11_28_2017_Foong_EECS Qual_Data Analyses_By Country.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How Much Do Men and Women Ask For in Hourly Bill Rate on Upwork (Top 10 Countries)? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "import httplib2\n",
    "import oauth2\n",
    "import urllib3\n",
    "import types\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from gender_detector import GenderDetector \n",
    "import psycopg2, psycopg2.extras\n",
    "from causalinference import CausalModel\n",
    "from causalinference.utils import random_data\n",
    "import httplib\n",
    "import base64\n",
    "import json # For Microsoft Face API\n",
    "import urllib as urllib # For Microsoft Face API\n",
    "import time \n",
    "import csv\n",
    "import datetime \n",
    "from statsmodels.formula.api import ols\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Initialize arrays for Causal Analysis \n",
    "user_count = 1\n",
    "bill_rate_array = []\n",
    "gender_array = []\n",
    "all_covariates_array = []\n",
    "\n",
    "# Read data from csv files and reformat\n",
    "all_data_file_name = '11_21_2017_upwork_analysis_worldwide_allskills.csv' # Filename for all data\n",
    "gender_data_file_name = '11_20_2017_upwork_gender_analysis_worldwide_allskills.csv' # Filename for gender data\n",
    "df = pd.read_csv(all_data_file_name)\n",
    "df_gender = pd.read_csv(gender_data_file_name)\n",
    "\n",
    "# Merge the files, then remove rows with an error, no gender identified, or ambiguous education\n",
    "merged = df.merge(df_gender, on='user_count')\n",
    "merged = merged[merged.bill_rate != 'error']\n",
    "merged = merged[merged.gender != 'unidentified']\n",
    "merged = merged[merged.gender != 'error']\n",
    "merged = merged[merged.education != 'None']\n",
    "merged = merged[merged.job_category != 'none']\n",
    "#merged = merged[merged.country == 'United States'] # Look only at data in the United States, which is the largest dataset\n",
    "\n",
    "all_bill_rates = merged.bill_rate.astype('float')\n",
    "merged['bill_rate'] = merged.bill_rate.astype('float')\n",
    "merged['work_experience'] = merged.work_experience.astype('float')\n",
    "all_work_experience = merged.work_experience\n",
    "all_education_id = merged.education_id\n",
    "all_job_category_id = merged.job_category_id\n",
    "all_country_id = merged.country_id\n",
    "all_genders = merged.gender\n",
    "female_count = 0\n",
    "male_count = 0\n",
    "\n",
    "# Converting covariates to a matrix on a dichotomous scale\n",
    "def make_dichotomous_matrix(id_value, covariate, final_matrix):\n",
    "    for option in list(set(covariate)):\n",
    "        if (id_value == option):\n",
    "            final_matrix.append(1)\n",
    "        else:\n",
    "            final_matrix.append(0)\n",
    "    return final_matrix\n",
    "\n",
    "covariate_matrix = []\n",
    "print make_dichotomous_matrix(20, all_country_id, covariate_matrix)\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Countries in the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the countries that are represented in our complete dataset of users on Upwork. We find that the top 10 countries represented are the United States, India, Ukraine, Philippines, Russia, Pakistan, Serbia, United Kingdom, Bangladesh, and Egypt.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "col_0                             count\n",
      "country                                \n",
      "United States                      1360\n",
      "India                               836\n",
      "Ukraine                             666\n",
      "Philippines                         530\n",
      "Russia                              351\n",
      "Pakistan                            317\n",
      "Serbia                              217\n",
      "Bangladesh                          216\n",
      "United Kingdom                      215\n",
      "Egypt                               192\n",
      "Romania                             133\n",
      "Canada                              118\n",
      "Australia                            97\n",
      "Italy                                84\n",
      "Germany                              84\n",
      "France                               76\n",
      "Indonesia                            72\n",
      "Brazil                               66\n",
      "Armenia                              62\n",
      "Bulgaria                             62\n",
      "Macedonia                            62\n",
      "Spain                                57\n",
      "Greece                               56\n",
      "Venezuela                            53\n",
      "Kenya                                48\n",
      "Poland                               47\n",
      "Belarus                              47\n",
      "Turkey                               44\n",
      "China                                43\n",
      "Mexico                               40\n",
      "...                                 ...\n",
      "Grenada                               3\n",
      "Uruguay                               3\n",
      "Ethiopia                              3\n",
      "Taiwan                                3\n",
      "Qatar                                 3\n",
      "Cameroon                              2\n",
      "Guatemala                             2\n",
      "Haiti                                 2\n",
      "Iceland                               2\n",
      "Trinidad and Tobago                   2\n",
      "Oman                                  2\n",
      "Paraguay                              2\n",
      "Rwanda                                2\n",
      "Nicaragua                             2\n",
      "Ghana                                 2\n",
      "Malta                                 2\n",
      "British Virgin Islands                1\n",
      "Panama                                1\n",
      "Barbados                              1\n",
      "Turkmenistan                          1\n",
      "Saint Vincent and the Grenadines      1\n",
      "Bahrain                               1\n",
      "Mauritius                             1\n",
      "Guyana                                1\n",
      "Myanmar                               1\n",
      "United States Virgin Islands          1\n",
      "Botswana                              1\n",
      "Tajikistan                            1\n",
      "Aruba                                 1\n",
      "Saint Lucia                           1\n",
      "\n",
      "[126 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "country_table = pd.crosstab(index=merged['country'], columns=\"count\")\n",
    "print country_table.sort_values(by=['count'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We created a subset of the sample with profiles from the top 10 countries represented in the dataset (n = 4900).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4900\n"
