allisonmorgan · October 26, 2020 21:53
diff --git a/eob_compared_to_nsf_sed.ipynb b/eob_compared_to_nsf_sed.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import numpy as np\n",
    "\n",
    "from scipy import stats\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From \"Expectations of brilliance underlie gender distributions across academic disciplines\": \n",
    "# https://science.sciencemag.org/content/sci/347/6219/262.full.pdf.\n",
    "eob_data = pd.read_csv('data.csv') # Thanks Andrei!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(eob_data.field_label.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>fab</td>       <th>  R-squared:         </th> <td>   0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   20.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Oct 2020</td> <th>  Prob (F-statistic):</th> <td>5.70e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:46:53</td>     <th>  Log-Likelihood:    </th> <td> -3228.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2323</td>      <th>  AIC:               </th> <td>   6461.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2321</td>      <th>  BIC:               </th> <td>   6472.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>    4.1095</td> <td>    0.066</td> <td>   61.937</td> <td> 0.000</td> <td>    3.979</td> <td>    4.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>perc_women</th> <td>   -0.0059</td> <td>    0.001</td> <td>   -4.548</td> <td> 0.000</td> <td>   -0.008</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.261</td> <th>  Durbin-Watson:     </th> <td>   1.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.878</td> <th>  Jarque-Bera (JB):  </th> <td>   0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.024</td> <th>  Prob(JB):          </th> <td>   0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.013</td> <th>  Cond. No.          </th> <td>    169.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    fab   R-squared:                       0.009\n",
       "Model:                            OLS   Adj. R-squared:                  0.008\n",
       "Method:                 Least Squares   F-statistic:                     20.68\n",
       "Date:                Mon, 26 Oct 2020   Prob (F-statistic):           5.70e-06\n",
       "Time:                        15:46:53   Log-Likelihood:                -3228.4\n",
       "No. Observations:                2323   AIC:                             6461.\n",
       "Df Residuals:                    2321   BIC:                             6472.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      4.1095      0.066     61.937      0.000       3.979       4.240\n",
       "perc_women    -0.0059      0.001     -4.548      0.000      -0.008      -0.003\n",
       "==============================================================================\n",
       "Omnibus:                        0.261   Durbin-Watson:                   1.967\n",
       "Prob(Omnibus):                  0.878   Jarque-Bera (JB):                0.234\n",
       "Skew:                          -0.024   Prob(JB):                        0.890\n",
       "Kurtosis:                       3.013   Cond. No.                         169.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As percentage of women in a field increases, the emphasis on brilliance is downplayed.\n",
    "res = smf.ols('fab ~ perc_women', data=eob_data[eob_data.female > 0]).fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation with NSF SED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supplemental materials for paper: \"Paradigms of Sex Research and Women in STEM\" (https://osf.io/4jmuw/). Thanks Jeff!\n",
    "nsf_sed = pd.read_csv('../../nsf_sed/osfstorage-archive/clean_nsf_gender_data.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subfield</th>\n",
       "      <th>year</th>\n",
       "      <th>pct_female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agricultural business and management</td>\n",
       "      <td>1970</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agricultural business and management</td>\n",
       "      <td>1971</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agricultural business and management</td>\n",
       "      <td>1972</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agricultural business and management</td>\n",
       "      <td>1973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agricultural business and management</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               subfield  year  pct_female\n",
       "0  Agricultural business and management  1970         NaN\n",
       "1  Agricultural business and management  1971         NaN\n",
       "2  Agricultural business and management  1972         NaN\n",
       "3  Agricultural business and management  1973         NaN\n",
       "4  Agricultural business and management  1974         NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsf_sed[['subfield', 'year', 'pct_female']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsf_sed_slopes = pd.DataFrame(columns = ['subfield', 'trend', 'pct_women'])\n",
    "\n",
    "for subfield in nsf_sed.subfield.unique():\n",
    "    nsf_sed_subfield = nsf_sed[nsf_sed.subfield == subfield].dropna(axis=0)\n",
    "    if len(nsf_sed_subfield) > 1:\n",
    "        avg_year = nsf_sed_subfield['year'].mean()\n",
    "        std_year = nsf_sed_subfield['year'].std()\n",
    "        nsf_sed_subfield['year_scaled'] = (nsf_sed_subfield['year'] - avg_year)/std_year\n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(nsf_sed_subfield['year_scaled'], nsf_sed_subfield['pct_female'])\n",
    "        \n",
    "        most_recent_year = nsf_sed_subfield.year.max()\n",
    "        pct_women = nsf_sed_subfield[nsf_sed_subfield.year == most_recent_year].pct_female.values[0]\n",
    "    else:\n",
    "        slope, intercept, r_value, p_value, std_err = np.nan, np.nan, np.nan, np.nan, np.nan\n",
    "        pct_women = np.nan\n",
    "    \n",
    "    nsf_sed_slopes = nsf_sed_slopes.append({'subfield': subfield, 'trend': slope, 'pct_women': pct_women}, ignore_index=True)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>subfield</th>\n",
       "      <th>trend</th>\n",
