SPENSER_notebook_7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41e1fe99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "import sys\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4d623ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read CSV file containing the people assigned an occupation (9 categories)\n",
    "## CSV obtained after running spensr_notebook_5\n",
    "df_persons_NE_occupation_dir = r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\economic_activity\\occupation' # use your path\n",
    "df_persons_NE_occupation_file = os.path.join(df_persons_NE_occupation_dir, \"df_after_occupation_20211126_v7.csv\")\n",
    "df_persons_NE_occupation = pd.read_csv(df_persons_NE_occupation_file, index_col=None, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c01ceb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column for the Occupation\n",
    "df_persons_NE_occupation['Income'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f8f1e24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a90b5499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2aae39ba",
   "metadata": {},
   "source": [
    "## Start calculating the income per occupation type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd8c676",
   "metadata": {},
   "source": [
    "### Occupation 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "57446555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "2\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_2_income = [14500,69000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_2_gap = 0.114\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [2]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - income_age_width_slot\n",
    "            income_max = occupation_income[0] + 0.2 * income_age_width_slot    \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - (income_age_width_slot/3)\n",
    "            income_max = occupation_income[0] + 4 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 1.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 5.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 11 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0]\n",
    "            income_max = occupation_income[0] + 3 * income_age_width_slot\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "1b87ff3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6840.0\n",
      "78690.0\n",
      "36142.27884607007\n",
      "33470.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate min, max, mean and median values:\n",
    "\n",
    "column = df_occupation_2[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fc9a82e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92a18edc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "947b3db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1013.7211539299315"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differences from the mean\n",
    "37156 - mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "e5083cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1005.0"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differences from the median\n",
    "34475 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "65316c6c",
   "metadata": {},
   "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": [
    "# histogram of the income for occupation 2:\n",
    "\n",
    "df_income.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "a0c7c915",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "253.0\n",
      "-51.0\n",
      "104.0\n",
      "850.0\n",
      "-480.0\n",
      "-252.0\n",
      "-652.0\n",
      "870.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(18173 - df_occupation_2.Income.quantile(0.1))\n",
    "print(23699 - df_occupation_2.Income.quantile(0.2))\n",
    "print(27144- df_occupation_2.Income.quantile(0.3))\n",
    "print(30920 - df_occupation_2.Income.quantile(0.4))\n",
    "print(37270 - df_occupation_2.Income.quantile(0.6))\n",
    "print(41868 - df_occupation_2.Income.quantile(0.7))\n",
    "print(47088 - df_occupation_2.Income.quantile(0.8))\n",
    "print(60380 - df_occupation_2.Income.quantile(0.9))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb7212b",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "4ffc47a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8270.0"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=16) & (df_occupation_2['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "b84e26fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12170.0"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=18) & (df_occupation_2['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "e27cc2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24270.0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=22) & (df_occupation_2['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "e2f257bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33280.0"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=30) & (df_occupation_2['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "0cd6a9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57470.0"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=40) & (df_occupation_2['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "964a51cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37240.0"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=50) & (df_occupation_2['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "df22901b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22440.0"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_2 = df_occupation_2.loc[(df_occupation_2['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "22ae6e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export data as csv file:\n",
    "df_occupation_2_saved = df_occupation_2\n",
    "\n",
    "df_occupation_2.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_2_20220201.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54047437",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "510ec7d5",
   "metadata": {},
   "source": [
    "### Occupation 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "d16b3b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "3\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_3_income = [13300,50300]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_3_gap = 0.102\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [3]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - income_age_width_slot\n",
    "            income_max = occupation_income[0] + 0.2 * income_age_width_slot    \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - (income_age_width_slot/3)\n",
    "            income_max = occupation_income[0] + 4.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 1.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6.75 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 6 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 11 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 7.25 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0]\n",
    "            income_max = occupation_income[0] + 3 * income_age_width_slot\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "7c672dbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6310.0\n",
      "56750.0\n",
      "29151.85517986915\n",
      "27720.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate min, max, mean and median values:\n",
    "\n",
