Unemployment_and_Participation_Rates_Eurozone.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "trained-english",
   "metadata": {},
   "source": [
    "# Eurozone and NL Participation,  Unemployment,  Job Vacancy, and Vacancies per Unemployed Rates\n",
    "In this notebook I look at:\n",
    "* The total population of the Eurozone and the Netherlands that works or is capable of working. It's referred to as the _Population Level_. In simpler words, it's the set of people who could in theory work. Given the different approaches in different Euro area countries, one can define it using different age groups, e.g. people aged from 15 to 74, or narrower as people aged from 20 to 64.\n",
    "* Another key concept in this notebook is the _Labor Force Level_ &mdash; it is a subset of the population level comprising only those who work or actively look for work. Naturally `Labor Force Level` < `Population Level`\n",
    "* Finally we arrive at the _Employment Level_ &mdash; it is a subset of the labor force level comprising only those who work. It follows that: `Employment Level` < `Labor Force Level` < `Population Level`.\n",
    "\n",
    "I then look at the different ratios involving the sizes of the above-mentioned sets:\n",
    "1. **Participation Rate**: The percentage of the population level comprising those who work or are actively looking for work, i.e. $\\frac{\\left|\\text{Labor Force Level}\\right|}{\\left|\\text{Population Level}\\right|}$\n",
    "2. **Unemployment Rate**: The percentage of the labor force level comprising those who are actively looking for work, i.e. $\\frac{\\left|\\text{Labor Force Level}\\right| - \\left|\\text{Employment Level}\\right|}{\\left|\\text{Labor Force Level}\\right|}$\n",
    "3. **Job Vacancy Rate** (aka _Unfilled Vacancies/Labor Force Ratio_): The number of unfilled vacancies as percentage of the labor force level, i.e. $\\frac{\\left|\\text{Unfilled vacancies}\\right|}{\\left|\\text{Labor Force Level}\\right|}$\n",
    "4. **Vacancies per Unemployed Rate** (aka _Unfilled Vacancies/Labor Force Ratio_): The number of unfilled vacancies as percentage of the labor force level, i.e. $\\frac{\\left|\\text{Unfilled vacancies}\\right|}{\\left|\\text{Unemployment Level}\\right|}$\n",
    "    \n",
    "    ($\\left|X\\right|$ denotes the size of set X)\n",
    "\n",
    "and how they've evolved since 2000.\n",
    "\n",
    "\n",
    "I also construct a [Beveridge Curve](https://en.wikipedia.org/wiki/Beveridge_curve) for the NL job market by plotting the unemployment rate against the vacancy rate.\n",
    "\n",
    "Throughout this notebook all non-percentage numbers are in the thousands of persons. I also consistently use seasonally and calendar unadjusted data given that Eurostat doesn't deliver all labour market data with a season  adjustment (e.g. the total population level). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "special-yeast",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.ticker as mtick\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader.data as web\n",
    "from datetime import date\n",
    "\n",
    "import eurostat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fitting-dakota",
   "metadata": {},
   "outputs": [],
   "source": [
    "report_start = date.fromisoformat('2000-01-01')\n",
    "report_end = date.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "intimate-spread",
   "metadata": {},
   "outputs": [],
   "source": [
    "NL_PFX = 'NL '\n",
    "EA_PFX = 'Euro area '\n",
    "POPULATION = 'Population'\n",
    "LABOUR_FORCE = 'Labour Force'\n",
    "UNEMPLOYMENT_LEVEL = 'Unemployment Level'\n",
    "UNFILLED_VAC = 'Unfilled Vacancies'\n",
    "MEAN_LEVEL_OF = 'Mean level of '\n",
    "PARTICIPATION_RATE = 'Participation Rate'\n",
    "MEAN_PARTICIPATION_RATE = MEAN_LEVEL_OF + PARTICIPATION_RATE\n",
    "EMPLOYMENT_POPULATION_RATE = 'Employment Rate'\n",
    "MEAN_EMPLOYMENT_POPULATION_RATE = MEAN_LEVEL_OF + EMPLOYMENT_POPULATION_RATE\n",
    "UNEMPLOYMENT_RATE = 'Unemployment Rate'\n",
    "MEAN_UNEMPLOYMENT_RATE = MEAN_LEVEL_OF + UNEMPLOYMENT_RATE\n",
    "UNFILLED_VACANCIES_POPULATION_RATE = 'Unfilled Vacancies/Population Rate'\n",
    "MEAN_UNFILLED_VACANCIES_POPULATION_RATE = MEAN_LEVEL_OF + UNFILLED_VACANCIES_POPULATION_RATE\n",
    "UNFILLED_VACANCIES_LABOR_FORCE_RATE = 'Job-vacancy Rate'\n",
    "MEAN_UNFILLED_VACANCIES_LABOR_FORCE_RATE = MEAN_LEVEL_OF + UNFILLED_VACANCIES_LABOR_FORCE_RATE\n",
    "UNEMPLOYMENT_RATIO_GRAPH_TITLE = 'Unemployment Rate'\n",
    "JOB_VAC_RATE_GRAPH_TITLE = 'Job-vacancy rate (Unfilled Vacancies/Labor Force)'\n",
    "JOB_VAC_PER_UNEMPLOYED = 'Job Vacancies per Unemployed'\n",
    "JOB_VAC_PER_UNEMPLOYED_GRAPH_TITLE = JOB_VAC_PER_UNEMPLOYED + ' Person'\n",
    "MEAN_VACANCIES_PER_UNEMPLOYED = 'Mean level of job vacancies per unemployed person'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "particular-shooting",
   "metadata": {},
   "source": [
    "#### Population and Labour Force Levels\n",
    "Eurostat collects statistics on labour force in the EU using different age brackets. Depending on which definition you choose, you get a different definition of the labour force. E.g. choosing the bracket `Y20-64` defines the labour force as people aged 20 to 64 that work or actively search for work. Similar for unemployment rates. Switching to `Y15-74` makes the labour force larger.\n",
    "\n",
    "Please adjust the constant `AGE_GROUP` in the cell below as you find appropriate. To be consistent with [the CBS](https://www.cbs.nl), I use the `Y15-74` age group for the Netherlands for determining the population and labour force levels.\n",
    "\n",
    "I use the following dataseries provided by [the Eurostat](https://ec.europa.eu/eurostat):\n",
    "\n",
