nilshg/scf1983

## scf1983
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The Distribution of US Wealth in the SCF 1983"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook creates the summary statistics necessary to gauge the results of [Gudat (2014)](http://webspace.qmul.ac.uk/ngudat/Gudat2014.html)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import tables as tab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, import the dataset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_stata('/Users/tew207/Dropbox/QMUL/PhD/PhD Data/SCF/1983/scf83b.dta')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One important variable is `b3005`, which is the full sample composite weight. From the SCF codebook:\n",
      "\n",
      "> This variable is equal to the non-response adjustment factor weight (B3002) times the 1983 post-stratification weight        (B3004). THIS IS THE RECOMMENDED WEIGHT TO USE WITH THE FULL AREA PROBABILITY SAMPLE.  This weight will \"blowup\" the 3,824  observation full area probability sample into the aggregate U.S. household population (including Alaska and Hawaii) as measured by the 1983 CPS.  The average value of B3005 is 21,945.3 and it totals 83,918,807."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Winfried's Code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, remove 159 observations that are excluded from the area probability sample observations in the SCF.\n",
      "`b3016` is the extended income FRB weigt, which is either the weight of the observation (ranging from 546 to 56,473), or 0 if the household is excluded from the area probability sample."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data[data.b3016 != 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, generate a variable `persons` that is 1 if there's only one adult living in the household, 2 if the respondent in `b3128` indicate that they were the the household head's spouse (`b3128==2`) or partner/common-law spouse (`b3128==3`)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['persons'] = 1\n",
      "data['persons'][np.logical_or(data.b3128==2, data.b3128==3)] = 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then generate 3-year age groups, while dropping households that are younger than 20 years old:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['age'] = data['b4503']\n",
      "data = data[data.age > 19]\n",
      "data['age_group'] = 1\n",
      "for i in range(1,20):\n",
      "    data['age_group'][np.logical_and(data.age >= 23 + 3*(i-1), data.age < 26 +3*(i-1))] = i+1\n",
      "    \n",
      "data['age_group'][data.age >= 80] = 21"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, we sort the data by the new age group variable and add a new column that holds the number of observations in each age group. To this end, we first select the value count corresponding to each age group for each observation, which gives us as series that has the age group as index and the value counts as values, and then use this series as a matrix to get rid of the index. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data.sort('age_group')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_obs = data.age_group.value_counts()[data['age_group']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['num_obs'] = num_obs.as_matrix()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The variables on financial and non-financial assets and debts are created as follows:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Financial Worth:\n",
      "\n",
      "1. `b3401`: Amount in unrestricted checking accounts\n",
      "2. `b3418`: Amount in all money market and call accounts\n",
      "3. `b3434`: Amount in all savings or share accounts\n",
      "4. `b3446`: Amount in IRA/Keogh accounts\n",
      "5. `b3453`: Amount in CD's\n",
      "6. `b3457`: Face value of US govt savings bonds\n",
      "7. `b3458`: Face value of bonds\n",
      "8. `b3462`: Amount in Stocks and Mutual Funds\n",
      "9. `b3470`: Amount in Trust Accounts\n",
      "10. `b3475`: Cash value of Life Insurance\n",
      "11. `b3477`: Value of loans owed to household\n",
      "12. `b3306`: Total thrift-type pension account assets\n",
      "13. `b3312`: Total defined contribution account pension assets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "finworth_vars = ['b3401', 'b3418', 'b3434', 'b3446', 'b3453', 'b3457', 'b3458', \n",
      "                'b3462', 'b3470', 'b3475', 'b3477', 'b3306', 'b3312']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['finworth'] = 0\n",
      "for i in finworth_vars:\n",
      "    data['finworth'] += data[i].clip_lower(0)   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Non-Financial worth:\n",
      "\n",
      "1. `b3708`: Current Value of Home\n",
      "2. `b3801`: Aggregate Value of other properties\n",
      "3. `b3902`: Gross market value of vehicles\n",
      "4. `b3501`: Net value of business with no management interest\n",
      "5. `b3601`: Value of Land Contracts and Notes\n",
      "6. `b3509`: Household's net share in the business (A)\n",
      "8. `b3522`: Household's net share in the business (B)\n",
      "7. `b3506`: Amount owed to household by business (A)\n",
      "9. `b3519`: Amount owed to household by business (B)\n",
