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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Real capital equivalence to wage-income"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "How much real capital has been necessary for risk-free interest to match annual wage?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Dependencies:*\n",
      "     - Linux, bash\n",
      "     - Python: matplotlib, pandas, numpy\n",
      "     - Modules: yi_1tools, yi_fred, yi_timeseries\n",
      "     \n",
      "*CHANGE LOG*\n",
      "    \n",
      "    2014-12-06  Update code and commentary.\n",
      "    2014-08-11  First version."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  NOTEBOOK settings and system details:\n",
      "\n",
      "#  Assume that the backend is LINUX (our particular distro is Ubuntu, running bash shell):\n",
      "print '\\n ::  TIMESTAMP of last notebook execution:'\n",
      "!date\n",
      "print '\\n ::  IPython version:'\n",
      "!ipython --version\n",
      "\n",
      "#  Automatically reload modified modules:\n",
      "%load_ext autoreload\n",
      "%autoreload 2   #  0 will disable autoreload.\n",
      "#  Generate plots inside notebook:\n",
      "%matplotlib inline\n",
      "\n",
      "#  DISPLAY options\n",
      "from IPython.display import Image \n",
      "#  e.g. Image(filename='holt-winters-equations.png', embed=True)\n",
      "from IPython.display import YouTubeVideo\n",
      "#  e.g. YouTubeVideo('1j_HxD4iLn8')\n",
      "from IPython.display import HTML # useful for snippets\n",
      "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
      "import pandas as pd\n",
      "print '\\n ::  pandas version:'\n",
      "print pd.__version__\n",
      "#      pandas DataFrames are represented as text by default; enable HTML representation:\n",
      "#      [Deprecated: pd.core.format.set_printoptions( notebook_repr_html=True ) ]\n",
      "pd.set_option( 'display.notebook_repr_html', False )\n",
      "\n",
      "#  MATH display, use %%latex, rather than the following:\n",
      "#                from IPython.display import Math\n",
      "#                from IPython.display import Latex\n",
      "\n",
      "print '\\n ::  Working directory (set as $workd):'\n",
      "workd, = !pwd\n",
      "print workd + '\\n'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  TIMESTAMP of last notebook execution:\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sun Dec  7 11:45:35 PST 2014\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  IPython version:\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.3.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  pandas version:\n",
        "0.15.0\n",
        "\n",
        " ::  Working directory (set as $workd):\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/yaya/Dropbox/ipy/fecon235/nb\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Some useful modules:       \n",
      "from yi_1tools import *\n",
      "from yi_fred import *\n",
      "from yi_timeseries import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "From wage data to real annual income"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#      GET DATA released monthly since 1964\n",
      "#      for private-sector nonfarm production/nonsupervisory jobs:\n",
      "wage = getfred( m4wage  )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#     wage in dollars/hour\n",
      "inc = wage * 2000\n",
      "#     Assume 40 hours/week at 50 weeks/year \n",
      "#     to derive ANNUAL INCOME for average nonfarm worker."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  We also want to consider dollars in today's real terms:\n",
      "deflator = getfred(m4defl)\n",
      "tail( deflator )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                   Y\n",
        "T                   \n",
        "2014-04-01  1.007236\n",
        "2014-05-01  1.004633\n",
        "2014-06-01  1.002719\n",
        "2014-07-01  1.001779\n",
        "2014-08-01  1.002172\n",
        "2014-09-01  1.001099\n",
        "2014-10-01  1.000000"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#    = REAL annual income\n",
      "rinc = todf( inc * deflator )\n",
      "stats( rinc )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                  Y\n",
        "count    610.000000\n",
        "mean   36882.474376\n",
        "std     2224.209179\n",
        "min    32852.481683\n",
        "25%    34894.258560\n",
        "50%    36603.214874\n",
        "75%    38345.660801\n",
        "max    41441.690745\n",
        "\n",
        " ::  Index on min:\n",
        "Y   1964-03-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Index on max:\n",
        "Y   2014-02-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Head:\n",
        "                       Y\n",
        "T                       \n",
        "1964-01-01  32899.763431\n",
        "1964-02-01  32874.224674\n",
        "1964-03-01  32852.481683\n",
        "1964-04-01  33096.498702\n",
        "1964-05-01  33080.111205\n",
        "1964-06-01  33160.762789\n",
        "1964-07-01  33137.010060\n",
        "\n",
        " ::  Tail:\n",
        "                       Y\n",
        "T                       \n",
        "2014-04-01  41296.675599\n",
        "2014-05-01  41270.318117\n",
        "2014-06-01  41271.898392\n",
        "2014-07-01  41293.348465\n",
        "2014-08-01  41429.776439\n",
        "2014-09-01  41385.426582\n",
        "2014-10-01  41400.000000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        " ::  Correlation matrix:\n",
        "   Y\n",
        "Y  1\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  plot of inc is an uninteresting upward slope, \n",
      "#     and it is difficult to gauge worth of \n",
      "#     old dollars from decades ago.\n",
      "#  Therefore, plot rinc\n",
      "plotfred(rinc, 'Real annual wage')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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VV1itv02b0reLR8O2bap5eapffBH/+ctDqn4L15AeGrZtU9261aZ//tO+LFOt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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad328dac>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  Finished: plotdf-Real_annual_wage.png\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is interesting that **real annual income ranges from 34K to 42K** in a non-monotonic way historically. Real earnings can go down dramatically when inflation rages."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Let see what the geometric growth rate of real annual income:\n",
