{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construction jobs analysis\n",
    "\n",
    "By [Ben Welsh](mailto:ben.welsh@latimes.com)\n",
    "\n",
    "The Los Angeles Times conducted an analysis of federal data to evaluate the makeup and pay of construction work. \n",
    "\n",
    "The analysis found that wages have been in decline nationwide for several decades. In LA County, this has coincided with a shift to a workforce made up overwhelmingly of Latinos, which leads a nationwide trend in the industry toward employing more Latino workers.\n",
    "\n",
    "Those results were published in the April 20, 2017 story [\"Immigrants flooded California construction. Worker pay sank. Here’s why\"](http://www.latimes.com/projects/la-fi-construction-trump/).\n",
    "\n",
    "The story cities other studies by UCLA, unionstats.com and other sources that are not reproduced here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How we did it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloaded data on wages from the Bureau of Labor Statistics'  [Current Employment Statistics Program](https://www.bls.gov/ces/) and prepared it for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "%run bls/01-download.ipynb\n",
    "%run bls/02-transform.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloaded U.S. Census data tracking the hispanic ethnicity of adult workers in Los Angeles by industry from the University of Minnesota's [IPUMS online data analysis system](https://usa.ipums.org/usa/sda/) and prepared it for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run ipums/01-transform.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding 1: Construction workers make more than 5 dollars an hour less today than they did in 1972, when pay in the field peaked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cpi\n",
    "import calculate\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in BLS data that tracks a number of metrics by industry "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls = pd.read_csv(\"./bls/output/bls_ce_transformed.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter down to unadjusted data, as recommended by BLS staff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls = bls[bls.seasonal == 'U']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter down to records that track large sectors of the economy the BLS calls \"super sectors.\" Construction is one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls = bls[bls.supersector_name == bls.industry_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter down to data tracking the average hourly pay for non-supervisors in each supersector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls = bls[\n",
    "    bls.data_type_text == 'AVERAGE HOURLY EARNINGS OF PRODUCTION AND NONSUPERVISORY EMPLOYEES'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter down to annual totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls = bls[bls.period == 'M13']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust the wages for inflation to 2016 dollars using the Consumer Price Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls['value_2016_dollars'] = bls.apply(\n",
    "    lambda x: cpi.to_2016_dollars(x.value, x.year),\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trim that data down and chart the change in Construction pay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls_construction = bls[\n",
    "    bls.supersector_name == 'Construction'\n",
    "].set_index(\"year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5daad84490>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UrYlx/TsR5N+MWWv3OToUpRxKE7+qN4viU/Hx8uAaJ11ApZmXJ9OGRrJuTxaJaTmODkcp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\nK6WUm/l/pnZHhIfeJgEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5da9ad6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bls_construction[['value_2016_dollars']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output data for a graphic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bls_construction[['value_2016_dollars']][bls_construction.index > 1972].to_csv(\"./bls/output/graphic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What as the peak year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1972    31.866958\n",
       "Name: value_2016_dollars, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bls_construction.sort_values(\n",
    "    \"value_2016_dollars\",\n",
    "    ascending=False\n",
    ").value_2016_dollars.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much do they make today?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2016    25.97\n",
       "Name: value_2016_dollars, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bls_construction.sort_index(\n",
    "    ascending=False\n",
    ").value_2016_dollars.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much has pay declined between the two years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_construction_pay = bls_construction.at[1972, 'value_2016_dollars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "construction_pay_today = bls_construction.at[2016, 'value_2016_dollars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.8969581339712924"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "construction_pay_today - max_construction_pay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding 2: At its peak, construction paid almost 10 dollars more than the average hourly job in the private sector. But as real pay steadily declined for decades, much of that gap was erased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull the same time series for the private sector as a whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls_overall = bls[\n",
    "    bls.supersector_name == 'Total private'\n",
    "].set_index(\"year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls_comparison = pd.merge(\n",
    "    bls_construction.reset_index()[['year', 'value_2016_dollars']],\n",
    "    bls_overall.reset_index()[['year', 'value_2016_dollars']],\n",
    "    on=\"year\",\n",
    "    suffixes=[\"_construction\", \"_private\"]\n",
    ").set_index(\"year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5da9ad68d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4H3/8cZkeb+sJXaYtFmNPXApxlzJ54rZm5XK++7s05ERSOl9ujOXUxXQ+GBmKm5O8/Pz2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\nhEMPgiAoCOHQgyAICkI49CAIgoLwP7ZS2HEfy3apAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5da9a78710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bls_comparison.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measure the difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls_comparison['diff'] = bls_comparison.apply(\n",
    "    lambda x: x.value_2016_dollars_construction - x.value_2016_dollars_private,\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What year was the gap the greatest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_2016_dollars_construction</th>\n",
       "      <th>value_2016_dollars_private</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>31.866958</td>\n",
       "      <td>22.392998</td>\n",
       "      <td>9.473961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value_2016_dollars_construction  value_2016_dollars_private      diff\n",
       "year                                                                       \n",
       "1972                        31.866958                   22.392998  9.473961"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bls_comparison.sort_values(\"diff\", ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the gap today?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_2016_dollars_construction</th>\n",
       "      <th>value_2016_dollars_private</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>25.97</td>\n",
       "      <td>21.54</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value_2016_dollars_construction  value_2016_dollars_private  diff\n",
       "year                                                                   \n",
       "2016                            25.97                       21.54  4.43"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bls_comparison.sort_index(ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding 3: Even as homebuilding exploded from 2011 to 2016, hourly wages for construction laborers rose slower than average pay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract out the pay for those two years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bls_11v16 = bls_comparison[\n",
    "    (bls_comparison.index == 2011) |\n",
    "    (bls_comparison.index == 2016)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the percentage increase for construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9157056562594472"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate.percentage_change(*bls_11v16.value_2016_dollars_construction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same for the overall private sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8461236753022447"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate.percentage_change(*bls_11v16.value_2016_dollars_private)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding 4: Seventy percent of construction workers in Los Angeles are Latino."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in construction industry worker totals by hispanic status, retrieved from the University of Minnesota's IPUMS compilation of U.S. Census data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "la_hispanics = pd.read_csv(\"./ipums/output/hispanics_la_combined.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot it by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5daa636710>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5daa618f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "la_hispanics[['year', 'latino_percent']].reset_index().set_index(\"year\").latino_percent.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output the totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>latino_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>24.155741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>43.102641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>53.354025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>68.171001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>66.138752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>69.562059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  latino_percent\n",
       "0  1980       24.155741\n",
       "1  1990       43.102641\n",
       "2  2000       53.354025\n",
       "3  2005       68.171001\n",
       "4  2010       66.138752\n",
       "5  2015       69.562059"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_hispanics[['year', 'latino_percent']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output data for a graphic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "la_hispanics[['year', 'latino_percent']].to_csv(\"./ipums/output/graphic.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
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