Country

Indicator

Value type

Values present

External debt stocks, total (DOD, current US$)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	3,428,116,976	+1.03%	90
Angola	57,031,749,206	-5.65%	26
Albania	11,363,954,733	+9.06%	65
Argentina	266,167,259,703	-0.275%	7
Armenia	15,838,832,193	-0.579%	52
Azerbaijan	14,532,679,536	-4.87%	57
Burundi	1,042,821,518	+9.19%	108
Benin	12,482,753,039	+16.3%	62
Burkina Faso	10,397,389,534	+5.55%	68
Bangladesh	101,447,458,740	+4.56%	18
Bosnia & Herzegovina	14,010,190,698	+4.67%	59
Belarus	36,704,544,070	-7.91%	35
Belize	1,509,919,205	+2.85%	103
Bolivia	16,306,937,351	+2.36%	51
Brazil	607,115,489,833	+4.77%	3
Bhutan	3,269,070,740	+3.44%	93
Botswana	2,078,815,547	+3.8%	100
Central African Republic	1,020,876,080	+0.577%	109
China	2,420,210,835,504	-1.14%	1
Côte d’Ivoire	36,547,882,004	+15%	36
Cameroon	15,331,905,271	+1.47%	54
Congo - Kinshasa	11,066,891,639	+13.9%	66
Congo - Brazzaville	7,779,155,931	-1.05%	73
Colombia	197,505,176,816	+7.11%	8
Comoros	381,715,460	+5.42%	118
Cape Verde	2,451,446,195	+1.7%	98
Costa Rica	39,025,118,295	+0.886%	34
Djibouti	3,428,662,710	+8.16%	89
Dominica	598,135,919	-0.118%	113
Dominican Republic	52,256,628,609	+8.32%	27
Algeria	7,315,265,960	+2.61%	75
Ecuador	60,563,619,919	-0.198%	24
Egypt	168,062,300,504	+3.05%	11
Eritrea	725,941,139	-0.16%	111
Ethiopia	33,290,272,173	+8.66%	39
Fiji	3,347,681,716	+12%	92
Gabon	7,588,099,792	-5.12%	74
Georgia	24,467,544,298	+2.02%	43
Ghana	43,741,980,956	+2.51%	30
Guinea	5,164,136,104	+5.31%	81
Gambia	1,325,048,605	+12.3%	104
Guinea-Bissau	1,128,036,823	+5.17%	106
Grenada	727,141,919	+3.91%	110
Guatemala	25,364,845,252	+0.377%	42
Guyana	2,952,573,717	+42.6%	96
Honduras	12,820,575,275	+1.44%	60
Haiti	2,637,609,832	+2.97%	97
Indonesia	406,054,424,835	+2.53%	6
India	646,787,065,275	+5.08%	2
Iran	9,900,872,535	+4.66%	71
Iraq	20,331,392,379	-10.5%	48
Jamaica	15,349,418,849	-2.85%	53
Jordan	44,629,840,155	+8.25%	29
Kazakhstan	163,154,522,933	+1.35%	13
Kenya	42,910,026,054	+3.26%	31
Kyrgyzstan	10,115,052,290	+3.79%	69
Cambodia	22,533,605,679	+0.266%	46
Laos	20,350,257,787	+6.39%	47
Lebanon	66,296,461,063	-1.17%	22
Liberia	2,078,056,984	+9.14%	101
St. Lucia	1,086,264,196	+19.5%	107
Sri Lanka	61,705,953,520	+5.09%	23
Lesotho	1,775,614,027	-1.87%	102
Morocco	69,267,283,213	+6.61%	20
Moldova	10,638,677,756	+9.41%	67
Madagascar	6,452,328,515	+8.67%	79
Maldives	4,000,222,208	+0.186%	86
Mexico	595,917,670,218	+1.79%	4
North Macedonia	12,614,290,436	+8.37%	61
Mali	6,457,154,422	+3.66%	78
Myanmar (Burma)	12,162,102,319	-2.99%	63
Montenegro	8,616,498,559	-1.36%	72
Mongolia	34,321,287,449	+2.6%	38
Mozambique	66,847,716,823	+3.83%	21
Mauritania	4,603,556,865	+0.383%	83
Mauritius	19,252,338,501	+9.06%	50
Malawi	3,604,446,499	+7.92%	88
Niger	5,612,994,783	+3.4%	80
Nigeria	102,481,701,292	-0.6%	17
Nicaragua	15,163,412,737	+1.78%	56
Nepal	9,968,496,284	+8.54%	70
Pakistan	130,847,370,987	+2.46%	15
Peru	90,067,566,772	+1.39%	19
Philippines	121,402,087,836	+9.14%	16
Papua New Guinea	15,320,623,208	-21.9%	55
Paraguay	26,135,416,108	+5.45%	41
Rwanda	11,383,983,446	+17.5%	64
Sudan	22,580,569,039	+0.624%	45
Senegal	39,950,111,017	+23.9%	33
Solomon Islands	527,817,630	+10.6%	114
Sierra Leone	2,381,922,454	-0.768%	99
El Salvador	22,741,503,455	+6.72%	44
Somalia	3,022,833,727	-26.1%	95
Serbia	49,000,132,003	+10.2%	28
São Tomé & Príncipe	454,273,049	+7.75%	116
Suriname	4,047,562,767	-3.51%	85
Eswatini	1,240,687,606	-0.574%	105
Syria	4,875,542,720	+0.278%	82
Chad	3,214,041,697	-3.2%	94
Togo	3,375,209,273	+4.83%	91
Thailand	193,626,206,257	-3.69%	9
Tajikistan	6,872,765,847	+2.22%	77
Turkmenistan	3,917,747,615	-15.7%	87
Timor-Leste	307,384,038	+6.11%	119
Tonga	196,453,499	-6.42%	120
Tunisia	41,278,696,730	+0.676%	32
Turkey	499,842,484,724	+9.16%	5
Tanzania	34,597,954,343	+13.9%	37
Uganda	19,393,466,640	-4.97%	49
Ukraine	176,645,481,677	+23.3%	10
Uzbekistan	59,184,351,624	+20.8%	25
St. Vincent & Grenadines	628,508,651	+13.5%	112
Vietnam	141,849,555,321	-3.26%	14
Vanuatu	470,663,727	+1.56%	115
Samoa	432,494,880	-8.12%	117
Kosovo	4,242,269,393	+15.9%	84
Yemen	7,283,022,249	-0.926%	76
South Africa	165,786,604,626	-3.46%	12
Zambia	29,029,302,611	+2.51%	40
Zimbabwe	14,213,387,011	+2.78%	58

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'DT.DOD.DECT.CD'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'DT.DOD.DECT.CD'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

External debt stocks, total (DOD, current US$)

Year: 2023

Acquire full dataset with Python or R from source