      "10. `b3481`: Value of antiques, art, collections and livestock"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nonfinworth_vars = ['b3708', 'b3801', 'b3902', 'b3501', 'b3601', 'b3509', 'b3506',\n",
      "                    'b3522', 'b3519', 'b3481']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['nonfinworth'] = 0\n",
      "for i in nonfinworth_vars:\n",
      "    data['nonfinworth'] += data[i]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Total debt:\n",
      "\n",
      "1. `b4001`: House Mortgage Total (1st and 2nd mortgage)\n",
      "2. `b3602`: Amount owed against land contracts/notes\n",
      "3. `b3802`: Amount outstanding on other property\n",
      "4. `b4102`: Total Credit Card Debt\n",
      "5. `b4125`: Amount owed on lines of credit\n",
      "6. `b4204`: Loans against cash value of life insurance\n",
      "7. `b4205`: Loans for automobile purchase\n",
      "8. `b4206`: Non-auto consumer loans\n",
      "9. `b3507`: Amount owed to businesses by household (A)\n",
      "10. `b3520`: Amount owed to businesses by household (B)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "debt_vars = ['b4001', 'b3602', 'b3802', 'b4102', 'b4125', 'b4204', 'b4205', 'b4206', 'b3507', 'b3520'] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['debt'] = 0\n",
      "for i in debt_vars:\n",
      "    data['debt'] += data[i].clip(0) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then, the household's net worth variables are:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['netfinworth'] = data['finworth'] - data['debt']\n",
      "data['networth'] = data['netfinworth'] + data['nonfinworth']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots()\n",
      "axes.hist(data['netfinworth'], bins=100)\n",
      "axes.\n",
      "\n",
      "fig.suptitle(\"Histrograms of SCF asset & debt distributions\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-16-a72705612708>, line 3)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-16-a72705612708>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    axes.\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['p'] = data['b3016']/sum(data['b3016'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(np.arange(1, 4000), data.sort('netfinworth').netfinworth[1:4000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[<matplotlib.lines.Line2D at 0xd4e8a58>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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Dlk9ERBM7fRp44okZXKBMUVFRkSxatEhiYmLEYDBIeXm5dHV1SXZ2tpjNZnE4HOL1epX3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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd4c8358>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w_by_age = []\n",
      "\n",
      "for i in range(1, 22):\n",
      "    w_by_age.append(data[data['age_group']==i].networth.as_matrix())\n",
      "\n",
      "f, ax = plt.subplots(figsize=(10,10))\n",
      "ax.boxplot(w_by_age, sym = '')\n",
      "ax.set_ylim([-100000, 1500000])\n",
      "f.suptitle(\"Boxplot of wealth distribution by age group\", fontsize=15);\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lZmYqPDxckvTII4/o0Ucfldfr1cGDB3XnnXd23dUAAACwGJ5gAAAA0MN4ggEA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\n8LH/AxvcIyiSJFEbAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x18f75ac8>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['education'] = data.b3113\n",
      "data['occupation'] = data.b3114"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w_by_ed = []\n",
      "\n",
      "for i in range(1, 6):\n",
      "    w_by_ed.append(data[data['education']==i].networth.as_matrix())\n",
      "\n",
      "f, ax = plt.subplots(figsize=(10,10))\n",
      "ax.boxplot(w_by_ed, sym = '')\n",
      "ax.set_ylim([-100000, 1500000])\n",
      "ax.set_xticklabels([\"0-8 Grades\", \"9-12 Grades\", \"High School\", \"Some College\", \"College Degree\"])\n",
      "f.suptitle(\"Boxplot of wealth distribution by education group\", fontsize=15);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XauPGjU7bmXqWmcvlavE6mcddM01JSVFKSsrpGxAAAEArFRQUqKCgoFnLtiis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\niCwYgzUiIiIiC8ZgjYiIiMiC/R828fIeyMd9xQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13f1e9b0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w_by_occ = []\n",
      "\n",
      "for i in range(1, 9):\n",
      "    w_by_occ.append(data[data['occupation']==i].networth.as_matrix())\n",
      "\n",
      "f, ax = plt.subplots(figsize=(10,10))\n",
      "ax.boxplot(w_by_occ, sym = '')\n",
      "ax.set_ylim([-100000, 1500000])\n",
      "ax.set_xticklabels([\"Professional/Technical\", \"Managers\", \"Self-Employed Managers\",\n",
      "                    \"Sales/Clerical\", \"Craftsmen, Protective Service\", \"Operatives, Laborers, Service Workers\",\n",
      "                    \"Farmers\", \"Miscellaneous\"], rotation = 45, ha = 'right')\n",
      "f.suptitle(\"Boxplot of wealth distribution by education group\", fontsize=15);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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J5eXlio6OliQFBAQoODhYe/fubXRZVVVVCgkJkdPprLcsoCPgXqIAgJMF+DPT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\n9GO4r/DJH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       "text": [
        "<matplotlib.figure.Figure at 0x13f27cf8>"
       ]
      }
     ],
     "prompt_number": 85
    }
   ],
   "metadata": {}
  }
 ]
}