{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies, ran on Python 3.9.12\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from linearmodels.panel import PanelOLS\n",
    "\n",
    "import matplotlib.pyplot as plt # for improving our visualizations\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def desc(df):\n",
    "    return df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CI(SE):\n",
    "    CI = [1.96*x for x in SE]\n",
    "    return CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Process(df, colordict):\n",
    "    df['CI'] = 1.96*df['SE']\n",
    "    df[\"Color\"] = df['IncomeCat'].apply(lambda x: colordict[x])\n",
    "    df.sort_values('Coef', inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_swap_dict(d):\n",
    "    return {v: k for k, v in d.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure Globals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"220\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#0173b2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#029e73;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d55e00;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "['#0173b2', 'tab:purple', '#029e73', '#d55e00']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colordict1 = {'HIC': '#D81B60', 'LLMIC': '#004D40', 'UMIC': '#1E88E5'}\n",
    "colordict2 = {'China': '#1E88E5', 'HIC': '#D81B60', 'LLMIC': '#004D40', 'UMIC': '#1E88E5', 'India': '#004D40', 'United States of America': '#D81B60'}\n",
    "colordict3 = {'HIC': '#e54813', 'LLMIC': '#3987f9', 'UMIC': '#95fb51'}\n",
    "\n",
    "palette1 = get_swap_dict(colordict1)\n",
    "palette2 = get_swap_dict(colordict2)\n",
    "palette3 = get_swap_dict(colordict3)\n",
    "\n",
    "IncomeCats1 = ['HIC', 'LLMIC','UMIC']\n",
    "IncomeCats2 = ['China', 'HIC', 'India', 'LLMIC','UMIC', 'United States of America']\n",
    "\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "\n",
    "wd = 0.075\n",
    "offset = 0.05\n",
    "\n",
    "custom_style = {'axes.facecolor': 'white',\n",
    "                'grid.color': '0.15',\n",
    "                'grid.linestyle':'--',\n",
    "               'grid.alpha':'0.25',\n",
    "               'axes.spines.left': True,\n",
    "               }\n",
    "sns.set_style(\"darkgrid\", rc=custom_style)\n",
    "palette = sns.color_palette(\"colorblind\", 4).as_hex()\n",
    "palette[1] = 'tab:purple'\n",
    "palette"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading In and Subsetting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>journal_abbr_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>60</td>\n",
       "      <td>Jjm0Bj15KE</td>\n",
       "      <td>88478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manuscript_id_original_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>114069</td>\n",
       "      <td>rEv9BvKQcc</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_full_name_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>196220</td>\n",
       "      <td>GCx43JV3Wx</td>\n",
       "      <td>1721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inv_year_month_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>92</td>\n",
       "      <td>q95ODPi3Fs</td>\n",
       "      <td>20539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_year_month_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>83</td>\n",
       "      <td>fwpD6LXSq0</td>\n",
       "      <td>548958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCRlead</th>\n",
       "      <td>787055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119068</td>\n",
       "      <td>0.323869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anon_policy_available</th>\n",
       "      <td>787055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.242546</td>\n",
       "      <td>0.428623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anon_manu</th>\n",
       "      <td>787055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07661</td>\n",
       "      <td>0.265971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>submitted_review</th>\n",
       "      <td>787055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.302516</td>\n",
       "      <td>0.459348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_team_size_bin</th>\n",
       "      <td>787023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.684327</td>\n",
       "      <td>1.368504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                count  unique         top    freq      mean  \\\n",
       "journal_abbr_deid              787055      60  Jjm0Bj15KE   88478       NaN   \n",
       "manuscript_id_original_deid    787055  114069  rEv9BvKQcc      64       NaN   \n",
       "rev_full_name_deid             787055  196220  GCx43JV3Wx    1721       NaN   \n",
       "inv_year_month_deid            787055      92  q95ODPi3Fs   20539       NaN   \n",
       "rev_year_month_deid            787055      83  fwpD6LXSq0  548958       NaN   \n",
       "...                               ...     ...         ...     ...       ...   \n",
       "SCRlead                      787055.0     NaN         NaN     NaN  0.119068   \n",
       "anon_policy_available        787055.0     NaN         NaN     NaN  0.242546   \n",
       "anon_manu                    787055.0     NaN         NaN     NaN   0.07661   \n",
       "submitted_review             787055.0     NaN         NaN     NaN  0.302516   \n",
       "ln_team_size_bin             787023.0     NaN         NaN     NaN  1.684327   \n",
       "\n",
       "                                  std  min  25%  50%  75%  max  \n",
       "journal_abbr_deid                 NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "manuscript_id_original_deid       NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "rev_full_name_deid                NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "inv_year_month_deid               NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "rev_year_month_deid               NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...                               ...  ...  ...  ...  ...  ...  \n",
       "SCRlead                      0.323869  0.0  0.0  0.0  0.0  1.0  \n",
       "anon_policy_available        0.428623  0.0  0.0  0.0  0.0  1.0  \n",
       "anon_manu                    0.265971  0.0  0.0  0.0  0.0  1.0  \n",
       "submitted_review             0.459348  0.0  0.0  0.0  1.0  1.0  \n",
       "ln_team_size_bin             1.368504  0.0  1.0  1.0  3.0  4.0  \n",
       "\n",
       "[21 rows x 11 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the data\n",
    "invited_rev_df = pd.read_csv('./invited_revs_deid_df.csv', low_memory=False)\n",
    "\n",
    "desc(invited_rev_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map integer to unique inv_year_month_deid\n",
    "num_index_dict = {i: x for i, x in enumerate(invited_rev_df['inv_year_month_deid'].unique())}\n",
    "invited_rev_df['num_index'] = invited_rev_df['inv_year_month_deid'].map({v: k for k, v in num_index_dict.items()})\n",
    "\n",
    "# Set index\n",
    "invited_rev_df = invited_rev_df.set_index(['manuscript_id_original_deid', 'num_index'],\n",
    "                        drop=False)\n",
    "           \n",
    "# Subset only reviewers who submitted a review\n",
    "submitted_rev_df = invited_rev_df.loc[invited_rev_df['submitted_review'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Corresponding Author income Dataframes\n",
    "invited_rev_income_df=invited_rev_df.loc[invited_rev_df['income_cat'].notna()]\n",
    "\n",
    "# Reset indices for Journal FE      \n",
    "invited_rev_income_df = invited_rev_income_df.set_index(['journal_abbr_deid', 'num_index'], drop=False)\n",
    "\n",
    "invited_rev_income_df = invited_rev_income_df.loc[invited_rev_income_df['inv_year_month_deid'].notna()]\n",
    "\n",
    "# Prep team_size for control                 \n",
    "invited_rev_income_df = invited_rev_income_df.loc[invited_rev_income_df['ln_team_size_bin'].notna()]\n",
    "\n",
    "### Corresponding Author income Dataframes\n",
    "submitted_rev_income_df = invited_rev_income_df.loc[invited_rev_income_df['submitted_review'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Lead Author income Dataframes\n",
    "invited_rev_leadincome_df=invited_rev_df.loc[invited_rev_df['lead_income_cat'].notna()]\n",
    "\n",
    "# Reset indices for Journal FE      \n",
    "invited_rev_leadincome_df = invited_rev_leadincome_df.set_index(['journal_abbr_deid', 'num_index'], drop=False)\n",
    "\n",
    "invited_rev_leadincome_df = invited_rev_leadincome_df.loc[invited_rev_leadincome_df['inv_year_month_deid'].notna()]\n",
    "\n",
    "# Prep team_size for control                 \n",
    "invited_rev_leadincome_df = invited_rev_leadincome_df.loc[invited_rev_leadincome_df['ln_team_size_bin'].notna()]\n",
    "\n",
    "### Lead Author income Dataframes\n",
    "submitted_rev_leadincome_df= invited_rev_leadincome_df.loc[invited_rev_leadincome_df['submitted_review'] == 1]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main Text\n",
    "- Fig. 1 and Homophily figures from R estimates\n",
    "- Fig. 2 and Access figures from estimates in Python\n",
    "- Back of the Envelope calculation in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig. 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax1, ax2] = plt.subplots(nrows=1,\n",
    "                        ncols=2,\n",
    "                        sharex=True,\n",
    "                        figsize=(14, 5))\n",
    "y_pos1=[1, 2, 3, 4]\n",
    "y_pos2=[1, 2, 3]\n",
    "\n",
    "capsize=0\n",
    "capthick=2\n",
    "elinewidth=3\n",
    "markersize=25\n",
    "alpha=1\n",
    "    \n",
    "ax1.axvline(x = 0, color = 'black', linestyle = '--', lw=2, alpha=0.5)\n",
    "ax1.text(-0.0035,2.05,'Non-SCR baseline',rotation=90, fontsize=12, alpha=0.75)\n",
    "\n",
    "ax1.errorbar(y=.75, x=0.0454, xerr=1.96*0.0052,\n",
    "             linestyle='None', lw=2,marker='.', markersize=markersize, elinewidth=elinewidth, capsize=capsize, capthick=capthick , color = 'black', alpha=alpha)\n",
    "    \n",
    "\n",
    "ax1.errorbar(y=1.5, x=0.037314661, xerr=1.96*0.015364554,\n",
    "             linestyle='None', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=capthick , color=palette[0], alpha=alpha)\n",
    "ax1.errorbar(y=2.15, x=0.06169491, xerr=1.96*0.007939438,\n",
    "             linestyle='None', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=capthick , color = palette[1], alpha=alpha)\n",
    "ax1.errorbar(y=2.75, x=0.029427, xerr=1.96*0.008055,\n",
    "             linestyle='None', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=capthick , color = palette[2], alpha=alpha)\n",
    "    \n",
    "ax1.invert_yaxis()\n",
    "\n",
    "# make one of the y-ticks bold\n",
    "ax1.get_yticklabels()[0].set_weight('bold')\n",
    "    \n",
    "ax1.text(-0.065, 1.2, 'A', transform=ax1.transAxes,\n",
    "      fontsize=35, fontweight='bold', va='top', ha='right')\n",
    "##############################\n",
    "y_pos3=[.5, .75, 1.5, 2.15, 2.75, 3]\n",
    "\n",
    "ax1.set_yticks(y_pos3, ['','Overall', 'Low- & Lower-Middle\\nIncome Countries',\n",
    "                        'Upper-Middle\\nIncome Countries','High\\nIncome Countries', ''],\n",
    "           fontsize=16)\n",
    "\n",
    "# make first ytick bold\n",
    "ax1.get_yticklabels()[1].set_weight('bold')\n",
    "   \n",
    "    \n",
    "ax1.set_title('Country homophily\\n', fontsize=20)  \n",
    "ax1.set_xlabel('Increase in Pr(Positive Review)\\n relative to non-SCR', fontsize=16)\n",
    "\n",
    "\n",
    "ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True, labelsize=14)\n",
    "ax1.tick_params(axis='y', which='both', left=False, right=False)\n",
    "ax1.spines[['bottom','right', 'top']].set_visible(False)\n",
    "ax1.spines['left'].set_visible(True)\n",
    "ax1.spines['left'].set_color('black')\n",
    "\n",
    "# set left spine to bold\n",
    "ax1.spines['left'].set_linewidth(2)\n",
    "\n",
    "# change alpha of grid\n",
    "ax1.grid(alpha=0.2, linestyle='--')\n",
    "ax1.yaxis.grid(False)\n",
    "ax1.set_xlim([-0.04,0.09])\n",
    "\n",
    "#####################\n",
    "\n",
    "y_pos=[.5,1.5, 2.15, 3]\n",
    "colors = ['#678FCF', '#67C3CF']\n",
    "alpha=1\n",
    "    \n",
    "ax2.axvline(x = 0, color = 'black', linestyle = '--', lw=2, alpha=0.5)\n",
    "ax2.text(-0.0035,2.05,'Non-SCR baseline',rotation=90, fontsize=12, alpha=0.75)\n",
    "\n",
    "ax2.errorbar(y=1.5,x=0.04957404,xerr=1.96*0.00760005,\n",
    "             linestyle='None', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=2, color = 'tab:red', alpha=alpha)\n",
    "ax2.errorbar(y=2.15,x=0.006423258,xerr=1.96*0.016299202,\n",
    "             linestyle='--', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=2, color = '#e54813', alpha=alpha)\n",
    "\n",
    "ax2.set_title('Effect of anonymization\\non country homophily', fontsize=20)  \n",
    "   \n",
    "ax2.set_yticks(y_pos, ['','Visible\\nAuthor ID', 'Hidden\\nAuthor ID', ''],\n",
    "       rotation=0, fontsize=16)\n",
    "ax2.tick_params(axis='x', which='major', labelsize=14)\n",
    "ax2.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True)\n",
    "ax2.tick_params(axis='y', which='both', left=False, right=False)\n",
