Country

Indicator

Value type

Values present

Multilateral debt service (% of public and publicly guaranteed debt service)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	100	0%	1
Angola	3.54	+23.8%	117
Albania	63.8	+5%	27
Argentina	35.1	+33.3%	75
Armenia	42.1	-20.2%	60
Azerbaijan	49.6	+2.38%	48
Burundi	73.2	-2.27%	15
Benin	37.8	-1.21%	66
Burkina Faso	75.5	-4.16%	14
Bangladesh	51.4	-6.74%	45
Bosnia & Herzegovina	60.6	-19.7%	30
Belarus	10.6	+10.8%	109
Belize	66.2	-8.15%	24
Bolivia	63.3	+66%	28
Brazil	14.7	+24.1%	102
Bhutan	33	+15%	78
Botswana	98.7	+4.06%	2
Central African Republic	79	+36.3%	11
China	8.64	+92.5%	112
Côte d’Ivoire	12	-21.2%	108
Cameroon	16.8	+68.6%	99
Congo - Kinshasa	37.6	-33.1%	67
Congo - Brazzaville	14.4	+18.4%	103
Colombia	17	-14.2%	98
Comoros	54	+55.7%	41
Cape Verde	40.5	-8.65%	63
Costa Rica	31.9	-23.9%	80
Djibouti	80.3	+18.8%	9
Dominica	47.8	+12.4%	52
Dominican Republic	18.1	+27.1%	95
Algeria	69.4	+12.1%	19
Ecuador	48.8	+26.6%	50
Egypt	35.4	+20.5%	72
Eritrea	37.3	+1.92%	68
Ethiopia	35.2	+101%	74
Fiji	67.3	+94.4%	23
Gabon	26.6	-15.2%	85
Georgia	66	+12.2%	25
Ghana	53.4	+342%	42
Guinea	25.4	-21.3%	87
Gambia	70.1	+6.02%	18
Guinea-Bissau	67.8	-11.8%	22
Grenada	48.2	+5.07%	51
Guatemala	58.8	+81.1%	35
Guyana	64.7	+7.43%	26
Honduras	56.3	+11.9%	38
Haiti	12.4	-35.3%	106
Indonesia	16.3	+107%	100
India	38.8	+26.5%	65
Iran	6.8	-85.5%	114
Iraq	12.2	+39.3%	107
Jamaica	27.4	+13.5%	83
Jordan	36.4	+176%	69
Kazakhstan	32.2	+67.8%	79
Kenya	35.8	-1.98%	71
Kyrgyzstan	42.5	-0.945%	59
Cambodia	28.3	+7.83%	82
Laos	18.7	-23.8%	94
Lebanon	96.4	+29.2%	5
Liberia	97.4	+7.03%	4
St. Lucia	43.6	-7.58%	58
Sri Lanka	59.7	+76.6%	33
Lesotho	70.6	-1.98%	17
Morocco	55.9	+62.2%	39
Moldova	97.9	+13.7%	3
Madagascar	58.7	-5.48%	36
Maldives	15.7	+109%	101
Mexico	17.9	+156%	96
North Macedonia	25.3	-49%	88
Mali	51.6	-2.25%	44
Myanmar (Burma)	17	+39%	97
Montenegro	19.5	+27.5%	92
Mongolia	13.3	+72%	105
Mozambique	25.5	+11.3%	86
Mauritania	59.5	+3.08%	34
Mauritius	5.03	-81.7%	116
Malawi	59.8	+6.84%	32
Niger	60.2	-2.94%	31
Nigeria	23.9	+9.43%	90
Nicaragua	90	+4.61%	7
Nepal	83.4	+4.01%	8
Pakistan	24.7	+57.6%	89
Peru	30.3	-26.2%	81
Philippines	40.6	+79.4%	62
Papua New Guinea	55.5	+30.2%	40
Paraguay	49.3	+34.2%	49
Rwanda	40.2	-19%	64
Sudan	51.1	+4.25%	46
Senegal	33.8	+6.53%	77
Solomon Islands	78.5	+0.292%	12
Sierra Leone	68.3	-0.604%	20
El Salvador	40.6	+28.5%	61
Somalia	100	0%	1
Serbia	34.2	-12.7%	76
São Tomé & Príncipe	50.7	+26.2%	47
Suriname	68.1	-25.8%	21
Eswatini	62.5	+0.59%	29
Chad	6.6	+19.5%	115
Togo	35.2	+2.07%	73
Thailand	10.5	+33.5%	110
Tajikistan	45.4	+24.5%	54
Turkmenistan	9	+62.8%	111
Timor-Leste	92.7	-7.28%	6
Tonga	19.3	-34.8%	93
Tunisia	43.6	+11.1%	57
Turkey	13.3	-2.81%	104
Tanzania	22	+7.88%	91
Uganda	43.8	-4.27%	56
Ukraine	75.7	+109%	13
Uzbekistan	53.4	+22.8%	43
St. Vincent & Grenadines	70.9	-7.56%	16
Vietnam	46.5	+11.5%	53
Vanuatu	27.1	+24.7%	84
Samoa	35.9	-2.41%	70
Kosovo	80	-0.854%	10
Yemen	100	0%	1
South Africa	8.43	+95.1%	113
Zambia	44.1	+64.4%	55
Zimbabwe	58	+164%	37

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.MLAT.PG.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.MLAT.PG.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

Multilateral debt service (% of public and publicly guaranteed debt service)

Year: 2023

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