    "column = df_occupation_3[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "12dc7eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "610.1448201308485"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differences to the mean value\n",
    "29762 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "ab7de7b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-229.0"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differences to the medeian value\n",
    "27491 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "8ef476ac",
   "metadata": {},
   "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": [
    "# Histogram:\n",
    "df_occupation_3.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "42974572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-222.0\n",
      "-340.0\n",
      "-594.0\n",
      "-24.0\n",
      "-757.0\n",
      "-411.0\n",
      "38.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(14338 - df_occupation_3.Income.quantile(0.1))\n",
    "print(18920 - df_occupation_3.Income.quantile(0.2))\n",
    "print(21546- df_occupation_3.Income.quantile(0.3))\n",
    "print(24716 - df_occupation_3.Income.quantile(0.4))\n",
    "#print(27491 - df_occupation_3.Income.quantile(0.5))\n",
    "print(30423 - df_occupation_3.Income.quantile(0.6))\n",
    "print(34889 - df_occupation_3.Income.quantile(0.7))\n",
    "print(39348 - df_occupation_3.Income.quantile(0.8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097696ac",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "dc834f9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7810.0"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=16) & (df_occupation_3['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "850d89d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11860.0"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=18) & (df_occupation_3['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "2cc662c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21180.0"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=22) & (df_occupation_3['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "8abe27f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28680.0"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=30) & (df_occupation_3['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "7fa2dc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44830.0"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=40) & (df_occupation_3['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "1b615011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30440.0"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=50) & (df_occupation_3['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "095f18d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18920.0"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_3 = df_occupation_3.loc[(df_occupation_3['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "95c6f33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_3_saved = df_occupation_3\n",
    "\n",
    "df_occupation_3_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_3_20220201.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b41805e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7ab05e39",
   "metadata": {},
   "source": [
    "### Occupation 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "id": "cdfe9e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "5\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_5_income = [11500,44000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_5_gap = 0.227\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [5]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - income_age_width_slot\n",
    "            income_max = occupation_income[0] + 0.2 * income_age_width_slot  \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] + 0.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 5.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 6 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 11 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 2.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 7 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0]\n",
    "            income_max = occupation_income[0] + 3.2 * income_age_width_slot\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "id": "9d6fbec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5100.0\n",
      "52610.0\n",
      "28027.58728290835\n",
      "27340.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the min, max, mean and median income values:\n",
    "column = df_occupation_5[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "id": "0f480313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "410.41271709165085"
      ]
     },
     "execution_count": 678,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the mean\n",
    "28438 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "id": "e54602a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-129.0"
      ]
     },
     "execution_count": 679,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the median\n",
    "27211 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "id": "dbc5bcdb",
   "metadata": {},
   "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": [
    "# histogram:\n",
    "\n",
    "df_occupation_5.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "id": "db657c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-328.0\n",
      "421.0\n",
      "-129.0\n",
      "418.0\n",
      "-671.0\n",
      "-286.0\n",
      "7.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(13832 - df_occupation_5.Income.quantile(0.1))\n",
    "print(19431 - df_occupation_5.Income.quantile(0.2))\n",
    "print(22111- df_occupation_5.Income.quantile(0.3))\n",
    "print(24878 - df_occupation_5.Income.quantile(0.4))\n",
    "#print( - df_occupation_5.Income.quantile(0.5))\n",
    "print(29589 - df_occupation_5.Income.quantile(0.6))\n",
    "print(33084 - df_occupation_5.Income.quantile(0.7))\n",
    "print(36517 - df_occupation_5.Income.quantile(0.8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7c1246",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "id": "0ec094d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7380.0"
      ]
     },
     "execution_count": 682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=16) & (df_occupation_5['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "id": "35e027e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11250.0"
      ]
     },
     "execution_count": 683,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=18) & (df_occupation_5['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "id": "591e28ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23180.0"
      ]
     },
     "execution_count": 684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=22) & (df_occupation_5['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "id": "0db0ff38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27850.0"
      ]
     },
     "execution_count": 685,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=30) & (df_occupation_5['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "id": "591a5d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43180.0"
      ]
     },
     "execution_count": 686,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=40) & (df_occupation_5['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "id": "247131c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29740.0"
      ]
     },
