Regional_wage_deflators.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regional wage deflators\n",
    "\n",
    "Does the use of the regional CPI-U change the wage estimate picture for low-wage workers?\n",
    "\n",
    "-----\n",
    "\n",
    "January 30, 2019\n",
    "\n",
    "Brian Dew, @bd_econ\n",
    "\n",
    "\n",
    "-----\n",
    "\n",
    "Check whether inflation is overstated for low wage workers. \n",
    "\n",
    "My suspicion is based on a few observations:\n",
    "\n",
    "1) Real wage estimates nearly always adjust for changing prices using the consumer price index for all urban consumers (CPI-U), or the historical CPI-U updated to current methods (CPI-U-RS).\n",
    "\n",
    "2) But the regional CPI-U shows higher rates of inflation in the west region\n",
    "\n",
    "3) And low-wage workers tend to be in the south and midwest (places where the local minimum wage is more likely to be the federal minimum wage)\n",
    "\n",
    "Therefore, measures of real wages and real wage growth at the bottom of the wage distribution may be biased by the choice of the wage deflator.\n",
    "\n",
    "To check this, calculate the real wage at the first decile using the CPI-U and again using the regional CPI-U.\n",
    "\n",
    "Two possible graphs: \n",
    "\n",
    "1) Share of bottom 10% of wage earners living in the south or midwest. \n",
    "\n",
    "2) First decile real wage showing two lines, one with each deflator.\n",
    "\n",
    "Then write up the results.\n",
    "\n",
    "\n",
    "-----\n",
    "\n",
    "Background and references:\n",
    "\n",
    "[Smith and Woodbury (2000)](https://www.urban.org/sites/default/files/publication/62251/309642-The-Low-Wage-Labor-Market-Challenges-and-Opportunities-for-Economic-Self-Sufficiency.PDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:11.551622Z",
     "start_time": "2020-10-15T23:40:11.365318Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AK': 'West',\n",
       " 'HI': 'West',\n",
       " 'WA': 'West',\n",
       " 'OR': 'West',\n",
       " 'CA': 'West',\n",
       " 'NV': 'West',\n",
       " 'ID': 'West',\n",
       " 'MT': 'West',\n",
       " 'UT': 'West',\n",
       " 'AZ': 'West',\n",
       " 'CO': 'West',\n",
       " 'WY': 'West',\n",
       " 'NM': 'West',\n",
       " 'ND': 'Midwest',\n",
       " 'SD': 'Midwest',\n",
       " 'NE': 'Midwest',\n",
       " 'KS': 'Midwest',\n",
       " 'MN': 'Midwest',\n",
       " 'IA': 'Midwest',\n",
       " 'MO': 'Midwest',\n",
       " 'WI': 'Midwest',\n",
       " 'IL': 'Midwest',\n",
       " 'IN': 'Midwest',\n",
       " 'MI': 'Midwest',\n",
       " 'OH': 'Midwest',\n",
       " 'TX': 'South',\n",
       " 'OK': 'South',\n",
       " 'AR': 'South',\n",
       " 'LA': 'South',\n",
       " 'MS': 'South',\n",
       " 'AL': 'South',\n",
       " 'TN': 'South',\n",
       " 'KY': 'South',\n",
       " 'WV': 'South',\n",
       " 'VA': 'South',\n",
       " 'MD': 'South',\n",
       " 'DE': 'South',\n",
       " 'DC': 'South',\n",
       " 'NC': 'South',\n",
       " 'SC': 'South',\n",
       " 'GA': 'South',\n",
       " 'FL': 'South',\n",
       " 'PA': 'Northeast',\n",
       " 'NJ': 'Northeast',\n",
       " 'NY': 'Northeast',\n",
       " 'CT': 'Northeast',\n",
       " 'RI': 'Northeast',\n",
       " 'MA': 'Northeast',\n",
       " 'NH': 'Northeast',\n",
       " 'VT': 'Northeast',\n",
       " 'ME': 'Northeast'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, pickle\n",
    "\n",
    "data_dir = '/home/brian/Documents/CPS/data/'\n",
    "\n",
    "cpi = pd.read_csv(f'{data_dir}clean/cpi.csv', index_col=[0], parse_dates=True)\n",
    "\n",
    "def pct_wage(group):\n",
    "    \"\"\"Return BLS-styled binned first decile wage\"\"\"\n",
    "    d = pd.DataFrame()\n",
    "    year = group['YEAR'].iloc[0]\n",
    "    month = group['MONTH'].iloc[0]\n",
    "    date = pd.to_datetime(f'{year}-{month}-01')\n",
    "    weight = 'PWORWGT'\n",
    "    percentile = 0.05\n",
    "    bin_size = 0.5 #($0.50)\n",
    "    bins = list(np.arange(0.25, 300, bin_size))\n",
    "    group['HRWAGE'] = group['HRWAGE'] * cpi.loc[date].ALL\n",
    "    for wage_var in ['RHRWAGE', 'HRWAGE']:\n",
    "        bin_cut = lambda x: pd.cut(x[wage_var], bins, include_lowest=True)\n",
    "        cum_sum = lambda x: x[weight].cumsum()\n",
    "        dft = (group.sort_values(wage_var)\n",
    "                    .assign(WAGE_RANGE = bin_cut, CS = cum_sum))\n",
    "        pct_point = dft[weight].sum() * percentile\n",
    "        pct_bin = (dft.iloc[(dft['CS'] - pct_point).abs().argsort()[:1]]\n",
    "                      .WAGE_RANGE.values[0])\n",
    "        wage_bins = list(dft['WAGE_RANGE'].unique())\n",
    "        pct_loc = wage_bins.index(pct_bin)\n",
    "        bin_below = dft[dft['WAGE_RANGE'] == wage_bins[pct_loc-1]].iloc[-1].CS\n",
    "        bin_above = dft[dft['WAGE_RANGE'] == wage_bins[pct_loc]].iloc[-1].CS\n",
    "        pct_value = ((((pct_point - bin_below) / \n",
    "                       (bin_above - bin_below)) * bin_size) + pct_bin.left)\n",
    "        if wage_var == 'RHRWAGE':\n",
    "            d.at[date, 'Regional CPI'] = pct_value\n",
    "    \n",
