1 Introduction

This report presents descriptive analysis of the Global Dynasties Dataset (GDD), a leader-level panel recording nominal heads of state or government and their familial ties for 10,119 country-years spanning 1946–2020. A leader is coded as dynastic (pred_bin = 1) if at least one family member previously held political office at any level (national, state, or local).

The analysis proceeds in two parts:

Regional analysis — global and cross-regional patterns of dynastic leadership, kin-type distribution, and regime variation.
South Asia deep-dive — a focused look at the eight South Asian countries in the dataset, where dynastic rates are exceptionally high.

Data source: Global Dynasties Dataset (Verma & Midha, 2026), sheet NEW Jan-Feb 2026. Variables prefixed fln_ are harmonized from the TED dataset (Nooruddin et al., 2022; Harvard Dataverse DOI: 10.7910/DVN/UXFY88).

2 Regime Type Definitions

The dataset classifies each country-year into one of six system categories. These are used throughout the analysis to contextualise dynastic patterns across regime types:

Regime Type	Definition
Parliamentary Democracy	Competitive multi-party elections; executive accountable to legislature; prime minister or equivalent as effective head of government (e.g. India, Sri Lanka under democratic periods).
Presidential Democracy	Competitive elections; directly elected executive president with a fixed term who is not accountable to legislature for survival in office (e.g. Maldives post-2008).
Mixed Democratic	Hybrid systems combining features of both parliamentary and presidential models, or semi-competitive elections with meaningful opposition but institutional ambiguities (e.g. Bangladesh under early 1970s arrangements).
Civilian Dictatorship	Non-democratic single-party or personalised rule without free elections, led by a civilian ruler (e.g. early Bangladesh under Mujibur Rahman’s one-party BAKSAL period; Sheikh Hasina’s later tenure coded thus).
Military Dictatorship	Executive power exercised by military officers through a coup or direct military governance; legislative and electoral competition suspended or tightly controlled (e.g. Pakistan under Ayub Khan, Zia-ul-Haq, Musharraf; Bangladesh under Zia ur-Rahman, Ershad).
Royal Dictatorship	Hereditary monarchy with no or minimal competitive elections; executive power concentrated in a royal family (e.g. Bhutan under monarchy, certain Gulf states).

Note: These categories are project-specific codings by the GDD team. They may differ from other datasets’ operationalisations (e.g. V-Dem, Polity). In particular, the Mixed Democratic category captures transitional or ambiguous cases that do not fit neatly into purely democratic or purely autocratic bins.

3 Setup and Data Loading

library(readxl)
library(tidyverse)
library(scales)
library(kableExtra)
library(RColorBrewer)

df <- read_excel(
  "D:/Populism and Democrary/Global Dynasty/Global_Dynasties_Dataset.xlsx",
  sheet = "NEW Jan-Feb 2026"
) %>%
  # Recode labels
  mutate(
    dynasty      = as.integer(pred_bin),
    female_leader = as.integer(fln_gender),
    decade       = paste0(floor(year / 10) * 10, "s"),

    relation_label = case_when(
      relation_code_pred == 2  ~ "Father",
      relation_code_pred == 3  ~ "Mother",
      relation_code_pred == 4  ~ "Son",
      relation_code_pred == 6  ~ "Husband",
      relation_code_pred == 8  ~ "Brother",
      relation_code_pred == 10 ~ "Grandfather",
      relation_code_pred == 11 ~ "Grandmother",
      relation_code_pred == 14 ~ "Uncle",
      relation_code_pred == 18 ~ "Cousin",
      relation_code_pred == 19 ~ "Other kin",
      TRUE                     ~ NA_character_
    ),

    pos_label = case_when(
      pos_code_pred == 2 ~ "Head of Government",
      pos_code_pred == 3 ~ "Cabinet Minister",
      pos_code_pred == 4 ~ "Member of Parliament",
      pos_code_pred == 5 ~ "Assembly / Chief Minister",
      pos_code_pred == 6 ~ "Mayor",
      pos_code_pred == 7 ~ "Councillor",
      pos_code_pred == 1 ~ "Other",
      TRUE               ~ NA_character_
    ),

    system_category = case_when(
      system_category == "Civilian Dictatorship"   ~ "Civilian Dictatorship",
      system_category == "Military Dictatorship"   ~ "Military Dictatorship",
      system_category == "Royal Dictatorship"      ~ "Royal Dictatorship",
      system_category == "Parliamentary Democracy" ~ "Parliamentary Democracy",
      system_category == "Presidential Democracy"  ~ "Presidential Democracy",
      system_category == "Mixed Democratic"        ~ "Mixed Democratic",
      TRUE                                         ~ NA_character_
    )
  )

# Drop the 5 rows missing Region
df <- df %>% filter(!is.na(Region))

cat("Total country-years:", nrow(df), "\n")

## Total country-years: 10114

cat("Dynastic leaders:", sum(df$dynasty == 1), "\n")

## Dynastic leaders: 2658

cat("Non-dynastic:", sum(df$dynasty == 0), "\n")

## Non-dynastic: 7456

cat("Years covered:", min(df$year), "–", max(df$year), "\n")

## Years covered: 1946 – 2020

4 Part I: Regional Analysis

4.1 Global Dynastic Prevalence

4.1.1 Overall Rate

overall <- df %>%
  summarise(
    Total        = n(),
    Dynastic     = sum(dynasty == 1),
    Non_dynastic = sum(dynasty == 0),
    Rate         = round(Dynastic / Total * 100, 1)
  )

overall %>%
  kbl(
    col.names = c("Total country-years", "Dynastic leaders",
                  "Non-dynastic leaders", "Dynastic rate (%)"),
    caption   = "Table 1: Global dynastic leadership (1946–2020)"
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE)

Table 1: Global dynastic leadership (1946–2020)
Total country-years	Dynastic leaders	Non-dynastic leaders	Dynastic rate (%)
10114	2658	7456	26.3

Across all 10114 country-years in the dataset, 2658 (26.3%) had a dynastic leader — a leader with at least one family member who previously held political office.

