{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture Support: Solow Growth Model: Initial and Alternative Scenarios: 2020-01-23" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# prepare the python environment with the numerical\n", "# analysis package (np), the database package (pd), &\n", "# the matlab clone plotting package (plt):\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# THESE ARE ALL QUANTITIES YOU CAN CHANGE AT WILL\n", "\n", "# choose the model parameters:\n", "n = 0.01 # the labor-force L proportional growth rate\n", "g = 0.02 # the labor-efficiency E proportional growth rate\n", "s = 0.12 # the share of production Y that is saved and invested\n", "δ = 0.03 # the capital depreciation rate\n", "θ = 1.00 # the elasticity of production Y with respect to capital \n", " # intensity κ\n", " #\n", " # additional variables in the model: output per worker y; the\n", " # capital stock K\n", "\n", "# choose starting values L_0, E_0, and κ_0 for the labor force, labor\n", "# efficiency, and capital intensity:\n", "L_0 = 1\n", "E_0 = 1\n", "κ_0 = 8\n", "\n", "# choose the period at which something changes in the model:\n", "T_alt = 50\n", "\n", "# choose the parameter that changes:\n", "Δs = 0.00 # a change in the savings-investment rate\n", "Δg = 0.00 # a change in the labor-efficiency growth rate\n", "Δn = 0.00 # a change in the labor-force growth rate\n", "ΔK = -0.7 # proportional change in the capital stock (due, \n", " # for example, to wartime destruction)\n", " \n", "# choose the length of time for which the simulation will run:\n", "T = 100\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# initialize the list of labor force values for the initial scenario:\n", "L = [L_0]\n", "\n", "# calculate the labor force for the duration of the\n", "# simulation:\n", "for t in range(T):\n", " L = L + [L[t]*np.exp(n)]\n", "\n", "# initialize the dataframe:\n", "solow_df = pd.DataFrame()\n", "\n", "# stuff the list of labor-force values into the dataframe:\n", "solow_df['L'] = L\n", "\n", "# initialize the list of labor efficiency values:\n", "E = [E_0]\n", "\n", "# calculate labor efficiency for the duration of the\n", "# simulation:\n", "for t in range(T):\n", " E = E + [E[t]*np.exp(g)]\n", "\n", "# stuff the list of labor-efficiency values into the dataframe:\n", "solow_df['E'] = E\n", "\n", "# initialize the list of capital-intensity values:\n", "κ = [κ_0]\n", "\n", "# calculate capital intensity for the duration of the\n", "# simulation:\n", "for t in range(T):\n", " κ = κ + [κ[t]*(1 + (s/κ[t] - (n+g+δ))/(1+θ))]\n", "\n", "# stuff the list of capital-intensity values into the dataframe:\n", "solow_df['κ'] = κ" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# initialize the list of labor force values for the alternative\n", "# scenario:\n", "L_alt = [L_0]\n", "\n", "# calculate the labor force for the duration of the\n", "# simulation:\n", "for t in range(T_alt):\n", " L_alt = L_alt + [L_alt[t]*np.exp(n)]\n", " \n", "for t in range(T_alt, T):\n", " L_alt = L_alt + [L_alt[t]*np.exp(n+Δn)]\n", "\n", "# stuff the list of labor-force values into the dataframe:\n", "solow_df['L_alt'] = L_alt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# initialize the list of labor efficiency values:\n", "E_alt = [E_0]\n", "\n", "# calculate labor efficiency for the duration of the\n", "# simulation:\n", "for t in range(T_alt):\n", " E_alt = E_alt + [E_alt[t]*np.exp(g)]\n", " \n", "for t in range(T_alt, T):\n", " E_alt = E_alt + [E_alt[t]*np.exp(g+Δg)]\n", "\n", "# stuff the list of labor-efficiency values into the dataframe:\n", "solow_df['E_alt'] = E_alt" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# initialize the list of capital-intensity values:\n", "κ_alt = [κ_0]\n", "\n", "# calculate capital intensity for the duration of the\n", "# simulation:\n", "for t in range(T_alt):\n", " κ_alt = κ_alt + [κ_alt[t]*(1 + ((s)/κ_alt[t] - (n+g+δ))/(1+θ))]\n", " \n", "κ_alt[T_alt] = np.exp(ΔK/(1+θ))*κ_alt[T_alt]\n", "\n", "for t in range(T_alt, T):\n", " κ_alt = κ_alt + [κ_alt[t]*(1 + ((s+Δs)/κ_alt[t] - (n+g+δ+Δn+Δg))/(1+θ))]\n", "\n", "\n", " \n", "# stuff the list of capital-intensity values into the dataframe:\n", "solow_df['κ_alt'] = κ_alt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# calculate the remaining variables in the model\n", "\n", "# initialize the lists of values:\n", "Y = []\n", "K = []\n", "y = []\n", "\n", "Y_alt = []\n", "K_alt = []\n", "y_alt = []\n", "\n", "# calculate the variables for the duration of the\n", "# simulation:\n", "for t in range(T+1):\n", " Y = Y + [(κ[t]**θ)*L[t]*E[t]]\n", " K = K + [(κ[t]*Y[t])]\n", " y = y + [Y[t]/L[t]]\n", "\n", "# stuff the lists of values into the dataframe:\n", "solow_df['Y'] = Y\n", "solow_df['K'] = K\n", "solow_df['y'] = y\n", "\n", "# calculate the variables for the duration of the\n", "# simulation:\n", "for t in range(T_alt+1):\n", " Y_alt = Y_alt + [(κ_alt[t]**θ)*L_alt[t]*E_alt[t]]\n", " K_alt = K_alt + [(κ_alt[t]*Y_alt[t])]\n", " y_alt = y_alt + [Y_alt[t]/L_alt[t]]\n", " \n", "for t in range(T_alt+1,T+1):\n", " Y_alt = Y_alt + [(κ_alt[t]**θ)*L_alt[t]*E_alt[t]]\n", " K_alt = K_alt + [(κ_alt[t]*Y_alt[t])]\n", " y_alt = y_alt + [Y_alt[t]/L_alt[t]]\n", "\n", "# stuff the lists of values into the dataframe:\n", "solow_df['Y_alt'] = Y_alt\n", "solow_df['K_alt'] = K_alt\n", "solow_df['y_alt'] = y_alt\n", "\n", "# plot the entire dataframe:\n", "solow_df.plot()\n", "\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title('All the Model Variables')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show the labor force:\n", "solow_df[['L','L_alt']].plot()\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title(\"Labor Force: Baseline (Blue) and Alternative (Orange) Scenarios\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show labor efficiency:\n", "solow_df[['E','E_alt']].plot()\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title(\"Labor Efficiency: Baseline (Blue) and Alternative (Orange) Scenarios\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show capital intensity:\n", "solow_df[['κ','κ_alt']].plot()\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title(\"Capital Intensity: Baseline (Blue) and Alternative (Orange) Scenarios\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show output per worker:\n", "solow_df[['y','y_alt']].plot()\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title(\"Output per Worker: Baseline (Blue) and Alternative (Orange) Scenarios\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show output:\n", "solow_df[['Y','Y_alt']].plot()\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title(\"Output: Baseline (Blue) and Alternative (Orange) Scenarios\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show the capital stock:\n", "solow_df[['K','K_alt']].plot()\n", "# set the y-axis minimum to zero, title the plot,\n", "# & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title(\"Capital Stock: Baseline (Blue) and Alternative (Orange) Scenarios\")\n", "plt.show()" ] } ], "metadata": { "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }