Country

Indicator

Value type

Values present

Total debt service (% of GNI)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	0.253	+38.3%	114
Angola	16.3	+3.04%	8
Albania	3.65	-3.32%	65
Argentina	7.24	+26.7%	29
Armenia	11.8	+12.9%	13
Azerbaijan	2.65	+20.2%	82
Burundi	1.68	+68.7%	97
Benin	4.87	+63.8%	46
Burkina Faso	4.41	-25.9%	52
Bangladesh	1.64	+15.6%	99
Bosnia & Herzegovina	6.11	+46.7%	36
Belarus	11.3	+17.7%	15
Belize	3.86	+36.4%	62
Bolivia	3.9	-30.6%	60
Brazil	10.7	+60.8%	16
Bhutan	4.86	+1.15%	47
Botswana	1.28	+46.8%	105
Central African Republic	1.41	+75.7%	102
China	2.13	-9.87%	90
Côte d’Ivoire	4.64	+51.3%	50
Cameroon	3.53	-15.6%	69
Congo - Kinshasa	0.756	+22.5%	111
Congo - Brazzaville	5.31	-14.8%	43
Colombia	8	-1.83%	24
Comoros	1.31	+249%	104
Cape Verde	6.47	+23.4%	33
Costa Rica	6.31	+56.1%	35
Djibouti	2.52	+17.8%	84
Dominica	7.16	+9.45%	30
Dominican Republic	4.46	+35.7%	51
Algeria	0.192	+41%	115
Ecuador	5.62	+20.5%	40
Egypt	5.65	+45.1%	39
Fiji	4.26	+4.07%	55
Gabon	6.33	+108%	34
Georgia	12.5	-12.7%	12
Ghana	1.71	-73.6%	95
Guinea	1.49	+38.5%	101
Gambia	2.33	+22.2%	87
Guinea-Bissau	2.76	-17.3%	79
Grenada	4.67	+0.422%	49
Guatemala	1.71	-61.3%	96
Guyana	2.72	+70.7%	80
Honduras	5.49	-5.09%	41
Haiti	0.324	+40%	113
Indonesia	4.38	-25.5%	53
India	2.29	+15.1%	88
Iran	0.0894	+99.6%	117
Iraq	1.66	+2.49%	98
Jamaica	12.7	+43.3%	10
Jordan	7.66	-19.7%	28
Kazakhstan	19	-12.9%	7
Kenya	3.65	+23.2%	66
Kyrgyzstan	7.87	+53%	25
Cambodia	5.94	-8.52%	38
Laos	8.36	+8.46%	21
Lebanon	21.8	+15.4%	4
Liberia	3.02	+40.7%	76
St. Lucia	3.07	+27.4%	75
Sri Lanka	3.48	-24.6%	70
Lesotho	5.07	+74.4%	44
Morocco	4.07	-12.8%	58
Moldova	7.79	+64.7%	27
Madagascar	1.72	+18.9%	94
Maldives	8.55	-32.7%	20
Mexico	3.1	-21%	74
North Macedonia	11.6	+42.8%	14
Mali	1.52	+5.81%	100
Myanmar (Burma)	1.39	-10.1%	103
Montenegro	10.3	-31.6%	18
Mongolia	26.8	+4.33%	2
Mozambique	22.7	-31.3%	3
Mauritania	3.68	+2.04%	64
Mauritius	12.6	+97.8%	11
Malawi	1.22	+6.36%	106
Niger	1.16	-42.6%	107
Nigeria	2.61	+51.8%	83
Nicaragua	13.8	-18.4%	9
Nepal	1.16	+40.6%	108
Pakistan	4.82	+6.95%	48
Peru	5.42	+67.3%	42
Philippines	2.68	+27.1%	81
Papua New Guinea	19.6	+31.4%	6
Paraguay	4.36	-17%	54
Rwanda	3.88	+45.1%	61
Sudan	0.533	+43%	112
Senegal	9.05	+25.2%	19
Solomon Islands	1.77	+142%	93
Sierra Leone	2.44	+42.7%	85
El Salvador	29	+157%	1
Somalia	3.32	+1,592%	72
Serbia	7.86	-12.4%	26
São Tomé & Príncipe	0.942	-35.1%	110
Suriname	20.6	+87%	5
Eswatini	3.92	+101%	59
Syria	0.149	+229%	116
Chad	2.2	-7.36%	89
Togo	2.8	-7.22%	78
Thailand	8.27	+47.3%	22
Tajikistan	3.78	+5.84%	63
Turkmenistan	1.93	-1.16%	92
Timor-Leste	1.03	+109%	109
Tonga	3.17	+55.8%	73
Tunisia	10.5	+10.2%	17
Turkey	6.9	-10.6%	32
Tanzania	2.89	+7.89%	77
Uganda	4.99	+139%	45
Ukraine	4.2	-34.8%	57
Uzbekistan	8.24	+4.75%	23
St. Vincent & Grenadines	3.55	+0.652%	68
Vietnam	6.96	+4.45%	31
Vanuatu	1.99	+6.72%	91
Samoa	4.23	-12.5%	56
Kosovo	2.41	-24.7%	86
South Africa	6.1	-11.9%	37
Zambia	3.61	-32.4%	67
Zimbabwe	3.34	+135%	71

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.TDS.DECT.GN.ZS'

# 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.TDS.DECT.GN.ZS'

# 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

Total debt service (% of GNI)

Year: 2023

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