SCF_data_experiment_Brian.ipynb

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    "# Survey Data\n",
    "\n",
    "The raw SCF survey data can be accessed from either the \"Full Public Data Set\" or the \"Summary Extract Public Data\" datasets. The only difference between the two is that the Extract dataset contains fewer variables, and the variable names were re-defined to make them a little bit more user friendly (e.g. total income = 'X5729' in the Full dataset and = 'income' in the Extract dataset). There are also a number of variables in the Extract dataset that were constructed from various variables in the Full dataset. For example the Extract dataset variable 'networth' doesn't appear in the Full dataset, but all the individual components required to construct 'networth' does.\n",
    "\n",
    "There IS a big difference between the codebooks for the two datasets, though. The [Full dataset codebook](http://www.federalreserve.gov/econresdata/scf/files/codebk2013.txt) is 45,000 lines of text, which can make it absurdly difficult to find what you're looking for-- especially if you're only looking for variable names or definitions. The [Extract dataset codebook](http://sda.berkeley.edu/data/scfcomb2013/Doc/hcbk.htm) is hosted by UCal Berkeley and is much, much easier to work with. \n",
    "\n",
    "Here are some tips/hints that should save you a lot of time. All of this info is in the codebook, the problem is finding it buried in the 45,000 lines of text.\n",
    "\n",
    "* ### ***J-codes vs. X-codes:***\n",
    "\n",
    "   Every question from the SCF survey (post 1989) is tabulated and given a code that begins with 'X'. For example, total income is coded as X5729 in the Full dataset and 'highest level of education completed by the head of household' is X5901.\n",
    "    \n",
    "   You'll also notice that each X-code has a corresponding J-code in the Full dataset (e.g. X5729 vs. J5729). The J-codes are essentially flags, and give additional information as to whether a particular observation/variable combo was altered/imputed/missing/etc.\n",
    "    \n",
    "   *Search for 'Definitions of the \"J\" Variables' in the main codebook to find the J-code definitions*\n",
    "    \n",
    "   *Search for 'LIST OF VARIABLES' in the  main codebook to find definitions of the various X-codes*\n",
    "   \n",
    "   ***Note: no J-codes for survey years before 1992. Variable names for these datasets start with a 'b' rather than 'X'. ***\n",
    "    \n",
    "\n",
    "* ### ***Weights:***\n",
    "\n",
    "   In order to make the ~5,000 households covered by the survey more representative of the ~125 million total households in America, each observation receives a weight. The weight is supposed to estimate how many households each observation in the dataset is supposed to represent. e.g. a weight of 1,050 implies that that particular household is estimated to be representative of 1,050 households in the US during that particular year.  \n",
    "    \n",
    "   In the Full dataset, the weight variable is X42001. In the Extract dataset, the weight variable is 'wgt'.\n",
    "   \n",
    "   These weights complicate things like determining standard errors or running regressions (apparently). There's a lot of info on this in the Full Dataset codebook. There's a dataset called \"Replicate Weight Files\" on the SCF website that is designed to help determine standard errors/confidence intervals/etc. \n",
    "    \n",
    "   ***Note: none of the variables in either dataset are pre-weighted.***  \n",
    "    \n",
    "    \n",
    "* ### ***The dataset you're seeing is 5x larger than actual dataset:***\n",
    "\n",
    "  The [Berkeley SCF site](http://sda.berkeley.edu/data/scfcomb2013/Doc/hcbk.htm) does a good job explaining this --\n",
    "  \n",
    "  \"Users should be aware that the dataset appears to have five times the number of cases as it should have. This dataset uses multiple imputation to supply estimates for any missing data point. Since there are five imputed values for each missing value, there are in effect five datasets, which have been combined into the current dataset. Therefore users should not attempt to calculate standard errors or confidence intervals for the point estimates produced by the SDA analysis programs. Although the (weighted) point estimates are accurate, the calculation of the complex standard errors and confidence intervals would require access to additional variables that are not included in the present dataset and would also require calculation algorithms that are not currently available in SDA.\n",
    "\n",
