.004    0.743 -0.012    0.493\n",
       "16                 sl -0.003    0.382 -0.039    0.373\n",
       "17                 dc  0.007    0.505    NaN      NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['variable', 'coef1', 'p-value1', 'coef2', 'p-value2']\n",
    "randomization_df = pd.DataFrame(columns=columns)\n",
    "randomization_df['variable'] = list(used_control_models.keys())\n",
    "\n",
    "for index, row in randomization_df.iterrows():\n",
    "    var = row['variable']\n",
    "    randomization_df.loc[index,'coef1'] = used_control_models[var].params[1]\n",
    "    randomization_df.loc[index,'p-value1'] = used_control_models[var].pvalues[1]\n",
    "    try:\n",
    "        randomization_df.loc[index,'coef2'] = result.params[var]\n",
    "        randomization_df.loc[index,'p-value2'] = result.pvalues[var]\n",
    "    except:\n",
    "        randomization_df.loc[index,'coef2'] = np.nan\n",
    "        randomization_df.loc[index,'p-value2'] = np.nan\n",
    "\n",
    "pd.set_option('display.float_format', '{:0.3f}'.format)\n",
    "randomization_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fisher Exact Test ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For m=10000, 0.13 minutes were needed.\n",
      "Number of simulated test statistics larger or equal to original test statistic:\t 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class FisherExactTest:\n",
    "    '''Performs Fisher Exact Test Simulation\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        self.rng = np.random.default_rng()\n",
    "        pass\n",
    "    \n",
    "    def calc_teststat(self, treatment):\n",
    "        '''Calculate sum of outputs by group.\n",
    "\n",
    "        Given the (altered) treament array whether individual is in the treatment group or not\n",
    "        and given the original output array, calculate the sum of outputs for each group.\n",
    "\n",
    "        Arguments:\n",
    "            treatment(np.array): Array of the treatment variable\n",
    "\n",
    "        Returns:\n",
    "            Fisher Test Stat(int): Value of the Fisher Test Statistics given the initial output and\n",
    "                the (altered) treatment population.\n",
    "        '''\n",
    "        treatment_output  = sum(np.multiply(treatment, self.output))\n",
    "        notreatment_output = sum(np.multiply(np.logical_not(treatment), self.output))\n",
    "        return treatment_output/self.n_treatment - notreatment_output/self.n_notreatment    \n",
    "\n",
    "    def initial_arrays(self, treatment, output):\n",
    "        '''Sets up original treament and output array and calculates teststat.\n",
    "        \n",
    "        Arguments:\n",
    "            treatment(pd.Series): Column of the treatment variable\n",
    "            \n",
    "            output(pd.Series): Column of the output variable\n",
    "            \n",
    "        Returns:\n",
    "            initial_teststat(int): Value of the Fisher Test Statistics given the initial output and\n",
    "                the initial treatment population.\n",
    "        '''\n",
    "        self.initial_treatment = np.array(treatment)\n",
    "        self.n_treatment = treatment.value_counts()[True] # Done here as it doesn't change and fastens iteration\n",
    "        self.n_notreatment = treatment.value_counts()[False]\n",
    "        self.output = np.array(output)\n",
    "        \n",
    "        self.initial_teststat = self.calc_teststat(self.initial_treatment)\n",
    "        return self.initial_teststat\n",
    "    \n",
    "    def simulation(self, m=1000):\n",
    "        '''Iterates over simulated permutations and returns how many were larger than initial one. '''\n",
    "        count_le = 0 # Used for counting whether alternative test statistic is larger than orginal test statistic\n",
    "        \n",
    "        start_time = time.perf_counter()\n",
    "        for i in range(0, m):\n",
    "            alt_perm = self.rng.permutation(self.initial_treatment) # Creates random alternative permutation\n",
    "            alt_t = self.calc_teststat(alt_perm) # Fisher test for alternative permutation\n",
    "            if alt_t >= self.initial_teststat: \n",
    "                count_le += 1\n",
    "        total_time = time.perf_counter() - start_time\n",
    "        \n",
    "        print('For m={}, {:.2f} minutes were needed.'.format(m, total_time/60))\n",
    "        print('Number of simulated test statistics larger or equal to original test statistic:\\t', count_le)     \n",
    "        return count_le\n",
    "\n",
    "\n",
    "bw = df.guest_black.apply(lambda x: True if x==0 else False if x==1 else np.NaN) # True if guest is white\n",