     ]
    }
   ],
   "source": [
    "merged = merged[(merged['country'] == 'United States') | (merged['country'] == 'India') | (merged['country'] == 'Ukraine') |\n",
    "                (merged['country'] == 'Philippines') | (merged['country'] == 'Russia') | (merged['country'] == 'Pakistan') |\n",
    "                (merged['country'] == 'Serbia') | (merged['country'] == 'Bangladesh') | (merged['country'] == 'United Kingdom') |\n",
    "                (merged['country'] == 'Egypt')]\n",
    "\n",
    "print len(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bill rate array length: 7292\n",
      "Gender array length: 7292\n",
      "Covariate array length: 4900\n",
      "Female count: 2445, Male count: 4847\n"
     ]
    }
   ],
   "source": [
    "# Data formatting \n",
    "for gender in all_genders:\n",
    "    if (gender == \"male\"):\n",
    "        gender_array.append(0)\n",
    "        male_count += 1\n",
    "    elif (gender == \"female\"): # Female as the treatment group\n",
    "        gender_array.append(1)\n",
    "        female_count += 1\n",
    "\n",
    "all_bill_rates = merged['']\n",
    "for rate in all_bill_rates:\n",
    "    rate = round(float(rate), 2)\n",
    "    bill_rate_array.append(rate)\n",
    "    \n",
    "\n",
    "for row in merged.itertuples():    \n",
    "    job_category_matrix = []\n",
    "    country_matrix = []\n",
    "    education_matrix = []\n",
    "    individual_covariate_matrix = []\n",
    "    \n",
    "    #Blocked out because not considering >1 country\n",
    "    #country_matrix = make_dichotomous_matrix(row.country_id, all_country_id, country_matrix)\n",
    "    #individual_covariate_matrix.extend(country_matrix)\n",
    "    \n",
    "    job_category_matrix = make_dichotomous_matrix(row.job_category_id, all_job_category_id, job_category_matrix)\n",
    "    education_matrix = make_dichotomous_matrix(row.education_id, all_education_id, education_matrix)\n",
    "    \n",
    "    individual_covariate_matrix.extend(job_category_matrix)\n",
    "    individual_covariate_matrix.extend(education_matrix)\n",
    "    individual_covariate_matrix.append(row.work_experience)\n",
    "    all_covariates_array.append(individual_covariate_matrix)\n",
    "\n",
    "# Check that arrays contain complete data\n",
    "print \"Bill rate array length: {0}\".format(len(bill_rate_array))\n",
    "print \"Gender array length: {0}\".format(len(gender_array))\n",
    "print \"Covariate array length: {0}\".format(len(all_covariates_array))\n",
    "print \"Female count: {0}, Male count: {1}\".format(female_count, male_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal Analysis\n",
    "\n",
    "When we conduct a causal analysis of the data, we find that, on average, women on Upwork in the United States post an hourly bill rate of USD \\$35.29, while men post an hourly bill rate of USD \\$44.70. \n",
    "\n",
    "Therefore, women in this sample of Upwork workers in the United States asked for an hourly bill rate that was, on average, 78.95% of what men in the sample asked for. In comparison, female, full-time, year-round workers in the United States earned 80% of men's annual average wages in 2015 (Institute for Women's Policy Research, 2016; see https://iwpr.org/issue/employment-education-economic-change/pay-equity-discrimination/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary Statistics\n",
      "\n",
      "                       Controls (N_c=691)         Treated (N_t=669)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Raw-diff\n",
      "--------------------------------------------------------------------------------\n",
      "              Y       44.695       49.860       35.286       26.106       -9.409\n",
      "\n",
      "                       Controls (N_c=691)         Treated (N_t=669)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Nor-diff\n",
      "--------------------------------------------------------------------------------\n",
      "             X0        0.250        0.434        0.278        0.448        0.063\n",
      "             X1        0.226        0.418        0.043        0.204       -0.554\n",
      "             X2        0.049        0.216        0.163        0.370        0.376\n",
      "             X3        0.052        0.222        0.009        0.094       -0.252\n",
      "             X4        0.029        0.168        0.048        0.214        0.098\n",
      "             X5        0.172        0.378        0.271        0.445        0.238\n",
      "             X6        0.025        0.155        0.012        0.109       -0.094\n",
      "             X7        0.059        0.236        0.021        0.143       -0.196\n",
      "             X8        0.059        0.236        0.052        0.223       -0.031\n",
      "             X9        0.007        0.085        0.016        0.127        0.085\n",
      "            X10        0.059        0.236        0.079        0.270        0.078\n",
      "            X11        0.012        0.107        0.007        0.086       -0.042\n",
      "            X12        0.621        0.486        0.592        0.492       -0.059\n",
      "            X13        0.223        0.416        0.271        0.445        0.111\n",
      "            X14        0.029        0.168        0.016        0.127       -0.084\n",
      "            X15        0.027        0.164        0.019        0.138       -0.053\n",
      "            X16        0.026        0.159        0.027        0.162        0.005\n",
      "            X17        0.074        0.262        0.075        0.263        0.004\n",
      "            X18        5.469        6.731        5.677        6.750        0.031\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y = np.array(bill_rate_array)\n",
    "D = np.array(gender_array)\n",
    "X = np.array(all_covariates_array)\n",
    "\n",
    "causal = CausalModel(Y, D, X)\n",
    "\n",
    "print(causal.summary_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When each person in the dataset was matched with a comparable person in the dataset (based on job category, highest level of education, and number of years of work experience), we found that women (the \"treatment\" group) post significantly lower bill rates compared to men (the \"control\" group) (z = -2.56, p = 0.01). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Treatment Effect Estimates: Matching\n",
      "\n",
      "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
      "--------------------------------------------------------------------------------\n",
      "           ATE     -7.343      2.869     -2.560      0.010    -12.966     -1.720\n",
      "           ATC     -8.733      3.104     -2.813      0.005    -14.817     -2.649\n",
