Country

Indicator

Value type

Values present

Net official development assistance received (constant 2021 US$)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	3,959,649,902	-15.6%	7
Angola	96,580,002	-60.9%	108
Albania	338,769,989	-46.5%	79
Argentina	407,309,998	+162%	72
Armenia	322,529,999	+99.2%	81
Azerbaijan	47,480,000	+386%	123
Burundi	600,179,993	-1.33%	62
Benin	873,530,029	+2.77%	47
Burkina Faso	1,446,619,995	-10.2%	32
Bangladesh	5,743,330,078	+12.9%	4
Bosnia & Herzegovina	295,260,010	-47.7%	85
Belarus	11,680,000	-91.6%	133
Belize	20,790,001	-73.1%	126
Bolivia	356,760,010	-29.6%	77
Brazil	602,469,971	-45.9%	61
Bhutan	208,770,004	+63.9%	93
Botswana	88,500,000	-8.22%	112
Central African Republic	700,840,027	+6.31%	55
China	-380,579,987	-32.5%	136
Côte d’Ivoire	2,091,419,922	+32.7%	17
Cameroon	1,228,920,044	+7.71%	37
Congo - Kinshasa	3,330,239,990	-7.75%	10
Congo - Brazzaville	738,789,978	+262%	52
Colombia	1,941,589,966	+1.29%	21
Comoros	146,770,004	-4.47%	98
Cape Verde	86,559,998	-40.4%	113
Costa Rica	665,599,976	+739%	57
Cuba	145,179,993	-4.74%	101
Djibouti	65,570,000	-62.7%	117
Dominica	62,480,000	-21%	118
Dominican Republic	408,880,005	-8.82%	71
Algeria	232,929,993	+8.62%	91
Ecuador	397,079,987	+32%	74
Egypt	6,080,490,234	-23.4%	3
Eritrea	57,270,000	+30.3%	121
Ethiopia	4,993,240,234	+22.7%	5
Fiji	384,350,006	-36.9%	76
Micronesia (Federated States of)	146,880,005	+12.3%	97
Gabon	145,809,998	+48%	99
Georgia	400,000,000	-48%	73
Ghana	1,077,420,044	-13%	41
Guinea	525,710,022	-11.2%	66
Gambia	284,880,005	+14.9%	86
Guinea-Bissau	156,160,004	-7.65%	95
Equatorial Guinea	11,930,000	-6.58%	132
Grenada	-97,019,997	-242%	135
Guatemala	437,029,999	-14.7%	70
Guyana	204,809,998	+47.5%	94
Honduras	817,440,002	+32.4%	49
Haiti	895,489,990	-5.88%	45
Indonesia	606,830,017	-3.05%	60
India	3,427,169,922	+9.31%	9
Iran	311,820,007	+2.65%	82
Iraq	1,710,560,059	-5.53%	25
Jamaica	95,419,998	+62.9%	109
Jordan	2,058,090,088	-40.3%	19
Kazakhstan	70,349,998	+22.2%	115
Kenya	2,732,570,068	-14%	11
Kyrgyzstan	755,520,020	+67%	51
Cambodia	1,685,550,049	+23.3%	26
Kiribati	94,769,997	+30.1%	110
Laos	576,460,022	+0.135%	63
Lebanon	1,470,430,054	+5.08%	31
Liberia	494,630,005	-18.4%	68
Libya	274,390,015	-20.1%	87
St. Lucia	36,299,999	-63.7%	125
Sri Lanka	18,570,000	-88%	128
Lesotho	152,809,998	-14.7%	96
Morocco	1,507,130,005	+56%	30
Moldova	932,710,022	+56.4%	44
Madagascar	1,042,920,044	-2.37%	42
Maldives	125,570,000	+11.7%	105
Mexico	515,369,995	-8.57%	67
Marshall Islands	138,520,004	+29.3%	102
North Macedonia	233,610,001	-30.9%	90
Mali	1,233,219,971	-14.2%	36
Myanmar (Burma)	1,078,780,029	-28.4%	40
Montenegro	106,470,001	-20.1%	106
Mongolia	297,089,996	+5.34%	84
Mozambique	2,623,939,941	+14.9%	13
Mauritania	342,730,011	-20.2%	78
Mauritius	81,900,002	-72.1%	114
Malawi	1,368,479,980	+16.2%	33
Malaysia	-5,500,000	-138%	134
Namibia	332,329,987	+82%	80
Niger	2,106,570,068	+16.5%	16
Nigeria	4,544,700,195	+28.8%	6
Nicaragua	1,168,380,005	+55%	38
Nepal	1,263,869,995	-20.9%	35
Nauru	36,660,000	+8.59%	124
Pakistan	1,871,550,049	-36%	22
Panama	145,630,005	+59.3%	100
Peru	850,559,998	+180%	48
Philippines	1,840,770,020	+12.7%	24
Palau	59,630,001	+20.6%	119
Papua New Guinea	681,590,027	-42.5%	56
North Korea	13,340,000	-33.7%	131
Paraguay	101,199,997	-42.2%	107
Palestinian Territories	2,336,330,078	+8.15%	14
Rwanda	1,120,550,049	-15.8%	39
Sudan	1,574,569,946	-58.8%	28
Senegal	1,521,969,971	+9.54%	29
Solomon Islands	264,420,013	-0.11%	89
Sierra Leone	545,580,017	-23.1%	65
El Salvador	730,419,983	+243%	53
Somalia	1,969,079,956	-18.2%	20
Serbia	558,000,000	+4.63%	64
South Sudan	2,061,469,971	-2.72%	18
São Tomé & Príncipe	56,849,998	-21.2%	122
Suriname	59,090,000	+105%	120
Eswatini	94,190,002	-24.7%	111
Syria	8,198,959,961	-15.4%	2
Chad	726,409,973	-0.552%	54
Togo	450,250,000	+28.4%	69
Thailand	627,530,029	+374%	58
Tajikistan	611,820,007	+10%	59
Turkmenistan	15,160,000	-47.2%	130
Timor-Leste	232,789,993	-8.83%	92
Tonga	306,510,010	+171%	83
Tunisia	1,289,650,024	+17.9%	34
Turkey	892,169,983	-15.8%	46
Tuvalu	66,180,000	+83.8%	116
Tanzania	2,728,719,971	+4.23%	12
Uganda	2,146,659,912	-16.3%	15
Ukraine	29,293,019,531	+1,223%	1
Uzbekistan	1,671,670,044	+43.9%	27
St. Vincent & Grenadines	15,760,000	-87.5%	129
Venezuela	271,769,989	-0.505%	88
Vietnam	19,450,001	-96.3%	127
Vanuatu	129,070,007	-22.7%	104
Samoa	130,619,995	+54.7%	103
Kosovo	395,480,011	-12.8%	75
Yemen	3,695,959,961	-4.98%	8
South Africa	1,030,119,995	-1.1%	43
Zambia	1,864,969,971	+71.5%	23
Zimbabwe	788,750,000	-20%	50

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.ODA.ODAT.KD'

# 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.ODA.ODAT.KD'

# 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

Net official development assistance received (constant 2021 US$)

Year: 2022

Acquire full dataset with Python or R from source