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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Growth of Federal debt and its burden on the US population"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We examine government debt in real terms, and the current debt per capita."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Dependencies:*\n",
      "     - Linux, bash\n",
      "     - Python: matplotlib, pandas\n",
      "     - Modules: yi_1tools.py, yi_fred.py\n",
      "     \n",
      "*CHANGE LOG*\n",
      "\n",
      "    2015-01-16  Code review.\n",
      "    2014-08-16  First version."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  NOTEBOOK settings and system details:   [00-tpl v14.09.28]\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   \n",
      "#           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": [
        "Sat Jan 17 12:42:01 PST 2015\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  IPython version:\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.3.0\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n",
        "\n",
        " ::  pandas version:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.15.0\n",
        "\n",
        " ::  Working directory (set as $workd):"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/yaya/Dropbox/ipy/fecon235/nb\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Some useful modules:       [ ] - print out EOF for reference.\n",
      "from yi_1tools import *\n",
      "from yi_fred import *\n",
      "from yi_plot import *\n",
      "from yi_timeseries import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Federal debt monthly (resampled from original quarterly series):\n",
      "debt = getfred( m4debt )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( debt )\n",
      "#  This series is denominated in millions of dollars,\n",
      "#  so we are looking at federal debt around \n",
      "#  1.8 * 10^7 * 10^6 = $18 trillion circa 2014Q3."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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HxYoHJOPy1FPQr1/jwO05b8dxHMPEOcqkAU+bOI7jbAQzZ8J++8GcObD11sWvv9lpExEZ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\neDuO47RA/h/6JTlgWaJmNwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad201b4c>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Deflator, which is monthly:\n",
      "defl = getfred( m4defl )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real debt\n",
      "rdebt = todf( defl * debt )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( rdebt )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HxYoHlI9Lrp73AcBMVZ2tqquAh4ATUsr0Ae4DUNX/AypEpG1jJL78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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad07f42c>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Population:\n",
      "pop = getfred( m4pop )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real debt per capita\n",
      "rdebtcap = todf( rdebt / pop )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( rdebtcap )\n",
      "#  shown in thousands of dollars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f0Jd2CiF/vjbqPdJKK+ROG4qLYwMfXtVxnIyISBHhGstJQCvCL+VF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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad2aadac>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In current dollars, each American (including children and elders) incurred Federal debt of \\$10K before the 1980s. Then there is a steady climb to around \\$35K in 2008. Thereafter, it is seen **rocketing** (roughly doubling since 2002). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "georet( rdebtcap[:'2002-01-01'], 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "[2.61, 2.63, 1.82, 12]"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "georet( rdebtcap['2002-01-01':], 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "[5.89, 5.91, 1.7, 12]"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Through 2002, the geometric growth rate of real debt per capita was 2.6% Since 2002, that rate has increased to 5.9% per annum."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  This confirms the doubling over 12 years at the geometric rate of 5.9% per annum:\n",
      "1.059 ** 12"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "1.98953471418589"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Total government debt will double in 12 years, given current real rate of increase at 5.9%."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Federal debt is currently over $55,000 per capita. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Most recent data:\n",
      "tail( rdebtcap )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "                    Y\n",
        "T                    \n",
        "2014-01-01  56.043304\n",
        "2014-02-01  55.993733\n",
        "2014-03-01  55.901460\n",
        "2014-04-01  55.778506\n",
        "2014-05-01  55.805470\n",
        "2014-06-01  55.867467\n",
        "2014-07-01  55.980663"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Prorate to only workers:\n",
      "56 / 0.62"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "90.3225806451613"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Assuming 62% of the population are workers (see fred-gdp-wage.ipynb), i.e. source of tax revenue (excluding corporations), **each worker carries approximately $90,000 of federal debt**. That figure is roughly equivalent to two years of average wage income! "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    }
   ],
   "metadata": {}
  }
 ]
}