     "execution_count": 687,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=50) & (df_occupation_5['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "id": "a9e5244d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18250.0"
      ]
     },
     "execution_count": 688,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_5 = df_occupation_5.loc[(df_occupation_5['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "id": "059bc79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_5_saved = df_occupation_5\n",
    "\n",
    "df_occupation_5_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_5_20220201.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a1e8a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0ed277b3",
   "metadata": {},
   "source": [
    "### Occupation 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "id": "d13d22d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "7\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_7_income = [9200,32000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_7_gap = 0.071\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [7]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] + 0.5 * income_age_width_slot  \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - 1 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 3.2 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 0.4 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6.3 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 9.5 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 1.75 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 7 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0] - 1 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 2.5 * income_age_width_slot\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "id": "56d2fae2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4440.0\n",
      "31950.0\n",
      "14751.257876751695\n",
      "13780.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the min, max, mean and median income value:\n",
    "column = df_occupation_7[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "id": "75c37d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "425.7421232483048"
      ]
     },
     "execution_count": 1008,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# difference to the mean\n",
    "15177 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "id": "d985a1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-23.0"
      ]
     },
     "execution_count": 1009,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# difference to the median\n",
    "13757 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "id": "8fa90e61",
   "metadata": {},
   "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": [
    "# histogram\n",
    "df_occupation_7.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "id": "de759a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-50.0\n",
      "-106.0\n",
      "-146.0\n",
      "-293.0\n",
      "374.0\n",
      "-31.0\n",
      "-386.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(6430 - df_occupation_7.Income.quantile(0.1))\n",
    "print(8274 - df_occupation_7.Income.quantile(0.2))\n",
    "print(9914 - df_occupation_7.Income.quantile(0.3))\n",
    "print(11697 - df_occupation_7.Income.quantile(0.4))\n",
    "#print( - df_occupation_5.Income.quantile(0.5))\n",
    "print(15964 - df_occupation_7.Income.quantile(0.6))\n",
    "print(18569 - df_occupation_7.Income.quantile(0.7))\n",
    "print(21034 - df_occupation_7.Income.quantile(0.8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ba7f90",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "id": "693ac68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5360.0"
      ]
     },
     "execution_count": 1014,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=16) & (df_occupation_7['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "id": "67a80990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7480.0"
      ]
     },
     "execution_count": 1015,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=18) & (df_occupation_7['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "id": "6156ea68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11650.0"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=22) & (df_occupation_7['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "id": "c3a38b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16470.0"
      ]
     },
     "execution_count": 1017,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=30) & (df_occupation_7['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "id": "4f7c8671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25030.0"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=40) & (df_occupation_7['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "id": "783f393e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18590.0"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=50) & (df_occupation_7['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "id": "c871f413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10550.0"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_7 = df_occupation_7.loc[(df_occupation_7['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "id": "27aeccba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_7_saved = df_occupation_7\n",
    "\n",
    "df_occupation_7_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_7_20220202.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f780151",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4806ea05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7a18d657",
   "metadata": {},
   "source": [
    "### Occupation 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1471,
   "id": "a39f1657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "8\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_8_income = [10000,38000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_8_gap = 0.219\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [8]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (2*occupation_income[0]/3)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - 1.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] - 0 * income_age_width_slot  \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] + 0.2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 5.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 2.2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 6 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 11 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 2.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 7 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0] - 0.2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 3.3 * income_age_width_slot\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1472,
   "id": "54bf0204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2970.0\n",
      "45270.0\n",
      "24660.416830283517\n",
      "23920.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the min, max, mean and median income values:\n",
    "column = df_occupation_8[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1473,
   "id": "a5e6c231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-278.4168302835169"
      ]
     },
     "execution_count": 1473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the mean\n",
    "24382 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1474,
