Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	-1,692,000		52
Angola	-2,195,845	+23.1%	55
Argentina	-996,000	-102%	47
Armenia	-3,792,000	-60.6%	60
Azerbaijan	-3,903,000	+0.515%	61
Burundi	-769,746	-5.24%	46
Benin	41,695,703	+47,833%	8
Burkina Faso	1,762,279	-156%	33
Bangladesh	515,833,000	+654%	1
Bolivia	-20,177,000	-0.811%	80
Bhutan	7,992,000	-81.3%	23
Botswana	-2,889,550	+1.04%	58
Central African Republic	-13,341		41
Côte d’Ivoire	20,681,763	+72.2%	15
Cameroon	10,421,591	-58.9%	20
Congo - Kinshasa	32,317,339	+53.9%	10
Congo - Brazzaville	2,616,070	+265%	30
Colombia	-2,666,000	-7.53%	57
Comoros	4,523,760	+123%	27
Cape Verde	-4,042,169	+5.72%	62
Djibouti	2,062,440	-53.3%	31
Dominican Republic	-6,321,000	-23.4%	67
Ecuador	-15,971,000	-11%	76
Egypt	-6,779,637	-0.646%	69
Eritrea	1,736,932	-196%	34
Ethiopia	-5,513,625	-130%	64
Fiji	0	-100%	40
Gabon	-58,698	+2%	43
Georgia	-66,293,000	+1.81%	86
Ghana	-9,630,500	+246%	72
Guinea	25,390,956	-5.29%	14
Gambia	-1,055,233	+24.4%	48
Guinea-Bissau	1,815,641	-28.7%	32
Guatemala	-7,243,000	-21.4%	70
Guyana	-11,324,000	0%	74
Honduras	-18,195,000	-6.95%	79
Indonesia	-50,799,000	-28.6%	84
Jamaica	-33,000	-50%	42
Jordan	0		40
Kazakhstan	-544,000	+0.928%	45
Kenya	28,284,509	+19.6%	13
Kyrgyzstan	14,076,000	-77.7%	16
Cambodia	291,533,000	+69.4%	3
Laos	-28,796,000	+1,728%	82
Liberia	31,327,474	+200%	11
Sri Lanka	104,098,000	-187%	4
Lesotho	-1,590,187	-23%	50
Morocco	-1,467,454	-0.495%	49
Madagascar	13,185,741	+188%	18
Maldives	241,000	-128%	38
Mexico	-10,000,000		73
Mali	5,577,659	-200%	25
Myanmar (Burma)	-32,591,000	+3.97%	83
Mongolia	-16,750,000	-253%	78
Mozambique	-1,815,641	-199%	53
Mauritania	1,671,564	+1.88%	35
Mauritius	-148,079	+3.41%	44
Malawi	34,598,563	+222%	9
Niger	9,968,015	-67.8%	21
Nigeria	12,702,815	-55.7%	19
Nicaragua	-21,431,000	0%	81
Nepal	354,948,000	+147%	2
Pakistan	-301,228,000	-240%	87
Peru	1,374,000	-33.5%	37
Philippines	-5,467,000	-44.7%	63
Papua New Guinea	-16,705,000	+3.66%	77
Paraguay	-8,390,000	-18.8%	71
Rwanda	56,128,780	+336%	7
Sudan	-6,763,629	+0.0608%	68
Senegal	7,378,626	-30.6%	24
Solomon Islands	9,668,000	+5.68%	22
Sierra Leone	3,053,638	-58.4%	29
El Salvador	-5,574,000	-49.1%	65
Somalia	-3,280,427	+48.3%	59
São Tomé & Príncipe	65,369	-82.7%	39
Suriname	0		40
Eswatini	-2,111,800	+0.768%	54
Chad	-2,600,062	+8%	56
Togo	1,586,184	-53.8%	36
Tajikistan	-12,880,000	-679%	75
Timor-Leste	4,007,000	-36.4%	28
Tonga	-1,655,000	+7.82%	51
Tunisia	0	-100%	40
Tanzania	89,403,966	+31.6%	5
Uganda	85,317,774	+31.5%	6
Uzbekistan	30,602,000	-85.9%	12
Vietnam	-55,347,000	-54.6%	85
Vanuatu	4,818,000	-8.85%	26
Samoa	-5,853,000	+112%	66
Zambia	13,678,005	+59.9%	17
Zimbabwe	0		40

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.RDBC.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.RDBC.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 concessional (NFL, current US$)

Year: 2023

Acquire full dataset with Python or R from source