      "           ATT     -5.908      3.493     -1.692      0.091    -12.754      0.938\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Matching each person in the dataset with a comparable person in the dataset\n",
    "\n",
    "causal.est_via_matching(bias_adj=True)\n",
    "print (causal.estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we estimated the propensity score or the probability of being a woman given the covariates (job category, highest level of education, and number of years of work experience). The resulting model takes into consideration the following covariates when estimating propensity score: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Estimated Parameters of Propensity Score\n",
      "\n",
      "                    Coef.       S.e.          z      P>|z|      [95% Conf. int.]\n",
      "--------------------------------------------------------------------------------\n",
      "     Intercept      0.096      0.097      0.987      0.324     -0.095      0.287\n",
      "            X1     -1.783      0.223     -7.992      0.000     -2.220     -1.346\n",
      "            X2      1.090      0.220      4.946      0.000      0.658      1.522\n",
      "            X3     -1.900      0.452     -4.206      0.000     -2.786     -1.015\n",
      "            X7     -1.375      0.361     -3.813      0.000     -2.082     -0.668\n",
      "            X5      0.152      0.169      0.901      0.368     -0.179      0.483\n",
      "           X13      0.130      0.165      0.791      0.429     -0.192      0.453\n",
      "            X6     -0.872      0.441     -1.977      0.048     -1.737     -0.008\n",
      "            X8     -0.318      0.254     -1.251      0.211     -0.816      0.180\n",
      "           X15     -0.702      0.427     -1.642      0.101     -1.539      0.136\n",
      "           X14     -0.751      0.451     -1.665      0.096     -1.635      0.133\n",
      "            X9      0.652      0.548      1.190      0.234     -0.422      1.727\n",
      "            X4      0.265      0.305      0.871      0.384     -0.332      0.863\n",
      "        X5*X13      0.753      0.335      2.246      0.025      0.096      1.410\n",
      "        X15*X4     14.639    900.316      0.016      0.987  -1749.980   1779.257\n",
      "        X7*X14      1.743      0.954      1.827      0.068     -0.127      3.612\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Propensity score estimation\n",
    "\n",
    "causal.est_propensity_s()\n",
    "print (causal.propensity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve covariate balance, we constructed a sample that removes people who have extreme propensity score values. This means that their covariate values lead them to have such high propensity to be a woman (or a man) that it is difficult to find another comparable person in the group. With this trimmed sample, women on average asked for an hourly bill rate of USD \\$35.33 and men asked for an hourly bill rate of USD \\$45.02. \n",
    "\n",
    "Therefore, women in this sample of Upwork workers in the United States asked for an hourly bill rate that was, on average, 78.48% of what men in the sample asked for. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary Statistics\n",
      "\n",
      "                       Controls (N_c=681)         Treated (N_t=667)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Raw-diff\n",
      "--------------------------------------------------------------------------------\n",
      "              Y       45.019       50.127       35.332       26.131       -9.687\n",
      "\n",
      "                       Controls (N_c=681)         Treated (N_t=667)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Nor-diff\n",
      "--------------------------------------------------------------------------------\n",
      "             X0        0.254        0.436        0.279        0.449        0.056\n",
      "             X1        0.217        0.413        0.043        0.204       -0.534\n",
      "             X2        0.050        0.218        0.163        0.370        0.374\n",
      "             X3        0.050        0.218        0.009        0.094       -0.244\n",
      "             X4        0.029        0.169        0.045        0.207        0.083\n",
      "             X5        0.175        0.380        0.271        0.445        0.234\n",
      "             X6        0.025        0.156        0.012        0.109       -0.096\n",
      "             X7        0.060        0.238        0.021        0.143       -0.200\n",
      "             X8        0.060        0.238        0.052        0.223       -0.034\n",
      "             X9        0.007        0.085        0.016        0.127        0.084\n",
      "            X10        0.060        0.238        0.079        0.271        0.076\n",
      "            X11        0.012        0.108        0.007        0.086       -0.044\n",
      "            X12        0.630        0.483        0.594        0.492       -0.074\n",
      "            X13        0.226        0.419        0.271        0.445        0.105\n",
      "            X14        0.026        0.161        0.016        0.127       -0.069\n",
      "            X15        0.016        0.126        0.016        0.127        0.003\n",
      "            X16        0.026        0.161        0.027        0.162        0.003\n",
      "            X17        0.075        0.263        0.075        0.264        0.000\n",
      "            X18        5.504        6.754        5.685        6.756        0.027\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Trimming\n",
    "\n",
    "causal.trim_s()\n",
    "causal.cutoff\n",
    "print (causal.summary_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve the credibility of the treatment effects, we stratified the sample into blocks of people who have more similar covariate values. This resulted in 7 strata. The propensity score ranges of the different strata are reported below, and summary statistics are included for the first two strata. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Stratification Summary\n",
      "\n",
      "              Propensity Score         Sample Size     Ave. Propensity   Outcome\n",
      "   Stratum      Min.      Max.  Controls   Treated  Controls   Treated  Raw-diff\n",
      "--------------------------------------------------------------------------------\n",
      "         1     0.141     0.174       182        35     0.157     0.158    -6.430\n",
      "         2     0.178     0.344        59        21     0.263     0.279    -5.352\n",
      "         3     0.353     0.477        62        48     0.434     0.440   -11.214\n",
      "         4     0.524     0.524       178       197     0.524     0.524    -5.249\n",
      "         5     0.556     0.562       121       154     0.560     0.560   -12.532\n",
      "         6     0.589     0.621        22        37     0.601     0.604    -2.179\n",
