section_7-5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.io\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "path = os.getcwd()\n",
    "\n",
    "matlab_exe_path = '''matlab'''\n",
    "\n",
    "julia_path = '''C:\\\\Users\\\\mwaugh\\\\AppData\\\\Local\\\\Programs\\\\Julia\\\\Julia-1.4.0\\\\bin\\\\julia.exe'''\n",
    "\n",
    "path = \"src\\\\calibration\"\n",
    "#fig_path = \"C:\\\\users\\mwaugh\\\\github\\\\perla_tonetti_waugh\\\\Figures\""
   ]
  },
  {
   "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",
    "## 7.5. The Role of Firm Dynamics and Adoption Costs\n",
    "\n",
    "#### Table of Contents\n",
    "\n",
    "- [GBM](#gbm)\n",
    "\n",
    "- [Delta Shock](#detla)\n",
    "\n",
    "- [Connection to Welfare Decomposition (Section 7.3)](#dcomp)\n",
    "\n",
    "The underlying MATLAB code is described (with links to the relevant ``.m`` files) in the [readme file in the calibraiton folder](/src/calibration/README.md).\n",
    "\n",
    "---\n",
    "\n",
    "### <a name=\"gbm\"></a> Importance of Firm Productivity Shocks (GBM)\n",
    "\n",
    "We uniformly scale up and down the GBM variance and mean for different values of the adoption cost parameter chi. The large value of chi is ten percent larger than the baseline calibrated value. The small value of chi is ten percent smaller than the baseline calibrated value. All other parameter values are fixed, i.e., we do not re-calibrate the model when changing these parameter values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 1. Compute outcomes for different GBM parameter values\n",
    "\n",
    "First, we compute a key input for the figures, saved to [``/output/robust/gbm/closest_chi_params.csv``](/output/robust/gbm/closest_chi_params.csv). Each row in this file contains the parameter values that generate a BGP equilibrium growth rate that matches the baseline aggregate growth when externally fixing a set value for chi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matlab_cmd = '''\"cd('src\\calibration');robust_no_recalibrate_gbm;\"'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calbirated values computed on date\n",
      "   20-Apr-2020\n",
      "\n",
      "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "Chi Values\n",
      "    0.1409\n",
      "\n",
      "Parameters for given chi which deliver growth closest to baseline growth\n",
      "  Columns 1 through 13\n",
      "\n",
      "    'g'    'theta'    'kappa'    'chi'    'mu'    'upsilon'    'zeta'    'delta'    'N'    'gamma'    'eta'    'Theta'    'd_0'\n",
      "\n",
      "  Column 14\n",
      "\n",
      "    'd_T'\n",
      "\n",
      "  Columns 1 through 13\n",
      "\n",
      "    0.0080    4.9890    0.1042    0.1409   -0.0251    0.0390    1.0000    0.0200   10.0000    1.0000         0    1.0000    3.0225\n",
      "\n",
      "  Columns 14 through 16\n",
      "\n",
      "    2.8202    0.0203    3.1669\n",
      "\n",
      "Chi Values\n",
      "    0.1268\n",
      "\n",
      "Parameters for given chi which deliver growth closest to baseline growth\n",
      "  Columns 1 through 13\n",
      "\n",
      "    'g'    'theta'    'kappa'    'chi'    'mu'    'upsilon'    'zeta'    'delta'    'N'    'gamma'    'eta'    'Theta'    'd_0'\n",
      "\n",
      "  Column 14\n",
      "\n",
      "    'd_T'\n",
      "\n",
      "  Columns 1 through 13\n",
      "\n",
      "    0.0079    4.9890    0.1042    0.1268   -0.0311    0.0483    1.0000    0.0200   10.0000    1.0000         0    1.0000    3.0225\n",
      "\n",
      "  Columns 14 through 16\n",
      "\n",
      "    2.8202    0.0203    3.1669\n",
      "\n",
      "Chi Values\n",
      "    0.1153\n",
      "\n",
      "Parameters for given chi which deliver growth closest to baseline growth\n",
      "  Columns 1 through 13\n",
      "\n",