    "ax2.spines[['bottom', 'right', 'top']].set_visible(False)\n",
    "ax2.invert_yaxis()\n",
    "ax2.spines['left'].set_visible(True)\n",
    "ax2.spines['left'].set_color('black')\n",
    "\n",
    "# set left spine to bold\n",
    "ax2.spines['left'].set_linewidth(2)\n",
    "# change alpha of grid\n",
    "ax2.grid(alpha=0.2, linestyle='--')\n",
    "ax2.yaxis.grid(False)\n",
    "\n",
    "ax2.set_xlabel('Increase in Pr(Positive Review)\\n relative to non-SCR', fontsize=16)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=.05)\n",
    "\n",
    "ax2.text(-0.065, 1.2, 'B', transform=ax2.transAxes,\n",
    "      fontsize=35, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig. 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84\n"
     ]
    }
   ],
   "source": [
    "# Identify countries with less than 100 unique manuscripts, remove from country-level analysis\n",
    "num_subs_by_country = submitted_rev_income_df.groupby('auth_country_deid')['manuscript_id_original_deid'].nunique().reset_index()\n",
    "lessthan100subs = num_subs_by_country[num_subs_by_country['manuscript_id_original_deid'] < 100]['auth_country_deid'].tolist()\n",
    "print(len(lessthan100subs))\n",
    "\n",
    "countrylevel_df = submitted_rev_income_df[~(submitted_rev_income_df['auth_country_deid'].isin(lessthan100subs))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jz/jnl3z7fd57s2zqk65_41cntm0000gr/T/ipykernel_30254/799445903.py:2: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  countrylevel_df.loc[countrylevel_df['income_cat'] == 'HIC', 'color'] = '#029e73'\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>journal_abbr_deid</th>\n",
       "      <th>manuscript_id_original_deid</th>\n",
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       "      <th>inv_year_month_deid</th>\n",
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       "      <th>Positivity</th>\n",
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      "text/plain": [
       "                            journal_abbr_deid manuscript_id_original_deid  \\\n",
       "journal_abbr_deid num_index                                                 \n",
       "2uvSmB4FfU        0                2uvSmB4FfU                  vzogPGSPaV   \n",
       "                  1                2uvSmB4FfU                  vzogPGSPaV   \n",
       "                  1                2uvSmB4FfU                  IVFCnJ8w7n   \n",
       "                  1                2uvSmB4FfU                  IVFCnJ8w7n   \n",
       "                  1                2uvSmB4FfU                  IVFCnJ8w7n   \n",
       "\n",
       "                            rev_full_name_deid inv_year_month_deid  \\\n",
       "journal_abbr_deid num_index                                          \n",
       "2uvSmB4FfU        0                 2qpQvpXAB1          4u7sPd9GWT   \n",
       "                  1                 WtUGimNjDh          LSGwgiMx6F   \n",
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       "                  1                 wdx3Oi9Ruw          LSGwgiMx6F   \n",
       "                  1                 3qGSSDnTXd          LSGwgiMx6F   \n",
       "\n",
       "                            rev_year_month_deid  Positivity auth_country_deid  \\\n",
       "journal_abbr_deid num_index                                                     \n",
       "2uvSmB4FfU        0                  gNZ963hbxy         1.0        wqRmBXWcDL   \n",
       "                  1                  zrFbHrXzJI         1.0        wqRmBXWcDL   \n",
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       "                  1                  kU0bEfWdb1         0.0        XEjFmKl0yz   \n",
       "                  1                  kU0bEfWdb1         0.0        XEjFmKl0yz   \n",
       "\n",
       "                            rev_country_deid lead_country_deid  \\\n",
       "journal_abbr_deid num_index                                      \n",
       "2uvSmB4FfU        0               ShuO2wZfuO        wqRmBXWcDL   \n",
       "                  1               drl7SrdxEA        wqRmBXWcDL   \n",
       "                  1               wqRmBXWcDL        XEjFmKl0yz   \n",
       "                  1               7R6g4GlTIC        XEjFmKl0yz   \n",
       "                  1               8pK0NResQ4        XEjFmKl0yz   \n",
       "\n",
       "                            lead_income_cat income_cat income_cat2  national  \\\n",
       "journal_abbr_deid num_index                                                    \n",
       "2uvSmB4FfU        0                    UMIC       UMIC       China       1.0   \n",
       "                  1                    UMIC       UMIC       China       1.0   \n",
       "                  1                     HIC        HIC         HIC       1.0   \n",
       "                  1                     HIC        HIC         HIC       1.0   \n",
       "                  1                     HIC        HIC         HIC       1.0   \n",
       "\n",
       "                             regional  final_decision_binary  SCR  SCRlead  \\\n",
       "journal_abbr_deid num_index                                                  \n",
       "2uvSmB4FfU        0               1.0                    1.0  0.0      0.0   \n",
       "                  1               1.0                    1.0  0.0      0.0   \n",
       "                  1               1.0                    0.0  0.0      0.0   \n",
       "                  1               1.0                    0.0  0.0      0.0   \n",
       "                  1               1.0                    0.0  0.0      0.0   \n",
       "\n",
       "                             anon_policy_available  anon_manu  \\\n",
       "journal_abbr_deid num_index                                     \n",
       "2uvSmB4FfU        0                              0        0.0   \n",
       "                  1                              0        0.0   \n",
       "                  1                              0        0.0   \n",
       "                  1                              0        0.0   \n",
       "                  1                              0        0.0   \n",
       "\n",
       "                             submitted_review  ln_team_size_bin  num_index  \\\n",
       "journal_abbr_deid num_index                                                  \n",
       "2uvSmB4FfU        0                       1.0               3.0          0   \n",
       "                  1                       1.0               3.0          1   \n",
       "                  1                       1.0               0.0          1   \n",
       "                  1                       1.0               0.0          1   \n",
       "                  1                       1.0               0.0          1   \n",
       "\n",
       "                                  color  \n",
       "journal_abbr_deid num_index              \n",
       "2uvSmB4FfU        0          tab:purple  \n",
       "                  1          tab:purple  \n",
       "                  1             #029e73  \n",
       "                  1             #029e73  \n",
       "                  1             #029e73  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map color to income category\n",
    "countrylevel_df.loc[countrylevel_df['income_cat'] == 'HIC', 'color'] = '#029e73'\n",
    "countrylevel_df.loc[countrylevel_df['income_cat'] == 'UMIC', 'color'] = 'tab:purple'\n",
    "countrylevel_df.loc[countrylevel_df['income_cat'] == 'LLMIC', 'color'] = '#0173b2'\n",
    "\n",
    "# Create dictionary where auth_country_deid is the key and color is the value\n",
    "country_color_dict = dict(zip(countrylevel_df['auth_country_deid'], countrylevel_df['color']))\n",
    "countrylevel_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "pSCR = PanelOLS.from_formula('SCR ~ auth_country_deid + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=countrylevel_df).fit(cov_type='clustered', cluster_entity = True)\n",
    "\n",
    "SCRheatmap_html = pSCR.summary.tables[1].as_html()\n",
    "SCRheatmap_df = pd.read_html(SCRheatmap_html, header=0, index_col=0)[0]\n",
    "SCRheatmap_df.reset_index(inplace=True)\n",
    "SCRheatmap_df.drop([0,63], inplace=True) # drop estimate of anon_manu and team_size\n",
    "SCRheatmap_df.reset_index(inplace=True)\n",
    "SCRheatmap_df.drop(columns=('level_0'), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Std. Err.</th>\n",
       "      <th>T-stat</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Lower CI</th>\n",
       "      <th>Upper CI</th>\n",
       "      <th>SCRAccess</th>\n",
       "      <th>auth_country_deid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>auth_country_deid[T.01RUywaEYk]</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>1.2683</td>\n",
       "      <td>0.2047</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>Not Significant</td>\n",
       "      <td>01RUywaEYk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>auth_country_deid[T.118DgncWzO]</td>\n",
       "      <td>0.0543</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>4.8404</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0764</td>\n",
       "      <td>0.0543</td>\n",
       "      <td>118DgncWzO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>auth_country_deid[T.1IsCikyr0z]</td>\n",
       "      <td>0.0304</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>3.4299</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0478</td>\n",
       "      <td>0.0304</td>\n",
       "      <td>1IsCikyr0z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>auth_country_deid[T.1VC2OqFzuM]</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.5535</td>\n",
       "      <td>0.5799</td>\n",
       "      <td>-0.0144</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>Not Significant</td>\n",
       "      <td>1VC2OqFzuM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>auth_country_deid[T.1qKy8VtrJA]</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>1.0443</td>\n",
       "      <td>0.2963</td>\n",
       "      <td>-0.0065</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>Not Significant</td>\n",
       "      <td>1qKy8VtrJA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>auth_country_deid[T.tMmqDD061N]</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>9.2785</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.1644</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>tMmqDD061N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>auth_country_deid[T.tu6LLpKwYF]</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.9089</td>\n",
       "      <td>0.3634</td>\n",
       "      <td>-0.0104</td>\n",
       "      <td>0.0285</td>\n",
       "      <td>Not Significant</td>\n",
       "      <td>tu6LLpKwYF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>auth_country_deid[T.wqRmBXWcDL]</td>\n",
       "      <td>0.2649</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>23.9070</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.2866</td>\n",
       "      <td>0.2649</td>\n",
       "      <td>wqRmBXWcDL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>auth_country_deid[T.y1owGpzrXe]</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>0.4013</td>\n",
       "      <td>0.6882</td>\n",
       "      <td>-0.0121</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>Not Significant</td>\n",
       "      <td>y1owGpzrXe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>auth_country_deid[T.z6kyUqGDMn]</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>2.5988</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>z6kyUqGDMn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              index  Parameter  Std. Err.   T-stat  P-value  \\\n",
       "0   auth_country_deid[T.01RUywaEYk]     0.0077     0.0061   1.2683   0.2047   \n",
       "1   auth_country_deid[T.118DgncWzO]     0.0543     0.0112   4.8404   0.0000   \n",
       "2   auth_country_deid[T.1IsCikyr0z]     0.0304     0.0089   3.4299   0.0006   \n",
       "3   auth_country_deid[T.1VC2OqFzuM]     0.0057     0.0103   0.5535   0.5799   \n",
       "4   auth_country_deid[T.1qKy8VtrJA]     0.0074     0.0071   1.0443   0.2963   \n",
       "..                              ...        ...        ...      ...      ...   \n",
       "57  auth_country_deid[T.tMmqDD061N]     0.1357     0.0146   9.2785   0.0000   \n",
       "58  auth_country_deid[T.tu6LLpKwYF]     0.0090     0.0099   0.9089   0.3634   \n",
       "59  auth_country_deid[T.wqRmBXWcDL]     0.2649     0.0111  23.9070   0.0000   \n",
       "60  auth_country_deid[T.y1owGpzrXe]     0.0031     0.0078   0.4013   0.6882   \n",
       "61  auth_country_deid[T.z6kyUqGDMn]     0.0206     0.0079   2.5988   0.0094   \n",
       "\n",
       "    Lower CI  Upper CI        SCRAccess auth_country_deid  \n",
       "0    -0.0042    0.0196  Not Significant        01RUywaEYk  \n",
       "1     0.0323    0.0764           0.0543        118DgncWzO  \n",
       "2     0.0130    0.0478           0.0304        1IsCikyr0z  \n",
       "3    -0.0144    0.0258  Not Significant        1VC2OqFzuM  \n",
       "4    -0.0065    0.0214  Not Significant        1qKy8VtrJA  \n",
       "..       ...       ...              ...               ...  \n",
       "57    0.1070    0.1644           0.1357        tMmqDD061N  \n",
