Country

Indicator

Value type

Values present

Grants, excluding technical cooperation (BoP, current US$)

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	3,749,710,000	-13.4%	5
Angola	158,570,000	-30.2%	81
Albania	227,500,000	-13.9%	71
Argentina	125,350,000	+106%	90
Armenia	106,320,000	-18.8%	98
Azerbaijan	58,790,000	+0.376%	116
Burundi	544,730,000	+8.67%	42
Benin	510,490,000	-13.1%	46
Burkina Faso	1,122,730,000	-12.8%	23
Bangladesh	1,257,650,000	-22%	19
Bosnia & Herzegovina	231,600,000	-19.1%	69
Belarus	78,440,000	+23.1%	108
Belize	16,750,000	-70.9%	129
Bolivia	198,220,000	+82.1%	75
Brazil	173,500,000	-25.1%	79
Bhutan	54,570,000	-10.5%	118
Botswana	71,730,000	-20.3%	112
Central African Republic	598,790,000	+4.2%	40
China	109,170,000	-31.3%	97
Côte d’Ivoire	793,460,000	+34.9%	30
Cameroon	724,060,000	+29.7%	36
Congo - Kinshasa	2,724,450,000	+1%	7
Congo - Brazzaville	110,170,000	+7%	96
Colombia	1,058,080,000	-14.2%	24
Comoros	95,270,000	-19.4%	102
Cape Verde	49,590,000	-25.9%	120
Costa Rica	69,990,000	-47.3%	113
Cuba	94,960,000	-8.8%	103
Djibouti	128,770,000	+10.9%	89
Dominica	20,210,000	-45.5%	126
Dominican Republic	105,080,000	-16.9%	99
Algeria	76,460,000	-32.1%	109
Ecuador	195,870,000	+1.99%	76
Egypt	5,559,280,000	-1.19%	3
Eritrea	55,290,000	+24.6%	117
Ethiopia	4,050,010,000	+21.1%	4
Fiji	79,930,000	-71.9%	107
Micronesia (Federated States of)	148,150,000	+13.4%	85
Gabon	18,220,000	-60.7%	128
Georgia	229,300,000	-16.5%	70
Ghana	527,220,000	-28.5%	43
Guinea	321,580,000	-18.4%	60
Gambia	201,970,000	+42.2%	73
Guinea-Bissau	115,360,000	+11.6%	93
Equatorial Guinea	12,450,000	-15%	132
Grenada	1,860,000	-93.5%	136
Guatemala	390,080,000	-13.4%	55
Guyana	23,520,000	-41.8%	125
Honduras	293,120,000	-19.9%	63
Haiti	820,770,000	-14%	29
Indonesia	836,730,000	-19.2%	28
India	690,710,000	-11.9%	37
Iran	122,110,000	-18.7%	91
Iraq	746,130,000	-43.5%	32
Jamaica	85,130,000	+31.3%	104
Jordan	1,341,540,000	-41.9%	17
Kazakhstan	73,960,000	+1.34%	111
Kenya	1,343,580,000	-4.16%	16
Kyrgyzstan	338,790,000	+5.53%	57
Cambodia	492,310,000	-3.01%	48
Kiribati	74,320,000	+32.7%	110
Laos	332,490,000	-13.4%	59
Lebanon	1,228,410,000	-6%	20
Liberia	347,270,000	-24.5%	56
Libya	192,710,000	-31.6%	77
St. Lucia	11,500,000	-78.3%	134
Sri Lanka	238,820,000	-1.42%	67
Lesotho	136,640,000	-8.8%	86
Morocco	414,530,000	-24.2%	54
Moldova	515,010,000	+82.3%	44
Madagascar	667,220,000	+12.1%	38
Maldives	100,190,000	+12.7%	101
Mexico	316,300,000	+25.2%	61
Marshall Islands	135,730,000	+26.1%	87
North Macedonia	113,700,000	-34.5%	95
Mali	980,750,000	-15.5%	26
Myanmar (Burma)	732,100,000	-19.8%	33
Montenegro	43,060,000	-33.3%	122
Mongolia	148,490,000	-12.3%	84
Mozambique	2,489,480,000	+17.9%	8
Mauritania	275,290,000	-31.1%	64
Mauritius	26,710,000	-28.1%	124
Malawi	1,053,970,000	+10.9%	25
Malaysia	46,920,000	-23.3%	121
Namibia	155,160,000	+2.72%	83
Niger	1,210,680,000	-6.88%	21
Nigeria	2,001,720,000	-6.99%	10
Nicaragua	134,630,000	-25.9%	88
Nepal	480,810,000	-28.3%	49
Nauru	31,350,000	+2.32%	123
Pakistan	1,166,590,000	+7.39%	22
Panama	53,500,000	-1.78%	119
Peru	335,970,000	-0.193%	58
Philippines	447,750,000	-10.8%	52
Palau	18,670,000	-14%	127
Papua New Guinea	436,980,000	-25.4%	53
North Korea	11,640,000	-42.7%	133
Paraguay	64,420,000	-25.3%	114
Palestinian Territories	2,073,570,000	+4.34%	9
Rwanda	726,070,000	-16%	35
Sudan	1,545,440,000	-56%	14
Senegal	512,020,000	-9.82%	45
Solomon Islands	199,870,000	+0.513%	74
Sierra Leone	476,790,000	-6.67%	51
El Salvador	207,420,000	+14.8%	72
Somalia	1,853,320,000	-24.5%	12
Serbia	296,810,000	-17.1%	62
South Sudan	1,981,750,000	+5.47%	11
São Tomé & Príncipe	80,240,000	+28.5%	106
Suriname	13,750,000	-31.5%	131
Eswatini	103,960,000	-7.81%	100
Syria	8,017,410,000	-14.8%	2
Chad	661,560,000	+7.37%	39
Togo	272,340,000	+16.4%	65
Thailand	169,070,000	+1.75%	80
Tajikistan	506,840,000	+25.1%	47
Turkmenistan	15,770,000	-35.9%	130
Timor-Leste	156,920,000	-12.5%	82
Tonga	116,700,000	+23.9%	92
Tunisia	551,910,000	+40.8%	41
Turkey	1,453,650,000	-28.7%	15
Tuvalu	60,950,000	+90.5%	115
Tanzania	1,284,040,000	-10.7%	18
Uganda	1,690,290,000	-4.28%	13
Ukraine	15,634,020,000	+1,614%	1
Uzbekistan	187,150,000	+8.04%	78
St. Vincent & Grenadines	5,000,000	-72.9%	135
Venezuela	255,890,000	+1.07%	66
Vietnam	480,280,000	-16.7%	50
Vanuatu	80,710,000	-35.9%	105
Samoa	113,960,000	+55.5%	94
Kosovo	236,650,000	-15.8%	68
Yemen	3,302,670,000	-14.8%	6
South Africa	872,510,000	-20.5%	27
Zambia	782,400,000	-8.42%	31
Zimbabwe	731,160,000	-22.4%	34

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 = 'BX.GRT.EXTA.CD.WD'

# 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 <- 'BX.GRT.EXTA.CD.WD'

# 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

Grants, excluding technical cooperation (BoP, current US$)

Year: 2022

Acquire full dataset with Python or R from source