      "georet( rinc, 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[0.45, 0.46, 0.83, 12]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thus real growth in wages (and thus income) since 1964 is about 0.45% per annum."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Interest rate used to derive capital equivalence"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#     We use three-month Treasury bills as our \"risk-free\" interest rate.\n",
      "irate = monthly(getfred( d4bills ))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  irate is used as a divisor in our calculations, \n",
      "#       so if it is zero or negative, that's a numerical disaster!\n",
      "\n",
      "#  Assert 25 bp FLOOR, esp. in the event of negative interest rates!\n",
      "irate[ irate < 0.25 ] = 0.25\n",
      "#            ^conditional example in pandas,\n",
      "#             no need for apply or max functions here.\n",
      "\n",
      "#  In practice, the floor could be maintained by increasing \n",
      "#  the duration of our Treasury bill, perhaps to a Treasury note.\n",
      "\n",
      "plotfred( irate )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Fra3Ft9/c3FxV16Nv594Onku3PxaLMWHCBID3/WVGVLXgG3A0MAPoDgjwIHBd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\nokWZj8ll2/LlmSs7Hnec5Ur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Ll+deJbl370Tu56RJlgJ02GFWtKlPH7j22tJ+qR3HyY/9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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad308eec>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note how that **25 basis point FLOOR** will make a vast difference at the zero end of interest rates: for otherwise, capital equivalence could be absurdly high as \\$350 million."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "So how much real capital is required for risk-free investment to equal annual wages?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rcap = todf( rinc / (irate * 10000.0) )\n",
      "# Real capital expressed in $million\n",
      "stats(rcap)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                Y\n",
        "count  610.000000\n",
        "mean     2.711055\n",
        "std      5.048234\n",
        "min      0.221214\n",
        "25%      0.542302\n",
        "50%      0.718531\n",
        "75%      1.085734\n",
        "max     16.576676\n",
        "\n",
        " ::  Index on min:\n",
        "Y   1981-05-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Index on max:\n",
        "Y   2014-02-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Head:\n",
        "                   Y\n",
        "T                   \n",
        "1964-01-01  0.932005\n",
        "1964-02-01  0.933927\n",
        "1964-03-01  0.928036\n",
        "1964-04-01  0.956546\n",
        "1964-05-01  0.953317\n",
        "1964-06-01  0.955642\n",
        "1964-07-01  0.957717\n",
        "\n",
        " ::  Tail:\n",
        "                    Y\n",
        "T                    \n",
        "2014-04-01  16.518670\n",
        "2014-05-01  16.508127\n",
        "2014-06-01  16.508759\n",
        "2014-07-01  16.517339\n",
        "2014-08-01  16.571911\n",
        "2014-09-01  16.554171\n",
        "2014-10-01  16.560000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        " ::  Correlation matrix:\n",
        "   Y\n",
        "Y  1\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "\"Normalcy\" before 2008, real capital equivalent looked like:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred(rcap[:'2007-12-31'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Trf0FC5LvP/FE1UcfDd5u2KEu+8CTcc45ZrwHD1YdObLm+n/6k+qZ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\ncQPuOI5TR/n/ZrPq0qzsP7oAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad2c120c>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Before the Great Recession, the real capital equivalent stayed under \\$4 million, most of time below \\$1 million. However, with the Fed's zero rate policy, the real capital equivalent has zoomed past \\$16 million! "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred(rcap)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M8JvfwDPPWO+P+rLHHjBzZnp6ikR6hbRsCccdR/B8ydwl6YQtIt1EpEJEZojI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\n8Jmq7h20ZL9T1R1il2VAQ/r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       "text": [
        "<matplotlib.figure.Figure at 0xad28172c>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Since the capital equivalence has gotten stratospheric recently due to the Fed's zero rate policy, it's easy to understand why investment has shifted into long-term bonds and risky equities."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The mean since 1964 of the real capital equivalent (computed around the risk-free interest rate) to real wages is around **\\$2.7 million**. Clearly one should be cautious of that figure because of the extraordinary range."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Equivalency break-even return"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So **supposing real capital is fixed at \\$1 million**, let's compute the equivalency rate of return to break-even with real wage. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bereturn = todf( rinc / 10000.0 )\n",
      "plotfred( bereturn )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Whe3JJ8NWExwzZ1rYWNu2cPXV0LWrfaGznZvcCZd337Xe90MPZWcRbadqFNQg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\nh2wJsxNOMAN+5pnwyy+pz12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       "text": [
        "<matplotlib.figure.Figure at 0xad26a66c>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bereturn.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                Y\n",
        "count  610.000000\n",
        "mean     3.688247\n",
        "std      0.222421\n",
        "min      3.285248\n",
        "25%      3.489426\n",
        "50%      3.660321\n",
        "75%      3.834566\n",
        "max      4.144169"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Conclusion: historically one needs around a 3.7% return on current \\$1 million to approximate the earnings of a nonfarm employee."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}