    "* [lfsq_pganws](https://ec.europa.eu/eurostat/databrowser/view/lfsq_pganws/default/table?lang=en&category=labour.employ.lfsq.lfsq_pop) &mdash; Population by sex, age, citizenship and labour status (1 000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "executed-indianapolis",
   "metadata": {},
   "outputs": [],
   "source": [
    "#AGE_GROUP = 'Y20-64'\n",
    "AGE_GROUP = 'Y15-74'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "challenging-stone",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieving labour fource in thousands of persons. Labour force are people aged 20 to 64 that work\n",
    "# or actively search for work. For the Netherlands an age bracket of 18 to 67 would be better. However\n",
    "# Eurostat doesn't provide it. It does provide alterate age brackets such as Y15-74.\n",
    "ea_nl_labour_force = eurostat.get_data_df('lfsq_pganws',\n",
    "               filter_pars={'startPeriod': report_start, 'unit': 'THS_PER', 'sex': 'T', 'age': AGE_GROUP,\n",
    "                            'citizen': 'TOTAL', 'wstatus': ['POP', 'ACT'],\n",
    "                            'geo': ['EA20', 'NL']}).iloc[:, 5:]\n",
    "ea_nl_labour_force = ea_nl_labour_force.set_index([ea_nl_labour_force.columns[0], ea_nl_labour_force.columns[1]]).T\n",
    "ea_nl_labour_force = ea_nl_labour_force.set_axis(pd.DatetimeIndex(ea_nl_labour_force.index, 'QS'))\n",
    "ea_nl_labour_force = ea_nl_labour_force.loc[ea_nl_labour_force.first_valid_index():]\n",
    "ea_nl_labour_force.columns.names = 2*[None]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulation-nation",
   "metadata": {},
   "source": [
    "#### Unemployment Level\n",
    "I use the following dataseries provided by [the Eurostat](https://ec.europa.eu/eurostat):\n",
    "\n",
    "* [une_rt_q](https://ec.europa.eu/eurostat/databrowser/view/une_rt_q/default/table?lang=en&category=labour.employ.lfsi.une) &mdash; Unemployment by sex and age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "waiting-blowing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieving the number of unemployed in thousands of persons. Using unadjusted data for consistency's sake with\n",
    "# population and labour force numbers.\n",
    "ea_nl_unemployed = eurostat.get_data_df('une_rt_q',\n",
    "               filter_pars={'startPeriod': report_start, 'unit': 'THS_PER', 'sex': 'T', 'age': AGE_GROUP,\n",
    "                            's_adj': 'NSA', 'geo': ['EA20', 'NL']}).iloc[:, 5:]\n",
    "\n",
    "ea_nl_unemployed = ea_nl_unemployed.set_index(ea_nl_unemployed.columns[0]).T\n",
    "ea_nl_unemployed = ea_nl_unemployed.set_axis(pd.DatetimeIndex(ea_nl_unemployed.index, 'QS'))\n",
    "ea_nl_unemployed = ea_nl_unemployed.loc[ea_nl_unemployed.iloc[:,1].first_valid_index():]\n",
    "ea_nl_unemployed.columns.names = [None]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handy-trigger",
   "metadata": {},
   "source": [
    "#### Unfilled Vacancies Level\n",
    "I use the following dataseries provided by [the Eurostat](https://ec.europa.eu/eurostat):\n",
    "\n",
    "* [jvs_q_nace2](https://ec.europa.eu/eurostat/databrowser/view/jvs_q_nace2/default/table?lang=en&category=labour.jvs) &mdash; Job vacancy statistics by NACE Rev. 2 activity - quarterly data (from 2001 onwards)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "external-certificate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieving the number of vacancies and converting into thousands of persons. Using unadjusted data for consistency's sake with\n",
    "# population and labour force numbers.\n",
    "ea_nl_vacancies = eurostat.get_data_df('jvs_q_nace2',\n",
    "               filter_pars={'startPeriod': report_start, 'nace_r2': 'A-S', 'sizeclas': 'TOTAL',\n",
    "                            's_adj': 'NSA', 'indic_em': 'JOBVAC', 'geo': ['EA20', 'NL']}).iloc[:, 5:]\n",
    "ea_nl_vacancies = ea_nl_vacancies.set_index(ea_nl_vacancies.columns[0]).T\n",
    "ea_nl_vacancies = ea_nl_vacancies.set_axis(pd.DatetimeIndex(ea_nl_vacancies.index, 'QS'))\n",
    "ea_nl_vacancies = ea_nl_vacancies.loc[ea_nl_vacancies.iloc[:,0].first_valid_index():]\n",
    "ea_nl_vacancies.columns.names = [None]\n",
    "ea_nl_vacancies /= 1e3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "naked-martial",
   "metadata": {},
   "source": [
    "#### Aggregating datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "heavy-sharp",
   "metadata": {},
   "outputs": [],
   "source": [
    "ea_labour_mkt = pd.concat([ea_nl_labour_force.loc[:, (ea_nl_labour_force.columns.get_level_values(0), 'EA20')],\n",
    "                           ea_nl_unemployed.loc[:,'EA20']], axis=1)\n",
    "ea_labour_mkt.columns = [POPULATION, LABOUR_FORCE, UNEMPLOYMENT_LEVEL]\n",
    "ea_labour_mkt.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "improved-overall",
   "metadata": {},
   "outputs": [],
   "source": [
    "ea_labour_mkt[PARTICIPATION_RATE] = ea_labour_mkt[LABOUR_FORCE] / ea_labour_mkt[POPULATION]\n",
    "ea_labour_mkt[UNEMPLOYMENT_RATE] = ea_labour_mkt[UNEMPLOYMENT_LEVEL] / ea_labour_mkt[LABOUR_FORCE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "above-concrete",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Labour Force</th>\n",
       "      <th>Unemployment Level</th>\n",
       "      <th>Participation Rate</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>255044.5</td>\n",
       "      <td>160064.5</td>\n",
       "      <td>15239.0</td>\n",
       "      <td>0.627594</td>\n",
       "      <td>0.095205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-01</th>\n",
       "      <td>255097.1</td>\n",
       "      <td>160537.5</td>\n",
       "      <td>15300.0</td>\n",
       "      <td>0.629319</td>\n",
       "      <td>0.095305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>254909.3</td>\n",
       "      <td>160287.7</td>\n",
       "      <td>15351.0</td>\n",
       "      <td>0.628803</td>\n",
