Country

Indicator

Value type

Values present

Net official flows from UN agencies, IFAD (current US$)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	1,404,280	-88.1%	42
Angola	1,069,690	-236%	47
Albania	-1,660,640	-4.89%	85
Armenia	-3,605,050	+0.982%	88
Azerbaijan	-2,949,390	+27.5%	87
Burundi	8,814,960	-12.9%	20
Benin	7,033,700	-5.36%	24
Burkina Faso	25,058,769	+23.1%	8
Bangladesh	71,008,148	+108%	1
Bosnia & Herzegovina	-2,887,850	+53.4%	86
Bolivia	850,630	-150%	53
Bhutan	1,229,970	-31.4%	45
Central African Republic	4,797,200	+157%	28
China	-17,701,969	-4.13%	95
Côte d’Ivoire	4,451,540	-11.9%	29
Cameroon	8,897,690	+87.7%	19
Congo - Kinshasa	5,405,380	+11.2%	27
Congo - Brazzaville	-789,000	-164%	78
Colombia	-1,116,180	+7.95%	79
Comoros	1,677,900	+386%	38
Cape Verde	1,047,970	-59.2%	48
Cuba	22,170	-90.2%	63
Djibouti	1,540,620	-23.4%	40
Dominica	-38,260	-4.35%	64
Ecuador	-401,170	-4.94%	75
Egypt	-10,249,060	-1.24%	94
Eritrea	2,140,650	+144%	36
Ethiopia	36,257,019	+14.5%	3
Georgia	-1,615,320	-6.25%	84
Ghana	15,844,730	-29.4%	13
Guinea	4,286,650	+163%	30
Gambia	2,469,650	+325%	35
Guinea-Bissau	1,277,660	+4.3%	44
Equatorial Guinea	-63,120	-4.36%	65
Grenada	1,092,350	-14.7%	46
Guatemala	-188,720	-4.69%	69
Guyana	2,064,280	-666%	37
Honduras	-1,416,940	+69.7%	82
Haiti	903,240	-32.3%	50
Indonesia	-6,203,040	-10.2%	92
India	-4,699,200	-157%	91
Iraq	500,000	+150%	57
Jordan	-374,980	+53.7%	72
Kenya	26,954,359	+29.8%	7
Kyrgyzstan	2,811,090	+51.1%	32
Cambodia	29,001,160	+138%	5
Kiribati	1,330,000	+46.6%	43
Laos	1,574,350	-253%	39
Liberia	18,110,439	+55.5%	11
Sri Lanka	786,970	+84.7%	54
Lesotho	2,675,210	+85.9%	34
Morocco	-772,740	-52.6%	77
Moldova	-178,380	-108%	68
Madagascar	9,531,720	-28.6%	17
Maldives	-218,810	-13.5%	70
Mexico	536,380	+42.7%	56
North Macedonia	-519,900	-4.95%	76
Mali	10,827,120	+90%	16
Montenegro	102,240	-73.6%	60
Mongolia	229,920	-71.4%	59
Mozambique	17,972,549	+53.4%	12
Mauritania	6,086,670	+84.6%	25
Malawi	34,129,639	+103%	4
Niger	20,368,660	+33.2%	9
Nigeria	27,323,021	+34.1%	6
Nicaragua	1,489,480	-28.4%	41
Nepal	5,918,310	-54.4%	26
Pakistan	55,699,219	+67.3%	2
Philippines	-4,032,410	+230%	90
Papua New Guinea	-1,155,760	-163%	80
Paraguay	623,120	-213%	55
Palestinian Territories	-106,470	-125%	67
Rwanda	20,231,140	+47%	10
Sudan	8,948,730	-1,560%	18
Senegal	2,680,090	-10.3%	33
Solomon Islands	-83,670	-4.92%	66
Sierra Leone	8,602,980	+58.8%	22
El Salvador	867,560	-443%	52
Somalia	-1,456,720	-43.1%	83
South Sudan	899,860	-8.16%	51
São Tomé & Príncipe	968,770	+200%	49
Eswatini	-292,580	-75.6%	71
Syria	-395,380	+2.43%	74
Chad	13,682,220	+160%	14
Togo	-377,480	-112%	73
Tajikistan	7,156,790	-28.2%	23
Tonga	307,170	-69.4%	58
Tunisia	68,210	-73.3%	61
Tanzania	-6,532,750	-35.4%	93
Uganda	4,216,570	-11.7%	31
Uzbekistan	8,728,560	-65.6%	21
Vietnam	-3,685,850	-190%	89
Samoa	62,060	+590%	62
Zambia	-1,177,910	-12.8%	81
Zimbabwe	11,389,080	+242%	15

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.NFL.IFAD.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.NFL.IFAD.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

Net official flows from UN agencies, IFAD (current US$)

Year: 2022

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