{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project Econ 323\n",
    "\n",
    "Canada's indigenous population is currently facing a water crisis. More than 100 First Nation communities have long term water boil advisories in place, meaning they are without access to clean drinking water. \n",
    "\n",
    "Many Canadian indigneous populations have faced these conditions for years, and some, for even decades. The average duration of a water boil advisory lasts eight years. In a water-rich country, indigenous nations are facing conditions similar to those in impoverished and developing countries. As a result of increasing pressure from indigenous and social justice organizations, in 2016, the Government of Canada committed to ending all long term drinking water advisories by 2021. \n",
    "\n",
    "In this project, I will be investigating how long term water boil advisories affect migration, education, and unemployment levels within indigenous nations in Canada. Using data from Statistics Canada and Indigenous Services Canada, I will be looking at long term water boil advisories for the period of 1996-2016. This project is based upon my existing thesis in Econ 494. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Cleaning of Data\n",
    "I take a data set in which I have merged individual census data for each affected Indigenous nation (which was previously merged within Python) and clean it further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as sm\n",
    "\n",
    "\n",
    "import qeds\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "from sklearn import (\n",
    "    linear_model, metrics, neural_network, pipeline, model_selection\n",
    ")\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>first_nation</th>\n",
       "      <th>population</th>\n",
       "      <th>total_married</th>\n",
       "      <th>married_commonlaw</th>\n",
       "      <th>notmarried_commonlaw</th>\n",
       "      <th>total_family</th>\n",
       "      <th>children</th>\n",
       "      <th>not_in_family</th>\n",
       "      <th>aboriginal_mother_tongue</th>\n",
       "      <th>...</th>\n",
       "      <th>employment_rate</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>multiple_on</th>\n",
       "      <th>advisory_on</th>\n",
       "      <th>married</th>\n",
       "      <th>child_perc</th>\n",
       "      <th>single_perc</th>\n",
       "      <th>movers_1yr_perc</th>\n",
       "      <th>movers_5yr_perc</th>\n",
       "      <th>high_school_more</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996</td>\n",
       "      <td>Shoal Lake No.40</td>\n",
       "      <td>164</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996</td>\n",
       "      <td>North Spirit Lake</td>\n",
       "      <td>157</td>\n",
       "      <td>110.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996</td>\n",
       "      <td>Sandy Lake</td>\n",
       "      <td>1611</td>\n",
       "      <td>950.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.618012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.816901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996</td>\n",
       "      <td>Toosey</td>\n",
       "      <td>75</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1996</td>\n",
       "      <td>Indian Island</td>\n",
       "      <td>52</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year       first_nation  population  total_married  married_commonlaw  \\\n",
       "0  1996   Shoal Lake No.40         164          100.0               35.0   \n",
       "1  1996  North Spirit Lake         157          110.0               35.0   \n",
       "2  1996         Sandy Lake        1611          950.0              450.0   \n",
       "3  1996             Toosey          75           55.0                0.0   \n",
       "4  1996      Indian Island          52           30.0                5.0   \n",
       "\n",
       "   notmarried_commonlaw  total_family  children  not_in_family  \\\n",
       "0                  70.0          40.0      30.0            0.0   \n",
       "1                  75.0          35.0      25.0            0.0   \n",
       "2                 500.0         355.0     290.0            0.0   \n",
       "3                  50.0          15.0      10.0            0.0   \n",
       "4                  25.0          15.0       0.0            0.0   \n",
       "\n",
       "   aboriginal_mother_tongue  ...  employment_rate  unemployment_rate  \\\n",
       "0                  0.666667  ...            0.450              0.167   \n",
       "1                  0.750000  ...            0.478              0.231   \n",
       "2                  0.618012  ...            0.416              0.186   \n",
       "3                  0.133333  ...            0.500              0.250   \n",
       "4                  0.272727  ...            0.333              0.500   \n",
       "\n",
       "   multiple_on  advisory_on   married  child_perc  single_perc  \\\n",
       "0            0            0  0.350000    0.750000          0.0   \n",
       "1            0            0  0.318182    0.714286          0.0   \n",
       "2            0            0  0.473684    0.816901          0.0   \n",
       "3            0            0  0.000000    0.666667          0.0   \n",
       "4            0            0  0.166667    0.000000          0.0   \n",
       "\n",
       "   movers_1yr_perc  movers_5yr_perc  high_school_more  \n",
       "0              0.0              0.0               0.0  \n",
       "1              0.0              0.0               0.0  \n",
       "2              0.0              0.0               0.0  \n",
       "3              0.0              0.0               0.0  \n",
       "4              0.0              0.0               0.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demo_data = pd.read_excel(r'master_data_2016_copy.xlsx')\n",
    "demo_df = pd.DataFrame(Demo_data)\n",
    "demo_df.head()\n",
    "\n",
    "#dropping columns\n",
    "demo_df.drop([\"  Married spouses and common-law partners\", \"  Lone parents\"], axis = 1, inplace = True)\n",
    "demo_df.drop([\"Number of Years Advisory was On \"], axis=1, inplace = True)\n",
    "\n",
    "#cleaning column names \n",
    "demo_df.rename(columns={\"Year\":\"year\", \"First Nation\":\"first_nation\", \"Population\": \"population\"}, inplace = True)\n",
    "demo_df.rename(columns={\"Total - Marital Status 15 years and over\":\"total_married\"}, inplace = True)\n",
    "demo_df.rename(columns={\"% of the Aboriginal identity population with an Aboriginal language as mother tongue\":\"aboriginal_mother_tongue\"}, inplace = True)\n",
    "demo_df.rename(columns={\"% of the Aboriginal identity population who speak an Aboriginal language most often at home\":\"aboriginal_home_language\"}, inplace = True)\n",
    "demo_df.rename(columns={\"  Children in census families\":\"children\"}, inplace = True)\n",
    "demo_df.rename(columns={\"  Married or living common law\":\"married_commonlaw\",\"  Not married and not living common law\":\"notmarried_commonlaw\"}, inplace = True)\n",
    "demo_df.rename(columns={\"Total - Family Characteristics\":\"total_family\",\"  Persons not in census families\":\"not_in_family\"}, inplace = True)\n",
    "demo_df.rename(columns={\"Total - Mobility status 1 year ago \":\"total_mobility_1yr\", \"Total - Mobility status 5 years ago\":\"total_mobility_5yr\"}, inplace = True)\n",
    "demo_df.rename(columns={\"  Non-movers\":\"non-movers_1yr\",\"  Movers\":\"movers_1yr\",\"  Non-movers.1\":\"non-movers_5yr\",\"  Movers.1\":\"movers_5yr\"}, inplace = True)\n",
    "demo_df.rename(columns={\"Total - Highest certificate, diploma or degree \":\"total_hs_higher_edu\",\"  No certificate, diploma or degree\":\"no_hs_higheredu\",\"  Secondary (high) school diploma or equivalency certificate\":\"hs_edu\"}, inplace = True)\n",
    "demo_df.rename(columns={\"   Persons with a trades; college or university certificate or diploma (below bachelor's degree)\":\"higher_edu_below\"}, inplace = True)\n",
    "demo_df.rename(columns={\"  University certificate, diploma or degree at bachelor level or above\":\"higher_edu_bach\"}, inplace = True)\n",
    "demo_df.rename(columns={\"Total - Labour force status \": \"total_lbr\",\"  In the labour force\":\"in_lbr_force\",\"    Employed\":\"employed\",\"    Unemployed\":\"unemployed\",\"  Not in the labour force\":\"not_in_lbr_force\"},inplace = True)\n",
    "demo_df.rename(columns={\"Participation rate\":\"participation_rate\", \"Employment rate\":\"employment_rate\",\"Unemployment rate\":\"unemployment_rate\",\"Multiple On\":\"multiple_on\",\"Advisory On\":\"advisory_on\"}, inplace = True)\n",
    "\n",
    "#converting all relevant columns into percentages represented in decimal format\n",
    "demo_df[\"employment_rate\"] = demo_df[\"employment_rate\"].div(100)\n",
    "demo_df[\"unemployment_rate\"] = demo_df[\"unemployment_rate\"].div(100)\n",
    "demo_df[\"participation_rate\"] = demo_df[\"participation_rate\"].div(100)\n",
    "demo_df[\"married\"] = demo_df[\"married_commonlaw\"]/demo_df[\"total_married\"]\n",
    "demo_df[\"child_perc\"] = demo_df[\"children\"]/demo_df[\"total_family\"]\n",
    "demo_df[\"single_perc\"] = demo_df[\"not_in_family\"]/demo_df[\"total_family\"]\n",