   "id": "5ae78e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-443.0"
      ]
     },
     "execution_count": 1474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the median:\n",
    "23477 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1475,
   "id": "138b312d",
   "metadata": {},
   "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": [
    "# Histogram\n",
    "df_occupation_8.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1476,
   "id": "1ff52ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-242.0\n",
      "305.0\n",
      "-417.0\n",
      "-123.0\n",
      "-572.0\n",
      "-137.0\n",
      "-60.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(12858 - df_occupation_8.Income.quantile(0.1))\n",
    "print(17575 - df_occupation_8.Income.quantile(0.2))\n",
    "print(19323 - df_occupation_8.Income.quantile(0.3))\n",
    "print(21477 - df_occupation_8.Income.quantile(0.4))\n",
    "#print( - df_occupation_8.Income.quantile(0.5))\n",
    "print(25768 - df_occupation_8.Income.quantile(0.6))\n",
    "print(28843 - df_occupation_8.Income.quantile(0.7))\n",
    "print(32000 - df_occupation_8.Income.quantile(0.8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c79dab9",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1479,
   "id": "7a1fadf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5385.0"
      ]
     },
     "execution_count": 1479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=16) & (df_occupation_8['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1480,
   "id": "f80ff2af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8640.0"
      ]
     },
     "execution_count": 1480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=18) & (df_occupation_8['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1481,
   "id": "3e85730b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19690.0"
      ]
     },
     "execution_count": 1481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=22) & (df_occupation_8['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1482,
   "id": "99ba5c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23390.0"
      ]
     },
     "execution_count": 1482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=30) & (df_occupation_8['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1483,
   "id": "a3d2f46d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36740.0"
      ]
     },
     "execution_count": 1483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=40) & (df_occupation_8['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1484,
   "id": "0962378b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25110.0"
      ]
     },
     "execution_count": 1484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=50) & (df_occupation_8['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1485,
   "id": "071f3e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15410.0"
      ]
     },
     "execution_count": 1485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_8 = df_occupation_8.loc[(df_occupation_8['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_8[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1486,
   "id": "4ad0e9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_8_saved = df_occupation_8\n",
    "\n",
    "df_occupation_8_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_8_20220202.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa0d7be1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68e440d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2693232c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4e99daac",
   "metadata": {},
   "source": [
    "### Occupation 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2071,
   "id": "d3df0f0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "9\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_9_income = [4000,28000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_9_gap = 0.133\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [9]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - income_age_width_slot\n",
    "            income_max = occupation_income[0] + 0.3 * income_age_width_slot    \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - 0.3 * income_age_width_slot \n",
    "            income_max = occupation_income[0] + 9 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] - 0.2 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 9.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 3 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 12 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] - 0.1 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 11 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0] - 0 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 2 * income_age_width_slot\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2072,
   "id": "231cd42a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1490.0\n",
      "34970.0\n",
      "13914.946368831439\n",
      "13130.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the min, max, mean and median income values:\n",
    "column = df_occupation_9[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2073,
   "id": "a422c4e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "296.05363116856097"
      ]
     },
     "execution_count": 2073,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the mean:\n",
    "14211 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2074,
   "id": "d1c44735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-188.0"
      ]
     },
     "execution_count": 2074,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# difference to the median:\n",
    "12942 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2075,
   "id": "91601347",
   "metadata": {},
   "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": [
    "# Histogram:\n",
    "df_occupation_9.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2076,
   "id": "6ef58100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-70.0\n",
      "107.0\n",
      "-203.0\n",
      "325.0\n",
      "-183.0\n",
      "-484.0\n",
      "-563.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(3480 - df_occupation_9.Income.quantile(0.1))\n",
    "print(5247 - df_occupation_9.Income.quantile(0.2))\n",
    "print(6937 - df_occupation_9.Income.quantile(0.3))\n",
    "print(9905 - df_occupation_9.Income.quantile(0.4))\n",
    "#print( - df_occupation_9.Income.quantile(0.5))\n",
    "print(16037 - df_occupation_9.Income.quantile(0.6))\n",
    "print(18876 - df_occupation_9.Income.quantile(0.7))\n",
    "print(21907 - df_occupation_9.Income.quantile(0.8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f56d08",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2077,
   "id": "25f6ed81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2300.0"
      ]
     },
     "execution_count": 2077,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=16) & (df_occupation_9['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2078,
   "id": "3ea517e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3120.0"
      ]
     },
     "execution_count": 2078,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=18) & (df_occupation_9['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2079,
   "id": "2f621681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14310.0"
      ]
     },
     "execution_count": 2079,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=22) & (df_occupation_9['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2080,