       "58   -0.0104    0.0285  Not Significant        tu6LLpKwYF  \n",
       "59    0.2431    0.2866           0.2649        wqRmBXWcDL  \n",
       "60   -0.0121    0.0184  Not Significant        y1owGpzrXe  \n",
       "61    0.0051    0.0362           0.0206        z6kyUqGDMn  \n",
       "\n",
       "[62 rows x 9 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get only significant estimates, otherwise put crossbars or smth\n",
    "SCRheatmap_df.loc[SCRheatmap_df['P-value'] < .05, 'SCRAccess'] = SCRheatmap_df['Parameter']\n",
    "SCRheatmap_df.loc[SCRheatmap_df['P-value'] >= .05, 'SCRAccess'] = 'Not Significant'\n",
    "\n",
    "# Get country names\n",
    "SCRheatmap_df['auth_country_deid'] = SCRheatmap_df['index'].str.split(pat = 'T.', regex=False)\n",
    "SCRheatmap_df['auth_country_deid'] = SCRheatmap_df['auth_country_deid'].apply(lambda lst: lst[-1])\n",
    "SCRheatmap_df['auth_country_deid'] = SCRheatmap_df['auth_country_deid'].str.replace(']', '', regex = False)\n",
    "\n",
    "SCRheatmap_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   auth_country_deid  num_unique_reviewers\n",
      "0         01RUywaEYk                   332\n",
      "1         118DgncWzO                  2449\n",
      "2         1IsCikyr0z                   258\n",
      "3         1VC2OqFzuM                   111\n",
      "4         1hNcbqbv4L                    10\n",
      "5         1qKy8VtrJA                   131\n",
      "6         21YOFRPI50                     2\n",
      "7         2l7aRi23dv                    22\n",
      "8         3nowERn9qZ                   158\n",
      "9         3qkBnAKMyN                     2\n",
      "10        4Kk2eQ7L0o                     2\n",
      "11        4Vh0LHpa3t                   379\n",
      "12        4cYC339BrY                    12\n",
      "13        4j79ul8fHR                   249\n",
      "14        4os6R6Jkav                   955\n",
      "15        4xYGrPJ9dw                    54\n",
      "16        5EyxET8BbW                  1649\n",
      "17        5MveUmKRG8                    45\n",
      "18        5kNN6T4gjA                    12\n",
      "19        6sM1RtEoVK                   471\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auth_country_deid</th>\n",
       "      <th>num_unique_invited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01RUywaEYk</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118DgncWzO</td>\n",
       "      <td>8418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1IsCikyr0z</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1VC2OqFzuM</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1hNcbqbv4L</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>ywwuO5vemq</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>yxhZgOoOr4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>z6kyUqGDMn</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>zFpYLaY6bP</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>zdT1m3IStM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    auth_country_deid  num_unique_invited\n",
       "0          01RUywaEYk                 808\n",
       "1          118DgncWzO                8418\n",
       "2          1IsCikyr0z                 603\n",
       "3          1VC2OqFzuM                 258\n",
       "4          1hNcbqbv4L                  22\n",
       "..                ...                 ...\n",
       "156        ywwuO5vemq                  19\n",
       "157        yxhZgOoOr4                   2\n",
       "158        z6kyUqGDMn                 436\n",
       "159        zFpYLaY6bP                   5\n",
       "160        zdT1m3IStM                   1\n",
       "\n",
       "[161 rows x 2 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Country aggregates\n",
    "numrevsbycountry = submitted_rev_df.groupby('rev_country_deid')['rev_full_name_deid'].nunique().reset_index()\n",
    "numrevsbycountry.rename({'rev_country_deid':'auth_country_deid', 'rev_full_name_deid':'num_unique_reviewers'}, axis=1,inplace=True) # renaming to auth country for uniformity\n",
    "print(numrevsbycountry.head(20))\n",
    "\n",
    "numinvsbycountry = invited_rev_df.groupby('rev_country_deid')['rev_full_name_deid'].nunique().reset_index()\n",
    "numinvsbycountry.rename({'rev_country_deid':'auth_country_deid', 'rev_full_name_deid':'num_unique_invited'}, axis=1,inplace=True) # renaming to auth country for uniformity\n",
    "numinvsbycountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge all country-level data\n",
    "geo_df = SCRheatmap_df[['auth_country_deid','SCRAccess']]\n",
    "geo_df = geo_df.merge(numrevsbycountry, how='left', on='auth_country_deid')\n",
    "geo_df = geo_df.merge(numinvsbycountry, how='left', on='auth_country_deid')\n",
    "\n",
    "# Use country_color_dict to map color to auth_country_deid\n",
    "geo_df['color'] = geo_df['auth_country_deid'].map(country_color_dict)\n",
    "geo_df.head()\n",
    "\n",
    "# Set not sig to 0, since not sig diff from 0\n",
    "geo_df = geo_df.replace('Not Significant', 0, regex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auth_country_deid</th>\n",
       "      <th>SCRAccess</th>\n",
       "      <th>num_unique_reviewers</th>\n",
       "      <th>num_unique_invited</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Tuc4GrgtL5</td>\n",
       "      <td>0.3016</td>\n",
       "      <td>14311</td>\n",
       "      <td>42100</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>wqRmBXWcDL</td>\n",
       "      <td>0.2649</td>\n",
       "      <td>12499</td>\n",
       "      <td>27556</td>\n",
       "      <td>tab:purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>tMmqDD061N</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>5195</td>\n",
       "      <td>10863</td>\n",
       "      <td>#0173b2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   auth_country_deid  SCRAccess  num_unique_reviewers  num_unique_invited  \\\n",
       "37        Tuc4GrgtL5     0.3016                 14311               42100   \n",
       "59        wqRmBXWcDL     0.2649                 12499               27556   \n",
       "57        tMmqDD061N     0.1357                  5195               10863   \n",
       "\n",
       "         color  \n",
       "37     #029e73  \n",
       "59  tab:purple  \n",
       "57     #0173b2  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top three countries by number of unique reviewers and store number of unique reviewers\n",
    "top3 = geo_df.nlargest(3, 'num_unique_reviewers')\n",
    "top3 # These are USA, China, and India, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>IncomeCat</th>\n",
       "      <th>Coef</th>\n",
       "      <th>SE</th>\n",
       "      <th>CI</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>income_cat2[T.LLMIC]</td>\n",
       "      <td>LLMIC</td>\n",
       "      <td>0.027014</td>\n",
       "      <td>0.005898</td>\n",
       "      <td>0.011560</td>\n",
       "      <td>#0173b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>income_cat2[T.UMIC]</td>\n",
       "      <td>UMIC</td>\n",
       "      <td>0.032668</td>\n",
       "      <td>0.006638</td>\n",
       "      <td>0.013010</td>\n",
       "      <td>tab:purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>income_cat2[T.HIC]</td>\n",
       "      <td>HIC</td>\n",
       "      <td>0.050192</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.014229</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>income_cat2[T.India]</td>\n",
       "      <td>India</td>\n",
       "      <td>0.135914</td>\n",
       "      <td>0.015167</td>\n",
       "      <td>0.029727</td>\n",
       "      <td>#0173b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>income_cat2[T.China]</td>\n",
       "      <td>China</td>\n",
       "      <td>0.264301</td>\n",
       "      <td>0.011220</td>\n",
       "      <td>0.021991</td>\n",
       "      <td>tab:purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income_cat2[T.United States of America]</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>0.300562</td>\n",
       "      <td>0.026401</td>\n",
       "      <td>0.051745</td>\n",
       "      <td>#029e73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     index                 IncomeCat  \\\n",
       "0                     income_cat2[T.LLMIC]                     LLMIC   \n",
       "1                      income_cat2[T.UMIC]                      UMIC   \n",
       "2                       income_cat2[T.HIC]                       HIC   \n",
       "3                     income_cat2[T.India]                     India   \n",
       "4                     income_cat2[T.China]                     China   \n",
       "5  income_cat2[T.United States of America]  United States of America   \n",
       "\n",
       "       Coef        SE        CI       Color  \n",
       "0  0.027014  0.005898  0.011560     #0173b2  \n",
       "1  0.032668  0.006638  0.013010  tab:purple  \n",
       "2  0.050192  0.007260  0.014229     #029e73  \n",
       "3  0.135914  0.015167  0.029727     #0173b2  \n",
       "4  0.264301  0.011220  0.021991  tab:purple  \n",
       "5  0.300562  0.026401  0.051745     #029e73  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### View regression results for columns in Table S9\n",
    "\n",
    "colordict4 = {'China': palette[1], 'HIC': palette[2], 'LLMIC': palette[0], 'UMIC': palette[1], 'India': palette[0], 'United States of America': palette[2]}\n",
    "colordict5 = {'HIC': palette[2], 'LLMIC': palette[0], 'UMIC': palette[1]}\n",
    "\n",
    "reg1 = PanelOLS.from_formula('SCR ~ income_cat + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=invited_rev_income_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg1.params.index if p.startswith('income_cat[T.')]\n",
    "df1 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg1.params[paramscell], \n",
    "                    'SE':reg1.std_errors[paramscell]})\n",
    "Process(df1, colordict5)\n",
    "\n",
    "reg3 = PanelOLS.from_formula('SCR ~ income_cat2 + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=invited_rev_income_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg3.params.index if p.startswith('income_cat2[T.')]\n",
    "df3 = pd.DataFrame({'IncomeCat': IncomeCats2, 'Coef': reg3.params[paramscell], \n",
    "                    'SE':reg3.std_errors[paramscell]})\n",
    "Process(df3, colordict4)\n",
    "\n",
    "reg2 = PanelOLS.from_formula('SCR ~ income_cat + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=submitted_rev_income_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg2.params.index if p.startswith('income_cat[T.')]\n",
    "df2 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg2.params[paramscell], \n",
    "                    'SE':reg2.std_errors[paramscell]})\n",
    "Process(df2, colordict5)\n",
    "\n",
    "reg4 = PanelOLS.from_formula('SCR ~ income_cat2 + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=submitted_rev_income_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg4.params.index if p.startswith('income_cat2[T.')]\n",
    "df4 = pd.DataFrame({'IncomeCat': IncomeCats2, 'Coef': reg4.params[paramscell], \n",
    "                    'SE':reg4.std_errors[paramscell]})\n",
    "Process(df4, colordict4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1670x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,  [ax1, ax2] = plt.subplots(nrows=1,\n",
    "                                    ncols=2,\n",
    "                                    figsize=(16.7,8))\n",
    "\n",
    "ax1.scatter(geo_df['num_unique_reviewers'], geo_df['SCRAccess'], c=geo_df['color'], alpha=.9)\n",
    "ax1.set_title('SCRs and reviewer pool composition\\n',fontsize=26)\n",
    "\n",
    "ax1.set_xlabel('Num. unique reviewers\\nin reviewer pool', fontsize=22)\n",
    "ax1.set_ylabel('Pr(Reviewer\\nfrom author country)', fontsize=22)\n",
    "ax1.tick_params(axis='x', labelrotation=0, labelsize=16)\n",
    "ax1.tick_params(axis='y', labelrotation=0, labelsize=18)\n",
    "ax1.xaxis.grid(False)\n",
    "ax1.yaxis.grid(False)\n",
    "ax1.spines[['top','bottom','right','left']].set_visible(True)\n",
    "ax1.spines[['top','bottom','right','left']].set_color('black')\n",
    "ax1.spines[['top','bottom','right','left']].set_linewidth(2)\n",
    "\n",
    "# Label top 3 points \"USA\", \"China\", \"India\"\n",
    "for i, txt in enumerate(geo_df['auth_country_deid']):\n",
    "    if txt == 'Tuc4GrgtL5':\n",
    "        ax1.annotate('USA', xy = (geo_df['num_unique_reviewers'].iloc[i] , geo_df['SCRAccess'].iloc[i]), \n",
    "                    xytext = (geo_df['num_unique_reviewers'].iloc[i]-1700, geo_df['SCRAccess'].iloc[i]-.001), fontsize=16)\n",
    "    if txt == 'wqRmBXWcDL':\n",
    "        ax1.annotate('China', xy = (geo_df['num_unique_reviewers'].iloc[i] , geo_df['SCRAccess'].iloc[i]), \n",
    "                    xytext = (geo_df['num_unique_reviewers'].iloc[i]-2000, geo_df['SCRAccess'].iloc[i]-.001), fontsize=16)\n",
    "    if txt == 'tMmqDD061N':\n",
    "        ax1.annotate('India', xy = (geo_df['num_unique_reviewers'].iloc[i] , geo_df['SCRAccess'].iloc[i]), \n",
    "                    xytext = (geo_df['num_unique_reviewers'].iloc[i]-1700, geo_df['SCRAccess'].iloc[i]-.001), fontsize=16)\n",
    "\n",
    "# Add legend for income categories\n",