       "      <td>0.095772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-01</th>\n",
       "      <td>255161.2</td>\n",
       "      <td>160840.9</td>\n",
       "      <td>16225.0</td>\n",
       "      <td>0.630350</td>\n",
       "      <td>0.100876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>253598.4</td>\n",
       "      <td>159443.3</td>\n",
       "      <td>17150.0</td>\n",
       "      <td>0.628724</td>\n",
       "      <td>0.107562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01</th>\n",
       "      <td>260108.8</td>\n",
       "      <td>170833.3</td>\n",
       "      <td>11093.0</td>\n",
       "      <td>0.656776</td>\n",
       "      <td>0.064935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>260509.1</td>\n",
       "      <td>171167.1</td>\n",
       "      <td>11154.0</td>\n",
       "      <td>0.657048</td>\n",
       "      <td>0.065164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>260976.7</td>\n",
       "      <td>171110.7</td>\n",
       "      <td>11665.0</td>\n",
       "      <td>0.655655</td>\n",
       "      <td>0.068172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-01</th>\n",
       "      <td>261184.9</td>\n",
       "      <td>171810.1</td>\n",
       "      <td>10782.0</td>\n",
       "      <td>0.657810</td>\n",
       "      <td>0.062755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-01</th>\n",
       "      <td>261361.2</td>\n",
       "      <td>172436.1</td>\n",
       "      <td>10767.0</td>\n",
       "      <td>0.659762</td>\n",
       "      <td>0.062441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population  Labour Force Unemployment Level  Participation Rate  \\\n",
       "2009-01-01    255044.5      160064.5            15239.0            0.627594   \n",
       "2009-04-01    255097.1      160537.5            15300.0            0.629319   \n",
       "2009-07-01    254909.3      160287.7            15351.0            0.628803   \n",
       "2009-10-01    255161.2      160840.9            16225.0            0.630350   \n",
       "2010-01-01    253598.4      159443.3            17150.0            0.628724   \n",
       "...                ...           ...                ...                 ...   \n",
       "2023-07-01    260108.8      170833.3            11093.0            0.656776   \n",
       "2023-10-01    260509.1      171167.1            11154.0            0.657048   \n",
       "2024-01-01    260976.7      171110.7            11665.0            0.655655   \n",
       "2024-04-01    261184.9      171810.1            10782.0            0.657810   \n",
       "2024-07-01    261361.2      172436.1            10767.0            0.659762   \n",
       "\n",
       "           Unemployment Rate  \n",
       "2009-01-01          0.095205  \n",
       "2009-04-01          0.095305  \n",
       "2009-07-01          0.095772  \n",
       "2009-10-01          0.100876  \n",
       "2010-01-01          0.107562  \n",
       "...                      ...  \n",
       "2023-07-01          0.064935  \n",
       "2023-10-01          0.065164  \n",
       "2024-01-01          0.068172  \n",
       "2024-04-01          0.062755  \n",
       "2024-07-01          0.062441  \n",
       "\n",
       "[63 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ea_labour_mkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "another-dispatch",
   "metadata": {},
   "outputs": [],
   "source": [
    "nl_labour_mkt = pd.concat([ea_nl_labour_force.loc[:, (ea_nl_labour_force.columns.get_level_values(0), 'NL')],\n",
    "                           ea_nl_unemployed.loc[:,'NL'], ea_nl_vacancies.loc[:,'NL']], axis=1)\n",
    "nl_labour_mkt.columns = [POPULATION, LABOUR_FORCE, UNEMPLOYMENT_LEVEL, UNFILLED_VAC]\n",
    "#nl_labour_mkt.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "environmental-sustainability",
   "metadata": {},
   "outputs": [],
   "source": [
    "nl_labour_mkt[PARTICIPATION_RATE] = nl_labour_mkt[LABOUR_FORCE] / nl_labour_mkt[POPULATION]\n",
    "nl_labour_mkt[UNEMPLOYMENT_RATE] = nl_labour_mkt[UNEMPLOYMENT_LEVEL] / nl_labour_mkt[LABOUR_FORCE]\n",
    "nl_labour_mkt[UNFILLED_VACANCIES_LABOR_FORCE_RATE] = nl_labour_mkt[UNFILLED_VAC] / nl_labour_mkt[LABOUR_FORCE]\n",
    "nl_labour_mkt[JOB_VAC_PER_UNEMPLOYED] = nl_labour_mkt[UNFILLED_VAC] / nl_labour_mkt[UNEMPLOYMENT_LEVEL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "particular-aaron",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Labour Force</th>\n",
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       "      <th>Unfilled Vacancies</th>\n",
       "      <th>Participation Rate</th>\n",
       "      <th>Unemployment Rate</th>\n",
       "      <th>Job-vacancy Rate</th>\n",
       "      <th>Job Vacancies per Unemployed</th>\n",
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       "      <th>2000-01-01</th>\n",
       "      <td>11892.7</td>\n",
       "      <td>7982.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.671244</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2000-04-01</th>\n",
       "      <td>11888.3</td>\n",
       "      <td>8074.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.679172</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2000-07-01</th>\n",
       "      <td>11901.8</td>\n",
       "      <td>8154.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.685115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-10-01</th>\n",
       "      <td>11914.7</td>\n",
       "      <td>8202.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.688435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>11961.0</td>\n",
       "      <td>8201.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.8</td>\n",
       "      <td>0.685678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026435</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-01</th>\n",
       "      <td>13317.3</td>\n",
       "      <td>10118.5</td>\n",
       "      <td>366.0</td>\n",
       "      <td>416.8</td>\n",
       "      <td>0.759801</td>\n",
       "      <td>0.036171</td>\n",
       "      <td>0.041192</td>\n",
       "      <td>1.138798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>13368.3</td>\n",
       "      <td>10145.2</td>\n",
       "      <td>349.0</td>\n",
       "      <td>389.2</td>\n",
       "      <td>0.758900</td>\n",
       "      <td>0.034401</td>\n",
       "      <td>0.038363</td>\n",
       "      <td>1.115186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>13375.3</td>\n",
       "      <td>10153.3</td>\n",
       "      <td>386.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>0.759108</td>\n",
       "      <td>0.038017</td>\n",
       "      <td>0.040676</td>\n",
       "      <td>1.069948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-01</th>\n",
       "      <td>13377.4</td>\n",
       "      <td>10186.9</td>\n",
       "      <td>365.0</td>\n",
       "      <td>418.9</td>\n",
       "      <td>0.761501</td>\n",
       "      <td>0.03583</td>\n",
       "      <td>0.041121</td>\n",
       "      <td>1.147671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-01</th>\n",
       "      <td>13379.1</td>\n",
       "      <td>10168.7</td>\n",
       "      <td>377.0</td>\n",
       "      <td>396.4</td>\n",
       "      <td>0.760044</td>\n",
       "      <td>0.037075</td>\n",
       "      <td>0.038982</td>\n",
       "      <td>1.051459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population  Labour Force Unemployment Level  Unfilled Vacancies  \\\n",
       "2000-01-01     11892.7        7982.9                NaN                 NaN   \n",
       "2000-04-01     11888.3        8074.2                NaN                 NaN   \n",
       "2000-07-01     11901.8        8154.1                NaN                 NaN   \n",
       "2000-10-01     11914.7        8202.5                NaN                 NaN   \n",
       "2001-01-01     11961.0        8201.4                NaN               216.8   \n",
       "...                ...           ...                ...                 ...   \n",
       "2023-07-01     13317.3       10118.5              366.0               416.8   \n",
       "2023-10-01     13368.3       10145.2              349.0               389.2   \n",
       "2024-01-01     13375.3       10153.3              386.0               413.0   \n",
       "2024-04-01     13377.4       10186.9              365.0               418.9   \n",
       "2024-07-01     13379.1       10168.7              377.0               396.4   \n",
       "\n",
       "            Participation Rate Unemployment Rate  Job-vacancy Rate  \\\n",
       "2000-01-01            0.671244               NaN               NaN   \n",
       "2000-04-01            0.679172               NaN               NaN   \n",
       "2000-07-01            0.685115               NaN               NaN   \n",
       "2000-10-01            0.688435               NaN               NaN   \n",
       "2001-01-01            0.685678               NaN          0.026435   \n",
       "...                        ...               ...               ...   \n",
       "2023-07-01            0.759801          0.036171          0.041192   \n",
       "2023-10-01            0.758900          0.034401          0.038363   \n",
       "2024-01-01            0.759108          0.038017          0.040676   \n",
       "2024-04-01            0.761501           0.03583          0.041121   \n",
       "2024-07-01            0.760044          0.037075          0.038982   \n",
       "\n",
       "           Job Vacancies per Unemployed  \n",
       "2000-01-01                          NaN  \n",
       "2000-04-01                          NaN  \n",
       "2000-07-01                          NaN  \n",
       "2000-10-01                          NaN  \n",
       "2001-01-01                          NaN  \n",
       "...                                 ...  \n",
       "2023-07-01                     1.138798  \n",
       "2023-10-01                     1.115186  \n",
       "2024-01-01                     1.069948  \n",
       "2024-04-01                     1.147671  \n",
       "2024-07-01                     1.051459  \n",
       "\n",
       "[99 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nl_labour_mkt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mobile-valuable",
   "metadata": {},
   "source": [
    "### Euro area\n",
    "#### Participation rate\n",
    "Participation rate &mdash; is percentage of population who work or are looking for work. Where by population we take residents who are aged from 15 to 74 and who can in theory work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "retained-brain",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = ea_labour_mkt[PARTICIPATION_RATE].plot(figsize=(20,10), grid=True,\n",
    "                                            title=EA_PFX + PARTICIPATION_RATE + f' ({AGE_GROUP})',\n",
    "                                            xticks=ea_labour_mkt.index[::2], xlabel='', rot=45)\n",
    "ax2 = ax.twiny()\n",
    "min_pr, max_pr = ea_labour_mkt[PARTICIPATION_RATE].min() - .01, ea_labour_mkt[PARTICIPATION_RATE].max() + .01\n",
    "ea_labour_mkt[PARTICIPATION_RATE].plot.bar(ax=ax2, xlabel='', ylim=(min_pr, max_pr), legend=False)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax2.xaxis.set_major_locator(mtick.NullLocator())\n",
    "ax.axhline(y=ea_labour_mkt[PARTICIPATION_RATE].mean(), ls='--', c='#1f77b4', label=MEAN_PARTICIPATION_RATE)\n",
    "ax.legend()\n",
    "_ = ax.set_xticklabels(list(map('{:%Y-%m}'.format, ea_labour_mkt.index[::2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gorgeous-installation",
   "metadata": {},
   "source": [