      "         7     0.679     0.789        57       175     0.759     0.761    -0.053\n",
      "\n",
      "\n",
      "Summary Statistics\n",
      "\n",
      "                       Controls (N_c=182)          Treated (N_t=35)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Raw-diff\n",
      "--------------------------------------------------------------------------------\n",
      "              Y       46.502       32.999       40.071       29.997       -6.430\n",
      "\n",
      "                       Controls (N_c=182)          Treated (N_t=35)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Nor-diff\n",
      "--------------------------------------------------------------------------------\n",
      "             X0        0.000        0.000        0.000        0.000          nan\n",
      "             X1        0.813        0.391        0.829        0.382        0.040\n",
      "             X2        0.000        0.000        0.000        0.000          nan\n",
      "             X3        0.187        0.391        0.171        0.382       -0.040\n",
      "             X4        0.000        0.000        0.000        0.000          nan\n",
      "             X5        0.000        0.000        0.000        0.000          nan\n",
      "             X6        0.000        0.000        0.000        0.000          nan\n",
      "             X7        0.000        0.000        0.000        0.000          nan\n",
      "             X8        0.000        0.000        0.000        0.000          nan\n",
      "             X9        0.000        0.000        0.000        0.000          nan\n",
      "            X10        0.000        0.000        0.000        0.000          nan\n",
      "            X11        0.000        0.000        0.000        0.000          nan\n",
      "            X12        0.643        0.480        0.686        0.471        0.090\n",
      "            X13        0.225        0.419        0.257        0.443        0.074\n",
      "            X14        0.000        0.000        0.000        0.000          nan\n",
      "            X15        0.000        0.000        0.000        0.000          nan\n",
      "            X16        0.005        0.074        0.029        0.169        0.177\n",
      "            X17        0.126        0.333        0.029        0.169       -0.370\n",
      "            X18        4.467        5.621        4.286        5.233       -0.033\n",
      "\n",
      "\n",
      "Summary Statistics\n",
      "\n",
      "                        Controls (N_c=59)          Treated (N_t=21)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Raw-diff\n",
      "--------------------------------------------------------------------------------\n",
      "              Y       46.947       29.641       41.595       23.719       -5.352\n",
      "\n",
      "                        Controls (N_c=59)          Treated (N_t=21)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Nor-diff\n",
      "--------------------------------------------------------------------------------\n",
      "             X0        0.051        0.222        0.048        0.218       -0.015\n",
      "             X1        0.000        0.000        0.000        0.000          nan\n",
      "             X2        0.000        0.000        0.000        0.000          nan\n",
      "             X3        0.000        0.000        0.000        0.000          nan\n",
      "             X4        0.000        0.000        0.000        0.000          nan\n",
      "             X5        0.000        0.000        0.000        0.000          nan\n",
      "             X6        0.288        0.457        0.381        0.498        0.194\n",
      "             X7        0.627        0.488        0.524        0.512       -0.207\n",
      "             X8        0.000        0.000        0.000        0.000          nan\n",
      "             X9        0.000        0.000        0.000        0.000          nan\n",
      "            X10        0.017        0.130        0.048        0.218        0.171\n",
      "            X11        0.017        0.130        0.000        0.000       -0.184\n",
      "            X12        0.492        0.504        0.286        0.463       -0.425\n",
      "            X13        0.373        0.488        0.524        0.512        0.302\n",
      "            X14        0.102        0.305        0.095        0.301       -0.021\n",
      "            X15        0.000        0.000        0.000        0.000          nan\n",
      "            X16        0.000        0.000        0.000        0.000          nan\n",
      "            X17        0.034        0.183        0.095        0.301        0.247\n",
      "            X18        5.237        5.761        4.095        4.805       -0.215\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ecf384/.conda/envs/eureka2/lib/python2.7/site-packages/causalinference/core/summary.py:110: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return (mean_t-mean_c) / np.sqrt((sd_c**2+sd_t**2)/2)\n"
     ]
    }
   ],
   "source": [
    "causal.stratify_s()\n",
    "\n",
    "print(causal.strata)\n",
    "print(causal.strata[0].summary_stats)\n",
    "print(causal.strata[1].summary_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we estimated the effect of the treatment (i.e., being a woman on Upwork in the United States) on average hourly bill rate using least square estimation, weighting, and matching. Across all estimation techniques, we found that women posted significantly lower average hourly bill rates compared to men (p < 0.01). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Treatment Effect Estimates: Weighting\n",
      "\n",
      "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
      "--------------------------------------------------------------------------------\n",
      "           ATE     -6.162      2.221     -2.774      0.006    -10.516     -1.808\n",
      "\n",
      "Treatment Effect Estimates: OLS\n",
      "\n",
      "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
      "--------------------------------------------------------------------------------\n",
      "           ATE     -6.459      2.422     -2.667      0.008    -11.205     -1.713\n",
      "           ATC     -6.883      2.281     -3.018      0.003    -11.353     -2.412\n",
      "           ATT     -6.027      2.887     -2.088      0.037    -11.684     -0.369\n",
      "\n",
      "Treatment Effect Estimates: Matching\n",
      "\n",
      "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
      "--------------------------------------------------------------------------------\n",
      "           ATE     -7.393      2.893     -2.556      0.011    -13.062     -1.724\n",
      "           ATC     -8.925      3.122     -2.859      0.004    -15.044     -2.807\n",