    "        if wage_var == 'HRWAGE':\n",
    "            d.at[date, 'Regular CPI'] = pct_value\n",
    "    return d\n",
    "\n",
    "\n",
    "cpsdd = pickle.load(open(f'{data_dir}cps_basic_dd.pkl', 'rb'))\n",
    "\n",
    "regions_map = cpsdd['January_2017_Record_Layout.txt']['map']['region']\n",
    "\n",
    "regions_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation/background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:11.717216Z",
     "start_time": "2020-10-15T23:40:11.552669Z"
    }
   },
   "outputs": [],
   "source": [
    "columns = ['MONTH', 'AGE', 'HRWAGE', 'STATE', 'BASICWGT', 'PWORWGT']\n",
    "df = (pd.read_feather(f'{data_dir}clean/cps2020.ft', columns=columns)\n",
    "        .query('MONTH==9')\n",
    "        .assign(REGION = lambda x: x['STATE'].map(regions_map)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What share of the population (all ages, including children) lives in each region?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:11.729708Z",
     "start_time": "2020-10-15T23:40:11.718600Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REGION\n",
       "Midwest      20.707195\n",
       "Northeast    17.326841\n",
       "South        38.097496\n",
       "West         23.868464\n",
       "Name: BASICWGT, dtype: float32"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.groupby('REGION').BASICWGT.sum()\n",
    "\n",
    "data / data.sum() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What share of the age 16-64 population lives in each region?\n",
    "\n",
    "A: about the same, 58% are in the south or midwest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:11.742940Z",
     "start_time": "2020-10-15T23:40:11.730814Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REGION\n",
       "Midwest      20.693659\n",
       "Northeast    17.157436\n",
       "South        37.916306\n",
       "West         24.232594\n",
       "Name: BASICWGT, dtype: float32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.query('16 <= AGE <= 64').groupby('REGION').BASICWGT.sum()\n",
    "\n",
    "data / data.sum() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What share of people making $8/hr or less live in each region?\n",
    "\n",
    "A: Much less likely in the west and much more likely in the south. 70% are in the south and midwest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:11.749866Z",
     "start_time": "2020-10-15T23:40:11.743959Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REGION\n",
       "Midwest      18.685289\n",
       "Northeast    15.789719\n",
       "South        52.684395\n",
       "West         12.840600\n",
       "Name: PWORWGT, dtype: float32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.query('HRWAGE <= 8').groupby('REGION').PWORWGT.sum()\n",
    "\n",
    "data / data.sum() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How does inflation vary by region?\n",
    "\n",
    "A: Higher than total in the west and lower that total in the midwest and south"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:11.754858Z",
     "start_time": "2020-10-15T23:40:11.750833Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Inflation rate as of December 2018, ALL and regions\n",
    "#(cpi.iloc[-13] - 1) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series for a longer-term look"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the wage trend at the fifth percentile* using the regional CPI for price adjustments?\n",
    "\n",
    "*Wage where about five percent of workers make less and 95 percent make more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:33.778201Z",
     "start_time": "2020-10-15T23:40:11.755776Z"
    },
    "code_folding": [],
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "columns = ['YEAR', 'MONTH', 'RHRWAGE', 'HRWAGE', 'PWORWGT']\n",
    "data = pd.DataFrame()\n",
    "for year in range(1989, 2021):\n",
    "    df = (pd.read_feather(f'{data_dir}clean/cps{year}.ft', columns=columns)\n",
    "            .query('RHRWAGE > 0')\n",
    "            .groupby(['YEAR', 'MONTH'])\n",
    "            .apply(pct_wage)\n",
    "            .reset_index()\n",
    "            .set_index('level_2')\n",
    "            .drop(['YEAR', 'MONTH'], axis=1))\n",
    "    data = data.append(df[['Regional CPI', 'Regular CPI']], sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:34.140836Z",
     "start_time": "2020-10-15T23:40:33.779553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "title = 'Real wage at fifth percentile, August 2019 USD'\n",
    "ax = data.plot(title=title)\n",
    "ax.set_xlabel('')\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('/home/brian/Documents/blog_posts/Regional_CPI_Wages/rw_by_cpi.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:34.247096Z",
     "start_time": "2020-10-15T23:40:34.142048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.read_csv('regularcpi_sa.csv').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-15T23:40:34.249704Z",
     "start_time": "2020-10-15T23:40:34.248049Z"
    }
   },
   "outputs": [],
   "source": [
    "#data['Regular CPI'].to_csv('regularcpi_nsa.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}