    "#### Unemployment rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "banned-anger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unempl_rate = ea_labour_mkt[UNEMPLOYMENT_RATE].dropna()\n",
    "ax = unempl_rate.plot(figsize=(20,10), grid=True, xticks=unempl_rate.index[::2], xlabel='',\n",
    "                      rot=45, title=EA_PFX + UNEMPLOYMENT_RATIO_GRAPH_TITLE + f' ({AGE_GROUP})')\n",
    "ax2 = ax.twiny()\n",
    "min_ur, max_ur = unempl_rate.min() - .01, unempl_rate.max() + .01\n",
    "unempl_rate.plot.bar(ax=ax2, xlabel='', legend=False, ylim=(min_ur, max_ur))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax2.xaxis.set_major_locator(mtick.NullLocator())\n",
    "ax.axhline(y=unempl_rate.mean(), ls='--', label=MEAN_UNEMPLOYMENT_RATE)\n",
    "ax.legend()\n",
    "_ = ax.set_xticklabels(list(map('{:%Y-%m}'.format, unempl_rate.index[::2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continental-chemistry",
   "metadata": {},
   "source": [
    "### The Netherlands\n",
    "#### Participation Rate\n",
    "[Het CBS](https://www.cbs.nl/en-gb/) distinguishes between netto and brutto participation rates. Netto participation rate is the ration of the number of people who work to the total number of people in the labour force. Brutto participation rate is the ration of the number of people who work or are looking for work to the total number of people in the labour force. Netto participation rate is therefore always smaller than the brutto participation rate. In this notebook I look exclusively at the brutto participation rate as it's more representative of the job market and is the most frequently used in literature.\n",
    "\n",
    "As I've mentioned in the beginning of this notebook, I use not seasonally adjusted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "regulated-saturday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = nl_labour_mkt[PARTICIPATION_RATE].plot(figsize=(20,10), grid=True,\n",
    "                                            title=NL_PFX + PARTICIPATION_RATE + f' ({AGE_GROUP})',\n",
    "                                            xticks=nl_labour_mkt.index[::2], xlabel='', rot=45)\n",
    "ax2 = ax.twiny()\n",
    "min_pr, max_pr = nl_labour_mkt[PARTICIPATION_RATE].min() - .01, nl_labour_mkt[PARTICIPATION_RATE].max() + .01\n",
    "nl_labour_mkt[PARTICIPATION_RATE].plot.bar(ax=ax2, xlabel='', ylim=(min_pr, max_pr), legend=False)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax2.xaxis.set_major_locator(mtick.NullLocator())\n",
    "ax.axhline(y=nl_labour_mkt[PARTICIPATION_RATE].mean(), ls='--', c='#1f77b4', label=MEAN_PARTICIPATION_RATE)\n",
    "ax.legend()\n",
    "_ = ax.set_xticklabels(list(map('{:%Y-%m}'.format, nl_labour_mkt.index[::2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "considerable-cleaning",
   "metadata": {},
   "source": [
    "#### Unemployment rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "environmental-package",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unempl_rate = nl_labour_mkt[UNEMPLOYMENT_RATE].dropna()\n",
    "ax = unempl_rate.plot(figsize=(20,10), grid=True, xticks=unempl_rate.index[::2], xlabel='',\n",
    "                      rot=45, title=NL_PFX + UNEMPLOYMENT_RATIO_GRAPH_TITLE + f' ({AGE_GROUP})')\n",
    "ax2 = ax.twiny()\n",
    "min_ur, max_ur = unempl_rate.min() - .01, unempl_rate.max() + .01\n",
    "unempl_rate.plot.bar(ax=ax2, xlabel='', legend=False, ylim=(min_ur, max_ur))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax2.xaxis.set_major_locator(mtick.NullLocator())\n",
    "ax.axhline(y=unempl_rate.mean(), ls='--', label=MEAN_UNEMPLOYMENT_RATE)\n",
    "ax.legend()\n",
    "_ = ax.set_xticklabels(list(map('{:%Y-%m}'.format, unempl_rate.index[::2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subsequent-cholesterol",
   "metadata": {},
   "source": [
    "#### Job vacancy rate\n",
    "_The Job Vacancy Rate_ (aka _The Unfilled Vacancies to Labor Force rate_) is by far the most well known measure of labour market tightness. It shows the ratio of unfilled vacancies to the labour force (i.e. the total pool of people who work or are looking for work). I like this measure more than _Job vacancies per unemployed_, which I describe further down in this notebook because in the present labour market an increasing number of vacancies are filled by job switchers rather than the unemployed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "theoretical-retention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job_vac_rate = nl_labour_mkt[UNFILLED_VACANCIES_LABOR_FORCE_RATE].dropna()\n",
    "ax = job_vac_rate.plot(figsize=(20,10), grid=True, xticks=job_vac_rate.index[::2], xlabel='', rot=45,\n",
    "                       title=NL_PFX + JOB_VAC_RATE_GRAPH_TITLE + f' ({AGE_GROUP})')\n",
    "ax2 = ax.twiny()\n",
    "job_vac_rate.plot.bar(ax=ax2, xlabel='', legend=False)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax2.xaxis.set_major_locator(mtick.NullLocator())\n",
    "ax.axhline(y=job_vac_rate.mean(), ls='--', label=MEAN_UNFILLED_VACANCIES_LABOR_FORCE_RATE)\n",
    "ax.legend()\n",
    "_ = ax.set_xticklabels(list(map('{:%Y-%m}'.format, job_vac_rate.index[::2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "british-velvet",
   "metadata": {},
   "source": [