    "demo_df[\"movers_1yr_perc\"] = demo_df[\"movers_1yr\"]/demo_df[\"total_mobility_1yr\"]\n",
    "demo_df[\"movers_5yr_perc\"] = demo_df[\"movers_5yr\"]/demo_df[\"total_mobility_5yr\"]\n",
    "\n",
    "#generating new column for hs and uni education\n",
    "demo_df[\"high_school_more\"] = demo_df[\"hs_edu\"] + demo_df[\"higher_edu_bach\"]+ demo_df[\"higher_edu_below\"]\n",
    "demo_df[\"high_school_more\"] = demo_df[\"high_school_more\"]/demo_df[\"total_hs_higher_edu\"]\n",
    "\n",
    "#filling Nan with 0's\n",
    "demo_df = demo_df.fillna(0)\n",
    "\n",
    "\n",
    "#preview of cleaned data  \n",
    "demo_df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary Graphs of the Data\n",
    "\n",
    "Here I prepare some visualizations to better understand the data I have cleaned. These graphs are meant to give some preliminary observations on the composition of the data before regressions are run. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For an initial visualization of the data, here I am going to look at the number of advisories over my time frame, 1996-2016. Since demographic data is only available for each census year in this time frame, I coded the occurance of an advisory for a census year if an advisory was on within the last five years of the relevant census year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'Advisory On', 'Advisory Off'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of advisories by year\n",
    "\n",
    "year = demo_df.groupby(\"year\")\n",
    "year.head()\n",
    "df_1996 = year.get_group(1996)\n",
    "#print (pd.value_counts(df_1996['advisory_on'].values, sort=False))\n",
    "\n",
    "df_2001 = year.get_group(2001)\n",
    "#print (pd.value_counts(df_2001['advisory_on'].values, sort=False))\n",
    "\n",
    "df_2006 = year.get_group(2006)\n",
    "#print(pd.value_counts(df_2006[\"advisory_on\"].values, sort=False))\n",
    "\n",
    "df_2011 = year.get_group(2011)\n",
    "#print(pd.value_counts(df_2011[\"advisory_on\"].values, sort=False))\n",
    "\n",
    "df_2016 = year.get_group(2016)\n",
    "#print(pd.value_counts(df_2016[\"advisory_on\"].values, sort=False))\n",
    "\n",
    "info = {\"year\":[1996, 2001, 2006, 2011, 2016], \"Advisory On\":[0,0,12,26,66],\"Advisory Off\":[46,58,80,47,27]}\n",
    "year_df = pd.DataFrame(data=info)\n",
    "year_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y = year_df[\"year\"]\n",
    "x = year_df['Advisory On']\n",
    "plt.bar(y,x, width = 3, color = [\"white\",\"white\",\"lightblue\",\"skyblue\",\"steelblue\"])\n",
    "plt.xticks(y)\n",
    "plt.grid(None)\n",
    "plt.title(\"Indigenous Water Boil Advisories per Census Year\", fontsize = 15)\n",
    "plt.xlabel(\"Census Year\", fontsize = 10)\n",
    "plt.ylabel(\"Count of Advisories\", fontsize = 10)\n",
    "plt.tight_layout()\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.savefig(\"boil_advisories_4.png\")\n",
    "#plt.savefig('myimage.svg', format='svg', dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the graph above, the number of advisories is increasing per year, with no advisories occuring in 1996 and 2001. Due to limitations of data, within my dataset, the number of advisories in 1996 and 2001 is zero, however in reality this is not the case. The reason there could be \"missing advisories\" is due to the fact that demographic data for the affected First Nation was not available for the relevant census year, therefore, the First Nation could not be marked as having an advisory. Note that the count of advisories means the number of First Nation communities that have water boil advisories within 5 years of the relevant census year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxpot of HS/Higher Education\n",
    "import seaborn as sns\n",
    "\n",
    "edu_boxplot = sns.boxplot(x=\"advisory_on\", y=\"high_school_more\", data=demo_df, palette = \"Blues\")\n",
    "edu_boxplot.set_xlabel(\"Advisory On\", fontsize = 13)\n",
    "edu_boxplot.set_ylabel(\"HS/Higher Education\", fontsize = 13)\n",
    "edu_boxplot.set_title(\"Proportion of Indigneous community with HS/Higher Education\", fontsize= 13)\n",
    "edu_boxplot.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot of unemployment rate\n",
    "unemp_boxplot = sns.boxplot(x=\"advisory_on\", y=\"unemployment_rate\", data=demo_df, palette = \"Blues\")\n",
    "unemp_boxplot.set_xlabel(\"Advisory On\", fontsize = 13)\n",
    "unemp_boxplot.set_ylabel(\"Unemployment Rate\", fontsize = 13)\n",
    "unemp_boxplot.set_title(\"Unemployment Rate Dynamics\", fontsize=16)\n",
    "unemp_boxplot.grid(False)\n",
    "#unemp_boxplot.legend(handles = unemp_boxplot, labels = [\"Advisory Off\", \"Advisory On\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot of migration (population)\n",
    "\n",
    "plt.figure(figsize=(5,9))\n",
    "pop_boxplot = sns.boxplot(x=\"advisory_on\", y=\"population\", data=demo_df, palette = \"Blues\")\n",
    "pop_boxplot.set_xlabel(\"Advisory On\", fontsize = \"13\")\n",
    "pop_boxplot.set_ylabel(\"Population\", fontsize = \"13\")\n",
    "pop_boxplot.set_title(\"Population Dynamics\", fontsize = 15)\n",
    "pop_boxplot.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zooming in \n",
    "pop_boxplot = sns.boxplot(x=\"advisory_on\", y=\"population\", data=demo_df, palette = \"Blues\")\n",
    "pop_boxplot.set_xlabel(\"Advisory On\", fontsize = 13)\n",
    "pop_boxplot.set_ylabel(\"Population\", fontsize = 13)\n",
    "pop_boxplot.set_title(\"Zoomed In Box Plot of Population Dynamics\", fontsize =15 )\n",
    "plt.axis([-1,2,0,1500])\n",
    "pop_boxplot.grid(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graphs, we see that when an advisory is turned on, the mean of the unemployment rate and attainment of a highschool/higher level education is higher than when an adivsory is off or not in place. For population (migration change) we see that it is slightly lower when an advisory is in effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Map of Indigenous Water Boil Advisories in Canada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a better idea of how long term water boil advisories are distributed across Canada, I'm going to make a map of the individual First Nation communities that are affected. First, I'll make my dataset of coordinates so I will be ready to put the point on the map. I created this dataset by searching up affected First Nation community names, and obtained coordinates from the GeoHacks website from Wikipedia, or from Google Maps. \n",
    "\n",
    "Using folium, I create a map of Canada with interactive points with the names of affected First Nation communities. Click to take a look at the names!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        ;

        
    
    
            var marker_884496d218e74b93b862c2eeff5c9aea = L.marker(
                [54.408849, -109.626871],
                {}
            ).addTo(map_19b19d9e7e4b4df697c919b3f1655307);
        
    
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            var marker_43b8c9d5684048ffa450fd8318c16aa2 = L.marker(
                [51.919541, -97.310067],
                {}
            ).addTo(map_19b19d9e7e4b4df697c919b3f1655307);
        
    
        var popup_1bc5eaa92a4f4e81ace7b9c07aae0738 = L.popup({"maxWidth": "100%"});

        
            var html_e42a8df920644cef96f84bd24c374564 = $(`<div id="html_e42a8df920644cef96f84bd24c374564" style="width: 100.0%; height: 100.0%;">Kinonjeoshtegon (Jackhead)</div>`)[0];
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        marker_43b8c9d5684048ffa450fd8318c16aa2.bindPopup(popup_1bc5eaa92a4f4e81ace7b9c07aae0738)
        ;

        
    
    
            var marker_77c5c64924734d7198e57eb77b934657 = L.marker(
                [50.728938, -122.217739],
                {}
            ).addTo(map_19b19d9e7e4b4df697c919b3f1655307);
        
    
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            var html_26143c84461b46a5925aba80771c6118 = $(`<div id="html_26143c84461b46a5925aba80771c6118" style="width: 100.0%; height: 100.0%;">Tsal'alh</div>`)[0];
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f1bb62a7b10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "map_1 = folium.Map(location=[55.585901, -105.750596], zoom_start = 5)\n",
    "map_data = pd.read_excel(r'map data 2.0.xlsx')\n",
    "map_df_1 = pd.DataFrame(map_data)\n",
    "map_df_1[\"Coordinates\"] = list(zip(map_df_1[\"Longitude\"], map_df_1[\"Latitude\"]))\n",
    "map_df_1.head()\n",
    "map_df_1['Latitude'] = map_df_1['Latitude'].astype(float)\n",
    "map_df_1[\"Longitude\"] = map_df_1[\"Longitude\"].astype(float)\n",
    "\n",
    "for i in range(0,len(map_df_1)):\n",
    "    folium.Marker([map_df_1.iloc[i]['Latitude'], map_df_1.iloc[i]['Longitude']], popup=map_df_1.iloc[i]['First Nation']).add_to(map_1)\n",
    "\n",
    "map_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the graph above, the provinces that are most affected by long term water boil advisories are Manitoba and Ontario. However, water boil advisories seem to plague the majority of Canada, with exception to the North West Territories, Nunavat, and the Yukon. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed Effects Regression\n",
    "\n",