    "  A weight variable (WGT) is provided for the analysis of the data. The sum of the weights for each year is the number of households in the U.S. for that year. However, if you run an analysis that includes all eight years in the dataset, the sum of the weights is eight times the average number of households in those years. Nevertheless, statistics like means and percentages will not be affected by those artificially inflated weighted N's.\"\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Downloading/Importing SCF Data\n",
    "\n",
    "The SCF datasets (both Full and Summary Extract versions) are available in (1) SAS, (2) Stata, and (3) Fixed-width formats. The easiest way to read in the data is via Pandas pd.read_stata function. \n",
    "\n",
    "The files are pretty large, so they're compressed in a .zip file. We'll have to unzip the file first before reading it into Python.\n",
    "\n",
    "*** We'll work with the Summary Extract data below ***"
   ]
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  {
   "cell_type": "code",
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YY1</th>\n",
       "      <th>Y1</th>\n",
       "      <th>wgt</th>\n",
       "      <th>hhsex</th>\n",
       "      <th>age</th>\n",
       "      <th>agecl</th>\n",
       "      <th>educ</th>\n",
       "      <th>edcl</th>\n",
       "      <th>married</th>\n",
       "      <th>kids</th>\n",
       "      <th>...</th>\n",
       "      <th>LLOAN11</th>\n",
       "      <th>LLOAN12</th>\n",
       "      <th>nwcat</th>\n",
       "      <th>inccat</th>\n",
       "      <th>assetcat</th>\n",
       "      <th>ninccat</th>\n",
       "      <th>NINC2CAT</th>\n",
       "      <th>NW10CAT</th>\n",
       "      <th>INC10CAT</th>\n",
       "      <th>NINC10CAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3100.802441</td>\n",
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       "      <th>1</th>\n",
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       "      <td>3090.352195</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3094.100275</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3098.507517</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3104.670102</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>5888.636780</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>5880.947979</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>5892.280696</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>5887.018574</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>5886.753895</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 324 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YY1  Y1          wgt  hhsex  age  agecl  educ  edcl  married  kids  \\\n",
       "0    1  11  3100.802441      1   54      3    11     2        2     1   \n",
       "1    1  12  3090.352195      1   54      3    11     2        2     1   \n",
       "2    1  13  3094.100275      1   54      3    11     2        2     1   \n",
       "3    1  14  3098.507517      1   54      3    11     2        2     1   \n",
       "4    1  15  3104.670102      1   54      3    11     2        2     1   \n",
       "5    2  21  5888.636780      1   22      1    16     4        2     0   \n",
       "6    2  22  5880.947979      1   22      1    16     4        2     0   \n",
       "7    2  23  5892.280696      1   22      1    16     4        2     0   \n",
       "8    2  24  5887.018574      1   22      1    16     4        2     0   \n",
       "9    2  25  5886.753895      1   22      1    16     4        2     0   \n",
       "\n",
       "     ...      LLOAN11  LLOAN12  nwcat  inccat  assetcat  ninccat  NINC2CAT  \\\n",
       "0    ...            0        0      1       1         1        2         1   \n",
       "1    ...            0        0      1       1         1        2         1   \n",
       "2    ...            0        0      1       1         1        2         1   \n",
       "3    ...            0        0      1       1         1        2         1   \n",
       "4    ...            0        0      1       1         1        2         1   \n",
       "5    ...            0        0      1       2         1        1         1   \n",
       "6    ...            0        0      1       2         1        1         1   \n",
       "7    ...            0        0      1       2         1        1         1   \n",
       "8    ...            0        0      1       2         1        1         1   \n",
       "9    ...            0        0      1       2         1        1         1   \n",
       "\n",
       "   NW10CAT  INC10CAT  NINC10CAT  \n",
