section_7-4_main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "autoscroll": "auto",
    "options": {
     "caption": false,
     "complete": true,
     "display_data": true,
     "display_stream": true,
     "dpi": 200,
     "echo": true,
     "evaluate": true,
     "f_env": null,
     "f_pos": "htpb",
     "f_size": [
      6,
      4
     ],
     "f_spines": true,
     "fig": true,
     "include": true,
     "name": null,
     "option_string": "",
     "results": "verbatim",
     "term": false,
     "wrap": "output"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os as os\n",
    "\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### [Equilibrium Technology Diffusion, Trade, and Growth](https://christophertonetti.com/files/papers/PerlaTonettiWaugh_DiffusionTradeAndGrowth.pdf) by Perla, Tonetti, and Waugh (AER 2020)\n",
    "\n",
    "## Section 7.4. The Welfare Effects of a Reduction in Trade Costs\n",
    "\n",
    "This is the main driver file that computes transition dynamics and generates figures and numbers reported in the paper. \n",
	"\n",
	"Table of Contents\n",
    "- [Compute Transition Dynamics](#trans)\n",
    "\n",
    "- [Figures](#figs)\n",
    "    - [Figure 1: Volume of Trade](#fig1)\n",
    "    - [Figure 2: Variety](#fig2)\n",
    "    - [Figure 3: Realocation Effects](#fig3)\n",
    "    - [Figure 4: Log Consumption](#fig4)\n",
    "    - [Figure 5: Productivity Growth](#fig5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## <a name=\"trans\"></a> Compute Transition Dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "julia_path = '''C:\\\\Users\\\\mwaugh\\\\AppData\\\\Local\\\\Programs\\\\Julia\\\\Julia-1.4.0\\\\bin\\\\julia.exe'''\n",
    "\n",
    "###Set correc Julia path for your configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook TransitionDynamics.ipynb to script\n",
      "[NbConvertApp] Writing 3460 bytes to TransitionDynamics.jl\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------------------------------------------------------\n",
      "SS to SS Baseline vs. 10% Lower Trade Costs\n",
      "\n",
      "\n",
      "Change in Growth, Steady State to Steady State\n",
      "Growth in Baseline: 0.79\n",
      "Growth in New SS: 1.03\n",
      "\n",
      "\n",
      "Imports/GDP, Steady State to Steady State\n",
      "Imports/GDP in Baseline: 10.6\n",
      "Imports/GDP in New SS: 14.4\n",
      "\n",
      "\n",
      "Consumption Equivalent Welfare Gain, Steady State to Steady State\n",
      "CE gains: 11.2\n",
      "\n",
      "\n",
      "-------------------------------------------------------------------------\n",
      "\n",
      "Computing Transition Dynamics\n",
      "\n",
      "cachename: 1128371551232770518\n",
      "\n",
      "solving without cache\n",
      "486.873108 seconds (1.80 G allocations: 694.058 GiB, 14.00% gc time)\n",
      "\n",
      "-------------------------------------------------------------------------\n",
      "\n",
      "Consumption Equivalent Welfare Gain, Inclusive of Transition Path\n",
      "CE gains: 10.8\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: using BenchmarkTools.params in module Main conflicts with an existing identifier.\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to script TransitionDynamics.ipynb\n",
    "\n",
    "julia_command = '''TransitionDynamics.jl'''\n",
    "\n",
    "!{julia_path} {julia_command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the transition path computed in [TransitionDynamics.ipynb](https://github.com/jlperla/PerlaTonettiWaugh.jl/blob/master/TransitionDynamics.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": "auto",
    "options": {
     "caption": false,
     "complete": true,
     "display_data": true,
     "display_stream": true,
     "dpi": 200,
     "echo": true,
     "evaluate": true,
     "f_env": null,
     "f_pos": "htpb",