    "Here, I will be looking at a simple fixed effects panel OLS regression for the outcomes of long term water boil advisories on migration, educational attainment, and unemployment rates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression on Migration (as observed through Population change)\n",
    "For the regression on migration, I will run one (First Nation community and year) fixed effects regression without additional controls, and another with additional controls. These controls will control for noise in the estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================================\n",
      "                         population I population II\n",
      "---------------------------------------------------\n",
      "Intercept                139.6264***  196.9917***  \n",
      "advisory_on              -20.4577     -15.7457     \n",
      "child_perc                            -76.8143**   \n",
      "movers_1yr_perc                       -224.3292    \n",
      "movers_5yr_perc                       41.3787      \n",
      "high_school_more                      -46.3176     \n",
      "aboriginal_mother_tongue              3.2227***    \n",
      "aboriginal_home_language              -2.5327**    \n",
      "===================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "demo_df.columns\n",
    "\n",
    "#regression on population\n",
    "lm_pop = list()\n",
    "#no controls, fixed effects\n",
    "lm_pop.append(smf.ols(formula=\"population ~ advisory_on + C(first_nation) + C(year)\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "#controls, fixed effects\n",
    "lm_pop.append(smf.ols(formula=\"population ~ advisory_on + C(first_nation) + C(year) + child_perc + movers_1yr_perc + movers_5yr_perc + high_school_more + aboriginal_mother_tongue + aboriginal_home_language\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "regressors = [\"Intercept\",\"advisory_on\", \"child_perc\", \"movers_1yr_perc\",\"movers_5yr_perc\",\"high_school_more\",\"aboriginal_mother_tongue\",\"aboriginal_home_language\"]\n",
    "tble = summary_col(lm_pop,regressor_order=regressors, stars = True)\n",
    "tble.tables[0] = tble.tables[0].loc[regressors]\n",
    "print (tble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the results observed above, our estimates with and without controls are not significant. The effects of water boil advisory on population change are quite small, decreasing the population by around 20 people without controls, and around 15 in the estimate with controls. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression on Educational Attainment (High School Certificate and/or Higher Education)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the regression on migration, I will run two fixed effect regressions controlling for variation that occurs First Nation community and years. One regression will run without additional controls, another will incorporate additional controls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "            high_school_more I high_school_more II\n",
      "--------------------------------------------------\n",
      "Intercept   0.0179             -0.2316**          \n",
      "advisory_on 0.0218             -0.0019            \n",
      "child_perc                     0.1647***          \n",
      "married                        0.4152***          \n",
      "==================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "#regression on attainment of high school education and/or higher level education\n",
    "lm_edu = list()\n",
    "#no controls, fixed effects\n",
    "lm_edu.append(smf.ols(formula=\"high_school_more ~ advisory_on + C(first_nation) + C(year)\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "#controls, fixed effects\n",
    "lm_edu.append(smf.ols(formula=\"high_school_more ~ advisory_on + C(first_nation) + C(year) + child_perc + married\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "regressors_1 = [\"Intercept\",\"advisory_on\", \"child_perc\", \"married\"]\n",
    "tble_1 = summary_col(lm_edu,regressor_order=regressors_1, stars = True)\n",
    "tble_1.tables[0] = tble_1.tables[0].loc[regressors_1]\n",
    "print (tble_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to our regression looking at migration through population change, our effects on high school and/or higher level attainment are insignificant. Without controls, it seems that the proportion of a indigneous community that would have achieved a high school diploma or higher would have actually increased by ~2%, and when I add controls, it would have decreased by less than 1%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression on Unemployment Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=========================================================\n",
      "                 unemployment_rate I unemployment_rate II\n",
      "---------------------------------------------------------\n",
      "Intercept        0.2145***           0.1384***           \n",
      "advisory_on      0.0021              -0.0171             \n",
      "child_perc                           0.1971***           \n",
      "married                              -0.1118             \n",
      "high_school_more                     0.2742***           \n",
      "=========================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "#regression on unemployment rate\n",
    "lm_unemp = list()\n",
    "#no controls, fixed effects\n",
    "lm_unemp.append(smf.ols(formula=\"unemployment_rate ~ advisory_on + C(first_nation) + C(year)\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "#controls, fixed effects\n",
    "lm_unemp.append(smf.ols(formula=\"unemployment_rate ~ advisory_on + C(first_nation) + C(year) + high_school_more + child_perc + married\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "regressors_2 = [\"Intercept\",\"advisory_on\", \"child_perc\", \"married\", \"high_school_more\"]\n",
    "tble_2= summary_col(lm_unemp,regressor_order=regressors_2, stars = True)\n",
    "tble_2.tables[0] = tble_2.tables[0].loc[regressors_2]\n",
    "print (tble_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The effects on unemployment rate are also found to be insignificant. For the regression without additional controls, the unemployment rate increases by less than 1%. For the regression with additional controls, it seems that the unemployment rate for an average indigneous community would actually decrease by around 2%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout these three regressions, what I find is that there are no significant effects on migration, high school and/or higher level educational attainment, or unemployment levels. This means that when a long term water boil advisory is in effect, indigenous populations do not move out of their communities, their levels of educational attainment are unaffected, and unemployment rates are unaffected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will implement a lasso regression as my regularization tool to look at which coefficients from my fixed effects regression could be eliminated due to overfitting. For each of my regressions looking at different outcomes, I will test out the different controls that are particular to each outcome variable and see how they perform under lasso regression. Instead of the fixed effects regression above, I will instead be implementing a simple linear regression, omitting time and First Nation community fixed effects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lasso</th>\n",
       "      <th>linreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aboriginal_mother_tongue</th>\n",
       "      <td>6.541572</td>\n",
       "      <td>6.144288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aboriginal_home_language</th>\n",
       "      <td>-1.897842</td>\n",
       "      <td>-1.187683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>advisory_on</th>\n",
       "      <td>-9.126670</td>\n",
       "      <td>-22.827865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_perc</th>\n",
       "      <td>346.293725</td>\n",
       "      <td>368.148840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movers_1yr_perc</th>\n",
       "      <td>-4.705196</td>\n",
       "      <td>-704.278714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movers_5yr_perc</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>105.654375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_school_more</th>\n",
       "      <td>363.173397</td>\n",
       "      <td>449.845937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               lasso      linreg\n",
       "aboriginal_mother_tongue    6.541572    6.144288\n",
       "aboriginal_home_language   -1.897842   -1.187683\n",
       "advisory_on                -9.126670  -22.827865\n",
       "child_perc                346.293725  368.148840\n",
       "movers_1yr_perc            -4.705196 -704.278714\n",
       "movers_5yr_perc            -0.000000  105.654375\n",
       "high_school_more          363.173397  449.845937"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lasso regression for controls pertaining to population\n",
    "\n",
    "demo_df.columns\n",