4.1.2 Trend Over Time

trend <- df %>%
  group_by(year) %>%
  summarise(
    rate    = mean(dynasty == 1) * 100,
    n_total = n()
  )

ggplot(trend, aes(x = year, y = rate)) +
  geom_line(colour = "#2c7bb6", linewidth = 0.8) +
  geom_smooth(method = "loess", span = 0.3, se = TRUE,
              colour = "#d7191c", fill = "#f7b6b2", alpha = 0.3) +
  scale_x_continuous(breaks = seq(1946, 2020, 10)) +
  scale_y_continuous(limits = c(0, 60), labels = label_percent(scale = 1)) +
  labs(
    title    = "Global Share of Dynastic Leaders, 1946–2020",
    subtitle = "Red smoothed line shows LOESS trend; shaded band is 95% CI",
    x        = "Year",
    y        = "Share of dynastic leaders (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor = element_blank())

Figure 1: Share of dynastic leaders globally by year (1946–2020)

4.1.3 By Decade

decade_df <- df %>%
  group_by(decade) %>%
  summarise(rate = mean(dynasty == 1) * 100, n = n()) %>%
  arrange(decade)

ggplot(decade_df, aes(x = decade, y = rate, fill = rate)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(rate, 1), "%")),
            vjust = -0.5, size = 4, fontface = "bold") +
  scale_fill_gradient(low = "#c6dbef", high = "#2171b5", guide = "none") +
  scale_y_continuous(limits = c(0, 40), labels = label_percent(scale = 1)) +
  labs(
    title = "Dynastic Leadership Rate by Decade",
    x     = "Decade",
    y     = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.x = element_blank())

Figure 2: Dynastic rate by decade

4.2 Dynastic Rates by Region

region_df <- df %>%
  group_by(Region) %>%
  summarise(
    Total    = n(),
    Dynastic = sum(dynasty == 1),
    Rate     = Dynastic / Total * 100
  ) %>%
  arrange(desc(Rate))

ggplot(region_df, aes(x = reorder(Region, Rate), y = Rate, fill = Rate)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(Rate, 1), "%")),
            hjust = -0.1, size = 4, fontface = "bold") +
  coord_flip() +
  scale_fill_gradient(low = "#fee8c8", high = "#b30000", guide = "none") +
  scale_y_continuous(limits = c(0, 75), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Leadership Rate by Region (1946–2020)",
    subtitle = "Percentage of country-years with a dynastic leader",
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.y = element_blank())

Figure 3: Dynastic leadership rate by region

region_df %>%
  mutate(Rate = paste0(round(Rate, 1), "%")) %>%
  rename(Region = Region, `Country-years` = Total,
         `Dynastic leaders` = Dynastic, `Dynastic rate` = Rate) %>%
  kbl(caption = "Table 2: Dynastic rates by region") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE)

Table 2: Dynastic rates by region
Region	Country-years	Dynastic leaders	Dynastic rate
South Asia	552	324	58.7%
Middle East	1169	584	50%
East Asia	1038	309	29.8%
North America	1161	341	29.4%
Australia and Oceania	309	74	23.9%
South America	776	168	21.6%
Africa	2735	568	20.8%
Western Europe	1363	196	14.4%
Eastern Europe	1011	94	9.3%

4.3 Regional Trends Over Time

region_trend <- df %>%
  mutate(period = floor(year / 5) * 5) %>%
  group_by(Region, period) %>%
  summarise(rate = mean(dynasty == 1) * 100, .groups = "drop")

ggplot(region_trend, aes(x = period, y = rate, colour = Region)) +
  geom_line(linewidth = 0.8) +
  geom_smooth(method = "loess", span = 0.5, se = FALSE, linetype = "dashed",
              linewidth = 0.4) +
  facet_wrap(~Region, ncol = 3, scales = "free_y") +
  scale_x_continuous(breaks = c(1950, 1980, 2010)) +
  scale_y_continuous(labels = label_percent(scale = 1)) +
  scale_colour_brewer(palette = "Set1", guide = "none") +
  labs(
    title    = "Dynastic Leadership Rate by Region Over Time",
    subtitle = "5-year averages; dashed line = LOESS trend",
    x        = "Year",
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 12) +
  theme(strip.text = element_text(face = "bold"),
        panel.grid.minor = element_blank())

Figure 4: Dynastic leadership trends by region (1946–2020)

4.4 Regime Type and Dynasticism

regime_df <- df %>%
  filter(!is.na(system_category)) %>%
  group_by(system_category) %>%
  summarise(
    Total    = n(),
    Dynastic = sum(dynasty == 1),
    Rate     = Dynastic / Total * 100
  ) %>%
  arrange(desc(Rate))

regime_colours <- c(
  "Royal Dictatorship"      = "#7b2d8b",
  "Civilian Dictatorship"   = "#d73027",
  "Military Dictatorship"   = "#fc8d59",
  "Mixed Democratic"        = "#fee090",
  "Parliamentary Democracy" = "#91bfdb",
  "Presidential Democracy"  = "#4575b4"
)

ggplot(regime_df,
       aes(x = reorder(system_category, Rate), y = Rate,
           fill = system_category)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(Rate, 1), "%")),
            hjust = -0.1, size = 4, fontface = "bold") +
  coord_flip() +
  scale_fill_manual(values = regime_colours, guide = "none") +
  scale_y_continuous(limits = c(0, 80), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Leadership Rate by Regime Type (1946–2020)",
    subtitle = "Percentage of country-years with a dynastic leader",
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.y = element_blank())

