6_demographics_bias.ipynb

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        "<a href=\"https://colab.research.google.com/github/ipeirotis/mturk_demographics/blob/master/6_demographics_bias.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
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        "id": "UKhEi_CQ9zfq"
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      "source": [
        "# Load the data and process it.\n",
        "import pandas as pd\n",
        "URL = 'https://github.com/ipeirotis/mturk_demographics/raw/master/dataset_29may2023_16_mar2015.zip'\n",
        "df = pd.read_csv(URL, low_memory=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df=df.dropna()\n",
        "grouped_size = df.groupby(['worker_id','hit_answered_date','gender','household_income','household_size','location_country','time_spent_on_mturk','marital_status','year_of_birth']).transform('size')\n",
        "# Map the group sizes to the original DataFrame based on index\n",
        "df['count'] = grouped_size\n",
        "\n",
        "# Display the dataset with the new \"row_count\" column\n",
        "print(len(df['worker_id'].unique()))\n",
        "print(df['count'].value_counts())"
      ],
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        "id": "iX0EVHW8B1DT",
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          "name": "stdout",
          "text": [
            "123508\n",
            "1.0    207279\n",
            "2.0       862\n",
            "3.0        60\n",
            "5.0         5\n",
            "Name: count, dtype: int64\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a new column for 'worker_id' counts\n",
        "df['participation'] = df['worker_id'].map(df['worker_id'].value_counts())\n",
        "print(len(df['worker_id'].unique()))"
      ],
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        "id": "IEzl6fRkTK4f",
        "outputId": "c240c37d-72f5-443a-9a9c-850d7aa1d700"
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      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "123508\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= df[['worker_id','gender','household_income','household_size','location_country','marital_status','year_of_birth','time_spent_on_mturk','count','participation']]\n",
        "\n",
        "#dem_df = df.drop_duplicates()\n",
        "dem_df = df.drop_duplicates(subset=['worker_id'])\n",
        "\n",
        "dem_df=dem_df.dropna()\n",
        "print(len(dem_df))"
      ],
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          "name": "stdout",
          "text": [
            "87318\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dem_df.head(5)"
      ],
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          "height": 226
        },
        "id": "CpQaS_rUp35s",
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      },
      "execution_count": 5,
      "outputs": [
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              "                                      worker_id  gender household_income  \\\n",
              "0  GIQvi1p3x6/xUloDxpXza3DJKzO9QxtN0yPb49FMiY8=    male  $25,000-$39,999   \n",
              "1  OuVAI9jQUJ1TrM8tfCrr70KsearC0kNbovFD9prQ91M=    male  $40,000-$59,999   \n",
              "2  rXYK7nenLvxPYqFIOk9Ik5ALPFsXma+Lah1+1Ct+5y4=  female  $40,000-$59,999   \n",
              "3  ndej+ke5aXKdkZmPuI2Vz878ZjtyA4r2+7D4zA05Pvw=    male  $75,000-$99,999   \n",
              "4  E2skbsh2TBfWqeUSwFDajfQfJpMB2hqjKkzFh26W2PY=  female  $75,000-$99,999   \n",
              "\n",
              "  household_size location_country marital_status  year_of_birth  \\\n",
              "0              2               ZZ        married         1982.0   \n",
              "1              3               ZZ        married         1930.0   \n",
              "2              2               ZZ         single         1994.0   \n",
              "3              4               ZZ        married         1991.0   \n",
              "4              4               ZZ        married         1996.0   \n",
              "\n",
              "  time_spent_on_mturk  count  participation  \n",
              "0  2-4 hours per week    1.0            4.0  \n",
              "1  2-4 hours per week    1.0            5.0  \n",
              "2  4-8 hours per week    1.0            4.0  \n",
              "3  2-4 hours per week    1.0           25.0  \n",
              "4  2-4 hours per week    1.0           21.0  "
            ],
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              "      <th>0</th>\n",
              "      <td>GIQvi1p3x6/xUloDxpXza3DJKzO9QxtN0yPb49FMiY8=</td>\n",
              "      <td>male</td>\n",
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              "      <td>2</td>\n",
              "      <td>ZZ</td>\n",
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              "      <td>1994.0</td>\n",
              "      <td>4-8 hours per week</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>ndej+ke5aXKdkZmPuI2Vz878ZjtyA4r2+7D4zA05Pvw=</td>\n",
              "      <td>male</td>\n",
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              "      <td>4</td>\n",
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              "      <td>1991.0</td>\n",
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              "      <td>female</td>\n",
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              "      <td>4</td>\n",
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              "      <td>1996.0</td>\n",
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              "      <td>21.0</td>\n",
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          "metadata": {},
          "execution_count": 5
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    {
      "cell_type": "code",
      "source": [
        "from scipy.special import logit\n",
        "import pandas as pd\n",
        "\n",
        "## Regression against the demographics:\n",
        "## DJ preprocessed the data using sql\n",
        "## 1 worker per row: count\n",
        "##    - count: number of identical rows\n",
        "##    - participation: number of submissions\n",
        "##  if count <> participation, worker changed his reponses (participation - count) times\n",
        "\n",
        "# dem_df = pd.read_csv(\"demographics_preprocessed.csv\")\n",
        "\n",
        "dem_df[\"household_income\"]=dem_df[\"household_income\"].astype(\"category\")\n",
        "dem_df[\"household_size\"]=dem_df[\"household_size\"].astype(\"category\")\n",
        "dem_df[\"location_country\"]=dem_df[\"location_country\"].astype(\"category\")\n",
        "dem_df[\"marital_status\"]=dem_df[\"marital_status\"].astype(\"category\")\n",
        "dem_df[\"year_of_birth\"]=dem_df[\"year_of_birth\"].astype(int)\n",
        "dem_df[\"time_spent_on_mturk\"]=dem_df[\"time_spent_on_mturk\"].astype(\"category\")\n",
        "\n",
        "dem_df['propensity'] = dem_df['participation']/29\n",
        "dem_df['life_event'] = dem_df['count']!= dem_df['participation']\n",
        "dem_df['life_event'] = dem_df['life_event'].apply(lambda x : 1 if x else 0)\n",
        "dem_df['location_country'] = dem_df['location_country'].apply(lambda x : x if x in ['US','IN','CA','GB','PH'] else 'OTHER')\n",
        "dem_df['age'] = 2023 - dem_df['year_of_birth']\n",
        "\n",
        "len(dem_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrsKwoqngiZP",
        "outputId": "c326c52f-a60d-4438-d83a-ade7a84cf055"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "87318"
            ]
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          "metadata": {},
          "execution_count": 6
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      "execution_count": 7,
      "metadata": {
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          "height": 1000
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        "id": "x7GK1CZJ9zfs",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:             propensity   R-squared:                       0.201\n",
              "Model:                            OLS   Adj. R-squared:                  0.201\n",
              "Method:                 Least Squares   F-statistic:                     731.5\n",
              "Date:                Wed, 30 Aug 2023   Prob (F-statistic):               0.00\n",
              "Time:                        13:33:31   Log-Likelihood:                 51696.\n",
              "No. Observations:               87318   AIC:                        -1.033e+05\n",
              "Df Residuals:                   87287   BIC:                        -1.030e+05\n",
              "Df Model:                          30                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "=========================================================================================================================\n",
              "                                                            coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------------------------------------------------\n",
              "Intercept                                                 0.0247      0.005      4.850      0.000       0.015       0.035\n",
              "C(location_country)[T.GB]                                -0.0047      0.005     -0.991      0.322      -0.014       0.005\n",
              "C(location_country)[T.IN]                                 0.0756      0.004     18.973      0.000       0.068       0.083\n",
              "C(location_country)[T.OTHER]                              0.0053      0.004      1.350      0.177      -0.002       0.013\n",
              "C(location_country)[T.PH]                                 0.0296      0.016      1.842      0.065      -0.002       0.061\n",
              "C(location_country)[T.US]                                 0.0018      0.004      0.496      0.620      -0.005       0.009\n",
              "C(gender)[T.male]                                         0.0007      0.001      0.799      0.424      -0.001       0.003\n",
              "C(household_income)[T.$100,000 or more]                   0.0177      0.002      7.414      0.000       0.013       0.022\n",
