20131105_JapanLifeTables.ipynb

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   "cells": [
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     "cell_type": "markdown",
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     "source": [
      "I recently watched this [Vice video on Youtube](http://www.youtube.com/watch?v=qpZbu7J7UL4) which is a coverage about the Japanese sex industry. Except for the fact that it looks like it was made with a message in mind that ressembles \"the Japanese are weird\", it cited government surveys about the population of Japan declining. Given that I have been interested in data relating to death statistics and more generally statistics about populations, I decided to examine the Japanese age pyramid.\n",
      "\n",
      "In this endeavour, the first step was to gather the right data. I was able to find life tables for Japan at the [Ministry of Health, Labor and Welfare website](http://www.mhlw.go.jp/english/database/db-hw/lifetb21th/dl/tables.pdf). \n",
      "\n",
      "In this article, I'm going to be working on the data for Japanese men. The linked file contains two interesting things:\n",
      "\n",
      "- the number of surviving people in an hypothetical cohort of 100000 people starting as babies\n",
      "- the probability of dying at a given age (death rate)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = \"\"\"Age,number of survivors,number of deaths,survivors rate,death rate,force of mortality,life expectancy,stationary L,stationary T\n",
      "0,100000,246,0.99754,0.00246,0.09375,79.55,99808,7955005\n",
      "1,99754,37,0.99963,0.00037,0.00057,78.75,99733,7855198\n",
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      "3,99690,18,0.99982,0.00018,0.00022,76.80,99681,7655761\n",
      "4,99672,13,0.99987,0.00013,0.00015,75.81,99665,7556080\n",
      "5,99659,11,0.99989,0.00011,0.00012,74.82,99653,7456415\n",
      "6,99647,10,0.99990,0.00010,0.00011,73.83,99642,7356762\n",
      "7,99637,9,0.99991,0.00009,0.00010,72.84,99632,7257120\n",
      "8,99628,8,0.99992,0.00008,0.00009,71.84,99623,7157488\n",
      "9,99619,8,0.99992,0.00008,0.00008,70.85,99615,7057865\n",
      "10,99612,8,0.99992,0.00008,0.00008,69.85,99608,6958249\n",
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      "12,99594,11,0.99989,0.00011,0.00010,67.87,99588,6759043\n",
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      "68,83588,1298,0.98447,0.01553,0.01503,16.44,82946,1374496\n",
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      "86,36852,3990,0.89173,0.10827,0.10871,5.59,34853,206085\n",
      "87,32862,3919,0.88074,0.11926,0.12062,5.21,30894,171231\n",
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      "92,14876,2855,0.80805,0.19195,0.20290,3.62,13421,53816\n",
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      "96,5529,1479,0.73256,0.26744,0.29704,2.69,4763,14859\n",
      "97,4051,1171,0.71095,0.28905,0.32578,2.49,3441,10096\n",
      "98,2880,898,0.68823,0.31177,0.35695,2.31,2410,6655\n",
      "99,1982,665,0.66440,0.33560,0.39077,2.14,1632,4245\n",
      "100,1317,475,0.63949,0.36051,0.42746,1.98,1065,2613\n",
      "101,842,325,0.61351,0.38649,0.46726,1.84,669,1548\n",
      "102,517,214,0.58652,0.41348,0.51044,1.70,402,879\n",
      "103,303,134,0.55858,0.44142,0.55729,1.58,231,478\n",
      "104,169,80,0.52977,0.47023,0.60811,1.46,126,247\n",
      "105,90,45,0.50020,0.49980,0.66325,1.35,65,121\n",
      "106,45,24,0.46998,0.53002,0.72307,1.25,32,56\n",
      "107,21,12,0.43925,0.56075,0.78796,1.16,14,24\n",
      "108,9,5,0.40818,0.59182,0.85837,1.07,6,10\n",
      "109,4,2,0.37696,0.62304,0.93474,0.99,2,4\n",
      "110,1,1,0.34578,0.65422,1.01761,0.92,1,1\"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's set up our pylab environment."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "rcParams['figure.figsize'] = (10, 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's read the data displayed above to a numpy array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data.split('\\n')[1:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = array([elem.split(',') for elem in data], dtype=np.double)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(111, 9)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's plot the survivors as a function of their age."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(data[:, 0], data[:, 1], label = \"number of survivors\")\n",
      "legend(loc='lower left')\n",
      "xlabel('age')\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3nzK3X21n/te/wuOPQ3Fxrf4Yj6LPuWep0unRnys/DXr//fczZMgQ0tLSiIiI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\nEQ+gpk1ERETEA6hpExEREfEA/w8d8+XdKra8NQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb1355ac>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's plot the death rate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(data[:, 0], data[:, 4], label = \"death rate for men\")\n",