    "df_fisher = pd.DataFrame({'white': bw, 'yes':df['yes']})\n",
    "df_fisher.dropna(inplace=True)\n",
    "\n",
    "FET = FisherExactTest()\n",
    "og_test = FET.initial_arrays(df_fisher.white, df_fisher.yes)\n",
    "FET.simulation(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional analysis - Discrimination against male ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replication of table 5 adding guest_male and changing interaction terms ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>yes I</th>   <th>yes II</th>   <th>yes III</th> <th>yes IIII</th> <th>yes IIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black</th>         <td>-0.08***</td> <td>-0.08***</td> <td>-0.08***</td> <td>-0.08***</td> <td>-0.08***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_male</th>            <td>-0.02</td>  <td>-0.05***</td>  <td>-0.04**</td>  <td>-0.03*</td>   <td>-0.04**</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_property</th>       <td>0.02</td>       <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.02)</td>      <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_guest_male</th>    <td>-0.06**</td>     <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.02)</td>      <td></td>         <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multiple_listings</th>       <td></td>      <td>0.09***</td>     <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>      <td>(0.02)</td>      <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multiple_male</th>           <td></td>       <td>0.01</td>       <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>      <td>(0.03)</td>      <td></td>         <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ten_reviews</th>             <td></td>         <td></td>      <td>0.14***</td>     <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>      <td>(0.02)</td>      <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ten_reviews_male</th>        <td></td>         <td></td>       <td>-0.00</td>      <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>      <td>(0.02)</td>      <td></td>         <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>young</th>                   <td></td>         <td></td>         <td></td>       <td>-0.01</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>      <td>(0.01)</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>young_male</th>              <td></td>         <td></td>         <td></td>      <td>-0.04*</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>      <td>(0.02)</td>      <td></td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>any_black</th>               <td></td>         <td></td>         <td></td>         <td></td>      <td>0.13***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>         <td></td>      <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>any_black_gm</th>            <td></td>         <td></td>         <td></td>         <td></td>       <td>-0.02</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>         <td></td>         <td></td>         <td></td>      <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>            <td>0.50***</td>  <td>0.48***</td>  <td>0.44***</td>  <td>0.52***</td>  <td>0.47***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>             <td>0.01</td>     <td>0.02</td>     <td>0.03</td>     <td>0.01</td>     <td>0.02</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td>0.01</td>     <td>0.02</td>     <td>0.03</td>     <td>0.01</td>     <td>0.02</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                     <td>6235</td>     <td>6235</td>     <td>6235</td>     <td>6235</td>     <td>6235</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2_adj</th>                <td>0.009</td>    <td>0.016</td>    <td>0.028</td>    <td>0.009</td>    <td>0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Implied coefficient</th>   <td>-0.08</td>    <td>-0.04</td>    <td>-0.04</td>    <td>-0.07</td>    <td>-0.06</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "=================================================================\n",
       "                     yes I    yes II  yes III  yes IIII yes IIIII\n",
       "-----------------------------------------------------------------\n",
       "guest_black         -0.08*** -0.08*** -0.08*** -0.08*** -0.08*** \n",
       "                    (0.02)   (0.02)   (0.02)   (0.02)   (0.02)   \n",
       "guest_male          -0.02    -0.05*** -0.04**  -0.03*   -0.04**  \n",
       "                    (0.02)   (0.02)   (0.02)   (0.02)   (0.02)   \n",
       "shared_property     0.02                                         \n",
       "                    (0.02)                                       \n",