   "id": "bde3eaa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15100.0"
      ]
     },
     "execution_count": 2080,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=30) & (df_occupation_9['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2082,
   "id": "26b770f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21700.0"
      ]
     },
     "execution_count": 2082,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=40) & (df_occupation_9['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2083,
   "id": "9dc00d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16870.0"
      ]
     },
     "execution_count": 2083,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=50) & (df_occupation_9['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2084,
   "id": "e9fdda8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6320.0"
      ]
     },
     "execution_count": 2084,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_9 = df_occupation_9.loc[(df_occupation_9['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_9[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2085,
   "id": "7c6dedf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_9_saved = df_occupation_9\n",
    "\n",
    "df_occupation_9_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_9_20220202.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b70454",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6030f632",
   "metadata": {},
   "source": [
    "### Occupation 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2199,
   "id": "bd5ef4e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "4\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_4_income = [9000,38000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_4_gap = 0.057 \n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [4]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - income_age_width_slot\n",
    "            income_max = occupation_income[0] + 0.2 * income_age_width_slot    \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - income_age_width_slot\n",
    "            income_max = occupation_income[0] + 4 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 1.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 4.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 11 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 2.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0]\n",
    "            income_max = occupation_income[0] + 3 * income_age_width_slot\n",
    "\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2200,
   "id": "9f173888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4370.0\n",
      "42060.0\n",
      "19208.751527660846\n",
      "18590.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the min, max, mean and median income values:\n",
    "column = df_occupation_4[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2201,
   "id": "dfb38fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "283.2484723391535"
      ]
     },
     "execution_count": 2201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the mean:\n",
    "19492 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2202,
   "id": "f8a18f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.0"
      ]
     },
     "execution_count": 2202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# difference to the median\n",
    "18628 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2203,
   "id": "dd92509f",
   "metadata": {},
   "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": [
    "# Histogram\n",
    "df_occupation_4.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2204,
   "id": "cf6c4d99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-209.0\n",
      "298.0\n",
      "302.0\n",
      "-452.0\n",
      "-613.0\n",
      "361.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(11511 - df_occupation_4.Income.quantile(0.2))\n",
    "print(14918 - df_occupation_4.Income.quantile(0.3))\n",
    "print(16982 - df_occupation_4.Income.quantile(0.4))\n",
    "#print( - df_occupation_9.Income.quantile(0.5))\n",
    "print(20358 - df_occupation_4.Income.quantile(0.6))\n",
    "\n",
    "print(22567 - df_occupation_4.Income.quantile(0.7))\n",
    "print(25791 - df_occupation_4.Income.quantile(0.8))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a1cdfa",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2206,
   "id": "fb52085a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5310.0"
      ]
     },
     "execution_count": 2206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=16) & (df_occupation_4['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2207,
   "id": "be78a3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7930.0"
      ]
     },
     "execution_count": 2207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=18) & (df_occupation_4['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2208,
   "id": "5b49d3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13430.0"
      ]
     },
     "execution_count": 2208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=22) & (df_occupation_4['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2209,
   "id": "ec5b242d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19800.0"
      ]
     },
     "execution_count": 2209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=30) & (df_occupation_4['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2210,
   "id": "dc834e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31030.0"
      ]
     },
     "execution_count": 2210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=40) & (df_occupation_4['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2211,
   "id": "4b47c87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21710.0"
      ]
     },
     "execution_count": 2211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=50) & (df_occupation_4['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2212,
   "id": "543660ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13130.0"
      ]
     },
     "execution_count": 2212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_4 = df_occupation_4.loc[(df_occupation_4['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2213,
   "id": "009ebbfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_4_saved = df_occupation_4\n",
    "\n",
    "df_occupation_4_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_4_20220215.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8154b87c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8ead6901",
   "metadata": {},
   "source": [
    "### Occupation 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2376,
   "id": "2455ce94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "1\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_1_income = [10000, 80000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 15\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation4digitsoc2010ashetable15\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table152019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (4) Table 15.12  Gender pay gap 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_1_gap = 0.125\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [1]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] + 0.2 * (occupation_income[0])   \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] + 2.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 1.25 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 6 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 1.75 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 12 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 1.5 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 8 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0]\n",