Figure 5: Dynastic rate by regime type

heat_df <- df %>%
  filter(!is.na(system_category)) %>%
  group_by(Region, system_category) %>%
  summarise(n = n(), rate = mean(dynasty == 1) * 100, .groups = "drop") %>%
  filter(n >= 20)   # suppress cells with very few observations

ggplot(heat_df, aes(x = system_category, y = Region, fill = rate)) +
  geom_tile(colour = "white", linewidth = 0.5) +
  geom_text(aes(label = ifelse(n >= 20, paste0(round(rate, 0), "%"), "")),
            size = 3.5, colour = "black") +
  scale_fill_gradient2(
    low      = "#f7fbff",
    mid      = "#6baed6",
    high     = "#08306b",
    midpoint = 30,
    name     = "Dynastic\nrate (%)"
  ) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 12)) +
  labs(
    title    = "Dynastic Rate by Regime Type and Region",
    subtitle = "Cells with fewer than 20 country-years suppressed",
    x        = NULL,
    y        = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(axis.text.x = element_text(angle = 30, hjust = 1, size = 10),
        panel.grid  = element_blank())

Figure 6: Dynastic rate — regime type × region heatmap

4.5 Who Do Dynastic Leaders Inherit From?

4.5.1 Global Kin-Type Distribution

kin_df <- df %>%
  filter(dynasty == 1, !is.na(relation_label)) %>%
  count(relation_label) %>%
  mutate(pct = n / sum(n) * 100) %>%
  arrange(desc(pct))

ggplot(kin_df, aes(x = reorder(relation_label, pct), y = pct,
                   fill = relation_label)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(pct, 1), "%")),
            hjust = -0.1, size = 4, fontface = "bold") +
  coord_flip() +
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 80), labels = label_percent(scale = 1)) +
  labs(
    title    = "Type of Dynastic Kin Relationship (Global, 1946–2020)",
    subtitle = "Among dynastic leaders (pred_bin = 1) with identified kin type",
    x        = NULL,
    y        = "Share of dynastic leaders (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.y = element_blank())

Figure 7: Type of kin relationship — dynastic predecessors (global)

4.5.2 Position Held by Dynastic Relative

pos_df <- df %>%
  filter(dynasty == 1, !is.na(pos_label)) %>%
  count(pos_label) %>%
  mutate(pct = n / sum(n) * 100) %>%
  arrange(desc(pct))

ggplot(pos_df, aes(x = reorder(pos_label, pct), y = pct, fill = pos_label)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(pct, 1), "%")),
            hjust = -0.1, size = 4, fontface = "bold") +
  coord_flip() +
  scale_fill_brewer(palette = "Set2", guide = "none") +
  scale_y_continuous(limits = c(0, 75), labels = label_percent(scale = 1)) +
  labs(
    title    = "Office Level of Dynastic Predecessor (Global, 1946–2020)",
    subtitle = "Among dynastic leaders with identified predecessor position",
    x        = NULL,
    y        = "Share of dynastic leaders (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.y = element_blank())

Figure 8: Office level held by dynastic predecessor

4.6 Gender and Dynasticism

gender_df <- df %>%
  mutate(gender_label = if_else(female_leader == 1, "Female leaders", "Male leaders")) %>%
  group_by(gender_label) %>%
  summarise(
    Total    = n(),
    Dynastic = sum(dynasty == 1),
    Rate     = Dynastic / Total * 100
  )

ggplot(gender_df, aes(x = gender_label, y = Rate, fill = gender_label)) +
  geom_col(colour = "white", width = 0.5) +
  geom_text(aes(label = paste0(round(Rate, 1), "%\n(n=", Dynastic, ")")),
            vjust = -0.4, size = 5, fontface = "bold") +
  scale_fill_manual(values = c("Female leaders" = "#d95f02",
                               "Male leaders"   = "#1f78b4"),
                    guide = "none") +
  scale_y_continuous(limits = c(0, 80), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Rate by Leader Gender (Global, 1946–2020)",
    subtitle = paste0("Female leaders n=", sum(df$female_leader==1),
                      "; Male leaders n=", sum(df$female_leader==0)),
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 14) +
  theme(panel.grid.major.x = element_blank())

Figure 9: Dynastic rates by gender

gender_decade <- df %>%
  mutate(gender_label = if_else(female_leader == 1, "Female", "Male")) %>%
  group_by(decade, gender_label) %>%
  summarise(rate = mean(dynasty == 1) * 100, n = n(), .groups = "drop") %>%
  filter(n >= 5)

ggplot(gender_decade, aes(x = decade, y = rate, colour = gender_label,
                          group = gender_label)) +
  geom_line(linewidth = 1.1) +
  geom_point(size = 3) +
  scale_colour_manual(values = c("Female" = "#d95f02", "Male" = "#1f78b4"),
                      name = "Leader gender") +
  scale_y_continuous(limits = c(0, 100), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Rate by Gender Over Decades",
    subtitle = "Cells with fewer than 5 observations suppressed",
    x        = "Decade",
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.minor = element_blank(),
        legend.position  = "bottom")

Figure 10: Dynastic rate for female leaders by decade

5 Part II: South Asia Deep-Dive

sa <- df %>% filter(Region == "South Asia")
cat("South Asia country-years:", nrow(sa), "\n")

## South Asia country-years: 552

cat("Dynastic leaders:", sum(sa$dynasty == 1),
    paste0("(", round(mean(sa$dynasty==1)*100, 1), "%)"), "\n")

## Dynastic leaders: 324 (58.7%)

South Asia stands out as the most dynastically concentrated region in the dataset. With a dynastic rate of 58.7% — compared to a global average of 26.3% — the eight countries in the region offer a rich case for understanding how familial political succession operates across different regime types and historical periods.

A note on how counts work in this dataset. The GDD is a country-year panel: each row represents one country in one year. When we say “India has 20 dynastic country-years,” this means India was governed by a dynastic leader in 20 separate calendar years — not that India had 20 different dynastic individuals. In fact, India had only 2 unique dynastic leaders (Indira Gandhi and Rajiv Gandhi) who together account for all 20 dynastic years. Likewise, Bangladesh’s figures begin only from 1971 (its year of independence), so all pre-1971 history is excluded. This distinction between country-years and unique leaders matters whenever interpreting counts — the tables in the next section make both explicit.