       "      <th>pct_women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agricultural business and management</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agricultural economics</td>\n",
       "      <td>12.220621</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agricultural and horticultural plant breeding</td>\n",
       "      <td>10.121519</td>\n",
       "      <td>51.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agricultural animal breeding</td>\n",
       "      <td>7.786816</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agronomy and crop science</td>\n",
       "      <td>8.881026</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        subfield      trend  pct_women\n",
       "0           Agricultural business and management   0.000000        0.0\n",
       "1                         Agricultural economics  12.220621       41.0\n",
       "2  Agricultural and horticultural plant breeding  10.121519       51.9\n",
       "3                   Agricultural animal breeding   7.786816       40.0\n",
       "4                      Agronomy and crop science   8.881026       31.4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsf_sed_slopes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Biochemistry', 'EvolutionaryBiology', 'MolecularBiology',\n",
       "       'Chemistry', 'EarthScience', 'Astronomy', 'Physics',\n",
       "       'ComputerScience', 'Mathematics', 'Psychology', 'Neuroscience',\n",
       "       'Economics', 'Political Science', 'Sociology', 'Linguistics',\n",
       "       'Engineering', 'Education', 'Spanish', 'History', 'Classics',\n",
       "       'ComparativeLiterature', 'MusicTheoryComposition', 'Philosophy',\n",
       "       'CommunicationStudies'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {'Anthropology': 'Anthropology', 'Astronomy': 'Astronomy', 'Biochemistry (biological sciences)': 'Biochemistry', 'Chemistry': 'Chemistry', 'Classics': 'Classics', 'Communication': 'CommunicationStudies', 'Comparative literature': 'ComparativeLiterature', 'Computer and information sciences': 'ComputerScience', 'Geological and earth sciences, general': 'EarthScience', 'Economics': 'Economics', 'Education': 'Education', 'Engineering': 'Engineering', 'Evolutionary biology': 'EvolutionaryBiology', 'History': 'History', 'Linguistics': 'Linguistics', 'Mathematics and statistics, general': 'Mathematics', 'Molecular biology': 'MolecularBiology', 'Music theory and composition': 'MusicTheoryComposition', 'Cognitive neuroscience': 'Neuroscience', 'Philosophy': 'Philosophy', 'Physics, general': 'Physics', 'Political science': 'Political Science', 'Psychology': 'Psychology', 'Sociology': 'Sociology', 'Spanish language and literature': 'Spanish'}\n",
    "\n",
    "nsf_sed_slopes['subfield'] = nsf_sed_slopes['subfield'].map(mapping)\n",
    "nsf_sed_slopes['subfield'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "field_mean_fab = eob_data.groupby(['field_label']).fab.mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fab_slopes_merged = nsf_sed_slopes.merge(field_mean_fab, how='inner', left_on='subfield', right_on='field_label')[['subfield', 'trend', 'pct_women', 'fab']]\n",
    "fab_slopes_merged = fab_slopes_merged.dropna(axis=0)\n",
    "fab_slopes_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Biochemistry\n",
       "1        EvolutionaryBiology\n",
       "2           MolecularBiology\n",
       "4               EarthScience\n",
       "5                  Astronomy\n",
       "6                    Physics\n",
       "8                Mathematics\n",
       "10              Neuroscience\n",
       "13                 Sociology\n",
       "14               Linguistics\n",
       "17                   Spanish\n",
       "19                  Classics\n",
       "20     ComparativeLiterature\n",
       "21    MusicTheoryComposition\n",
       "22                Philosophy\n",
       "Name: subfield, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fab_slopes_merged.subfield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00762057420301425 0.7213469750212782 0.020907065653016504\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "\n",
    "ax.scatter(fab_slopes_merged.trend, fab_slopes_merged.fab, edgecolor='white')\n",
    "ax.set_xlabel('Slope of percent female trend line (Lockhart, NSF SED)')\n",
    "ax.set_ylabel('Average expectation of brilliance\\nper field (Leslie et. al., Science 2015)')\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(fab_slopes_merged.trend, fab_slopes_merged.fab)\n",
    "print(slope, p_value, std_err)\n",
    "\n",
    "ax.plot(range(-3, 17), [intercept for x in range(-3, 17)], color='k', linestyle=':')\n",
    "\n",
    "shift = 0.5\n",
    "for field in ['Astronomy', 'Philosophy', 'Physics', 'Mathematics', 'Sociology', 'Linguistics', 'Classics', 'EvolutionaryBiology']:\n",
    "    field_point = fab_slopes_merged[fab_slopes_merged.subfield == field]\n",
    "    ax.text(field_point.trend + shift, field_point.fab, field)\n",
    "\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('correlation_between_EOB_and_SED_trends.pdf', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.016525411353549544 0.013104409815010404 0.005754899515784754\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "\n",
    "ax.scatter(fab_slopes_merged.pct_women, fab_slopes_merged.fab, edgecolor='white')\n",
    "ax.set_xlabel('Recent estimates of percent female (Lockhart, NSF SED)')\n",
    "ax.set_ylabel('Average expectation of brilliance\\nper field (Leslie et. al., Science 2015)')\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(fab_slopes_merged.pct_women, fab_slopes_merged.fab)\n",
    "print(slope, p_value, std_err)\n",
    "ax.plot(range(0, 80), [intercept + slope*x for x in range(0, 80)], color='k', linestyle=':')\n",
    "\n",
    "shift = 3\n",
    "for field in ['Astronomy', 'Philosophy', 'Physics', 'Mathematics', 'Sociology', 'Linguistics', 'Classics', 'EvolutionaryBiology']:\n",
    "    field_point = fab_slopes_merged[fab_slopes_merged.subfield == field]\n",
    "    ax.text(field_point.pct_women + shift, field_point.fab, field)\n",
    "\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('correlation_between_EOB_and_SED_female.pdf', dpi=500)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
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