    "            income_max = occupation_income[0] + 2 * income_age_width_slot\n",
    "\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2377,
   "id": "ee2229dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4690.0\n",
      "99870.0\n",
      "38127.08237121925\n",
      "34500.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the min, max, mean and median income values:\n",
    "column = df_occupation_1[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2378,
   "id": "482bdae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6577.9176287807495"
      ]
     },
     "execution_count": 2378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the mean:\n",
    "44705 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2379,
   "id": "29a36cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-830.0"
      ]
     },
     "execution_count": 2379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the median:\n",
    "33670 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2380,
   "id": "f104ebf8",
   "metadata": {},
   "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": [
    "# Histogram:\n",
    "df_occupation_1.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2381,
   "id": "24251ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-223.0\n",
      "420.0\n",
      "-237.0\n",
      "-269.0\n",
      "928.0\n",
      "799.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(19267 - df_occupation_1.Income.quantile(0.2))\n",
    "print(23950 - df_occupation_1.Income.quantile(0.3))\n",
    "print(27933 - df_occupation_1.Income.quantile(0.4))\n",
    "#print( - df_occupation_9.Income.quantile(0.5))\n",
    "print(40801 - df_occupation_1.Income.quantile(0.6))\n",
    "\n",
    "print(48528 - df_occupation_1.Income.quantile(0.7))\n",
    "print(52169 - df_occupation_1.Income.quantile(0.75))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67465d63",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2382,
   "id": "7bc215e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5910.0"
      ]
     },
     "execution_count": 2382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=16) & (df_occupation_1['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2383,
   "id": "f91c6f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8610.0"
      ]
     },
     "execution_count": 2383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=18) & (df_occupation_1['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2384,
   "id": "52a58c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16430.0"
      ]
     },
     "execution_count": 2384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=22) & (df_occupation_1['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2385,
   "id": "37077e8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35770.0"
      ]
     },
     "execution_count": 2385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=30) & (df_occupation_1['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2386,
   "id": "66e3d457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58790.0"
      ]
     },
     "execution_count": 2386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=40) & (df_occupation_1['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2387,
   "id": "f04ab413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43790.0"
      ]
     },
     "execution_count": 2387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=50) & (df_occupation_1['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2388,
   "id": "e2b0388c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17270.0"
      ]
     },
     "execution_count": 2388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_1 = df_occupation_1.loc[(df_occupation_1['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2389,
   "id": "609f03b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_1_saved = df_occupation_1\n",
    "\n",
    "df_occupation_1_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_1_20220215.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e82977d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4c3e83b3",
   "metadata": {},
   "source": [
    "### Occupation 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2542,
   "id": "5c2f3079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation running:\n",
      "6\n",
      "Merging dataframes:\n",
      "Job done. check the results\n"
     ]
    }
   ],
   "source": [
    "## Income data\n",
    "\n",
    "# Source: ONS\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "\n",
    "# Select estimated min and max income values based on the percentile values given in previous source.\n",
    "## These values are related to a specific region of England (in this case, North-East of England)\n",
    "occupation_6_income = [9000,31000]\n",
    "\n",
    "##########################################################################################\n",
    "\n",
    "## Dataset: Earnings and hours worked, region by occupation by two-digit SOC: ASHE Table 3\n",
    "## Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/regionbyoccupation2digitsocashetable3\n",
    "## Year: 2019 revised edition of this dataset\n",
    "## File downloaded: table32019revised.zip\n",
    "## Table: Work Region Occupation SOC10 (2) Table 3.7a   Annual pay - Gross 2019.xls\n",
    "\n",
    "#### Gender pay gap per occupation type (MEAN value) North East values\n",
    "occupation_6_gap = 0.132\n",
    "\n",
    "##########################################################################################\n",
    "##########################################################################################\n",
    "\n",
    "## code developed:\n",
    "\n",
    "# Name all occupation types to loop through them in the code:\n",
    "occupations = [6]\n",
    "\n",
    "\n",
    "for occupation in occupations:\n",
    "\n",
    "    \n",
    "    #Select the people of the df_occupation that is related to the selected OA area:\n",
    "    (globals()[f\"df_occupation_{occupation}\"]) = df_persons_NE_occupation.loc[(df_persons_NE_occupation['Occupation'] == occupation)]\n",
    "    \n",
    "    # Select the Min and Max income values of each occupation category:\n",
    "    occupation_income = (globals()[f\"occupation_{occupation}_income\"])\n",
    "    \n",
    "    \n",
    "    # Calculate the income age slot\n",
    "    ## this value will indicate the range in which agents will be assigned the income based on their age\n",
    "    income_age_width_slot = (occupation_income[1] - occupation_income[0])/ 10\n",
    "    \n",
    "    \n",
    "    ## Do the calculus just agent by agent\n",
    "    for idx_person, person in (globals()[f\"df_occupation_{occupation}\"]).iterrows():\n",
    "        \n",
    "        # Identify the slot of income based on the age (UK values)\n",
    "        ## An estimated range of income was guessed for each person based on their ages.\n",
    "        ## The estimated income by age was identified from: ONS\n",
    "        ### Earnings and hours worked, age group: ASHE Table 6\n",
    "        ### Link: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/agegroupashetable6\n",
    "        ## Year: 2019 revised edition of this dataset\n",
    "        ## File downloaded: table62019revised.zip\n",
    "        ### Table: Age Group Table 6.7a   Annual pay - Gross 2019.xls\n",
    "        #### Agents aged 40-49 will earn more than any other, then agents aged 30-39, then 50-59, then 25-29, then 22-29, then 60+, then 18-21 and finally 16-17\n",
    "                    \n",
    "        \n",
    "        # Define the income range based on the age\n",
    "        if (person['Age'] <= 17):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] - (occupation_income[0]/3)\n",
    "        elif (person['Age'] > 17 and person['Age'] <= 21):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] + 0.2 * (occupation_income[0])   \n",
    "        elif (person['Age'] > 21 and person['Age'] < 30):\n",
    "            income_min = occupation_income[0] - (occupation_income[0]/2)\n",
    "            income_max = occupation_income[0] + 5.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 30 and person['Age'] < 40):\n",
    "            income_min = occupation_income[0] + 0 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 7.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 40 and person['Age'] < 50):\n",
    "            income_min = occupation_income[0] + 0.1 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 12 * income_age_width_slot \n",
    "        elif (person['Age'] >= 50 and person['Age'] < 60):\n",
    "            income_min = occupation_income[0] + 0.1 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 8.5 * income_age_width_slot\n",
    "        elif (person['Age'] >= 60):\n",
    "            income_min = occupation_income[0] - 0.1 * income_age_width_slot\n",
    "            income_max = occupation_income[0] + 4 * income_age_width_slot\n",
    "\n",
    "\n",
    "            \n",
    "        # Value identified as the income value of the person based on the occupation type and age range\n",
    "        income_selected_by_occupation_age = random.randint(int(income_min), int(income_max))\n",
    "        \n",
    "     \n",
    "        # Incorporate the gender gap\n",
    "        ## If male, then increase the income half of the gender income gap\n",
    "        ## If female, decrease the income half of the gender income gap\n",
    "        if (person['Sex'] == 1):   # Male\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age + (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        else:\n",
    "            income_selected_by_occupation_age_sex = income_selected_by_occupation_age - (income_selected_by_occupation_age * (globals()[f\"occupation_{occupation}_gap\"])/2)\n",
    "        \n",
    "                       \n",
    "        # Transform the income value into an integer and round it to the 10 closest value\n",
    "        income_chosen = int(income_selected_by_occupation_age_sex)\n",
    "        income_round = round(income_chosen,-1)\n",
    "        \n",
    "        # Update the value in the column \"Income\"\n",
    "        (globals()[f\"df_occupation_{occupation}\"]).at[idx_person,'Income'] = income_round\n",
    "        \n",
    "\n",
    "print('Job done. check the results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2543,
   "id": "d18c1e94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4200.0\n",
      "37730.0\n",
      "15423.398751748475\n",
      "14690.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate the min, max, mean and median income values:\n",
    "column = df_occupation_6[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2544,
   "id": "c6314e89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-293.39875174847475"
      ]
     },
     "execution_count": 2544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# difference to the mean:\n",
    "15130 -  mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2545,
   "id": "5b1c7679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "337.0"
      ]
     },
     "execution_count": 2545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to the median:\n",
    "15027 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2546,
   "id": "0a51de4c",
   "metadata": {},
   "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": [
    "# Histogram:\n",
    "df_occupation_6.hist(column='Income', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2547,
   "id": "a9ad42ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-289.0\n",
      "-57.999999999956344\n",
      "322.0\n",
      "318.0\n",
      "-203.0\n",
      "-319.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(10031 - df_occupation_6.Income.quantile(0.25))\n",
    "print(11100 - df_occupation_6.Income.quantile(0.3))\n",
    "print(13232 - df_occupation_6.Income.quantile(0.4))\n",
    "#print( - df_occupation_6.Income.quantile(0.5))\n",
    "print(16788 - df_occupation_6.Income.quantile(0.6))\n",
    "\n",
    "print(18457 - df_occupation_6.Income.quantile(0.7))\n",
    "print(20911 - df_occupation_6.Income.quantile(0.8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aece133c",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2548,
   "id": "26944caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5075.0"
      ]
     },
     "execution_count": 2548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_16_17_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=16) & (df_occupation_6['Age'] <=17)]\n",
    "\n",
    "df_income_16_17_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2549,
   "id": "32a91d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7370.0"
      ]
     },
     "execution_count": 2549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_18_21_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=18) & (df_occupation_6['Age'] <=21)]\n",
    "\n",
    "df_income_18_21_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2551,
   "id": "58fbec90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12300.0"
      ]
     },
     "execution_count": 2551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_22_29_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=22) & (df_occupation_6['Age'] <=29)]\n",
    "\n",
    "df_income_22_29_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2552,
   "id": "5e976a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16450.0"
      ]
     },
     "execution_count": 2552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_30_39_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=30) & (df_occupation_6['Age'] <=39)]\n",
    "\n",
    "df_income_30_39_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2554,
   "id": "216a7d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21120.0"
      ]
     },
     "execution_count": 2554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_40_49_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=40) & (df_occupation_6['Age'] <=49)]\n",
    "\n",
    "df_income_40_49_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2553,
   "id": "675e05c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17350.0"
      ]
     },
     "execution_count": 2553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_50_59_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=50) & (df_occupation_6['Age'] <=59)]\n",
    "\n",
    "df_income_50_59_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2550,
   "id": "d8738013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12550.0"
