ECCU’s High-Income Paradox: Wealth Doesn’t Always Mean Resilience

Photo: Wikipedia

Introduction

The World Bank creates a yearly income classification for all countries with a population of over 30,000. This classification divides countries into four income groups: low, lower-middle, upper-middle, and high income. These groups are based on Gross National Income (GNI) per capita, measured in US dollars. The classification is used for analytical purposes and to determine eligibility for World Bank loans and other support programs. This classification stays the same throughout the fiscal year (from July 1 to June 30).

According to Table 1, income group definitions fall into the following categories for 2024 Fiscal Year:

• Low-income countries: GNI per capita of $1,145 or less.
• Lower-middle-income countries: GNI per capita between $1,146 and $4,515.
• Upper-middle-income countries: GNI per capita between $4,516 and $14,005.
• High-income countries: GNI per capita of $14,006 or more.

library(gt)
library(dplyr)
library(tidyverse)

# Create the data frame
income_thresholds <- tibble::tibble(
  ` ` = c("July 1, 2024 - for FY25 (new)", "July 1, 2023 - for FY24 (previous)"),
  `Low Income` = c("≤ 1,145", "≤ 1,135"),
  `Lower-middle Income` = c("1,146 - 4,515", "1,136 - 4,465"),
  `Upper-middle Income` = c("4,516 - 14,005", "4,466 - 13,845"),
  `High Income` = c("> 14,005", "> 13,845")
)

# Create the gt table
income_thresholds_table <- income_thresholds %>%
  gt() %>%
  # Add a title (optional)
  # tab_header(
  #   title = "World Bank Income Classifications (Gross National Income per capita, US$)"
  # ) %>%
  # Style the header
  cols_align("center") %>%
  cols_width(
    ` ` ~ px(200),
    everything() ~ px(150)
  ) %>%
  # Add color to the cells
  tab_style(
    style = cell_fill(color = "aliceblue"), # Light blue
    locations = cells_body(
      columns = `Low Income`
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "skyblue"), # Plum
    locations = cells_body(
      columns = `Lower-middle Income`
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#90EE90"), # Light green
    locations = cells_body(
      columns = `Upper-middle Income`
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#3CB371"), # Medium sea green
    locations = cells_body(
      columns = `High Income`
    )
  ) %>%
  # Add a source note (optional)
  # tab_source_note(
  #   source_note = "Source: World Bank"
  # )
  opt_table_font(
    font = "Arial"
  )
income_thresholds_table

Table 1: Classification

	Low Income	Lower-middle Income	Upper-middle Income	High Income
July 1, 2024 - for FY25 (new)	≤ 1,145	1,146 - 4,515	4,516 - 14,005	> 14,005
July 1, 2023 - for FY24 (previous)	≤ 1,135	1,136 - 4,465	4,466 - 13,845	> 13,845

Methodology

According to the World Bank, to keep income classification thresholds fixed in real terms, they are adjusted annually for inflation using the Special Drawing Rights (SDR) deflator, a weighted average of the GDP deflators of China, Japan, the United Kingdom, the United States, and the Euro Area.

library(ggplot2)
library(plotly)
library(dplyr)
library(scales)

# Create the data frame
income_data <- tibble::tibble(
  income_group = factor(c("Low Income", "Lower Middle", "Upper Middle", "High Income"),
                        levels = c("Low Income", "Lower Middle", "Upper Middle", "High Income")),
  lower_bound = c(0, 1146, 4516, 14006),
  upper_bound = c(1145, 4515, 14005, Inf),
  midpoint = c(572.5, 2830.5, 9260.5, 20000), # Approximate midpoints for visualization
  color_group = c("Low Income", "Lower Middle", "Upper Middle", "High Income") # For consistent coloring
) %>%
  mutate(income_range_label = case_when(
    upper_bound == Inf ~ paste0(">", format(lower_bound - 1, big.mark = ",")),
    TRUE ~ paste0("$", format(lower_bound, big.mark = ","), "-$", format(upper_bound, big.mark = ","))
  ))

# Define colors to match the image
income_colors <- c("Low Income" = "#FFB347", # Light orange
                   "Lower Middle" = "#0099f9", # Light blue
                   "Upper Middle" = "#00124D", # dark blue
                   "High Income" = "#007E33")  # Dark green

# Create the ggplot
income_plot <- ggplot(income_data, aes(x = income_group, y = midpoint, fill = color_group,
                                       text = paste("Income Group:", income_group, "<br>",
                                                    "Range:", income_range_label))) +
  geom_bar(stat = "identity", width = 0.6) +
  geom_text(aes(label = income_range_label, y = midpoint + (max(midpoint) * 0.05)), # Adjust y for spacing
           vjust = "bottom", hjust = "center", size = 3.5) + # Adjust appearance
  scale_fill_manual(values = income_colors) +
  scale_y_continuous(labels = scales::comma, expand = c(0, 0), limits = c(0, max(income_data$midpoint) * 1.2)) + # Adjust limits
  labs(title = "Figure 1: Income Groups in 2025",
       subtitle = "Annual GNI per capita (US$)",
       x = NULL,
       y = "Approximate Midpoint (US$)",
       fill = "Income Group") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
        plot.subtitle = element_text(size = 12, hjust = 0.5),
        legend.position = "none") # Remove legend as colors are self-explanatory

# Convert to plotly
income_plotly <- ggplotly(income_plot, tooltip = "text")

# Display the plotly chart
income_plotly

Figure 1: Bar Chart Showing Income Classification

Some key highlights include (Figure 2):

• Globally, the share of low-income countries has declined significantly, dropping from 30.7% in 2000 to just 11.9% today.

