The Global Passport Divide: Regional Inequality in Visa-Free Access

Europe dominates global passport power while Africa, Asia, and Middle East lag significantly behind.

TidyTuesday
2025
Analysis of 2024 Henley Passport Index data revealing global passport power inequality. European passports provide 91 more visa-free destinations than the global median, while African, Asian, and Middle Eastern passports face significant restrictions. Visualizes both regional performance gaps and underlying compositional differences across passport strength tiers.
Author

Steven Ponce

Published

September 8, 2025

Figure 1: Two-panel chart showing global passport power inequality in 2024. Top panel: horizontal bar chart displaying regional differences from global median visa-free access, with Europe leading at +91 destinations above the median, followed by the Caribbean (+53) and the Americas (+44), while Africa (-36), Asia (-31), and the Middle East (-27) fall below the median. Bottom panel: stacked horizontal bar chart showing regional composition by performance tiers, revealing Europe has 29 Global Elite countries and 7 Strong Performers. In comparison, Africa is dominated by 43 Emerging Markets countries and 8 Restricted Access countries. Charts demonstrate Europe’s passport dominance, alongside significant disadvantages in Africa, Asia, and the Middle East.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,   # Easily Install and Load the 'Tidyverse'
    ggtext,      # Improved Text Rendering Support for 'ggplot2'
    showtext,    # Using Fonts More Easily in R Graphs
    janitor,     # Simple Tools for Examining and Cleaning Dirty Data
    scales,      # Scale Functions for Visualization
    glue,        # Interpreted String Literals,
    patchwork    # The Composer of Plots
  )})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 11,
  height = 12,
  units  = "in",
  dpi    = 300
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 36)

country_lists <- tt$country_lists |> clean_names()
rank_by_year <- tt$rank_by_year |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(country_lists)
glimpse(rank_by_year)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

#' On 5 May 2023, the World Health Organization (WHO) officially declared 
#' that COVID-19 was no longer a Public Health Emergency of International 
#' Concern (PHEIC).

# Calculate regional performance vs global median for 2024
regional_gaps_2024 <- rank_by_year |>
  filter(year == 2024) |>
  mutate(
    global_median_2024 = median(visa_free_count, na.rm = TRUE)
  ) |>
  group_by(region) |>
  summarise(
    regional_median = median(visa_free_count, na.rm = TRUE),
    global_median = first(global_median_2024),
    countries_count = n(),
    .groups = "drop"
  ) |>
  mutate(
    gap_from_global = regional_median - global_median,
    performance_category = ifelse(gap_from_global >= 0, "Above Global Median", "Below Global Median"),
    region_clean = case_when(
      region == "NORTH_AMERICA" ~ "Americas",
      region == "SOUTH_AMERICA" ~ "Americas",
      region == "CENTRAL_AMERICA" ~ "Americas",
      region == "EUROPE" ~ "Europe",
      region == "ASIA" ~ "Asia",
      region == "AFRICA" ~ "Africa",
      region == "MIDDLE_EAST" ~ "Middle East",
      region == "OCEANIA" ~ "Oceania",
      region == "CARIBBEAN" ~ "Caribbean",
      TRUE ~ str_to_title(str_replace_all(region, "_", " "))
    )
  ) |>
  # Combine Americas if they exist separately
  group_by(region_clean) |>
  summarise(
    gap_from_global = weighted.mean(gap_from_global, countries_count),
    countries_count = sum(countries_count),
    performance_category = ifelse(gap_from_global >= 0, "Above Global Median", "Below Global Median"),
    .groups = "drop"
  ) |>
  arrange(desc(gap_from_global))

# Create performance clusters for 2024
performance_clusters_2024 <- rank_by_year |>
  filter(year == 2024) |>
  mutate(
    region_clean = case_when(
      region == "NORTH_AMERICA" ~ "Americas",
      region == "SOUTH_AMERICA" ~ "Americas",
      region == "CENTRAL_AMERICA" ~ "Americas",
      region == "EUROPE" ~ "Europe",
      region == "ASIA" ~ "Asia",
      region == "AFRICA" ~ "Africa",
      region == "MIDDLE_EAST" ~ "Middle East",
      region == "OCEANIA" ~ "Oceania",
      region == "CARIBBEAN" ~ "Caribbean",
      TRUE ~ str_to_title(str_replace_all(region, "_", " "))
    ),
    performance_cluster = case_when(
      visa_free_count >= 180 ~ "Global Elite (180+)",
      visa_free_count >= 150 ~ "Strong Performers (150-179)",
      visa_free_count >= 100 ~ "Middle Powers (100-149)",
      visa_free_count >= 50 ~ "Emerging Markets (50-99)",
      TRUE ~ "Restricted Access (<50)"
    ),
    performance_cluster = factor(performance_cluster, levels = c(
      "Global Elite (180+)", "Strong Performers (150-179)",
      "Middle Powers (100-149)", "Emerging Markets (50-99)",
      "Restricted Access (<50)"
    ))
  )