    "X_pop = demo_df.drop([\"year\", \"higher_edu_bach\", \"total_lbr\",\"in_lbr_force\", \"first_nation\", \"population\", \"total_married\", \"not_in_family\", \"married_commonlaw\", \"notmarried_commonlaw\", \"employed\", \"employment_rate\", \"single_perc\", \"total_family\", \"children\",\"total_mobility_1yr\",\"non-movers_1yr\",\"movers_1yr\",\"total_mobility_5yr\",\"non-movers_5yr\",\"movers_5yr\",\"total_hs_higher_edu\",\"no_hs_higheredu\",\"hs_edu\",\"higher_edu_below\",\"unemployed\",\"not_in_lbr_force\",\"participation_rate\",\"unemployment_rate\",\"multiple_on\",\"married\"], axis=1).copy()\n",
    "X_pop.head()\n",
    "y_pop = demo_df[\"population\"]\n",
    "\n",
    "lasso_model_pop = linear_model.Lasso()\n",
    "lasso_model_pop.fit(X_pop, y_pop)\n",
    "\n",
    "lr_model_pop = linear_model.LinearRegression()\n",
    "lr_model_pop.fit(X_pop, y_pop)\n",
    "\n",
    "lasso_coefs_pop = pd.Series(dict(zip(list(X_pop), lasso_model_pop.coef_)))\n",
    "lr_coefs_pop = pd.Series(dict(zip(list(X_pop), lr_model_pop.coef_)))\n",
    "coefs_pop = pd.DataFrame(dict(lasso=lasso_coefs_pop, linreg=lr_coefs_pop))\n",
    "coefs_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that most of the control variables used do not contribute to overfitting within the regression, this is very good news! The only variable that could cause an overfitting issue and should be eliminated from my regression is movers_5yr_perc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "#alphas = np.exp(np.linspace(10, -2, 50))\n",
    "alphas, coefs_lasso_pop, _ = linear_model.lasso_path(X_pop, y_pop, alphas=None, fit_intercept=True, max_iter=10000)\n",
    "\n",
    "# plotting\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "colors = cycle(qeds.themes.COLOR_CYCLE)\n",
    "log_alphas = -np.log10(alphas)\n",
    "for coef_l, c, name in zip(coefs_lasso_pop, colors, list(X_pop)):\n",
    "   ax.plot(log_alphas, coef_l, c=c)\n",
    "   ax.set_xlabel('-Log(alpha)')\n",
    "   ax.set_ylabel('lasso coefficients')\n",
    "   ax.set_title('Lasso Path for Coefficients of Controls in Migration Regression')\n",
    "   ax.axis('tight')\n",
    "   maxabs = np.max(np.abs(coef_l))\n",
    "   i = [idx for idx in range(len(coef_l)) if abs(coef_l[idx]) >= (0.9*maxabs)][0]\n",
    "   xnote = log_alphas[i]\n",
    "   ynote = coef_l[i]\n",
    "   ax.annotate(name, (xnote, ynote), color=c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lasso</th>\n",
       "      <th>linreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>advisory_on</th>\n",
       "      <td>6.541572</td>\n",
       "      <td>6.144288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>-1.897842</td>\n",
       "      <td>-1.187683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_perc</th>\n",
       "      <td>-9.126670</td>\n",
       "      <td>-22.827865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                lasso     linreg\n",
       "advisory_on  6.541572   6.144288\n",
       "married     -1.897842  -1.187683\n",
       "child_perc  -9.126670 -22.827865"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for regression on completion of high school and/or some or all higher level education\n",
    "demo_df.columns\n",
    "X_edu = demo_df.drop([\"year\",\"higher_edu_bach\",\"total_lbr\",\"in_lbr_force\", \"aboriginal_home_language\",\"aboriginal_mother_tongue\", \"high_school_more\",\"movers_1yr_perc\",\"movers_5yr_perc\", \"first_nation\", \"population\", \"total_married\", \"not_in_family\", \"married_commonlaw\", \"notmarried_commonlaw\", \"employed\", \"employment_rate\", \"single_perc\", \"total_family\", \"children\",\"total_mobility_1yr\",\"non-movers_1yr\",\"movers_1yr\",\"total_mobility_5yr\",\"non-movers_5yr\",\"movers_5yr\",\"total_hs_higher_edu\",\"no_hs_higheredu\",\"hs_edu\",\"higher_edu_below\",\"unemployed\",\"not_in_lbr_force\",\"participation_rate\",\"unemployment_rate\",\"multiple_on\"], axis=1).copy()\n",
    "X_edu.head()\n",
    "y_edu = demo_df[\"high_school_more\"]\n",
    "\n",
    "lasso_model_edu = linear_model.Lasso()\n",
    "lasso_model_edu.fit(X_edu, y_edu)\n",
    "\n",
    "lr_model_edu = linear_model.LinearRegression()\n",
    "lr_model_edu.fit(X_edu, y_edu)\n",
    "\n",
    "lasso_coefs_edu = pd.Series(dict(zip(list(X_edu), lasso_model_pop.coef_)))\n",
    "lr_coefs_edu = pd.Series(dict(zip(list(X_edu), lr_model_pop.coef_)))\n",
    "coefs_edu = pd.DataFrame(dict(lasso=lasso_coefs_edu, linreg=lr_coefs_edu))\n",
    "coefs_edu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like from above, we don't have to eliminate any of our control variables! Let's also see a visualization below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "#alphas = np.exp(np.linspace(10, -2, 50))\n",
    "alphas, coefs_lasso_edu, _ = linear_model.lasso_path(X_edu, y_edu, alphas = None, fit_intercept=True, max_iter=10000)\n",
    "\n",
    "# plotting\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "colors = cycle(qeds.themes.COLOR_CYCLE)\n",
    "log_alphas = -np.log10(alphas)\n",
    "for coef_l_e, c, name in zip(coefs_lasso_edu, colors, list(X_edu)):\n",
    "   ax.plot(log_alphas, coef_l_e, c=c)\n",
    "   ax.set_xlabel('-Log(alpha)')\n",
    "   ax.set_ylabel('lasso coefficients')\n",
    "   ax.set_title('Lasso Path for Coefficients of Controls in Education Regression')\n",
    "   ax.axis('tight')\n",
    "   maxabs = np.max(np.abs(coef_l_e))\n",
    "   i = [idx for idx in range(len(coef_l_e)) if abs(coef_l_e[idx]) >= (0.9*maxabs)][0]\n",
    "   xnote = log_alphas[i]\n",
    "   ynote = coef_l_e[i]\n",
    "   ax.annotate(name, (xnote, ynote), color=c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lasso</th>\n",
       "      <th>linreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>advisory_on</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.137155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_perc</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.348416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_school_more</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lasso    linreg\n",
       "advisory_on         0.0  0.001543\n",
       "married             0.0 -0.137155\n",
       "child_perc          0.0  0.348416\n",
       "high_school_more    0.0  0.232869"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for regression on unemployment rates\n",
    "demo_df.columns\n",
    "X_unemp = demo_df.drop([\"year\",\"higher_edu_bach\",\"total_lbr\",\"in_lbr_force\", \"aboriginal_home_language\",\"aboriginal_mother_tongue\",\"movers_1yr_perc\",\"movers_5yr_perc\", \"first_nation\", \"population\", \"total_married\", \"not_in_family\", \"married_commonlaw\", \"notmarried_commonlaw\", \"employed\", \"employment_rate\", \"single_perc\", \"total_family\", \"children\",\"total_mobility_1yr\",\"non-movers_1yr\",\"movers_1yr\",\"total_mobility_5yr\",\"non-movers_5yr\",\"movers_5yr\",\"total_hs_higher_edu\",\"no_hs_higheredu\",\"hs_edu\",\"higher_edu_below\",\"unemployed\",\"not_in_lbr_force\",\"participation_rate\",\"unemployment_rate\",\"multiple_on\"], axis=1).copy()\n",
    "X_unemp.head()\n",
    "y_unemp = demo_df[\"unemployment_rate\"]\n",
    "\n",
    "lasso_model_unemp = linear_model.Lasso()\n",
    "lasso_model_unemp.fit(X_unemp, y_unemp)\n",
    "\n",
    "lr_model_unemp = linear_model.LinearRegression()\n",
    "lr_model_unemp.fit(X_unemp, y_unemp)\n",
    "\n",
    "lasso_coefs_unemp = pd.Series(dict(zip(list(X_unemp), lasso_model_unemp.coef_)))\n",
    "lr_coefs_unemp = pd.Series(dict(zip(list(X_unemp), lr_model_unemp.coef_)))\n",
    "coefs_unemp = pd.DataFrame(dict(lasso=lasso_coefs_unemp, linreg=lr_coefs_unemp))\n",
    "coefs_unemp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is surprising - all of the controls, including the treatment variable, contribute to overfitting within the regression! However, I can't chuck out the treatment variable, or else, I would have no regression. But, I can disinclude the other controls and just use fixed effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rCw8ePMCECRPkKv7S0lJs27ZNNmjWxcVF9tn58+fx/fffY8uWLfjhhx8UyjY8PBxr1qzBoEGD6o1BKi4uDgDQo0cPGBkZqbSM1I8//ojc3FzMnz8fPj4+sukzZ87EihUrsHPnTri6usq6iMTGxmLv3r1wdHTE2rVr5b5P2tT7008/4dNPP4WRkRHmz58vG+Tcv39/uTJxcHBATEyMSuUl9eTJE2zcuBGVlZVK99Hjx48bXMeff/6JgIAAvPTSS1ixYoVcK9vBgwfx+++/Y//+/XJ3vaSysrKwfft22Xa/9tprWLZsGa5cuYIFCxbAwsICDg4OMDQ0lF2oKtuuc+fOQSQS4bvvvlPovvPkyZMGtwEArl69iqtXr6Jbt25yJ8HXX38da9euRVRUFE6cOIG//e1vsLGxkbW6xMbGKnTZq0t1dTXu3r0LABg4cKBKcTUlvme11H6WevjwIcaOHYv33ntPrm6TSCTYunUriouL8f7778slFZGRkfjss8+wdetW7N69G3p6enWu//Lly6isrMSnn36q0GJaUlKikCg1xc2bN/HVV1/J1TN79uyBn58fLly4oNLNgJbQmDh+/vlnJCYmYsGCBXLTKysrsX79ely+fBnDhg1TuAP54MEDeHl5YfHixbL68fDhw9i7dy8+/fRTDBw4EB9++KHsgtHV1RXffvstjhw5glWrVinEHB4ejrfeegvTpk2TTQsMDMTXX38NX19f7Nixo96HGWRnZ+Onn36Crq4uNm/eLHen/Pfff8fBgwexY8cOrFmzBsDTc5GdnR2CgoLw5MkTuSRJLBYjJCQEXbt2VZospKSk4Pvvv5fVCzNmzMBbb72Fo0ePwtDQEL6+vrKukPPnz8eSJUtw4cIFzJ8/X9ZKUVxcjG+++QYikQibN2+GnZ2dbP2pqalYsWIFfH198fPPPyscm6qW7/z58xETEyNLCFpq3N+VK1dQVlYGU1NTdO7cWe6zxtbdJSUl2LFjB7S0tPDNN9/ItYpIr6HqIhaLYWdnh2+++Ubht9/Y41rV+j4qKkr29KAlS5bIzVddXY3S0tJ69x3Q+HO6VHOvifjY0VZQ11NXpINtIiIiZNOkT/dQ1udOIBDAxMRE7u+65q09X1VVFa5cuQJdXV288cYbcrFYWVlhzpw5kEgkOH/+fKO26+bNm9i/fz/279+PHTt24O2330Z0dDSMjY3lDkgzMzOld2p79uwJFxcXxMTEoKqqqlHfDTy9K/rsCdTNzQ3W1ta4d+9eo9f3rFu3bqGwsBDDhg2TSwYAYNKkSejZsycePXoku9iqbciQISonAwBkLQhWVlaNijEnJwcRERGwtLRU2BfdunXD1KlTUVlZiT///FM2/eTJk5BIJHj33XcVko9x48bBwcEBt27dQklJSaNiUdXly5dRUlKCSZMmKd1HqgxAPnHiBLS0tPB///d/Csf/7