• China transitioned out of low-income status in 1999 and, by 2010, had achieved upper-middle-income status, a position it holds today.

• Similarly, India, which was classified as a low-income country two decades ago, has experienced rapid growth driven by infrastructure investments, economic reforms, and the expansion of its services sector.

Figure 2: Tree map

The World Bank also highlighted the following trends

• 100% of South Asian countries were classified as low-income countries in 1987, whereas this share has fallen to just 13% in

• In the Middle East and North Africa there is a higher share of low-income countries in 2023 (10%) than in 1987, when no countries were classified to this category.

• In Latin America and the Caribbean, the share of high-income countries has climbed from 9% in 1987 to 44% in 2023.

• Europe and Central Asia has a slightly lower share of high-income countries in 2023 (69%) than it did in 1987 (71%).

The ECCU

The Eastern Caribbean Currency Union (ECCU) has a combination of both high-income and upper-middle-income economies (Figure 3). This reflects the diverse economic conditions across the member countries. Factors like tourism, financial services, and in some cases, citizenship-by-investment programs, contribute to the GNI per capita, which determines these classifications. However, these economies are also vulnerable to external shocks, such as natural disasters and fluctuations in the global economy, which can impact their economic stability and potentially shift their income group status.

library(ggplot2)
library(dplyr)
library(wbstats)
library(tidyr)

# Define the countries and years
countries <- c("VC", "KN", "LC", "GD", "DM", "AG")  # Country codes
years <- c(2022, 2023, 2024)
indicator_code <- "NY.GNP.PCAP.CD" # GNI per capita (Atlas method, current US$)

# Fetch the data from the World Bank
gni_data <- wb_data(
  indicator = indicator_code,
  country = countries,
  start_date = min(years),
  end_date = max(years)
) %>%
  # Select relevant columns
  select(country, date, iso2c, iso3c, NY.GNP.PCAP.CD) %>%
  # Filter for the desired years
  filter(date %in% years) %>%
  # Rename columns for clarity
  rename(Country = country, Year = date, GNI_per_capita = NY.GNP.PCAP.CD) %>%
  # Get income group
  mutate(Income_Group = rep(c("High Income","Upper Middle", "Upper Middle", "High Income", "Upper Middle","Upper Middle"), each = 3)) #Made up, change as needed
 
gni_data_filtered <- gni_data %>%
  group_by(Year) %>%
  filter(!all(is.na(GNI_per_capita))) %>%
  ungroup()

# Create the horizontal bar chart with reversed order
gni_chart <- gni_data_filtered %>%
  arrange(desc(GNI_per_capita)) %>%  # Arrange the data by value in descending order
  mutate(Country = factor(Country, levels = rev(c("St. Kitts and Nevis", "Antigua and Barbuda", "St. Lucia", "Grenada", "St. Vincent and the Grenadines", "Dominica")))) %>%
  ggplot(aes(x = Country, y = GNI_per_capita, fill = Income_Group)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_y_continuous(labels = function(x) paste0(x/1000, "k")) +
  facet_wrap(~Year, scales = "free_y") +
  coord_flip() +
  scale_fill_manual(values = c(
    "Low Income" = "cornflowerblue",
    "Lower Middle" = "#AEEA00",
    "Upper Middle" = "#00124D",
    "High Income" = "#FCB001"
  )) +
  labs(
    title = "Figure 3: GNI per Capita by Country and Year",
    x = "Country",
    y = "GNI per Capita (US$)",
    fill = "Income Group"
  ) +
  theme_minimal() +
  theme(plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
        axis.text.x = element_text(hjust = 1),
        legend.title = element_blank(),
        legend.key.size = unit(0.5, "lines"),
        legend.position = "bottom"
  )

# Display the chart
print(gni_chart)   

Figure 3: Classification and GNI per capita for ECCU Independent Countries

The reliance on specific sectors and inherent vulnerabilities present both opportunities and challenges for the ECCU. While the high and upper-middle-income statuses indicate a degree of economic development, these countries are vulnerable to shocks. For instance, countries like Antigua and Barbuda, despite facing significant vulnerabilities to economic and environmental shocks due to their small size and reliance on sectors like tourism, often find it difficult to secure IDA loans. This is because the International Development Association (IDA), the World Bank’s concessional lending arm, primarily allocates resources based on a country’s Gross National Income (GNI) per capita, which places Antigua and Barbuda and St Kitts and Nevis in the high-income category. While this income level suggests a degree of economic development, it does not fully account for the unique challenges these nations face, where a single hurricane or a global recession can have devastating and long-lasting impacts, creating a mismatch between their actual needs and their eligibility for concessional financing.

ECCU member countries must therefore pursue sustainable and diversified growth strategies. This includes building resilience to climate change, promoting inclusive economic policies, and addressing issues like high public debt. By doing so, ECCU countries can aim to maintain or even improve their income group status, leading to greater prosperity and improved living standards for their citizens.