bi-sep/electricity_kenya.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c5fc2bff-487f-456d-972e-b54c3b6b8dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "gdp_data = pd.read_csv('imf_gdp_capita.csv').rename(columns={'GDP per capita, current prices\\n (U.S. dollars per capita)':'Country'})\n",
    "gdp_data = gdp_data[gdp_data[\"Country\"] == \"Kenya\"]\n",
    "\n",
    "gdp_data = pd.melt(gdp_data, id_vars=['Country'], var_name='Year', value_name='GDP_per_capita')\n",
    "gdp_data['Year'] = pd.to_numeric(gdp_data['Year'])\n",
    "gdp_data['GDP_per_capita'] = pd.to_numeric(gdp_data['GDP_per_capita'])\n",
    "\n",
    "el_access_data = pd.read_csv('share-of-the-population-with-access-to-electricity.csv').rename(columns={'Entity':'Country', 'Access to electricity (% of population)':'electricity_access'})\n",
    "el_access_data = el_access_data[el_access_data[\"Country\"] == \"Kenya\"]\n",
    "\n",
    "\n",
    "electricity_data = pd.read_csv('share-elec-by-source.csv').rename(columns={'Entity':'Country'})\n",
    "electricity_data = electricity_data[electricity_data[\"Country\"] == \"Kenya\"]\n",
    "\n",
    "renewable_columns = ['Hydro - % electricity', 'Solar - % electricity', \n",
    "                     'Wind - % electricity', 'Other renewables excluding bioenergy - % electricity']\n",
    "non_renewable_columns = ['Coal - % electricity', 'Gas - % electricity', \n",
    "                         'Oil - % electricity', 'Nuclear - % electricity']\n",
    "\n",
    "electricity_data['Renewable'] = electricity_data[renewable_columns].sum(axis=1)\n",
    "electricity_data['Non-renewable'] = electricity_data[non_renewable_columns].sum(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9cf6077e-536d-43e7-8663-c9087b8af7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation with GDP Growth:\n",
      "                    electricity_access  Renewable  GDP_growth\n",
      "electricity_access            1.000000   0.834904    0.048234\n",
      "Renewable                     0.834904   1.000000    0.124918\n",
      "GDP_growth                    0.048234   0.124918    1.000000\n",
      "\n",
      "Regression Results with GDP Growth as Control:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              Renewable   R-squared:                       0.704\n",
      "Model:                            OLS   Adj. R-squared:                  0.673\n",
      "Method:                 Least Squares   F-statistic:                     22.62\n",
      "Date:                Sun, 17 Nov 2024   Prob (F-statistic):           9.42e-06\n",
      "Time:                        21:29:20   Log-Likelihood:                -73.771\n",
      "No. Observations:                  22   AIC:                             153.5\n",
      "Df Residuals:                      19   BIC:                             156.8\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                 48.4419      3.787     12.791      0.000      40.515      56.368\n",
      "electricity_access     0.5613      0.084      6.651      0.000       0.385       0.738\n",
      "GDP_growth             0.1767      0.260      0.679      0.505      -0.368       0.721\n",
      "==============================================================================\n",
      "Omnibus:                        0.546   Durbin-Watson:                   1.538\n",
      "Prob(Omnibus):                  0.761   Jarque-Bera (JB):                0.060\n",
      "Skew:                           0.121   Prob(JB):                        0.970\n",
      "Kurtosis:                       3.083   Cond. No.                         101.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# Merge data on the Year column\n",
    "merged_data = pd.merge(gdp_data, el_access_data[['Year', 'electricity_access']], on='Year', how='left')\n",
    "merged_data = pd.merge(merged_data, electricity_data[['Year', 'Renewable']], on='Year', how='left')\n",
    "\n",
    "# Calculate year-on-year GDP growth\n",
    "merged_data['GDP_growth'] = merged_data['GDP_per_capita'].pct_change() * 100  # Percentage change\n",