      "    'g'    'theta'    'kappa'    'chi'    'mu'    'upsilon'    'zeta'    'delta'    'N'    'gamma'    'eta'    'Theta'    'd_0'\n",
      "\n",
      "  Column 14\n",
      "\n",
      "    'd_T'\n",
      "\n",
      "  Columns 1 through 13\n",
      "\n",
      "    0.0079    4.9890    0.1042    0.1153   -0.0372    0.0579    1.0000    0.0200   10.0000    1.0000         0    1.0000    3.0225\n",
      "\n",
      "  Columns 14 through 16\n",
      "\n",
      "    2.8202    0.0203    3.1669\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!{matlab_exe_path} -batch {matlab_cmd}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 2. Create Figure 6 in PTW 2020\n",
    "\n",
    "The code below reads in the output from matlab and then plots the results to generate Figure 6 of PTW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnames = ['gold', 'gnew', 'gdiff', \"welfare\", 'upsilon']\n",
    "\n",
    "mat = scipy.io.loadmat(path + \"\\\\output\\\\robust\\\\gbm\\\\norecalibrate_values_gbm_1.mat\")\n",
    "\n",
    "nocaldf = pd.DataFrame(mat[\"record_values\"])\n",
    "\n",
    "nocaldf.columns = cnames\n",
    "nocaldf[\"gdiff\"] = -nocaldf[\"gdiff\"] \n",
    "\n",
    "nocaldf.sort_values([\"upsilon\"], inplace = True)\n",
    "\n",
    "base_chi = str(round(mat[\"chi_value\"][0][0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat(path + \"\\\\output\\\\robust\\\\gbm\\\\norecalibrate_values_gbm_0.9.mat\")\n",
    "\n",
    "nocaldf_lowchi = pd.DataFrame(mat[\"record_values\"])\n",
    "\n",
    "nocaldf_lowchi.columns = cnames\n",
    "nocaldf_lowchi[\"gdiff\"] = -nocaldf_lowchi[\"gdiff\"] \n",
    "\n",
    "nocaldf_lowchi.sort_values([\"upsilon\"], inplace = True)\n",
    "\n",
    "low_chi = str(round(mat[\"chi_value\"][0][0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat(path + \"\\\\output\\\\robust\\\\gbm\\\\norecalibrate_values_gbm_1.1.mat\")\n",
    "\n",
    "nocaldf_higchi = pd.DataFrame(mat[\"record_values\"])\n",
    "\n",
    "nocaldf_higchi.columns = cnames\n",
    "nocaldf_higchi[\"gdiff\"] = -nocaldf_higchi[\"gdiff\"] \n",
    "nocaldf_higchi.sort_values([\"upsilon\"], inplace = True)\n",
    "\n",
    "hig_chi = str(round(mat[\"chi_value\"][0][0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2,figsize = (12,10))\n",
    "\n",
    "fig.tight_layout(pad = 6)\n",
    "\n",
    "\n",
    "position = (0,0)\n",
    "\n",
    "ax[position].plot(nocaldf.upsilon, 100*nocaldf[\"gdiff\"], lw = 4)\n",
    "\n",
    "ax[position].plot(nocaldf_lowchi.upsilon, 100*nocaldf_lowchi[\"gdiff\"], lw = 4, color = 'r')\n",
    "\n",
    "ax[position].plot(nocaldf_higchi.upsilon, 100*nocaldf_higchi[\"gdiff\"], lw = 4, color = 'k')\n",
    "\n",
    "xticks = [0.01*float(item) for item in list(range(1,11,1)) ]\n",
    "\n",
    "ax[position].set_xticks(xticks)\n",
    "\n",
    "ax[position].set_xlim(0.004,0.07)\n",
    "ax[position].set_ylim(0.10,0.35)\n",
    "ax[position].set_ylabel(\"\\n Change in Productivity Growth \\n Percentage Points\", fontsize = 12)\n",
    "\n",
    "ax[position].spines[\"right\"].set_visible(False)\n",
    "ax[position].spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax[position].vlines(0.048, 0.00, 0.234,  \n",
    "           color='k',      \n",
    "           linestyle='--',\n",
    "           lw = 3)    \n",
    "\n",
    "ax[position].hlines(0.234, -0.001, 0.048 ,           \n",
    "           color='k',       \n",
    "           label=\"Calibrated Values\",\n",
    "           linestyle='--',\n",
    "           lw = 3)    \n",
    "\n",
    "\n",
    "###################################################################################\n",
    "\n",
    "position = (0,1)\n",
    "\n",
    "ax[position].plot(nocaldf.upsilon, 100*nocaldf[\"gold\"], lw = 4,  label = \"Calibrated χ = \" + base_chi)\n",
    "\n",
    "ax[position].plot(nocaldf_lowchi.upsilon, 100*nocaldf_lowchi[\"gold\"], lw = 4, color = 'red', label = \"Large χ = \" + low_chi)\n",
