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

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan -8,537,000 -5.57% 80
Angola Angola -54,507,000 +5.12% 88
Albania Albania -45,852,000 -5.95% 87
Armenia Armenia -66,623,000 +4.03% 90
Azerbaijan Azerbaijan -15,606,000 -68.3% 82
Burundi Burundi -4,619,000 +14.4% 76
Benin Benin 376,957,000 +55.5% 15
Burkina Faso Burkina Faso 175,390,000 +19.2% 21
Bangladesh Bangladesh 1,412,170,000 +10.5% 4
Bosnia & Herzegovina Bosnia & Herzegovina -120,425,000 +23.3% 94
Bolivia Bolivia -6,350,000 -45.8% 79
Bhutan Bhutan 44,666,000 -18.6% 33
Botswana Botswana -90,000 -21.1% 63
Central African Republic Central African Republic -2,504,000 +175% 74
China China -163,452,000 -21.4% 95
Côte d’Ivoire Côte d’Ivoire 909,489,000 +24.4% 5
Cameroon Cameroon 175,006,000 -5.67% 22
Congo - Kinshasa Congo - Kinshasa 860,647,000 +101% 6
Congo - Brazzaville Congo - Brazzaville 61,421,000 -44% 30
Comoros Comoros 11,636,000 -8.02% 50
Cape Verde Cape Verde 49,910,000 -0.623% 32
Djibouti Djibouti 41,654,000 +63.9% 34
Dominica Dominica 35,931,000 -0.948% 36
Ecuador Ecuador -66,000 -49.6% 62
Egypt Egypt -59,915,000 -32% 89
Eritrea Eritrea 0 61
Ethiopia Ethiopia 674,963,000 +15.2% 8
Fiji Fiji 3,806,000 -95.7% 53
Georgia Georgia -110,454,000 +24.3% 93
Ghana Ghana 509,762,000 +66.9% 12
Guinea Guinea 33,562,000 +26.6% 38
Gambia Gambia 1,659,000 +44.1% 58
Guinea-Bissau Guinea-Bissau 24,446,000 +70.3% 44
Grenada Grenada 35,479,000 +34.9% 37
Guyana Guyana 53,853,000 +83.3% 31
Honduras Honduras 30,003,000 -164% 40
Indonesia Indonesia -187,915,000 +2.12% 96
India India -1,819,389,000 +23.9% 98
Iraq Iraq -16,373,000 -0.752% 84
Jordan Jordan 26,610,000 -882% 41
Kenya Kenya 804,926,000 -14.5% 7
Kyrgyzstan Kyrgyzstan 78,909,000 +391% 28
Cambodia Cambodia 365,351,000 +45.5% 16
Laos Laos 72,581,000 +39.4% 29
Lebanon Lebanon 813,000 59
Liberia Liberia 120,038,000 +7.35% 25
St. Lucia St. Lucia 15,573,000 +38.9% 47
Sri Lanka Sri Lanka 410,549,000 -460% 14
Lesotho Lesotho 26,372,000 +16.8% 42
Morocco Morocco -196,000 -43.4% 66
Moldova Moldova 12,294,000 -88.7% 49
Madagascar Madagascar 328,791,000 +150% 17
Maldives Maldives 1,686,000 -292% 57
North Macedonia North Macedonia -16,269,000 -1.92% 83
Mali Mali 99,506,000 +792% 26
Myanmar (Burma) Myanmar (Burma) -69,036,000 +58.4% 91
Montenegro Montenegro -5,349,000 -17.6% 77
Mongolia Mongolia 10,419,000 -59.7% 51
Mozambique Mozambique 1,711,000 -120% 56
Mauritania Mauritania 36,036,000 -491% 35
Mauritius Mauritius -230,000 -41.5% 67
Malawi Malawi 218,477,000 +8.45% 18
Niger Niger 92,364,000 -83% 27
Nigeria Nigeria 1,493,501,000 -33% 3
Nicaragua Nicaragua 25,971,000 -32.4% 43
Nepal Nepal 178,426,000 -55% 20
Pakistan Pakistan 1,509,318,000 +190% 2
Philippines Philippines -2,475,000 -47.5% 73
Papua New Guinea Papua New Guinea 17,454,000 +25.5% 46
Paraguay Paraguay -568,000 -21.2% 72
Rwanda Rwanda 542,201,000 +113% 11
Sudan Sudan -37,224,000 -3.51% 86
Senegal Senegal 603,119,000 +6.69% 10
Solomon Islands Solomon Islands 20,441,000 +53.2% 45
Sierra Leone Sierra Leone 9,856,000 -55.2% 52
El Salvador El Salvador -360,000 0% 70
Somalia Somalia -11,758,000 -3.31% 81
Serbia Serbia -34,504,000 -32.6% 85
São Tomé & Príncipe São Tomé & Príncipe -308,000 +0.654% 69
Eswatini Eswatini -149,000 0% 65
Syria Syria 0 61
Chad Chad -6,146,000 +13.4% 78
Togo Togo 194,150,000 +123% 19
Tajikistan Tajikistan 3,430,000 -87% 54
Timor-Leste Timor-Leste 2,904,000 -18.8% 55
Tonga Tonga 388,000 -170% 60
Tunisia Tunisia -105,000 -70.8% 64
Tanzania Tanzania 1,681,621,000 +25% 1
Uganda Uganda 174,621,000 -24.8% 23
Ukraine Ukraine 441,307,000 -22.4% 13
Uzbekistan Uzbekistan 642,453,000 -14.7% 9
St. Vincent & Grenadines St. Vincent & Grenadines 14,404,000 -16.1% 48
Vietnam Vietnam -640,888,000 +179% 97
Vanuatu Vanuatu -510,000 -110% 71
Samoa Samoa -3,606,000 +3.24% 75
Kosovo Kosovo 31,912,000 -44.8% 39
Yemen Yemen -79,479,000 +1.35% 92
Zambia Zambia 161,234,000 -78.1% 24
Zimbabwe Zimbabwe -248,000 -47.1% 68 

                    
# 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.MIDA.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.MIDA.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))