    "\n",
    "# Drop NaN values that might have been generated by pct_change\n",
    "merged_data.dropna(subset=['electricity_access', 'Renewable', 'GDP_growth'], inplace=True)\n",
    "\n",
    "\n",
    "# Step 1: Correlation analysis (now with GDP growth included)\n",
    "correlation = merged_data[['electricity_access', 'Renewable', 'GDP_growth']].corr()\n",
    "print(\"Pearson Correlation with GDP Growth:\")\n",
    "print(correlation)\n",
    "\n",
    "# Step 2: Regression analysis (with GDP growth as a control)\n",
    "X = merged_data[['electricity_access', 'GDP_growth']]  # Include GDP growth as a predictor\n",
    "y = merged_data['Renewable']\n",
    "\n",
    "# Add constant for intercept\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Run the regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print regression results\n",
    "print(\"\\nRegression Results with GDP Growth as Control:\")\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "76beea43-efc1-4184-92dc-2c8e7285bf8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot scatter plot with regression line\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "sns.regplot(\n",
    "    x='electricity_access', y='Renewable', data=merged_data, \n",
    "    scatter_kws={'color': 'blue', 'label': 'Data points'}, \n",
    "    line_kws={'color': 'red', 'linewidth': 2, 'label': 'Regression line'}\n",
    ")\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Access to Electricity vs. Share of Renewable Electricity in Kenya', fontsize=16)\n",
    "plt.xlabel('Access to Electricity (% of Population)', fontsize=12)\n",
    "plt.ylabel('Share of Renewable Electricity (%)', fontsize=12)\n",
    "\n",
    "\n",
    "# Add the legend\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('regression.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0aa2149a-7bbc-4454-890a-5bcb801fb4bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Year & GDP per capita & Population percentage with access to electricity & Share of electricity produced from renewable sources & GDP growth rate \\\\\n",
      "\\midrule\n",
      "2000 & 617.139000 & 15.175694 & 40.371230 & -4.745007 \\\\\n",
      "2001 & 617.047000 & 17.048136 & 59.459465 & -0.014908 \\\\\n",
      "2002 & 611.893000 & 18.912030 & 67.572815 & -0.835269 \\\\\n",
      "2003 & 668.475000 & 16.000000 & 73.357664 & 9.247042 \\\\\n",
      "2004 & 692.709000 & 22.642206 & 62.824677 & 3.625266 \\\\\n",
      "2005 & 778.323000 & 24.522501 & 59.701494 & 12.359302 \\\\\n",
      "2006 & 854.981000 & 26.422052 & 56.591209 & 9.849124 \\\\\n",
      "2007 & 1028.226000 & 28.342442 & 67.016490 & 20.263023 \\\\\n",
      "2008 & 1118.755000 & 30.280056 & 65.040647 & 8.804387 \\\\\n",
      "2009 & 1123.268000 & 23.000000 & 53.435117 & 0.403395 \\\\\n",
      "2010 & 1176.311000 & 19.200000 & 67.877096 & 4.722203 \\\\\n",
      "2011 & 1178.599000 & 36.157864 & 63.218392 & 0.194506 \\\\\n",
      "2012 & 1396.220000 & 38.125990 & 71.725830 & 18.464380 \\\\\n",
      "2013 & 1490.422000 & 40.092150 & 72.524755 & 6.746931 \\\\\n",
      "2014 & 1613.101000 & 36.000000 & 71.304350 & 8.231159 \\\\\n",
      "2015 & 1625.176000 & 41.600000 & 85.015288 & 0.748558 \\\\\n",
      "2016 & 1688.852000 & 53.100000 & 83.884296 & 3.918099 \\\\\n",
      "2017 & 1805.398000 & 55.831993 & 74.422903 & 6.900901 \\\\\n",
      "2018 & 1987.302000 & 61.180614 & 85.093162 & 10.075562 \\\\\n",
      "2019 & 2107.735000 & 69.700000 & 86.919106 & 6.060126 \\\\\n",
      "2020 & 2067.987000 & 71.492714 & 92.327584 & -1.885816 \\\\\n",
      "2021 & 2208.691000 & 76.542450 & 90.057995 & 6.803911 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(merged_data.drop(['Country'], axis=1).rename(columns={'GDP_per_capita':'GDP per capita', 'electricity_access':'Population percentage with access to electricity', 'GDP_growth':'GDP growth rate', 'Renewable':'Share of electricity produced from renewable sources'}).to_latex(index=False))\n"
   ]
  }
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