      "           ATT     -5.829      3.520     -1.656      0.098    -12.728      1.070\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Treatment effect estimation\n",
    "\n",
    "causal.est_via_ols()\n",
    "causal.est_via_weighting()\n",
    "causal.est_via_matching(bias_adj=True)\n",
    "print(causal.estimates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average Bill Rate by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean bill rates\n",
      "gender\n",
      "female    35.285770\n",
      "male      44.695181\n",
      "Name: bill_rate, dtype: float64\n",
      "Standard deviation of bill rates\n",
      "gender\n",
      "female    26.105574\n",
      "male      49.860480\n",
      "Name: bill_rate, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "gender_table= merged.groupby('gender')['bill_rate'].mean()\n",
    "print \"Mean bill rates\"\n",
    "print gender_table\n",
    "\n",
    "gender_table= merged.groupby('gender')['bill_rate'].std()\n",
    "print \"Standard deviation of bill rates\"\n",
    "print gender_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Bill Rate by Gender\n",
    "\n",
    "The distribution of bill rate between men and women on Upwork is described below. The range of bill rates for women was smaller (range = 192) than the range of bill rates for men (range = 996). The overall distribution of bill rates for men and women is also described below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall bill rate minimum: $3.0\n",
      "Overall bill rate maximum: $999.0\n",
      "Overall bill rate range: $996.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f459230a7d0>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rauvF33jMbUluS5IDBw702tradh5+XOvr61lbW8vhg4dXcr7jubav3dXzn2rPzZGTZ4ar\nYY47Z4arcarmuKV3fVfVF2Yj0j/R3T+z7H6yqvYv9+9P8tSy/4kkF296+EXLPgBgm7byru9KcnuS\nj3T3D266664k1y3b1yV5z6b9h6rqnKq6NMllSe5d3ZIBYO/YyqXvr03ynUl+t6o+tOz710nekuTO\nqro+yaNJ3pQk3X1/Vd2Z5IFsvGP8xu5+duUrB4A94ISh7u5fS1LHufvK4zzm1iS37mBdAEB8MhkA\njCbUADCYUAPAYEINAIMJNQAMJtQAMJhQA8BgQg0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCY\nUAPAYEINAIMJNQAMJtQAMJhQA8BgQg0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYEIN\nAIMJNQAMJtQAMJhQA8BgQg0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYEINAIMJNQAM\nJtQAMJhQA8BgQg0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYEINAIMJNQAMJtQAMJhQ\nA8BgQg0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYEINAIMJNQAMJtQAMJhQA8BgQg0A\ngwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYCcMdVW9s6qeqqoPb9p3QVXdXVUPLd/P33Tf\nzVX1cFU9WFVX7dbCAWAv2Mor6h9LcvXz9t2U5J7uvizJPcvtVNUVSQ4lefXymLdX1VkrWy0A7DEn\nDHV3vz/JHz1v9zVJ7li270jyxk37j3T30939SJKHk7x+RWsFgD3nZH9Hva+7jy7bn0yyb9m+MMlj\nm457fNkHAJyEs3d6gu7uqurtPq6qbkhyQ5Ls27cv6+vrO11KkuTYsWNZX1/P5W+9fCXnO55VrXeq\n5+bIyTPD1TDHnTPD1ThVczzZUD9ZVfu7+2hV7U/y1LL/iSQXbzruomXfX9DdtyW5LUkOHDjQa2tr\nJ7mUP299fT1ra2s5fPDwSs53PNf2tbt6/lPtuTly8sxwNcxx58xwNU7VHE/20vddSa5btq9L8p5N\n+w9V1TlVdWmSy5Lcu7MlAsDedcJX1FX1U0nWkryiqh5PckuStyS5s6quT/JokjclSXffX1V3Jnkg\nyTNJbuzuZ3dp7QBwxjthqLuPe433yuMcf2uSW3eyqNPB4XrxS+u39C0v0UoAOJP5ZDIAGEyoAWAw\noQaAwYQaAAYTagAYTKgBYDChBoDBhBoABhNqABhMqAFgMKEGgMGEGgAGE2oAGEyoAWAwoQaAwYQa\nAAYTagAYTKgBYDChBoDBhBoABhNqABhMqAFgMKEGgMGEGgAGE2oAGEyoAWAwoQaAwYQaAAYTagAY\nTKgBYDChBoDBhBoABhNqABhMqAFgMKEGgMGEGgAGE2oAGEyoAWAwoQaAwYQaAAY7+1Qv4Ex1uA6/\n6P239C0v0UoAOJ15RQ0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYEINAIMJNQAMJtQA\nMJhQA8BgQg0Agwk1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYEINAIMJNQAMdvapXgAv7HAd\nftH7b+lbXqKVAHAqeUUNAIMJNQAMJtQAMJhQA8BgQg0Agwk1AAzmz7NOUyf6863En3ABnAm8ogaA\nwYQaAAYTagAYTKgBYDBvJjtFtvJmsN1+Dm82A5hv10JdVVcn+eEkZyV5R3e/Zbeei5PzYiG//K2X\nJ2sv3VoAeGG7EuqqOivJf0ryjUkeT/IbVXVXdz+wG8/HTF7RA+zcbv2O+vVJHu7uj3X3nyQ5kuSa\nXXouADhj7dal7wuTPLbp9uNJvmaXngsAdmTyFcDq7tWftOofJbm6u//Zcvs7k3xNd3/XpmNuSHLD\ncvMrkjy4oqd/RZI/XNG59jJz3DkzXA1z3DkzXI1Vz/HLu/uVJzpot15RP5Hk4k23L1r2/T/dfVuS\n21b9xFX1we4+sOrz7jXmuHNmuBrmuHNmuBqnao679Tvq30hyWVVdWlV/KcmhJHft0nMBwBlrV15R\nd/czVfVdSf5HNv48653dff9uPBcAnMl27e+ou/sXkvzCbp3/Raz8cvoeZY47Z4arYY47Z4arcUrm\nuCtvJgMAVsNnfQPAYGdUqKvq6qp6sKoerqqbTvV6pqqqi6vqV6rqgaq6v6revOy/oKrurqqHlu/n\nb3rMzctcH6yqq07d6mepqrOq6req6r3LbTPcpqo6r6reXVUfraqPVNXfNsftqap/ufyz/OGq+qmq\nepkZnlhVvbOqnqqqD2/at+25VdXrqup3l/v+Y1XVKtd5xoR608eWflOSK5JcW1VXnNpVjfVMku/t\n7iuSvCHJjcusbkpyT3dfluSe5XaW+w4leXWSq5O8fZk3yZuTfGTTbTPcvh9O8ovd/TeSvCYb8zTH\nLaqqC5N8d5ID3f2V2XgD76GY4Vb8WDZmsNnJzO1HkvzzJJctX88/546cMaGOjy3dsu4+2t2/uWx/\nLhv/x3hhNuZ1x3LYHUneuGxfk+RIdz/d3Y8keTgb897TquqiJN+S5B2bdpvhNlTVlyb5e0luT5Lu\n/pPu/kzMcbvOTvJFVXV2ki9O8vsxwxPq7vcn+aPn7d7W3Kpqf5K/3N0f6I03ff34psesxJkU6hf6\n2NILT9FaThtVdUmSr0ry60n2dffR5a5PJtm3bJvtC/uhJN+X5M827TPD7bk0yR8k+dHlVwjvqKqX\nxxy3rLufSPLWJJ9IcjTJZ7v7l2KGJ2u7c7tw2X7+/pU5k0LNNlXVuUl+Osn3dPcfb75v+TdDfxJw\nHFX1rUme6u77jneMGW7J2Un+VpIf6e6vSvL5LJcan2OOL275Heo12fiXnlcleXlVfcfmY8zw5EyZ\n25kU6hN+bCn/X1V9YTYi/RPd/TPL7ieXyzhZvj+17Dfbv+hrk3xbVX08G79m+fqqelfMcLseT/J4\nd//6cvvd2Qi3OW7dNyR5pLv/oLv/NMnPJPk7McOTtd25PbFsP3//ypxJofaxpVu0vCPx9iQf6e4f\n3HTXXUmuW7avS/KeTfsPVdU5VXVpNt4sce9Ltd6Juvvm7r6ouy/Jxv/Wfrm7vyNmuC3d/ckkj1XV\nVyy7rkzyQMxxOz6R5A1V9cXLP9tXZuN9J2Z4crY1t+Uy+R9X1RuW+f/TTY9Zje4+Y76SfHOS/53k\n95J8/6lez9SvJF+Xjcs5v5PkQ8vXNyf5K9l4l+NDSf5nkgs2Peb7l7k+mOSbTvXPMOkryVqS9y7b\nZrj9+b02yQeX/z3+XJLzzXHbMzyc5KNJPpzkvyY5xwy3NLefysbv9f80G1d3rj+ZuSU5sMz+95K8\nLcuHia3qyyeTAcBgZ9KlbwA44wg1AAwm1AAwmFADwGBCDQCDCTUADCbUADCYUAPAYP8XAmMr6bLI\n+TAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f459230a110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_bill_rates = merged['bill_rate']\n",
    "\n",
    "all_bill_range = np.ptp(all_bill_rates)\n",
    "all_bill_min = np.amin(all_bill_rates)\n",
    "all_bill_max = np.amax(all_bill_rates)\n",
    "\n",
    "print (\"Overall bill rate minimum: ${0}\".format(all_bill_min))\n",
    "print(\"Overall bill rate maximum: ${0}\".format(all_bill_max))\n",
    "print(\"Overall bill rate range: ${0}\".format(all_bill_range))\n",
    "\n",
    "merged.hist(column='bill_rate', figsize=(8,8), color=\"purple\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female bill rate minimum: $3.0\n",
      "Female bill rate maximum: $195.0\n",
      "Female bill rate range: $192.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f458c030110>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45742604d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_bill_rates = merged[merged['gender'] == 'female']['bill_rate']\n",
    "\n",
    "female_bill_range = np.ptp(female_bill_rates)\n",
    "female_bill_min = np.amin(female_bill_rates)\n",
    "female_bill_max = np.amax(female_bill_rates)\n",
    "\n",
    "female_df = merged[merged['gender'] == 'female']\n",
    "\n",
    "print (\"Female bill rate minimum: ${0}\".format(female_bill_min))\n",
    "print(\"Female bill rate maximum: ${0}\".format(female_bill_max))\n",
    "print(\"Female bill rate range: ${0}\".format(female_bill_range))\n",
    "\n",
    "female_df.hist(column='bill_rate', figsize=(8,8), color=\"red\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male bill rate minimum: $3.0\n",
      "Male bill rate maximum: $999.0\n",
      "Male bill rate range: $996.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4553792c90>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45537a01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_bill_rates = merged[merged['gender'] == 'male']['bill_rate']\n",
    "\n",
    "male_bill_range = np.ptp(male_bill_rates)\n",
    "male_bill_min = np.amin(male_bill_rates)\n",
    "male_bill_max = np.amax(male_bill_rates)\n",
    "\n",
    "male_df = merged[merged['gender'] == 'male']\n",
    "\n",
    "print (\"Male bill rate minimum: ${0}\".format(male_bill_min))\n",
    "print(\"Male bill rate maximum: ${0}\".format(male_bill_max))\n",
    "print(\"Male bill rate range: ${0}\".format(male_bill_range))\n",
    "\n",
    "male_df.hist(column='bill_rate', figsize=(8,8), color=\"blue\", bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing Outliers from Male and Female Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noticing the large range of bill rates for male workers on Upwork, we removed outliers whose bill rate was more or less than two standard deviations of the average bill rate for male and female workers. In this revised sample, the average hourly bill rate for men was USD \\$44.70 (std = USD \\$49.82), and the range was USD \\$122.00. For women, the average hourly bill rate was USD \\$35.29 (std = USD \\$26.09), and the range was USD \\$82.00. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male bill rate average: 44.6951808973\n",
      "Male bill rate standard deviation: 49.8243879957\n",
      "Male bill rate minimum: $3.0\n",
      "Male bill rate maximum: $125.0\n",
      "Male bill rate range: $122.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ecf384/.conda/envs/eureka2/lib/python2.7/site-packages/ipykernel_launcher.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "/home/ecf384/.conda/envs/eureka2/lib/python2.7/site-packages/ipykernel_launcher.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4552bdbf90>]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4552bf1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_sd = np.std(merged[merged['gender'] == 'male']['bill_rate'])\n",
    "male_mean = np.mean(merged[merged['gender'] == 'male']['bill_rate'])\n",
    "print(\"Male bill rate average: {0}\".format(male_mean))\n",
    "print(\"Male bill rate standard deviation: {0}\".format(male_sd))\n",
    "male_no_outliers_df = merged[merged['gender'] == 'male']\n",
    "male_no_outliers_df = male_no_outliers_df[merged['bill_rate'] > male_mean - 2 * male_sd]\n",
    "male_no_outliers_df = male_no_outliers_df[merged['bill_rate'] < male_mean + 2 * male_sd]\n",
    "\n",
    "male_bill_range = np.ptp(male_no_outliers_df['bill_rate'])\n",
    "male_bill_min = np.amin(male_no_outliers_df['bill_rate'])\n",
    "male_bill_max = np.amax(male_no_outliers_df['bill_rate'])\n",
    "\n",
    "print (\"Male bill rate minimum: ${0}\".format(male_bill_min))\n",
    "print(\"Male bill rate maximum: ${0}\".format(male_bill_max))\n",
    "print(\"Male bill rate range: ${0}\".format(male_bill_range))\n",
    "\n",
    "male_no_outliers_df.hist(column='bill_rate', figsize=(8,8), color=\"blue\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female bill rate average: 35.2857698057\n",
      "Female bill rate standard deviation: 26.0860553534\n",
      "Female bill rate minimum: $3.0\n",
      "Female bill rate maximum: $85.0\n",
      "Female bill rate range: $82.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ecf384/.conda/envs/eureka2/lib/python2.7/site-packages/ipykernel_launcher.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "/home/ecf384/.conda/envs/eureka2/lib/python2.7/site-packages/ipykernel_launcher.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4552991910>]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAHiCAYAAAA597/kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBoABCDIADECQAWAA/x9CKiqpV+6AOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45529ca9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_sd = np.std(merged[merged['gender'] == 'female']['bill_rate'])\n",
    "female_mean = np.mean(merged[merged['gender'] == 'female']['bill_rate'])\n",
    "print(\"Female bill rate average: {0}\".format(female_mean))\n",
    "print(\"Female bill rate standard deviation: {0}\".format(female_sd))\n",
    "female_no_outliers_df = merged[merged['gender'] == 'female']\n",
    "female_no_outliers_df = female_no_outliers_df[merged['bill_rate'] > female_mean - 2 * female_sd]\n",
    "female_no_outliers_df = female_no_outliers_df[merged['bill_rate'] < female_mean + 2 * female_sd]\n",
    "\n",
    "female_bill_range = np.ptp(female_no_outliers_df['bill_rate'])\n",
    "female_bill_min = np.amin(female_no_outliers_df['bill_rate'])\n",
    "female_bill_max = np.amax(female_no_outliers_df['bill_rate'])\n",
    "\n",
    "print (\"Female bill rate minimum: ${0}\".format(female_bill_min))\n",
    "print(\"Female bill rate maximum: ${0}\".format(female_bill_max))\n",
    "print(\"Female bill rate range: ${0}\".format(female_bill_range))\n",
    "\n",
    "female_no_outliers_df.hist(column='bill_rate', figsize=(8,8), color=\"red\", bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bill Rate Across Job Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4592356e50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LjIiImK2m5B61pE2ABwK/ANa3fVn91F+B9afie0RERMxGkw5qSWsDhwGvtX318OdsG/AE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\nNku5QwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f458c0c3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_table= merged.groupby('job_category')['bill_rate'].mean()\n",
    "gender_table.plot(kind=\"bar\", figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Covariates\n",
    "\n",
    "Below we report on the distribution of the covariate values for the sample. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Highest Level of Education by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45532a14d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVDCDWpKkghnUkiQVzKCWJKlgBrUkSQUzqCVJKphBLUlSwQxqSZIKZlBLklQwg1qSpIIZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f457429c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "education_table = pd.crosstab(merged['education'], merged['gender'])\n",
    "education_table.plot(kind=\"bar\", figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primary Job Category by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4551bcd6d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7ITYzM7MBb36MUR8CXNt0fXju9r5F0nvnw/ObmZkNWAv15MGSjgWmA+fnQ5OAVSPieUkb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\nQgghVFh01CGEEEKFRUcdQgghVNj/ASkxytQT3m/FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45521b7410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job_category_table = pd.crosstab(merged['job_category'], merged['gender'])\n",
    "job_category_table.plot(kind=\"bar\", figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Work Experience by Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of work experience (years) for men and women in the U.S. on Upwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f45524566d0>]], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Wl0nZb2Hy3oPl/UePmveGcjNZJIkaWH4rG9Jkhq25ILaR5cOXpJ3JXkiyWf7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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4552462d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get work experience distribution \n",
    "merged.hist(column='work_experience', figsize=(8,8), color=\"purple\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4551fa14d0>]], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2v6p6c1V9cPrnfWfWPbeqrquqD1TV923WwAFgN1jNGfUrk1x8wrLnJHlLd5+f5C3Tx6mq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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4552133f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Work experience distribution for women \n",
    "female_df = merged[merged['gender'] == 'female']\n",
    "female_df.hist(column='work_experience', figsize=(8,8), color=\"red\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4551e85210>]], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HcPqytmuAU6vqnwB/CZzXN+9zVbWjG141nm5KkrQ5DQ3qqvoQcN+ytj+pqge7yY8CJ6xB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\ng1qSpIYZ1JIkNcygliSpYQa1JEkN+/9vpmCx6RJoZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4551ec8dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Work experience distribution for men \n",
    "male_df = merged[merged['gender'] == 'male']\n",
    "male_df.hist(column='work_experience', figsize=(8,8), color=\"blue\", bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Analyses for Evaluating Statistical Significance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Average Bill Rate Across Genders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average bill rate is significantly different between men and women in the U.S. on Upwork (p < 0.001). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              bill_rate   R-squared:                       0.014\n",
      "Model:                            OLS   Adj. R-squared:                  0.013\n",
      "Method:                 Least Squares   F-statistic:                     18.83\n",
      "Date:                Tue, 28 Nov 2017   Prob (F-statistic):           1.54e-05\n",
      "Time:                        15:22:35   Log-Likelihood:                -6945.0\n",
      "No. Observations:                1360   AIC:                         1.389e+04\n",
      "Df Residuals:                    1358   BIC:                         1.390e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept         35.2858      1.546     22.828      0.000      32.254      38.318\n",
      "gender[T.male]     9.4094      2.169      4.339      0.000       5.155      13.663\n",
      "==============================================================================\n",
      "Omnibus:                     2335.956   Durbin-Watson:                   2.037\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          3302663.433\n",
      "Skew:                          11.088   Prob(JB):                         0.00\n",
      "Kurtosis:                     243.397   Cond. No.                         2.64\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                sum_sq      df          F    PR(>F)\n",
      "gender    3.009471e+04     1.0  18.828004  0.000015\n",
      "Residual  2.170629e+06  1358.0        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "# Computing statistical significance of bill rate between men and women on Upwork\n",
    "model = ols(\"bill_rate ~ gender\", merged).fit()\n",
    "anova_table = sm.stats.anova_lm(model, typ=2)\n",
    "print(model.summary())\n",
    "print anova_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Average Bill Rate Across Job Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average bill rate is significantly different between workers in different job categories in the U.S. on Upwork (p < 0.001). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              bill_rate   R-squared:                       0.068\n",
      "Model:                            OLS   Adj. R-squared:                  0.060\n",
      "Method:                 Least Squares   F-statistic:                     8.910\n",
      "Date:                Tue, 28 Nov 2017   Prob (F-statistic):           1.80e-15\n",
      "Time:                        15:22:40   Log-Likelihood:                -6906.6\n",
      "No. Observations:                1360   AIC:                         1.384e+04\n",
      "Df Residuals:                    1348   BIC:                         1.390e+04\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================================\n",
      "                                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                     56.2362      4.475     12.567      0.000      47.458      65.015\n",
      "job_category[T.Admin Support]                -30.6756      5.538     -5.539      0.000     -41.539     -19.812\n",
      "job_category[T.Customer Service]             -30.8162     10.731     -2.872      0.004     -51.867      -9.766\n",
      "job_category[T.Data Science & Analytics]     -13.4262      6.906     -1.944      0.052     -26.974       0.122\n",
      "job_category[T.Design & Creative]            -18.3645      4.926     -3.728      0.000     -28.028      -8.701\n",
      "job_category[T.Engineering & Architecture]    -6.6830      8.995     -0.743      0.458     -24.328      10.962\n",
      "job_category[T.IT & Networking]              -10.7124      7.501     -1.428      0.153     -25.427       4.002\n",
      "job_category[T.Legal]                         47.7638     11.709      4.079      0.000      24.794      70.733\n",
      "job_category[T.Sales & Marketing]             -1.3166      6.018     -0.219      0.827     -13.122      10.489\n",
      "job_category[T.Translation]                  -29.6304      7.021     -4.220      0.000     -43.404     -15.857\n",
      "job_category[T.Web, Mobile & Software Dev]   -12.0182      5.315     -2.261      0.024     -22.445      -1.591\n",
      "job_category[T.Writing]                      -19.6525      5.010     -3.923      0.000     -29.480      -9.825\n",
      "==============================================================================\n",
      "Omnibus:                     2438.511   Durbin-Watson:                   2.030\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4402437.109\n",
      "Skew:                          12.090   Prob(JB):                         0.00\n",
      "Kurtosis:                     280.679   Cond. No.                         17.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                    sum_sq      df         F        PR(>F)\n",