              "C(household_income)[T.$15,000-$24,999]                    0.0059      0.002      2.469      0.014       0.001       0.011\n",
              "C(household_income)[T.$25,000-$39,999]                    0.0088      0.002      4.018      0.000       0.004       0.013\n",
              "C(household_income)[T.$40,000-$59,999]                    0.0061      0.002      2.906      0.004       0.002       0.010\n",
              "C(household_income)[T.$60,000-$74,999]                    0.0108      0.002      4.826      0.000       0.006       0.015\n",
              "C(household_income)[T.$75,000-$99,999]                    0.0131      0.002      5.874      0.000       0.009       0.017\n",
              "C(household_income)[T.Less than $10,000]                  0.0047      0.003      1.860      0.063      -0.000       0.010\n",
              "C(household_size)[T.2]                                   -0.0123      0.002     -6.698      0.000      -0.016      -0.009\n",
              "C(household_size)[T.3]                                   -0.0194      0.002    -10.830      0.000      -0.023      -0.016\n",
              "C(household_size)[T.4]                                   -0.0211      0.002    -11.625      0.000      -0.025      -0.018\n",
              "C(household_size)[T.5 ]                                  -0.1152      0.134     -0.860      0.390      -0.378       0.147\n",
              "C(household_size)[T.5+]                                  -0.0154      0.002     -7.242      0.000      -0.020      -0.011\n",
              "C(marital_status)[T.divorced]                            -0.0031      0.004     -0.872      0.383      -0.010       0.004\n",
              "C(marital_status)[T.engaged]                             -0.0105      0.003     -3.045      0.002      -0.017      -0.004\n",
              "C(marital_status)[T.married]                             -0.0149      0.002     -6.766      0.000      -0.019      -0.011\n",
              "C(marital_status)[T.single]                              -0.0039      0.002     -1.698      0.089      -0.008       0.001\n",
              "C(time_spent_on_mturk)[T.2-4 hours per week]             -0.0030      0.002     -1.561      0.118      -0.007       0.001\n",
              "C(time_spent_on_mturk)[T.20-40 hours per week]            0.0068      0.002      3.491      0.000       0.003       0.011\n",
              "C(time_spent_on_mturk)[T.4-8 hours per week]             -0.0019      0.002     -1.029      0.304      -0.005       0.002\n",
              "C(time_spent_on_mturk)[T.8-20 hours per week]             0.0052      0.002      2.884      0.004       0.002       0.009\n",
              "C(time_spent_on_mturk)[T.Less than 1 hour per week]      -0.0011      0.003     -0.377      0.706      -0.007       0.005\n",
              "C(time_spent_on_mturk)[T.More than 40 hours per week]     0.0041      0.002      1.836      0.066      -0.000       0.009\n",
              "C(life_event)[T.1]                                        0.1284      0.001    133.018      0.000       0.127       0.130\n",
              "age                                                       0.0005   4.01e-05     13.552      0.000       0.000       0.001\n",
              "==============================================================================\n",
              "Omnibus:                   121269.717   Durbin-Watson:                   1.751\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         34672936.924\n",
              "Skew:                           8.206   Prob(JB):                         0.00\n",
              "Kurtosis:                      99.233   Cond. No.                     1.17e+04\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 1.17e+04. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>propensity</td>    <th>  R-squared:         </th>  <td>   0.201</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.201</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   731.5</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Wed, 30 Aug 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>13:33:31</td>     <th>  Log-Likelihood:    </th>  <td>  51696.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td> 87318</td>      <th>  AIC:               </th> <td>-1.033e+05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td> 87287</td>      <th>  BIC:               </th> <td>-1.030e+05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    30</td>      <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                            <td></td>                               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>                                             <td>    0.0247</td> <td>    0.005</td> <td>    4.850</td> <td> 0.000</td> <td>    0.015</td> <td>    0.035</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(location_country)[T.GB]</th>                             <td>   -0.0047</td> <td>    0.005</td> <td>   -0.991</td> <td> 0.322</td> <td>   -0.014</td> <td>    0.005</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(location_country)[T.IN]</th>                             <td>    0.0756</td> <td>    0.004</td> <td>   18.973</td> <td> 0.000</td> <td>    0.068</td> <td>    0.083</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(location_country)[T.OTHER]</th>                          <td>    0.0053</td> <td>    0.004</td> <td>    1.350</td> <td> 0.177</td> <td>   -0.002</td> <td>    0.013</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(location_country)[T.PH]</th>                             <td>    0.0296</td> <td>    0.016</td> <td>    1.842</td> <td> 0.065</td> <td>   -0.002</td> <td>    0.061</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(location_country)[T.US]</th>                             <td>    0.0018</td> <td>    0.004</td> <td>    0.496</td> <td> 0.620</td> <td>   -0.005</td> <td>    0.009</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(gender)[T.male]</th>                                     <td>    0.0007</td> <td>    0.001</td> <td>    0.799</td> <td> 0.424</td> <td>   -0.001</td> <td>    0.003</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.$100,000 or more]</th>               <td>    0.0177</td> <td>    0.002</td> <td>    7.414</td> <td> 0.000</td> <td>    0.013</td> <td>    0.022</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.$15,000-$24,999]</th>                <td>    0.0059</td> <td>    0.002</td> <td>    2.469</td> <td> 0.014</td> <td>    0.001</td> <td>    0.011</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.$25,000-$39,999]</th>                <td>    0.0088</td> <td>    0.002</td> <td>    4.018</td> <td> 0.000</td> <td>    0.004</td> <td>    0.013</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.$40,000-$59,999]</th>                <td>    0.0061</td> <td>    0.002</td> <td>    2.906</td> <td> 0.004</td> <td>    0.002</td> <td>    0.010</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.$60,000-$74,999]</th>                <td>    0.0108</td> <td>    0.002</td> <td>    4.826</td> <td> 0.000</td> <td>    0.006</td> <td>    0.015</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.$75,000-$99,999]</th>                <td>    0.0131</td> <td>    0.002</td> <td>    5.874</td> <td> 0.000</td> <td>    0.009</td> <td>    0.017</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_income)[T.Less than $10,000]</th>              <td>    0.0047</td> <td>    0.003</td> <td>    1.860</td> <td> 0.063</td> <td>   -0.000</td> <td>    0.010</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_size)[T.2]</th>                                <td>   -0.0123</td> <td>    0.002</td> <td>   -6.698</td> <td> 0.000</td> <td>   -0.016</td> <td>   -0.009</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_size)[T.3]</th>                                <td>   -0.0194</td> <td>    0.002</td> <td>  -10.830</td> <td> 0.000</td> <td>   -0.023</td> <td>   -0.016</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_size)[T.4]</th>                                <td>   -0.0211</td> <td>    0.002</td> <td>  -11.625</td> <td> 0.000</td> <td>   -0.025</td> <td>   -0.018</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_size)[T.5 ]</th>                               <td>   -0.1152</td> <td>    0.134</td> <td>   -0.860</td> <td> 0.390</td> <td>   -0.378</td> <td>    0.147</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(household_size)[T.5+]</th>                               <td>   -0.0154</td> <td>    0.002</td> <td>   -7.242</td> <td> 0.000</td> <td>   -0.020</td> <td>   -0.011</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(marital_status)[T.divorced]</th>                         <td>   -0.0031</td> <td>    0.004</td> <td>   -0.872</td> <td> 0.383</td> <td>   -0.010</td> <td>    0.004</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(marital_status)[T.engaged]</th>                          <td>   -0.0105</td> <td>    0.003</td> <td>   -3.045</td> <td> 0.002</td> <td>   -0.017</td> <td>   -0.004</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(marital_status)[T.married]</th>                          <td>   -0.0149</td> <td>    0.002</td> <td>   -6.766</td> <td> 0.000</td> <td>   -0.019</td> <td>   -0.011</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(marital_status)[T.single]</th>                           <td>   -0.0039</td> <td>    0.002</td> <td>   -1.698</td> <td> 0.089</td> <td>   -0.008</td> <td>    0.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(time_spent_on_mturk)[T.2-4 hours per week]</th>          <td>   -0.0030</td> <td>    0.002</td> <td>   -1.561</td> <td> 0.118</td> <td>   -0.007</td> <td>    0.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(time_spent_on_mturk)[T.20-40 hours per week]</th>        <td>    0.0068</td> <td>    0.002</td> <td>    3.491</td> <td> 0.000</td> <td>    0.003</td> <td>    0.011</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(time_spent_on_mturk)[T.4-8 hours per week]</th>          <td>   -0.0019</td> <td>    0.002</td> <td>   -1.029</td> <td> 0.304</td> <td>   -0.005</td> <td>    0.002</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(time_spent_on_mturk)[T.8-20 hours per week]</th>         <td>    0.0052</td> <td>    0.002</td> <td>    2.884</td> <td> 0.004</td> <td>    0.002</td> <td>    0.009</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(time_spent_on_mturk)[T.Less than 1 hour per week]</th>   <td>   -0.0011</td> <td>    0.003</td> <td>   -0.377</td> <td> 0.706</td> <td>   -0.007</td> <td>    0.005</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(time_spent_on_mturk)[T.More than 40 hours per week]</th> <td>    0.0041</td> <td>    0.002</td> <td>    1.836</td> <td> 0.066</td> <td>   -0.000</td> <td>    0.009</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(life_event)[T.1]</th>                                    <td>    0.1284</td> <td>    0.001</td> <td>  133.018</td> <td> 0.000</td> <td>    0.127</td> <td>    0.130</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>age</th>                                                   <td>    0.0005</td> <td> 4.01e-05</td> <td>   13.552</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>121269.717</td> <th>  Durbin-Watson:     </th>   <td>   1.751</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>34672936.924</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>            <td> 8.206</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>        <td>99.233</td>   <th>  Cond. No.          </th>   <td>1.17e+04</td>  \n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.17e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}                                           &    propensity    & \\textbf{  R-squared:         } &      0.201    \\\\\n\\textbf{Model:}                                                   &       OLS        & \\textbf{  Adj. R-squared:    } &      0.201    \\\\\n\\textbf{Method:}                                                  &  Least Squares   & \\textbf{  F-statistic:       } &      731.5    \\\\\n\\textbf{Date:}                                                    & Wed, 30 Aug 2023 & \\textbf{  Prob (F-statistic):} &      0.00     \\\\\n\\textbf{Time:}                                                    &     13:33:31     & \\textbf{  Log-Likelihood:    } &     51696.    \\\\\n\\textbf{No. Observations:}                                        &       87318      & \\textbf{  AIC:               } &  -1.033e+05   \\\\\n\\textbf{Df Residuals:}                                            &       87287      & \\textbf{  BIC:               } &  -1.030e+05   \\\\\n\\textbf{Df Model:}                                                &          30      & \\textbf{                     } &               \\\\\n\\textbf{Covariance Type:}                                         &    nonrobust     & \\textbf{                     } &               \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                                                                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{Intercept}                                                &       0.0247  &        0.005     &     4.850  &         0.000        &        0.015    &        0.035     \\\\\n\\textbf{C(location\\_country)[T.GB]}                               &      -0.0047  &        0.005     &    -0.991  &         0.322        &       -0.014    &        0.005     \\\\\n\\textbf{C(location\\_country)[T.IN]}                               &       0.0756  &        0.004     &    18.973  &         0.000        &        0.068    &        0.083     \\\\\n\\textbf{C(location\\_country)[T.OTHER]}                            &       0.0053  &        0.004     &     1.350  &         0.177        &       -0.002    &        0.013     \\\\\n\\textbf{C(location\\_country)[T.PH]}                               &       0.0296  &        0.016     &     1.842  &         0.065        &       -0.002    &        0.061     \\\\\n\\textbf{C(location\\_country)[T.US]}                               &       0.0018  &        0.004     &     0.496  &         0.620        &       -0.005    &        0.009     \\\\\n\\textbf{C(gender)[T.male]}                                        &       0.0007  &        0.001     &     0.799  &         0.424        &       -0.001    &        0.003     \\\\\n\\textbf{C(household\\_income)[T.\\$100,000 or more]}                &       0.0177  &        0.002     &     7.414  &         0.000        &        0.013    &        0.022     \\\\\n\\textbf{C(household\\_income)[T.\\$15,000-\\$24,999]}                &       0.0059  &        0.002     &     2.469  &         0.014        &        0.001    &        0.011     \\\\\n\\textbf{C(household\\_income)[T.\\$25,000-\\$39,999]}                &       0.0088  &        0.002     &     4.018  &         0.000        &        0.004    &        0.013     \\\\\n\\textbf{C(household\\_income)[T.\\$40,000-\\$59,999]}                &       0.0061  &        0.002     &     2.906  &         0.004        &        0.002    &        0.010     \\\\\n\\textbf{C(household\\_income)[T.\\$60,000-\\$74,999]}                &       0.0108  &        0.002     &     4.826  &         0.000        &        0.006    &        0.015     \\\\\n\\textbf{C(household\\_income)[T.