5.1 Dynastic Leaders at a Glance: India, Pakistan & Bangladesh

5.1.1 India

india_leaders <- df %>%
  filter(Country == "India") %>%
  arrange(year) %>%
  group_by(nominal_leader) %>%
  summarise(
    `Years in power`   = paste0(min(year), "–", max(year)),
    `No. of years`     = n(),
    Dynastic           = if_else(max(dynasty) == 1, "✔ Yes", "✘ No"),
    `Regime type`      = paste(unique(system_category), collapse = " / "),
    `Dynastic lineage` = first(na.omit(dynasty_desc)),
    .groups = "drop"
  ) %>%
  mutate(
    `Dynastic lineage` = str_replace_all(`Dynastic lineage`, "\r\n", "; "),
    `Dynastic lineage` = str_trunc(`Dynastic lineage`, 120)
  ) %>%
  rename(Leader = nominal_leader)

india_leaders %>%
  kbl(caption = "Table 5: All leaders of India (1947–2020) — dynastic status") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = TRUE, font_size = 12) %>%
  column_spec(3, bold = TRUE) %>%
  column_spec(4, color = if_else(india_leaders$Dynastic == "✔ Yes",
                                  "#bd0026", "grey40"), bold = TRUE) %>%
  row_spec(which(india_leaders$Dynastic == "✔ Yes"), background = "#fff0f0")

Table 5: All leaders of India (1947–2020) — dynastic status
Leader	Years in power	No. of years	Dynastic	Regime type	Dynastic lineage
Atal Bihari Vajpayee	1998–2003	6	✘ No	Parliamentary Democracy	Father: Krishna Bihari Vajpayee, school teacher; Mother: Krishna Devi
Chandra Shekhar	1990–1990	1	✘ No	Parliamentary Democracy	Son: Pankaj Shekhar Singh, Politician; Son: Neeraj Shekhar, Politician-Member of Parliament
Charan Singh	1979–1979	1	✘ No	Parliamentary Democracy	Son: Ajit Singh, Politician-Minister; Grandson: Jayant Chaudhary, Politician-Minister
H.D. Deve Gowda	1996–1996	1	✘ No	Parliamentary Democracy	Father: Dodde Gowda, Farmer; Mother: Devamma, home maker ; Son: HD Revanna, Politician-Member of Asseambly; Grands…
Inder Kumar Gujral	1997–1997	1	✘ No	Parliamentary Democracy	Father: Avtar Narain ; Mother: Pushpa Gujral ; Son: Naresh Gujral, Politician-Member of Parliament
Indira Gandhi	1966–1983	15	✔ Yes	Parliamentary Democracy	Father: Jawaharlal Nehru, Politician-Prime Minister; Son: Rajiv Gandhi, Politician-Prime Minister; Son: Sanjay Gandh…
Jawaharlal Nehru	1947–1963	17	✘ No	Parliamentary Democracy	Father: Motilal Nehru, Barrister ; Mother: Swarup Rani Thussu ; Daughter: Indira Gandhi, Politician-Prime Minister
Lal Bahadur Shastri	1964–1965	2	✘ No	Parliamentary Democracy	Father: Sharada Prasad Srivastava, Teacher; Mother: Ramdulari Devi; Son: Anil Shastri, Politician-Minister; Son: Su…
Manmohan Singh	2004–2013	10	✘ No	Parliamentary Democracy	Father: Gurmukh Singh ; Mother: Amrit Kaur
Morarji Desai	1977–1978	2	✘ No	Parliamentary Democracy	father: Ranchhodji Nagarji Desai, school teacher ; Mother: Vajiaben Desai
Narendra Modi	2014–2020	7	✘ No	Parliamentary Democracy	Father: Damodardas Mulchand Modi, Grocer; Mother: Hiraben Modi
P.V. Narasimha Rao	1991–1995	5	✘ No	Parliamentary Democracy	Father: Sitarama Rao ; Mother: Rukma Baiagrarian ; Son: PV Ranga Rao, Politician-Member of Assembly; Son : P.V. Raje…
Rajiv Gandhi	1984–1988	5	✔ Yes	Parliamentary Democracy	Father: Feroze Gandhi, Politician-Member of Parliament; Mother: Indira Gandhi, Politician-Prime Minister; Brother: Sa…
Vishwanath Pratap Singh	1989–1989	1	✘ No	Parliamentary Democracy	Son: Ajeya Singh, Politician

India: Only 2 out of 14 leaders were dynastic: Indira Gandhi (daughter of Jawaharlal Nehru; served 1966–1984 across two stints) and Rajiv Gandhi (son of Indira Gandhi; served 1984–1989). Together they account for 20 dynastic country-years out of India’s 74 total. Narendra Modi is coded non-dynastic — his father was a grocer with no political office.

5.1.2 Pakistan

pak_leaders <- df %>%
  filter(Country == "Pakistan") %>%
  arrange(year) %>%
  group_by(nominal_leader) %>%
  summarise(
    `Years in power`   = paste0(min(year), "–", max(year)),
    `No. of years`     = n(),
    Dynastic           = if_else(max(dynasty) == 1, "✔ Yes", "✘ No"),
    `Regime type`      = paste(unique(system_category), collapse = " / "),
    `Dynastic lineage` = first(na.omit(dynasty_desc)),
    .groups = "drop"
  ) %>%
  mutate(
    `Dynastic lineage` = str_replace_all(`Dynastic lineage`, "\r\n", "; "),
    `Dynastic lineage` = str_trunc(`Dynastic lineage`, 120)
  ) %>%
  rename(Leader = nominal_leader)

pak_leaders %>%
  kbl(caption = "Table 6: All leaders of Pakistan (1947–2020) — dynastic status") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = TRUE, font_size = 12) %>%
  column_spec(3, bold = TRUE) %>%
  column_spec(4, color = if_else(pak_leaders$Dynastic == "✔ Yes",
                                  "#bd0026", "grey40"), bold = TRUE) %>%
  row_spec(which(pak_leaders$Dynastic == "✔ Yes"), background = "#fff0f0")