       "0        2         2          4  \n",
       "1        2         2          4  \n",
       "2        2         2          4  \n",
       "3        2         2          4  \n",
       "4        2         2          4  \n",
       "5        1         3          2  \n",
       "6        1         3          2  \n",
       "7        1         3          2  \n",
       "8        1         3          2  \n",
       "9        1         3          2  \n",
       "\n",
       "[10 rows x 324 columns]"
      ]
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   ],
   "source": [
    "import pandas as pd   #The data package\n",
    "import sys            #The code below wont work for any versions before Python 3. This just ensures that (allegedly).\n",
    "assert sys.version_info >= (3,5)\n",
    "\n",
    "\n",
    "import requests\n",
    "import io\n",
    "import zipfile     #Three packages we'll need to unzip the data\n",
    "\n",
    "\"\"\"\n",
    "The next two lines of code converts the URL into a format that works\n",
    "with the \"zipfile\" package.\n",
    "\"\"\"\n",
    "url2013 = 'http://www.federalreserve.gov/econresdata/scf/files/scfp2013s.zip'\n",
    "url2013_requested = requests.get(url2013)  \n",
    "\n",
    "\"\"\"\n",
    "Next, zipfile downloads, unzips, and saves the file to your computer. 'url2013_unzipped' \n",
    "contains the file path for the file.\n",
    "\"\"\"\n",
    "zipfile2013 = zipfile.ZipFile(io.BytesIO(url2013_requested.content))        \n",
    "url2013_unzipped = zipfile2013.extract(zipfile2013.namelist()[0]) \n",
    "\n",
    "\n",
    "df2013 = pd.read_stata(url2013_unzipped)\n",
    "\n",
    "\n",
    "\n",
    "df2013.head(10)       #Returns the first 10 rows of the dataframe"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We'll also be looking at prior-year surveys, so I'll condense the unzipping processes above into a function out of laziness\n",
    "\n",
    "**Note: The Summary Extract datasets are not available for survey years prior to 1989.**"
   ]
  },
  {
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   "source": [
    "def unzip_survey_file(year = '2013'):\n",
    "    import requests, io, zipfile\n",
    "    import pandas as pd\n",
    "    \n",
    "    if int(year) <1989:\n",
    "        url = 'http://www.federalreserve.gov/econresdata/scf/files/'+year+'_scf'+year[2:]+'bs.zip'\n",
    "    \n",
    "    else: \n",
    "        url = 'http://www.federalreserve.gov/econresdata/scf/files/scfp'+year+'s.zip'    \n",
    "        \n",
    "    url = requests.get(url)\n",
    "    url_unzipped = zipfile.ZipFile(io.BytesIO(url.content))\n",
    "    return url_unzipped.extract(url_unzipped.namelist()[0])\n",
    "\n",
    "df1983 = pd.read_stata(unzip_survey_file(year = '1983'))\n",
    "df1992 = pd.read_stata(unzip_survey_file(year = '1992'))\n",
    "df2001 = pd.read_stata(unzip_survey_file(year = '2001'))\n",
    "\n",
    "\"\"\"\n",
    "There is no Summary Extract dataset for 1983, so we'll rename the variable names in the 1983 Full \n",
    "dataset so that they correspond to the variable names in the other survey years.\n",
    "\n",
    "Also, 161 out of the 4262 total households covered in the 1983 survey actually reported having \n",
    "negative income. This isn't the case for the other survey years we are considering, and it \n",
    "complicates our analysis a bit below. Because of this, we drop any obs. that report negative \n",
    "incomes before proceeding. This has a near-zero impact on any of our results, since all but 2 \n",
    "of these observations recieve a weight of zero. The two non-zero weight observations reporting\n",
    "negative incomes account for only <0.05% of the total population, so not much is lost be \n",
    "excluding them.\n",
    "\n",
    "Going forward: it might be worthwhile to figure out why there are instances of negative incomes\n",
    "in the 1983 survey yet none for the other years. \n",
    "\"\"\"\n",
    "df1983 = df1983.rename(columns = {'b3201':'income', 'b3324':'networth', 'b3015' : 'wgt'})\n",
    "\n",
    "df1983 = df1983[df1983['income']>=0]"
   ]
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   "metadata": {},
   "source": [
    "# Distribution of Income in the United States\n",
    "\n",
    "How equal, or unequal, is the distribution of income across households in the US? On way to visualize this is by plotting what's known as a [Lorenz curve](https://en.wikipedia.org/wiki/Lorenz_curve).\n",
    "\n",