     "f_size": [
      6,
      4
     ],
     "f_spines": true,
     "fig": true,
     "include": true,
     "name": null,
     "option_string": "",
     "results": "verbatim",
     "term": false,
     "wrap": "output"
    }
   },
   "outputs": [],
   "source": [
    "output_location = \"data/\"\n",
    "hash_value = \"11368798858269829579\"\n",
    "output_location = output_location + hash_value + \".csv\"\n",
    "df = pd.read_csv(output_location) # front-loaded with values from stationary equilibrium computation\n",
    "fig_path = \"figures/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The dataframe is front-loaded with the time-0 stationary solution. The transition dynamics start with change at ``loc[5]``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"figs\"></a> Figures 1-5 in PTW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "### <a name=\"fig1\"></a>Figure 1: Volume of Trade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "autoscroll": "auto",
    "options": {
     "caption": false,
     "complete": true,
     "display_data": true,
     "display_stream": true,
     "dpi": 200,
     "echo": true,
     "evaluate": true,
     "f_env": null,
     "f_pos": "htpb",
     "f_size": [
      6,
      4
     ],
     "f_spines": true,
     "fig": true,
     "include": true,
     "name": null,
     "option_string": "",
     "results": "verbatim",
     "term": false,
     "wrap": "output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "\n",
    "before = df.t <= 0\n",
    "after = df.t >= 0\n",
    "\n",
    "ax.plot(df.t, 100*(1-df.λ_ii), color = \"blue\", lw = 5,\n",
    "        label = \"Imports/GDP\", ls = \"-\", alpha = 0.78)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax.set_ylabel(\"Imports/GDP\", fontsize = 14) \n",
    "ax.set_xlim(-20,50)\n",
    "ax.set_ylim(9,15)\n",
    "\n",
    "plt.savefig(fig_path + \"trade.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage Point Change in Imports/GDP Immediately After Change in d\n",
      "3.3645\n",
      "\n",
      "Percentage Point Change in Imports/GDP at New SS\n",
      "3.7817\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage Point Change in Imports/GDP Immediately After Change in d\")\n",
    "print(round(100*((1-df.λ_ii).iloc[5]-(1-df.λ_ii).iloc[0]),4))\n",
    "print(\"\")\n",
    "print(\"Percentage Point Change in Imports/GDP at New SS\")\n",
    "print(round(100*((1-df.λ_ii).iloc[-1]-(1-df.λ_ii).iloc[0]),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### <a name=\"fig2\"></a>Figrue 2: Domestic Variety "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": "auto",
    "options": {
     "caption": false,
     "complete": true,
     "display_data": true,
     "display_stream": true,
     "dpi": 200,
     "echo": true,
     "evaluate": true,
     "f_env": null,
     "f_pos": "htpb",
     "f_size": [
      6,
      4
     ],
     "f_spines": true,
     "fig": true,
     "include": true,
     "name": null,
     "option_string": "",
     "results": "verbatim",
     "term": false,
     "wrap": "output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "\n",
    "before = df.t <= 0\n",
    "after = df.t >= 0\n",
    "\n",
    "ax.plot(df.t, df.Ω/df.Ω.iloc[0], color = \"red\", lw = 5,\n",
    "        label = \"\", ls = \"-\")\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax.set_ylabel(\"Domestic Variety \\n Normalized, Initial S.S. = 1\", fontsize = 14) \n",
    "\n",
    "ax.set_ylim(0.94,1.04)\n",
    "\n",
    "ax.set_xlim(-20,50)\n",
    "\n",
    "plt.savefig(fig_path + \"omega.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized Variety Prior to Change in d (Relative to Initial SS)\n",
      "1.0\n",
      "\n",
      "Normalized Variety Immediately After Change in d (Relative to Initial SS)\n",
      "0.9995\n",
      "\n",
      "Normalized Variety At New SS (Relative to Initial SS)\n",
      "0.9498\n"