Table 6: All leaders of Pakistan (1947–2020) — dynastic status
Leader	Years in power	No. of years	Dynastic	Regime type	Dynastic lineage
Agha Mohammad Yahya Khan	1969–1970	2	✘ No	Military Dictatorship	Father : Saadat Ali Khan, Police Officer
Benazir Bhutto	1988–1995	5	✔ Yes	Military Dictatorship / Parliamentary Democracy	Father: Zulfikar Ali Bhutto, Politician-Prime Minister; Mother : Nusrat Bhutto; Son: Bilawal Zardari, Politician-Mini…
Chauhdry Mohammad Ali	1955–1955	1	✘ No	Parliamentary Democracy	Son: Chaudhry Ahmed Mukhtar, Politician-Minister; Grandson: Chaudhry Moonis Elahi, Politician-Member of Parliament
Hwaja Nazim ad-Din	1951–1952	2	✔ Yes	Parliamentary Democracy	Father: Khwaja Nizamuddin; Mother: Nawabzadi Bilqis Banu; Grandfather: Khwaja Ahsanullah, Politician-Nawab of Dacca; …
Imran Khan	2018–2020	3	✘ No	Parliamentary Democracy	Father: Ikramullah Khan Niazi, Civil engineer; Mother: Shaukat Khanum
Iskander Ali Mirza	1956–1957	2	✘ No	Parliamentary Democracy	Father: Sahibzada Sayyid Muhammad Fateh Ali Mirza, Landowner; Mother: Dilshad Begum
Liaquat Ali Khan	1947–1950	4	✘ No	Parliamentary Democracy	Wife : Ra’ana Liaquat Ali Khan, Politician-Governor
Miraj Khalid	1996–1996	1	✘ No	Parliamentary Democracy	NA
Mohammad Ali Bogra	1953–1954	2	✔ Yes	Parliamentary Democracy	Father: Nawabzada Altaf Ali Chowdhury, Politician; Grandfather : Syed Nawab Ali Chowdhury, Politican-Minister; Uncle:…
Mohammad Ayub Khan	1958–1968	11	✘ No	Military Dictatorship	Father: Mir Dad Khan, Army Officer; Brother: Sardar Bahadur Khan, Politician-Minister; Son: Gohar Khan, Politician-Mi…
Mohammad Zia-ul-Haq	1977–1987	11	✘ No	Military Dictatorship	Father : Muhammad Akbar Ali, Civil Servant; Son : Muhammad Ijaz-ul-Haq, Politician-Minister
Nawaz Sharif	1990–2016	9	✘ No	Parliamentary Democracy	Father: Muhammed Sharif, Businessman; Mother: Begum Shamim Akhta; Brothers: Shehbaz Sharif, Politician-Prime Ministe…
Pervez Musharraf	1999–2007	9	✘ No	Parliamentary Democracy / Military Dictatorship	Father : Syed Musharrafuddin, Accountant; Mother : Zarin
Shahid Khaqan Abbasi	2017–2017	1	✔ Yes	Parliamentary Democracy	Father: Khaqan Abbasi, Politician-Minister; Sister: Sadia Abbasi, Politician-Member of Parliament
Yousaf Raza Gilani	2008–2012	5	✔ Yes	Military Dictatorship / Parliamentary Democracy	Father: Makhdoom Syed Alamdar Hussain Gilani, Politician-Minister; Son: Abdul Qadir Gillani, Politician-Member of Par…
Zulfikar Ali Bhutto	1971–1976	6	✔ Yes	Military Dictatorship / Mixed Democratic	Father: Shah Nawaz Bhutto, Politician-Member of Assembly; Mother: Khursheed Begum; Daughter: Benazir Bhutto, Politici…

Pakistan: Dynastic leaders include the Bhutto family (Zulfikar Ali Bhutto and his daughter Benazir Bhutto), Hwaja Nazim ad-Din (grandfather was Nawab of Dacca), Mohammad Ali Bogra (father and grandfather were politicians), Yousaf Raza Gilani (father was a minister), and Shahid Khaqan Abbasi (father was also a minister). Nawaz Sharif appears in multiple terms but is coded non-dynastic as his father was a businessman rather than a politician.

5.1.3 Bangladesh

bd_leaders <- df %>%
  filter(Country == "Bangladesh") %>%
  arrange(year) %>%
  group_by(nominal_leader) %>%
  summarise(
    `Years in power`   = paste0(min(year), "–", max(year)),
    `No. of years`     = n(),
    Dynastic           = if_else(max(dynasty) == 1, "✔ Yes", "✘ No"),
    `Regime type`      = paste(unique(system_category), collapse = " / "),
    `Dynastic lineage` = first(na.omit(dynasty_desc)),
    .groups = "drop"
  ) %>%
  mutate(
    `Dynastic lineage` = str_replace_all(`Dynastic lineage`, "\r\n", "; "),
    `Dynastic lineage` = str_trunc(`Dynastic lineage`, 120)
  ) %>%
  rename(Leader = nominal_leader)

bd_leaders %>%
  kbl(caption = "Table 7: All leaders of Bangladesh (1971–2020) — dynastic status") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = TRUE, font_size = 12) %>%
  column_spec(3, bold = TRUE) %>%
  column_spec(4, color = if_else(bd_leaders$Dynastic == "✔ Yes",
                                  "#bd0026", "grey40"), bold = TRUE) %>%
  row_spec(which(bd_leaders$Dynastic == "✔ Yes"), background = "#fff0f0")