       "shared_guest_male   -0.06**                                      \n",
       "                    (0.02)                                       \n",
       "multiple_listings            0.09***                             \n",
       "                             (0.02)                              \n",
       "multiple_male                0.01                                \n",
       "                             (0.03)                              \n",
       "ten_reviews                           0.14***                    \n",
       "                                      (0.02)                     \n",
       "ten_reviews_male                      -0.00                      \n",
       "                                      (0.02)                     \n",
       "young                                          -0.01             \n",
       "                                               (0.01)            \n",
       "young_male                                     -0.04*            \n",
       "                                               (0.02)            \n",
       "any_black                                               0.13***  \n",
       "                                                        (0.02)   \n",
       "any_black_gm                                            -0.02    \n",
       "                                                        (0.02)   \n",
       "Intercept           0.50***  0.48***  0.44***  0.52***  0.47***  \n",
       "                    (0.01)   (0.01)   (0.01)   (0.01)   (0.01)   \n",
       "R-squared           0.01     0.02     0.03     0.01     0.02     \n",
       "                    0.01     0.02     0.03     0.01     0.02     \n",
       "N                   6235     6235     6235     6235     6235     \n",
       "R2_adj              0.009    0.016    0.028    0.009    0.021    \n",
       "Implied coefficient -0.08    -0.04    -0.04    -0.07    -0.06    \n",
       "=================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['shared_guest_male'] = df['shared_property'] * df['guest_male']\n",
    "df['multiple_male'] = df['multiple_listings'] * df['guest_male']\n",
    "df['ten_reviews_male'] = df['ten_reviews'] * df['guest_male']\n",
    "df['young_male'] = df['young'] * df['guest_male']\n",
    "df['any_black_gm'] = df['any_black'] * df['guest_male']\n",
    "\n",
    "df_model = df[['yes','guest_black', 'guest_male', 'name_by_city', 'shared_property', 'shared_guest_male', \n",
    "               'multiple_listings', 'multiple_male', 'ten_reviews', 'ten_reviews_male',\n",
    "              'young', 'young_male', 'any_black', 'any_black_gm']].dropna()\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + shared_property + shared_guest_male', data=df_model)\n",
    "result1 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + multiple_listings + multiple_male', data=df_model)\n",
    "result2 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + ten_reviews + ten_reviews_male', data=df_model)\n",
    "result3 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + young + young_male', data=df_model)\n",
    "result4 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + any_black + any_black_gm', data=df_model)\n",
    "result5 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "tble5 = summary_col([result1, result2, result3, result4, result5], stars=True, float_format='%.2f', \n",
    "                   regressor_order=['guest_black', 'guest_male', 'shared_property', 'shared_guest_male', \n",
    "                        'multiple_listings', 'multiple_male', 'ten_reviews', 'ten_reviews_male',\n",
    "                        'young', 'young_male', 'any_male', 'any_black', 'any_black_gm'],  \n",
    "                   info_dict={'N':lambda x: \"{0:d}\".format(int(x.nobs)),'R2_adj':lambda x: \"{:.3f}\".format(x.rsquared_adj),\n",
    "                             'Implied coefficient':lambda x: \"{0:.2f}\".format(float(x.params[2]+x.params[4]))})\n",
    "tble5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the relationship between gender of guest and shared property ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>yes I</th>   <th>yes II</th>   <th>yes III</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black</th> <td>-0.08***</td> <td>-0.09***</td> <td>-0.07***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>             <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_male</th>  <td>-0.05***</td> <td>-0.08***</td>   <td>-0.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>             <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>0.51***</td>  <td>0.53***</td>  <td>0.50***</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>             <td>(0.01)</td>   <td>(0.02)</td>   <td>(0.01)</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>     <td>0.01</td>     <td>0.01</td>     <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>              <td>0.01</td>     <td>0.01</td>     <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>             <td>6235</td>     <td>2974</td>     <td>3261</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R²_adj</th>        <td>0.008</td>    <td>0.014</td>    <td>0.004</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "======================================\n",