    "\n",
    "ax[position].plot(nocaldf_higchi.upsilon, 100*nocaldf_higchi[\"gold\"], lw = 4, color = 'k', label = \"Small χ = \" + hig_chi)\n",
    "\n",
    "xticks = [0.01*float(item) for item in list(range(1,11,1)) ]\n",
    "\n",
    "ax[position].set_xticks(xticks)\n",
    "\n",
    "ax[position].set_xlim(0.004,0.07)\n",
    "ax[position].set_ylim(0.0,3.10)\n",
    "#ax.set_ylim(0,0.40)\n",
    "ax[position].set_xlabel(\"\\n GBM Variance Parameter\", fontsize = 12)\n",
    "ax[position].set_ylabel(\"\\n Initial SS Productivity Growth\", fontsize = 12)\n",
    "\n",
    "ax[position].hlines(0.79, -0.001, 0.048,\n",
    "           color='k',       \n",
    "           label=\"Calibrated Values\",\n",
    "           linestyle='--',\n",
    "           lw = 3)  \n",
    "\n",
    "ax[position].vlines(0.048, 0, 0.79,   \n",
    "           color='k',   \n",
    "           linestyle='--',\n",
    "           lw = 3)  \n",
    "\n",
    "ax[position].spines[\"right\"].set_visible(False)\n",
    "ax[position].spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax[position].legend(bbox_to_anchor=(0., -1.25, 1., .102),frameon = False, fontsize = 14, loc = 4)\n",
    "\n",
    "#########################################################################################\n",
    "\n",
    "position = (1,0)\n",
    "\n",
    "series = \"welfare\"\n",
    "\n",
    "ax[position].plot(nocaldf.upsilon, 100*nocaldf[series], lw = 4,  label = \"Calibrated χ\")\n",
    "\n",
    "ax[position].plot(nocaldf_lowchi.upsilon, 100*nocaldf_lowchi[series], lw = 4, color = 'red', label = \"Large χ\")\n",
    "\n",
    "ax[position].plot(nocaldf_higchi.upsilon, 100*nocaldf_higchi[series], lw = 4, color = 'k', label = \"Small χ\")\n",
    "\n",
    "xticks = [0.01*float(item) for item in list(range(1,11,1)) ]\n",
    "\n",
    "ax[position].set_xticks(xticks)\n",
    "\n",
    "ax[position].set_xlim(0.004,0.07)\n",
    "ax[position].set_ylim(5,15)\n",
    "#ax.set_ylim(0,0.40)\n",
    "ax[position].set_xlabel(\"\\n GBM Variance Parameter\", fontsize = 12)\n",
    "ax[position].set_ylabel(\"\\n Welfare Gain, Percent\", fontsize = 12)\n",
    "\n",
    "ax[position].spines[\"right\"].set_visible(False)\n",
    "ax[position].spines[\"top\"].set_visible(False)\n",
    "\n",
    "\n",
    "ax[position].vlines(0.048, 0, 11.18, \n",
    "           color='k',  \n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "ax[position].hlines(11.18, -0.001, 0.048 , \n",
    "           color='k',       \n",
    "           label=\"Calibrated Values\", \n",
    "           linestyle='--',\n",
    "           lw = 3)  \n",
    "\n",
    "#############################################################################################\n",
    "position = (1,1)\n",
    "\n",
    "ax[position].axis('off')\n",
    "\n",
    "#plt.savefig(fig_path + \"\\\\gbm_chi.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Discussion of these results from PTW text\n",
    "The first thing to observe from Figure 6 is that the percentage point change in productivity is nearly constant across different values of the variance parameter. In other words, the variance does not much affect the response of growth to a change in trade costs.\n",
    "\n",
    "The parameter which does influence the change in growth is the adoption cost parameter. The three\n",
    "different lines on the left panel in Figure 6 illustrate this point. A small value of chi (top black line) corresponds to small costs of adoption. When adoption costs are small, growth is more responsive to changes in trade costs. In contrast, a large value of chi (bottom red line) corresponds to large adoption costs and a smaller response of growth to trade costs. The closed form equations available in the non-GBM version of the model deliver some insight. Equation 35 shows that the change in the growth rate for a given change in trade costs is larger when adoption costs are smaller.\n",