     ]
    }
   ],
   "source": [
    "print(\"Normalized Variety Prior to Change in d (Relative to Initial SS)\")\n",
    "print(round((df.Ω/df.Ω.iloc[0]).iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Normalized Variety Immediately After Change in d (Relative to Initial SS)\")\n",
    "print(round((df.Ω/df.Ω.iloc[0]).iloc[6],4))\n",
    "print(\"\")\n",
    "print(\"Normalized Variety At New SS (Relative to Initial SS)\")\n",
    "print(round((df.Ω/df.Ω.iloc[0]).iloc[-1],4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### <a name=\"fig3\"></a>Figure 3: Reallocation Effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2,figsize = (12,7))\n",
    "\n",
    "fig.tight_layout(pad = 6)\n",
    "\n",
    "fig.text(-0.01, 0.5, \"Breakdown of Labor Outside of Production,\\n Percentage Point Change relative to SS\"\n",
    "         , va='center', rotation='vertical',fontsize = 14)\n",
    "\n",
    "############################################################################################################\n",
    "postion = (0,0)\n",
    "\n",
    "ax[postion].plot(df.t, 100*((df.L_tilde_x) - (df.L_tilde_x.iloc[0])), color = \"black\", \n",
    "           lw = 5, ls = \"-.\", alpha = 0.70, label = \"Exporting Costs\")\n",
    "\n",
    "ax[postion].spines[\"top\"].set_visible(False)\n",
    "ax[postion].spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax[postion].set_xlim(-20,50)\n",
    "ax[postion].set_ylim(-1.0,1.0)\n",
    "\n",
    "ax[postion].set_title(\"Exporter Costs\")\n",
    "\n",
    "############################################################################################################\n",
    "\n",
    "postion = (0,1)\n",
    "\n",
    "ax[postion].plot(df.t, 100*((df.L_tilde_a) - (df.L_tilde_a.iloc[0])), color = \"black\",\n",
    "           lw = 5, ls = \"-\", alpha = 0.70, label = \"Adoption Costs\")\n",
    "\n",
    "ax[postion].spines[\"top\"].set_visible(False)\n",
    "ax[postion].spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax[postion].set_xlim(-20,50)\n",
    "ax[postion].set_ylim(-1.0,1.0)\n",
    "ax[postion].set_title(\"Adoption Costs\")\n",
    "\n",
    "############################################################################################################\n",
    "\n",
    "postion = (1,0)\n",
    "\n",
    "ax[postion].plot(df.t, 100*(df.L_tilde_E - df.L_tilde_E.iloc[0]) , color = \"blue\", \n",
    "           lw = 5, ls = \"-\", alpha = 0.70, label = \"Entry Costs\")\n",
    "\n",
    "ax[postion].spines[\"top\"].set_visible(False)\n",
    "ax[postion].spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax[postion].set_xlim(-20,50)\n",
    "ax[postion].set_ylim(-2.5,1.0)\n",
    "\n",
    "ax[postion].set_title(\"Entry Costs\")\n",
    "\n",
    "############################################################################################################\n",
    "\n",
    "postion = (1,1)\n",
    "\n",
    "ax[postion].plot(df.t, 100*(df.L_tilde - df.L_tilde.iloc[0])\n",
    "           , color = \"red\", lw = 5, ls = \"-\", alpha = 0.80, label = \"Total Fixed Costs\")\n",
    "\n",
    "ax[postion].spines[\"top\"].set_visible(False)\n",
    "ax[postion].spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax[postion].set_xlim(-20,50)\n",
    "ax[postion].set_ylim(-1.50,1.0)\n",
    "\n",
    "\n",
    "ax[postion].set_title(\"Total Labor Outside of Production\")\n",
    "\n",
    "############################################################################################################\n",
    "plt.savefig(fig_path + \"fixed_costs.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exporter Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exporter Costs Prior to Change in d (Percentage Point Change relative to SS)\n",
      "0.0\n",
      "\n",
      "Exporter Costs Immediately After Change in d  (Percentage Point Change relative to SS)\n",
