Technical cooperation grants (BoP, current US$)

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 226,460,000 -7.23% 13
Angola Angola 41,940,000 -16.2% 80
Albania Albania 98,720,000 -5.17% 46
Argentina Argentina 32,310,000 +4.23% 91
Armenia Armenia 43,110,000 -27.8% 79
Azerbaijan Azerbaijan 66,830,000 -28.4% 62
Burundi Burundi 35,580,000 +24.3% 84
Benin Benin 92,990,000 +19% 51
Burkina Faso Burkina Faso 98,810,000 -9.79% 45
Bangladesh Bangladesh 310,170,000 +29.1% 5
Bosnia & Herzegovina Bosnia & Herzegovina 82,700,000 -11.7% 54
Belarus Belarus 59,810,000 -25.9% 67
Belize Belize 5,870,000 +44.9% 121
Bolivia Bolivia 60,180,000 -66.4% 66
Brazil Brazil 170,570,000 -7.54% 23
Bhutan Bhutan 18,480,000 +23.7% 103
Botswana Botswana 8,860,000 -14.1% 117
Central African Republic Central African Republic 24,360,000 -27.4% 100
China China 407,910,000 -16.6% 4
Côte d’Ivoire Côte d’Ivoire 105,860,000 -2.11% 40
Cameroon Cameroon 151,230,000 -6.31% 30
Congo - Kinshasa Congo - Kinshasa 124,280,000 -22.6% 37
Congo - Brazzaville Congo - Brazzaville 28,080,000 +0.717% 98
Colombia Colombia 159,010,000 -5.11% 27
Comoros Comoros 19,290,000 +15.4% 102
Cape Verde Cape Verde 16,240,000 -35.9% 111
Costa Rica Costa Rica 16,810,000 -12.2% 110
Cuba Cuba 18,400,000 -14.7% 105
Djibouti Djibouti 15,550,000 +55.5% 112
Dominica Dominica 2,150,000 +88.6% 133
Dominican Republic Dominican Republic 35,180,000 +129% 87
Algeria Algeria 162,420,000 -0.429% 25
Ecuador Ecuador 58,630,000 -10.6% 69
Egypt Egypt 226,320,000 +3.29% 14
Eritrea Eritrea 3,310,000 -11.7% 129
Ethiopia Ethiopia 268,330,000 +42.1% 8
Fiji Fiji 47,280,000 -11.7% 78
Micronesia (Federated States of) Micronesia (Federated States of) 5,140,000 +55.8% 123
Gabon Gabon 28,340,000 +0.998% 97
Georgia Georgia 64,610,000 -6.17% 64
Ghana Ghana 165,150,000 +4.45% 24
Guinea Guinea 47,600,000 -5.87% 77
Gambia Gambia 28,700,000 -25% 94
Guinea-Bissau Guinea-Bissau 18,110,000 +3.01% 107
Equatorial Guinea Equatorial Guinea 1,620,000 -6.36% 135
Grenada Grenada 2,140,000 +62.1% 134
Guatemala Guatemala 52,950,000 -6.7% 73
Guyana Guyana 3,600,000 -32.7% 127
Honduras Honduras 35,080,000 +5.89% 88
Haiti Haiti 53,190,000 +19.7% 72
Indonesia Indonesia 257,500,000 -9.76% 10
India India 490,470,000 -4.67% 2
Iran Iran 172,230,000 +10.8% 22
Iraq Iraq 220,990,000 +1.03% 16
Jamaica Jamaica 15,010,000 +77.2% 113
Jordan Jordan 236,770,000 +6.17% 12
Kazakhstan Kazakhstan 34,310,000 -7.97% 89
Kenya Kenya 225,320,000 +41.9% 15
Kyrgyzstan Kyrgyzstan 47,720,000 -1.95% 76
Cambodia Cambodia 135,840,000 +13.2% 33
Kiribati Kiribati 18,240,000 -2.88% 106
Laos Laos 95,570,000 -2.44% 50
Lebanon Lebanon 251,650,000 +47% 11
Liberia Liberia 39,720,000 +11% 82
Libya Libya 71,590,000 +11.2% 59
St. Lucia St. Lucia 5,090,000 +113% 124
Sri Lanka Sri Lanka 52,320,000 -11.4% 74
Lesotho Lesotho 5,760,000 +18.8% 122
Morocco Morocco 279,260,000 +1.81% 7
Moldova Moldova 98,140,000 -28.3% 47