NmzYWJigitXrihd9o033pDbbj09PYwZMwY1NTVISkpSeTu0tLQgFAqV3gFT9U6v9LGXCxYskPuNaGtry06I0rv7TVVYWCj7fTX2+GpOfC21n6VEIhEWLlyo0BoRHx+P1NRU9OrVSy4ZAJ4mQB4eHigoKMDNmzfrXX999amBgUGLJASjR49WuICU3mFuiTqrpeMoLCzE5cuX4eDgoFC/aGtr4/XXXwcAufpFSk9PD6+//rrc+WbMmDEAnl7sLly4UO63M3LkSIhEIty/f19pzB07dsSUKVPkpg0fPhx9+/ZFeno64uPj693mP//8E1VVVZg6dapCt6i5c+fCwsICYWFhyM3NlU2fOnUqqqqqcOHCBbn5z58/j6qqKqWtA9L11b5otLGxQb9+/VBRUYEpU6bIjYsyNDTE0KFDUVVVhdTUVNn0y5cvo7CwEPPmzZNLBgDIurXk5eUhMjJS4ftb8zjLzs6WXQf8+uuv+Pzzz7F161aIRCK8++67Ct2gGlt337x5EyUlJRg1apRCFylvb+8Gb6AtXLhQIRloynGtan1fXz0iFAobjLcp53Sp5l4TsYWgFZSVleHkyZMIDg5Geno6ysrKIJFIZJ/XroC6dOmCnj174tq1a8jKypI1K/bq1UvhhzVmzBgEBQXhww8/xIgRI9C/f3/06dNHIctPS0tDeXk5HB0dlV6oSO8aSvsaqurWrVu4desWgKc/og4dOmDatGmYNWuWwkVdaGgozpw5g+TkZBQUFKC6ulru84KCAlhYWDTq+x0cHJR2VbC0tJQbw9BU0v1RV1Y9YMAAJCUlITk5WaG52tHRsVHfJT0eGvu4TunJs1+/fkovWAYMGIDjx4/LlW18fDyEQiGCg4MRHByssExlZSVqamqQkZHRqL65qpKWjbQptbHKy8tx//59GBkZ1fnYX5FIhLy8PBQUFMglxwCUbpO0Gbkxg7XHjBmDn3/+Ge+88w5GjhwJJycn9O3bt1HHsbRclN1l7N69O8zMzJCRkYHS0tIGu/rUpXZd01jNia+l9rOUjY2N0oHr0hifTdqlBg4ciODgYCQnJ8suSJUZNWoUTp06hXXr1mHYsGEYMGAAHB0dW3TQn7J9Ik3SWvNBAarGkZiYiOrqamhpaWH//v0Ky0gTzbS0NIXPOnXqpHAhJv1tGBkZKXRzEwqFMDU1lTsf1ubk5KT0cbhOTk6Ij4/H/fv30a9fP6XLAvUfJ9ra2ujXrx9u3LiB5ORk2XE6duxY7NmzB+fOncOsWbMgEAhQU1OD8+fPQ1dXF+PGjVP6Xc92b6297co+kyYPtbddmuCkpKQo3ffp6ekAnu77Z+vS1jzOsrOzZePKpHR0dPDZZ58pnDubUnfXPsc9S09PD927d6/zfRPa2tpKu+c05bhWtb53dnaGlZUVjhw5gqSkJLi7u6Nv377o0aOH0uuVZzXlnC7V3GsiJgRqVlVVhVWrViExMRH29vYYNWoUTExMZFnmgQMH5AaICoVCfPnllzh06BACAwOxd+9eAE+zzZEjR+KNN96QXdS/9NJL+Pzzz3Hs2DFcunRJdqfO3t4ePj4+GD58OICnd2MA5U+BAf5XGUnnU5WyQb7KnDx5Ert27YKRkREGDhwIa2tr6OjoQCAQyPrvPztIVhXKWh2Ap/uwpqam0et7VnP2W2OfWCStVFTpLlOb9Lvr+j7pemvf7S8sLER1dbVCJf4sVZo2m0Iac1PfVVFUVCTrPtfQNpSVlSkkBMr6x0or0cYcN6+88gpMTU1x5swZ+Pv7y05wjo6OWLBgQYPjKICn+8LQ0LDOp3CYm5sjPz8fJSUlTU4IpPVNVVUVcnJy0KlTJ5WXbU58LbWfper6HUqP7bp+A9LpDbV49erVCxs3bsShQ4cQHBwsu0spvev27N3pplBWZzV2n0gviutL9Bq6waBqHNIuqklJSfW26iirK+r7jvrq7mdvFknVVf61u9jUp6HjRFldaWBggHHjxsHf3x8RERFwc3NDeHg4srOzMXHixDrv9irbPmm51bdfareUS/f9s60TzyorK1Pp+5vz26tP7QH0RUVFCA0Nxfbt2/H1119j8+bNcl2GmlJ3S8ujofKv6zNlv4GmHNeq1vcGBgbYvHkzDhw4gFu3bslacAwNDTFx4kT8/e9/r7frYlPO6VLNvSZiQqBmt27dQmJiotJR5Hl5eUp/FEZGRli4cCEWLlyIzMxMxMXF4cKFC7h48SKys7Oxbt062bxubm5wc3NDeXk5EhMTcfv2bZw5cwbffPMN1q1bh/79+8tOzPn5+UpjlHZXUcezcKurq7F//36Ym5tj27ZtCtm0su42z4vW3G9OTk4Ant7FKioqUnkcgfS76xq0LH1EZe2KwsDAAFVVVTh06FBzQm4yacy5ublKB2Q3RLot3bp1w/fff9+isTXWmDFjMGbMGJSUlODu3bsICQnBuXPnsHbtWnz33XcK/WefZWhoiMLCQpSXlyu96JaWa10VvSqEQiH69OmD2NhYREVFNSohaI34VNXQxW1dv4HGxNi7d2/8+9//RmVlJe7fv4+IiAj4+/vL3i1Q1x3h1iTdjtrjyZ5VUFAAoPl1k/S7Xn75ZSxdurRZ62quuuph6fSGtrWh40RZXQk87Tbk7++PgIAAuLm5ycbJ1dVdqKVI49i6dataWmrVwcjICGPHjoVIJMLGjRuxZcsWbN68WfbbbUrdLV2mofJXpqE6o7HHtar1vaWlJf75z3/i3XffRVpaGmJjYxEQEIDjx4+juLi43keyN+Wc3lI4hkDNMjIyAEB2t7622NjYBpe3tbXF+PHjsW7dOlhZWSE6OlrpnRBdXV30798fb7zxBhYuXAiJRCLrztOlSxfo6uoiJSVF6WDHqKgoAGjSxVlDCgoKUFxcjD59+igkA6WlpUqbvaR3Ulr6TkZjSfdHdHS00s+l+60lKmsbGxsMHDgQFRUVOHLkSIPzS1tUpM3P8fHxSltZlMXYp08flJaW4sGDB82OuymkTxAJCwtr0vL6+vqwt7dHWlqayoN3m6Ixx6GBgQHc3Nzw1ltvYebMmaioqMDt27cbXE56jClr8n748CHy8/PRuXPnJrcOSE2ePBkAcOzYMaV3FGurfRy1RnzN/b3XFyPQtN+ptrY2HB0dMW/ePNmNnIbGILQWaRcIabcHZe7cuSM3b1M5OjpCS0tLtr62dOfOHaXHiPSBDMq64tRW33FSWVkp66Lz7HnQzs4OLi4uCAkJQUJCAkJDQ9GzZ89mPYBDFdJ6Ut37Xh3n25EjR8LFxQWJiYm4evWqbHpT6m5puSrbD2VlZU06jzX3uFa1vhcIBOjatSumTJmCr7/+Gtra2kq76dbWlHN6S2FCoGbS/vzPXlRmZmbi119/VZg/MzMTDx8+VJheWlqK8vJyCIVCWdOf9Dmzz5JmltIxByKRCGPHjkV5eTn27t2rMH7h8OHDEAgEmDhxYtM2sh6mpqbQ1dVFUlKSXPNbVVUVdu3aJbuTVZu0i0dju8+0NA8PDxgbGyMoKEghebt48SKSkpJgZ2fX6PECdVm6dCkMDQ1x9OhR+Pn5KT3Z5+fnY8eOHbh27RqAp/1C3dzckJOTg6NHj8rN+/DhQwQEBEBbW1uu77R0MPsPP/yAnJwche+oqKiQnWTVYdy4cTA0NMS5c+fkBtRLKYvpWTNmzEBVVRV8fX2V3iktKSlp9jgSY2NjCASCOo/DsLAwpYPhn/391Uf6m/vtt98Ufh8///wzACh9vFxjjRo1Cu7u7vjrr7/w5ZdfKu2nLX0x2bffftuq8TW0nxvSt29fdO3aFYmJiQoD7aKiohAcHAwTE5MGn8Mtfc/Is6R3IFtiUHFLsLGxgbOzMwoKCpT2f87OzsaxY8cAQGGQdWOZmppizJgxuH//Pvbv36+0TsrJyZEbDKsuGRkZskd9SgUGBiI+Ph6dO3du8LGj0jvXZ86cUYj38OHDyM3Nhbu7u9KujFOnTkVNTQ3Wr1+PmpoatbcOAMCECRNgZGSEP/74Q2lLukQiQVxcXJO629amrvPta6+9BgDYt2+fXD3Z2Lrbw8MDhoaGuH79ukL3nkOHDjVpTERTjmtV6/uHDx8iMzNTYT5pV92GzgtNOae3FHYZaqatW7fW+dnChQsxZMgQdOzYESdOnMDDhw/Ro0cPPH78GKGhoXB3d1f4ET548ADr16+Hg4MDunXrBgsLC1m/vMLCQsyYMUPW/+yXX35BVlYW+vfvL+uX/+DBA0RERMDY2Fh2VxB4+pSQuLg4nD9/Hvfv38eAAQNQXFyMwMBA2ZMMWurCtjYtLS14eXnBz88P//znP+Hh4YHKykrExMSgqKgILi4uCslS586dYW1tjTt37sj6IGppaWHIkCHNvuPVGHp6enj//fexYcMGrF69GsOGDYONjQ1SUlIQFhYGQ0NDLF++vNEDgevSpUsXfPHFF9iwYQP27NmDU6dOYeDAgbC0tERFRQUePXqEuLg4VFVVyT2d55133sHKlSvx+++/Izo6Go6OjrJnFldUVOCf//yn3CBvFxcXvPnmm9izZw+WLl2KQYMGwdbWFhUVFXj8+DHi4uJgbW2N7777rkW261kmJib46KOPsGHDBqxZswYDBw5Ejx49UFZWhrS0NNkL0+ozYcIEJCcn4/Tp01i8eLHsSQrFxcXIzs5GbGwsXHoLyEYAACAASURBVF1dFR7d2xh6enro27cv7ty5gy+++AI9e/aEUCiEk5MTnJ2dsXnzZohEIvTr10+W+N+7dw9xcXGwtbVVeMGSMqNGjUJISAiuXr2Kd955By+99BKEQiFCQ0ORnp6OAQMGYPr06U3eBiktLS2sXLkS27ZtQ1BQEBYvXgwXFxd07doVIpEIOTk5iI6ORl5enlxrZmvE19B+bohAIMD777+P1atXY+vWrbhx4wbs7e2RmZmJoKAgiEQiLF++vN5+u8DT1pOIiAg4OzvD1tYWBgYGSE9PR2hoKHR0dGQvM3oeLFu2DB9//DEOHTqEsLAwODs7Q1dXF1lZWQgJCUFZWRlmzZql0jiWhixduhR//fUXDhw4gD///BNOTk4wNzeHWCxGeno6EhISsGjRIrW/cdXNzQ27d+9GWFgYunfvLnsPgY6OjtyjtOtibW2NJUuWYMeOHfjggw8wYsQImJubIz4+HrGxsbCyssLbb7+tdFkPDw9YWloiNzcXhoaGGD16tDo2UY6xsTE++eQTrFu3Dh999BFcXFxgZ2cHkUiEx48fIzExEY8fP8aBAwealay6urrixo0b+OGHHzBs2DDo6+vD0NAQL7/8crPil753KTQ0FOfPn8fUqVMBNL7uNjAwwFtvvYUtW7bgX//6F0aMGCEbJJucnAxnZ2fExsY2+jzc2ONa1fo+MjISu3fvlj2QwMzMDGKxGLdu3UJNTQ1mzZrVYGyNPae3FCYEzVT7TbrPmj9/PkxNTbFu3Trs2bMHMTExuHPnDmxsbDB37lzMmDED169fl1umV69emDNnDmJjYxEREYHCwkKYmJigS5cu+Mc//iF3kTFnzhzcvHkTSUlJsotqS0tLTJ8+Ha+88orcAWNkZIRNmzbhyJEjCAoKwokTJ6CtrS17+/GwYcNaeM/8z6uvvgpTU1OcP38eZ8+ehYGBAQYOHIjXXnsN+/btU5hfS0sLq1atwn//+1+EhYXh2rVrkEgksLS0bNWEAHj6+NBNmzbh8OHDiIqKQlFREUxNTTFu3DjMmzdP4U3FzdW7d2/s2LEDFy5cwK1btxAeHo6ioiJoa2vDxsYGnp6emDRpktxj82xsbLBt2zYcOnQIISEhuHPnDvT09ODs7FznRcHf/vY39OvXD6dOnUJcXBxCQ0Ohr68PCwsLjBo1SuFttS1t0KBB2Lp1K44cOYKoqCjExMRAX18fnTp1UvkFeUuXLoW7uzsCAgJkCaahoSEsLS0xderUFrmD8sEHH2D37t2IjY3F7du3UVNTAx8fHzg7O2PBggWIiIjAgwcPEB4eDpFIhA4dOsDHxwcvv/yyyuNAPvjgAzg5OeHChQuyd4t06tQJb775Jry8vJQ+5q4p9PX18cknnyAqKgoXL17E3bt3ERsbi+rqapiZmcHR0RGjR49WeAZ/a8RX335WRe/evbF161YcPHgQkZGRCA8Ph6GhITw8PODt7d1gdxLg6V1gY2NjJCQkICEhAZWVlbC0tMS4ceMwY8YMtV/wNkbHjh3x3Xff4cSJE7ILrsrKSpiYmGDAgAHw9PRs8EVsqjIwMMD69etx4cIFXLlyBTdv3kR5eTnMzMxgY2OD1157TaXkt7kcHR3h4+OD3377DadPnwYA2XlE1e4TU6ZMQadOnXD06FHcvHkTZWVlsLCwwMsvvwxvb+86B3IKhUKMHTsWfn5+GDt2bIPJZUtxcXHB999/j+PHjyM8PBx3796FUCiEubk5HB0d8frrrze7L/mECROQk5ODK1eu4MSJE6iqqoK1tXWzEwLg6blf+obh8ePHy8YhNbbuHjNmDIyMjHDw4EHcuHED2tracHZ2xqZNm/DLL78AaPx4mcYe16rW925ubrIbayEhISguLoaZmRn69OkDLy8vuLq6NhhbU87pLUFQWlra9GfSEREREanJxYsX4evrCx8fH5VefKguq1evRmRkJH744QeF9xhQ26iursbixYuRm5uLgwcPtlqi1l5xDAERERFRHZKSkhAZGQkXFxcmA22guLhYYbykRCLBoUOH8PjxY7i7uzMZaAHsMkRERET0jNOnTyM3NxeXL1+GQCDAq6++2tYhaaR79+5hw4YNcHV1hbW1NcrKypCQkID79+/D2NgYixYtausQ2wUmBERERETPOHbsGHJycmBra4vly5c3+CQjUo9OnTrBw8MD8fHxCA8PR2VlJSwsLDB58mR4e3srvPWamoZjCNQgOzsbAHiQEhEREdFzjy0EatTQC4A0UWFhIYyNjds6DGpBLNP2h2XavrA82x+WafvTnDJtiTEUHFRMRERERKTBmBAQEREREWkwJgRERERERBqMCQERERERkQZjQkBEREREpMGYEBARERERaTAmBEREREREGowJARERERGRBmNCQERERESkwZgQEBERERFpMCYEREREREQajAkBEREREZEGY0JARERERKTBmBAQEREREWkwJgRERERERBqMCQERERERkQZjQkBEREREpMGYEBARERERaTAmBEREREREGowJARERERGRBmNCQERERESkwZgQEBERERFpMCYEREREREQajAkBEREREZEGY0JARERERKTBmBAQEREREWkwJgRERERERBqMCQERERERkQZjQkBEREREpMGYEBARERERaTAmBEREREREGowJARERERGRBmNCQERERESkwZgQEBERERFpMCYEREREREQajAkBEREREZEGY0JARERERKTBmBAQEREREWkwJgRERERERBqMCQERERERkQZjQkBEREREpMGYEBARERERaTAmBEREREREGowJARERERGRBmNCQERERESkwZgQEBERERFpMCYEREREREQajAkBEREREZEGY0JARERERKTBmBAQEREREWkwJgRERERERBqMCQERERERkQZjQkBEREREpMGYEBARERERaTAmBEREREREGkzU1gE0lr+/P44ePQqxWAw7OzssXrwYTk5Odc6fkpKCnTt34t69ezAyMoKnpyfmzZsHgUCgMG9cXBw+/fRTdOnSBT/++KM6N4OIiIiI6LnwQrUQXL9+Hbt27YK3tzd8fX3Rt29frF27FtnZ2UrnLykpwerVq2FmZoYtW7ZgyZIlOHbsGI4fP64wb1FREbZu3YoBAwaoezOIiIiIiJ4bL1RCcPz4cYwfPx6TJ09G165dsXTpUpibmyMgIEDp/FeuXEF5eTmWL18Oe3t7DB8+HLNmzcLx48chkUjk5v3uu+8wfvx49OnTpzU2hYiIiIjoufDCJASVlZVISkqCq6ur3HRXV1fEx8crXebu3btwcnKCrq6u3Px5eXnIysqSTfP394dYLIa3t7d6giciIiIiek69MGMICgoKUFNTAzMzM7npZmZmiIqKUrqMWCyGlZWVwvwAkJ+fD1tbW6SkpOCPP/7Apk2bIBQKG4zj7NmzOHfuXL3zfPLJJwCAwsLCBtenaSQSCfdLO8MybX9Ypu0Ly7P9YZm2P80pUz09vWZ//wuTEEgpGwzc1PkrKyuxceNGvPnmm7C1tVVpfZ6envD09Kx3HumYBmNjY9UD1RCFhYXcL+0My7T9YZm2LyzP9odl2v60dZm+MAmBiYkJtLS0IBaL5abn5+crtBpImZubK50feNpSkJeXh9TUVPj6+sLX1xfA0wxNIpHglVdewZo1a+Dm5qaGrSEiIiIiej68MAmBtrY2evbsicjISIwYMUI2PTIyEsOGDVO6TJ8+ffDrr7+ioqICOjo6svktLCxgY2OD6upq/PDDD3LL+Pv7IzIyEqtWrYK1tbX6NoiIiIiI6DnwwgwqBoAZM2bg0qVLOHfuHFJTU/HTTz8hLy8PU6ZMAQDs2bMHq1atks0/evRo6OrqYtu2bXj48CGCgoLg5+eHGTNmQCAQQCQSwd7eXu6fmZkZtLW1YW9vD319/bbaVCIiIiKiVvHCtBAAwMiRI1FQUIBDhw4hLy8P9vb2WLNmjexOfl5eHjIzM2XzGxoa4ssvv8TOnTuxfPlyGBkZYebMmZgxY0ZbbQIRERER0XNFUFpaKml4NmoM6aBiExOTNo7k+dPWg2ao5bFM2x+WafvC8mx/WKbtT3PKtCWeMvRCdRkiIiIiIqKWxYSAiIiIiEiDMSEgIiIiItJgTAiIiIiIiDQYEwIiIiIiIg3GhICIiIiISIMxISAiIiIi0mBMCIiIiIiINBgTAiIiIiIiDcaEgIiIiIhIgzEhICIiIiLSYEwIiIiIiIg0GBMCIiIiIiINxoSAiIiIiEiDMSEgIiIiItJgTAiIiIiIiDQYEwIiIiIiIg3GhICIiIiISIMxISAiIiIi0mBMCIiIiIiINBgTAiIiIiIiDcaEgIiIiIhIgzEhICIiIiLSYEwIiIiIiIg0GBMCIiIiIiINxoSAiIiIiEiDMSEgIiIiItJgTAiIiIiIiDQYEwIiIiIiIg3GhICIiIiISIMxISAiIiIi0mBMCIiIiIiINBgTAiIiIiIiDcaEgIiIiIhIgzEhICIiIiLSYEwIiIiIiIg0GBMCIiIiIiINxoSAiIiIiEiDMSEgIiIiItJgTAiIiIiIiDQYEwIiIiIiIg3GhICIiIiISIMxISAiIiIi0mBMCIiIiIiINBgTAiIiIiIiDcaEgIiIiIhIgzEhICIiIiLSYEwIiIiIiIg0GBMCIiIiIiINxoSAiIiIiEiDMSEgIiIiItJgTAiIiIiIiDQYEwIiIiIiIg3GhICIiIiISIMxISAiIiIi0mBMCIiIiIiINBgTAiIiIiIiDcaEgIiIiIhIgzEhICIiIiLSYEwIiIiIiIg0GBMCIiIiIiINxoSAiIiIiEiDMSEgIiIiItJgTAiIiIiIiDQYEwIiIiIiIg3GhICIiIiISIMxISAiIiIi0mBMCIiIiIiINJiorQNoLH9/fxw9ehRisRh2dnZYvHgxnJyc6pw/JSUFO3fuxL1792BkZARPT0/MmzcPAoEAABAUFISAgADcv38flZWV6Nq1K7y9vTF06NDW2iQiIiIiojbzQrUQXL9+Hbt27YK3tzd8fX3Rt29frF27FtnZ2UrnLykpwerVq2FmZoYtW7ZgyZIlOHbsGI4fPy6bJzY2Fi4uLlizZg22bdsGd3d3rF+/HnFxca21WUREREREbeaFaiE4fvw4xo8fj8mTJwMAli5ditu3byMgIAALFixQmP/KlSsoLy/H8uXLoaurC3t7e6SlpeH48eOYMWMGBAIBlixZIreMj48PQkNDcfPmzXpbHoiIiIiI2oMXpoWgsrISSUlJcHV1lZvu6uqK+Ph4pcvcvXsXTk5O0NXVlZs/Ly8PWVlZdX5XaWkpjIyMWiZwIiIiIqLn2AvTQlBQUICamhqYmZnJTTczM0NUVJTSZcRiMaysrBTmB4D8/HzY2toqLOPv74/c3FyMHTtW6TrPnj2Lc+fO1RvrJ598AgAoLCysdz5NJJFIuF/aGZZp+8MybV9Ynu0Py7T9aU6Z6unpNfv7X5iEQEo6GFgd8wcGBuKXX37BypUrYW1trXQeT09PeHp61rse6ZgGY2Nj1QPVEIWFhdwv7QzLtP1hmbYvLM/2h2Xa/rR1mb4wXYZMTEygpaUFsVgsNz0/P1+h1UDK3Nxc6fwAFJYJDAzEli1b8MEHH/AJQ0RERESkMV6YhEBbWxs9e/ZEZGSk3PTIyEj07dtX6TJ9+vRBXFwcKioq5Oa3sLCAjY2NbNr169exZcsWvP/++xg+fLh6NoCIiIiI6Dn0wiQEADBjxgxcunQJ586dQ2pqKn766Sfk5eVhypQpAIA9e/Zg1apVsvlHjx4NXV1dbNu2DQ8fPkRQUBD8/PxkTxgCgGvXruHbb7/FggUL4OzsDLFYDLFYzL55RERERKQRXqgxBCNHjkRBQQEOHTqEvLw82NvbY82aNbL+/nl5ecjMzJTNb2hoiC+//BI7d+7E8uXLYWRkhJkzZ2LGjBmyeQICAlBdXY1du3Zh165dsunOzs7YsGFD620cEREREVEbEJSWlkraOoj2Rjqo2MTEpI0jef609aAZanks0/aHZdq+sDzbH5Zp+9OcMm2Jpwy9UF2GiIiIiIioZTU7IZBIJCgrK2uJWIiIiIiIqJWpnBAEBQXh119/lZvm5+eH2bNnY+7cufjqq6+YGBARERERvWBUTgiOHz8ue4Y/ANy7dw+//fYbHB0dMXnyZNy+fRtHjx5VS5BERERERKQeKj9lKD09HSNGjJD9fe3aNRgbG+Pzzz+HtrY2hEIhrl27hvnz56slUCIiIiIiankqtxCUlZXJjWIODw+Hm5sbtLW1AQAODg7Iyclp+QiJiIiIiEhtVE4IOnTogHv37gEAMjIykJqaCldXV9nnBQUF0NHRafkIiYiIiIhIbVTuMjR27Fjs378feXl5ePToEYyMjDBkyBDZ5/fu3UPnzp3VEiQREREREamHygnB7NmzUVlZidDQUFhZWWHZsmUwNDQE8PRlCnFxcZg+fbraAiUiIiIiopbHNxWrAd9UXDe+XbH9YZm2PyzT9oXl2f6wTNufF+ZNxatWrUJUVFSdn0dHR2PVqlXNDoiIiIiIiFqPyglBTEwMxGJxnZ8/efIEsbGxLRIUERERERG1DpUTgobk5ORAV1e3pVZHREREREStoN5BxTdv3sStW7dkf587d05pt6GioiJERUWhd+/eLR8hERERERGpTb0JwcOHD3Ht2jUAgEAgwN27d5GYmKgwn56eHvr27YslS5aoJ0oiIiIiIlKLehOCuXPnYu7cuQCA6dOn47333sOYMWNaIy4iIiIiImoFKr+H4OTJk+qMg4iIiIiI2oDKCUFtZWVlKCwshESi+AoDa2vrZgdFREREREStQ+WEoLKyEn/88QfOnz+PgoKCOuc7ceJEiwRGRERERETqp3JC8J///AcXLlzAkCFD4OzsDCMjI3XGRURERERErUDlhCAwMBDjx4/HsmXL1BkPERERERG1IpVfTFZTU8P3DBARERERtTMqJwRubm6Ij49XZyxERERERNTKVE4Ili5digcPHmDfvn0Qi8XqjImIiIiIiFqJymMIFi1aBIlEgocPH+LQoUMQCoUQCARy8wgEAvj5+bV4kEREREREpB4qJwQjRoxQSACIiIiIiOjFpnJCsHz5cnXGQUREREREbUDlMQRERERERNT+NCohyM7Oxvfff4/FixfD29sbMTExAIAnT55g+/btSEpKUkuQRERERESkHionBKmpqXj//fcRGBiITp06oby8HDU1NQAAU1NTJCQk4MyZM2oLlIiIiIiIWp7KCcGvv/4KfX197NixAx988AEkEonc5+7u7rhz506LB0hEREREROqjckIQFxeHadOmwdzcXOnThqytrZGbm9uiwRERERERkXqpnBBUVVVBT0+vzs8LCwshFApbJCgiIiIiImodKicE3bp1Q3R0tNLPJBIJgoOD0aNHjxYLjIiIiIiI1E/lhGD69OkICgrCH3/8gcLCQgBATU0NUlNTsXHjRiQlJWHmzJlqC5SIiIiIiFqeyi8mGzVqFLKzs7Fv3z4cOHAAALB27VoAgJaWFhYuXAh3d3e1BElEREREROqhckIAALNnz8bo0aMRFBSEjIwMSCQS2NraYvjw4bCxsVFXjEREREREpCaNSggAoEOHDnjllVfUEQsREREREbWyRr2pmIiIiIiI2pc6WwgWLVoELS0t7NixAyKRCIsWLVL6/oHaBAIBdu3a1eJBEhERERGRetSZEDg7O0MgEMiSAOnfRERERETUfghKS0slbR1Ee5OdnQ0AMDExaeNInj+FhYUwNjZu6zCoBbFM2x+WafvC8mx/WKbtT3PKtL4XB6uKYwiIiIiIiDSYygnB+fPnsX79+jo/37BhAy5dutQiQRERERERUetQOSEICAiAubl5nZ9bWFjA39+/RYIiIiIiIqLWoXJCkJ6ejm7dutX5uZ2dHTIyMloiJiIiIiIiaiUqJwQCgQAFBQV1fl5YWIiampoWCYqIiIiIiFqHyglBz549cfXqVVRUVCh8Vl5ejitXrsDBwaFFgyMiIiIiIvVSOSGYPXs20tPTsXLlSgQGBiItLQ3p6ekIDAzExx9/jPT0dMyZM0edsRIRERERUQur88Vkz3J1dcV7772Hn376CRs3bpRNl0gkMDAwwLJlyzBo0CC1BElEREREROqhckIAAOPGjYOHhwciIiKQmZkJiUSCjh07wtXVFQYGBuqKkYiIiIiI1KRRCQEAGBgYYPjw4eqIhYiIiIiIWhnfVExEREREpMHqbCGYPn06BAIB/Pz8oK2tLfu7ISdOnGjRAImIiIiISH3qTAjmzZsHgUAAoVAo9zcREREREbUfdSYEEyZMgKmpKbS0nvYqmj9/fqsFRUREREREraPOMQSLFy9GcHCw7O9Vq1YhKiqqVYIiIiIiIqLWUWdCIBKJUFlZKfs7JiYGYrG4VYIiIiIiIqLWUWeXITs7O5w5cwampqaydwykpaUhNja23hU6Ozu3bIRERERERKQ2gtLSUomyDyIiIrBx40aUlJSotCKJRAKBQMCnDAHIzs4GAJiYmLRxJM+fwsJCGBsbt3UY1IJYpu0Py7R9YXm2PyzT9qc5Zaqnp9fs76+zhcDV1RW7d+9GcnIy8vPzsWnTJkybNg39+vVr9pcSEREREdHzoc6EIDs7G6ampujfvz8AICAgAB4eHhgwYECrBUdEREREROql8lOG6MVx94ccPDySr/QzQUUmkjZeROrBFLnpeUUVcHz3LG4l5kJQkQnT+LEQlibIlglbnoH8kGS5ZcatvoLdFx8AAISlCTCNHwtBRSYAIGXZGohPX2rWdhTdikDCq/9G2PIMFD+qaNa6GlKeV9Uq30NERET0vKmzheDZpwzFxsZi0qRJrRIUqZfLxG0o6f5tnZ9LtDugoNcRSISm9a7Hb+Uw6OsKlX7W9asPIdDVbVacRERERKR+fMqQBtLWK4ZQF6iuawaBEBKRRb3rkFRXw9xIW/Ht1dVVAAChSfMHO1XVKB3vXk9QlYBAu9nf+zypqZZAS8g3hBMREZH68ClDatDWTxmK+igS1bplgEE6tJ44Q6BVA4vuN2ExrggVIfrITeuG6oIimI09jK4dspEu7oqMP99CRb4ttGqKYaIfgirDPnhsrYdRbw2AoCITof+qganRRUSKndBdZAWhViGshpxGX6cLqCixRkHuANh0P4ez/xmMJZIJ+CnvIqK79MOir16HSKiFtFN5yPgzD1o1OtASlsKqexRK+1dgUJdzEFRkITn2ddwJGgwjiQEENSXIq7gPmzF30M8oApGXNsLJ81ek3X0ZhelWqDIENuc+wOl/fodq8ynQqsxCUUImkkIWoCjHGkI9wNoxFr0G7YQAxXhUaIlVf45H96yhGCY0gz60YNBZG12mm2DdjQSIiysx2s4SvS/qYIPWffQeaIrP5vaDvo7y1o+CpHIk/piLXksskH6mEKVZlTDsogOH181RnlOFR8eeoDynGsY9ddB9vjlEhk975klqJPjrYhEeBxejqrAGetYidJpiDPP++gCedluK+TIb3V8zQ05wCYoeVqCrlymsRxqi6EEF0vwLUPKoEkIDAcyc9NDFywRCvTp7/bUaPu2i/WGZti8sz/aHZdr+8ClDpB6lHdClZwQEY2KQF22CrPgJyM89jq5uFkAaoGNbDFzXwb25X6M4rAraWjX4tToc86f1R1XUcJTlClBVlgDgf4PIC4uGwLw6Ao4LdVAWE4yUkL/jSbwAHQ0S0HnSDQBA5bQpOOvmgZLPrsPBxhAAkBdZgszLBcgqugHRmL5w7ZwJ/exkdKu6jviyD6F1zwHZYdUYMPAP7A/tCqfRQ9DziRD9+5zBff+nx1ty2KvoOXQfdCd1xuFDw7FMxx6SKl3o5h3GE52FiAp4FZZugN3rJhDE+SLhzzmo1PkGHydFY2LvQiyzHYqiAlP8JsiAia0O/tHBDvf+kwe93gKEJYlhr6uP3tDBB6/0xnsno9HN2gBLJ/eodxenny1E1xkmEOpr4f5vYiTvyYOWtgD23mYQCIDkPWJknC2E3aynXa+yrhUj83IR7OeYwrCrDnJvlyD5v2L0WyGCQef/tWykny5El+km6DbPDAKhACUZlUjcmYtOnsboNtcMVSU1SD32BCkH8tHjzfpbcoiIiIgaUu/tRQMDA/Tv3x8jR46Es7MzPDw8MHLkyHr/UdsTCCQwskxDl1c6o8Ow2ei9dCJE1enIeuSMNAcnAEB0XkcUJpvCKE0bJWld0W/UbuTY5KDGwQw93jREdZUuzMoK8DC7WLZefUQidbITrLrchP2wQFRKBCgr6I7ivF64J5oKABjs0QudLfWhLdSCe08LiIRaKL6bCUFVEdKmeWDE9H7oOlAXdv3PI+ZqL1w7+BcyA3VgYHoHdu4X4TjXAmPm9IHzBH/k/+UKcfzTi2n9oSYwGjoSJoID+EGcAr1qLRTldEaVwQCk35kIbVMR7Lw7wUTvCqy7BqLLyxbIvmWABzlmmD3p7yiJMUX3GWb4++vdcTg+AxgtgraxFno81oeRngjvefUEAAzsZgZPV1sEJ+Q1uJ87TzGGcQ9dGHTShvUwQxSnVKKLlwmM7HVgaKcDy8H6KEgql82fdaUItmONYDnIAHrWInSeYgIjBx1k/lkkt17rkYawGKgPXUsRdMyEyPqzCBau+rAdawS9DiIY2evAfo4ZxNFlqCyss+MXERERkUrqbCF41vr169UZh8r8/f1x9OhRiMVi2NnZYfHixXBycqpz/pSUFOzcuRP37t2DkZERPD09MW/ePLm+7zExMdi9ezcePXoECwsLzJo1C1OmTGmNzVELLWE1jCwf4cuzHXHh7lXkFVXgx0ohKoTGyH5SBjsAVQITCA0NUJZVCQgA4w738QG0YbgpCVkGRdA1XgpRSTVOhf2F/5tgBAAo18rFpJG9ICw5DImhI4oF1dCR6EG3e1dYdOkNZjrCfwAAIABJREFUZB7G37feQveuuVhUXgX9qpqn8UgeAgJbDPlTgAfng5DfMRZ9XxFB50ENdM2qIREBAvFDFBTpw8laDAAQliXCwr4Kum9Z4NYRwE5rFQzSkyGQlMPCohgQA5WlRqjW64KyrCoY2utAoCWAsOweqnUdYNjTDIKabAyxMYeoCJBUA0bddeBqpgctAZCcXYLu3XRgkiREz46GEAn/lxtbm+oiKkX5U5pq0+/4v7v6IuOny+t3+t80bWMhqoqe7oPqshpUPqmBUXcduXUYO+jgyZ1yuWmGXeXHQRSnVqI8pwp5EaUKMZTnVkPbWHnXJiIiIiJVqJwQAE/7xh88eBDR0dF48uQJVq9ejf79++PJkyfYt28fJk2ahJ49e6orVly/fh27du3C22+/jX79+uHMmTNYu3YtfvzxR1hbWyvMX1JSgtWrV8PJyQlbtmxBWloafH19oaenh5kzZwIAMjMz8fnnn2PixIlYsWIF7ty5gx07dsDExATDhw9X27aom5ZWNZ4UV+ALn4GwMdND0Te3Ia7UQlX10wvUGmgBgqf92oGnw0geiExgN38+HHppIX+nBAIJcCo0A/83oTcAQCASoLOlPlDydP6n/wugpasDvf/f3/4z7364lKCFwtIqHPt/7d15fFTV/f/x92Rjsi8QNgkBDGtECDtiv6QoCBUQwbJYQUG+Ll9pLf7UhwulqJUiVsHaKkUUFEQfIKsVEkELUgKtYgIECBLZwr4kIduQZDLz+4Nm6pgEJ2EmQ+a+no+Hj5p7z5n7uTlg73vuPfdsOqypyZflH1SmiotLZB/9kCIKuyjvcAd9vfZnShti14Fsm9oUSLJf+bRAx4W5TZeKBunYR3lSkDRj51169clE/d/fvlVytwRpi2S3m2T3u/Lc3I/nNjv5zyyZHzapbG+XnMJA5T67C/OZTdVch1eZAPzjz6muzh9t8wuq+hlN+oWo2cCwKl2DIgkDAADg2rg8IzEnJ0e//e1vtX37drVs2VKlpaWy2a5cXEZGRurgwYPasGGDxwqVpLVr1+q2227THXfcobi4OD388MOKjo7Wxo0bq22/ZcsWlZaWavr06YqPj9eAAQM0ZswYrV27Vvb/XPGlpKQoJiZGDz/8sOLi4nTHHXdo0KBBWrNmjUfPxZMqrP6y2U36v1vzdUunJrqxeZgku9O1aaGlXHa7FBRaJtlNKrzQVtuDmqvihhtkD2qs0pJoSdLx8yXan1MgSWoUcOWPS0WjePlbspyOGWA5IElKahet/3dXRzWNMqu8wq4tmecV0SFeUfYSZVrz1XxMlLo+9L2K8+JUnl+gwNbhMgVI9pi2igizyOLX6soxzO3lV3JEpZeufKN+4JxZH6RZtSkrUKP6t3Y6trlZgIqOlslus6vC3F7+pYdVnJ0vu59d/z6XJ2v4lYv3wiNlSj+cJ5tdahcbouKjZSo0188jN/5mPwVG+qnosPM6B4WHy2RudvVcHtIqUJYzVpljA6r8UyU8AAAA1JLLdwiWLFmi4OBg/elPf5Kfn58mTpzotL9Xr17avn272wusVF5eruzsbMc3+5WSkpJ04MCBavtkZWUpMTFRjX7wPvykpCQtW7ZMZ8+eVfPmzZWVlaWkpCSnfj169NCXX34pq9WqgIBa3UTxqqw/rZbdEiPb5TCdOxeunpff0cElZ5R9trWamsIUE+Gv7E1n1S4oSqqwq6CoTGe37pPNP1xZ26aot/1bXXjnW+23N5F/QIRMVru6BPrplcXpmqafKaDMpgMvbVFQcFN1H3pKEYEl8isvlj1vl8qPHpQ5VPrHvDSZLNGKybOoLKhC/qmHlG2LlCX8FrXbuEVf7Dim9s2CZPKz6p62K3Vif1cFhvZVSX4nHf/mNuV8fUSXIqxqZE5Wj9F/VdO+fio8LN0WcUl7vl2jN249I8vfzJI6qKKkXOc2f69LWXtUmt9Gu587oKCQcMXf3FM5Oy8qOOKibig9rw/eW6h+YUnK/ugGvW89qWFBwcqff1zW0gh9azquonNWZf95p6R2OvLuLp23FKm09Mq5VqeiPFhSnA69tl0mvyuh2FoWJqmlU5/yy5GyWpo4tpnKo3QqpbEubvtOfgGXZS2NUPnlGJkjjuvAS6WyVQQ4avAP+O9jRDZrkCwFrZXxzH4FNLokk8kmW0WQKspD1Sj0nAf+JAEAgNoyBeeq05OjvV1Gnbh8tbtv3z6NHTtW0dHRKigoqLK/adOmunjxoluL+6GCggLZbDZFRUU5bY+KitLu3bur7ZOXl6cmTZpUaS9J+fn5at68ufLy8tStW7cqbSoqKlRQUKCYGOe3uKSkpCg1NfWqtT777LOSrrxCqj798DGX0rMxWpLeVyM6f6WbexUq64uHde7if19L1SXITza7tLq4QofDzum1UIsSjo+QqcKiRoG7VGHuIhVZlWz20zrrfzr958voMkukvtsxVuYAq1p3O6Emzb/Xrt23KfmWtZqXX67DBaVaZJduaeSvG4P8ZC2zKSC8t0KDAxQmkypyzyjx52/oZGa4+gz6h4KCP1XWzgnamzFWIQHBKi0qlu1Smb5LGaQmNx6SDkuPJf9dJ/wLdPao81hJkp+fVebwkyoraaKSvBuVta2NmrROV6efLVJKYKmO5jfWjK8aqa01VPf6t1Kw/GSzlsocfkKWYmvVx3o8JKBRvux2P5VZmshuC5DJv0yNwk45XfxXxy+gTOaIHJVbmuhyQStJJpn8yxUQWHTVfgAAoP7Y7XW/9rPb7XXu69HXjv6Y1Wq96gELCwvl7+/555mrLITlhvY/blP5OFF1fYcOHaqhQ4de9fMq1yGo73cEd37KOZX2VbIkqURS65uk1pJ6/WfflZkefdTjB+3b/Od/y4vaa8+ss2p3/zj9uluwfu1o8X8/aJ2sFpKkrrJJSpJ0SY9r3R8r91f/OyosLFRMxQEFn9kv8/CNKpNUJqlVV6mVU8tOkq5M7O51t7Rh1yzN/Gifts3++Q/WB/i9JMk5IlaKl0WjJEmxkv5W7QuwbtSCKttayvWl9Vq6uC3uKp/R1sXPuH7xPmzfw5j6FsbT9zCmvsfbY+ryHII2bdpoz5491e6z2+3asWOHbrzx6u9tvxYRERHy8/NTXl6e0/b8/Pwqdw0qRUdHV9te+u+dguraXLp0Sf7