      "0.7077\n",
      "\n",
      "Exporter Costs at New SS  (Percentage Point Change relative to SS)\n",
      "0.7338\n"
     ]
    }
   ],
   "source": [
    "cost_measure = 100*((df.L_tilde_x) - (df.L_tilde_x.iloc[0]))\n",
    "\n",
    "print(\"Exporter Costs Prior to Change in d (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Exporter Costs Immediately After Change in d  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[5],4))\n",
    "print(\"\")\n",
    "print(\"Exporter Costs At New SS  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[-1],4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adoption Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adoption Costs Prior to Change in d (Percentage Point Change relative to SS)\n",
      "0.0\n",
      "\n",
      "Adoption Costs Immediately After Change in d  (Percentage Point Change relative to SS)\n",
      "0.3635\n",
      "\n",
      "Adoption Costs at New SS  (Percentage Point Change relative to SS)\n",
      "0.1888\n"
     ]
    }
   ],
   "source": [
    "cost_measure = 100*((df.L_tilde_a) - (df.L_tilde_a.iloc[0]))\n",
    "\n",
    "print(\"Adoption Costs Prior to Change in d (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Adoption Costs Immediately After Change in d  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[5],4))\n",
    "print(\"\")\n",
    "print(\"Adoption Costs At New SS  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[-1],4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entry Costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entry Costs Prior to Change in d (Percentage Point Change relative to SS)\n",
      "0.0\n",
      "\n",
      "Entry Costs Immediately After Change in d  (Percentage Point Change relative to SS)\n",
      "-2.2344\n",
      "\n",
      "Entry Costs At New SS  (Percentage Point Change relative to SS)\n",
      "-0.5428\n"
     ]
    }
   ],
   "source": [
    "cost_measure = 100*(df.L_tilde_E - df.L_tilde_E.iloc[0])\n",
    "\n",
    "print(\"Entry Costs Prior to Change in d (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Entry Costs Immediately After Change in d  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[5],4))\n",
    "print(\"\")\n",
    "print(\"Entry Costs At New SS  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[-1],4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Labor Outside of Production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Labor Outside of Production Prior to Change in d (Percentage Point Change relative to SS)\n",
      "0.0\n",
      "\n",
      "Total Labor Outside of Production Immediately After Change in d  (Percentage Point Change relative to SS)\n",
      "-1.1632\n",
      "\n",
      "Total Labor Outside of Production At New SS  (Percentage Point Change relative to SS)\n",
      "0.3798\n"
     ]
    }
   ],
   "source": [
    "cost_measure = 100*(df.L_tilde - df.L_tilde.iloc[0])\n",
    "\n",
    "print(\"Total Labor Outside of Production Prior to Change in d (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Total Labor Outside of Production Immediately After Change in d  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[5],4))\n",
    "print(\"\")\n",
    "print(\"Total Labor Outside of Production At New SS  (Percentage Point Change relative to SS)\")\n",
    "print(round(cost_measure.iloc[-1],4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### <a name=\"fig4\"></a>Figure 4: Log Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "\n",
    "before = df.t <= 0\n",
    "after = df.t >= 0\n",
    "\n",
    "ax.plot(df.t, (np.log(df.c) + df.log_M) - (np.log(df.c) + df.log_M).iloc[4] +1, color = \"red\", lw = 5,\n",
    "       label = \"Log Consumption\")\n",
    "\n",
    "ax.plot(df.t, (df.log_M) - (df.log_M).iloc[4] + 1, color = \"k\", ls = \"-.\", alpha = 0.5, lw = 3,\n",
    "       label = \"Log Productivity\")\n",
    "        \n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax.set_ylabel(\"Log Consumption & Productivity\\n Normalized, t-1 = 1\", fontsize = 14) \n",
    "\n",
    "#ax.set_ylim(1-0.06,1+0.25)\n",
    "ax.set_xlim(-20,50)\n",
    "\n",
    "ax.legend(fontsize = 14, frameon = False)\n",
    "\n",
    "axins = zoomed_inset_axes(ax, # The original ax\n",
    "                          1.5, # zoom level\n",
    "                          loc=4, # location\n",
    "                          borderpad=2)  # space around it relative to figure\n",
    "\n",
    "axins.plot(df.t, (np.log(df.c) + df.log_M) - (np.log(df.c) + df.log_M).iloc[4] +1, color = \"red\", lw = 5,\n",
    "       label = \"Log Consumption\")\n",
    "\n",
    "axins.plot(df.t, (df.log_M) - (df.log_M).iloc[4]+1, color = \"k\", ls = \"-.\", alpha = 0.5, lw = 3,\n",
    "       label = \"Log Productivity\")\n",
    "        \n",
    "axins.set_xlim(-5,5)\n",
    "axins.set_ylim(1-0.06,1+0.10)\n",
    "\n",
    "mark_inset(ax, axins, loc1=3, loc2=2, alpha = 0.15)\n",
    "\n",
    "axins.spines[\"top\"].set_visible(False)\n",
    "axins.spines[\"right\"].set_visible(False)\n",
    "\n",
    "plt.savefig(fig_path + \"log_consumption_inset.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized Consumption Prior to Change in d (Relative to Initial SS)\n",
      "1.0\n",
      "\n",
      "Normalized Consumption Immediately After Change in d (Relative to Initial SS)\n",
      "1.0335\n",
      "\n",
      "Normalized Consumption At New SS (Relative to Initial SS)\n",
      "0.9912\n"
     ]
    }
   ],
   "source": [
    "print(\"Normalized Consumption Prior to Change in d (Relative to Initial SS)\")\n",
    "print(round((df.c/df.c.iloc[0]).iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Normalized Consumption Immediately After Change in d (Relative to Initial SS)\")\n",
    "print(round((df.c/df.c.iloc[0]).iloc[5],4))\n",
    "print(\"\")\n",
    "print(\"Normalized Consumption At New SS (Relative to Initial SS)\")\n",
    "print(round((df.c/df.c.iloc[0]).iloc[-1],4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### <a name=\"fig5\"></a> Figure 5: Productivity Growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "autoscroll": "auto",
    "options": {
     "caption": false,
     "complete": true,
     "display_data": true,
     "display_stream": true,
     "dpi": 200,
     "echo": true,
     "evaluate": true,
     "f_env": null,
     "f_pos": "htpb",
     "f_size": [
      6,
      4
     ],
     "f_spines": true,
     "fig": true,
     "include": true,
     "name": null,
     "option_string": "",
     "results": "verbatim",
     "term": false,
     "wrap": "output"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "\n",
    "before = df.t <= 0\n",
    "after = df.t >= 0\n",
    "\n",
    "ax.plot(df.t, 100*(df.g), color = \"red\", lw = 5,\n",
    "        label = \"Growth Rate\", ls = \"-\", alpha = 0.78)\n",
    "\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "ax.set_ylabel(\"Productivity Growth\", fontsize = 14) \n",
    "ax.set_xlim(-20,50)\n",
    "ax.set_ylim(0.6,1.10)\n",
    "\n",
    "plt.savefig(fig_path + \"growth.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Productivity Growth at Initial SS\n",
      "0.0079\n",
      "\n",
      "Productivity Growth Immediately After Change in d\n",
      "0.009\n",
      "\n",
      "Productivity Growth at New SS\n",
      "0.0103\n"
     ]
    }
   ],
   "source": [
    "print(\"Productivity Growth at Initial SS\")\n",
    "print(round(df.g.iloc[0],4))\n",
    "print(\"\")\n",
    "print(\"Productivity Growth Immediately After Change in d\")\n",
    "print(round(df.g.iloc[5],4))\n",
    "print(\"\")\n",
    "print(\"Productivity Growth at New SS\")\n",
    "print(round(df.g.iloc[-1],4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}