Country

Indicator

Value type

Values present

IMF repurchases and charges (TDS, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	27,956,332	+111%	76
Angola	472,054,596	+241%	13
Albania	138,467,623	+74.7%	35
Argentina	21,015,156,199	+12.9%	1
Armenia	128,935,842	+120%	38
Azerbaijan	26,985,145	+190%	79
Burundi	17,299,948	+122%	89
Benin	38,052,416	+152%	70
Burkina Faso	27,800,249	+5.35%	77
Bangladesh	351,267,125	+80.7%	16
Bosnia & Herzegovina	158,871,903	+326%	30
Belarus	52,125,299	+190%	60
Belize	2,218,524	+190%	110
Bolivia	20,112,124	+190%	87
Brazil	687,149,997	+190%	10
Bhutan	1,303,366	+190%	119
Botswana	12,570,744	+190%	97
Central African Republic	31,499,567	+145%	71
China	1,846,952,244	+190%	5
Côte d’Ivoire	434,387,721	+112%	14
Cameroon	90,830,065	+715%	46
Congo - Kinshasa	78,179,277	+190%	49
Congo - Brazzaville	11,985,097	+190%	98
Colombia	367,036,919	+170%	15
Comoros	8,447,199	+166%	101
Cape Verde	1,626,206	+189%	115
Costa Rica	161,450,620	+486%	29
Djibouti	2,327,916	+41.9%	109
Dominica	2,602,730	+15.6%	108
Dominican Republic	221,394,781	+864%	24
Algeria	156,946,870	+190%	31
Ecuador	774,236,720	+219%	9
Egypt	3,916,917,470	+110%	2
Eritrea	1,550,165	+190%	116
Ethiopia	144,805,690	+776%	32
Fiji	8,233,751	+190%	102
Gabon	190,631,609	+203%	26
Georgia	78,013,855	+115%	50
Ghana	178,184,932	+27.8%	27
Guinea	61,522,341	+77.3%	57
Gambia	9,953,340	+132%	100
Guinea-Bissau	7,171,848	+110%	105
Grenada	5,117,412	+29.1%	106
Guatemala	31,204,742	+190%	72
Guyana	13,331,152	+190%	93
Honduras	109,318,651	+740%	42
Haiti	23,959,120	+43%	83
Indonesia	328,300,137	+190%	18
India	844,139,557	+190%	8
Iran	247,151,266	+190%	23
Iraq	139,221,361	-59.4%	34
Jamaica	302,830,472	+102%	19
Jordan	267,065,951	+279%	22
Kazakhstan	74,167,792	+190%	52
Kenya	118,119,372	+26.3%	39
Kyrgyzstan	90,855,412	+214%	45
Cambodia	12,837,554	+190%	94
Laos	7,757,495	+190%	103
Lebanon	40,833,908	+190%	67
Liberia	55,399,056	+78.1%	59
St. Lucia	1,788,960	+189%	113
Sri Lanka	277,849,069	+57.1%	21
Lesotho	12,658,792	+44%	96
Morocco	164,122,720	+172%	28
Moldova	89,299,910	+111%	47
Madagascar	67,532,232	+152%	56
Maldives	1,428,767	+189%	118
Mexico	581,211,827	+190%	11
North Macedonia	70,064,257	+927%	55
Mali	49,514,565	+97.5%	62
Myanmar (Burma)	116,202,344	+449%	40
Montenegro	28,103,078	+869%	75
Mongolia	79,996,252	+135%	48
Mozambique	41,918,490	+34.8%	66
Mauritania	18,069,695	+49%	88
Mauritius	11,890,379	+190%	99
Malawi	40,009,466	+21.9%	69
Niger	43,283,222	+74.1%	65
Nigeria	1,168,649,660	+808%	6
Nicaragua	24,366,406	+183%	81
Nepal	20,656,416	+140%	86
Pakistan	1,859,755,113	+52.1%	4
Peru	96,359,698	+190%	44
Philippines	142,629,856	+190%	33
Papua New Guinea	21,021,945	+216%	85
Paraguay	14,702,555	+190%	92
Rwanda	55,461,756	+53.1%	58
Sudan	40,715,178	+190%	68
Senegal	134,147,972	+627%	37
Solomon Islands	7,214,537	+586%	104
Sierra Leone	75,468,490	+63.4%	51
El Salvador	134,909,714	+831%	36
Somalia	347,076,877	+7,474%	17
Serbia	101,227,643	+436%	43
São Tomé & Príncipe	2,079,783	+98.7%	112
Suriname	16,883,725	+221%	90
Eswatini	24,192,980	+501%	82
Syria	28,596,676	+190%	74
Chad	49,179,719	+88.6%	63
Togo	27,554,784	+289%	78
Thailand	206,533,474	+190%	25
Tajikistan	12,694,811	+107%	95
Turkmenistan	15,226,836	+190%	91
Timor-Leste	1,646,217	+189%	114
Tonga	1,009,875	+189%	120
Tunisia	568,191,508	+189%	12
Turkey	282,422,189	+190%	20
Tanzania	29,167,649	+190%	73
Uganda	26,478,206	+190%	80
Ukraine	3,415,647,200	+41.4%	3
Uzbekistan	112,479,013	+516%	41
St. Vincent & Grenadines	1,528,820	+71.3%	117
Vietnam	72,436,196	+190%	53
Vanuatu	4,260,953	+43.8%	107
Samoa	2,131,810	+5.5%	111
Kosovo	23,039,027	+38.3%	84
Yemen	48,664,776	+80.4%	64
South Africa	935,367,168	+504%	7
Zambia	71,758,499	+190%	54
Zimbabwe	51,829,140	+190%	61

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.DIMF.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.TDS.DIMF.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

IMF repurchases and charges (TDS, current US$)

Year: 2023

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