{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture Support: Solow Growth Model Simulator: 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": [ "# SET PARAMETERS, INITIAL CONDITIONS, AND SCENARIO LENGTH IN THIS CELL\n", "#\n", "# 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 = 1.09 # the elasticitiy 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 length of time for which the simulation will run:\n", "T = 300\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# initialize the list of labor force values:\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", "# plot the labor force over time\n", "solow_df.L.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('Size of Labor Force')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 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", "# plot the L and E values in the dataframe over time:\n", "solow_df[['L','E']].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('Values of Labor Efficiency and Labor Force')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Memo**:\n", "\n", "The definition of capital intensity: $ \\kappa = \\frac{K}{Y} $ \n", "The proportional growth rate of output: $ g_Y = \\theta g_{\\kappa} + n + g $ \n", "The change in the capital stock: $ \\frac{dK}{dt} = sY - \\delta K $ \n", "The proportional growth rate of the capital stock: $ g_{K} = \\frac{1}{K} \\frac{dK}{dt} = s\\frac{Y}{K} - \\delta = \\frac{s}{\\kappa} - \\delta $ \n", "The proportional growth rate of capital intensity is: $ g_\\kappa = g_K - g_Y\n", "\n", "So the proportional rate of growth of capital-intensity $ \\kappa $ is:\n", "\n", ">$ g_\\kappa = \\left( \\frac{s}{\\kappa} - \\delta \\right) - \\left( \\theta g_{\\kappa} + n + g \\right) $\n", "\n", "> $ (1+\\theta) g_\\kappa = \\frac{s}{\\kappa} - \\delta - n - g $\n", "\n", ">$ g_\\kappa = \\frac{s/\\kappa - (n+g+\\delta)}{1+\\theta} $\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 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\n", "\n", "# plot capital intensity:\n", "solow_df.κ.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('Capital Intensity')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "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 three variables calculated so far over time:\n", "solow_df[['L','E', 'κ']].plot()\n", "\n", "# set the y-axis minimum to zero, & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title('Labor Force, Labor Efficiency, Capital Intensity')\n", "plt.show()" ] }, { "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", "# 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", "# 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show output per worker:\n", "solow_df.y.plot()\n", "# set the y-axis minimum to zoero, & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title('Output per Worker')\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 output:\n", "solow_df.Y.