    "The Lorenz curve plots the proportion of overall income earned (y-axis) against the bottom x% of earners (x-axis). If total income in the US was uniformly distributed across all households, than we would expect the bottom 50% of earners to account for 50% of the total income. Likewise, the bottom 80% of earners should account for 80% of total income, etc. etc. The Lorenz curve in this \"perfect equality\" case would just be a straight line going from (x,y) = (0%,0%) to (x,y) = (100%,100%).\n",
    "\n",
    "In reality, income in most countries is far from being uniformly distributed. As we will see, the bottom 50% of earners in the U.S. accounted for XX% of total income during 2013 while the bottom 80% accounted for just XX%. This means the Lorenz curve for the U.S. won't be linear, but instead \"bowed\" towards the bottom right-hand corner. The heralded Gini coefficient is actually estimated by measuring the area between a country's *actual* Lorenz curve and the linear Lorenz curve we'd see in the \"perfect equality\" case.\n",
    "\n",
    "***Note:*** It would make our lives a lot easier if the income percentile groupings of households in our *sample* accurately reflected the *actual* income percentile groupings in the US. This isn't the case, unfortunately, and we'll have to weight the observations in order to get a more accurate picture of the income distribution.\n",
    "\n",
    "The function below produces accurate, weighted percentiles for income, net worth, or whatever other variable in the SCF dataset we're interest in. About 99% of code was taken from [here](http://stackoverflow.com/a/29677616), but I modified it a bit to suit our needs better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def weighted_percentiles(data, variable, weights, percentiles = [], \n",
    "                         dollar_amt = False, subgroup = None, limits = []):\n",
    "    \"\"\"\n",
    "    data               specifies what dataframe we're working with\n",
    "    \n",
    "    variable           specifies the variable name (e.g. income, networth, etc.) in the dataframe\n",
    "    \n",
    "    percentiles = []   indicates what percentile(s) to return (e.g. 90th percentile = .90)\n",
    "    \n",
    "    weights            corresponds to the weighting variable in the dataframe\n",
    "    \n",
    "    dollar_amt = False returns the percentage of total income earned by that percentile \n",
    "                       group (i.e. bottom 80% of earners earned XX% of total)\n",
    "                         \n",
    "    dollar_amt = True  returns the $ amount earned by that percentile (i.e. 90th percentile\n",
    "                       earned $X)\n",
    "                         \n",
    "    subgroup = ''      isolates the analysis to a particular subgroup in the dataset. For example\n",
    "                       subgroup = 'age' would return the income distribution of the age group \n",
    "                       determined by the limits argument\n",
    "                       \n",
    "    limits = []        Corresponds to the subgroup argument. For example, if you were interesting in \n",
    "                       looking at the distribution of income across heads of household aged 18-24,\n",
    "                       then you would input \"subgroup = 'age', limits = [18,24]\"\n",
    "                         \n",
    "    \"\"\"\n",
    "    import numpy \n",
    "    a  = list()\n",
    "    data[variable+weights] = data[variable]*data[weights]\n",
    "    if subgroup is None:\n",
    "        tt = data\n",
    "    else:\n",
    "        tt = data[data[subgroup].astype(int).isin(range(limits[0],limits[1]+1))] \n",
    "    values, sample_weight = tt[variable], tt[weights]\n",
    "    \n",
    "    for index in percentiles: \n",
    "        values = numpy.array(values)\n",
    "        index = numpy.array(index)\n",
    "        sample_weight = numpy.array(sample_weight)\n",
    "\n",
    "        sorter = numpy.argsort(values)\n",
    "        values = values[sorter]\n",
    "        sample_weight = sample_weight[sorter]\n",
    "\n",
    "        weighted_percentiles = numpy.cumsum(sample_weight) - 0.5 * sample_weight\n",
    "        weighted_percentiles /= numpy.sum(sample_weight)\n",
    "        a.append(numpy.interp(index, weighted_percentiles, values))\n",
    "    \n",
    "    if dollar_amt is False:    \n",
    "        return[tt.loc[tt[variable]<=a[x],\n",
    "                      variable+weights].sum()/tt[variable+weights].sum() for x in range(len(percentiles))]\n",
    "    else:\n",
    "        return a\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