      "legend(loc='upper left')\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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27e2uRvxN9xYUERHxo6IiuO46ePdd6NfP7mrECrq3oDRKc/TWU+bWU+bWC7fM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\nlYiIiIgPqbkSERER8aH/D6m6Lgw9c3oxAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb13e1cc>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From there, we can build a simple model of an evolving population. The state equation we're going to use is the following (adapted from [http://en.wikipedia.org/wiki/Demographic_analysis](http://en.wikipedia.org/wiki/Demographic_analysis)):\n",
      "$$\n",
      "P_{new} = P_{old} + B - D \n",
      "$$\n",
      "\n",
      "where $P$ is the population number, $B$ is the number of births and $D$ the number of deaths.\n",
      "We will describe the population by a vector $P = P_i$ containing the respective population numbers for age $i$, i.e. $P_{12}$ is the population aged between 12 and 13.\n",
      "For each iteration, $P_0$ gets incremented by the number of births while all other age groups $i$ get decremented by $P_i * d_i$ where $d_i$ is the death probability for the age group in question.\n",
      "\n",
      "Looking for the birth rates, I found the following figure [here](http://www.mhlw.go.jp/english/database/db-hw/populate/dl/02.pdf) for the year 2011: 8.3 children per 1000 population per year. We will ignore the infant and neonatal death rates for this figure.\n",
      "\n",
      "Looking for the current age pyramid, I found the following [link](http://www.e-stat.go.jp/SG1/estat/ListE.do?lid=000001112823). Unfortunately, it does not provide a very fine grained population pyramid, therefore I'll be interpolating it linearly on one year age categories for the following simulation.\n",
      "\n",
      "First, let's do the data formatting."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "age_data_men = \"\"\"0\t529000\n",
      "1\t543000\n",
      "2\t529000\n",
      "3\t529000\n",
      "4\t544000\n",
      "5\t2746000\n",
      "10\t2983000\n",
      "15\t3068000\n",
      "20\t3117000\n",
      "25\t3495000\n",
      "30\t3889000\n",
      "35\t4712000\n",
      "40\t4727000\n",
      "45\t4077000\n",
      "50\t3802000\n",
      "55\t3917000\n",
      "60\t4997000\n",
      "65\t3914000\n",
      "70\t3426000\n",
      "75\t2730000\n",
      "80\t1822000\n",
      "85\t899000\n",
      "90\t265000\n",
      "95\t60000\n",
      "100\t7000\"\"\"\n",
      "\n",
      "age_data_men = array([elem.split('\\t') for elem in age_data_men.split('\\n')], dtype=np.int32)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll use the following approach for interpolation. First, we'll build a function that returns the number of people above a given age. Second, we'll build a function that gives the number of people in an age interval by substracting two numbers from the previous function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def population_above(age, age_data=age_data_men):\n",
      "    \"\"\"returns population above age\n",
      "    \"\"\"\n",
      "    if age >= age_data[-1, 0]:\n",
      "        return age_data[-1, 1]\n",
      "    index = (age_data[:, 0] >= age).nonzero()[0][0]\n",
      "    if age_data[index, 0] == age:\n",
      "        return sum(age_data[index:, 1])\n",
      "    else:\n",
      "        return sum(age_data[index:, 1]) + (age_data[index, 0] - age) / float(age_data[index, 0] - age_data[index - 1, 0]) * age_data[index - 1, 1]\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def population_between(start, end, age_data=age_data_men):\n",
      "    \"\"\"returns population between start and end ages\n",
      "    start and end are expected to be integers\"\"\"\n",
      "    return population_above(start, age_data=age_data) - population_above(end, age_data=age_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now test this function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print population_between(0, 1), 529000\n",
      "print population_between(1, 2), 543000\n",
      "print population_between(5, 10), 2746000\n",
      "print population_between(5, 15), (2746000 + 2983000)\n",
      "print population_between(5, 6), (2746000 / 5.)\n",
      "print population_between(10, 11), (2983000 / 5.)\n",
      "print population_between(5, 11), (2746000 + 2983000 / 5.)\n",
      "print population_between(9, 10), (2746000 / 5.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "529000 529000\n",
        "543000 543000\n",
        "2746000 2746000\n",
        "5729000 5729000\n",
        "549200.0 549200.0\n",
        "596600.0 596600.0\n",
        "3342600.0 3342600.0\n",
        "549200.0 549200.0\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From there, we can construct the age pyramid for men."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ages = arange(100)\n",
      "pyramid = [population_between(age, age + 1) for age in ages]\n",
      "plot(ages, pyramid)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[<matplotlib.lines.Line2D at 0xb1c332c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ASEtLo6ysjObmZg4fPsyOHTuYOXNmjz8AERGRQNa/P4waBX/9q9WViFV61DP3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\nAPv/SrXuNZ6yVQAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb2c0c8c>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Just a quick check:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sum(pyramid), sum(age_data_men[:, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "61320000.0 61327000\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now iterate the simulation using the curve of death probability and the birth data. In the next cell, we're defining a function that constructs a new age pyramid from an old pyramid taking into account deaths and births."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def construct_new_pyramid(old_pyramid, death_probs, birth_rate):\n",
      "    births = birth_rate / 1000 * sum(old_pyramid)\n",
      "    new_pyramid = old_pyramid * (1 - death_probs)\n",
      "    new_pyramid[-2] += new_pyramid[-1]\n",
      "    new_pyramid[1:] = new_pyramid[0:-1]\n",
      "    new_pyramid[0] = births\n",
      "    return new_pyramid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "death_probs = data[:, 4][:ages.shape[0]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(ages, pyramid, label='start population')\n",
      "plot(ages, construct_new_pyramid(pyramid, death_probs, 8.3), label='population after one year')\n",
      "legend(loc='lower left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.legend.Legend at 0xb1cffec>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tf//GYW5gwECOZB+xTYNEpyJhTnQqlZWwZQvcdZdl/ePkj7kp9Ca5R6MQollq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\n6XAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb1f704c>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As one can see, the population shifts to the right, while newborn babies are added to the left of the plot. We can iterate this behavious several times to construct estimates in the future for the years 2023, 2033 and 2043."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def iterate_pyramid(pyramid, death_probs, birth_rate, years):\n",
      "    for year in range(years):\n",
      "        pyramid = construct_new_pyramid(pyramid, death_probs, birth_rate)\n",
      "    return pyramid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(ages, pyramid, label='2013')\n",
      "plot(ages, iterate_pyramid(pyramid, death_probs, 8.3, 10), label='2023')\n",
      "plot(ages, iterate_pyramid(pyramid, death_probs, 8.3, 20), label='2033')\n",
      "plot(ages, iterate_pyramid(pyramid, death_probs, 8.3, 30), label='2043')\n",
      "legend(loc='lower left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.legend.Legend at 0xb389b2c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mCAo62VxdIThY/Y1yp6HX6pk5YCb/3fZfVo1Zpfp8Bysr6e7sjE6rhdhYEQRx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\naTMwAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb07ab4c>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also plot population estimates according to this simulation model for one hundred years in the future:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "future_years = arange(100)\n",
      "total_men_population = [sum(iterate_pyramid(pyramid, death_probs, 8.3, future_year)) for future_year in future_years]\n",
      "plot(future_years, array(total_men_population) / 1e6)\n",
      "title('Total men population in Japan')\n",
      "xlabel('Years in the future')\n",
      "ylabel('Population (million)')\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KsvWMWSwWWrZsmaMTZjswFWMimUpKgjlzYPp0qFTJKMq6dtXm5CIijmLXYqxh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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb13d32c>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As one can see, there is no convergence, only steady decline. This is due to the fact that the birth rate is given per 1000 people. Thus, when the total population declines, the number of births declines and so on. This is better seen on a 1000 year outlook, where the population goes to zero in the long run:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "future_years = arange(1000)\n",
      "total_men_population = [sum(iterate_pyramid(pyramid, death_probs, 8.3, future_year)) for future_year in future_years]\n",
      "plot(future_years, array(total_men_population) / 1e6)\n",
      "title('Total men population in Japan')\n",
      "xlabel('Years in the future')\n",
      "ylabel('Population (million)')\n",
      "grid(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Cdu0cHZGIiIiYVZE9Zr169WLDhg1FPne1ixcv0r17dy5dukRmZib33nsv06ZN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\ngAHg6uroyERERIqntOoWFWb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HDvz9739n0qRJ9O/fnzvvvLPI4w8aNAiATp068c0331zz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb4308ac>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}