      "job_category  1.491618e+05    11.0  8.909848  1.797546e-15\n",
      "Residual      2.051562e+06  1348.0       NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "# Computing statistical significance of bill rate between people from different job categories on Upwork worldwide\n",
    "model = ols(\"bill_rate ~ job_category\", merged).fit()\n",
    "anova_table = sm.stats.anova_lm(model, typ=2)\n",
    "print(model.summary())b\n",
    "print anova_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Average Bill Rate Across Highest Levels of Education"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average bill rate is significantly different between workers with different highest levels of education (p < 0.01)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              bill_rate   R-squared:                       0.015\n",
      "Model:                            OLS   Adj. R-squared:                  0.011\n",
      "Method:                 Least Squares   F-statistic:                     4.041\n",
      "Date:                Tue, 28 Nov 2017   Prob (F-statistic):            0.00121\n",
      "Time:                        14:56:13   Log-Likelihood:                -6944.2\n",
      "No. Observations:                1360   AIC:                         1.390e+04\n",
      "Df Residuals:                    1354   BIC:                         1.393e+04\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=============================================================================================\n",
      "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "Intercept                    31.5326      3.982      7.919      0.000      23.721      39.344\n",
      "education[T.Bachelor]         7.7809      4.219      1.844      0.065      -0.495      16.057\n",
      "education[T.Doctorate]       21.4190      8.217      2.607      0.009       5.300      37.538\n",
      "education[T.High School]     -7.1632      8.118     -0.882      0.378     -23.088       8.762\n",
      "education[T.Master]          11.6402      4.543      2.562      0.011       2.729      20.552\n",
      "education[T.Professional]    23.6877      7.768      3.049      0.002       8.449      38.926\n",
      "==============================================================================\n",
      "Omnibus:                     2355.285   Durbin-Watson:                   2.011\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          3469308.384\n",
      "Skew:                          11.275   Prob(JB):                         0.00\n",
      "Kurtosis:                     249.403   Cond. No.                         12.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                 sum_sq      df         F    PR(>F)\n",
      "education  3.235442e+04     5.0  4.040629  0.001209\n",
      "Residual   2.168369e+06  1354.0       NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "# Computing statistical significance of bill rate between people with different education on Upwork worldwide\n",
    "model = ols(\"bill_rate ~ education\", merged).fit()\n",
    "anova_table = sm.stats.anova_lm(model, typ=2)\n",
    "print(model.summary())\n",
    "print anova_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Average Bill Rate Across All Covariates and Genders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average bill rate is significantly different between workers, taking into account all covariate values and gender (p < 0.001). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              bill_rate   R-squared:                       0.060\n",
      "Model:                            OLS   Adj. R-squared:                  0.054\n",
      "Method:                 Least Squares   F-statistic:                     10.77\n",
      "Date:                Tue, 28 Nov 2017   Prob (F-statistic):           8.59e-15\n",
      "Time:                        15:25:31   Log-Likelihood:                -6912.3\n",
      "No. Observations:                1360   AIC:                         1.384e+04\n",
      "Df Residuals:                    1351   BIC:                         1.389e+04\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=============================================================================================\n",
      "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "Intercept                    18.5969      4.217      4.410      0.000      10.325      26.869\n",
      "education[T.Bachelor]         7.4755      4.134      1.808      0.071      -0.633      15.584\n",
      "education[T.Doctorate]       11.7393      8.138      1.443      0.149      -4.224      27.703\n",
      "education[T.High School]     -6.1051      7.947     -0.768      0.442     -21.695       9.484\n",
      "education[T.Master]           8.7673      4.497      1.950      0.051      -0.055      17.589\n",
      "education[T.Professional]    17.5330      7.671      2.285      0.022       2.484      32.582\n",
      "gender[T.male]               10.0561      2.129      4.723      0.000       5.879      14.233\n",
      "job_category_id               1.2579      0.309      4.069      0.000       0.651       1.864\n",
      "work_experience               0.8070      0.161      5.028      0.000       0.492       1.122\n",
      "==============================================================================\n",
      "Omnibus:                     2401.242   Durbin-Watson:                   2.032\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          3979756.399\n",
      "Skew:                          11.716   Prob(JB):                         0.00\n",
      "Kurtosis:                     266.973   Cond. No.                         96.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                       sum_sq      df          F        PR(>F)\n",
      "education        1.566352e+04     5.0   2.045834  6.971135e-02\n",
      "gender           3.415520e+04     1.0  22.305296  2.567967e-06\n",
      "job_category_id  2.534922e+04     1.0  16.554492  5.000805e-05\n",
      "work_experience  3.870406e+04     1.0  25.275965  5.635230e-07\n",
      "Residual         2.068732e+06  1351.0        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "# Computing statistical significance using model with all covariates\n",
    "model = ols(\"bill_rate ~ job_category_id + education + gender + work_experience\", merged).fit()\n",
    "anova_table = sm.stats.anova_lm(model, typ=2)\n",
    "print(model.summary())\n",
    "print anova_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Things to Look Into Next\n",
    "\n",
    "https://matplotlib.org/devdocs/gallery/shapes_and_collections/scatter.html#sphx-glr-gallery-shapes-and-collections-scatter-py\n",
    "\n",
    "http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html\n"
   ]
  }
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