    "ax1.scatter([], [], c='#029e73', label='HIC')\n",
    "ax1.scatter([], [], c='tab:purple', label='UMIC')\n",
    "ax1.scatter([], [], c='#0173b2', label='LLMIC')\n",
    "ax1.legend(loc='lower right', fontsize=16, title='Income Group', title_fontsize='16')\n",
    "\n",
    "ax1.text(-0.065, 1.2, 'A', transform=ax1.transAxes,\n",
    "      fontsize=35, fontweight='bold', va='top', ha='right')\n",
    "    \n",
    "# Light grid\n",
    "ax1.grid(True, which='major', axis='both', linestyle='--', linewidth=1, color='gray')\n",
    "\n",
    "##############\n",
    "ypos = [0, .3, .7, 1.3, 1.7 , 2.3, 2.7, 3]\n",
    "\n",
    "\n",
    "ax2.barh(y=.3, width=df4['Coef'][0], xerr=df4['CI'][0], color=df4['Color'][0], height=.3)\n",
    "ax2.barh(y=.7, width=df4['Coef'][3], xerr=df4['CI'][3], color=df4['Color'][3], height=.3, alpha = .8)\n",
    "\n",
    "ax2.barh(y=1.3, width=df4['Coef'][1], xerr=df4['CI'][1], color=df4['Color'][1], height=.3)\n",
    "ax2.barh(y=1.7, width=df4['Coef'][4], xerr=df4['CI'][4], color=df4['Color'][4], height=.3, alpha = .8)\n",
    "\n",
    "ax2.barh(y=2.3, width=df4['Coef'][2], xerr=df4['CI'][2], color=df4['Color'][2], height=.3)\n",
    "ax2.barh(y=2.7, width=df4['Coef'][5], xerr=df4['CI'][5], color=df4['Color'][5], height=.3, alpha = .8)\n",
    "\n",
    "\n",
    "\n",
    "ax2.set_title('SCRs by author country\\n', fontsize=26)\n",
    "ax2.set_yticks(ypos, ['','Low- & Lower-Middle\\nIncome Countries','India', \n",
    "                      'Upper-Middle\\nIncome Countries', 'China', \n",
    "                      'High\\nIncome Countries', 'USA', ''], rotation=0, fontsize=22)\n",
    "\n",
    "\n",
    "ax2.set_xlabel('Pr(Reviewer\\nfrom author country)', fontsize=22)\n",
    "\n",
    "ax2.text(-0.065, 1.2, 'B', transform=ax2.transAxes,\n",
    "      fontsize=35, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "    \n",
    "\n",
    "for ax in [ax2]:\n",
    "    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True,labelsize=16)\n",
    "    ax.tick_params(axis='y', which='both', left=False, right=False, labelsize=18)\n",
    "    ax.invert_yaxis()\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['left'].set_color('black')\n",
    "\n",
    "    # set left spine to bold\n",
    "    ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "    # change alpha of grid\n",
    "    ax.grid(alpha=0.2, linestyle='--')\n",
    "    ax.yaxis.grid(False)\n",
    "\n",
    "    # set xticks every .1\n",
    "    ax.set_xticks(np.arange(0, 0.5, 0.1))\n",
    "    #set xlim 0-.5\n",
    "    ax.set_xlim(0,.41)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      num_unique_reviewers  SCRAccess\n",
      "num_unique_reviewers               1.00000    0.97662\n",
      "SCRAccess                          0.97662    1.00000\n",
      "                    num_unique_invited  SCRAccess\n",
      "num_unique_invited            1.000000   0.959057\n",
      "SCRAccess                     0.959057   1.000000\n"
     ]
    }
   ],
   "source": [
    "# Pearson correlation between number of unique reviewers and SCRAccess\n",
    "print(geo_df[['num_unique_reviewers', 'SCRAccess']].corr(method='pearson'))\n",
    "print(geo_df[['num_unique_invited', 'SCRAccess']].corr(method='pearson'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back of the Envelope Calculation: SCR Access -> Positivity -> Acceptance Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1. Relationship between Acceptance and Positivity\n",
    "reset_submitted_rev_df = submitted_rev_df.reset_index(drop=True)\n",
    "bote_df = reset_submitted_rev_df.groupby('manuscript_id_original_deid').agg({\n",
    "    'Positivity': 'mean', \n",
    "    'final_decision_binary': 'first', \n",
    "    'ln_team_size_bin': 'first', \n",
    "    'num_index': 'first', \n",
    "    'auth_country_deid': 'first', \n",
    "    'journal_abbr_deid': 'first', \n",
    "    'SCR': 'sum',  # SCR is now number of SCRs on manuscript\n",
    "    'anon_manu': 'first'\n",
    "}).reset_index()\n",
    "bote_df = bote_df.set_index(['journal_abbr_deid', 'num_index'], drop=True)\n",
    "\n",
    "# Prep team_size for control                 \n",
    "bote_df = bote_df.loc[bote_df['ln_team_size_bin'].notna()]\n",
    "bote_df = bote_df.loc[bote_df['auth_country_deid'].notna()]\n",
    "bote_df = bote_df.loc[bote_df['Positivity'].notna()]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            PanelOLS Estimation Summary                            \n",
      "===================================================================================\n",
      "Dep. Variable:     final_decision_binary   R-squared:                        0.5951\n",
      "Estimator:                      PanelOLS   R-squared (Between):              0.8315\n",
      "No. Observations:                 113054   R-squared (Within):               0.5951\n",
      "Date:                   Thu, Oct 03 2024   R-squared (Overall):              0.5970\n",
      "Time:                           16:28:15   Log-likelihood                -2.633e+04\n",
      "Cov. Estimator:                Clustered                                           \n",
      "                                           F-statistic:                      1128.2\n",
      "Entities:                             60   P-value                           0.0000\n",
      "Avg Obs:                          1884.2   Distribution:              F(147,112847)\n",
      "Min Obs:                          19.000                                           \n",
      "Max Obs:                       1.356e+04   F-statistic (robust):          3.914e+16\n",
      "                                           P-value                           0.0000\n",
      "Time periods:                         83   Distribution:              F(147,112847)\n",
      "Avg Obs:                          1362.1                                           \n",
      "Min Obs:                          1.0000                                           \n",
      "Max Obs:                          2880.0                                           \n",
      "                                                                                   \n",
      "                                        Parameter Estimates                                        \n",
      "===================================================================================================\n",
      "                                 Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Positivity                          0.9347     0.0117     79.852     0.0000      0.9118      0.9576\n",
      "auth_country_deid[T.01RUywaEYk]    -0.0304     0.0226    -1.3462     0.1782     -0.0747      0.0139\n",
      "auth_country_deid[T.118DgncWzO]     0.0014     0.0134     0.1014     0.9193     -0.0249      0.0277\n",
      "auth_country_deid[T.1IsCikyr0z]    -0.0208     0.0196    -1.0630     0.2878     -0.0592      0.0176\n",
      "auth_country_deid[T.1VC2OqFzuM]    -0.0175     0.0278    -0.6296     0.5289     -0.0721      0.0371\n",
      "auth_country_deid[T.1hNcbqbv4L]    -0.0647     0.0933    -0.6934     0.4881     -0.2477      0.1182\n",
      "auth_country_deid[T.1qKy8VtrJA]    -0.0443     0.0193    -2.2987     0.0215     -0.0820     -0.0065\n",
      "auth_country_deid[T.21YOFRPI50]    -0.0976     0.0651    -1.4974     0.1343     -0.2252      0.0301\n",
      "auth_country_deid[T.2l7aRi23dv]    -0.1304     0.0466    -2.7961     0.0052     -0.2217     -0.0390\n",
      "auth_country_deid[T.3nowERn9qZ]    -0.1027     0.0147    -6.9706     0.0000     -0.1315     -0.0738\n",
      "auth_country_deid[T.4Kk2eQ7L0o]     0.0288     0.0277     1.0423     0.2973     -0.0254      0.0831\n",
      "auth_country_deid[T.4Vh0LHpa3t]    -0.0808     0.0135    -5.9994     0.0000     -0.1072     -0.0544\n",
      "auth_country_deid[T.4cYC339BrY]    -0.0265     0.0434    -0.6107     0.5414     -0.1115      0.0585\n",
      "auth_country_deid[T.4j79ul8fHR]    -0.0830     0.0156    -5.3122     0.0000     -0.1136     -0.0524\n",
      "auth_country_deid[T.4os6R6Jkav]    -0.0688     0.0148    -4.6607     0.0000     -0.0977     -0.0398\n",
      "auth_country_deid[T.4xYGrPJ9dw]    -0.0506     0.0330    -1.5319     0.1255     -0.1154      0.0141\n",
      "auth_country_deid[T.5EyxET8BbW]    -0.0136     0.0120    -1.1342     0.2567     -0.0370      0.0099\n",
      "auth_country_deid[T.5MveUmKRG8]    -0.1114     0.0314    -3.5438     0.0004     -0.1730     -0.0498\n",
      "auth_country_deid[T.5kNN6T4gjA]    -0.0550     0.0340    -1.6187     0.1055     -0.1215      0.0116\n",
      "auth_country_deid[T.6sM1RtEoVK]    -0.1028     0.0085    -12.050     0.0000     -0.1195     -0.0861\n",
      "auth_country_deid[T.7CnpUNYfhd]     0.0800     0.0812     0.9849     0.3247     -0.0792      0.2391\n",
      "auth_country_deid[T.7R6g4GlTIC]     0.0225     0.0180     1.2524     0.2104     -0.0127      0.0577\n",
      "auth_country_deid[T.7gJDsWXLq5]     0.0096     0.0201     0.4779     0.6327     -0.0297      0.0489\n",
      "auth_country_deid[T.87x5hnkwOa]    -0.4339     0.0065    -66.932     0.0000     -0.4466     -0.4212\n",
      "auth_country_deid[T.8GGdVsGk6z]    -0.8397     0.0143    -58.612     0.0000     -0.8677     -0.8116\n",
      "auth_country_deid[T.8LzEw1g3PG]     0.0292     0.0313     0.9345     0.3500     -0.0321      0.0905\n",
      "auth_country_deid[T.8pK0NResQ4]     0.0182     0.0191     0.9524     0.3409     -0.0193      0.0557\n",
      "auth_country_deid[T.9pbpdOsJYC]    -0.0067     0.0106    -0.6354     0.5251     -0.0274      0.0140\n",
      "auth_country_deid[T.9rJz0V1MlX]    -0.1357     0.0241    -5.6350     0.0000     -0.1829     -0.0885\n",
      "auth_country_deid[T.ACrU4zDcnS]    -0.0079     0.0023    -3.4423     0.0006     -0.0124     -0.0034\n",
      "auth_country_deid[T.Asb9dAsIWp]    -0.0522     0.0490    -1.0643     0.2872     -0.1482      0.0439\n",
      "auth_country_deid[T.B2SSiUPAel]    -0.0163     0.0346    -0.4692     0.6389     -0.0841      0.0516\n",
      "auth_country_deid[T.BREeliOgcn]    -0.0275     0.0099    -2.7818     0.0054     -0.0469     -0.0081\n",
      "auth_country_deid[T.C1AHEvrvwq]    -0.1573     0.0707    -2.2238     0.0262     -0.2959     -0.0187\n",
      "auth_country_deid[T.CNeykzbH8t]    -0.1301     0.0176    -7.3799     0.0000     -0.1646     -0.0955\n",
      "auth_country_deid[T.E8CBulBho4]    -0.0559     0.0124    -4.5034     0.0000     -0.0803     -0.0316\n",
      "auth_country_deid[T.EVV2RJeulh]    -0.0005     0.0142    -0.0322     0.9743     -0.0283      0.0274\n",
      "auth_country_deid[T.EwXsMZHFYX]     0.0036     0.0229     0.1571     0.8752     -0.0413      0.0485\n",
      "auth_country_deid[T.Ezn1B2oxgz]    -0.1086     0.0221    -4.9082     0.0000     -0.1520     -0.0653\n",
      "auth_country_deid[T.FCpM4cXbjl]    -0.0607     0.0117    -5.1873     0.0000     -0.0837     -0.0378\n",
      "auth_country_deid[T.FTwRRliPEJ]     0.0071     0.0112     0.6311     0.5280     -0.0149      0.0290\n",
      "auth_country_deid[T.FaPD8dn8Mm]    -0.0329     0.0014    -22.976     0.0000     -0.0357     -0.0301\n",
      "auth_country_deid[T.Fw0V4m7Uwj]    -0.0467     0.0267    -1.7479     0.0805     -0.0991      0.0057\n",
      "auth_country_deid[T.G3O2srQTKC]     0.1414     0.0506     2.7958     0.0052      0.0423      0.2405\n",
      "auth_country_deid[T.GdJz1hBZV7]     0.0829     0.1242     0.6675     0.5045     -0.1605      0.3263\n",
      "auth_country_deid[T.I31DdNyhLb]    -0.0124     0.0029    -4.2903     0.0000     -0.0181     -0.0067\n",
      "auth_country_deid[T.I8YLf36AhP]    -0.1051     0.0038    -27.328     0.0000     -0.1126     -0.0975\n",
      "auth_country_deid[T.IEM89n03IA]    -0.1412     0.0817    -1.7277     0.0840     -0.3014      0.0190\n",
      "auth_country_deid[T.IT1mIovDbK]    -0.0325     0.0179    -1.8174     0.0692     -0.0676      0.0025\n",
      "auth_country_deid[T.Je0BmRB6vF]     0.0247     0.0650     0.3805     0.7036     -0.1027      0.1522\n",
      "auth_country_deid[T.Jm3MRCR1Pt]    -0.1808     0.1641    -1.1018     0.2706     -0.5025      0.1409\n",
      "auth_country_deid[T.JrXfBEnzFE]    -0.0468     0.0461    -1.0154     0.3099     -0.1371      0.0435\n",
      "auth_country_deid[T.JuQ4rzWhyA]    -0.0179     0.0223    -0.8027     0.4222     -0.0615      0.0258\n",
      "auth_country_deid[T.KIKtoFowCx]    -0.0238     0.0202    -1.1760     0.2396     -0.0635      0.0159\n",
      "auth_country_deid[T.KN7KJA6fNM]     0.1187     0.0466     2.5443     0.0110      0.0273      0.2101\n",
      "auth_country_deid[T.LMiYt4S6rd]    -0.0173     0.0685    -0.2521     0.8010     -0.1515      0.1170\n",
      "auth_country_deid[T.LhwZe0GVjy]     0.0352     0.0386     0.9112     0.3622     -0.0405      0.1108\n",
      "auth_country_deid[T.M2uxCK1Vyf]     0.2703     0.0878     3.0772     0.0021      0.0981      0.4425\n",
      "auth_country_deid[T.MtZoMAqCoe]    -0.0751     0.0233    -3.2202     0.0013     -0.1208     -0.0294\n",
      "auth_country_deid[T.OB4RmJoJPb]    -0.6125     0.0091    -67.621     0.0000     -0.6303     -0.5948\n",
      "auth_country_deid[T.OktMOgnq2p]     0.0459     0.0335     1.3720     0.1701     -0.0197      0.1115\n",
      "auth_country_deid[T.PFIk5CtSAp]    -0.0310     0.0144    -2.1514     0.0314     -0.0592     -0.0028\n",