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i0tvXhl59177ANnV6T0/yBH4SO91OaR/LBdu8a4Bfvcru68vRXfOZ43NZRG7GzgXp7Rg+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\nlQP/D6MuJ2kdqBWYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1087be208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt                            \n",
    "\n",
    "def figureprefs(data, variable = 'income', labels = False, legendlabels = []):\n",
    "    \n",
    "    percentiles = [i * 0.05 for i in range(20)]+[0.99, 1.00]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10,8));\n",
    "\n",
    "    ax.set_xticks([i*0.1 for i in range(11)]);       #Sets the tick marks\n",
    "    ax.set_yticks([i*0.1 for i in range(11)]);\n",
    "\n",
    "    vals = ax.get_yticks()                           #Labels the tick marks\n",
    "    ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals]);\n",
    "    ax.set_xticklabels(['{:3.0f}%'.format(x*100) for x in vals]);\n",
    "\n",
    "    ax.set_title('Lorenz Curve: United States, 1983 vs. 2013',  #Axes titles\n",
    "                  fontsize=18, loc='center');\n",
    "    ax.set_ylabel('Cumulative Percent of Total Income', fontsize = 12);\n",
    "    ax.set_xlabel('Percent of Familes Ordered by Incomes', fontsize = 12);\n",
    "    \n",
    "    if type(data) == list:\n",
    "        values = [weighted_percentiles(data[x], variable,\n",
    "                    'wgt', dollar_amt = False, percentiles = percentiles) for x in range(len(data))]\n",
    "        for index in range(len(data)):\n",
    "            plt.plot(percentiles,values[index],\n",
    "                     linewidth=2.0, marker = 's',clip_on=False,label=legendlabels[index]);\n",
    "            for num in [10, 19, 20]:\n",
    "                ax.annotate('{:3.1f}%'.format(values[index][num]*100), \n",
    "                    xy=(percentiles[num], values[index][num]),\n",
    "                    ha = 'right', va = 'center', fontsize = 12);\n",
    "\n",
    "    else:\n",
    "        values = weighted_percentiles(data, variable,\n",
    "                    'wgt', dollar_amt = False, percentiles = percentiles)\n",
    "        plt.plot(percentiles,values,\n",
    "                     linewidth=2.0, marker = 's',clip_on=False,label=legendlabels);\n",
    "\n",
    "    plt.plot(percentiles,percentiles, linestyle =  '--', color='k',\n",
    "            label='Perfect Equality');\n",
    "   \n",
    "    legend(loc = 2)\n",
    "\n",
    "    \n",
    "\n",
    "years_graph = [df2013, df1983]\n",
    "labels = ['2013', '1983']\n",
    "\n",
    "figureprefs(years_graph, variable = 'income', legendlabels = labels);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is the distribution of income in the U.S. becoming more or less equal?\n",
    "\n",
    "It's clear from the graph above that the distribution of income in the U.S. has become more unequal since 1983. This is reflected by the fact that the Lorenz curve has clearly shifted to the right. The top 1% of earners increased their share from 12.6% (100%-80.3%) of total income in 1983 to 19.7% in 2013. The top 5% increased their share from 26% in 1983 to 36.2% in 2013.\n",
    "\n",
    "We try to illustrate which income percentile groups become worse-off or better-off below. The graph we produce below plots average annual (real) income growth between 1983 and 2013 against income percentile. \n",
    "\n",
    "To see what's going on here, consider the following: a family in the 50th percentile made \\$46,075 per year in 1983 (in 2013 dollars) while in 2013 a family in the 50th percentile earned \\$46,668 per year (again in 2013 dollars). The real growth in income for a family in the 50th percentile was therefore just 1.29% (0.046% per year on average). A family in the 90th percentile, however, saw their income increase a whopping 70.23% (2.40% per year on average) in real terms. This definitely makes it seem like higher income families have fared relatively better over the past 31 years than lower, or average income families.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/pandas/io/wb.py:19: FutureWarning: \n",
      "The pandas.io.wb module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.\n",
      "After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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kyQy/l7Qn6R/5DNI4n71ILXp7ZXXvJ7WMPCvpTtIYvoNI3dAN1X1VlYHA08Arkm4hdYmv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\nfr6pUyNIbNrU/bPPstuXL3c/+OA43y9+ETV93bplv7y7dXN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DzyMiRUgBn4gUBTPb28xe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\n/wGCNXCmVlbRqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b65c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\"\"\"\n",