    "\n",
    "Even though the elasticity of growth to trade costs is not sensitive to the value of the GBM variance parameter",
    "holding adoption costs constant, the value of the GBM variance parameter",
    "—and, thus, the firm dynamics data—strongly influences the calibrated value of the adoption cost. The right panel in Figure 6 illustrates this point by tracing out how the growth rate in the initial steady state varies with the GBM variance parameter",
    ". For a given value, there is a near linear decrease in the steady state growth rate as the variance increases. Across chi values, the slope is essentially the same, but the intercept shifts, with smaller chi values leading to higher growth rates. This is intuitive—lower adoption costs lead to more adoption and faster economic growth.\n",
    "\n",
    "The implication of these observations is that data on firm dynamics influences the inferred adoption cost and, thus, the elasticity of growth to trade costs. For example, holding fixed our target of an aggregate growth rate of 0.79 percent, if the transition matrix of relative size (Table 3) had pushed for us to find a smaller value for the GBM variance parameter",
    ", then the right panel of Figure 6 shows this would have lead us to calibrate a larger value for chi. Combining this observation with the left panel of Figure 6, our calibration strategy would have then led to a smaller increase in the growth rate for the same decrease in trade costs.\n",
    "\n",
    "The lower panel of Figure 6 shows that the welfare gains from trade (comparing BGPs) are nearly constant across values of the GBM variance parameter",
    ", but sensitive to the value of chi, just like the elasticity of growth to trade costs. Thus, the value of chi is crucial for determining both the change in growth and the welfare gains from trade. Even though the GBM variance parameter",
    " does not much affect the welfare gains from trade when holding all other parameters constant, different values of the GBM variance parameter",
    " (which are associated with different firm dynamics moments) affect the calibration of  chi. It is in this sense that not just firm heterogeneity, but firm dynamics, matter\n",
    "for the welfare gains from trade in our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our discussion above, which compares our gains from trade to those in Sampson (2016), strongly suggests this point as well. When the GBM process is shut down and the model is re-calibrated, the gains from trade are still larger than what the ACR formula would imply, but they are far more modest and in line with what Sampson finds. Recall from Section 7.3 that much of the welfare gains arise because the equilibrium has an inefficiently low growth rate and that changes in trade costs change the growth rate. Using the decomposition from Section 7.3, we find that the different values of chi associated with different\n",
    "values of the GBM variance parameter",
    "affect the welfare gains from trade almost completely because of a change in the sensitivity\n",
    "of growth to the trade cost and not because of different levels of inefficiency.\n",
    "\n",
    "This point is illustrated in the Connection to the Welfare Decomposition section below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### <a name=\"delta\"></a> Importance of the Exit Shock\n",
    "\n",
    "We uniformly scale up and down the exit shock for different values of the adoption cost parameter chi. The large value of chi is ten percent larger than the baseline calibrated value. The small value of chi is ten percent smaller than the baseline calibrated value. All other parameter values are fixed, i.e., we do not re-calibrate the model when changing these parameter values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 1. Compute outcomes for different Delta parameter values\n",