      "auth_country_deid[T.PWczyEI9kS]    -0.0734     0.0214    -3.4362     0.0006     -0.1152     -0.0315\n",
      "auth_country_deid[T.PuK2jxQE4t]    -0.0894     0.0309    -2.8875     0.0039     -0.1500     -0.0287\n",
      "auth_country_deid[T.QWDE9ZD8Gy]    -0.0272     0.0237    -1.1501     0.2501     -0.0737      0.0192\n",
      "auth_country_deid[T.QmvV2WNfhA]     0.0165     0.0237     0.6990     0.4845     -0.0298      0.0629\n",
      "auth_country_deid[T.REIHJvK5Pu]     0.0259     0.0017     15.235     0.0000      0.0225      0.0292\n",
      "auth_country_deid[T.RPwx00oxJM]    -0.0508     0.0081    -6.2501     0.0000     -0.0667     -0.0348\n",
      "auth_country_deid[T.SXpLzg8pTr]    -0.0493     0.0307    -1.6046     0.1086     -0.1095      0.0109\n",
      "auth_country_deid[T.ShuO2wZfuO]    -0.0031     0.0117    -0.2618     0.7935     -0.0259      0.0198\n",
      "auth_country_deid[T.SuXM9YQISL]     0.0051     0.0201     0.2561     0.7979     -0.0342      0.0445\n",
      "auth_country_deid[T.T0tYdAJu1n]    -0.0667     0.0441    -1.5131     0.1303     -0.1530      0.0197\n",
      "auth_country_deid[T.Tuc4GrgtL5]     0.0055     0.0108     0.5143     0.6071     -0.0156      0.0267\n",
      "auth_country_deid[T.U2wr1ahHoP]    -0.1512     0.0193    -7.8204     0.0000     -0.1890     -0.1133\n",
      "auth_country_deid[T.V8r4v5lrXE]    -0.0691     0.0163    -4.2315     0.0000     -0.1011     -0.0371\n",
      "auth_country_deid[T.VGv0un84VE]    -0.0740     0.1276    -0.5801     0.5618     -0.3242      0.1761\n",
      "auth_country_deid[T.VXbExi2Zwd]    -0.1502     0.0984    -1.5263     0.1269     -0.3431      0.0427\n",
      "auth_country_deid[T.VqqeLFJ0rC]     0.0442     0.0026     17.093     0.0000      0.0392      0.0493\n",
      "auth_country_deid[T.VypHvUjEiY]    -0.0673     0.0432    -1.5560     0.1197     -0.1520      0.0175\n",
      "auth_country_deid[T.WYHUNQMYxH]     0.0101     0.0123     0.8168     0.4140     -0.0141      0.0342\n",
      "auth_country_deid[T.X6qR13Pzt7]    -0.1081     0.0288    -3.7465     0.0002     -0.1646     -0.0515\n",
      "auth_country_deid[T.XEjFmKl0yz]    -0.0313     0.0127    -2.4609     0.0139     -0.0563     -0.0064\n",
      "auth_country_deid[T.XLhzgzLwx2]     0.0102     0.0268     0.3828     0.7018     -0.0422      0.0627\n",
      "auth_country_deid[T.XOu57u6P4c]    -0.1412     0.0872    -1.6190     0.1055     -0.3122      0.0297\n",
      "auth_country_deid[T.XXk4dJDKXi]    -0.4500     0.3449    -1.3048     0.1920     -1.1259      0.2259\n",
      "auth_country_deid[T.Xzt8Z9tRhQ]    -0.0928     0.0547    -1.6955     0.0900     -0.2001      0.0145\n",
      "auth_country_deid[T.Y1PADFt9cG]    -0.0274     0.0139    -1.9664     0.0493     -0.0548  -8.904e-05\n",
      "auth_country_deid[T.ZTHOLofOLp]    -0.0573     0.0513    -1.1172     0.2639     -0.1579      0.0432\n",
      "auth_country_deid[T.ZdlqdA6fep]    -0.0906     0.0258    -3.5144     0.0004     -0.1411     -0.0401\n",
      "auth_country_deid[T.ZsebRKkaew]    -0.0042     0.0124    -0.3383     0.7352     -0.0285      0.0201\n",
      "auth_country_deid[T.aPDGfGcKaB]    -0.0060     0.0160    -0.3746     0.7080     -0.0372      0.0253\n",
      "auth_country_deid[T.al9cj72660] -5.759e-05     0.0063    -0.0091     0.9927     -0.0124      0.0123\n",
      "auth_country_deid[T.bS9aAjmVDp]    -0.1530     0.0371    -4.1226     0.0000     -0.2257     -0.0802\n",
      "auth_country_deid[T.blpP7b2o1O]    -0.2447     0.0059    -41.293     0.0000     -0.2563     -0.2330\n",
      "auth_country_deid[T.dYtMI8V8b3]     0.0681     0.0127     5.3587     0.0000      0.0432      0.0930\n",
      "auth_country_deid[T.drl7SrdxEA]    -0.1722     0.0712    -2.4193     0.0156     -0.3117     -0.0327\n",
      "auth_country_deid[T.e2NvNp9jP0]    -0.0096     0.0172    -0.5599     0.5755     -0.0433      0.0241\n",
      "auth_country_deid[T.eBLSjg6QtK]    -0.0724     0.0158    -4.5922     0.0000     -0.1033     -0.0415\n",
      "auth_country_deid[T.eZTgBszCZj]    -0.1646     0.0905    -1.8196     0.0688     -0.3420      0.0127\n",
      "auth_country_deid[T.ej9ySOv2Am]    -0.0450     0.1098    -0.4102     0.6817     -0.2602      0.1701\n",
      "auth_country_deid[T.fpXuUK9yqU]    -0.2954     0.2015    -1.4661     0.1426     -0.6902      0.0995\n",
      "auth_country_deid[T.gH1Uv3udJi]    -0.1616     0.3713    -0.4352     0.6635     -0.8892      0.5661\n",
      "auth_country_deid[T.gWWAXmhYit]     0.0901     0.0136     6.6026     0.0000      0.0633      0.1168\n",
      "auth_country_deid[T.gcvMwWjPy1]    -0.0504     0.0166    -3.0410     0.0024     -0.0828     -0.0179\n",
      "auth_country_deid[T.h1NPw5EnRL]    -0.0467     0.0173    -2.6958     0.0070     -0.0806     -0.0127\n",
      "auth_country_deid[T.h8ixbcPZA3]    -0.0764     0.0130    -5.8851     0.0000     -0.1018     -0.0510\n",
      "auth_country_deid[T.hqjkuTlbAN]    -0.0300     0.0354    -0.8468     0.3971     -0.0993      0.0394\n",
      "auth_country_deid[T.hxlyd3MlML]    -0.0420     0.0542    -0.7755     0.4380     -0.1482      0.0642\n",
      "auth_country_deid[T.iH1lEaZyWN]    -0.0477     0.0410    -1.1627     0.2450     -0.1282      0.0327\n",
      "auth_country_deid[T.igVOZNIFMr]    -0.1493     0.0732    -2.0389     0.0415     -0.2929     -0.0058\n",
      "auth_country_deid[T.j60dGwNulY]    -0.4561     0.0968    -4.7107     0.0000     -0.6458     -0.2663\n",
      "auth_country_deid[T.jRFqxxWu8q]    -0.0918     0.0166    -5.5397     0.0000     -0.1242     -0.0593\n",
      "auth_country_deid[T.kFz70cXQZA]    -0.0472     0.0357    -1.3223     0.1861     -0.1171      0.0227\n",
      "auth_country_deid[T.kGSCpHNDxf]    -0.0550     0.0605    -0.9087     0.3635     -0.1736      0.0636\n",
      "auth_country_deid[T.mV6vFBqRnE]    -0.0627     0.0489    -1.2823     0.1997     -0.1585      0.0331\n",
      "auth_country_deid[T.mYZjHz6LV3]    -0.1825     0.1164    -1.5689     0.1167     -0.4106      0.0455\n",
      "auth_country_deid[T.mai5g9Zpwd]    -0.0692     0.0432    -1.5996     0.1097     -0.1539      0.0156\n",
      "auth_country_deid[T.moDl7qxHXV]     0.0364     0.0119     3.0561     0.0022      0.0131      0.0597\n",
      "auth_country_deid[T.n0pDFBZwqe]    -0.1064     0.0203    -5.2361     0.0000     -0.1462     -0.0666\n",
      "auth_country_deid[T.nJYYwjWuLH]    -0.2179     0.1762    -1.2369     0.2161     -0.5631      0.1274\n",
      "auth_country_deid[T.nycWVY9VeY]    -0.0817     0.0683    -1.1952     0.2320     -0.2157      0.0523\n",
      "auth_country_deid[T.p8Au9KrEmE]    -0.1086     0.0128    -8.4984     0.0000     -0.1337     -0.0836\n",
      "auth_country_deid[T.peJYYrEj3h]     0.0524     0.0129     4.0440     0.0001      0.0270      0.0777\n",
      "auth_country_deid[T.qAhEUKbRqM]    -0.0874     0.2095    -0.4174     0.6764     -0.4980      0.3231\n",
      "auth_country_deid[T.qHDHuXsZ7i]    -0.0261     0.0075    -3.4565     0.0005     -0.0408     -0.0113\n",
      "auth_country_deid[T.qPUIdXiciV]    -0.0743     0.0268    -2.7693     0.0056     -0.1268     -0.0217\n",
      "auth_country_deid[T.qb5gOP4KHh]    -0.0637     0.0215    -2.9578     0.0031     -0.1059     -0.0215\n",
      "auth_country_deid[T.qrMndoEQGw]     0.0486     0.0178     2.7379     0.0062      0.0138      0.0834\n",
      "auth_country_deid[T.qzJwJQ3vdR]     0.0274     0.0075     3.6743     0.0002      0.0128      0.0421\n",
      "auth_country_deid[T.sSJoGDbVbV]     0.1723     0.0680     2.5349     0.0113      0.0391      0.3054\n",
      "auth_country_deid[T.tFPRTbrLRL]    -0.0558     0.0112    -4.9863     0.0000     -0.0777     -0.0339\n",
      "auth_country_deid[T.tMmqDD061N]    -0.0620     0.0074    -8.3431     0.0000     -0.0766     -0.0474\n",
      "auth_country_deid[T.teGZdfVGtL]    -0.1062     0.1117    -0.9503     0.3419     -0.3251      0.1128\n",
      "auth_country_deid[T.tu6LLpKwYF]    -0.0044     0.0178    -0.2455     0.8060     -0.0392      0.0305\n",
      "auth_country_deid[T.uNK0igBPbO]    -0.0865     0.0285    -3.0361     0.0024     -0.1423     -0.0307\n",
      "auth_country_deid[T.wBYyDVytII]    -0.0895     0.0294    -3.0486     0.0023     -0.1470     -0.0320\n",
      "auth_country_deid[T.wjSoOFCsym]     0.0097     0.0328     0.2956     0.7675     -0.0546      0.0739\n",
      "auth_country_deid[T.wqRmBXWcDL]    -0.0638     0.0081    -7.8723     0.0000     -0.0797     -0.0479\n",
      "auth_country_deid[T.xbZRCf3ifS]    -0.2738     0.0766    -3.5747     0.0004     -0.4240     -0.1237\n",
      "auth_country_deid[T.xbgyay5rTA]     0.0387     0.0679     0.5697     0.5689     -0.0945      0.1719\n",
      "auth_country_deid[T.y1owGpzrXe]    -0.0309     0.0210    -1.4759     0.1400     -0.0720      0.0101\n",
      "auth_country_deid[T.y8HGPDnbVR]     0.0215     0.0032     6.7878     0.0000      0.0153      0.0277\n",
      "auth_country_deid[T.yYgBow6nJf]     0.0378     0.0067     5.6185     0.0000      0.0246      0.0509\n",
      "auth_country_deid[T.ywwuO5vemq]    -0.1185     0.0528    -2.2431     0.0249     -0.2220     -0.0150\n",
      "auth_country_deid[T.yxhZgOoOr4]    -0.0155     0.0030    -5.2306     0.0000     -0.0213     -0.0097\n",
      "auth_country_deid[T.z6kyUqGDMn]    -0.0584     0.0186    -3.1302     0.0017     -0.0949     -0.0218\n",
      "auth_country_deid[T.zdT1m3IStM]     0.0248     0.0026     9.6854     0.0000      0.0198      0.0298\n",
      "ln_team_size_bin                    0.0076     0.0011     7.1267     0.0000      0.0055      0.0097\n",
      "===================================================================================================\n",
      "\n",
      "F-test for Poolability: 44.241\n",
      "P-value: 0.0000\n",
      "Distribution: F(59,112847)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    }
   ],
   "source": [
    "# Effect of positive review on acceptance\n",
    "reg = PanelOLS.from_formula('final_decision_binary ~ Positivity + ln_team_size_bin + auth_country_deid + EntityEffects', \n",
    "                            data=bote_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "print(reg.summary)\n",
    "\n",
    "# Sanity check passed: Acceptance is highly correlated with review Positivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            PanelOLS Estimation Summary                            \n",
      "===================================================================================\n",
      "Dep. Variable:     final_decision_binary   R-squared:                        0.0618\n",
      "Estimator:                      PanelOLS   R-squared (Between):             -0.0470\n",
      "No. Observations:                 113054   R-squared (Within):               0.0618\n",
      "Date:                   Thu, Oct 03 2024   R-squared (Overall):              0.0306\n",
      "Time:                           16:28:30   Log-likelihood                -7.382e+04\n",
      "Cov. Estimator:                Clustered                                           \n",
      "                                           F-statistic:                      50.597\n",
      "Entities:                             60   P-value                           0.0000\n",
      "Avg Obs:                          1884.2   Distribution:              F(147,112847)\n",
      "Min Obs:                          19.000                                           \n",
      "Max Obs:                       1.356e+04   F-statistic (robust):         -1.163e+18\n",
      "                                           P-value                           1.0000\n",
      "Time periods:                         83   Distribution:              F(147,112847)\n",
      "Avg Obs:                          1362.1                                           \n",
      "Min Obs:                          1.0000                                           \n",
      "Max Obs:                          2880.0                                           \n",
      "                                                                                   \n",
      "                                        Parameter Estimates                                        \n",
      "===================================================================================================\n",
      "                                 Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "---------------------------------------------------------------------------------------------------\n",
      "SCR                                 0.0671     0.0094     7.1525     0.0000      0.0487      0.0855\n",
      "auth_country_deid[T.01RUywaEYk]     0.6549     0.0323     20.262     0.0000      0.5916      0.7183\n",
      "auth_country_deid[T.118DgncWzO]     0.7029     0.0162     43.344     0.0000      0.6711      0.7347\n",
      "auth_country_deid[T.1IsCikyr0z]     0.5924     0.0208     28.436     0.0000      0.5515      0.6332\n",
      "auth_country_deid[T.1VC2OqFzuM]     0.5595     0.0525     10.650     0.0000      0.4565      0.6625\n",
      "auth_country_deid[T.1hNcbqbv4L]     0.4978     0.1736     2.8674     0.0041      0.1575      0.8381\n",
      "auth_country_deid[T.1qKy8VtrJA]     0.4757     0.0226     21.026     0.0000      0.4313      0.5200\n",
      "auth_country_deid[T.21YOFRPI50]     0.5061     0.1613     3.1384     0.0017      0.1900      0.8222\n",
      "auth_country_deid[T.2l7aRi23dv]     0.4872     0.0514     9.4819     0.0000      0.3865      0.5879\n",
