Country

Indicator

Value type

Values present

Net financial flows, RDB nonconcessional (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Angola	-37,396,064	-3.71%	68
Albania	-24,019,522	-186%	65
Argentina	864,366,000	-43.7%	5
Armenia	14,888,265	-86.9%	46
Azerbaijan	-128,387,120	-12.8%	79
Benin	16,192,688	-44.4%	45
Burkina Faso	3,739,340	-56.3%	52
Bangladesh	1,062,259,000	+38.4%	4
Bosnia & Herzegovina	96,701,259	+561%	29
Belarus	45,414,139	-72.2%	39
Belize	-6,626,000	-228%	61
Bolivia	310,789,000	+214%	14
Brazil	237,584,000	-177%	16
Bhutan	6,821,000	-26.9%	50
Botswana	-109,621,480	-459%	78
China	-17,223,000	-107%	64
Côte d’Ivoire	146,894,811	-43.8%	24
Cameroon	94,145,177	-36.5%	30
Congo - Kinshasa	-43,355,261	-50.6%	70
Congo - Brazzaville	101,514,464	+1,900%	27
Colombia	647,979,000	+126%	6
Cape Verde	-9,155,579	-142%	63
Costa Rica	49,796,000	-84.2%	38
Dominican Republic	235,417,000	-1,987%	17
Algeria	-64,840,121	+3.8%	73
Ecuador	373,686,000	-28.6%	11
Egypt	441,143,002	+120,326%	8
Ethiopia	-74,810,804	+2,905%	75
Fiji	-3,493,000	-103%	59
Gabon	-29,719,945	-183%	67
Georgia	217,150,965	-35.3%	20
Ghana	-1,458,116	+20.4%	58
Guatemala	-53,229,000	-32.5%	72
Guyana	59,213,000	-62%	35
Honduras	34,059,000	-90.2%	42
Haiti	-500,000	-200%	55
Indonesia	1,336,909,000	+104%	2
India	1,239,432,000	-51.4%	3
Jamaica	-96,497,000	-569%	77
Jordan	-26,203,422	+123%	66
Kazakhstan	-51,889,964	-173%	71
Kenya	230,044,755	-28.4%	19
Kyrgyzstan	13,489,857	+140%	47
Laos	-617,000	+10.2%	56
Sri Lanka	497,154,000	-22.3%	7
Morocco	100,446,774	-76.9%	28
Moldova	-42,279,177	-119%	69
Mexico	-525,939,000	-305%	81
North Macedonia	77,345,869	+318%	33
Montenegro	-6,745,191	-242%	62
Mongolia	144,059,000	-15.7%	25
Mauritania	-1,115,265	-1.02%	57
Mauritius	231,390,810	-1,304%	18
Nigeria	58,205,895	-849%	36
Nicaragua	1,951,000	-95.5%	53
Pakistan	394,981,000	-72.7%	9
Peru	315,127,000	-36.6%	13
Philippines	1,461,175,000	-9.98%	1
Papua New Guinea	9,295,000	-96.9%	49
Paraguay	190,646,000	-70.8%	21
Rwanda	45,006,813	+198%	40
Sudan	0		54
Senegal	265,039,530	+95.3%	15
El Salvador	31,216,000	-37.7%	43
Somalia	0		54
Serbia	141,480,332	-2,951%	26
Suriname	155,846,000	-15.6%	23
Eswatini	9,681,194	-87.6%	48
Thailand	-330,571,000	+704%	80
Tajikistan	52,067,337	-10.3%	37
Turkmenistan	90,930,000	-45.3%	32
Timor-Leste	5,419,000	-11.4%	51
Tunisia	-76,526,026	+28.1%	76
Turkey	35,820,084	-60.8%	41
Tanzania	163,019,462	+53.1%	22
Uganda	63,856,927	-3.18%	34
Ukraine	322,381,863	+186%	12
Uzbekistan	388,047,000	-40.5%	10
Vietnam	-71,396,000	+272%	74
Kosovo	-5,249,744	-215%	60
South Africa	92,588,341	-157%	31
Zambia	17,219,906	-67.1%	44
Zimbabwe	0		54

Python
R

                    
# 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.RDBN.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.RDBN.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))

Compare

Net financial flows, RDB nonconcessional (NFL, current US$)

Year: 2023

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