Country

Indicator

Value type

Values present

Use of IMF credit (DOD, current US$)

Source: worldbank.org, 01.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	1,127,013,175	+0.437%	52
Angola	5,449,429,790	-2.41%	16
Albania	524,728,312	-17%	83
Argentina	47,612,397,027	-8.95%	2
Armenia	669,625,943	-11.6%	75
Azerbaijan	709,747,230	+0.813%	72
Burundi	435,441,543	+15.7%	87
Benin	1,141,974,133	+21%	51
Burkina Faso	632,346,312	+18.4%	76
Bangladesh	3,932,112,861	+31.9%	21
Bosnia & Herzegovina	930,090,965	-10.5%	60
Belarus	1,370,959,562	+0.813%	45
Belize	58,342,501	+0.813%	112
Bolivia	528,961,279	+0.813%	82
Brazil	18,072,742,641	+0.813%	5
Bhutan	34,268,924	+0.813%	120
Botswana	330,639,708	+0.813%	94
Central African Republic	504,139,051	+4.42%	86
China	48,576,699,225	+0.813%	1
Côte d’Ivoire	4,002,292,913	+21.2%	19
Cameroon	2,055,437,787	+9.71%	36
Congo - Kinshasa	3,997,330,078	+12.3%	20
Congo - Brazzaville	706,466,847	+33.7%	73
Colombia	8,650,931,119	+0.813%	11
Comoros	60,773,607	+5.98%	111
Cape Verde	110,827,274	+24.6%	103
Costa Rica	2,225,619,181	+11.6%	35
Djibouti	103,894,867	+0.813%	105
Dominica	42,396,759	-2.96%	118
Dominican Republic	1,374,464,003	-9.71%	43
Algeria	4,127,862,452	+0.813%	18
Ecuador	9,233,103,013	-1.64%	10
Egypt	19,319,376,392	-11.2%	4
Eritrea	40,784,072	+0.813%	119
Ethiopia	1,161,232,458	-7.24%	50
Fiji	216,553,519	+0.813%	99
Gabon	1,373,901,844	-7.17%	44
Georgia	1,062,760,619	-1.97%	56
Ghana	3,630,001,073	+16.2%	26
Guinea	845,336,357	-4.4%	64
Gambia	251,088,094	+1.33%	98
Guinea-Bissau	108,381,410	+14.2%	104
Grenada	66,133,577	-4.62%	107
Guatemala	820,705,987	+0.813%	65
Guyana	350,621,193	+0.813%	92
Honduras	1,102,822,873	+5.17%	53
Haiti	552,721,343	+24.3%	80
Indonesia	8,634,611,050	+0.813%	12
India	22,201,707,945	+0.813%	3
Iran	6,500,342,126	+0.813%	15
Iraq	3,661,648,375	+0.813%	25
Jamaica	1,611,393,458	+2%	42
Jordan	2,527,873,185	-3.1%	31
Kazakhstan	1,950,690,960	+0.813%	37
Kenya	3,799,188,290	+12.1%	23
Kyrgyzstan	575,385,730	-10.3%	79
Cambodia	337,629,807	+0.813%	93
Laos	204,043,792	+0.813%	100
Lebanon	1,073,963,560	+0.813%	54
Liberia	726,948,775	-4.03%	70
St. Lucia	75,774,814	+0.813%	106
Sri Lanka	2,853,755,333	+22.7%	27
Lesotho	176,795,825	-2.59%	101
Morocco	3,915,614,351	+0.813%	22
Moldova	1,178,109,319	+16.9%	49
Madagascar	1,343,533,153	+2%	48
Maldives	66,023,560	+0.813%	108
Mexico	15,286,469,262	+0.813%	7
North Macedonia	522,570,907	-7.52%	84
Mali	850,776,827	-3.28%	63
Myanmar (Burma)	1,629,888,373	-2.64%	41
Montenegro	173,322,242	-9.75%	102
Mongolia	379,795,798	-12.8%	90
Mozambique	1,070,704,645	+4.28%	55
Mauritania	595,950,841	+7.11%	77
Mauritius	312,740,494	+0.813%	95
Malawi	714,608,098	+1.58%	71
Niger	735,782,328	-0.183%	68
Nigeria	7,873,960,877	-8.73%	13
Nicaragua	675,853,973	+0.813%	74
Nepal	733,277,430	+15.9%	69
Pakistan	11,532,067,245	+0.091%	8
Peru	2,534,348,083	+0.813%	30
Philippines	3,751,297,395	+0.813%	24
Papua New Guinea	1,036,547,079	+21.5%	57
Paraguay	386,704,055	+0.813%	89
Rwanda	813,435,479	+32.7%	66
Sudan	2,401,192,744	+0.813%	33
Senegal	2,266,353,610	+40.8%	34
Solomon Islands	63,593,796	-6.39%	109
Sierra Leone	880,219,765	-1.3%	61
El Salvador	878,089,194	-9.15%	62
Somalia	374,635,737	-42%	91
Serbia	2,712,979,312	+9.69%	28
São Tomé & Príncipe	61,460,542	-0.8%	110
Suriname	548,255,830	+41.8%	81
Eswatini	257,883,650	-4.08%	97
Syria	752,119,838	+0.813%	67
Chad	964,325,006	-3.18%	58
Togo	595,137,790	-1.97%	78
Thailand	5,432,055,170	+0.813%	17
Tajikistan	520,641,586	+0.813%	85
Turkmenistan	400,495,076	+0.813%	88
Timor-Leste	43,287,627	+0.813%	117
Tonga	45,090,831	+0.813%	116
Tunisia	2,711,233,799	-12.8%	29
Turkey	7,428,003,327	+0.813%	14
Tanzania	1,759,847,855	+21.9%	40
Uganda	1,907,267,824	+24.5%	38
Ukraine	16,476,879,008	+14.2%	6
Uzbekistan	1,369,578,984	-3.52%	46
St. Vincent & Grenadines	52,879,223	-0.237%	115
Vietnam	1,905,152,011	+0.813%	39
Vanuatu	55,851,021	-3.14%	114
Samoa	57,447,608	-0.534%	113
Kosovo	263,621,971	+12.7%	96
Yemen	950,933,802	-0.555%	59
South Africa	9,901,055,894	-4.14%	9
Zambia	2,450,342,126	+19%	32
Zimbabwe	1,363,159,095	+0.813%	47

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.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.DOD.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

Use of IMF credit (DOD, current US$)

Year: 2023

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