      "auth_country_deid[T.3nowERn9qZ]     0.3577     0.0161     22.190     0.0000      0.3261      0.3893\n",
      "auth_country_deid[T.4Kk2eQ7L0o]     0.4962     0.3262     1.5212     0.1282     -0.1431      1.1355\n",
      "auth_country_deid[T.4Vh0LHpa3t]     0.3727     0.0308     12.088     0.0000      0.3122      0.4331\n",
      "auth_country_deid[T.4cYC339BrY]     0.4209     0.0869     4.8414     0.0000      0.2505      0.5913\n",
      "auth_country_deid[T.4j79ul8fHR]     0.4174     0.0205     20.373     0.0000      0.3772      0.4576\n",
      "auth_country_deid[T.4os6R6Jkav]     0.4254     0.0219     19.414     0.0000      0.3825      0.4684\n",
      "auth_country_deid[T.4xYGrPJ9dw]     0.5480     0.0405     13.527     0.0000      0.4686      0.6275\n",
      "auth_country_deid[T.5EyxET8BbW]     0.6483     0.0129     50.230     0.0000      0.6230      0.6735\n",
      "auth_country_deid[T.5MveUmKRG8]     0.4197     0.0373     11.240     0.0000      0.3466      0.4929\n",
      "auth_country_deid[T.5kNN6T4gjA]     0.4242     0.0892     4.7548     0.0000      0.2493      0.5990\n",
      "auth_country_deid[T.6sM1RtEoVK]     0.3453     0.0340     10.167     0.0000      0.2788      0.4119\n",
      "auth_country_deid[T.7CnpUNYfhd]     0.7569     0.0714     10.601     0.0000      0.6169      0.8968\n",
      "auth_country_deid[T.7R6g4GlTIC]     0.7592     0.0203     37.388     0.0000      0.7194      0.7990\n",
      "auth_country_deid[T.7gJDsWXLq5]     0.7336     0.0254     28.870     0.0000      0.6838      0.7834\n",
      "auth_country_deid[T.87x5hnkwOa]     0.0847     0.0053     15.841     0.0000      0.0743      0.0952\n",
      "auth_country_deid[T.8GGdVsGk6z]     0.2057     0.0065     31.885     0.0000      0.1930      0.2183\n",
      "auth_country_deid[T.8LzEw1g3PG]     0.6647     0.0269     24.714     0.0000      0.6120      0.7174\n",
      "auth_country_deid[T.8pK0NResQ4]     0.7764     0.0174     44.546     0.0000      0.7422      0.8106\n",
      "auth_country_deid[T.9pbpdOsJYC]     0.6615     0.0130     50.733     0.0000      0.6360      0.6871\n",
      "auth_country_deid[T.9rJz0V1MlX]     0.3226     0.0463     6.9703     0.0000      0.2319      0.4133\n",
      "auth_country_deid[T.ACrU4zDcnS]     0.0234     0.0043     5.3901     0.0000      0.0149      0.0319\n",
      "auth_country_deid[T.Asb9dAsIWp]     0.2783     0.0808     3.4430     0.0006      0.1199      0.4367\n",
      "auth_country_deid[T.B2SSiUPAel]     0.6257     0.0943     6.6377     0.0000      0.4409      0.8105\n",
      "auth_country_deid[T.BREeliOgcn]     0.6343     0.0124     50.952     0.0000      0.6099      0.6587\n",
      "auth_country_deid[T.C1AHEvrvwq]     0.3511     0.1029     3.4116     0.0006      0.1494      0.5529\n",
      "auth_country_deid[T.CNeykzbH8t]     0.2743     0.0343     7.9951     0.0000      0.2071      0.3416\n",
      "auth_country_deid[T.E8CBulBho4]     0.4794     0.0185     25.855     0.0000      0.4431      0.5158\n",
      "auth_country_deid[T.EVV2RJeulh]     0.7221     0.0212     34.137     0.0000      0.6807      0.7636\n",
      "auth_country_deid[T.EwXsMZHFYX]     0.6783     0.0250     27.129     0.0000      0.6293      0.7273\n",
      "auth_country_deid[T.Ezn1B2oxgz]     0.2933     0.0238     12.329     0.0000      0.2467      0.3400\n",
      "auth_country_deid[T.FCpM4cXbjl]     0.5278     0.0161     32.753     0.0000      0.4962      0.5594\n",
      "auth_country_deid[T.FTwRRliPEJ]     0.7090     0.0157     45.299     0.0000      0.6784      0.7397\n",
      "auth_country_deid[T.FaPD8dn8Mm]    -0.1162     0.0030    -39.143     0.0000     -0.1220     -0.1103\n",
      "auth_country_deid[T.Fw0V4m7Uwj]     0.4673     0.0344     13.567     0.0000      0.3998      0.5349\n",
      "auth_country_deid[T.G3O2srQTKC]     0.8831     0.0490     18.021     0.0000      0.7871      0.9791\n",
      "auth_country_deid[T.GdJz1hBZV7]     0.5105     0.2399     2.1277     0.0334      0.0402      0.9807\n",
      "auth_country_deid[T.I31DdNyhLb]    -0.0477     0.0049    -9.7780     0.0000     -0.0573     -0.0381\n",
      "auth_country_deid[T.I8YLf36AhP]    -0.2957     0.0097    -30.511     0.0000     -0.3147     -0.2767\n",
      "auth_country_deid[T.IEM89n03IA]     0.4034     0.0780     5.1724     0.0000      0.2506      0.5563\n",
      "auth_country_deid[T.IT1mIovDbK]     0.6245     0.0290     21.544     0.0000      0.5677      0.6813\n",
      "auth_country_deid[T.Je0BmRB6vF]     0.5526     0.1032     5.3561     0.0000      0.3504      0.7548\n",
      "auth_country_deid[T.Jm3MRCR1Pt]     0.1459     0.0929     1.5711     0.1162     -0.0361      0.3279\n",
      "auth_country_deid[T.JrXfBEnzFE]     0.4639     0.0524     8.8503     0.0000      0.3612      0.5666\n",
      "auth_country_deid[T.JuQ4rzWhyA]     0.5737     0.0256     22.407     0.0000      0.5235      0.6239\n",
      "auth_country_deid[T.KIKtoFowCx]     0.5842     0.0326     17.896     0.0000      0.5203      0.6482\n",
      "auth_country_deid[T.KN7KJA6fNM]     0.7271     0.2179     3.3361     0.0008      0.2999      1.1542\n",
      "auth_country_deid[T.LMiYt4S6rd]     0.4075     0.1023     3.9826     0.0001      0.2069      0.6080\n",
      "auth_country_deid[T.LhwZe0GVjy]     0.5996     0.1233     4.8630     0.0000      0.3579      0.8413\n",
      "auth_country_deid[T.M2uxCK1Vyf]     1.0477     0.0059     176.88     0.0000      1.0360      1.0593\n",
      "auth_country_deid[T.MtZoMAqCoe]     0.4951     0.0455     10.874     0.0000      0.4059      0.5844\n",
      "auth_country_deid[T.OB4RmJoJPb]    -0.0108     0.0071    -1.5084     0.1315     -0.0248      0.0032\n",
      "auth_country_deid[T.OktMOgnq2p]     0.4112     0.1456     2.8235     0.0048      0.1258      0.6967\n",
      "auth_country_deid[T.PFIk5CtSAp]     0.5227     0.0333     15.719     0.0000      0.4575      0.5879\n",
      "auth_country_deid[T.PWczyEI9kS]     0.4922     0.0299     16.468     0.0000      0.4336      0.5508\n",
      "auth_country_deid[T.PuK2jxQE4t]     0.3759     0.0547     6.8674     0.0000      0.2686      0.4831\n",
      "auth_country_deid[T.QWDE9ZD8Gy]     0.4856     0.0364     13.331     0.0000      0.4142      0.5570\n",
      "auth_country_deid[T.QmvV2WNfhA]     0.2792     0.2837     0.9842     0.3250     -0.2769      0.8353\n",
      "auth_country_deid[T.REIHJvK5Pu]     0.0529     0.0050     10.626     0.0000      0.0431      0.0627\n",
      "auth_country_deid[T.RPwx00oxJM]     0.5639     0.0123     45.943     0.0000      0.5399      0.5880\n",
      "auth_country_deid[T.SXpLzg8pTr]     0.5895     0.0511     11.525     0.0000      0.4892      0.6897\n",
      "auth_country_deid[T.ShuO2wZfuO]     0.6435     0.0134     48.068     0.0000      0.6173      0.6698\n",
      "auth_country_deid[T.SuXM9YQISL]     0.6133     0.0343     17.857     0.0000      0.5460      0.6807\n",
      "auth_country_deid[T.T0tYdAJu1n]     0.6567     0.0534     12.291     0.0000      0.5520      0.7614\n",
      "auth_country_deid[T.Tuc4GrgtL5]     0.6587     0.0097     68.071     0.0000      0.6398      0.6777\n",
      "auth_country_deid[T.U2wr1ahHoP]     0.3334     0.0291     11.453     0.0000      0.2763      0.3904\n",
      "auth_country_deid[T.V8r4v5lrXE]     0.3677     0.0329     11.172     0.0000      0.3032      0.4322\n",
      "auth_country_deid[T.VGv0un84VE]     0.3749     0.1660     2.2585     0.0239      0.0495      0.7002\n",
      "auth_country_deid[T.VXbExi2Zwd]     0.2642     0.1001     2.6400     0.0083      0.0680      0.4603\n",
      "auth_country_deid[T.VqqeLFJ0rC]     0.1197     0.0034     35.622     0.0000      0.1131      0.1263\n",
      "auth_country_deid[T.VypHvUjEiY]     0.4117     0.0767     5.3700     0.0000      0.2614      0.5620\n",
      "auth_country_deid[T.WYHUNQMYxH]     0.7313     0.0117     62.608     0.0000      0.7084      0.7542\n",
      "auth_country_deid[T.X6qR13Pzt7]     0.3704     0.0798     4.6434     0.0000      0.2141      0.5268\n",
      "auth_country_deid[T.XEjFmKl0yz]     0.5800     0.0213     27.277     0.0000      0.5383      0.6217\n",
      "auth_country_deid[T.XLhzgzLwx2]     0.6742     0.0381     17.676     0.0000      0.5994      0.7489\n",
      "auth_country_deid[T.XOu57u6P4c]     0.2729     0.1922     1.4201     0.1556     -0.1038      0.6496\n",
      "auth_country_deid[T.XXk4dJDKXi]     0.3999     0.3575     1.1186     0.2633     -0.3008      1.1006\n",
      "auth_country_deid[T.Xzt8Z9tRhQ]     0.3406     0.1118     3.0476     0.0023      0.1216      0.5597\n",
      "auth_country_deid[T.Y1PADFt9cG]     0.6398     0.0172     37.171     0.0000      0.6061      0.6736\n",
      "auth_country_deid[T.ZTHOLofOLp]     0.5513     0.0735     7.4990     0.0000      0.4072      0.6954\n",
      "auth_country_deid[T.ZdlqdA6fep]     0.5428     0.0424     12.796     0.0000      0.4597      0.6260\n",
      "auth_country_deid[T.ZsebRKkaew]     0.6675     0.0174     38.361     0.0000      0.6334      0.7016\n",
      "auth_country_deid[T.aPDGfGcKaB]     0.7186     0.0199     36.037     0.0000      0.6795      0.7577\n",
      "auth_country_deid[T.al9cj72660]     0.3961     0.0035     114.09     0.0000      0.3893      0.4029\n",
      "auth_country_deid[T.bS9aAjmVDp]     0.2348     0.0493     4.7594     0.0000      0.1381      0.3315\n",
      "auth_country_deid[T.blpP7b2o1O]     0.1450     0.0049     29.470     0.0000      0.1354      0.1547\n",
      "auth_country_deid[T.dYtMI8V8b3]     0.9735     0.0054     180.22     0.0000      0.9629      0.9841\n",
      "auth_country_deid[T.drl7SrdxEA]     0.3667     0.1410     2.6005     0.0093      0.0903      0.6431\n",
      "auth_country_deid[T.e2NvNp9jP0]     0.6946     0.0261     26.654     0.0000      0.6435      0.7457\n",
      "auth_country_deid[T.eBLSjg6QtK]     0.3837     0.0347     11.069     0.0000      0.3158      0.4517\n",
      "auth_country_deid[T.eZTgBszCZj]     0.2917     0.1060     2.7518     0.0059      0.0839      0.4994\n",
      "auth_country_deid[T.ej9ySOv2Am]     0.3811     0.1312     2.9046     0.0037      0.1239      0.6383\n",
      "auth_country_deid[T.fpXuUK9yqU]     0.0273     0.0385     0.7100     0.4777     -0.0481      0.1028\n",
      "auth_country_deid[T.gH1Uv3udJi]     0.3422     0.2256     1.5166     0.1294     -0.1001      0.7845\n",
      "auth_country_deid[T.gWWAXmhYit]     1.1004     0.0034     320.45     0.0000      1.0937      1.1071\n",
      "auth_country_deid[T.gcvMwWjPy1]     0.6291     0.0222     28.338     0.0000      0.5855      0.6726\n",
      "auth_country_deid[T.h1NPw5EnRL]     0.5973     0.0212     28.210     0.0000      0.5558      0.6388\n",
      "auth_country_deid[T.h8ixbcPZA3]     0.4936     0.0238     20.713     0.0000      0.4469      0.5403\n",
      "auth_country_deid[T.hqjkuTlbAN]     0.3830     0.1451     2.6403     0.0083      0.0987      0.6673\n",
      "auth_country_deid[T.hxlyd3MlML]     0.5046     0.1340     3.7658     0.0002      0.2420      0.7673\n",
      "auth_country_deid[T.iH1lEaZyWN]     0.4174     0.0912     4.5752     0.0000      0.2386      0.5963\n",
      "auth_country_deid[T.igVOZNIFMr]     0.5203     0.1136     4.5812     0.0000      0.2977      0.7430\n",
      "auth_country_deid[T.j60dGwNulY]     0.0306     0.0120     2.5466     0.0109      0.0070      0.0541\n",
      "auth_country_deid[T.jRFqxxWu8q]     0.4005     0.0238     16.812     0.0000      0.3538      0.4472\n",
      "auth_country_deid[T.kFz70cXQZA]     0.5116     0.0692     7.3909     0.0000      0.3759      0.6473\n",
      "auth_country_deid[T.kGSCpHNDxf]     0.5576     0.0714     7.8107     0.0000      0.4177      0.6975\n",
      "auth_country_deid[T.mV6vFBqRnE]     0.5652     0.0629     8.9857     0.0000      0.4419      0.6885\n",
      "auth_country_deid[T.mYZjHz6LV3]     0.4950     0.1088     4.5504     0.0000      0.2818      0.7082\n",
      "auth_country_deid[T.mai5g9Zpwd]     0.4035     0.1795     2.2483     0.0246      0.0517      0.7553\n",
      "auth_country_deid[T.moDl7qxHXV]     0.8449     0.0107     78.974     0.0000      0.8240      0.8659\n",
      "auth_country_deid[T.n0pDFBZwqe]     0.3166     0.0277     11.421     0.0000      0.2623      0.3710\n",
      "auth_country_deid[T.nJYYwjWuLH]     0.4268     0.3221     1.3253     0.1851     -0.2044      1.0581\n",
      "auth_country_deid[T.nycWVY9VeY]     0.3747     0.0788     4.7566     0.0000      0.2203      0.5291\n",
      "auth_country_deid[T.p8Au9KrEmE]     0.3063     0.0205     14.907     0.0000      0.2660      0.3465\n",
      "auth_country_deid[T.peJYYrEj3h]     0.5307     0.3136     1.6924     0.0906     -0.0839      1.1453\n",
      "auth_country_deid[T.qAhEUKbRqM]     0.2314     0.1031     2.2444     0.0248      0.0293      0.4336\n",
      "auth_country_deid[T.qHDHuXsZ7i]     0.5673     0.0162     34.986     0.0000      0.5356      0.5991\n",
      "auth_country_deid[T.qPUIdXiciV]     0.4108     0.0391     10.514     0.0000      0.3342      0.4874\n",
      "auth_country_deid[T.qb5gOP4KHh]     0.4565     0.0293     15.583     0.0000      0.3990      0.5139\n",
      "auth_country_deid[T.qrMndoEQGw]     0.9696     0.0119     81.196     0.0000      0.9462      0.9930\n",
      "auth_country_deid[T.qzJwJQ3vdR]     0.0545     0.0249     2.1879     0.0287      0.0057      0.1033\n",
      "auth_country_deid[T.sSJoGDbVbV]     0.6339     0.1845     3.4350     0.0006      0.2722      0.9956\n",