       "             yes I    yes II  yes III \n",
       "--------------------------------------\n",
       "guest_black -0.08*** -0.09*** -0.07***\n",
       "            (0.02)   (0.02)   (0.02)  \n",
       "guest_male  -0.05*** -0.08*** -0.02   \n",
       "            (0.02)   (0.02)   (0.02)  \n",
       "Intercept   0.51***  0.53***  0.50*** \n",
       "            (0.01)   (0.02)   (0.01)  \n",
       "R-squared   0.01     0.01     0.00    \n",
       "            0.01     0.01     0.00    \n",
       "N           6235     2974     3261    \n",
       "R²_adj      0.008    0.014    0.004   \n",
       "======================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols('yes ~ guest_black + guest_male', data=df_model)\n",
    "result_all = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "df_model_male = df_model[df_model['shared_property'] == 1]\n",
    "model = smf.ols('yes ~ guest_black + guest_male', data=df_model_male)\n",
    "result_male = model.fit(cov_type='cluster', cov_kwds={'groups': df_model_male['name_by_city']})\n",
    "\n",
    "df_model_female = df_model[df_model['shared_property'] == 0]\n",
    "model = smf.ols('yes ~ guest_black + guest_male', data=df_model_female)\n",
    "result_female = model.fit(cov_type='cluster', cov_kwds={'groups': df_model_female['name_by_city']})\n",
    "\n",
    "\n",
    "tble = summary_col([result_all, result_male, result_female], stars=True, float_format='%.2f', regressor_order=['guest_black', 'guest_male', 'Intercept'], info_dict={'N':lambda x: \"{0:d}\".format(int(x.nobs)), 'R²_adj':lambda x: \"{0:.3f}\".format(float(x.rsquared_adj))})\n",
    "tble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the relationship between guest's gender and race ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>yes I</th>   <th>yes II</th>   <th>yes III</th> <th>yes IIII</th> <th>yes IIIII</th> <th>yes IIIIII</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black</th>             <td>-0.08***</td> <td>-0.09***</td> <td>-0.07***</td> <td>-0.08***</td> <td>-0.08***</td>   <td>-0.08***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>     <td>(0.03)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_male</th>              <td>-0.05***</td> <td>-0.08***</td>   <td>-0.02</td>  <td>-0.05***</td>  <td>-0.05**</td>     <td>-0.03</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>   <td>(0.02)</td>     <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black_male</th>            <td></td>         <td></td>         <td></td>         <td></td>       <td>0.01</td>       <td>0.02</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>         <td></td>         <td></td>         <td></td>      <td>(0.03)</td>     <td>(0.04)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_property</th>             <td></td>         <td></td>         <td></td>         <td></td>         <td></td>         <td>0.03</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>         <td></td>         <td></td>         <td></td>         <td></td>        <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_guest_male</th>           <td></td>         <td></td>         <td></td>         <td></td>         <td></td>        <td>-0.05*</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>         <td></td>         <td></td>         <td></td>         <td></td>        <td>(0.03)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shared_guest_black</th>          <td></td>         <td></td>         <td></td>         <td></td>         <td></td>         <td>-0.02</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>         <td></td>         <td></td>         <td></td>         <td></td>        <td>(0.03)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>guest_black_male_shared</th>     <td></td>         <td></td>         <td></td>         <td></td>         <td></td>         <td>-0.02</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td></td>         <td></td>         <td></td>         <td></td>         <td></td>        <td>(0.05)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.51***</td>  <td>0.53***</td>  <td>0.50***</td>  <td>0.51***</td>  <td>0.51***</td>    <td>0.50***</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>(0.01)</td>   <td>(0.02)</td>   <td>(0.01)</td>   <td>(0.01)</td>   <td>(0.01)</td>     <td>(0.02)</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R-squared</th>                 <td>0.01</td>     <td>0.01</td>     <td>0.00</td>     <td>0.01</td>     <td>0.01</td>       <td>0.01</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>0.01</td>     <td>0.01</td>     <td>0.00</td>     <td>0.01</td>     <td>0.01</td>       <td>0.01</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>N</th>                         <td>6235</td>     <td>2974</td>     <td>3261</td>     <td>6235</td>     <td>6235</td>       <td>6235</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R2_adj</th>                    <td>0.008</td>    <td>0.014</td>    <td>0.004</td>    <td>0.008</td>    <td>0.008</td>      <td>0.009</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "\n",
       "================================================================================\n",
       "                         yes I    yes II  yes III  yes IIII yes IIIII yes IIIIII\n",
       "--------------------------------------------------------------------------------\n",
       "guest_black             -0.08*** -0.09*** -0.07*** -0.08*** -0.08***  -0.08***  \n",
       "                        (0.02)   (0.02)   (0.02)   (0.02)   (0.02)    (0.03)    \n",
       "guest_male              -0.05*** -0.08*** -0.02    -0.05*** -0.05**   -0.03     \n",
       "                        (0.02)   (0.02)   (0.02)   (0.02)   (0.02)    (0.02)    \n",
       "guest_black_male                                            0.01      0.02      \n",
       "                                                            (0.03)    (0.04)    \n",
       "shared_property                                                       0.03      \n",
       "                                                                      (0.02)    \n",
       "shared_guest_male                                                     -0.05*    \n",
       "                                                                      (0.03)    \n",
       "shared_guest_black                                                    -0.02     \n",
       "                                                                      (0.03)    \n",
       "guest_black_male_shared                                               -0.02     \n",
       "                                                                      (0.05)    \n",
       "Intercept               0.51***  0.53***  0.50***  0.51***  0.51***   0.50***   \n",
       "                        (0.01)   (0.02)   (0.01)   (0.01)   (0.01)    (0.02)    \n",
       "R-squared               0.01     0.01     0.00     0.01     0.01      0.01      \n",
       "                        0.01     0.01     0.00     0.01     0.01      0.01      \n",
       "N                       6235     2974     3261     6235     6235      6235      \n",
       "R2_adj                  0.008    0.014    0.004    0.008    0.008     0.009     \n",
       "================================================================================\n",
       "Standard errors in parentheses.\n",
       "* p<.1, ** p<.05, ***p<.01\n",
       "\"\"\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['guest_black_male'] = df['guest_black'] * df['guest_male']\n",
    "df['guest_black_male_shared'] = df['guest_black'] * df['guest_male'] * df['shared_property']\n",
    "\n",
    "df_model = df[['yes','guest_black', 'guest_male', 'name_by_city', 'shared_property', 'shared_guest_male', \n",
    "               'guest_black_male', 'shared_guest_black', 'guest_black_male_shared']].dropna()\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male', data=df_model)\n",
    "result0 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + guest_black_male', data=df_model)\n",
    "result1 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "model = smf.ols('yes ~ guest_black + guest_male + guest_black_male + shared_property + shared_guest_male + shared_guest_black + guest_black_male_shared', data=df_model)\n",
    "result3 = model.fit(cov_type='cluster', cov_kwds={'groups': df_model['name_by_city']})\n",
    "\n",
    "tble6 = summary_col([result_all, result_male, result_female, result0, result1, result3], stars=True, float_format='%.2f', \n",
    "                   regressor_order=['guest_black', 'guest_male', 'guest_black_male', 'shared_property', 'shared_guest_male', 'shared_guest_black',\n",
    "                        'guest_black_male_shared'],  \n",
    "                   info_dict={'N':lambda x: \"{0:d}\".format(int(x.nobs)),'R2_adj':lambda x: \"{:.3f}\".format(x.rsquared_adj)})\n",
    "tble6"
   ]
  }
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