Madagascar Madagascar 80,450,000 +3.61% 55
Maldives Maldives 4,960,000 -20% 125
Mexico Mexico 105,030,000 -2.32% 42
Marshall Islands Marshall Islands 8,060,000 +120% 118
North Macedonia North Macedonia 39,600,000 -17.6% 83
Mali Mali 153,910,000 +5.19% 29
Myanmar (Burma) Myanmar (Burma) 75,110,000 -28.9% 57
Montenegro Montenegro 14,970,000 -62.4% 114
Mongolia Mongolia 71,800,000 +3.82% 58
Mozambique Mozambique 158,530,000 +7.84% 28
Mauritania Mauritania 28,350,000 -0.106% 96
Mauritius Mauritius 18,000,000 -20.5% 108
Malawi Malawi 67,950,000 -24.6% 61
Malaysia Malaysia 35,200,000 -19.3% 86
Namibia Namibia 68,730,000 +43.4% 60
Niger Niger 95,640,000 -15.4% 49
Nigeria Nigeria 191,860,000 -16.9% 20
Nicaragua Nicaragua 18,450,000 -29.2% 104
Nepal Nepal 100,730,000 +14.9% 43
Nauru Nauru 4,230,000 +27.8% 126
Pakistan Pakistan 190,420,000 -17.6% 21
Panama Panama 12,780,000 +48.4% 115
Peru Peru 95,760,000 -17.8% 48
Philippines Philippines 159,530,000 +1.08% 26
Palau Palau 6,950,000 +205% 119
Papua New Guinea Papua New Guinea 136,440,000 -19% 32
North Korea North Korea 2,240,000 +0.448% 132
Paraguay Paraguay 30,540,000 -14.5% 93
Palestinian Territories Palestinian Territories 193,970,000 +16.7% 19
Rwanda Rwanda 99,890,000 +12.2% 44
Sudan Sudan 88,930,000 +22.5% 53
Senegal Senegal 206,450,000 -3.02% 18
Solomon Islands Solomon Islands 30,990,000 -29.9% 92
Sierra Leone Sierra Leone 32,990,000 -40.5% 90
El Salvador El Salvador 28,680,000 -10.8% 95
Somalia Somalia 126,510,000 +48.1% 36
Serbia Serbia 140,960,000 -0.921% 31
South Sudan South Sudan 105,350,000 +51.7% 41
São Tomé & Príncipe São Tomé & Príncipe 8,910,000 +9.59% 116
Suriname Suriname 1,260,000 -64.8% 136
Eswatini Eswatini 5,990,000 +15.4% 120
Syria Syria 300,630,000 +3.67% 6
Chad Chad 49,470,000 +21.8% 75
Togo Togo 56,850,000 +14.5% 70
Thailand Thailand 66,530,000 +10.3% 63
Tajikistan Tajikistan 41,530,000 -48.2% 81
Turkmenistan Turkmenistan 3,270,000 -53.7% 130
Timor-Leste Timor-Leste 54,210,000 -15.3% 71
Tonga Tonga 26,310,000 +135% 99
Tunisia Tunisia 267,650,000 -17.9% 9
Turkey Turkey 416,050,000 -30.3% 3
Tuvalu Tuvalu 3,490,000 -22.1% 128
Tanzania Tanzania 106,040,000 -18.8% 39
Uganda Uganda 129,510,000 +1.2% 34
Ukraine Ukraine 657,880,000 +72.7% 1
Uzbekistan Uzbekistan 59,300,000 -21.9% 68
St. Vincent & Grenadines St. Vincent & Grenadines 2,380,000 +222% 131
Venezuela Venezuela 16,930,000 -27.6% 109
Vietnam Vietnam 219,630,000 -10.7% 17
Vanuatu Vanuatu 35,290,000 -0.423% 85
Samoa Samoa 20,970,000 -13.8% 101
Kosovo Kosovo 80,150,000 +14% 56
Yemen Yemen 128,590,000 +43.5% 35
South Africa South Africa 107,380,000 -23% 38
Zambia Zambia 90,920,000 +15.4% 52
Zimbabwe Zimbabwe 61,200,000 +6.12% 65 

                    
# 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.TECH.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.TECH.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))