Table 7: All leaders of Bangladesh (1971–2020) — dynastic status
Leader	Years in power	No. of years	Dynastic	Regime type	Dynastic lineage
Abdus Sattar	1981–1981	1	✘ No	Civilian Dictatorship	NA
Abu Sadat Mohammad Sayem	1975–1976	2	✘ No	Civilian Dictatorship / Military Dictatorship	NA
Abul Fazal Mohammad Chowdhury	1982–1982	1	✘ No	Military Dictatorship	NA
Hasina Wazed	2009–2015	7	✔ Yes	Civilian Dictatorship	Father : Mujibur Rahman, Politician; Mother : Sheikh Fazilatunnesa Mujib; Son : Sajeeb Ahmed Wajed, Politician; Fathe…
Hossain Mohammad Ershad	1983–1989	7	✔ Yes	Military Dictatorship / Mixed Democratic	Father : Mokbul Hossain, Politician-Minister; Mother : Mazida Khatun; Brother: GM Quader, Politician-Member of Parlia…
Iajuddin Ahmed	2006–2008	3	✘ No	Parliamentary Democracy / Military Dictatorship	NA
Khaleda Zia	1991–2005	10	✔ Yes	Parliamentary Democracy	Father : Iskandar Ali Majumder, Businessman; Mother : Taiyaba Majumder; Husband : Ziaur Rahman, Politician-President…
Mujibur Rahman	1972–1974	3	✘ No	Civilian Dictatorship	Father : Sheikh Lutfur Rahman, Law Clerk; Mother : Sayera Khatun, housewife; Daughter : Sheikh Hasina, Politician-Pr…
Shahabuddin Ahmed	1990–1990	1	✘ No	Mixed Democratic	Father: Talukdar Resat Ahmed Bhuiyan, Philanthropist;
Sheikh Hasina Wajed	1996–2020	10	✔ Yes	Parliamentary Democracy / Civilian Dictatorship	Father : Mujibur Rahman, Politician; Mother : Sheikh Fazilatunnesa Mujib; Son : Sajeeb Ahmed Wajed, Politician; Fathe…
Tajuddin Ahmed	1971–1971	1	✘ No	Civilian Dictatorship	Father : Maulavi Muhammad Yasin Khan ; Mother : Meherunnesa Khanam; Brother : Afsaruddin Ahmad, Politician-Member of…
Zia ur-Rahman	1977–1980	4	✘ No	Military Dictatorship	Father : Mansur Rahman, Chemist; Mother : Jahanara Khatun; Wife : Khaleda Zia, Politician-Prime Minister; Son : Tariq…

Bangladesh (data from 1971 onwards): The country enters the dataset only at independence in 1971, so there is no pre-1971 data. Sheikh Mujibur Rahman (the founding leader, 1972–1974) is coded non-dynastic (pred_bin = 0) because he had no dynastic predecessor — he was the first of his family in national politics. His daughter Sheikh Hasina (multiple terms from 1996) is dynastic as Mujib’s successor. Khaleda Zia is dynastic as the widow of President Zia ur-Rahman. Hossain Mohammad Ershad (military ruler, 1983–1989) is dynastic through his father, who was a minister.

5.2 Country-Level Dynastic Rates

sa_country <- sa %>%
  group_by(Country) %>%
  summarise(
    Total    = n(),
    Dynastic = sum(dynasty == 1),
    Rate     = Dynastic / Total * 100
  ) %>%
  arrange(desc(Rate))

# Add global average line value
global_rate <- mean(df$dynasty == 1) * 100

ggplot(sa_country, aes(x = reorder(Country, Rate), y = Rate, fill = Rate)) +
  geom_col(colour = "white", width = 0.7) +
  geom_hline(yintercept = global_rate, linetype = "dashed",
             colour = "grey40", linewidth = 0.8) +
  annotate("text", x = 0.6, y = global_rate + 2,
           label = paste0("Global avg: ", round(global_rate, 1), "%"),
           hjust = 0, size = 3.5, colour = "grey30") +
  geom_text(aes(label = paste0(round(Rate, 1), "%")),
            hjust = -0.1, size = 4, fontface = "bold") +
  coord_flip() +
  scale_fill_gradient(low = "#ffffb2", high = "#bd0026", guide = "none") +
  scale_y_continuous(limits = c(0, 90), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Leadership Rate by South Asian Country (1946–2020)",
    subtitle = "Dashed line = global average",
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.y = element_blank())

Figure 11: Dynastic rate by South Asian country

sa_country %>%
  mutate(
    Rate        = paste0(round(Rate, 1), "%"),
    vs_global   = paste0(round(Dynastic/Total*100 - global_rate, 1), " pp")
  ) %>%
  rename(Country = Country, `Country-years` = Total,
         `Dynastic leaders` = Dynastic, `Dynastic rate` = Rate,
         `vs. Global avg` = vs_global) %>%
  kbl(caption = "Table 3: South Asian dynastic rates vs. global average") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE) %>%
  column_spec(5, bold = TRUE, color = "#bd0026")

Table 3: South Asian dynastic rates vs. global average
Country	Country-years	Dynastic leaders	Dynastic rate	vs. Global avg
Bhutan	75	61	81.3%	55.1 pp
Nepal	75	59	78.7%	52.4 pp
Afghanistan	75	52	69.3%	43.1 pp
Maldives	56	37	66.1%	39.8 pp
Sri Lanka	73	44	60.3%	34 pp
Bangladesh	50	30	60%	33.7 pp
Pakistan	74	21	28.4%	2.1 pp
India	74	20	27%	0.7 pp