      "auth_country_deid[T.tFPRTbrLRL]     0.4986     0.0157     31.744     0.0000      0.4678      0.5294\n",
      "auth_country_deid[T.tMmqDD061N]     0.4140     0.0106     39.149     0.0000      0.3933      0.4347\n",
      "auth_country_deid[T.teGZdfVGtL]     0.4203     0.2839     1.4805     0.1387     -0.1361      0.9768\n",
      "auth_country_deid[T.tu6LLpKwYF]     0.6881     0.0312     22.072     0.0000      0.6270      0.7492\n",
      "auth_country_deid[T.uNK0igBPbO]     0.4985     0.0930     5.3592     0.0000      0.3162      0.6808\n",
      "auth_country_deid[T.wBYyDVytII]     0.5297     0.0480     11.042     0.0000      0.4357      0.6238\n",
      "auth_country_deid[T.wjSoOFCsym]     0.7430     0.0617     12.043     0.0000      0.6221      0.8639\n",
      "auth_country_deid[T.wqRmBXWcDL]     0.4574     0.0114     40.089     0.0000      0.4350      0.4797\n",
      "auth_country_deid[T.xbZRCf3ifS]     0.3956     0.0784     5.0437     0.0000      0.2419      0.5493\n",
      "auth_country_deid[T.xbgyay5rTA]     0.3264     0.1255     2.6010     0.0093      0.0804      0.5723\n",
      "auth_country_deid[T.y1owGpzrXe]     0.5446     0.0407     13.392     0.0000      0.4649      0.6243\n",
      "auth_country_deid[T.y8HGPDnbVR]     0.1911     0.0033     57.348     0.0000      0.1846      0.1976\n",
      "auth_country_deid[T.yYgBow6nJf]     0.1133     0.0069     16.329     0.0000      0.0997      0.1269\n",
      "auth_country_deid[T.ywwuO5vemq]     0.4171     0.1137     3.6677     0.0002      0.1942      0.6400\n",
      "auth_country_deid[T.yxhZgOoOr4]    -0.0084     0.0056    -1.5155     0.1297     -0.0193      0.0025\n",
      "auth_country_deid[T.z6kyUqGDMn]     0.5744     0.0266     21.586     0.0000      0.5222      0.6265\n",
      "auth_country_deid[T.zdT1m3IStM]     0.1004     0.0034     29.240     0.0000      0.0937      0.1071\n",
      "ln_team_size_bin                    0.0318     0.0024     13.410     0.0000      0.0272      0.0365\n",
      "===================================================================================================\n",
      "\n",
      "F-test for Poolability: 109.04\n",
      "P-value: 0.0000\n",
      "Distribution: F(59,112847)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    }
   ],
   "source": [
    "# Effect of SCR count on acceptance\n",
    "reg = PanelOLS.from_formula('final_decision_binary ~ SCR + ln_team_size_bin + auth_country_deid + EntityEffects', \n",
    "                            data=bote_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "print(reg.summary)\n",
    "\n",
    "# Interpretation: For each SCR that reviews you, the odds of acceptance increase by ~6pp. \n",
    "# Effect here is 6.7pp, was 6.8pp in full data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean SCR access is 0.14650331587546253\n",
      "Mean SCR effect on acceptance rate is 0.00981572216365599\n"
     ]
    }
   ],
   "source": [
    "SCR_access_mean = submitted_rev_df['SCR'].mean()\n",
    "print('Mean SCR access is', SCR_access_mean)\n",
    "print('Mean SCR effect on acceptance rate is', SCR_access_mean*0.067) # Using estimate in noise injected replication\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCR access for USA is 0.3041097945102745\n",
      "USA SCR effect on acceptance rate is 0.02037535623218839\n",
      "USA acceptance would go from 0.7060896955152243 to 0.6955300614466918\n"
     ]
    }
   ],
   "source": [
    "# Identified USA earlier via greatest number of unique reviewers\n",
    "USA_SCR_access = submitted_rev_df.loc[submitted_rev_df['auth_country_deid'] == 'Tuc4GrgtL5']['SCR'].mean()\n",
    "USA_acceptance = submitted_rev_df.loc[submitted_rev_df['auth_country_deid'] == 'Tuc4GrgtL5']['final_decision_binary'].mean()\n",
    "print('SCR access for USA is', USA_SCR_access)\n",
    "print('USA SCR effect on acceptance rate is', USA_SCR_access*0.067)\n",
    "\n",
    "print('USA acceptance would go from', USA_acceptance, 'to', \n",
    "      USA_acceptance - (USA_SCR_access*0.067 - SCR_access_mean*0.067))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCR access for low access country is 0.01892744479495268\n",
      "Low access country SCR effect on acceptance rate is 0.0012681388012618297\n",
      "Low access country acceptance would go from 0.5299684542586751 to 0.5385160376210693\n"
     ]
    }
   ],
   "source": [
    "# Cannot identify Vietnam, but can use another country with low SCR access\n",
    "low_SCR_access = submitted_rev_df.loc[submitted_rev_df['auth_country_deid'] == '5MveUmKRG8']['SCR'].mean()\n",
    "low_SCR_access_acceptance = submitted_rev_df.loc[submitted_rev_df['auth_country_deid'] == '5MveUmKRG8']['final_decision_binary'].mean()\n",
    "print('SCR access for low access country is', low_SCR_access)\n",
    "print('Low access country SCR effect on acceptance rate is', low_SCR_access*0.067)\n",
    "\n",
    "print('Low access country acceptance would go from', low_SCR_access_acceptance, 'to', \n",
    "      low_SCR_access_acceptance - (low_SCR_access*0.067 - SCR_access_mean*0.067))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset columns for summary tables\n",
    "invited_rev_sumstats = invited_rev_df[['journal_abbr_deid', 'manuscript_id_original_deid', \n",
    "                          'final_decision_binary', 'submitted_review', 'SCR',\n",
    "                          'rev_country_deid', 'auth_country_deid', 'anon_manu', 'national', \n",
    "                          'regional']]\n",
    "submitted_rev_sumstats = submitted_rev_df[['journal_abbr_deid', 'manuscript_id_original_deid', \n",
    "                          'final_decision_binary', 'SCR', 'rev_country_deid', \n",
    "                          'auth_country_deid', 'anon_manu', 'national', 'regional']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.302516</td>\n",
       "      <td>0.459348</td>\n",
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       "    <tr>\n",
       "      <th>SCR</th>\n",
       "      <td>787055.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.32427</td>\n",
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       "    <tr>\n",
       "      <th>rev_country_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>161</td>\n",
       "      <td>Tuc4GrgtL5</td>\n",
       "      <td>177657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>auth_country_deid</th>\n",
       "      <td>787055</td>\n",
       "      <td>147</td>\n",
       "      <td>wqRmBXWcDL</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>anon_manu</th>\n",
       "      <td>787055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07661</td>\n",
       "      <td>0.265971</td>\n",
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       "      <th>national</th>\n",
       "      <td>786855.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.741472</td>\n",
       "      <td>0.437826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>787023.0</td>\n",
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       "      <td>0.376957</td>\n",
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      "text/plain": [
       "                                count  unique         top    freq      mean  \\\n",
       "journal_abbr_deid              787055      60  Jjm0Bj15KE   88478       NaN   \n",
       "manuscript_id_original_deid    787055  114069  rEv9BvKQcc      64       NaN   \n",
       "final_decision_binary        787055.0     NaN         NaN     NaN  0.634818   \n",
       "submitted_review             787055.0     NaN         NaN     NaN  0.302516   \n",
       "SCR                          787055.0     NaN         NaN     NaN   0.11941   \n",
       "rev_country_deid               787055     161  Tuc4GrgtL5  177657       NaN   \n",
       "auth_country_deid              787055     147  wqRmBXWcDL  225812       NaN   \n",
       "anon_manu                    787055.0     NaN         NaN     NaN   0.07661   \n",
       "national                     786855.0     NaN         NaN     NaN  0.741472   \n",
       "regional                     787023.0     NaN         NaN     NaN  0.828487   \n",
       "\n",
       "                                  std  min  25%  50%  75%  max  \n",
       "journal_abbr_deid                 NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "manuscript_id_original_deid       NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "final_decision_binary        0.481481  0.0  0.0  1.0  1.0  1.0  \n",
       "submitted_review             0.459348  0.0  0.0  0.0  1.0  1.0  \n",
       "SCR                           0.32427  0.0  0.0  0.0  0.0  1.0  \n",
       "rev_country_deid                  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "auth_country_deid                 NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "anon_manu                    0.265971  0.0  0.0  0.0  0.0  1.0  \n",
       "national                     0.437826  0.0  0.0  1.0  1.0  1.0  \n",
       "regional                     0.376957  0.0  1.0  1.0  1.0  1.0  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc(invited_rev_sumstats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.484472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCR</th>\n",
       "      <td>238097.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.146503</td>\n",
       "      <td>0.353611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_country_deid</th>\n",
       "      <td>238097</td>\n",
       "      <td>143</td>\n",
       "      <td>Tuc4GrgtL5</td>\n",
       "      <td>45681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auth_country_deid</th>\n",
       "      <td>238097</td>\n",
       "      <td>147</td>\n",
       "      <td>wqRmBXWcDL</td>\n",
       "      <td>68965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anon_manu</th>\n",
       "      <td>238097.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071416</td>\n",
       "      <td>0.257519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>national</th>\n",
       "      <td>238037.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.742977</td>\n",
       "      <td>0.436993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regional</th>\n",
       "      <td>238092.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.830721</td>\n",
       "      <td>0.374999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                count  unique         top   freq      mean  \\\n",
       "journal_abbr_deid              238097      60  Jjm0Bj15KE  19864       NaN   \n",
       "manuscript_id_original_deid    238097  113232  rEv9BvKQcc     17       NaN   \n",
       "final_decision_binary        238097.0     NaN         NaN    NaN  0.623645   \n",
       "SCR                          238097.0     NaN         NaN    NaN  0.146503   \n",
       "rev_country_deid               238097     143  Tuc4GrgtL5  45681       NaN   \n",
       "auth_country_deid              238097     147  wqRmBXWcDL  68965       NaN   \n",
       "anon_manu                    238097.0     NaN         NaN    NaN  0.071416   \n",
       "national                     238037.0     NaN         NaN    NaN  0.742977   \n",
       "regional                     238092.0     NaN         NaN    NaN  0.830721   \n",
       "\n",
       "                                  std  min  25%  50%  75%  max  \n",
       "journal_abbr_deid                 NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "manuscript_id_original_deid       NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "final_decision_binary        0.484472  0.0  0.0  1.0  1.0  1.0  \n",
       "SCR                          0.353611  0.0  0.0  0.0  0.0  1.0  \n",
       "rev_country_deid                  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "auth_country_deid                 NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "anon_manu                    0.257519  0.0  0.0  0.0  0.0  1.0  \n",
       "national                     0.436993  0.0  0.0  1.0  1.0  1.0  \n",
       "regional                     0.374999  0.0  1.0  1.0  1.0  1.0  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc(submitted_rev_sumstats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCR Benefit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig. S4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1 = plt.subplots(\n",
    "                        figsize=(6,6))\n",
    "ypos=[1, 1.35, 1.65, 2]\n",
    "    \n",
    "ax1.axvline(x = 0, color = 'black', linestyle = '--', lw=2, alpha=0.5)\n",
    "ax1.text(-0.0045,1.5,'Non-SCR baseline',rotation=90, fontsize=12, alpha=0.75)\n",
    "ax1.errorbar(y=1.35,x=0.047429, xerr=1.96*0.005208,\n",
    "             linestyle='None', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=2, color = 'tab:red', alpha=alpha)\n",
    "ax1.errorbar(y=1.65,x=0.019315,xerr=1.96*0.017610,\n",
    "             linestyle='--', lw=2,marker='.', markersize=markersize, capsize=capsize, elinewidth=elinewidth, capthick=2, color = '#e54813', alpha=alpha)\n",
    "\n",
    "ax1.set_xlabel('Increase in Pr(Positive Review)\\n relative to non-SCR', fontsize=16)\n",
    "\n",
    "ax1.set_title('Comparing SCR and non-SCR\\nwithin-manuscript positivity by anonymization status\\n', fontsize=20)\n",
    "\n",
    "for ax in [ax1]:\n",
    "    \n",
    "    ax.set_yticks(ypos, ['','Visible\\nAuthor ID', 'Hidden\\nAuthor ID', ''],\n",