+/ob5y1ZwqFR5ey0qvWBV0dEyHf4gTwGhforo1OinO3uYpaxC2aeLtCD1sH55S6saFwsDAABA3bh8h2DkyJH605/+pI8//tix3oDNZlNOTo6WL1+u7Oxs/e53v/NYoYGBgUpISFBGRoZuvfVWx/aMjAzdcsst1fbp1KmTlixZorKyMgUFBTnax8TEqFmzZo42O3fudOqXkZGhhISEBjV/4FrYK+w6tbFQpRcr5BdoUmh8oDpOayL/Rq7lxdObCnV68w8eX7GXymT/z4TzFofU/Rfz/7sv/B4p9AaXa1u06bAWpBxWjxuj9dgw5zdYLUj5Xn9LPVxtv54J0Vr0WK9q9/2UYyvydXFX1Vd8SlLjnsGKH1t9AAUAAGiITBaLxeUnqD/55BN9+OGHstlsstvtjkdq/Pz89MADD+iuu+7yWKHSldeOvv7663rkkUfUpUsXbdy4UZs2bXK8dvT999/Xd999p5dfflmSVFxcrEceeURdu3bVuHHjdPLkSc2fP18TJkxweu3otGnTdMcdd2jo0KHav3+/FixYoCeffLLOrx2tfGQoIiLCPSd+nbMW22QtsTl+NllzZbKXS5L8AqWgyP+2LbCYFBbpnkdi8ovLdKm4vNp95iB/NYuq2zN15YUVqrhc/V8Lf7OJ9/7/iLdvc8L9GFPfwnj6HsbU91zLmLpjDkGtAoEknT9/XmlpaTp16pTsdruaN2+uAQMGOL5x97TKhclyc3MVHx+vqVOn6qabrjz1PW/ePGVmZurdd991tK9cmOy7775TWFiYhg0bVu3CZIsWLXIsTHbPPfdc08JkRgsEtcF/xHwPY+p7GFPfwnj6HsbU9zS4QICfRiCoGf8R8z2Mqe9hTH0L4+l7GFPf4+1AUOuH5E+fPq1vvvnGcdHbrFkz9ezZUy1atLjmYgAAAADUr1oFgnfffVfr1693vJazkslk0siRI/Xggw+6tTgAAAAAnuVyIFi7dq3WrVunfv36afTo0WrdurUk6fjx41qzZo3Wr1+vJk2aeHxiMQAAAAD3cXkdgs8//1w9e/bUc889p06dOikkJEQhISHq1KmTnn32WfXo0UMpKSmerBUAAACAm7kcCM6cOaPevXvXuL9Xr146e/asW4oCAAAAUD9cDgTh4eE6efJkjftPnTrFjHcAAACggXE5EPTt21cbNmzQ5s2bnSYV2+12ffHFF9qwYYP69u3rkSIBAAAAeIbLk4onTZqkrKwsvfnmm3r//fcdrxk9ffq0Ll26pLZt22rSpEkeKxQAAACA+7kcCMLCwvTaa68pNTVVX3/9tWMdgnbt2qlPnz4aMmSIAgMDPVYoAAAAAPer1ToEgYGBGj58uIYPH+6pegAAAADUI5fnEOTm5mrfvn017t+3b5/y8vLcUhQAAACA+uFyIHjvvff0wQcf1Lh/2bJlWrx4sVuKAgAAAFA/XA4EmZmZ6tWrV437e/Toob1797qlKAAAAAD1w+VAUFBQoLCwsBr3h4WF6dKlS24pCgAAAED9cDkQNG7cWNnZ2TXuP3TokKKiotxSFAAAAID64XIg6N+/v7744gt99dVXVfZt27ZNX375pfr37+/W4gAAAAB4lsuvHR0/frwyMjL02muvacWKFYqPj5ckHTt2TDk5OWrdurXuvfdejxUKAAAAwP1cDgQhISGaO3euVq9erbS0NO3cuVOS1KJFC40fP1533323zGazxwoFAAAA4H61WpjMbDbr3nvv5U4AAAAA4CNcnkMAAAAAwPcQCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMLBavXa0oqJCX375pb7++mudO3dOktS0aVP16dNHP//5z+Xv7++RIgEAAAB4hsuBoKSkRDNnztShQ4cUHBysZs2aSZL27Nmjf/3rX0pJSdGLL76okJAQjxULAAAAwL1cDgRLly7VoUOH9OCDD2rYsGEKDAyUJFmtVm3cuFGLFi3SsmXL9NBDD3msWAAAAADu5fIcgh07dmjo0KEaOXKkIwxIUkBAgEaMGKE77rhDaWlpHikSAAAAgGe4HAgKCgoUHx9f4/42bdqooKDALUUBAAAAqB8uB4KmTZsqIyOjxv0ZGRlq2rSpW4oCAAAAUD9cDgS33367du7cqXnz5unYsWOyWq2yWq06duyY3njjDf3rX//SkCFDPFkrAAAAADdzeVLxmDFjdPbsWaWmpmrLli1O++x2u4YOHarRo0e7uz4AAAAAHuRyIDCZTHrsscc0fPjwKusQ9O7d+6rzCwAAAABcn2q1MJkkxcfHc/EPAAAA+AiXA0Fubq4uXLigDh06OLbl5ORo3bp1Kioq0sCBA9W/f3+PFAkAAADAM1wOBAsXLlR+fr7mzJkj6cprSJ955hmVlJQoKChIO3bs0PPPP68+ffp4rFgAAAAA7uXyW4YOHjyoHj16OH7esmWLiouLNX/+fH344Yfq3LmzVq9e7ZEiAQAAAHhGrRYmi4mJcfz89ddfKzExUfHx8QoICNDPfvYzHT9+3CNFAgAAAPAMlwNBWFiYcnNzJUmXL1/W/v37lZSU5NhvMplUXl7u/goBAAAAeIzLcwg6d+6sDRs2KC4uTrt27ZLValXfvn0d+0+ePKnGjRt7pEgAAAAAnuHyHYL7779fgYGB+uMf/6jPP/9cI0aMUFxcnCSpoqJC27dv10033eSxQgEAAAC4n8t3CFq0aKEFCxbo+PHjCgkJUbNmzRz7SktL9cgjj6ht27YeKRIAAACAZ9RqYTJ/f/9qL/pDQkLUr18/txUFAAAAoH64HAgyMzN1+PBhjRw50rFty5Yt+vjjj1VUVKT/+Z//0dSpU+Xn5/JTSAAAAAC8zOWr948++kgHDhxw/JyTk6M33nhDJpNJCQkJ+uyzz/Tpp596pEgAAAAAnuFyIDh+/Lg6duzo+Hnr1q1q1KiRXnvtNc2aNUvJycnavHmzR4oEAAAA4BkuB4KSkhKFhYU5ft61a5e6d++ukJAQSVKXLl109uxZ91cIAAAAwGNcDgTR0dGOlYgvXryow4cPOy1MZrFYmD8AAAAANDAuTyru37+/PvvsM5WXl+vQoUMKDAxUnz59HPuPHDmi5s2be6RIAAAAAJ7hciD41a9+pfz8fG3ZskUhISF6/PHHFR0dLenK40RpaWm68847PVYoAAAAAPczWSwW+7V+iM1mk8ViUaNGjRQQUKulDXzSuXPnJEkRERFeruT6U1hYqPDwcG+XATdiTH0PY+pbGE/fw5j6nmsZU7PZfM3Hd8vVu5+fn0JDQ93xUQAAAADqUa0DQVZWlrKzs1VcXCybzea0z2Qyafz48W4rDgAAAIBnuRwIiouL9eKLLyorK0t2u10mk0l2+5WnjSr/nUAAAAAANCwuvyd0yZIlys7O1vTp07Vw4ULZ7Xa98MILWrBggQYPHqx27drpgw8+8GStAAAAANzM5UDw73//W0OGDFFycrJjMTI/Pz+1bNlS06ZNU+PGjbVo0SKPFQoAAADA/VwOBIWFhWrbtq0kOd4kdPnyZcf+nj17ateuXW4uDwAAAIAn1Wql4ry8PElSSEiIgoODdeLECcf+wsLCKpOMAQAAAFzfXJ5U3KFDB2VmZmrcuHGSrtwRWLNmjWJiYmS327Vu3Tp16tTJY4UCAAAAcD+X7xAMHz5crVq1UllZmSRpypQpCg8P17x58zR//nxFRETooYce8lihAAAAANzvmlYqttvtOnr0qPz8/NSqVSv5+/u7s7YGi5WKa8bqir6HMfU9jKlvYTx9D2Pqexr0SsUmk8kx0RgAAABAw1NjIKj8lru2mjZtWudiAAAAANSvGgPB1KlTZTKZav2B69atu6aCAAAAANSfGgPBb37zmzoFAgAAAAANR42B4Pbbb6/POgAAAAB4gcuvHQUAAADge67pLUP1qby8XO+99562bt2qsrIydevWTY8++qiaNGly1X7bt2/Xhx9+qNOnT6tFixaaOHGi+vfv79i/cuVKpaWl6eTJkwoMDFTHjh11//33Kz4+3tOnBAAAAHhdg7lD8M477ygtLU1PPfWU5syZo5KSEr344ouqqKiosU9WVpbmzp2rgQMH6s9//rMGDhyoOXPm6ODBg442e/fu1Z133qlXX31VL7/8svz9/TVjxgwVFhbWx2kBAAAAXtUgAkFxcbE2bdqkyZMnKykpSQkJCXriiSd09OhR7d69u8Z+69at080336xx48YpLi5O48aNU9euXbV+/XpHmxdffFG333674uPj1aZNGz3xxBMqKCjQgQMH6uPUAAAAAK9qEIEgOztbVqtVSUlJjm2xsbFq1arVVS/cs7KynPpIUo8ePa7ax2KxyGazKTQ09NoLBwAAAK5zDWIOQV5envz8/BQREeG0PTo6Wnl5eTX2y8/PV1RUlNO2qKioq/ZZuHCh2rVrp06dOlW7PyUlRampqVet99lnn5UkHjuqht1u5/fiYxhT38OY+hbG0/cwpr7nWsbUbDZf8/G9GgiWLl2qFStWXLXN7Nmza9xnt9trvVbC1fosWrRIBw4c0CuvvCJ/f/9q2wwdOlRDhw696jEqV3kODw+vVW1GUFhYyO/FxzCmvocx9S2Mp+9hTH2Pt8fUq4Fg5MiRSk5Ovmqb2NhY2Ww22Ww2FRQUKDIy0rEvPz9fiYmJNfaNiopSfn6+07ZLly5VuWsgXZm0vG3bNr388stq3rx57U4EAAAAaKC8GggiIyOdLvBrkpCQoICAAKWnpzsCxIULF3TixAl17ty5xn6dOnVSenq6Ro8e7diWnp5epc/ChQu1bds2zZ49W3FxcXU7GQAAAKABahCTikNDQzV48GAtXrxYGRkZ+v777/X666+rTZs26tatm6Pd888/r/fff9/x88iRI7Vnzx6tXLlSOTk5Wrlypfbu3auRI0c62rz99tvavHmznnzySYWFhSkvL095eXmyWCz1eo4AAACANzSIScWSNHXqVPn7+2vu3LkqLS1Vt27dNH36dKdn/c+cOeO0UFnnzp319NNPa+nSpVq+fLmaN2+up59+Wh07dnS02bBhgyRpxowZTsebMGGC7r33Xg+fFQAAAOBdJovFYvd2Eb6mclLxj9+KBO9PmoH7Maa+hzH1LYyn72FMfc+1jKk73jLUIB4ZAgAAAOAZBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADC/B2Aa4qLy/Xe++9p61bt6qsrEzdunXTo48+qiZNmly13/bt2/Xhhx/q9OnTatGihSZOnKj+/ftX23bFihVaunSp7rzzTj3yyCOeOA0AAADgutJg7hC88847SktL01NPPaU5c+aopKREL774oioqKmrsk5WVpblz52rgwIH685//rIEDB2rOnDk6ePBgtW1TU1PVpk0bD54FAAAAcH1pEIGguLhYmzZt0uTJk5WUlKSEhAQ98cQTOnr0qHbv3l1jv3Xr1unmm2/WuHHjFBcXp3Hjxqlr165av359lc9/7bXX9Jvf/EZhYWGePh0AAADgutEgAkF2drasVquSkpIc22JjY9WqVSsdOHCgxn5ZWVlOfSSpR48eVfr85S9/0YABA9StWzf3Fg4AAABc5xrEHIK8vDz5+fkpIiLCaXt0dLTy8vJq7Jefn6+oqCinbVFRUU59UlNTdfr0aT3xxBMu1ZKSkqLU1NSrtnn22WclSYWFhS59ppHY7XZ+Lz6GMfU9jKlvYTx9D2Pqe65lTM1m8zUf36uBYOnSpVqxYsVV28yePbvGfXa7XSaTqVbH/GGfEydO6IMPPtCcOXMUGBjoUv+hQ4dq6NChV21z7tw5SVJ4eHitajOCwsJCfi8+hjH1PYypb2E8fQ9j6nu8PaZeDQQjR45UcnLyVdvExsbKZrPJZrOpoKBAkZGRjn35+flKTEyssW9UVJTy8/Odtl26dMlx1yArK0sFBQWaNm2aY7/NZtO+ffu0ceNGffLJJy4HBQAAAKAh8mogiIyMdLrAr0lCQoICAgKUnp7uCBAXLlzQiRMn1Llz5xr7derUSenp6Ro9erRjW3p6uqNPv3791L59e6c+8+fPV8uWLTV27FgFBDSIJ6oAAACAOmsQV7yhoaEaPHiwFi9erKioKIWHh+vdd99VmzZtnCYCP//88+rQoYPuv/9+SVfuQDzzzDNauXKl+vXrp507d2rv3r165ZVXJElhYWFV3ipkNpsVHh6u+Pj4+jtBAAAAwEsaRCCQpKlTp8rf319z585VaWmpunXrpunTp8vf39/R5syZM04LlXXu3FlPP/20li5dquXLl6t58+Z6+umn1bFjR2+cAgAAAHDdMVksFru3i/A1lZOKf/xWJHh/0gzcjzH1PYypb2E8fQ9j6nuuZUzd8ZahBrEOAQAAAADPIBAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAyMQAAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAzMZLFY7N4uwtecO3fO2yUAAADAIJo2bXpN/blDAAAAABgYdwhQr6ZPn6558+Z5uwy4EWPqexhT38J4+h7G1Pd4e0y5QwAAAAAYGIEAAAAAMDACAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABkYgAAAAAAzMf8aMGbO8XQSMJSEhwdslwM0YU9/DmPoWxtP3MKa+x5tjykrFAAAAgIHxyBAAAABgYAQCAAAAwMAIBAAAAICBEQgAAAAAAwvwdgEwNrvdrlmzZunbb7/VM888owEDBni7JNTRm2++qT179ig3N1dms1mdO3fW/fffr7i4OG+XhjooLCzU8uVDzlxHAAANOElEQVTLlZ6ervPnzysiIkK9e/fWfffdp4iICG+XhzpKSUnRV199pcOHD6u4uFiLFi1Ss2bNvF0WauGzzz7T6tWrlZeXp9atW+t///d/lZiY6O2yUAeZmZlas2aNsrOzlZubq8cff1y33367V2rhDgG8as2aNfLz44+hL0hISNBvf/tbvfXWW3rhhRdkt9s1Y8YMWa1Wb5eGOsjNzdXFixc1efJkvfnmm3riiSeUmZmpV1991dul4RqUlpYqKSlJEyZM8HYpqINt27bpnXfe0dixY/XGG2+oc+fOmjVrls6dO+ft0lAHly9fVnx8vB566CEFBQV5tRauxOA1hw4d0qeffqrHH3/c26XADYYNG6bExEQ1a9ZMCQkJuu+++5Sbm6szZ854uzTUQXx8vJ577jn17dtXLVu2VNeuXTVlyhTt3r1bJSUl3i4PdXTXXXfpl7/8pbp06eLtUlAHa9eu1W233aY77rhDcXFxevjhhxUdHa2NGzd6uzTUQa9evTRp0iQNGDDA61+OEgjgFSUlJXr11Vf12GOPKSoqytvlwM0uX76szZs3KzY2lscRfEhJSYkCAwPVqFEjb5cCGE55ebmys7OVlJTktD0pKUkHDhzwUlXwFcwhgFe89dZb6tmzp3r16uXtUuBGn332mZYsWaLLly/rhhtu0B/+8AcFBgZ6uyy4QVFRkZYtW6YhQ4bI39/f2+UAhlNQUCCbzVblS7SoqCjt3r3bS1XBVxAI4DZLly7VihUrrtpm9uzZOn/+vI4cOaJ58+bVU2WoK1fHtGvXrpKk5ORkJSUlKTc3V2vWrNErr7yiV155RWazuT7KhQtqO6bSlTs+L730kho3bqzJkyd7ukTUUl3GFA2XyWTydgnwQQQCuM3IkSOVnJx81TaxsbHavHmzcnJy9Mtf/tJp39y5c9WxY0fNnTvXg1WiNlwd00qhoaEKDQ1Vy5Yt1bFjR02YMEFpaWkaNGiQhyuFq2o7phaLRS+88IIkaebMmV6f+IaqajumaJgiIiLk5+envLw8p+35+fk8eotrRiCA20RGRioyMvIn202aNEmjR4922jZt2jRNnjxZ/fr181R5qANXx7Qmdrtd5eXlbqwI16o2Y1pSUqJZs2ZJkmbNmqXg4GAPVoa6uta/p2gYAgMDlZCQoIyMDN16662O7RkZGbrlllu8WBl8AYEA9a5x48Zq3Lhxle2xsbFq3ry5FyrCtTp16pTS0tLUvXt3RURE6OLFi/rkk08UGBioPn36eLs81EFJSYlmzpypkpISzZgxQ6WlpSotLZUkhYWFMTekgcrLy1NeXp5OnjwpScrJyVFxcbFiY2MVHh7u5erwU0aNGqXXX39d7du3V5cuXbRx40bl5uZq2LBh3i4NdWCxWHT69GlJks1m0/nz53X48GGFhYWpadOm9VqLyWKx2Ov1iEA1RowYwcJkDdj58+f1l7/8Rd9//72Ki4sVFRWlxMREjRs3joXJGqi9e/fqueeeq3Yfz6M3XMuXL9dHH31UZbs3F0RC7VQuTJabm6v4+HhNnTpVN910k7fLQh3U9N/ZQYMGafr06fVaC4EAAAAAMDDWIQAAAAAMjEAAAAAAGBiBAAAAADAwAgEAAABgYAQCAAAAwMAIBAAAAICBEQgAAB6Xm5urMWPGKD09vdZ99+7dqxEjRmjv3r11OvaIESP017/+tU59q3Pp0iWNGTNG33zzjds+EwC8iUAAAAZQeVH91VdfeeX4H3/8seLj45WUlOSV47tTZGSkBg8erGXLlsluZykfAA0fgQAA4FGFhYXavHmzfvGLX3i7FLcZNmyYvv/+e+3Zs8fbpQDANSMQAAA8asuWLbLb7erXr5+3S3Gb+Ph4xcXFafPmzd4uBQCuWYC3CwAAXD9KS0v10Ucfadu2bcrNzVVMTIySk5M1fvx4BQYGOtrZ7XatWLFCKSkpKigoULt27fTggw/q/ffflyT98Y9/dLTduXOn2rdvr7CwMKdjHTlyRGvXrtX+/ft18eJFBQcHq3v37nrggQcUGxt71TrnzZunbdu2acGCBXr77beVmZkps9msQYMGaeLEiQoIqPp/b998842WLl2qnJwcNWnSRL/61a80cOBAx/7CwkKtWLFCGRkZOnv2rOx2u9q3b6/77rtPXbp0qfJ53bt31+bNm1VRUSF/f3/XfsEAcB3iDgEAQNKVi/zZs2dr1apVuummmzR16lR16tRJK1as0Ny5c53aLl26VMuWLVPr1q01ZcoUdejQQS+88IIuXrzo1K6iokIHDx5UQkJCleNlZGTo5MmTSk5O1sMPP6zBgwfrm2++0fPPP6/S0lKX6v3973+vkJAQPfDAA0pMTNTq1au1YMGCKm0PHjyoN954Q3379tWUKVNkNpv1+uuvKycnx9HmzJkzSktLU/fu3TV58mSNHTtWFy5c0IwZM3Ts2LEqn9m+fXtZLBYdPXr0J2sFgOsZdwgAAJKkr7/+Wt9++63GjRun++67T5J05513KioqSuvXr1dGRoa6d++