Country

Indicator

Value type

Values present

External debt stocks, long-term (DOD, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	1,874,433,293	-0.145%	96
Angola	46,651,516,910	-7.93%	27
Albania	9,585,649,421	+12.7%	62
Argentina	163,605,136,250	-0.818%	8
Armenia	12,062,333,251	+1.1%	55
Azerbaijan	13,210,640,306	-6.09%	53
Burundi	607,085,975	+4.97%	109
Benin	9,458,471,906	+14.3%	64
Burkina Faso	9,765,043,223	+4.81%	61
Bangladesh	83,284,235,879	+10.3%	17
Bosnia & Herzegovina	10,189,244,733	+4.89%	59
Belarus	24,952,537,507	-12%	38
Belize	1,440,022,704	+2.83%	99
Bolivia	14,984,901,072	+1.42%	50
Brazil	508,551,146,192	+2.99%	3
Bhutan	3,233,877,816	+3.63%	85
Botswana	1,457,909,838	-6.71%	98
Central African Republic	442,796,838	+3.01%	112
China	1,084,231,056,087	-4.45%	1
Côte d’Ivoire	29,561,955,091	+10.3%	35
Cameroon	12,418,492,118	+0.494%	54
Congo - Kinshasa	6,467,510,547	+16.2%	72
Congo - Brazzaville	6,875,467,270	-3.72%	70
Colombia	168,933,403,698	+7.97%	7
Comoros	320,487,437	+5.7%	117
Cape Verde	1,933,793,921	+0.702%	95
Costa Rica	31,377,966,114	+5.07%	34
Djibouti	2,846,358,659	+16.7%	87
Dominica	378,619,161	+6.13%	114
Dominican Republic	46,917,295,606	+7.72%	26
Algeria	1,274,563,508	-1.24%	102
Ecuador	49,590,821,905	+0.44%	24
Egypt	119,259,905,561	+7.35%	12
Eritrea	641,297,476	-0.768%	108
Ethiopia	31,914,034,150	+9.87%	33
Fiji	2,337,596,197	+3.22%	91
Gabon	6,090,214,800	-1.66%	73
Georgia	19,238,122,679	+0.779%	44
Ghana	36,353,558,558	+4.18%	29
Guinea	4,021,210,042	+6.59%	79
Gambia	982,634,487	+10.2%	105
Guinea-Bissau	1,018,181,455	+4.29%	104
Grenada	605,738,922	+7.06%	110
Guatemala	24,461,739,265	+1.28%	40
Guyana	2,541,674,086	+52.4%	89
Honduras	10,345,182,402	-2.41%	58
Haiti	2,084,800,488	-1.52%	93
Indonesia	342,815,206,785	+0.991%	5
India	498,265,210,330	+7.01%	4
Iran	1,115,504,480	-0.762%	103
Iraq	15,698,548,004	-12.5%	48
Jamaica	11,120,315,391	-7.39%	56
Jordan	26,122,608,969	+14.4%	37
Kazakhstan	141,946,187,973	-0.496%	9
Kenya	36,295,438,589	+1.94%	30
Kyrgyzstan	8,414,743,560	+3.48%	68
Cambodia	17,823,586,872	+1.68%	46
Laos	20,006,050,785	+10.1%	43
Lebanon	48,084,690,342	-4.78%	25
Liberia	1,351,029,051	+17.8%	100
St. Lucia	930,117,382	+26.3%	106
Sri Lanka	51,602,903,242	+7.95%	22
Lesotho	1,595,453,298	-1.12%	97
Morocco	55,330,533,879	+8.2%	21
Moldova	6,637,628,437	+5.9%	71
Madagascar	4,980,338,748	+12.1%	77
Maldives	3,835,721,648	+10.3%	80
Mexico	520,492,250,956	+0.962%	2
North Macedonia	8,934,140,529	+12.2%	65
Mali	5,540,991,605	+4.61%	76
Myanmar (Burma)	10,487,748,018	-3.18%	57
Montenegro	8,269,469,317	-0.741%	69
Mongolia	32,028,335,651	+2.99%	32
Mozambique	64,843,341,463	+3.63%	20
Mauritania	3,749,372,024	-1.93%	81
Mauritius	9,496,040,007	+5.46%	63
Malawi	2,876,075,401	+13%	86
Niger	4,833,771,078	+3.98%	78
Nigeria	75,667,394,754	+3.67%	18
Nicaragua	13,457,709,764	+2.86%	52
Nepal	8,817,681,854	+7.15%	67
Pakistan	110,436,967,743	+2.81%	14
Peru	74,774,001,689	-1.34%	19
Philippines	100,555,010,441	+10.6%	16
Papua New Guinea	14,265,408,991	-9.17%	51
Paraguay	22,985,461,577	+4.08%	42
Rwanda	10,127,366,897	+16.4%	60
Sudan	15,364,548,060	+0.731%	49
Senegal	32,825,557,406	+21.5%	31
Solomon Islands	423,404,833	+20.9%	113
Sierra Leone	1,344,715,324	-0.163%	101
El Salvador	18,951,426,261	+5.44%	45
Somalia	1,996,933,695	-13.6%	94
Serbia	45,317,495,692	+14.9%	28
São Tomé & Príncipe	370,751,011	+9.32%	115
Suriname	3,293,605,027	-3.02%	84
Eswatini	926,069,589	-4.24%	107
Syria	3,501,530,081	+0.102%	83
Chad	2,215,097,557	-3.27%	92
Togo	2,679,689,484	+9.71%	88
Thailand	120,419,572,088	-5.44%	11
Tajikistan	5,674,280,260	+1.66%	75
Turkmenistan	3,517,252,539	-15.2%	82
Timor-Leste	254,756,411	+3.38%	119
Tonga	151,362,668	-8.38%	120
Tunisia	24,795,860,931	+2.42%	39
Turkey	318,390,696,397	+5.45%	6
Tanzania	28,460,964,290	+15.6%	36
Uganda	17,000,767,514	-0.881%	47
Ukraine	139,131,489,929	+23.5%	10
Uzbekistan	50,995,848,160	+23.5%	23
St. Vincent & Grenadines	575,628,523	+15%	111
Vietnam	104,835,215,346	-1.64%	15
Vanuatu	362,290,706	-3.13%	116
Samoa	308,627,273	-9.67%	118
Kosovo	2,525,228,737	+16.1%	90
Yemen	5,814,628,864	-1.38%	74
South Africa	113,666,637,732	-4.03%	13
Zambia	23,210,616,192	-1.77%	41
Zimbabwe	8,911,016,023	+4.89%	66

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.DLXF.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.DLXF.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, long-term (DOD, current US$)

Year: 2023

Acquire full dataset with Python or R from source