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('Value of Production')\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 the capital stock:\n", "solow_df.K.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('Value of Capital Stock')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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Af7lsKs3N8J3H1ma6FBGRPhWqMB97whA+ev54Hlq6jZVv7M90OSIifSZUYQ7wibeeRHFejK8+8pouwiUig0bowrwoL8bnLp3Coo17eXjZ9kyXIyLSJ0IX5gDXnDWO08cW8/U/r9LNn0VkUAhlmEcjxtevPJW9dfXc/viaTJcjIpJ2oQxzgBljirh+9onc/dJmVmzTj6EiEm6hDXOA2y6dwgn5OXzpjyt0VUURCbVQh/nQ3Bj/dsU0lm/bzy//vjHT5YiIpE2owxzg3aePYs7U4Xz7sTVsrK7LdDkiImkR+jA3M77xnhnkZEX4/O+Wa7hFREIp9GEOMGJoLl959ylUbt7Hr17clOlyRERSblCEOcDVZ4wOhltWa7hFREIn6TA3s6iZLTWzR9JZULocG26J8pn7ltLQ1JzpkkREUqY7e+afBlalq5C+MGJoLv/9nhks37af25/QlRVFJDySCnMzGwO8E/h5estJv8tnjOTas8fx02fX8/zrVZkuR0QkJZLdM/8+8M9Ah2MTZjbPzCrNrLKqqn+H5JevmM5Jwwu49YHlVB+sz3Q5IiK91mWYm9kVwG53X9xZO3ef7+4V7l5RVlaWsgLTIS87yv+79gz2H27ktgeW06zDFUVkgEtmz/x84N1mtgm4D3ibmf0mrVX1gWkjh/LlK6bz7Noqvv+kxs9FZGDrMszd/V/dfYy7jweuAf7m7telvbI+8KFzxvG+M8fww7+t44nXdmW6HBGRHhs0x5m3x8z4+lWnctqYIm69fxnrqw5muiQRkR7pVpi7+zPufkW6ismE3FiUn1x3JrGsCDfdvZjaI7qZhYgMPIN6z7zF6OI8fvTBM9hUXccnf7uUxrhOKBKRgUVhHjhvUinfuHoGz62t4ssPr9TNoEVkQMnKdAH9yfvPGsvmvXX8+On1jDshn5svnpTpkkREkqIwb+O2t09h697DfPOvqxlVnMuVM0dnuiQRkS4pzNuIRIxv/8Np7DpwhNseWE5BThZzpo3IdFkiIp3SmHk7crKi/PzDFUwfNZSb71nCi+urM12SiEinFOYdKMyNcddHz2Z8yRBuvKuSpVv2ZbokEZEOKcw7MSw/m9987BxKC3O44ReLFOgi0m8pzLswfGguv71xNsOGZHP9nYt4edPeTJckInIchXkSRhfn8cBN5zK8MIcb7lykMXQR6XcU5kkqL8rlvptmM/aEPD76y5d5UhfmEpF+RGHeDcMLc7n3xtlMKS9k3t2V3LNwc6ZLEhEBFObdVlKQw703zubCyWV88Q8ruf3xNTr1X0QyTmHeA/k5WfzshgreX5G4FvqtDyznSGM802WJyCCmM0B7KBaN8M33nsaYYUO4/Ym1bKg6yB3XV1BelJvp0kRkENKeeS+YGZ+aczJ3XH8m63Yf5N0/eoElOhZdRDJAYZ4Cl55SzkOfOJ/cWJQP3LGAX7ywUePoItKnugxzM8s1s0VmttzMXjWz/+iLwgaaKeWF/OmW87lochlffeQ1brp7MfsP6a5FItI3ktkzrwfe5u6nAzOBy8xsdnrLGpiKh2Tzsxsq+NI7p/G31buZ+8PnWbRRZ4yKSPp1Geae0HKn41jw0BhCB8yMf3zLRB68+TyiEeMD8xfwtUde09EuIpJWSY2Zm1nUzJYBu4En3H1hO23mmVmlmVVWVVWlus4BZ+bYYh799Fu47pwTufOFjcz94fO6UJeIpE1SYe7ucXefCYwBzjazU9tpM9/dK9y9oqysLNV1Dkj5OVl87apT+c3HzqG+sZn3/uRFvvGXVdTVN2W6NBEJmW4dzeLuNcAzwGVpqSakLji5lL9+5i184KyxzH9uA3O++yyPvLJdR7yISMokczRLmZkVB6/zgEuA1ekuLGwKc2P813tO4/c3n0dJQTa3/HYp1925kHW7D3b9YRGRLiSzZz4SeNrMXgFeJjFm/kh6ywqvM08cxp9uuYCvXXkKK7bt57LvP8e//XElu2uPZLo0ERnALB1/6ldUVHhlZWXKlxs21Qfr+d4Ta7nv5a3kZEX42AUTuPHCiQzNjWW6NBHpY2a22N0revx5hXnmbayu47uPr+GRV3YwbEiMmy6axHWzT6QgR5fOERksFOYhsmLbfr712Gqef72aorwYHz5vPB89bzzD8rMzXZqIpJnCPISWb63hf55Zx2Ov7mJIdpRrzx7Hh88dz7iSIZkuTUTSRGEeYmt31fLTZ9bz8PLtNLszZ+pwbjh3PBecVEokYpkuT0RSSGE+COzcf4TfLtzMbxdtofpgAxNL87n27HFcdcZoygpzMl2eiKSAwnwQqW+K8+iKndy1YBNLt9SQFTEunjKc9505hrdNHU52lq5oLDJQ9TbMdbjEAJKTFeWqM0Zz1RmjeX1XLQ8u2cZDS97gyVW7GDYkxqWnlDN3xkjOnVRCLKpgFxlMtGc+wDXFm3n+9Wr+sPQNnlq1i7qGOMVDYrxj+gjmzhjJeZNKtccuMgBoz3yQy4pGeOvU4bx16nCONMZ5bm0Vf1mxg7+s2MkDldvIz45y/kmlXDxlOBdPKWNUcV6mSxaRNFCYh0huLMo7TinnHaeUU98U54XXq3lq9W6eXVPF46/tAmDyiAIunjKccyeWUDF+GIU621QkFDTMMgi4O+t2H+SZNVU8vWY3L2/aS2PciRicOrqIcyacwDkTSjhr/AkUDVG4i2SCjmaRbjvcEGfJln0s3LCHlzbuZdmWGhrizQBMKM3n9DFFnD62mNPGFHPKqKHkxqIZrlgk/DRmLt2WF4yjn39SKQBHGuMs21rDki37WL61hpc27OWPy7YDkBUxpo4sZPrIoUwtH8rU8kKmlBdSUqDj20X6E4W5kBuLMntiCbMnlhydtnP/EZZvq2H51hqWb6vhqVW7eaBy29H5ZYU5iWAfUciEsnwmlCYeIwpzdXaqSAYozKVd5UW5lBeVc+kp5UenVdXWs3rnAdbsrGX1zlrW7Kzl7pc2U9/UfLRNXizKiSVDmFiWz/iSxGP0sDxGFecxsihXQzYiaaIwl6SVFeZQVljGW04+do/X5mZnx4EjbKquY0N1HZuq69hYXcfqHbU8/uoumprf/JtMaUE2o4sT4d7yGFmUm1h2QQ6lhTnkZ0cx0969SHcozKVXIhFjdHEeo4vzjo7Bt2iMN7Oj5ghv1Bxme8tj/2G27TvM2l21PLOmisON8eOWmReLUlqYnQj3gpzgSySHkoIcivNiFA+JUZyXTfGQGEPzYhTmZGloRwa9LsPczMYCvwbKgWZgvrv/IN2FycAXi0YYVzKkw0v3ujs1hxrZsf8I1Qfrqaqtf/PzwXo27zlE5eZ97DvUQEcHXkUMivJiFA/JDp5jFOXFyM/JoiAni/zsLPJzohTkZFGQm/Wm6QU5iXn5OVnkZEX0F4EMWMnsmTcBt7n7EjMrBBab2RPu/lqaa5OQMzOG5WcndfONpngzew81cOBwIzWHgsfhRmoONbD/8LH3+w83sq+ugY3VddTVN3Gwvokjjc1dLh8SXwq5sSi5sSh5sSg5sQi5WVFyYxHysqPB65ZH5Fi7rAixrAixaITsrAjZUSMWjRx9ZGe9+X1OVstrO/qZWDRCVtSImhGNGFmRxLO+XCRZXYa5u+8AdgSva81sFTAaUJhLn8mKRhhemMvwwtxuf7Yp3kxdfZyDDU1HA76uvomDR469rmuIc7ghzpHGOEea4hxuaOZIU5z6xjiHG+McaWym5lAjhxvj1Dc2J9oF85pTf6rGURGDaKQl4CNELPHfImLHAr8l/CMtz2aJL4bIsS+HaDDdjDc9RyzxpWoE7yNgtDO/9XuCdq3ft9MuYgZt3h/9agq+pKzV25a5Ld9fb5rX5kvtWBtrt33becc+d2y9x3/u2Pp7XCdtVti6QeeTeq1bY+ZmNh44A1jYzrx5wDyAcePGpaA0kdTIikYoGhJJy9mt7k5Ts9MYb6axyWmINydeB4+GpsS8hngzjU3Bc9xbzX/z+3hzYnnNLc/B8uNtHq3bxJubiTuJ51bzW7dvaGrGgWZ3mj1Rt3tH7x2HN7/349+3fK6lfXNz0I5j7ZodCD4XD8bJ0nCeotCNM0DNrAB4FvhPd3+os7Y6A1REktXyRQKJL4KWaS3vj807/sugo3neZhlv+py33/5oLR0sGz9+Wa2X016StpevHUXuuJL89J8BamYx4PfAPV0FuYhId7QMz7SZmolSBrQuL3RtiYGkO4FV7n57+ksSEZHuSuauBecD1wNvM7NlwWNumusSEZFuSOZolhfQ3zwiIv2a7icmIhICCnMRkRBQmIuIhIDCXEQkBBTmIiIhoDAXEQkBhbmISAgozEVEQkBhLiISAgpzEZEQUJiLiISAwlxEJAQU5iIiIaAwFxEJAYW5iEgIKMxFREJAYS4iEgLJ3AP0F2a228xW9kVBIiLSfcnsmf8KuCzNdYiISC90Gebu/hywtw9qERGRHtKYuYhICKQszM1snplVmlllVVVVqhYrIiJJSFmYu/t8d69w94qysrJULVZERJKgYRYRkRBI5tDEe4EFwBQz22ZmH0t/WSIi0h1ZXTVw92v7ohAREek5DbOIiISAwlxEJAQU5iIiIaAwFxEJAYW5iEgIKMxFREJAYS4iEgIKcxGREFCYi4iEgMJcRCQEFOYiIiGgMBcRCQGFuYhICCjMRURCQGEuIhICCnMRkRBQmIuIhIDCXEQkBJIKczO7zMzWmNk6M/tCuosSEZHuSeaGzlHgx8DlwHTgWjObnu7CREQkecnsmZ8NrHP3De7eANwHXJneskREpDuykmgzGtja6v024Jy2jcxsHjAveFtvZit7X16/VApUZ7qINFL/Bjb1b+Ca0psPJxPm1s40P26C+3xgPoCZVbp7RW8K66/C3DdQ/wY69W/gMrPK3nw+mWGWbcDYVu/HANt7s1IREUmtZML8ZeBkM5tgZtnANcCf0luWiIh0R5fDLO7eZGa3AI8BUeAX7v5qFx+bn4ri+qkw9w3Uv4FO/Ru4etU3cz9u+FtERAYYnQEqIhICCnMRkRBIaZiH8bR/M9tkZivMbFnLoUNmdoKZPWFmrwfPwzJdZ7LM7Bdmtrv1eQAd9ccSfhhsz1fMbFbmKk9OB/37ipm9EWzDZWY2t9W8fw36t8bMLs1M1ckxs7Fm9rSZrTKzV83s08H0UGy/TvoXlu2Xa2aLzGx50L//CKZPMLOFwfa7PzjQBDPLCd6vC+aP73QF7p6SB4kfR9cDE4FsYDkwPVXLz9QD2ASUtpn2LeALwesvAN/MdJ3d6M+FwCxgZVf9AeYCj5I412A2sDDT9fewf18BPtdO2+nBv9McYELw7zea6T500reRwKzgdSGwNuhDKLZfJ/0Ly/YzoCB4HQMWBtvlAeCaYPpPgZuD158Afhq8vga4v7Plp3LPfDCd9n8lcFfw+i7gqgzW0i3u/hywt83kjvpzJfBrT3gJKDazkX1Tac900L+OXAnc5+717r4RWEfi33G/5O473H1J8LoWWEXiDO1QbL9O+teRgbb93N0PBm9jwcOBtwEPBtPbbr+W7fogMMfM2juJE0jtMEt7p/13tiEGCgceN7PFwSULAEa4+w5I/AMEhmesutToqD9h2qa3BEMNv2g1LDZg+xf8yX0Gib270G2/Nv2DkGw/M4ua2TJgN/AEib8maty9KWjSug9H+xfM3w+UdLTsVIZ5Uqf9D0Dnu/ssEleN/KSZXZjpgvpQWLbpT4BJwExgB/DdYPqA7J+ZFQC/Bz7j7gc6a9rOtIHYv9BsP3ePu/tMEmfSnw1Ma69Z8Nyt/qUyzEN52r+7bw+edwN/ILEBdrX8uRo8785chSnRUX9CsU3dfVfwP1Ez8DOO/Sk+4PpnZjESQXePuz8UTA7N9muvf2Hafi3cvQZ4hsSYebGZtZzA2boPR/sXzC+ikyHEVIZ56E77N7N8MytseQ28A1hJol8fDpp9GHg4MxWmTEf9+RNwQ3BUxGxgf8uf8wNJm3Hiq0lsQ0j075rgqIEJwMnAor6uL1nBeOmdwCp3v73VrFBsv476F6LtV2ZmxcHrPOASEr8LPA28L2jWdvu1bNf3AX/z4NfQdqX419q5JH6BXg98MdO/HqegPxNJ/Fq+HHi1pU8kxq2eAl4Pnk/IdK3d6CS/wHAAAACYSURBVNO9JP5UbSTxzf+xjvpD4s+8HwfbcwVQken6e9i/u4P6Xwn+BxnZqv0Xg/6tAS7PdP1d9O0CEn9mvwIsCx5zw7L9OulfWLbfacDSoB8rgS8H0yeS+BJaB/wOyAmm5wbv1wXzJ3a2fJ3OLyISAjoDVEQkBBTmIiIhoDAXEQkBhbmISAgozEVEQkBhLiISAgpzEZEQ+P9OYUlra+ej7QAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show capital intensity:\n", "solow_df.κ.plot()\n", "# set the y-axis minimum to zoero, & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title('Capital Intensity')\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 labor efficiency:\n", "solow_df.E.plot()\n", "# set the y-axis minimum to zoero, & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title('Labor Efficiency')\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 size of labor force:\n", "solow_df.L.plot()\n", "# set the y-axis minimum to zoero, & tell python to show the plot:\n", "plt.ylim(bottom=0)\n", "plt.title('Size of Labor Force')\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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }