Global Salaries: Doctors vs. Nurses

Comparison of the salaries of the Government Doctors and Nurses across the Globe

The data used in this infographic is sourced from the Worldwide Bureaucracy Indicators (WWBI), a comprehensive cross-national dataset on public sector employment and wages developed by the World Bank’s Bureaucracy Lab. The WWBI aims to provide insights into the personnel dimensions of state capability, the public sector’s footprint within the labor market, and the fiscal implications of public sector wages. Constructed from nationally representative household surveys, these indicators offer a detailed picture of public sector labor markets worldwide. The dataset includes 192 indicators covering 202 countries and territories between 2000 and 2018, based on 53 million unique survey observations. For this visualization, two specific indicators from the WWBI have been utilized: Pay compression ratio in the public sector, by occupation, focusing on hospital doctors and hospital nurses, with a reference to clerical staff. The data for both indicators is from 2017 and covers 151 economies for hospital doctors and 149 economies for hospital nurses, last updated on March 18, 2024.

A scatterplot comparing the salaries of Doctors and Nurses in the public sector in different countries. Every country pays doctors more than nurses, but some pay doctors much more than their nurses.

How I made this graphic?

Loading required libraries, data import & creating custom functions

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(janitor)              # Cleaning names etc.

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours
library(patchwork)            # Combining plots
library(ggbeeswarm)           # For beeswarm plots

# library(ggfx)                 # Outer glow in the map
# library(magick)               # Adding images to plot
# library(ggthemes)             # Themes for ggplot2
# 
# # Mapping tools
# library(rnaturalearth)        # Maps of the World   
# library(sf)                   # All spatial objects in R
# library(geojsonio)            # To read geojson files into R

# Load Data
wwbi_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_data.csv')
wwbi_series <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_series.csv')
wwbi_country <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-30/wwbi_country.csv')

Visualization Parameters

# Font for titles
font_add_google("Bevan",
  family = "title_font"
) 

# Font for the caption
font_add_google("Stint Ultra Condensed",
  family = "caption_font"
) 

# Font for plot text
font_add_google("Josefin Slab",
  family = "body_font"
) 

showtext_auto()

# Background Colour
bg_col <- "grey90"

# Colour for the text
text_col <- "grey20" 

# Colour for highlighted text
text_hil <- "grey30" 

# Annotation colour
ann_col <- "red"

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")

Code to generate info-graphics for comparison of different job-pairs:

sel_names <- wwbi_series |> 
  filter(str_detect(indicator_name, 
                    "Pay compression ratio in public sector, by occupation:")) |> 
  mutate(
    indicator_name = str_remove(
      indicator_name,
      "Pay compression ratio in public sector, by occupation: "
    )
  ) |> 
  mutate(
    indicator_name = str_remove(
      indicator_name,
      " \\(clerk as reference\\)"
    )
  )

sel_names[3,1]
xdf1 <- wwbi_data |> 
  filter(indicator_code %in% sel_names$indicator_code) |> 
  left_join(sel_names) |> 
  select(country_code, indicator_name, value)

xdf1
library(GGally)

xdf2 <- xdf1 |> 
  pivot_wider(
    id_cols = country_code,
    names_from = indicator_name,
    values_from = value
  )
  
# GGally::ggpairs(
#   data = xdf2,
#   columns = 2:ncol(plotdf)
# ) +
#   coord_fixed()

# Identified Combinations
# Doctors vs. Nurses
# Judges vs. Senior Officers

Government Salaries: Doctors vs. Nurses

# List of countries with flags in ggflags
flag_cons <- ggflags::lflags |> names()

bg_col <- "white"        # Background Colour
myfill <- c("#006aff",
            "#ff9900")     # Various fill colours
mycol <- "blue"           # Various colours for annotations  

# Important Countries to display
display_cons <- c(
  "India", "United States", "China",
  "United Kingdom", "Pakistan",
  "Qatar", "Thailand",
  "Australia", "Chile", "Ethiopia"
  )

# Plot rectangles for background
rectangle1 <- tibble(
  x = c(0, 0, 1, 1, 0),
  y = c(0, 1, 1, 0, 0)
)

rectangle2 <- tibble(
  x = c(0, 1, 1, 0, 0),
  y = c(1, 1, 5, 5, 1)
)

# Text for the Plot
plot_title <- "Govt. Salaries : Doctors vs. Nurses"

plot_caption <- paste0(
  "Data: **World Bank (2017):** Worldwide Bureaucracy Indicators", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

plot_subtitle <- glue::glue("
In every country, public-sector doctors are paid more than nurses, and in <b style='color:{mycol}'>some countries</b>,<br>doctors get more than twice. This graph compares their salaries relative to those of clerks<br>in the same country. Countries further away from the diagonal line have a bigger wage<br>gap between doctors and nurses. Those countries higher up along the diagonal line pay<br>both doctors and nurses much more than they pay their clerks. The number in brackets<br>is ratio of doctors to nurses’ salaries.
")

# Base Test Size
ts = 80


# Drop some overplotted countries
cn_to_drop <- c(
  "tc", "iq", "dk", "sk", "ec",
  "ps", "by", "es", "eg", "gw",
  "ky", "bt"
)

# Getting final data-set ready
plotdf <- xdf1 |> 
  filter(str_detect(indicator_name, "Hospital")) |> 
  mutate(
    indicator_name = snakecase::to_snake_case(indicator_name)) |>
  pivot_wider(
    id_cols = country_code,
    names_from = indicator_name,
    values_from = value
  ) |> 
  mutate(
    display_ratio = round(hospital_doctor / hospital_nurse, 1)
  ) |> 
  left_join(
    wwbi_country |> 
              select(country_code, 
                     short_name,
                     code2 = x2_alpha_code)
    ) |> 
  mutate(code2 = str_to_lower(code2)) |> 
  filter(code2 %in% flag_cons) |> 
  mutate(
    size_var = if_else(
      short_name %in% display_cons,
      24,
      12)
    ) |> 
  arrange(desc(size_var), hospital_doctor) |> 
  mutate(col_var = if_else(
    display_ratio > 2.1,
    "a",
    "b"
  )) |> 
  filter(hospital_doctor < 4.2) |> 
  filter(!(code2 %in% cn_to_drop))

# Checking overplotted countries
# plotdf |> 
#   ggplot(aes(x = hospital_nurse, y = hospital_doctor,
#              label = code2)) + 
#   geom_text(alpha = 0.5) +
#   coord_fixed(xlim = c(1, 2),
#               ylim = c(1.5, 2.5))

# The actual graphic -------------------------------------------

g_base <- plotdf |> 
  ggplot(
    mapping = aes(
      x = hospital_nurse,
      y = hospital_doctor
    )
  ) +
  
  # Annotations --------------------------------------
  geom_abline(
    slope = 1,
    colour = text_col,
    alpha = 0.5,
    linewidth = 1
  ) +
  
  annotate(
    geom = "text",
    x = 3.3, y = 3.33,
    hjust = 1, vjust = 0,
    label = "Line of equality: Doctors and Nurses get paid equally",
    angle = 45,
    size = 24, 
    family = "body_font",
    colour = text_col
  ) +
  
  annotate(
    geom = "text",
    x = 1.03, y = 0.8,
    hjust = 0, vjust = 1,
    label = str_wrap("Countries in the light-blue rectangle pay doctors and nurses less than clerks.", 30),
    lineheight = 0.25,
    angle = 0,
    size = 24, 
    family = "body_font",
    colour = myfill[1] |> darken(0.4)
  ) +
  
  annotate(
    geom = "text",
    x = 0.23, y = 4,
    hjust = 1, vjust = 1,
    label = str_wrap("Countries in the light-orange rectangle pay their nurses less than clerks, but doctors more than clerks.", 80),
    lineheight = 0.25,
    angle = 90,
    size = 24, 
    family = "body_font",
    colour = myfill[2] |> darken(0.4)
  ) +
  
  # Rectangles for the background
  geom_polygon(
    data = rectangle1, 
    aes(x = x, y = y), 
    fill = myfill[1], 
    color = "transparent",
    alpha = 0.25
    ) +
  
  geom_polygon(
    data = rectangle2, 
    aes(x = x, y = y), 
    fill = myfill[2], 
    color = "transparent",
    alpha = 0.25
    ) +
  
  # The actual data to be plotted
  ggflags::geom_flag(
    mapping = aes(
      country = code2
    ),
    size = 10
  ) +
  # During trials, used geom_point instead of geom_flag
  # geom_point(
  #   alpha = 0.2,
  #   size = 5,
  #   pch = 16
  # ) +
  
  # Names of Countries and Ratio
  geom_text(
    mapping = aes(
      label = paste0(short_name, "\n(", display_ratio, ")"),
      size = size_var,
      colour = col_var
    ),
    nudge_y = -0.04,
    family = "body_font",
    check_overlap = TRUE,
    lineheight = 0.25,
    vjust = 1
  ) +
  scale_size_identity() +
  scale_colour_manual(values = c(mycol, text_col)) +
  
  
  # Scales and Coordinates
  coord_fixed(
    ylim = c(0.5, 4.2),
    xlim = c(0.2, 3.6)
    
  ) +
  scale_y_continuous(
    breaks = 1:4,
    expand = expansion(0)
  ) +
  scale_x_continuous(
    breaks = 1:3,
    expand = expansion(0)
  ) +
  
  # Lables
  labs(
    x = "Public Nurses' Salary (times that of a clerk)",
    y = "Public Doctors' Salary (times that of a clerk)",
    title = plot_title,
    subtitle = plot_subtitle,
    caption = plot_caption
  ) +
  
  # Themeing customization
  theme_minimal(
    base_family = "body_font",
    base_size = ts
  ) +
  
  theme(
    panel.grid.minor.y = element_blank(),
    legend.position = "none",
    panel.grid.minor.x = element_blank(),
    plot.caption = element_textbox(
      family = "caption_font",
      colour = text_hil,
      hjust = 1
    ),
    plot.title = element_markdown(
      family = "title_font",
      colour = text_hil,
      hjust = 0,
      size = 2.3 * ts,
      margin = margin(10,0,5,0, "mm")
    ),
    plot.subtitle = element_markdown(
      family = "body_font",
      colour = text_col,
      hjust = 0,
      lineheight = 0.35,
      margin = margin(0,0,10,0, "mm")
    ),
    panel.grid.major = element_line(
      linetype = 3,
      linewidth = 0.5,
      colour = "grey75"
    ),
    axis.ticks = element_line(
      colour = text_col,
      linewidth = 0.75
    ),
    axis.title = element_text(
      colour = text_col,
      hjust = 1,
      margin = margin(0,0,0,0, "mm"),
      size = 1.5 * ts
    ),
    axis.text = element_text(
      colour = text_col,
      hjust = 1, 
      size = ts,
      margin = margin(0,0,0,0,"mm")
    ),
    axis.line = element_line(
      linetype = 1,
      colour = text_col,
      linewidth = 0.75,
      arrow = arrow()
    ),
    plot.title.position = "plot"
  )

Adding Inset and QR code

inset_text1 <- str_wrap(
  "About the Data: The data used in this infographic is sourced from the Worldwide Bureaucracy Indicators (WWBI), a comprehensive cross-national dataset on public sector employment and wages developed by the World Bank's Bureaucracy Lab. The WWBI aims to provide insights into the personnel dimensions of state capability, the public sector's footprint within the labor market, and the fiscal implications of public sector wages. Constructed from nationally representative household surveys, these indicators offer a detailed picture of public sector labor markets worldwide. The dataset includes 192 indicators covering 202 countries and territories between 2000 and 2018, based on 53 million unique survey observations. For this visualization, two specific indicators from the WWBI have been utilized: Pay compression ratio in the public sector, by occupation, focusing on hospital doctors and hospital nurses, with a reference to clerical staff. The data for both indicators is from 2017 and covers 151 economies for hospital doctors and 149 economies for hospital nurses, last updated on March 18, 2024.", 
  width = 55, 
  whitespace_only = FALSE
  ) |> 
  str_view()

# A QR Code for the infographic
url_graphics <- paste0(
  "https://aditya-dahiya.github.io/projects_presentations/data_vizs/",
  # The file name of the current .qmd file
  "tidy_wbi_doctors_nurses",         
  ".html"
)
# remotes::install_github('coolbutuseless/ggqr')
# library(ggqr)
plot_qr <- ggplot(
  data = NULL, 
  aes(x = 0, y = 0, label = url_graphics)
  ) + 
  ggqr::geom_qr(
    colour = text_hil, 
    fill = bg_col,
    size = 1.5
    ) +
  coord_fixed() +
  theme_void() +
  theme(plot.background = element_rect(
    fill = NA, 
    colour = NA
    ),
    panel.background = element_rect(
      fill = NA,
      colour = text_col,
      linewidth = 1
    )
  )

# Compiling the plots

g <- g_base +
  annotate(
    geom = "label",
    x = 2.5,
    y = 1.9,
    label = inset_text1,
    family = "body_font",
    lineheight = 0.3,
    hjust = 0,
    vjust = 1,
    size = 14,
    colour = text_col,
    fill = bg_col,
    label.padding = unit(5, "mm"),
    label.size = 0
  ) +
  inset_element(
    p = plot_qr,
    left = 0.88, right = 1.05,
    bottom = 1.04, top = 1.16,
    align_to = "plot",
    clip = FALSE
  ) + 
  plot_annotation(
    theme = theme(
      plot.background = element_rect(
        fill = "transparent",
        colour = "transparent"
      )
    )
  )

Saving the graphic

# Saving the plot
ggsave(
  filename = here::here("data_vizs", "a4_tidy_wbi_doctors_nurses.png"),
  plot = g,
  width = 210 * 2,    # Best Twitter Aspect Ratio = 4:5
  height = 297 * 2,   
  units = "mm",
  bg = bg_col
)

library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", "a4_tidy_wbi_doctors_nurses.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(here::here("data_vizs", "thumbnails", "tidy_wbi_doctors_nurses.png"))