    "\n",
    "This calls the matlab code to perform this operation. The code appendix below describes each of the different components. The line below executes matlab from the command line/terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "matlab_cmd = '''\"cd('src\\calibration');robust_no_recalibrate_delta;\"'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calbirated values computed on date\n",
      "   20-Apr-2020\n",
      "\n",
      "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "Chi Values\n",
      "    0.1409\n",
      "\n",
      "Chi Values\n",
      "    0.1268\n",
      "\n",
      "Chi Values\n",
      "    0.1153\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!{matlab_exe_path} -batch {matlab_cmd}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 2. Create Figure 7 in PTW 2020\n",
    "\n",
    "The code below reads in the output from matlab and then plots the results to create Figure 7 of PTW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnames = ['gold', 'gnew', 'gdiff', \"welfare\", 'delta']\n",
    "\n",
    "mat = scipy.io.loadmat(path + \"\\\\output\\\\robust\\\\delta\\\\norecalibrate_values_delta_1.mat\")\n",
    "\n",
    "nocaldf = pd.DataFrame(mat[\"record_values\"])\n",
    "\n",
    "nocaldf.columns = cnames\n",
    "nocaldf[\"gdiff\"] = -nocaldf[\"gdiff\"] \n",
    "\n",
    "nocaldf.sort_values([\"delta\"], inplace = True)\n",
    "\n",
    "base_chi = str(round(mat[\"chi_value\"][0][0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat(path + \"\\\\output\\\\robust\\\\delta\\\\norecalibrate_values_delta_0.9.mat\")\n",
    "\n",
    "nocaldf_lowchi = pd.DataFrame(mat[\"record_values\"])\n",
    "\n",
    "nocaldf_lowchi.columns = cnames\n",
    "nocaldf_lowchi[\"gdiff\"] = -nocaldf_lowchi[\"gdiff\"] \n",
    "\n",
    "nocaldf_lowchi.sort_values([\"delta\"], inplace = True)\n",
    "\n",
    "low_chi = str(round(mat[\"chi_value\"][0][0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat(path + \"\\\\output\\\\robust\\\\delta\\\\norecalibrate_values_delta_1.1.mat\")\n",
    "\n",
    "nocaldf_higchi = pd.DataFrame(mat[\"record_values\"])\n",
    "\n",
    "nocaldf_higchi.columns = cnames\n",
    "nocaldf_higchi[\"gdiff\"] = -nocaldf_higchi[\"gdiff\"]\n",
    "\n",
    "nocaldf_higchi.sort_values([\"delta\"], inplace = True)\n",
    "\n",
    "hig_chi = str(round(mat[\"chi_value\"][0][0],3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2,figsize = (12,10))\n",
    "\n",
    "fig.tight_layout(pad = 6)\n",
    "\n",
    "\n",
    "position = (0,0)\n",
    "\n",
    "ax[position].plot(nocaldf.delta, 100*nocaldf[\"gdiff\"], lw = 4)\n",
    "\n",
    "ax[position].plot(nocaldf_lowchi.delta, 100*nocaldf_lowchi[\"gdiff\"], lw = 4, color = 'r')\n",
    "\n",
    "ax[position].plot(nocaldf_higchi.delta, 100*nocaldf_higchi[\"gdiff\"], lw = 4, color = 'k')\n",
    "\n",
    "xticks = [0.01*float(item) for item in list(range(1,11,1)) ]\n",
    "\n",
    "ax[position].set_xticks(xticks)\n",
    "\n",
    "ax[position].set_xlim(0.01,0.04)\n",
    "ax[position].set_ylim(0.15,0.4)\n",
    "#ax[position].set_xlabel(\"\\n GBM Variance Parameter\", fontsize = 12)\n",
    "ax[position].set_ylabel(\"\\n Change in Productivity Growth \\n Percentage Points\", fontsize = 12)\n",
    "\n",
    "ax[position].spines[\"right\"].set_visible(False)\n",
    "ax[position].spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax[position].vlines(0.020, 0.00, 0.234,           # Set the value equall to the average\n",
    "           color='k',       # make the color red\n",
    "           #label='Trade Shock', # this is the label (shows up in the legend)\n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "ax[position].hlines(0.234, -0.001, 0.020 ,           # Set the value equall to the average\n",