\\$75,000-\\$99,999]}                &       0.0131  &        0.002     &     5.874  &         0.000        &        0.009    &        0.017     \\\\\n\\textbf{C(household\\_income)[T.Less than \\$10,000]}               &       0.0047  &        0.003     &     1.860  &         0.063        &       -0.000    &        0.010     \\\\\n\\textbf{C(household\\_size)[T.2]}                                  &      -0.0123  &        0.002     &    -6.698  &         0.000        &       -0.016    &       -0.009     \\\\\n\\textbf{C(household\\_size)[T.3]}                                  &      -0.0194  &        0.002     &   -10.830  &         0.000        &       -0.023    &       -0.016     \\\\\n\\textbf{C(household\\_size)[T.4]}                                  &      -0.0211  &        0.002     &   -11.625  &         0.000        &       -0.025    &       -0.018     \\\\\n\\textbf{C(household\\_size)[T.5 ]}                                 &      -0.1152  &        0.134     &    -0.860  &         0.390        &       -0.378    &        0.147     \\\\\n\\textbf{C(household\\_size)[T.5+]}                                 &      -0.0154  &        0.002     &    -7.242  &         0.000        &       -0.020    &       -0.011     \\\\\n\\textbf{C(marital\\_status)[T.divorced]}                           &      -0.0031  &        0.004     &    -0.872  &         0.383        &       -0.010    &        0.004     \\\\\n\\textbf{C(marital\\_status)[T.engaged]}                            &      -0.0105  &        0.003     &    -3.045  &         0.002        &       -0.017    &       -0.004     \\\\\n\\textbf{C(marital\\_status)[T.married]}                            &      -0.0149  &        0.002     &    -6.766  &         0.000        &       -0.019    &       -0.011     \\\\\n\\textbf{C(marital\\_status)[T.single]}                             &      -0.0039  &        0.002     &    -1.698  &         0.089        &       -0.008    &        0.001     \\\\\n\\textbf{C(time\\_spent\\_on\\_mturk)[T.2-4 hours per week]}          &      -0.0030  &        0.002     &    -1.561  &         0.118        &       -0.007    &        0.001     \\\\\n\\textbf{C(time\\_spent\\_on\\_mturk)[T.20-40 hours per week]}        &       0.0068  &        0.002     &     3.491  &         0.000        &        0.003    &        0.011     \\\\\n\\textbf{C(time\\_spent\\_on\\_mturk)[T.4-8 hours per week]}          &      -0.0019  &        0.002     &    -1.029  &         0.304        &       -0.005    &        0.002     \\\\\n\\textbf{C(time\\_spent\\_on\\_mturk)[T.8-20 hours per week]}         &       0.0052  &        0.002     &     2.884  &         0.004        &        0.002    &        0.009     \\\\\n\\textbf{C(time\\_spent\\_on\\_mturk)[T.Less than 1 hour per week]}   &      -0.0011  &        0.003     &    -0.377  &         0.706        &       -0.007    &        0.005     \\\\\n\\textbf{C(time\\_spent\\_on\\_mturk)[T.More than 40 hours per week]} &       0.0041  &        0.002     &     1.836  &         0.066        &       -0.000    &        0.009     \\\\\n\\textbf{C(life\\_event)[T.1]}                                      &       0.1284  &        0.001     &   133.018  &         0.000        &        0.127    &        0.130     \\\\\n\\textbf{age}                                                      &       0.0005  &     4.01e-05     &    13.552  &         0.000        &        0.000    &        0.001     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:}       & 121269.717 & \\textbf{  Durbin-Watson:     } &      1.751    \\\\\n\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 34672936.924  \\\\\n\\textbf{Skew:}          &    8.206   & \\textbf{  Prob(JB):          } &       0.00    \\\\\n\\textbf{Kurtosis:}      &   99.233   & \\textbf{  Cond. No.          } &   1.17e+04    \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n [2] The condition number is large, 1.17e+04. This might indicate that there are \\newline\n strong multicollinearity or other numerical problems."
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import math\n",
        "import statsmodels.formula.api as sm\n",
        "\n",
        "formula = \"propensity ~ C(location_country) + C(gender) +  C(household_income)+ C(household_size) + C(marital_status) + C(time_spent_on_mturk)+ age + C(life_event)\"\n",
        "\n",
        "result = sm.ols(formula=formula, data=dem_df).fit()\n",
        "result.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "CyBZl-dX9zft"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "mpl.rc(\"figure\", figsize=(16, 8))\n",
        "mpl.rcParams.update({'font.size': 22})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "ALu_4Z-x9zft",
        "outputId": "918069b4-445f-4783-e41f-4b248ac880e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "dem_df.propensity.plot.hist(log=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "id": "ab2lvOnq9zft",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d5c7c90-66f0-4cf5-f653-b68d7fccce96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81.36352183971232"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "len(dem_df[dem_df['propensity']<0.1])/len(dem_df)*100"
      ]
    }
  ],
  "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": "3.5.2"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}