u/Px8rVmzRr169dLMmTNlMpkkXXmM5s0331Tjxo0dn3n+/HmVlpaqWbNmVY43bNgw3X333U7bevfurWeeeUY7duxQcnLyVeu1Wq1KTEzUtGnTHLXOmzdPn3/+uUaNGqVWrVo52h4/flxvvvmm4uLiJEkDBgzQlClTtGnTJk2ZMkWS1KZNGy1cuNDp2/6hQ4fq0Ucf1fr16/XrX//a6fjNmzeXJOXk5OjGG2+8aq0AcD3jDgEAQNKVQGAymTRq1Cin7WPGjHHsl658s2+1WnXnnXc6woAk3XbbbQoNDXXqW1BQIElVHheSJLPZ7Ph3i8WigoICtWrVSqGhocrOznap5hEjRjj9PHz4cNntdu3atctpe9euXR1hQJKio6PVqlUrnTlzxrEtMDDQEQbKyspUUFAgm82m9u3bV1tP5TlVniMANFTcIQAAH1JRUVHlArW6i/HqnDt3TtHR0VXax8TEKDQ0VOfOnZN05Vt/SWrZsqVTO39//2rvBEiq9vWcRUVFWrJkidLS0lRYWOi0r7i4+CfrNZlMVWq44YYbHOfyQ02bNq3SPywsTEVFRY6fbTabVq1apdTUVJ09e9apbU3nVVkHADRkBAIA8CEXLlzQ1KlTnbbNnj3bS9VIERERkuR04V1p7ty52rdvn+6++261a9dOwcHBMplMmjt3rmw2m1vr8POr/ob4D4PKqlWr9MEHH2jQoEG67777FBERIT8/P61cudLpTkKlynOqPEcAaKgIBADgQ6Kjo/XSSy85bWvbtq2OHDnyk32bNm2q9PR0FRUVOd0lyMvLU3FxseNb9so3AJ06dcrpG/qKigqdPXtWbdu2dWyLjY2V2Wyu8o17UVGR0tPTde+992rChAmO7WVlZS7dHZCuXMyfOnVK8fHxjm0nT550nEttbdu2TV27dtX06dOdti9fvrza9qdPn5Ykp0eRAKAhYg4BAPiQoKAgde/e3ekfVx8Z6t27t+x2u9atW+e0ffXq1Y790pXXbfr7++uzzz5z+ob9iy++qHIx7+/vrw4dOlR5Br/yG/sfP0q0bt26Wt0d+PTTT51+/vvf/y5J6tGjh8uf8cOaflzPgQMHlJWVVW37Q4cOKTg42CmQAEBDxB0CADCQHTt2OL7Z/qFbb71VvXv3Vo8ePfTxxx/r/Pnzat++vbKysrRlyxb17dtX3bt3lyRFRUVp1KhRWrVqlWbNmqU+ffro1KlT+sc//qEWLVpUeaa+b9++Wrx4sdOdh5CQEHXt2lWrVq2S1WpVbGys9u/fr8zMTIWHh7t0LgEBAdq3b59effVVdenSRXv37tX27ds1ePDgOn1r37dvXy1fvlyvv/66EhMTderUKaWmpiouLk6XL1+u0j4jI0O9e/dmDQIADR6BAAAM5J///Kf++c9/VtneunVr3XDDDXruuef00UcfaevWrdq6datiYmI0duxYjR8/3qn9pEmTZDablZKSoszMTLVr104zZ87U3/72N6cFzCQpOTlZixcvVlpamoYMGeLY/uSTT2rhwoVKSUlxvEL05Zdf1u9+9zuXzsVkMumFF17Q22+/rSVLligoKEijRo3SpEmT6vCbke655x6VlpZqy5Yt2r59u1q3bq2nnnpKX331lTIzM53aHjt2TCdOnNBDDz1Up2MBwPXEZLFYqr76AQCAWqqoqNDEiRPVv3//Ku/sf+utt3To0CHNmzfPLceqXKm48nGm+rZgwQIdOHBA8+fP5y1DABo85hAAAGqtupWEN2/erMLCQt18881V9o0fP17Hjx/Xt99+Wx/ledSlS5e0adMmTZw4kTAAwCfwyBAAoNa2bdum1NRU9e7dW+Hh4Tp06JC++OILtW3bVrfcckuV9jExMVq1apUXKnW/yMhInzkXAJAIBACAOmjTpo3MZrPWrVun4uJiRUZGavDgwZo4cWKVOQQAgOsbcwgAAAAAA2MOAQAAAGBgBAIAAADAwAgEAAAAgIERCAAAAAADIxAAAAAABvb/AX9mUczWWUMfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "alphas = np.exp(np.linspace(10, -2, 50))\n",
    "alphas, coefs_lasso_unemp, _ = linear_model.lasso_path(X_unemp, y_unemp, alphas = alphas, fit_intercept=True, max_iter=10000)\n",
    "\n",
    "# plotting\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "colors = cycle(qeds.themes.COLOR_CYCLE)\n",
    "log_alphas = -np.log10(alphas)\n",
    "for coef_l, c, name in zip(coefs_lasso_unemp, colors, list(X_unemp)):\n",
    "   ax.plot(log_alphas, coef_l, c=c)\n",
    "   ax.set_xlabel('-Log(alpha)')\n",
    "   ax.set_ylabel('lasso coefficients')\n",
    "   ax.set_title('Lasso Path for Coefficients of Controls in Unemployment Regression')\n",
    "   ax.axis('tight')\n",
    "   maxabs = np.max(np.abs(coef_l))\n",
    "   i = [idx for idx in range(len(coef_l)) if abs(coef_l[idx]) >= (0.9*maxabs)][0]\n",
    "   xnote = log_alphas[i]\n",
    "   ynote = coef_l[i]\n",
    "   ax.annotate(name, (xnote, ynote), color=c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regressions with Lasso Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Revised Regression on Migration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================================\n",
      "                         population I population II population III\n",
      "------------------------------------------------------------------\n",
      "Intercept                139.6264***  196.9917***   197.1705***   \n",
      "advisory_on              -20.4577     -15.7457      -16.5453      \n",
      "child_perc                            -76.8143**    -77.1064**    \n",
      "movers_1yr_perc                       -224.3292     -167.4374     \n",
      "movers_5yr_perc                       41.3787                     \n",
      "high_school_more                      -46.3176      -39.0160      \n",
      "aboriginal_mother_tongue              3.2227***     3.2881***     \n",
      "aboriginal_home_language              -2.5327**     -2.5524**     \n",
      "==================================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "#regression on population\n",
    "lm_pop_1 = list()\n",
    "#no controls, fixed effects\n",
    "lm_pop_1.append(smf.ols(formula=\"population ~ advisory_on + C(first_nation) + C(year)\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "#old controls before lasso regression, fixed effects \n",
    "lm_pop_1.append(smf.ols(formula=\"population ~ advisory_on + C(first_nation) + C(year) + child_perc + movers_1yr_perc + movers_5yr_perc + high_school_more + aboriginal_mother_tongue + aboriginal_home_language\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "#new regression with considerations from the lasso regression, fixed effects\n",
    "lm_pop_1.append(smf.ols(formula=\"population ~ advisory_on + C(first_nation) + C(year) + child_perc + movers_1yr_perc + high_school_more + aboriginal_mother_tongue + aboriginal_home_language\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "regressors_pop_1 = [\"Intercept\",\"advisory_on\", \"child_perc\", \"movers_1yr_perc\",\"movers_5yr_perc\",\"high_school_more\",\"aboriginal_mother_tongue\",\"aboriginal_home_language\"]\n",
    "table_pop_1 = summary_col(lm_pop_1,regressor_order=regressors_pop_1, stars = True)\n",
    "table_pop_1.tables[0] = table_pop_1.tables[0].loc[regressors_pop_1]\n",
    "print (table_pop_1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By eliminating the control that looked at the proportion of people who moved within the last five years, our estimate when the advisory is turned on slightly decreases to 16, as seen in the population III column. This estimate says that when an advisory is turned on, more people migrate out of their community than previously seen with the population II regression. It is important to note that all three estimates are still not signficant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Revised Regression on Education\n",
    "\n",
    "From the lasso regression, I found out that all of the control variables I introduced didn't contribute to overfitting! Here are the results of the original regression, with and without additional controls below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "            high_school_more I high_school_more II\n",
      "--------------------------------------------------\n",
      "Intercept   0.0179             -0.2316**          \n",
      "advisory_on 0.0218             -0.0019            \n",
      "child_perc                     0.1647***          \n",
      "married                        0.4152***          \n",
      "==================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "#regression on attainment of high school education and/or higher level education\n",
    "lm_edu = list()\n",
    "#no controls, fixed effects\n",
    "lm_edu.append(smf.ols(formula=\"high_school_more ~ advisory_on + C(first_nation) + C(year)\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "#controls, fixed effects\n",
    "lm_edu.append(smf.ols(formula=\"high_school_more ~ advisory_on + C(first_nation) + C(year) + child_perc + married\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "regressors_1 = [\"Intercept\",\"advisory_on\", \"child_perc\", \"married\"]\n",
    "tble_1 = summary_col(lm_edu,regressor_order=regressors_1, stars = True)\n",
    "tble_1.tables[0] = tble_1.tables[0].loc[regressors_1]\n",
    "print (tble_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the additional control variables don't contribute to overfitting, they might give a more accurate estimate than high_school_more I, which only controls for fixed effects. In high_school_more II I see find that when there is a long term water boil advisory in place for an indigenous community, the proportion of people within the community who attain a high school education and or higher education decreases by ~ 0.2%, although this effect is not significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Revised Regression on Unemployment Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============================\n",
      "            unemployment_rate\n",
      "-----------------------------\n",
      "Intercept   0.2145***        \n",
      "advisory_on 0.0021           \n",
      "=============================\n",
      "Standard errors in\n",
      "parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "#regression on unemployment rate\n",
    "lm_unemp = list()\n",
    "#no controls, fixed effects\n",
    "lm_unemp.append(smf.ols(formula=\"unemployment_rate ~ advisory_on + C(first_nation) + C(year)\", data=demo_df,\n",
    "                  missing=\"drop\").fit(cov_type='HC0'))\n",
    "\n",
    "regressors_2 = [\"Intercept\",\"advisory_on\"]\n",
    "tble_2= summary_col(lm_unemp,regressor_order=regressors_2, stars = True)\n",
    "tble_2.tables[0] = tble_2.tables[0].loc[regressors_2]\n",
    "print (tble_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the lasso regression indicated that I was overfitting my model by incorporating my additonal controls, I only run one regression - one with fixed effects and no other controls. I see that the effect of a long term water boil advisory on unemployment rates is still not signficant, however, the impact is quite small at 0.2% increase."
   ]
  }
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