    "           color='k',       # make the color red\n",
    "           label=\"Calibrated Values\", # this is the label (shows up in the legend)\n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "position = (0,1)\n",
    "\n",
    "ax[position].plot(nocaldf.delta, 100*nocaldf[\"gold\"], lw = 4,  label = \"Calibrated χ = \" + base_chi)\n",
    "\n",
    "ax[position].plot(nocaldf_lowchi.delta, 100*nocaldf_lowchi[\"gold\"], \n",
    "                  lw = 4, color = 'red', label = \"Large χ = \" + low_chi)\n",
    "\n",
    "ax[position].plot(nocaldf_higchi.delta, 100*nocaldf_higchi[\"gold\"], \n",
    "                  lw = 4, color = 'k', label = \"Small χ = \" + hig_chi)\n",
    "\n",
    "#ax[1].plot(nocaldf_bigchi.upsilon, 100*nocaldf_bigchi[\"gold\"], lw = 4, color = 'k', label = \"Large 1/chi\")\n",
    "\n",
    "xticks = [0.01*float(item) for item in list(range(1,11,1)) ]\n",
    "\n",
    "ax[position].set_xticks(xticks)\n",
    "\n",
    "ax[position].set_xlim(0.01,0.04)\n",
    "ax[position].set_ylim(0.20,1.4)\n",
    "#ax.set_ylim(0,0.40)\n",
    "ax[position].set_xlabel(\"\\n Exit Shock Parameter\", fontsize = 12)\n",
    "ax[position].set_ylabel(\"\\n Initial SS Productivity Growth\", fontsize = 12)\n",
    "\n",
    "ax[position].vlines(0.02, 0.00, 0.79,           # Set the value equall to the average\n",
    "           color='k',       # make the color red\n",
    "           #label='Trade Shock', # this is the label (shows up in the legend)\n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "ax[position].hlines(0.79, -0.001, 0.020 ,           # Set the value equall to the average\n",
    "           color='k',       # make the color red\n",
    "           label=\"Calibrated Values\", # this is the label (shows up in the legend)\n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "ax[position].spines[\"right\"].set_visible(False)\n",
    "ax[position].spines[\"top\"].set_visible(False)\n",
    "\n",
    "ax[position].legend(bbox_to_anchor=(0., -1.25, 1., .102),frameon = False, fontsize = 14, loc = 4)\n",
    "\n",
    "#########################################################################################\n",
    "position = (1,0)\n",
    "\n",
    "series = \"welfare\"\n",
    "\n",
    "ax[position].plot(nocaldf.delta, 100*nocaldf[series], lw = 4,  label = \"Calibrated χ\")\n",
    "\n",
    "ax[position].plot(nocaldf_lowchi.delta, 100*nocaldf_lowchi[series], lw = 4, color = 'red', label = \"Large χ\")\n",
    "\n",
    "ax[position].plot(nocaldf_higchi.delta, 100*nocaldf_higchi[series], lw = 4, color = 'k', label = \"Small χ\")\n",
    "\n",
    "#ax[1].plot(nocaldf_bigchi.upsilon, 100*nocaldf_bigchi[\"gold\"], lw = 4, color = 'k', label = \"Large 1/chi\")\n",
    "\n",
    "xticks = [0.01*float(item) for item in list(range(1,11,1)) ]\n",
    "\n",
    "ax[position].set_xticks(xticks)\n",
    "\n",
    "ax[position].set_xlim(0.01,0.04)\n",
    "ax[position].set_ylim(6,20)\n",
    "#ax.set_ylim(0,0.40)\n",
    "ax[position].set_xlabel(\"\\n Exit Shock Parameter\", fontsize = 12)\n",
    "ax[position].set_ylabel(\"\\n Welfare Gain, Percent\", fontsize = 12)\n",
    "\n",
    "ax[position].spines[\"right\"].set_visible(False)\n",
    "ax[position].spines[\"top\"].set_visible(False)\n",
    "\n",
    "\n",
    "ax[position].vlines(0.02, 0, 11.18,           # Set the value equall to the average\n",
    "           color='k',       # make the color red\n",
    "           #label='Trade Shock', # this is the label (shows up in the legend)\n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "ax[position].hlines(11.18, -0.001, 0.02 ,           # Set the value equall to the average\n",
    "           color='k',       # make the color red\n",
    "           label=\"Calibrated Values\", # this is the label (shows up in the legend)\n",
    "           linestyle='--',\n",
    "           lw = 3)     # thickness of the line\n",
