{
"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": {
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\n",
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