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

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 0 -100% 71
Angola Angola 44,989,801 -683% 40
Albania Albania -39,623,166 -362% 103
Argentina Argentina 1,987,351,278 +541% 2
Armenia Armenia 33,956,161 -489% 45
Azerbaijan Azerbaijan -41,313,524 -31.7% 105
Burundi Burundi 26,145,544 +53.3% 47
Benin Benin -11,302,313 -117% 93
Burkina Faso Burkina Faso 151,299,505 +76.5% 18
Bangladesh Bangladesh 83,566,025 -40.5% 30
Bosnia & Herzegovina Bosnia & Herzegovina 15,224,459 -18% 53
Belarus Belarus 80,032,916 +521% 31
Belize Belize 14,257,290 -20.9% 54
Bolivia Bolivia -144,071,139 -144% 110
Brazil Brazil 249,233,865 -36.3% 12
Bhutan Bhutan -2,303,589 +350% 77
Botswana Botswana -7,966,857 -231% 87
Central African Republic Central African Republic 2,338,316 -37% 62
China China -146,843,536 -7.21% 111
Côte d’Ivoire Côte d’Ivoire 303,622,623 -0.369% 9
Cameroon Cameroon 59,531,231 -48.4% 36
Congo - Kinshasa Congo - Kinshasa -9,265,691 -69.4% 89
Congo - Brazzaville Congo - Brazzaville -37,331,508 +81.3% 102
Colombia Colombia 260,507,174 -41% 11
Comoros Comoros 3,051,400 +455% 60
Cape Verde Cape Verde -7,307,695 +35.8% 86
Costa Rica Costa Rica -73,336,000 -22.7% 108
Djibouti Djibouti 144,926,171 +1,882% 20
Dominica Dominica 1,817,606 -73.2% 64
Dominican Republic Dominican Republic 427,620,053 +44.4% 6
Ecuador Ecuador 136,896,865 -187% 21
Egypt Egypt 85,352,822 -93.1% 29
Eritrea Eritrea -2,099,441 -0.073% 76
Ethiopia Ethiopia 36,544,137 -25.1% 43
Fiji Fiji -563,000 -107% 74
Gabon Gabon -40,638,774 -841% 104
Georgia Georgia 192,191,400 +26.1% 16
Ghana Ghana -13,835,522 -102% 96
Guinea Guinea 96,892,373 +65.9% 27
Gambia Gambia 61,446,234 +78.2% 35
Guinea-Bissau Guinea-Bissau 7,807,863 -321% 56
Grenada Grenada -9,988,748 +2,142% 91
Guatemala Guatemala -66,509,450 -28.6% 107
Guyana Guyana 7,541,445 -45.4% 57
Honduras Honduras -21,531,779 -108% 98
Haiti Haiti -3,769,000 -0.0265% 81
Indonesia Indonesia 6,867,007 -80.2% 58
India India 127,725,122 -52.8% 23
Iraq Iraq 0 -100% 71
Jamaica Jamaica -28,850,640 -16.2% 99
Jordan Jordan 196,126,049 -222% 14
Kazakhstan Kazakhstan 434,864 -257% 68
Kenya Kenya 135,965,310 -202% 22
Kyrgyzstan Kyrgyzstan 27,450,656 +45% 46
Cambodia Cambodia 26,134,934 -39.9% 48
Laos Laos 21,221,838 -13.4% 51
Lebanon Lebanon -15,103,178 -53.2% 97
Liberia Liberia 4,813,377 -34.6% 59
St. Lucia St. Lucia -4,847,107 +94.4% 83
Sri Lanka Sri Lanka -5,049,465 -73.9% 84
Lesotho Lesotho -8,712,745 +155% 88
Morocco Morocco -37,243,317 -124% 101
Moldova Moldova 831,661 -97.1% 67
Madagascar Madagascar 43,796,425 -35% 42
Maldives Maldives 124,458,709 +1,659% 24
Mexico Mexico -81,160,000 +1,300% 109
North Macedonia North Macedonia 66,769,437 -3,548% 33
Mali Mali 106,060,809 -35.4% 26
Myanmar (Burma) Myanmar (Burma) -1,142,000 -70.4% 75
Montenegro Montenegro 2,271,825 -58.6% 63
Mongolia Mongolia -2,538,186 +55.8% 78
Mozambique Mozambique 1,322,693 -57.6% 66
Mauritania Mauritania -45,921,434 -6.06% 106
Mauritius Mauritius 1,596,858 -2,358% 65
Malawi Malawi 92,743,484 +69.1% 28
Niger Niger -2,745,718 -106% 79
Nigeria Nigeria 2,336,233,964 +2,880% 1
Nicaragua Nicaragua 368,098,757 +13% 8
Nepal Nepal -9,646,369 +148% 90
Pakistan Pakistan 12,773,884 -86.1% 55
Peru Peru 299,081,090 -1,317% 10
Philippines Philippines -4,526,079 -40.5% 82
Papua New Guinea Papua New Guinea 15,541,632 -446% 52
Paraguay Paraguay 430,491,164 -30.9% 5
Rwanda Rwanda 180,637,184 +533% 17
Sudan Sudan -29,170,761 -2.96% 100
Senegal Senegal -10,407,785 -138% 92
Solomon Islands Solomon Islands -146,761 +1.67% 72
Sierra Leone Sierra Leone -6,136,793 -68% 85
El Salvador El Salvador 1,051,214,040 +712% 3
Serbia Serbia 107,904,678 -45.5% 25
São Tomé & Príncipe São Tomé & Príncipe 25,845,993 -3,448% 49
Suriname Suriname 56,141,641 +91.2% 38
Eswatini Eswatini 2,894,473 -23.8% 61
Syria Syria 0 71
Chad Chad 58,174,943 +1,680% 37
Togo Togo -11,739,740 -211% 94
Tajikistan Tajikistan 51,379,466 +932% 39
Turkmenistan Turkmenistan 34,833,855 -74.1% 44
Tonga Tonga 384,099 -337% 69
Tunisia Tunisia 499,499,302 -257% 4
Turkey Turkey -707,828,220 -50.8% 112
Tanzania Tanzania 61,551,066 +99.3% 34
Uganda Uganda 195,454,439 -997% 15
Ukraine Ukraine 204,880,588 -93.1% 13
Uzbekistan Uzbekistan 147,946,291 -30.8% 19
St. Vincent & Grenadines St. Vincent & Grenadines 44,532,994 +52.1% 41
Vietnam Vietnam -12,397,207 -32% 95
Samoa Samoa -359,596 -63.2% 73
Kosovo Kosovo 23,538,146 -33.5% 50
Yemen Yemen 72,023 -99.2% 70
South Africa South Africa 70,223,324 +601% 32
Zambia Zambia -3,200,571 -117% 80
Zimbabwe Zimbabwe 384,111,750 +16,048% 7 

                    
# 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.MOTH.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.MOTH.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))