5.3 Trends Over Time

sa_trend <- sa %>%
  group_by(Country, year) %>%
  summarise(dynasty = mean(dynasty == 1), .groups = "drop")

ggplot(sa_trend, aes(x = year, y = dynasty * 100, colour = Country)) +
  geom_line(linewidth = 0.9, alpha = 0.85) +
  scale_colour_brewer(palette = "Dark2", name = "Country") +
  scale_x_continuous(breaks = seq(1950, 2020, 10)) +
  scale_y_continuous(limits = c(-5, 110), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Leadership Over Time in South Asia",
    subtitle = "Each line = 1 if dynastic leader in power that year, 0 otherwise",
    x        = "Year",
    y        = "Dynastic leader (0/1)"
  ) +
  theme_minimal(base_size = 12) +
  theme(legend.position  = "bottom",
        panel.grid.minor = element_blank())

Figure 12: Dynastic leadership rate over time — South Asian countries

sa_period <- sa %>%
  mutate(period = floor(year / 5) * 5) %>%
  group_by(period) %>%
  summarise(rate = mean(dynasty == 1) * 100, .groups = "drop") %>%
  mutate(group = "South Asia")

global_period <- df %>%
  mutate(period = floor(year / 5) * 5) %>%
  group_by(period) %>%
  summarise(rate = mean(dynasty == 1) * 100, .groups = "drop") %>%
  mutate(group = "Global")

bind_rows(sa_period, global_period) %>%
  ggplot(aes(x = period, y = rate, colour = group, group = group)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 2.5) +
  scale_colour_manual(values = c("South Asia" = "#bd0026", "Global" = "#2166ac"),
                      name = NULL) +
  scale_x_continuous(breaks = seq(1946, 2020, 10)) +
  scale_y_continuous(limits = c(0, 80), labels = label_percent(scale = 1)) +
  labs(
    title    = "5-Year Dynastic Rate: South Asia vs. Global (1946–2020)",
    x        = "Year",
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position  = "bottom",
        panel.grid.minor = element_blank())

Figure 13: 5-year rolling dynastic rate — South Asia vs global

5.4 Who Do South Asian Leaders Inherit From?

kin_compare <- bind_rows(
  df %>% filter(dynasty == 1, !is.na(relation_label)) %>%
    count(relation_label) %>% mutate(pct = n/sum(n)*100, group = "Global"),
  sa %>% filter(dynasty == 1, !is.na(relation_label)) %>%
    count(relation_label) %>% mutate(pct = n/sum(n)*100, group = "South Asia")
)

ggplot(kin_compare, aes(x = reorder(relation_label, pct), y = pct,
                        fill = group)) +
  geom_col(position = "dodge", colour = "white", width = 0.7) +
  coord_flip() +
  scale_fill_manual(values = c("Global" = "#2166ac", "South Asia" = "#bd0026"),
                    name = NULL) +
  scale_y_continuous(labels = label_percent(scale = 1)) +
  labs(
    title    = "Kin-Type Distribution: South Asia vs. Global",
    subtitle = "Among dynastic leaders with identified kin type",
    x        = NULL,
    y        = "Share of dynastic leaders (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position  = "bottom",
        panel.grid.major.y = element_blank())

Figure 14: Kin-type distribution — South Asia vs global

sa_kin_ctry <- sa %>%
  filter(dynasty == 1, !is.na(relation_label)) %>%
  count(Country, relation_label) %>%
  group_by(Country) %>%
  mutate(pct = n / sum(n) * 100) %>%
  ungroup()

ggplot(sa_kin_ctry, aes(x = Country, y = pct, fill = relation_label)) +
  geom_col(colour = "white", width = 0.8) +
  scale_fill_brewer(palette = "Paired", name = "Kin type") +
  scale_y_continuous(labels = label_percent(scale = 1)) +
  labs(
    title    = "Kin-Type Composition by South Asian Country",
    subtitle = "Among dynastic leaders only",
    x        = NULL,
    y        = "Share (%)"
  ) +
  theme_minimal(base_size = 12) +
  theme(axis.text.x  = element_text(angle = 30, hjust = 1),
        legend.position = "bottom")

Figure 15: Kin-type by South Asian country

5.5 Gender and Dynasticism in South Asia

sa_gender <- sa %>%
  mutate(gender_label = if_else(female_leader == 1, "Female", "Male")) %>%
  group_by(Country, gender_label) %>%
  summarise(rate = mean(dynasty == 1) * 100, n = n(), .groups = "drop") %>%
  filter(n >= 3)

ggplot(sa_gender, aes(x = Country, y = rate, fill = gender_label)) +
  geom_col(position = "dodge", colour = "white", width = 0.7) +
  scale_fill_manual(values = c("Female" = "#d95f02", "Male" = "#1f78b4"),
                    name = "Leader gender") +
  scale_y_continuous(limits = c(0, 100), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Rate by Gender and Country — South Asia",
    subtitle = "Cells with fewer than 3 observations suppressed",
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position    = "bottom",
        panel.grid.major.x = element_blank())

Figure 16: Dynastic rate by gender — South Asia

5.6 Regime Type and Dynasticism in South Asia

sa_regime <- sa %>%
  filter(!is.na(system_category)) %>%
  group_by(system_category) %>%
  summarise(
    Total    = n(),
    Dynastic = sum(dynasty == 1),
    Rate     = Dynastic / Total * 100
  ) %>%
  filter(Total >= 10) %>%
  arrange(desc(Rate))

ggplot(sa_regime, aes(x = reorder(system_category, Rate), y = Rate,
                      fill = Rate)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(Rate, 1), "%\n(n=", Total, ")")),
            hjust = -0.1, size = 3.8, fontface = "bold") +
  coord_flip() +
  scale_fill_gradient(low = "#fdcc8a", high = "#b30000", guide = "none") +
  scale_y_continuous(limits = c(0, 90), labels = label_percent(scale = 1)) +
  labs(
    title    = "Dynastic Rate by Regime Type — South Asia",
    subtitle = "Regime types with fewer than 10 country-years suppressed",
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 13) +
  theme(panel.grid.major.y = element_blank())

