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

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0 -100% 59
Angola Angola -1,075,956,035 +54.7% 117
Albania Albania 53,537,238 -9.78% 36
Argentina Argentina -809,242,947 -54.5% 115
Armenia Armenia -62,352,142 -136% 95
Azerbaijan Azerbaijan -50,426,635 -12.9% 92
Burundi Burundi 5,977,320 -20.5% 55
Benin Benin -20,839,997 -122% 79
Burkina Faso Burkina Faso 43,486,648 -11.2% 39
Bangladesh Bangladesh 3,660,785,885 -32.2% 1
Bosnia & Herzegovina Bosnia & Herzegovina 69,076,771 -219% 32
Belarus Belarus -526,241,452 -4.77% 111
Belize Belize 42,524,848 +2,180% 40
Bolivia Bolivia 178,904,629 +5.28% 18
Brazil Brazil -200,429,909 -75.9% 103
Bhutan Bhutan 66,877,448 +62% 34
Botswana Botswana 1,126,376 -142% 58
Central African Republic Central African Republic 11,893,644 +624% 53
China China -624,140,134 -47.1% 112
Côte d’Ivoire Côte d’Ivoire 373,215,710 -32.7% 13
Cameroon Cameroon -344,292,367 +736% 107
Congo - Kinshasa Congo - Kinshasa 50,409,336 -413% 37
Congo - Brazzaville Congo - Brazzaville -206,975,567 +11.5% 104
Colombia Colombia -677,584,448 -479% 113
Comoros Comoros -1,234,792 -108% 61
Cape Verde Cape Verde -20,953,567 +1,901% 80
Costa Rica Costa Rica 33,233,518 -51.1% 43
Djibouti Djibouti 92,975,403 -2,470% 28
Dominica Dominica -9,105,777 +12.4% 68
Dominican Republic Dominican Republic 161,866,802 +53.4% 19
Algeria Algeria -33,150,736 -25% 89
Ecuador Ecuador -396,068,778 -53.3% 109
Egypt Egypt 1,720,266,283 +120% 5
Eritrea Eritrea -8,578,765 -1.73% 67
Ethiopia Ethiopia 2,052,263,541 -615% 3
Fiji Fiji 4,228,562 -92.1% 56
Gabon Gabon -68,195,432 +316% 98
Georgia Georgia -28,288,624 -151% 86
Ghana Ghana -23,214,733 -146% 83
Guinea Guinea -83,502,453 +359% 100
Gambia Gambia 26,628,738 -18.2% 46
Guinea-Bissau Guinea-Bissau -3,285,533 +121% 63
Grenada Grenada 22,945,855 -2,934% 47
Guatemala Guatemala -24,483,760 -17.8% 84
Guyana Guyana 99,341,062 +499% 27
Honduras Honduras -9,276,041 -117% 69
Haiti Haiti -25,421,000 -196% 85
Indonesia Indonesia -58,001,108 -92.4% 94
India India 3,318,583,705 +0.133% 2
Iran Iran -18,760,395 +21.6% 76
Iraq Iraq -352,235,547 -32.6% 108
Jamaica Jamaica -11,989,767 -210% 72
Jordan Jordan 420,212,847 +28.3% 11
Kazakhstan Kazakhstan 27,684,074 -259% 45
Kenya Kenya -833,705,017 +136% 116
Kyrgyzstan Kyrgyzstan -84,839,521 +484% 101
Cambodia Cambodia 366,483,476 -30.6% 14
Laos Laos -22,971,807 -144% 82
Lebanon Lebanon 22,667,701 -197% 48
Liberia Liberia 2,999,809 -1,089% 57
St. Lucia St. Lucia 112,486,188 +189,256% 25
Sri Lanka Sri Lanka 1,854,489,087 +165% 4
Lesotho Lesotho 12,561,278 -44.3% 52
Morocco Morocco 67,710,456 -86.4% 33
Moldova Moldova 135,868,070 +44% 20
Madagascar Madagascar 117,397,463 -40.7% 22
Maldives Maldives 239,723,352 +101% 17
Mexico Mexico 86,947,385 -136% 30
North Macedonia North Macedonia 110,425,522 +1,975% 26
Mali Mali -40,735,486 -234% 90
Myanmar (Burma) Myanmar (Burma) 78,546,385 -168% 31
Montenegro Montenegro -87,606,545 +437% 102
Mongolia Mongolia -48,093,313 -171% 91
Mozambique Mozambique 295,020,832 -455% 15
Mauritania Mauritania -64,907,299 -4.17% 96
Mauritius Mauritius 40,635,439 -50% 41
Malawi Malawi -31,932,018 +1,532% 88
Niger Niger -19,496,552 -270% 77
Nigeria Nigeria 906,423,003 +31.3% 8
Nicaragua Nicaragua -14,478,328 -25.4% 73
Nepal Nepal 116,879,212 +112% 23
Pakistan Pakistan 1,430,342,566 -355% 6
Peru Peru 47,011,844 -85.7% 38
Philippines Philippines 1,074,084,966 +7.3% 7
Papua New Guinea Papua New Guinea 381,774,520 -43.1% 12
Paraguay Paraguay -16,221,580 -577% 75
Rwanda Rwanda 91,022,415 +16% 29
Sudan Sudan -65,825,328 -8.57% 97
Senegal Senegal 8,718,302 -106% 54
Solomon Islands Solomon Islands 13,557,568 -1,293% 51
Sierra Leone Sierra Leone -14,609,142 +14.3% 74
El Salvador El Salvador 28,620,457 -302% 44
Serbia Serbia 280,481,424 -79.4% 16
São Tomé & Príncipe São Tomé & Príncipe -889,915 -40% 60
Suriname Suriname -22,624,797 +783% 81
Eswatini Eswatini -8,188,016 -35% 66
Syria Syria 0 59
Chad Chad 117,553,023 +408% 21
Togo Togo -11,538,662 -39.7% 71
Thailand Thailand -215,622,004 -197% 105
Tajikistan Tajikistan -79,411,588 -15.9% 99
Turkmenistan Turkmenistan -681,178,069 +7.12% 114
Timor-Leste Timor-Leste -1,490,502 -129% 62
Tonga Tonga -11,444,856 +109% 70
Tunisia Tunisia 854,482,999 +11,576% 9
Turkey Turkey -258,237,033 -147% 106
Tanzania Tanzania 20,018,425 -112% 49
Uganda Uganda -30,593,138 -126% 87
Ukraine Ukraine 57,396,841 -98.4% 35
Uzbekistan Uzbekistan 439,691,477 +219% 10
St. Vincent & Grenadines St. Vincent & Grenadines 16,372,331 +10.8% 50
Vietnam Vietnam -426,368,646 -49.1% 110
Vanuatu Vanuatu -6,599,229 -131% 64
Samoa Samoa -19,801,629 +8.28% 78
Kosovo Kosovo -7,937,978 +8.55% 65
Yemen Yemen 0 59
South Africa South Africa 114,056,169 -74.4% 24
Zambia Zambia -54,362,933 -200% 93
Zimbabwe Zimbabwe 33,739,733 -87.8% 42 

                    
# 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.BLAT.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.BLAT.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))