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

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 685,567 -2.76% 15
Angola Angola 341,657 -41.3% 63
Albania Albania 193,678 -25.3% 94
Argentina Argentina 95,214 -36.7% 110
Armenia Armenia 547,254 +4.53% 24
Azerbaijan Azerbaijan 425,760 +142% 45
Burundi Burundi 379,745 +7.02% 54
Benin Benin 541,074 -7.81% 26
Burkina Faso Burkina Faso 430,712 -39.5% 43
Bangladesh Bangladesh 953,059 -21.6% 6
Bosnia & Herzegovina Bosnia & Herzegovina 150,951 -31.3% 101
Belarus Belarus 111,253 -45.5% 108
Belize Belize 182,543 +43.4% 96
Bolivia Bolivia 401,951 -31.1% 49
Brazil Brazil 48,717 -47.4% 119
Bhutan Bhutan 594,353 -23.7% 20
Botswana Botswana 262,604 -60.5% 78
Central African Republic Central African Republic 381,937 -7.34% 53
China China 543,859 -12% 25
Côte d’Ivoire Côte d’Ivoire 298,281 -46.1% 72
Cameroon Cameroon 859,334 +36.4% 9
Congo - Kinshasa Congo - Kinshasa 235,568 -76.8% 88
Congo - Brazzaville Congo - Brazzaville 404,593 -3.8% 48
Colombia Colombia 98,938 -46.5% 109
Comoros Comoros 119,048 -77.3% 107
Cape Verde Cape Verde 498,074 +19.3% 34
Costa Rica Costa Rica 58,457 -22.3% 118
Cuba Cuba 19,524 -89.1% 122
Djibouti Djibouti 398,162 -13.7% 50
Dominica Dominica 84,070 -62.2% 113
Dominican Republic Dominican Republic 392,530 +14.8% 52
Algeria Algeria 201,137 -59.7% 92
Ecuador Ecuador 199,777 -53.5% 93
Egypt Egypt 178,852 -84.9% 97
Eritrea Eritrea 513,699 -33.3% 31
Ethiopia Ethiopia 935,342 +23% 7
Fiji Fiji 58,838 -78.6% 116
Gabon Gabon 176,160 -53.2% 98
Georgia Georgia 427,545 +55.9% 44
Ghana Ghana 437,053 -40.7% 42
Guinea Guinea 600,702 -9.82% 19
Gambia Gambia 326,930 -59.4% 68
Guinea-Bissau Guinea-Bissau 341,666 -31.9% 62
Equatorial Guinea Equatorial Guinea 125,526 -21.3% 105
Grenada Grenada 780 -98.1% 127
Guatemala Guatemala 487,509 -9.1% 38
Guyana Guyana 94,287 +69.7% 111
Honduras Honduras 540,776 -23.1% 27
Haiti Haiti 1,136,262 +29.2% 2
Indonesia Indonesia 522,345 +4.64% 30
India India 411,200 -51.5% 47
Iran Iran 58,676 -86.8% 117
Iraq Iraq 253,685 -60.7% 84
Jamaica Jamaica 300,444 -4.22% 71
Jordan Jordan 497,391 -27.7% 35
Kazakhstan Kazakhstan 150,693 -66.2% 102
Kenya Kenya 594,192 +0.854% 21
Kyrgyzstan Kyrgyzstan 266,959 -62.8% 76
Cambodia Cambodia 508,201 -20.6% 33
Laos Laos 328,745 -27.3% 66
Lebanon Lebanon 360,508 -59.5% 56
Liberia Liberia 239,810 -60.8% 86
St. Lucia St. Lucia 86,548 -48.3% 112
Sri Lanka Sri Lanka 327,499 -40.5% 67
Lesotho Lesotho 252,121 -49.4% 85
Morocco Morocco 261,666 -34.4% 79
Moldova Moldova 257,297 -47.2% 81
Madagascar Madagascar 536,884 -2.56% 28
Maldives Maldives 125,350 -21.8% 106
Mexico Mexico 21,182 -84.6% 121
North Macedonia North Macedonia 394,555 -30.1% 51
Mali Mali 489,728 -15.2% 37
Myanmar (Burma) Myanmar (Burma) 862,991 +300% 8
Montenegro Montenegro 16,959 -85.7% 123
Mongolia Mongolia 446,434 -13.7% 39
Mozambique Mozambique 754,381 -7.35% 10
Mauritania Mauritania 358,994 -54.9% 58
Mauritius Mauritius 412,377 +152% 46
Malawi Malawi 685,694 -5.8% 14
Namibia Namibia 349,171 -13.2% 60
Niger Niger 608,449 -37.8% 18
Nigeria Nigeria 690,978 -14.8% 13
Nicaragua Nicaragua 983,528 +35.7% 5
Nepal Nepal 1,049,430 +52.7% 3
Pakistan Pakistan 710,772 -5.34% 12
Panama Panama 886 -96.5% 126
Peru Peru 272,083 -19.9% 75
Philippines Philippines 341,960 -65.7% 61
Palau Palau 34,582 -53.9% 120
Papua New Guinea Papua New Guinea 669,986 +186% 16
North Korea North Korea 511,708 -33.7% 32
Paraguay Paraguay 592,671 +62.1% 22
Rwanda Rwanda 370,645 -23.5% 55
Sudan Sudan 215,290 -71.7% 89
Senegal Senegal 352,647 -7.51% 59
Solomon Islands Solomon Islands -404 -102% 128
Sierra Leone Sierra Leone 525,043 +65.3% 29
El Salvador El Salvador 359,460 -20.9% 57
Somalia Somalia 331,319 +9.6% 65
Serbia Serbia 137,946 -63% 104
South Sudan South Sudan 334,906 -55% 64
São Tomé & Príncipe São Tomé & Príncipe 720,772 +18.4% 11
Suriname Suriname 82,321 -58.2% 114
Eswatini Eswatini 264,393 -52.8% 77
Syria Syria 170,481 -64.8% 99
Chad Chad 258,171 -18.7% 80
Togo Togo 444,960 -36.6% 40
Thailand Thailand 79,673 -40% 115
Tajikistan Tajikistan 317,158 -65.8% 69
Timor-Leste Timor-Leste 254,770 -54.8% 82
Tonga Tonga 272,381 -15.7% 74
Tunisia Tunisia 300,482 -30.4% 70
Turkey Turkey 237,653 -24.6% 87
Tuvalu Tuvalu 4,916 -97.9% 125
Tanzania Tanzania 1,147,654 +72.7% 1
Uganda Uganda 147,877 -61.2% 103
Ukraine Ukraine 610,936 +145% 17
Uzbekistan Uzbekistan 585,733 +24.1% 23
St. Vincent & Grenadines St. Vincent & Grenadines 212,622 +53.7% 90
Venezuela Venezuela 284,817 -27.6% 73
Vietnam Vietnam 438,524 +17.1% 41
Vanuatu Vanuatu 254,755 +136% 83
Samoa Samoa 155,568 -42.2% 100
Kosovo Kosovo 10,706 -81.7% 124
Yemen Yemen 187,617 -60.2% 95
South Africa South Africa 205,974 -43.4% 91
Zambia Zambia 493,623 +102% 36
Zimbabwe Zimbabwe 987,337 +17.4% 4 

                    
# 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.FAOG.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.FAOG.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))