Net financial flows, multilateral (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan -10,229,000 +13.1% 100
Angola Angola 651,069,000 -10.8% 27
Albania Albania 5,952,000 -31.5% 88
Argentina Argentina 3,506,058,000 +30.6% 5
Armenia Armenia 118,748,000 +166% 61
Azerbaijan Azerbaijan -353,085,000 -15.6% 116
Burundi Burundi 20,757,000 +70.1% 81
Benin Benin 423,543,000 +25% 36
Burkina Faso Burkina Faso 332,191,000 +39.4% 42
Bangladesh Bangladesh 3,700,687,000 +26.1% 3
Bosnia & Herzegovina Bosnia & Herzegovina -18,950,000 -64.5% 103
Belarus Belarus -134,141,000 +1,004% 110
Belize Belize 7,990,000 -65.6% 86
Bolivia Bolivia 275,958,000 -45.8% 43
Brazil Brazil 1,135,083,000 +241% 17
Bhutan Bhutan 57,175,000 -46.3% 69
Botswana Botswana -101,624,000 -221% 108
Central African Republic Central African Republic -179,000 -106% 93
China China -785,984,000 +113% 118
Côte d’Ivoire Côte d’Ivoire 1,426,647,000 +6.2% 13
Cameroon Cameroon 421,524,000 -25.6% 37
Congo - Kinshasa Congo - Kinshasa 840,343,000 +154% 23
Congo - Brazzaville Congo - Brazzaville 142,111,000 +7.97% 57
Colombia Colombia 1,409,277,000 -45.9% 14
Comoros Comoros 19,211,000 +26.2% 82
Cape Verde Cape Verde 28,664,000 -53% 75
Costa Rica Costa Rica -172,359,000 -111% 113
Djibouti Djibouti 188,643,000 +408% 50
Dominica Dominica 37,048,000 -12.6% 72
Dominican Republic Dominican Republic 1,010,480,000 +281% 20
Algeria Algeria -64,840,000 +3.8% 104
Ecuador Ecuador 1,406,574,000 -22% 15
Egypt Egypt 1,782,256,000 -8.71% 11
Eritrea Eritrea -363,000 -90.7% 94
Ethiopia Ethiopia 631,183,000 -2.98% 28
Fiji Fiji 1,564,000 -99.6% 90
Gabon Gabon -68,566,000 -184% 105
Georgia Georgia 447,578,000 -1.79% 35
Ghana Ghana 484,838,000 -55.6% 33
Guinea Guinea 155,845,000 +39.5% 54
Gambia Gambia 62,050,000 +78.4% 67
Guinea-Bissau Guinea-Bissau 34,070,000 +155% 73
Grenada Grenada 26,568,000 -5.95% 78
Guatemala Guatemala -180,594,000 -174% 114
Guyana Guyana 108,467,000 -42% 62
Honduras Honduras 24,335,000 -95.7% 80
Haiti Haiti -4,269,000 +13.2% 97
Indonesia Indonesia 3,019,970,000 +23.6% 7
India India 3,392,469,000 +0.24% 6
Iran Iran -1,230,000 -102% 96
Iraq Iraq -171,149,000 +30.7% 112
Jamaica Jamaica -168,852,000 +285% 111
Jordan Jordan 883,050,000 +553% 21
Kazakhstan Kazakhstan -309,878,000 -273% 115
Kenya Kenya 1,700,731,000 +18.4% 12
Kyrgyzstan Kyrgyzstan 244,183,000 +17.4% 44
Cambodia Cambodia 730,270,000 +54.9% 26
Laos Laos 74,240,000 -7.14% 66
Lebanon Lebanon 150,318,000 -2.61% 55
Liberia Liberia 156,179,000 +20.5% 53
St. Lucia St. Lucia 11,143,000 +37.2% 85
Sri Lanka Sri Lanka 1,150,681,000 +48.4% 16
Lesotho Lesotho 16,069,000 -5.96% 83
Morocco Morocco 547,604,000 -63.1% 29
Moldova Moldova 176,686,000 -59.5% 52
Madagascar Madagascar 385,773,000 +89.8% 38
Maldives Maldives 129,888,000 +1,175% 59
Mexico Mexico -1,830,860,000 -439% 120
North Macedonia North Macedonia 233,722,000 +1,010% 45
Mali Mali 211,144,000 +24.4% 48
Myanmar (Burma) Myanmar (Burma) -103,369,000 +29.8% 109
Montenegro Montenegro -15,552,000 +4,775% 102
Mongolia Mongolia 132,683,000 -36.1% 58
Mozambique Mozambique 1,218,000 -132% 91
Mauritania Mauritania -9,329,000 -83.8% 98
Mauritius Mauritius 212,583,000 -621% 47
Malawi Malawi 345,819,000 +29.5% 41
Niger Niger 99,586,000 -84% 64
Nigeria Nigeria 3,897,792,000 +64.6% 2
Nicaragua Nicaragua 374,590,000 -2.93% 40
Nepal Nepal 523,728,000 -2.43% 30
Pakistan Pakistan 2,251,915,000 -22.5% 9
Peru Peru 1,040,871,000 +59.8% 19
Philippines Philippines 3,691,539,000 +45.9% 4
Papua New Guinea Papua New Guinea 28,456,000 -90.2% 76
Paraguay Paraguay 830,406,000 -37.9% 24
Rwanda Rwanda 826,379,000 +120% 25
Sudan Sudan -73,158,000 -2.97% 106
Senegal Senegal 865,129,000 +17.1% 22
Solomon Islands Solomon Islands 29,962,000 +34.1% 74
Sierra Leone Sierra Leone 6,773,000 -33.3% 87
El Salvador El Salvador 1,072,664,000 +329% 18
Somalia Somalia -15,038,000 +4.63% 101
Serbia Serbia 147,132,000 +57.7% 56
São Tomé & Príncipe São Tomé & Príncipe 25,603,000 -3,763% 79
Suriname Suriname 225,940,000 +5.09% 46
Eswatini Eswatini 27,158,000 -83.9% 77
Syria Syria 0 92
Chad Chad 49,429,000 -1,184% 70
Togo Togo 183,996,000 +82.1% 51
Thailand Thailand -413,971,000 +232% 117
Tajikistan Tajikistan 96,100,000 +1.73% 65
Turkmenistan Turkmenistan 125,764,000 -58.8% 60
Timor-Leste Timor-Leste 11,564,000 -24.9% 84
Tonga Tonga -883,000 -60.7% 95
Tunisia Tunisia 471,399,000 +127% 34
Turkey Turkey 100,252,000 -1,684% 63
Tanzania Tanzania 1,995,595,000 +28.7% 10
Uganda Uganda 519,250,000 +52.2% 31
Ukraine Ukraine 24,778,241,000 +86.7% 1
Uzbekistan Uzbekistan 2,322,189,000 -19.4% 8
St. Vincent & Grenadines St. Vincent & Grenadines 57,901,000 +30.2% 68
Vietnam Vietnam -792,056,000 +107% 119
Vanuatu Vanuatu 4,308,000 -59.3% 89
Samoa Samoa -9,819,000 +35.8% 99
Kosovo Kosovo 38,136,000 -55.6% 71
Yemen Yemen -79,407,000 +14.6% 107
South Africa South Africa 513,775,000 -45.2% 32
Zambia Zambia 188,958,000 -76.8% 49
Zimbabwe Zimbabwe 383,275,000 -112,828% 39 

                    
# 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.MLAT.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.MLAT.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))