    "\n",
    "###############################################################################################\n",
    "position = (1,1)\n",
    "\n",
    "ax[position].axis('off')\n",
    "\n",
    "#plt.savefig(fig_path + \"\\\\delta_chi.pdf\", bbox_inches = \"tight\", dip = 3600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Discussion of Figure from paper\n",
    "\n",
    "Similarly to the GBM variance case, the delta parameter interacts with the adoption cost parameter to affect the calibrated value of chi. The right panel in Figure 6 illustrates this point by tracing out how the growth rate in the initial steady state varies with delta. For a given chi value, the steady state growth rate increases with delta; across chi values, smaller chi values (lower adoption costs) lead to higher growth rates. Figure 6 shows that larger delta values (i.e., more entry observed in the data) would induce the calibration to infer larger chi values. But because these two parameters have opposite effects on economic growth, the change\n",
    "in parameter values generates offsetting effects and leaves the model’s elasticity of growth to trade costs unchanged\n",
    "\n",
    "The welfare gains from trade display a similar pattern. The bottom panel of Figure 7 shows that the welfare gains from trade increase with the value of delta, holding all else fixed. Again, however, larger values of delta generate larger calibrated values of chi, which offset to keep the welfare gains from trade largely unchanged. Re-calibrating the model holding fixed different values for delta verifies this observation—welfare only increases slightly as delta increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"dcomp\"></a> Connection to the Welfare Decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This calls Julia to perform the same welfare decomposition exercise as that done in ``section_7-3.ipynb``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook ChiUpsilonDelta.ipynb to script\n",
      "[NbConvertApp] Writing 2986 bytes to ChiUpsilonDelta.jl\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to script ChiUpsilonDelta.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "julia_command = '''ChiUpsilonDelta.jl'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------\n",
      "\n",
      "Baseline\n",
      "Growth in Baseline: 0.79\n",
      "Baseline Inefficincy term (CE units, percent of increase): 44.696\n",
      "Baseline Semi-Elasticity of Growth: -2.828\n",
      "-------------------------------------------------------------------------\n",
      "\n",
      "Large Chi case\n",
      "Growth in Large Chi case: 0.8\n",
      "Large Chi Inefficincy term (CE units, percent of increase): 44.125\n",
      "Large Chi Semi-Elasticity of Growth: -2.136\n",
      "-------------------------------------------------------------------------\n",
      "\n",
      "Small Chi case\n",
      "Growth in Small Chi case: 0.79\n",
      "Small Chi Inefficincy term (CE units, percent of increase): 45.145\n",
      "Small Chi Semi-Elasticity of Growth: -3.63\n"
     ]
    }
   ],
   "source": [
    "!{julia_path} {julia_command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decomposition from Section 7.3, shows how different values of $\\chi$s affect the welfare gains from trade. We find that the different values of $\\chi$ associated with different values of $\\upsilon^{2}$ affect the welfare gains from trade almost completely because of a change in the sensitivity of growth to the trade cost $\\left(\\frac{\\mathrm{d} f_{g}}{\\mathrm{d} d}\\right)$ (i.e., the semi-elasticity of growth changes substantially) and not because of different levels of inefficiency $\\left(U_1 \\frac{ \\partial f_{c}}{ \\partial g} + U_2\\right)$ (which are relativly simmilar across different specifications)."
   ]
  }
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