    "Note: All Summary Extract data for survey years 1989 and later have been adjusted for inflation\n",
    "(2013=100). This isn't the case for survey data prior to 1989, so we'll have to adjust the 1983 \n",
    "data manually.\n",
    "\"\"\"\n",
    "\n",
    "from pandas.io import wb                                            # World Bank api\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)  #Ignore these two lines\n",
    "\n",
    "\n",
    "cpi = wb.download(indicator='FP.CPI.TOTL' , country= 'USA', start=1983, end=2013)  #CPI\n",
    "\n",
    "\"\"\"\n",
    "The World Bank CPI series is indexed so that 2010 = 100. We'll have to re-index it so that 2013 = 100\n",
    "to be consistent with the other data.\n",
    "\"\"\"\n",
    "cpi1983 = (100/cpi['FP.CPI.TOTL'][2013-2013])*cpi['FP.CPI.TOTL'][2013-1983]/100\n",
    "df1983['realincome'] = df1983['income']/cpi1983\n",
    "\n",
    "\n",
    "\n",
    "percentiles = [i * 0.01 for i in range(1,100)]+[0.99]+[0.999]\n",
    "\n",
    "incomes = pd.DataFrame({'2001': weighted_percentiles(df2001, 'income', 'wgt', dollar_amt = True, percentiles =percentiles),\n",
    "'2013': weighted_percentiles(df2013, 'income', 'wgt', dollar_amt = True, percentiles = percentiles),\n",
    "'1992': weighted_percentiles(df1992, 'income', 'wgt', dollar_amt = True, percentiles = percentiles),\n",
    "'1983': weighted_percentiles(df1983, 'realincome', \"wgt\", dollar_amt = True, percentiles = percentiles)})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plt.plot(percentiles,(incomes['2013']-incomes['1983'])/incomes['1983']/(2013-1983+1),\n",
    "         linewidth = 2.0, label = '1983-2013');\n",
    "yvals = ax.get_yticks()\n",
    "ax.set_xticks([i * 0.1 for i in range(11)])\n",
    "xvals = ax.get_xticks()\n",
    "ax.set_yticklabels(['{:3.2f}%'.format(x*100) for x in yvals]);\n",
    "ax.set_xticklabels(['{:3.0f}'.format(x*100) for x in xvals]);\n",
    "ax.set_title('Annual real income growth by income percentile',  #Axes titles\n",
    "                  fontsize=18, loc='center');\n",
    "ax.axhline(y=0,xmin = 0, xmax = 1, linestyle = '--', color = 'k');\n",
    "ax.set_ylabel('Average annual growth rate of real income');\n",
    "ax.set_xlabel('Income percentile');\n",
    "legend(loc=2);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The figure above is pretty revealing, but the picture might be even more stark for the period 1992-2001, which corresponds roughly to President Clinton's two terms in office (or as close as we can get with the SCF, which is conducted every three years). \n",
    "\n",
    "This isn't to say that the other decades aren't interesting, but this timeframe seems to be one with the most rapid growth in top incomes.\n",
    "\n",
    "Real income growth exploded for top earners in the 1990s. The top 1% of earners saw their income grow by 8.35% per year on average in real terms between 1992 and 2001. The top 0.1% faired even better, with average real income growth of 18.70%.\n",
    "\n",
    "That being said, just about every income percentile saw better-than-average growth between 1992 and 2001. Median income (50th percentile) grew 2.15 per year on average in real terms, which is much better than the 0.046% per year average real income growth the same percentile group experienced over the period 1983-2013.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IiEgDpOBP0hYrKZs1axYAo0aNYurUqSxbtoyNGzcyceJEjjrqqErLHHPMMbRo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\njDHGGBND/j9sva/7xOOzHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105929748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "plt.plot(percentiles,(incomes['2001']-incomes['1992'])/incomes['1992']/(2001-1992+1),\n",
    "         linewidth = 2.0, label = '1992-2001');\n",
    "plt.plot(percentiles,(incomes['2013']-incomes['1983'])/incomes['1983']/(2013-1983+1),\n",
    "         linewidth = 2.0, label = '1983-2013');\n",
    "yvals = ax.get_yticks()\n",
    "ax.set_xticks([i * 0.1 for i in range(11)])\n",
    "xvals = ax.get_xticks()\n",
    "ax.set_yticklabels(['{:3.2f}%'.format(x*100) for x in yvals]);\n",
    "ax.set_xticklabels(['{:3.0f}'.format(x*100) for x in xvals]);\n",
    "ax.set_title('Annual real income growth by income percentile',  #Axes titles\n",
    "                  fontsize=18, loc='center');\n",
    "ax.axhline(y=0,xmin = 0, xmax = 1, linestyle = '--', color = 'k');\n",
    "ax.set_ylabel('Average annual growth rate of real income');\n",
    "ax.set_xlabel('Income percentile');\n",
    "legend(loc=2);"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}