Country

Indicator

Value type

Values present

Net ODA received (% of gross capital formation)

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	161	-36.4%	5
Angola	0.356	-74.7%	95
Albania	6.17	-49.5%	63
Argentina	0.351	+99%	96
Armenia	6.88	+35.4%	61
Azerbaijan	0.499	+378%	92
Burundi	129	-22.8%	8
Benin	15.6	-6.6%	43
Burkina Faso	42.4	-19.7%	20
Bangladesh	3.52	-10.7%	76
Bosnia & Herzegovina	4.22	-53.8%	72
Belarus	0.102	-87.6%	105
Belize	3.18	-78.2%	77
Bolivia	4.57	-38.8%	70
Brazil	0.163	-52.2%	102
Bhutan	12	+16.1%	49
Botswana	1.64	-12.5%	84
Central African Republic	118	+14.1%	9
China	-0.00364	-49.9%	109
Côte d’Ivoire	10.1	+10.3%	53
Cameroon	13.9	-0.619%	46
Congo - Kinshasa	15.7	-33.8%	42
Congo - Brazzaville	21.3	+241%	35
Colombia	2.75	-13.4%	82
Comoros	80.5	-8.15%	10
Cape Verde	16.8	-34.2%	39
Costa Rica	5.22	+695%	68
Djibouti	-15.7	-52.6%	110
Dominican Republic	1.05	-32.5%	90
Algeria	0.272	-7.69%	97
Ecuador	1.45	+6.88%	88
Egypt	6.15	-40.2%	64
Ethiopia	15.3	+17.4%	45
Fiji	39	-40.1%	21
Gabon	4.07	+40.1%	73
Georgia	6.25	-68.3%	62
Ghana	8.95	-2.07%	57
Guinea	17.9	-17%	37
Gambia	43.2	+6.62%	19
Guinea-Bissau	38.5	-16.1%	22
Equatorial Guinea	1.11	-12.2%	89
Guatemala	2.76	-21.9%	81
Honduras	9.49	+4.88%	54
Haiti	27.7	+10.1%	28
Indonesia	0.169	+0.691%	101
India	0.252	-18.3%	99
Iran	0.193	-3.77%	100
Iraq	2.25	-44.2%	83
Kazakhstan	0.131	+19.1%	104
Kenya	12.2	-14.4%	48
Kyrgyzstan	16.9	-8.63%	38
Cambodia	11.2	-8.59%	52
Kiribati	166	+15%	4
Lebanon	554	+391%	1
Libya	5.8	-27.7%	66
Sri Lanka	0.0546	-88.5%	106
Lesotho	23.8	-8.88%	30
Morocco	3.57	+60.8%	75
Moldova	22.3	+37.5%	33
Madagascar	29.4	-35.2%	27
Maldives	5.86	-31%	65
Mexico	0.148	-24.8%	103
Marshall Islands	205	-1.48%	3
North Macedonia	4.46	-40.4%	71
Mali	23.1	-12.5%	32
Montenegro	5.44	-36.2%	67
Mongolia	3.92	-22%	74
Mozambique	31.4	-10.3%	25
Mauritania	6.99	-39%	60
Mauritius	2.89	-77.6%	80
Malaysia	0.00508	-70.8%	108
Namibia	13	+54.1%	47
Niger	44.5	+1.24%	16
Nicaragua	33.1	+48.6%	23
Nepal	7.79	-36.7%	59
Pakistan	3.16	-45.3%	78
Panama	0.475	+9.56%	93
Peru	1.51	+151%	87
Philippines	1.62	-17.6%	86
Palau	56.7	+0.0738%	14
Paraguay	0.833	-54.4%	91
Palestinian Territories	44	-6.14%	17
Rwanda	31.4	-30.1%	24
Sudan	138	-57%	7
Senegal	11.5	-12.4%	51
Solomon Islands	69.1	-32.7%	12
Sierra Leone	49.4	-29.6%	15
El Salvador	9.08	+189%	55
Somalia	80.3	-43.1%	11
Serbia	2.89	-10.4%	79
Eswatini	9.07	-31.7%	56
Chad	21.7	-0.178%	34
Togo	24.5	+16%	29
Thailand	0.401	+340%	94
Tajikistan	15.5	-13.1%	44
Timor-Leste	65.8	+34.4%	13
Tonga	212	+249%	2
Tunisia	15.7	+7.43%	41
Turkey	0.252	-38.8%	98
Tanzania	8.55	-8.38%	58
Uganda	19.2	-27%	36
Ukraine	146	+1,811%	6
Uzbekistan	5.07	+27.1%	69
Vietnam	0.0366	-91.6%	107
Vanuatu	30.9	-17.9%	26
Samoa	43.5	+51.9%	18
Kosovo	11.6	-13.7%	50
South Africa	1.64	-15.2%	85
Zambia	23.3	+48.2%	31
Zimbabwe	16.3	-30%	40

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.GI.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.ODA.ODAT.GI.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

Net ODA received (% of gross capital formation)

Year: 2022

Acquire full dataset with Python or R from source