# Calculate cluster composition by region
cluster_composition <- performance_clusters_2024 |>
  count(region_clean, performance_cluster, .drop = FALSE) |>
  group_by(region_clean) |>
  mutate(
    total_countries = sum(n),
    proportion = n / total_countries,
    percentage = round(proportion * 100, 1)
  ) |>
  ungroup() |>
  filter(total_countries > 0) |>
  arrange(region_clean, performance_cluster)
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors(
  palette = c(
    "Above Global Median" = "#2A9D8F",
    "Below Global Median" = "#E76F51",
    "Global Elite (180+)" = "#264653",
    "Strong Performers (150-179)" = "#2A9D8F",
    "Middle Powers (100-149)" = "#457B9D",
    "Emerging Markets (50-99)" = "#F4A261",
    "Restricted Access (<50)" = "#E76F51"
  )
)

### |- titles and caption ----
title_text <- str_glue("The Global Passport Divide: Regional Inequality in Visa-Free Access")

subtitle_text <- str_glue(
  "Europe dominates global passport power while Africa, Asia, and Middle East lag significantly behind"
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 36,
  source_text = "Henley Passport Index Data"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.2), color = colors$title, margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.78), margin = margin(b = 20)),

    # Axis elements
    axis.line = element_blank(),
    axis.ticks = element_blank(),

    # Grid elements
    panel.grid.major = element_line(color = "gray90", linetype = "solid", linewidth = 0.3),
    # panel.grid.major.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),

    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
    axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),

    # Legend elements
    legend.position = "bottom",
    legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |-  P1: Regional power gaps ----
p1 <- regional_gaps_2024 |>
  mutate(
    region_clean = fct_reorder(region_clean, gap_from_global),
    label_position = ifelse(gap_from_global >= 0, gap_from_global + 2, gap_from_global - 2),
    label_hjust = ifelse(gap_from_global >= 0, 0, 1)
  ) |>
  ggplot(aes(x = region_clean, y = gap_from_global, fill = performance_category)) +
  # Geoms
  geom_col(width = 0.75, alpha = 0.9) +
  geom_hline(yintercept = 0, color = "#212121", size = 0.8) +
  geom_text(
    aes(
      y = label_position,
      label = paste0(ifelse(gap_from_global >= 0, "+", ""), round(gap_from_global, 0)),
      hjust = label_hjust
    ),
    color = colors$text,
    family = fonts$text,
    fontface = "bold",
    size = 4
  ) +
  # Scales
  scale_fill_manual(values = colors$palette) +
  scale_y_continuous(
    limits = c(-50, 100),
    breaks = seq(-60, 100, 25),
    expand = c(0.02, 0)
  ) +
  coord_flip() +
  # Labs
  labs(
    title = "Regional Passport Performance vs Global Median (2024)",
    subtitle = str_glue(
      "Difference in median visa-free access from global median\n",
      "Positive values indicate above-average regional performance"
    ),
    fill = "Performance Category:",
    x = NULL,
    y = "Difference from Global Median (Visa-Free Destinations)"
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "grey90", size = 0.5)
  )

### |- P2: Performance cluster composition ----
p2 <- cluster_composition |>
  left_join(
    regional_gaps_2024 |> select(region_clean, gap_from_global),
    by = "region_clean"
  ) |>
  mutate(
    region_clean = fct_reorder(region_clean, gap_from_global),
    label_text = ifelse(n > 0, as.character(n), "")
  ) |>
  ggplot(aes(x = proportion, y = region_clean, fill = performance_cluster)) +
  # Geoms
  geom_col(position = "fill", alpha = 0.9, width = 0.75) +
  geom_text(
    aes(label = label_text),
    position = position_fill(vjust = 0.5),
    color = "white",
    family = fonts$text,
    fontface = "bold",
    size = 3.5
  ) +
  # Scales
  scale_fill_manual(values = colors$palette) +
  scale_x_continuous(
    labels = percent_format(accuracy = 1),
    expand = c(0, 0)
  ) +
  # Labs
  labs(
    title = "Regional Composition by Passport Performance Clusters (2024)",
    subtitle = str_glue(
      "Proportion of countries in each performance tier by region\n",
      "Numbers show count of countries in each cluster"
    ),
    fill = "Performance\nCluster:",
    x = "Proportion of Regional Countries",
    y = NULL,
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "grey90", size = 0.5),
  ) +
  # Guides
  guides(fill = guide_legend(nrow = 2, byrow = TRUE))

### |-  Combined plots ----
combined_plots <- p1 / p2 +
  plot_layout(heights = c(1, 1))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.5),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size = rel(0.80),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.5),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2025, 
  week = 36, 
  width  = 10,
  height = 12
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.0.6        xfun_0.49          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5       
 [9] tools_4.4.0        generics_0.1.3     parallel_4.4.0     curl_6.0.0        
[13] gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3    ggplotify_0.1.2   
[17] lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2       munsell_0.5.1     
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] labeling_0.4.3     rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3     bit64_4.5.2       
[45] timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2 gitcreds_0.1.2    
[49] bit_4.5.0          hms_1.1.3          evaluate_1.0.1     knitr_1.49        
[53] markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5    
[57] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         vroom_1.6.5       
[61] svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[65] fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_36.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 36: [Henley Passport Index Data](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-09-09)
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