    "       rotation=0, fontsize=16)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=16)\n",
    "    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True)\n",
    "    ax.tick_params(axis='y', which='both', left=False, right=False)\n",
    "    ax.spines[['bottom', 'right', 'top']].set_visible(False)\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['left'].set_color('black')\n",
    "    # set left spine to bold\n",
    "    ax.spines['left'].set_linewidth(2)\n",
    "    # change alpha of grid\n",
    "    ax.grid(alpha=0.2, linestyle='--')\n",
    "    ax.yaxis.grid(False)\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "ax1.set_xlabel('Increase in Pr(Positive Review)\\n relative to non-SCR', fontsize=16)\n",
    "\n",
    "ax1.set_xlim([-0.03,0.07])\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCR Access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,  ax3 = plt.subplots(nrows=1,\n",
    "                                    ncols=1,\n",
    "                                    sharex=True,\n",
    "                                   figsize=(9,7))\n",
    "\n",
    "ypos = [0, .3, .7, 1.3, 1.7 , 2.3, 2.7, 3]\n",
    "ax3.barh(y=.3, width=df3['Coef'][0], xerr=df3['CI'][0], color=df3['Color'][0], height=.3)\n",
    "ax3.barh(y=.7, width=df3['Coef'][3], xerr=df3['CI'][3], color=df3['Color'][3], height=.3, alpha = .8)\n",
    "\n",
    "ax3.barh(y=1.3, width=df3['Coef'][1], xerr=df3['CI'][1], color=df3['Color'][1], height=.3)\n",
    "ax3.barh(y=1.7, width=df3['Coef'][4], xerr=df3['CI'][4], color=df3['Color'][4], height=.3, alpha = .8)\n",
    "\n",
    "ax3.barh(y=2.3, width=df3['Coef'][2], xerr=df3['CI'][2], color=df3['Color'][2], height=.3)\n",
    "ax3.barh(y=2.7, width=df3['Coef'][5], xerr=df3['CI'][5], color=df3['Color'][5], height=.3, alpha = .8)\n",
    "\n",
    "ax3.set_yticks(ypos, ['','Low- & Lower-Middle\\nIncome Countries','India', 'Upper-Middle\\nIncome Countries', 'China', 'High\\nIncome Countries', 'USA', ''], rotation=0, fontsize=18)\n",
    "\n",
    "ax3.set_title('Review Invitations\\n', fontsize=22)\n",
    "ax3.set_xlabel('Pr(Editor invites reviewer\\nfrom author country)', fontsize=20)\n",
    "ax3.yaxis.tick_left()\n",
    "\n",
    "for ax in [ax3]:\n",
    "    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True,labelsize=16)\n",
    "    ax.tick_params(axis='y', which='both', left=False, right=False, labelsize=18)\n",
    "    ax.invert_yaxis()\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['left'].set_color('black')\n",
    "\n",
    "    # set left spine to bold\n",
    "    ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "    # change alpha of grid\n",
    "    ax.grid(alpha=0.2, linestyle='--')\n",
    "    ax.yaxis.grid(False)\n",
    "\n",
    "    # set xticks every .1\n",
    "    ax.set_xticks(np.arange(0, 0.5, 0.1))\n",
    "    #set xlim 0-.5\n",
    "    ax.set_xlim(0,.41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>index</th>\n",
       "      <th>IncomeCat</th>\n",
       "      <th>Coef</th>\n",
       "      <th>SE</th>\n",
       "      <th>CI</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lead_income_cat[T.LLMIC]</td>\n",
       "      <td>LLMIC</td>\n",
       "      <td>0.091278</td>\n",
       "      <td>0.008765</td>\n",
       "      <td>0.017180</td>\n",
       "      <td>#004D40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lead_income_cat[T.HIC]</td>\n",
       "      <td>HIC</td>\n",
       "      <td>0.104778</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>0.019604</td>\n",
       "      <td>#D81B60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lead_income_cat[T.UMIC]</td>\n",
       "      <td>UMIC</td>\n",
       "      <td>0.202440</td>\n",
       "      <td>0.010554</td>\n",
       "      <td>0.020686</td>\n",
       "      <td>#1E88E5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      index IncomeCat      Coef        SE        CI    Color\n",
       "0  lead_income_cat[T.LLMIC]     LLMIC  0.091278  0.008765  0.017180  #004D40\n",
       "1    lead_income_cat[T.HIC]       HIC  0.104778  0.010002  0.019604  #D81B60\n",
       "2   lead_income_cat[T.UMIC]      UMIC  0.202440  0.010554  0.020686  #1E88E5"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### View regression results for Lead Author column in Table S10\n",
    "\n",
    "reg3 = PanelOLS.from_formula('SCR ~ income_cat + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=submitted_rev_income_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg3.params.index if p.startswith('income_cat[T.')]\n",
    "df3 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg3.params[paramscell], \n",
    "                    'SE':reg3.std_errors[paramscell]})\n",
    "Process(df3, colordict1)\n",
    "\n",
    "reg4 = PanelOLS.from_formula('SCRlead ~ lead_income_cat + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                            data=submitted_rev_leadincome_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg4.params.index if p.startswith('lead_income_cat[T.')]\n",
    "df4 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg4.params[paramscell], \n",
    "                    'SE':reg4.std_errors[paramscell]})\n",
    "Process(df4, colordict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax3, ax4] = plt.subplots(nrows=1,\n",
    "                                    ncols=2 ,\n",
    "                                    sharex=True,\n",
    "                                   figsize=(8, 3))\n",
    "###\n",
    "\n",
    "ax3.barh(y=0, width=df3['Coef'][0], xerr=df3['CI'][0], color=palette[0], height=.5)\n",
    "ax3.barh(y=1, width=df3['Coef'][2], xerr=df3['CI'][2], color=palette[1], height=.5)\n",
    "ax3.barh(y=2, width=df3['Coef'][1], xerr=df3['CI'][1], color=palette[2], height=.5)\n",
    "\n",
    "ax3.set_title('Corresponding Author\\n', fontsize=20)\n",
    "ax3.set_yticks(range(3), ['Low- & Lower-Middle\\nIncome Countries',\n",
    "                        'Upper-Middle\\nIncome Countries','High\\nIncome Countries'],\n",
    "           rotation=0, fontsize=20)   \n",
    "\n",
    "ax3.set_xlabel('Pr(Reviewer\\nfrom author country)', fontsize=20)\n",
    "\n",
    "ax4.barh(y=0, width=df4['Coef'][0], xerr=df4['CI'][0], color=palette[0], height=.5)\n",
    "ax4.barh(y=1, width=df4['Coef'][2], xerr=df4['CI'][2], color=palette[1], height=.5)\n",
    "ax4.barh(y=2, width=df4['Coef'][1], xerr=df4['CI'][1], color=palette[2], height=.5)\n",
    "\n",
    "ax4.set_title('Lead Author\\n', fontsize=22)\n",
    "ax4.set_xlabel('Pr(Reviewer\\nfrom author country)', fontsize=20)\n",
    "ax4.get_yaxis().set_ticks([])\n",
    "\n",
    "for ax in [ax3, ax4]:\n",
    "    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True,labelsize=16)\n",
    "    ax.tick_params(axis='y', which='both', left=False, right=False, labelsize=18)\n",
    "    ax.invert_yaxis()\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['left'].set_color('black')\n",
    "\n",
    "    # set left spine to bold\n",
    "    ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "    # change alpha of grid\n",
    "    ax.grid(alpha=0.2, linestyle='--')\n",
    "    ax.yaxis.grid(False)\n",
    "\n",
    "    # set xticks every .1\n",
    "    ax.set_xticks(np.arange(0, 0.21, 0.1))\n",
    "    # #set xlim 0-.5\n",
    "    # ax.set_xlim(0,.41)\n",
    "\n",
    "\n",
    "for i, ax in enumerate([ax3, ax4]):\n",
    "    ax.text(-0.065, 1.2, 'ABCDEFGHIJKL'[i], transform=ax.transAxes,\n",
    "      fontsize=20, fontweight='bold', va='top', ha='right')\n",
    "###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>index</th>\n",
       "      <th>IncomeCat</th>\n",
       "      <th>Coef</th>\n",
       "      <th>SE</th>\n",
       "      <th>CI</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>income_cat[T.LLMIC]</td>\n",
       "      <td>LLMIC</td>\n",
       "      <td>0.095596</td>\n",
       "      <td>0.009165</td>\n",
       "      <td>0.017963</td>\n",
       "      <td>#004D40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>income_cat[T.HIC]</td>\n",
       "      <td>HIC</td>\n",
       "      <td>0.101878</td>\n",
       "      <td>0.010188</td>\n",
       "      <td>0.019969</td>\n",
       "      <td>#D81B60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>income_cat[T.UMIC]</td>\n",
       "      <td>UMIC</td>\n",
       "      <td>0.207150</td>\n",
       "      <td>0.010993</td>\n",
       "      <td>0.021546</td>\n",
       "      <td>#1E88E5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 index IncomeCat      Coef        SE        CI    Color\n",
       "0  income_cat[T.LLMIC]     LLMIC  0.095596  0.009165  0.017963  #004D40\n",
       "1    income_cat[T.HIC]       HIC  0.101878  0.010188  0.019969  #D81B60\n",
       "2   income_cat[T.UMIC]      UMIC  0.207150  0.010993  0.021546  #1E88E5"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### View regression results for National and Regional Teams columns in Table S10\n",
    "\n",
    "reg1 = PanelOLS.from_formula('SCR ~ income_cat + ln_team_size_bin + anon_manu + EntityEffects',     \n",
    "                             data=submitted_rev_income_df).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg1.params.index if p.startswith('income_cat[T.')]\n",
    "df1 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg1.params[paramscell], \n",
    "                    'SE':reg1.std_errors[paramscell]})\n",
    "Process(df1, colordict1)\n",
    "\n",
    "reg2 = PanelOLS.from_formula('SCR ~ income_cat + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                             data=submitted_rev_income_df.loc[submitted_rev_income_df['national'] == 1]).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg2.params.index if p.startswith('income_cat[T.')]\n",
    "df2 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg2.params[paramscell], \n",
    "                    'SE':reg2.std_errors[paramscell]})\n",
    "Process(df2, colordict1)\n",
    "\n",
    "reg3 = PanelOLS.from_formula('SCR ~ income_cat + ln_team_size_bin + anon_manu + EntityEffects', \n",
    "                             data=submitted_rev_income_df.loc[submitted_rev_income_df['regional'] == 1]).fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "paramscell = [p for p in reg3.params.index if p.startswith('income_cat[T.')]\n",
    "df3 = pd.DataFrame({'IncomeCat': IncomeCats1, 'Coef': reg3.params[paramscell], \n",
    "                    'SE':reg3.std_errors[paramscell]})\n",
    "Process(df3, colordict1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax1, ax2, ax3] = plt.subplots(nrows=1,\n",
    "                                    ncols=3,\n",
    "                               sharex =True,\n",
    "                                   figsize=(12, 3))\n",
    "\n",
    "###\n",
    "\n",
    "ax1.barh(y=0, width=df3['Coef'][0], xerr=df3['CI'][0], color=palette[0], height=.5)\n",
    "ax1.barh(y=1, width=df3['Coef'][2], xerr=df3['CI'][2], color=palette[1], height=.5)\n",
    "ax1.barh(y=2, width=df3['Coef'][1], xerr=df3['CI'][1], color=palette[2], height=.5)\n",
    "\n",
    "ax1.set_title('All Teams\\n', fontsize=20)\n",
    "ax1.set_xlabel('Pr(Reviewer\\nfrom author country)', fontsize=20)\n",
    "ax1.set_yticks(range(3), ['Low- & Lower-Middle\\nIncome Countries',\n",
    "                        'Upper-Middle\\nIncome Countries','High\\nIncome Countries'],\n",
    "           rotation=0, fontsize=20)   \n",
    "\n",
    "ax2.barh(y=0, width=df1['Coef'][0], xerr=df1['CI'][0], color=palette[0], height=.5)\n",
    "ax2.barh(y=1, width=df1['Coef'][2], xerr=df1['CI'][2], color=palette[1], height=.5)\n",
    "ax2.barh(y=2, width=df1['Coef'][1], xerr=df1['CI'][1], color=palette[2], height=.5)\n",
    "ax2.set_title('National Teams\\n', fontsize=22)\n",
    "ax2.set_xlabel('Pr(Reviewer\\nfrom author country)', fontsize=20)\n",
    "ax2.get_yaxis().set_ticks([])\n",
    "\n",
    "###\n",
    "\n",
    "ax3.barh(y=0, width=df1['Coef'][0], xerr=df1['CI'][0], color=palette[0], height=.5)\n",
    "ax3.barh(y=1, width=df1['Coef'][2], xerr=df1['CI'][2], color=palette[1], height=.5)\n",
    "ax3.barh(y=2, width=df1['Coef'][1], xerr=df1['CI'][1], color=palette[2], height=.5)\n",
    "ax3.set_title('Regional Teams\\n', fontsize=22)\n",
    "ax3.set_xlabel('Pr(Reviewer\\nfrom author country)', fontsize=20)\n",
    "ax3.get_yaxis().set_ticks([])\n",
    "# no y tick labels\n",
    "\n",
    "for ax in [ax1,ax2,ax3]:\n",
    "    ax.tick_params(axis='x', which='major', labelsize=16)\n",
    "    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom =True)\n",
    "    ax.tick_params(axis='y', which='both', left=False, right=False)\n",
    "    ax.spines[['bottom', 'right', 'top']].set_visible(False)\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['left'].set_color('black')\n",
    "    # set left spine to bold\n",
    "    ax.spines['left'].set_linewidth(2)\n",
    "    # change alpha of grid\n",
    "    ax.grid(alpha=0.2, linestyle='--')\n",
    "    ax.yaxis.grid(False)\n",
    "    ax.invert_yaxis()\n",
    "    \n",
    "for i, ax in enumerate([ax1, ax2, ax3]):\n",
    "    ax.text(-0.065, 1.2, 'ABCDEFGHIJKL'[i], transform=ax.transAxes,\n",
    "      fontsize=18, fontweight='bold', va='top', ha='right')\n",
    "\n"
   ]
  }
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