      ]
     },
     "execution_count": 2550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_60_120_occupation_6 = df_occupation_6.loc[(df_occupation_6['Age'] >=60)]\n",
    "\n",
    "df_income_60_120_occupation_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "215825ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save data as csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2555,
   "id": "09d4abb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_occupation_6_saved = df_occupation_6\n",
    "\n",
    "df_occupation_6_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_occupation_6_20220216.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff42d8f5",
   "metadata": {},
   "source": [
    "## Concatenate all results in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2569,
   "id": "68732ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [df_occupation_1, df_occupation_2, df_occupation_3, df_occupation_4, df_occupation_5, df_occupation_6, df_occupation_7, df_occupation_8, df_occupation_9]\n",
    "\n",
    "df_income_all = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54cc7191",
   "metadata": {},
   "source": [
    "## Save the dataframe with all income values as a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2566,
   "id": "296bc4e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_income_all_saved = df_income_all\n",
    "\n",
    "df_income_all_saved.to_csv(r'C:\\Users\\b9055315\\Documents\\PhD_PROJECT\\Synthetic_population_developement\\SPENSER\\Data\\2019\\income\\df_income_all_saved_20220216.csv', encoding='utf-8', header=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c51ef1",
   "metadata": {},
   "source": [
    "## Check global results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2559,
   "id": "7082d151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1490.0\n",
      "99870.0\n",
      "25634.970058154257\n",
      "23050.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate min, max, mean and median income values:\n",
    "column = df_income_all[\"Income\"]\n",
    "min_value = column.min()\n",
    "max_value = column.max()\n",
    "mean_value = column.mean()\n",
    "median_value = column.median()\n",
    "\n",
    "print(min_value)\n",
    "print(max_value)\n",
    "print(mean_value)\n",
    "print(median_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2560,
   "id": "dca5f6ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-448.0"
      ]
     },
     "execution_count": 2560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference to median\n",
    "22602 - median_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2561,
   "id": "15b8ec49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "704.0299418457435"
      ]
     },
     "execution_count": 2561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference to mean\n",
    "26339 - mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2563,
   "id": "e3765ce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-767.0\n",
      "-433.0\n",
      "149.0\n",
      "-510.0\n",
      "-448.0\n",
      "-124.0\n",
      "175.0\n",
      "193.0\n",
      "-50.0\n"
     ]
    }
   ],
   "source": [
    "# calculate the differences between observed (ONS) and calculated percentiles for occupation 2:\n",
    "\n",
    "print(7993 - df_income_all.Income.quantile(0.1))\n",
    "print(12607 - df_income_all.Income.quantile(0.2))\n",
    "print(16679 - df_income_all.Income.quantile(0.3))\n",
    "print(19560 - df_income_all.Income.quantile(0.4))\n",
    "print(22602 - df_income_all.Income.quantile(0.5))\n",
    "print(26126 - df_income_all.Income.quantile(0.6))\n",
    "print(30605 - df_income_all.Income.quantile(0.7))\n",
    "print(36523 - df_income_all.Income.quantile(0.8))\n",
    "print(45580 - df_income_all.Income.quantile(0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2564,
   "id": "ff65ef18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8760.0\n",
      "13040.0\n",
      "16530.0\n",
      "20070.0\n",
      "23050.0\n",
      "26250.0\n",
      "30430.0\n",
      "36330.0\n",
      "45630.0\n"
     ]
    }
   ],
   "source": [
    "print(df_income_all.Income.quantile(0.1))\n",
    "print(df_income_all.Income.quantile(0.2))\n",
    "print(df_income_all.Income.quantile(0.3))\n",
    "print(df_income_all.Income.quantile(0.4))\n",
    "print(df_income_all.Income.quantile(0.5))\n",
    "print(df_income_all.Income.quantile(0.6))\n",
    "print(df_income_all.Income.quantile(0.7))\n",
    "print(df_income_all.Income.quantile(0.8))\n",
    "print(df_income_all.Income.quantile(0.9))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f901bffb",
   "metadata": {},
   "source": [
    "##### Check the order of the median income per range of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2570,
   "id": "6b053033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5500.0"
      ]
     },
     "execution_count": 2570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_1 = df_income_all.loc[(df_income_all['Age'] >=16) & (df_income_all['Age'] <=17)]\n",
    "\n",
    "df_income_all_1[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2571,
   "id": "dad328f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8230.0"
      ]
     },
     "execution_count": 2571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_2 = df_income_all.loc[(df_income_all['Age'] >=18) & (df_income_all['Age'] <=21)]\n",
    "\n",
    "df_income_all_2[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2572,
   "id": "0d600652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17210.0"
      ]
     },
     "execution_count": 2572,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_3 = df_income_all.loc[(df_income_all['Age'] >=22) & (df_income_all['Age'] <=29)]\n",
    "\n",
    "df_income_all_3[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2573,
   "id": "669a634a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24940.0"
      ]
     },
     "execution_count": 2573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_4 = df_income_all.loc[(df_income_all['Age'] >=30) & (df_income_all['Age'] <=39)]\n",
    "\n",
    "df_income_all_4[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2574,
   "id": "9ad05196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39880.0"
      ]
     },
     "execution_count": 2574,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_5 = df_income_all.loc[(df_income_all['Age'] >=40) & (df_income_all['Age'] <=49)]\n",
    "\n",
    "df_income_all_5[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2575,
   "id": "3ea53990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26430.0"
      ]
     },
     "execution_count": 2575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_6 = df_income_all.loc[(df_income_all['Age'] >=50) & (df_income_all['Age'] <=59)]\n",
    "\n",
    "df_income_all_6[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2576,
   "id": "34837afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15560.0"
      ]
     },
     "execution_count": 2576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_all_7 = df_income_all.loc[(df_income_all['Age'] >=60)]\n",
    "\n",
    "df_income_all_7[\"Income\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a969006",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "850f4aa6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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