Figure 17: Dynastic rate by regime type — South Asia

5.7 Country × Regime Breakdown

sa_ctry_reg <- sa %>%
  filter(!is.na(system_category)) %>%
  group_by(Country, system_category) %>%
  summarise(n = n(), rate = mean(dynasty == 1) * 100, .groups = "drop") %>%
  filter(n >= 5)

ggplot(sa_ctry_reg, aes(x = system_category, y = rate, fill = system_category)) +
  geom_col(colour = "white", width = 0.7) +
  geom_text(aes(label = paste0(round(rate, 0), "%")),
            vjust = -0.4, size = 3) +
  facet_wrap(~Country, ncol = 4, scales = "free_x") +
  scale_fill_brewer(palette = "Set1", guide = "none") +
  scale_y_continuous(limits = c(0, 110), labels = label_percent(scale = 1)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  labs(
    title    = "Dynastic Rate by Regime Type — Each South Asian Country",
    subtitle = "Cells with fewer than 5 country-years suppressed",
    x        = NULL,
    y        = "Dynastic rate (%)"
  ) +
  theme_minimal(base_size = 11) +
  theme(axis.text.x  = element_text(angle = 40, hjust = 1, size = 8),
        strip.text   = element_text(face = "bold"))

Figure 18: Dynastic rate by country and regime type — South Asia

6 Summary of Key Findings

findings <- tibble(
  `#` = 1:8,
  Finding = c(
    paste0("Globally, ", round(mean(df$dynasty==1)*100,1),
           "% of country-years had a dynastic leader (1946–2020)."),
    "South Asia has the highest regional dynastic rate, far exceeding the global average.",
    "Royal dictatorships show the highest dynastic rates across regime types; parliamentary democracies the lowest among democratic systems.",
    "Paternal inheritance (father as predecessor) dominates globally — over two-thirds of dynastic leaders had a father in politics.",
    "Female leaders are substantially more likely to be dynastic than male leaders, consistent with the 'dynastic gateway' hypothesis.",
    "The gender gap in dynastic rates has narrowed over time but remains pronounced in South Asia.",
    "In South Asia, father-to-child transmission accounts for the majority of dynastic ties, with spousal ties (husband as predecessor) prominent in several countries.",
    "Dynastic rates in South Asia remain persistently high even under democratic regimes, distinguishing it from most other regions."
  )
)

findings %>%
  kbl(caption = "Table 4: Key descriptive findings") %>%
  kable_styling(bootstrap_options = c("striped","hover"), full_width = TRUE) %>%
  column_spec(1, bold = TRUE, width = "3em") %>%
  column_spec(2, width = "90%")

Table 4: Key descriptive findings
#	Finding
1	Globally, 26.3% of country-years had a dynastic leader (1946–2020).
2	South Asia has the highest regional dynastic rate, far exceeding the global average.
3	Royal dictatorships show the highest dynastic rates across regime types; parliamentary democracies the lowest among democratic systems.
4	Paternal inheritance (father as predecessor) dominates globally — over two-thirds of dynastic leaders had a father in politics.
5	Female leaders are substantially more likely to be dynastic than male leaders, consistent with the ‘dynastic gateway’ hypothesis.
6	The gender gap in dynastic rates has narrowed over time but remains pronounced in South Asia.
7	In South Asia, father-to-child transmission accounts for the majority of dynastic ties, with spousal ties (husband as predecessor) prominent in several countries.
8	Dynastic rates in South Asia remain persistently high even under democratic regimes, distinguishing it from most other regions.

7 Data and Reproducibility

Dataset: Global Dynasties Dataset (GDD), sheet NEW Jan-Feb 2026 Variables derived from TED/FLN: Nooruddin et al. (2022), Harvard Dataverse doi:10.7910/DVN/UXFY88 Analysis software: R R version 4.3.0 (2023-04-21 ucrt); packages: tidyverse, readxl, kableExtra, RColorBrewer, scales Author: Arslan Niyazi · RPubs

sessionInfo()

## R version 4.3.0 (2023-04-21 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_India.utf8  LC_CTYPE=English_India.utf8   
## [3] LC_MONETARY=English_India.utf8 LC_NUMERIC=C                  
## [5] LC_TIME=English_India.utf8    
## 
## time zone: Asia/Calcutta
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] RColorBrewer_1.1-3 kableExtra_1.4.0   scales_1.4.0       lubridate_1.9.4   
##  [5] forcats_1.0.1      stringr_1.6.0      dplyr_1.1.4        purrr_1.0.4       
##  [9] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_4.0.1     
## [13] tidyverse_2.0.0    readxl_1.4.5      
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## loaded via a namespace (and not attached):
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##  [5] lattice_0.21-8    stringi_1.8.7     hms_1.1.3         digest_0.6.37    
##  [9] magrittr_2.0.3    evaluate_1.0.5    grid_4.3.0        timechange_0.3.0 
## [13] fastmap_1.2.0     Matrix_1.5-4      cellranger_1.1.0  jsonlite_2.0.0   
## [17] mgcv_1.8-42       fansi_1.0.4       viridisLite_0.4.2 jquerylib_0.1.4  
## [21] cli_3.6.1         rlang_1.1.1       splines_4.3.0     withr_3.0.2      
## [25] cachem_1.1.0      yaml_2.3.10       tools_4.3.0       tzdb_0.5.0       
## [29] vctrs_0.6.5       R6_2.6.1          lifecycle_1.0.5   pkgconfig_2.0.3  
## [33] pillar_1.9.0      bslib_0.9.0       gtable_0.3.6      glue_1.6.2       
## [37] systemfonts_1.2.2 xfun_0.52         tidyselect_1.2.1  rstudioapi_0.17.1
## [41] knitr_1.50        farver_2.1.2      nlme_3.1-162      htmltools_0.5.8.1
## [45] labeling_0.4.3    rmarkdown_2.30    svglite_2.1.3     compiler_4.3.0   
## [49] S7_0.2.0

Global Dynasties Dataset: Descriptive Analysis

Regional Patterns and a South Asia Deep-Dive (1946–2020)

Arslan

June 15, 2026