    "#### Job vacancies per unemployed person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "expensive-victory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ggAhaAAAAAAAAQsR1o3opym7VoRM12l1YZXY5OMO+4hpJUr+kGMVG2U2uBgAQSAQtAAAAAAAAISI+OkKzRvSSJL27o8jkanCmfcXNwdcwZrMAQKdD0AIAAAAAABBCbhmbKkn6YFeRXG6Ti0GrfUXVkqRhvQlaAKCzIWgBAAAAAAAIIVcPSlZyXKRO1Tq0r9JidjlocTpo4fosAND5ELQAAAAAAACEELvNqhvHNM9q2XyCoCUYNDhcOlpWJ4kZLQDQGXFlLgAAAAAAgCCR8dAij8bvPGXVoRM1GpbazU8VoSMOFFfL5TYUazfUMz7K7HIAAAFG0AIAAAAACFulNY1mlwD43d/WHNEzXx5vdhlhx9PQS5Iy4g1ZLMwyAoDOhlOHAQAAAADC1jMrD5pdAuB3i3YV6/DJGrPLgKSZqW6zSwAAmICgBQAAAAAQlo6V1epfm4+bXQbgd25DenbVIbPL6PSmD+2h/lyeBQA6JYIWAAAAAEBYmr8sR063YXYZQEC8u6NAR0trzS6jU7t/xkCzSwAAmISgBQAAAAAQdnblV+r97EKzywACYurg5OZZLauZ1WKmwT2ZzgIAnRVBCwAAAAAg7PxxyX5J0k2X9DG5EsD/fjAtU5L0zvYCHStjVgsAAIFG0AIAAAAACCvrDp7U+kOlirRZ9cCsIWaXA/jd6LRETR3SQy63wawWAABMQNACAAAAAAgbbrehxz5sns3y1cv7KT0pxuSKgMC4b/ogSdLb2wqUd6rO5GoAAOhcCFoAAAAAAGHj/Z2F2lNYpbgou75/DRemRucxrm83XT0oWU63ob99xKwWAAACiaAFAAAAABAWGp0uPb70gCTpO1MGKCk20uSKgMCa1zKr5a1P85VfzqwWAAAChaAFAAAAABAW/r35uPLL69UjPkp3XtXf7HKAgJuQkaQrB3aXw2Xo7x8dNrscAAA6DYIWAAAAAEDIq25w6JlVzadL+uGMQYqJtJtcEWCOedMHS5Le3Jqnwop6k6sBAKBzIGgBAAAAAIS8F9Ye0anaJg1IjtXtE9LNLgcwzcT+Sbp8QJIcLkPPrWFWCwAAgUDQAgAAAAAIaSeqG/TCuqOSpAevHaIIGy910bmdntXyny15Kq5sMLkaAADCH88+AQAAAAAh7akVB1XvcOmS9K6aPbKX2eUAprt8QJImZiSpyeVmVgsAAAFA0AIAAAAACFlHTtboP5/kSZIeum6oLBaLyRUB5rNYLJo3Y5Ak6fUtx3WiilktAAD4E0ELAAAAACBkzV92QC63oWuGpujyAd3NLgcIGldkdtf4ft3U6HTr+bVHzC4HAICwRtACAAAAAAhJ24+Xa/GuYlks0k9mDzG7HCCoWCwWzZvePKvlX5uP6WR1o8kVAQAQvuxmF+CJuian6pqc59xutVgUHWE7a1x7LmZsfZNLhow2x1pkUZfI8491OJxqdDX/LCIiovX2BodLbqPt5UpSTKTd9LFdImytU/AbnW41upofuwjj3Gn5Z491yeVuf7nRdpus1uaxTU63nG63T8ZG2W2yXWDs6e3hchs6vTUcLrccrvaXG2mzyt5yYU1PxjpdbjWdZ2yEzdp6wU5PxrrchuqanO1uD7vVqkj7Z2Mbna52l3vmWLfbUIOPxtqsFkXZm/cNwzBU72h7rMPhlPOMts83Vgrcfu/pMaLJ0fb26Mgxor2xZuz3DsfZfXt2jDj/fh/IY4Sjne3RkWNEW2PNOkbojPo8PUZ0dL8PxDGire3R0WOEp2P9ud/bzvg+kM8j2hvr7X7f1vYIzPMI3x4jTmtyuuUw2t8evn4e0dbYizlGfH57BOp5hF+OEUb7zxPPGeuj5xGejr3Qfn/m9oiSNSRea7Q3tuk8z9ulYHytce5+f+b2SLDbW8c2OJz6/aJ9kqQbR/dR36SYsx7zM/d7T51v23lyjPBkHZ48j/BkHZ4cI7zlyTHCU+fbFp7s956sw5N92ZPl+3q57TnzGDG+X1eNTkvUzvxKPbv6oH46e5jprzXaGtt4nte1wfBao6Pqmpw6c5W+Wu7nefKcw1N1Tc52X0d58jziQus4kyfPDTxZh/uMx+hijhEdWVd7Pr/fe7PctrZHW88jvF3+mTx9reHJOiLO2LUvdrlnMgzDo2OEN87cl9vaHhfznENq/3fIk9caniw/8ozjg9N1/tdRnqzDV8eI8/n8McJff0u9YTEMH/1W+1F+fr7S09OV+t1/yJ6QfM7Ppw3poVe+ObH1+2EPL2n34HlZ/yS98e1Jrd+P++1ynaptanPs6LREvff9q1q/v/KxVSqoqG9z7KCUOC2/f0rr9zP/skYHT9S0OTa1a7Q2PDS99fsb/7peO/Mr2xybFBupbQ/PbP3+i89v0uajp9oc2yXCpn2/nd36/Tdf2aLVB062OVaSch+b0/r/7/3rUy3eVdzu2L2/ubb1idCP3tiud7YXtjv201/MUPe4KEnSw+/u1v99fKzdset+Mk3pSTGSpD8s3qf/Pc905mU/mqzBPeMlSU8sz9FTKw+2O3bhvVdqTHpXSdLzaw7r0Q/3tzv2tTsn6KrBPSVJ/9yUq18u3NPu2Je/MUHXDG0eu2Brnh58a2e7Y5/98jjNGd1bkrRoZ5Hu/fe2dsc+/oXRum1CuiRp1f4S3fmPre2O/c3cEbpjUoYkadPhMn3phY/bHfuz64bq21MyJUnZeRWa++yGdsfOmz5IP5o5WJKUU1KtWU+sbXfsPZMH6H+yhkmS8k7V6eo/rW537Ncu76ff3jRSklRW06jxv1vR7tiJPdz6132zFRERobomp4b/cmm7Y7NG9dLfvjK+9fuMhxa1Ozb0jhFdtOGha1q/N+sY8dQkp7KyshQREeHRMeKBN7P132357Y4NtWPE63dfrkmZzachMesY8djNI9SlOFtZWVlad/hUpz1G3DouTX++fYwkmXqMeO3OCVq8eLGysrJ02WMfddpjhLfPI3x9jOgVH6HFixdrpzVTL21of2w4HyOC6XmEw+HQS28t1mPZ7X+eK9yPEcH0PMLhcOiax5Yqr7btF/6hfoy48x+faNX+E+2OPfN5xPm2sac8OUZ4wpNjhCc8OUZ46vR29uR5hC+3hSfHCE94cozwhCfHCE8d/O2s1g90nu8Y0TsxWpt+FrrvR5j1WsMT949y6ru3N7+O8uUx4rTcx+Z49DzCl/ucJ88jPOHJaw1PfGViuibajiorK0tVjW6fHSOks3+HO/I8wpvtkBQb6ZPnEZ7w5LWGJ7pE2LTzl9NbX0fd89r28x4jPOHpMcLT34Pcx+Z49H6EL/c5T15reOLZL4/TrGHJWrx4sSx9x+m+N3zznMNfx4jTch+bc84xwllVqoK/f0N5eXlKS0vz6fo8xanDAAAAAAAhxeU2tO14udllACGnuiF4PvkLAEA4CakZLXsOHFRGRsY5Pw+dU4c5tHTpMs2+9lolxEa33h4sU3U7Op2/pr5Riz9comuvnXXWKdDaGhvcpw5r3h43zrlO0VGRzbeF4KnDauobtHTpsja3R2idOsyhFcuW6cbrmz/5E7qnDmtqc3uE3qnDHPpoxbLWGS2he+owR5vbI/ROHebS8qVLlJWVJYvVZv5pgbw+ddi52yM0Tx3mbv0klqOdU/G0tdzgO3XYudsjFE8d5nI5tXjxYs2YNVsWW/unAgj+U4edvT1C9dRhDodDHyxarGtmtv088fPLDd5Th322PaIiI0PitUZbYx0Oh959f7FmtfO8XQq+1xptnzrss+2REBMti8Wi/36arwcWZCsuyqblP5qixJhz+ztzv/f006V7f3Ntuz9r7xhxvllUHVnHhZ5HeLr80+vw5Bjh6TpOf6Lbk+OJL7dFW/u9t4/TmS60L3u7rTt6jPCmhzNntLR1jPho/wl979/b1SXCqg0PTVdSbPNr4GB5P6Kxqe3XUZJ5rzXcbkMjf7Ws3fu3Jfvha7Rq+TJdP6f5ddSFnkd4s61zH5vj0XMOb/a59l5Htfc84mKPfxd6buDtfu12ubRyWfPrKLvdft7nBt4e/6SO/b33ZpbD6cepre3R1vOIYb9c4tXyz3Sh1xoXs60jLEbr6yiXrG0eI7zZ1kcfzfLoGNH/Z4s9Wn7uY3M+d+qwc7fHxTznkNr/W9feaw1fPOcw3C4tXrxYs66dLcN67usob9bhyWsNb49/nz9G5ObmasSQQUExoyWkrtESE2k/64/x+cZ5ssyOOvNA481Yh8VQlO3cn3lyrsZgGBtltyrK1vzYRUSc//E7/aS3IyLtVkV2cJKVL8ae3h62M86heOYB4UI8GWv34Pyonoy1WS2KibR3aHucHtsRVj+NtVjaH+uwGDrztM3nG9uWYBjbJdImu6Vj28OT44kZ+73DcvYTE8+OEf4Z681+f3o/P9/28GS5Zh0jHA6jw2PP5Ml+H4hjxIW2hyf7vZnHCIfjsxe1gXwe0R5v9/sLbQ//PY/w7VhXy3PsSLv1gs9LTvPXc46LOUacb3v4+3mEr8daLR17nij57nnExYyVzt2Xz94etvOO9WS55+OvY0RkB5+3S8HyWqP911Exkc3XZ2lwuPSX5TmSpHunDVLvrl06vPyO6ui28+QY4ck6PDmeeLIOT44RnvDkGOEpf+33nqzjYntr7/7+eszaOkZcN6q3RqYe1u6CKr247oh+MnuopODY76MjbLKpY6+jAvlaY+GOgg7f/7SYSLvOvEzDxRwjzsdXx4i2xETaO/Q66mL2+/Pdz5PnBhdax5mvoy7mGNGRdflzuR3ZHp48N/j88s/H2+vLtLUOh8Phs+We6XTIInm233vizH25I9vDUx3ZFhez37e1fIe7+YWU3dbx11GerMNfzw0+f4zw1/7nDU4dBgAAAAAIGa99fEwFFfXqlRCtb16ZYXY5QEiwWCy675pBkqRXN+aqos4314YJV01Ot/68LMfsMgAAIYSgBQAAAAAQEirrHfrr6kOSpB/NHOTTT8QC4W7m8J4a3jtBtU0uvbT+qNnlBLX/fHJcx0/VmV0GACC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sRZTdpm9c2V+3X5quf2zM1fNrjiinpEbf/0+2hne1atpMpxIjIswuEwCATsXhcmvj4TIt2V2kpXtKdKq2qfVn8dF2zRzWU7NH9tLkwT1ar7N277/NqhYAAAAAggtBiwnioyP00HVD9ZXL+urxpQf0Xnah2SUhTKzJOamfvrVTxVUNslike64eoB/NHByUF56PibTre1MH6iuX9dOL647ohXVHtLdC+torW7Uzv8qjZXE6PQAAPNfgcGn9wVJ9uLtYK/aVqLLe0fqzrjERmjW8p64b1VtXZiabNiMWAAAAAEIBQYuJ0pNi9PSXxhK04KI1uKRfLNyrN7Y2z2LJ6B6j+beN0YSMJJMru7DELhF6YNYQXT0wSd94ebPHIQsAAOi4+iaX1uSc0OJdxVq1/4RqGp2tP0uOi9K1I3rqupG9ddmAJEXYCFcAAAAAoCMIWoAQ1uR0a8OhMv0x26ZTjc0hyzeuyNBPZg9RTGRo7d5j07vqhyNd+sfROBVWNphdDgAAYeVgSbWeWnlQK/edUL3D1Xp7r4RozR7ZS9eN7KUJGUmyWS0mVgkAAAAAoSm03okFOiGX21BBeb2OltUqt7RWR1v+5ZbVKr+8Xi63Icmi1K7Revy2MboiM9nskr3Ws4v05j0TddXja80uBQCAkJbx0KIOjSuuatA/NubqHxtzORUnAAAAAHiJoAUIAm63oeKqhuYgpaxWR082BylHS2uVd6peTS53u/eNibRpbDeH/nr3FeoW16XDb6ycFmxvqvRMiDa7BAAAAAAAAADoMIKWEPTMyoP61tUD1CUy+C5wjvYZhqHSmqbm2SifC1Ryy2rV4Gg/TIm0W9UvKUb9k2PVPzlWGS1f+yfHqlu0VR9++KHiotidAQCAtPrACbNLAAAAAIBOhXdmQ9Cfl+foX5uP68Frh+jmsamyci7tNjU6XTpR1aiiygYVVdarqLJBxS3/L275f12DTX/Ys0Z2q1V2m0U2q0V2q0U2q7Xlq+Wzr7Z2bj9zvO3s2y0Wi4oqG3S0tEa5pXVnXXD28+xWi9JbwpSM7rHqnxzTGqj0TuzS7jnTHQ6Hvx5CAAAQQg6dqNbvFu3TRwdOml0KAAAAAHQqBC0hKLVrFxVU1OuBBdl6ZeNR/TxruCZldje7rIBqcLhUUtWgwooGFVedGaJ8FqSU1jR1YEkWVTsa/V5v69oszduvdWZK989mpqR266IImzVgtQAAgPBQUdekJ1cc1P99fEwut6EIm0UOl2F2WQAAAADQaRC0hKCVD0zRKxty9bfVh7S7oEpfeuFjzRiWop9lDVNmjzizy/OJosp6HT1Z2xygVLXMSKloaP3+VG1HQpTmU271SYxWr8Ro9U7s0vK1+f/dY2zavHGDJl15lSxWm+Y+u8HjOh+5YbhcbkNOt9H81WXI5Xa3fu9yG0pJiGoNVGY+sVb55fXKL6/XuoOlF1x+sF0/BQAABA+ny61/bT6uJ1bkqKKueYbrjGE99fM5wzRt/kfmFgcAAAAAnQhBSwiKjrDpu1MzdfuEND254qD+veW4Vuw7odUHTuorl/XVD2cMVlJspNlleqW8tkl/Wrpf//kkT8YFPogZHWFVn5bw5HSA0iuxy1nBSreYCFks7Z9yKy9WGtEnQREREV7V+80r+3t1PwAAgIuxJuekfvfBXh08USNJGtIzXg9fP1xXDUo2uTIAAAAA6HwIWkJY97go/famkfr6Ff306OL9Wrn/hP656Zje2V6gedMH6Y5JGYq0h8apqNxuQ29szdMfl+xv/UTmgB6xrUFK78RoPbPq0Fn3aXC4daS0VkdKa9tcJrNBAABAuDl8ska/X7RPq/Y3X/C+W0yE7p81RF+6NF12TkEKAAAAAKYgaAkDA1Pi9dI3LtXGQ6X63aJ92ltUpd8t2qfXPj6m/8kappnDe6r/zxZ7tMxAhhS78iv1i4W7lZ1XIUka2itev71ppC7NSDpr3OeDFuC0/cVVGtorwewyAADwm8o6h55aeVD/3JQrp9uQ3WrR16/I0H3TBymxi3czcwEAAAAAvkHQEkauGJis939wld76NE+PL81Rblmd7vm/T3VFZnezS2tTZZ1Djy/br39tPi7DkOKi7Lp/5mDdMakfn8iER257bpNeuGOCLh8QnL/rAAB4y+ly6/VP8vSXZQdU3jLrd/rQFP18zjANCJNr8wEAAABAqCNoCTM2q0VfvLSv5ozuo7+tPqQX1x/VxsNlZpd1Frfb0Fvb8vXYh/tbL2o/95I++nnWMKUkRJtcHUJRdYNTd7y8RU998RJdN6q32eUAAOAT6w+W6rcf7NWBkmpJ0qCUOD18/XBNHtzD5MoAAAAAAGciaAlTcVF2/WT2UH1pYl899uF+LdpVZHZJkqQ9hZX65cI9+vRYuaTmNwx+M3ekJgXprBuEhlnDe2rZ3hJ979/b9Ju5I/W1y/uZXRIAAF47Wlqr3y/apxX7SiRJXWMidP/MwfryxL7M+gUAAACAIETQEubSk2L07FfGadFDi0yto6rBob8sy9E/N+XKbUgxkTb9cMYgffPK/orgDQNcpL9/dbweXrhb/958XA+/u1snqhp0/8zBslgsZpcGAECH1Tulx5Yc0D8/Pi6Hq/k6LF+b1E/zpg9S15hIs8sDAAAAALSDoAV+ZRiG3t1RoN8v2q/SmkZJ0pzRvfWLOcPUO7GLydUhXNisFv3+ppFKiY/SkysO6plVh3SyulG/u2kkn/wFAAS9BodLb2zJ0+PbbapxHpMkTRvSQz+fM1wDU7gOCwAAAAAEO4IW+M2B4mo9vHC3thw9JUkakByrX88doasHcV5x+J7FYtEPZwxWj/goPfzubv3nkzyV1jTpmS+NVZdIm9nlAQBwjpySar2+5bje3lagynqHJIsGJMfqlzcM19QhKWaXBwAAAADoIIIW+FxNo1NPrcjRyxty5XIbio6w6gfXDNK3ru6vKDtveMO/vnJZPyXHRem+17drxb4SffWlzXrp6xM45QoAICg0OFxatLNIDyzIbvPnR0pr9Y1XPjnn9tzH5vi7NAAAAACAlwha0Kb3swt1/ejeHl3jwjAMfbCzSL9btFclVc2nCbt2RE89fP1wpXWL8VepwDmuHdFLr33rMt31j0/06bFyfeG5TfrnnRPVpyunqwMAmONA8enZK/mqanCaXQ4AAAAAwIcIWtCmH7y+Xa9uzNXD1w/XmPSuFxx/6ESNHnlvtzYcKpMk9eseo1/dOELTOO0FTHJpRpLe+u4V+vrLW3ToRI1u+dtGvXrnRA3pFW92aQCATqK+yaVFu4r0783HtO14Revtad26KL+83rzCAAAAAAA+5dVVom+//XbZ7XZZLBbFxcXplVdeaXfsHXfcocTERFmtVlmtViUlJZ13PIJDlwibth4r19xnN+j+N3eouLKhzXF1TU79ccl+XffUWm04VKYou1U/mjFYS384mZAFphvcM17//e4VGpgSp+KqBt323Ea9uTVPp2qbzC4NABDG9hdX6ZGFuzXxDyv04wXZ2na8QnarRbNH9NI/75yotQ9OM7tEAAAAAIAPeTyjZd68eVqwYIG++c1v6qabbtJPf/pT3XXXXZo4caJGjBhxzvi1a9fquuuu080336z4+HjNmzdPd955p0aNGqUJEyb4pAn43uofT9Wflu7X29sK9Pa2An24q1jfmZKpeyYPkN0iGYa0dE+J/vDhARW2hDDXDE3Rr24Yob7dOU0Ygkefrl301ncm6a5Xt+rTY+X6yVs7ZWn5HfYE58YHAJxPXZNTH+ws0utbjmv7GbNX0pO66P9d2le3jU9TSkK0eQUCAAAAAPzG46DlpZde0qhRo/Tyyy9LkrKyshQVFaUHHnhAS5YsOWd8bm7uWd/PmDFDUVFR+t///V+CliDWKzFaf7n9En19UoZ+88FefXqsXE+syNF/Pjmu70zur9f3WbX/4+aLuKZ27aJf3ThCM4f3NLlqoG1dYyL1r29dpufWHNbSPSXaV1RldkkAgDCxt7BKr285rne3F6i6sfnaK3arRbNG9NSXJvbVlZnJslo7fs07AAAAAEDo8ShoqampUW1trebM+eyT3Xa7XRkZGcrOzu7QMkpLSyVJvXr1andMVVWVqqo+eyO0qKhIkuRwOORwODwp+RwXe/+LWcfp20Oph+G9YvX6XRO0eHeJHl+Wo4KKBj3y/j5JVkXYLLr7qv76zuT+6hJpC0hdnrhQPb7YHv7uuSPLv9g+zNwn2hrjTT0duY9N0r1T+uveKf1VVNmgyfPX+nwdZ44Ltv3BU/QRXOgjuNBHcDGjj9pGpxbvLtZ/tuZrZ/5nz1nTu3XRFyek6dZxfZQcFyVJcrmccrkufp3B8vc6mJcfiHWEQw+BWAc9dJ51hEMPgVgHPQTPegK1fH+uJxA9+LuPQO0T/uwjkPu1v/oI9LEpVPv4/PL90YcZxz5f92HW37pgej3rUdCSk5MjSerfv/9Zt3fv3l0lJSUdWsasWbNkt9t1//33tzvmxhtv1Jo1a865fe3atdq7d+/nbvVsUs7ixYs9Gu/FpJ8LrmP58uUXtQ7Pe7j4dVgk/XCw9FGRRasLreoXb+iWDLdSmnK0ekWO5m3y/HF6apLTw3v453H6bHv4flufy3/b2ts+gmGfOFNzH8G3Tzz1+ocaEG/I1sErW527n4cm+ggu9BFc6CO4BKKP/FppY4lVW0stanQ1z1KxWgyNTjJ0RYqhQYnVstbs05a1+zqwtND+e+3NOsx4Dmv+8gOxDrZ1cCw/EOtgW/tj+YFYRyB68PzvYPBta8nTPoJvW5+uv+N9BF8PZy7fX30Eer/uWB/B3YPUkT6Cb79uax3n7yO494kztd9H6PQg+a4Ps/5en57UEQw87+gizJ49W3v37tWbb76prl27tjvuvffeO2dGy8SJEzV58mRlZGScNXbepmUe1ZCVleXReE+Xf751OBwOLV++XDNnzlRERITX6/C0B1+u4yZJTU1NWrFixVl9+PJxao+vH6fPb49Q7EG6+D7M3CfOdGYf2rTa58v/PE/7+Otem+Ki7LpqYHdNHZysKYOTWz+tfKb29vNQQx/BhT6CC30EF3/14XS5VVzVqIKKeh08UaN3thdqZ8Fnz0/7JjXPXrllbJ82/x5cSKj+vb6YdZj5HNas5QdiHWzr4Fh+INbBtvbP8gOxjkD04OnfwWDc1pJnfQTjtp45c6ZHz0uCsYesrCyPn18F4z7haR/B2oPU8ee7wbhfn7mOjvQRrPvEmS7URyj0IPm+D7P+Xn/+siVm8ihoGTx4sCTp6NGjZ91eVlam+Pj48973+uuv19KlS/Xqq6/qC1/4wnnHJiQkKCEh4ZzbIyIiLvoFdCDeSLjQOi62j2Do4fSYYO6jo8u/mD6CpYfTY72pJ1h+n7wZezH38VT32EiV1TZpyZ4SLdnTPINvTFqipg1N0TVDUzSyT+JZ5+D3xfEqGNBHcKGP4EIfwcXTPgzD0MmaRuWdqld+eZ3yTtUp71S98srrlFdep6KKBjndxtnrsFl07Yhe+vLEvrp8QPeAXnsl2P5eB+PyA7GOcOghEOugh86zjnDoIRDrCFQP/lxPoJ4rhHofp5fvzz4C+fvqrz4CvV/7ow8zjk2+7sOs42uo9dHe8n3Zh5l/h3zVh5m/T8HCo6AlLi5OsbGxWrRokR599FFJktPpVG5ubvOn0NuRlZWlDz/8UC+88ILuuOOOi6sYAEzwyc9nKDu/Qqv3n9CqAye0u6BK2fmVys6v1JMrDqpHfJSmDu6hyYO6K7da2n68QlabTYYkt9uQ25AMGTIMyTAkt2E0/8wwZBhGy23Nb/w1v6/X/NXd+rPmN/vchiGLLBqVlqjMHnEmPiIAEPwq6xzNwcmpupav9a3f55fXq9Hp9mh5DpehD3YW6YOdRWfdnvvYnHbuAQAAAADoDDw+ddhdd92lp59+Wnfffbfmzp2rBx98UIZhaP78+ZKkzMxMpaSkaNOmTZKk6667TkuWLNGPf/xjTZw4UTt37pQkpaSkqFevXj5sBQD8x2q1aGzfbhrbt5vunzVEJVUN+ujACa3af0LrD5bqZHWjFnyarwWf5kuy64ndW/xe0+i0RM29JFU3jOmtlPhov68PADzhdhv69Hi5KuscsljU8s8ii878Kllb/i+LZJHls9ssah0jWWS1nHs/qflDP7vLLSr9+LgKKxtbZqc0ByrVDee/HpzVIvVO7KLUbl2U3i1G6UnNXx9YkO3PhwYAAAAAEGY8DlqeeuopFRYW6pVXXtGLL76o2NhYvfDCCxo5cqSk5tOIWSyfnUbh9AV15s+f3xrGSNKUKVP00UcfXWT5AGCOngnR+uKlffXFS/uq0enSJ0fLW0KXkyqtrNapRs9OJzMmLVGyNL+RePpNR2vLu4zWljcfrdbm2xocLm07XqGd+ZXamV+p336wt0Pr4BPXAALB5Ta0eFeRnl19SPuLqwO0Vpu0f3+bP0mOi1RatxilJ8UovVuXlq/NoUrvxC6KtFvPuQ9BCwAAAADAEx4HLZK0YMGCdn9WUVFx1vdO5/k/SQgAoS7KbtNVg5J11aBkORwOLV68WPM2eXZ4Xfj9qzwaX1bTqPG/W+HRfQDAn5wut97LLtT9b3oWUgztFX/WKRWN1q+f+78MuVvO9FVQUd/h5ZfWNGnrL9o/xS0AAAAAABfLq6AFAGCu7nFRZpcAAJKkJqdb72zP198+OqxjZXUe33/JDyd7fJ+MhxZ5fB8AAAAAAPyFoAUAOon5Sw/oprF9NDAl3uxSAISBBodLCz7N13MfHW6dYZIUG6lTtU0mVwYAAAAAQGARtABAJ/HX1Yf019WHNKJPgm66JFU3XtJHPROizS4LQIipb3Lp31uO63/XHlZJVaMkqUd8lL49eYC+fFlfDf/lUpMrBAAAAAAgsAhaAKCTmDEsRR8dOKk9hVXaU1ilP3y4T1dkdtfcS1I1e2QvJURHmF0igCBW0+jUax8f04vrjqi0pnnWSu/EaH13aqZun5Cu6AibyRUCAAAAAGAOghYA6CRe/PqlOlXbpEW7irRwe4G2HivXhkNl2nCoTL94d7dmDEvRTZekauqQFFnMLhZA0KhqcOjVDbl6acNRVdQ5JEnpSV30vakDdcu4VEXZCVgAAAAAAJ0bQQsAdCJJsZH62uX99LXL+ynvVJ3eyy7UO9sLdOhEjRbvKtbiXcVK7BKh2SN6KoPLLACdWkVdk15ef1SvbMxVdYNTkjQgOVbfmzZQcy/powib1eQKAQAAAAAIDgQtANBJpSfF6N5pA/W9qZnaU1ilhTsK9F52oUqqGvXG1nxZZFNu5B79+Nph6hEfZXa5AAKktKZRL647qv/blKvaJpckaVBKnL5/zUBdP7qPbFbmvAEAAAAAcCaCFgDo5CwWi0amJmpkaqIeum6YNh8p06sbj2rp3hN6Y2uB3tha0OFl5T42x4+VAvCnkqoGPb/miP695ZgaHG5J0vDeCfrBNQN17YheshKwAAAAAADQJoIWAEArm9WiKwYm69J+iRr08DKzywEQAIUV9XpxwwG9sTVPTc7mgGVMelfdd81AXTM0RRYLAQsAAAAAAOdD0AIAANAJHTtVp/8cturHW9bL4TIkSZdmdNMPrhmkqwclE7AAAAAAANBBBC0AAABhzjAMHT5Zq0+PndInueX69Fi5jpbWSrJKMnRFZnf94JpBunxAEgELAAAAAAAeImgBAAAIM41Ol3YXVGprbrk+yS3XtuPlOlXb1O74jYfLtPFwWZs/49pLAAAAAACcH0ELAMBnDMPg0/CACSrqmvTpsfKW2SqnlJ1f2Xq9ldOi7FY1fu42AAAAAABw8QhaAAA+c8vfN+rh64drXN9uZpcChC3DMHT8VJ225pZr67FT2ppbroMnas4Z1z02UhMyumlCvyRNyOimEX0SNfgXH5pQMQAAAAAA4Y2gBQDgM9uPV+iWv23U3Ev66Kezh6pP1y5mlwSEPIfLrb2FVfok95Q+PVaurcfKdbK68ZxxA3rE6tJ+SRqf0U2XZiQpo3sMM8wAAAAAAAgAghYAgM/cNj5Nb23L18IdhVq6p1j3XD1A356Sqdgo/twgNBmGoaLKBuWUVLf8q9HBkmqVldv0ct5mRdltstssstusirBaFGGzym5r/hpxxu12m/Wz26zNYyJbxrZ1X4ukvUVV2ppbrh15Fap3uM6qK8Jm0ei0rprQr5vGt/zrHhdlzoMEAAAAAEAnxztfAACfefy2Mfr6FRn6zQd7teXoKT296pDe2JqnB68dqlvGpspq5dP1CE6GYehkTaNyimuUU1KtgyeqdaC4WgdLalTd6GzjHhbl11YGrL7ELhHNoUrLbJVRqYmKjrAFbP0AAAAAAKB9BC0AAJ8amZqoN+65XEt2F+sPH+5T3ql6/XhBtv65KVcPXz9cl2YkmV0iOrny2iYdKKnWwZJqHWiZpZJTUq2KOkeb4+1Wi/onx7Z5HZTzefDaIXK6DDndbjW53M3/d7nlcBtyON1yug05XG45Wn7mcBtam3OyzWVV1ju0cv8Jrdx/4qzbcx+b41FNAAAAAADA9whaAAA+Z7FYdN2o3po2NEX/2Jirv646pJ35lbrtuU2aM6q3HrpuqNKTYswuE2GuqsGhgy1ByoHi07NUalRac+71TSTJYpEyusdqUEqchvSK16Ce8RrSM179k2MVabcq46FFHq3/3mkDPa7Z03UAAAAAAADzEbQAAPwmOsKm70zJ1K3j0vSX5Tl645PjWrSrSMv3leiuq/rrW1f157oS8AnDMLS3qEqLdhZpT2GVckqqVVTZ0O74tG5dNLhnfMu/OA3uGa+BKXGcjgsAAAAAAHiMoAUA4Hc94qP06C2jdMekfvrdor3acKhMf//osJ5bc1gj+iToqoE9dPWgZI3v1403uuGRkqoGvbu9QO9sL9D+4uoO3y+/vF7rf3qNHysDAAAAAACdBUELACBghvVO0Gt3XaaV+07oyZU52l1Q1frvuTWHL3h/rkcBSaprcmrZnhL9d1u+NhwqldswuyIAAAAAANCZEbQAAALKYrFoxvCemjG8J9ejQIe53YY+PlKm/24r0JLdRaptcrX+bEK/brplXJr+551dJlYIAAAAAAA6K4IWAEDImPXEmubTjA1O1mX9kxQTyZ+xcHewpFpvby/Qu9sLzrrmSt+kGN0yLlU3j01Vv+6xkkTQAgAAAAAATME7VACAkJFTUqOckhq9vOGoIm1WjevXVVcP6qGrBiZrZGqibFaL2SXCB8pqGvVedqHe3lagXQWVrbcnRNt1/Zg+umVsqsb36yaLhe0NAAAAAADMR9ACAAgZf/3yWK0/WKp1B0tVUFGvj4+c0sdHTunxpQfUNSZCV2Ym66pBybpqYLLSk2LMLhceaHC4tHLfCb2zPV8fHTgpZ8uFV+xWi6YOSdGt41I1bWiKoiNsJlcKAAAAAABwNoIWAEDIuH50H10/uo8Mw1BuWZ3WHTypdQdL9fHhMlXUObRoV5EW7SqSJGV0j9EVmUmqKraoYP1RRdrtslststmszV+tltavn/3/3J/Zbe3cbrXKZmv+Pik2UhE2q8mPTugxDENbj5Xr7W0F+mBnoaobnK0/G5OWqJvHpuqGMX3UPS7KxCoBAAAAAADOj6AFABByLBaL+ifHqn9yrO6YlCGny63s/AqtzSnV+kOl2pFXodyyOuWW1Umy6YPjB/1aj81qUVq3Lsro3lxTRvcYZbTUl9q1i+yEMGc5dqpO7+8s0TvbC3T8VF3r7X0So3XT2FTdMi5VA1PiTawQAAAAAACg4whaAAAhz26zany/JI3vl6QfzRysqgaHPj5cpk2HS/XKxmMeLWtUaqKcbkMut7vl62f/Tn/vdLlbv290Nv//WFmdjpXVaU3OyfMu/zdzR7QGMn26dukU15VxuQ3llFTr48Mn9c/dNh3dtL71Z7GRNl03qrduGZeqy/t3l7UTPB4AAAAAACC8ELQAAMJOQnSEZo3opWmDu3sctLz/g6s8Gp/x0CKPxv9y4Z7W/0farEp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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vac_per_unempl = nl_labour_mkt[JOB_VAC_PER_UNEMPLOYED].dropna()\n",
    "ax = vac_per_unempl.plot(figsize=(20,10), grid=True, xticks=vac_per_unempl.index[::2], xlabel='', rot=45,\n",
    "                         title=NL_PFX + JOB_VAC_PER_UNEMPLOYED_GRAPH_TITLE + ' ({:})'.format(AGE_GROUP))\n",
    "ax2 = ax.twiny()\n",
    "vac_per_unempl.plot.bar(ax=ax2, xlabel='', legend=False)\n",
    "ax2.xaxis.set_major_locator(mtick.NullLocator())\n",
    "ax.axhline(y=vac_per_unempl.mean(), ls='--', label=MEAN_VACANCIES_PER_UNEMPLOYED)\n",
    "ax.legend()\n",
    "_ = ax.set_xticklabels(list(map('{:%Y-%m}'.format, vac_per_unempl.index[::2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olive-funeral",
   "metadata": {},
   "source": [
    "### The Beveridge Curve\n",
    "[The Beveridge Curve](https://en.wikipedia.org/wiki/Beveridge_curve) compares the unemployment rate to the job-vacancy rate (the number of job vacancies divided by the total labour force) and shows how this changes over time. I've looked at each of these rates individually earlier in this notebook, in this section I plot one against the other.\n",
    "\n",
    "The Beveridge curve tends to show an inverse relationship — unfilled vacancies tend to increase as unemployment rate decreases. The logic being that when few are looking for a job, employers take longer to fill in their vacancies. Those years where the vacancy rate is higher for the same unemployment rate constitute less efficient a labor market where 1) skills mismatches between what employers are looking for and what job seekers have, or 2) vacancies being filled predominantly by job switchers rather than the jobless, or 3) lower willingness on the part of the unemployed to take a given job might be the reasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "british-albuquerque",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bvg_curve_df = nl_labour_mkt.loc[:, [UNEMPLOYMENT_RATE, UNFILLED_VACANCIES_LABOR_FORCE_RATE]]\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "sc = ax.scatter(x=bvg_curve_df.iloc[:,0], y=bvg_curve_df.iloc[:,1], c=mdates.date2num(bvg_curve_df.index))\n",
    "ax.xaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "ax.set_xlabel(bvg_curve_df.columns[0])\n",
    "ax.set_ylabel(bvg_curve_df.columns[1])\n",
    "ax.set_title(NL_PFX + 'Beveridge Curve ({:})'.format( AGE_GROUP))\n",
    "# Add labels to the last three months for ease of understanding the current trend\n",
    "for i in range(-1, -4, -1):\n",
    "    plt.text(bvg_curve_df.iloc[i,0], bvg_curve_df.iloc[i,1], f'{bvg_curve_df.index[i]:%Y-%m}')\n",
    "ax.grid(True)\n",
    "loc = mdates.AutoDateLocator()\n",
    "_ = fig.colorbar(sc, ticks=loc, format=mdates.AutoDateFormatter(loc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "heated-stationery",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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