Country

Indicator

Value type

Values present

IFC, private nonguaranteed (NFL, US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	0	-100%	49
Angola	-38,888,889	+250%	76
Albania	31,783,088	-937%	25
Argentina	-139,947,844	-14.7%	85
Armenia	-5,289,207	-85.8%	62
Azerbaijan	20,000,000		34
Burundi	-1,250,000	-140%	54
Benin	0		49
Burkina Faso	11,463,746	+72.9%	41
Bangladesh	42,541,191	-183%	23
Bosnia & Herzegovina	-249,588	-83.4%	50
Belarus	-8,000,000	-86.3%	64
Belize	0		49
Bolivia	0	-100%	49
Brazil	1,106,842,805	-21.4%	1
Bhutan	0		49
Botswana	0	-100%	49
Central African Republic	0		49
China	5,840,007	-97.2%	45
Côte d’Ivoire	75,264,544	+104%	15
Cameroon	31,174,266	+94.3%	26
Congo - Kinshasa	73,000,000	+4,686%	17
Congo - Brazzaville	9,852,117		44
Colombia	273,707,597	-51.2%	6
Comoros	-3,562,463	-255%	59
Cape Verde	24,916,475	-3,641%	31
Costa Rica	-44,766,259	+21%	80
Djibouti	0		49
Dominica	0		49
Dominican Republic	51,641,448	-214%	21
Algeria	0		49
Ecuador	19,179,372	-85.8%	35
Egypt	178,354,698	-694%	7
Eritrea	0		49
Ethiopia	75,000,000	+477%	16
Fiji	0		49
Gabon	21,515,860	+120%	33
Georgia	-56,346,361	+484%	81
Ghana	-64,113,271	-21.3%	84
Guinea	-23,146,187	-10.7%	71
Gambia	-306,507	+94.7%	51
Guinea-Bissau	0		49
Grenada	0		49
Guatemala	159,500,362	+40.9%	8
Guyana	0		49
Honduras	-4,698,556	-108%	61
Haiti	-1,910,167	-60.5%	56
Indonesia	133,866,800	-56.5%	10
India	151,753,805	-64%	9
Iran	0		49
Iraq	-39,240,953	-159%	77
Jamaica	-40,000,000		78
Jordan	-38,027,637	+2.18%	75
Kazakhstan	91,562,079	+1.17%	13
Kenya	13,481,912	-88%	38
Kyrgyzstan	17,496,788	+160%	36
Cambodia	-19,996,718	-169%	70
Laos	12,457,935	-3,165%	40
Lebanon	0		49
Liberia	0	-100%	49
St. Lucia	0		49
Sri Lanka	-36,674,818	-133%	74
Lesotho	0		49
Morocco	123,610,259	+603%	11
Moldova	10,489,394		43
Madagascar	25,558,385	+255%	29
Maldives	-1,363,648	-200%	55
Mexico	23,724,077	-159%	32
North Macedonia	0		49
Mali	769,345	-255%	47
Myanmar (Burma)	-56,432,059	+90.5%	82
Montenegro	-2,483,784	-15%	58
Mongolia	79,331,734	-328%	14
Mozambique	26,254,989	-360%	28
Mauritania	-63,066,667	+663%	83
Mauritius	-8,333,333	-0.00001%	65
Malawi	-600,000	+50%	53
Niger	24,925,803		30
Nigeria	51,303,773	+1,334%	22
Nicaragua	-28,399,182	-23.3%	73
Nepal	11,000,000	-33.9%	42
Pakistan	-43,693,664	-21.1%	79
Peru	68,350,419	-56.3%	18
Philippines	99,381,917	-47.8%	12
Papua New Guinea	0		49
Paraguay	16,666,667	+150%	37
Rwanda	-4,229,187	-65%	60
Sudan	0		49
Senegal	41,664,336	-72.8%	24
Solomon Islands	0		49
Sierra Leone	0	-100%	49
El Salvador	-18,706,369	-208%	69
Somalia	0		49
Serbia	62,404,018	-321%	19
São Tomé & Príncipe	0		49
Suriname	0		49
Eswatini	0		49
Syria	0		49
Chad	-5,723,237	+80.3%	63
Togo	-9,651,769	-39.9%	66
Thailand	445,281,744	+1.36%	4
Tajikistan	-2,242,406	-117%	57
Turkmenistan	0		49
Timor-Leste	-437,500		52
Tonga	0		49
Tunisia	429,284	-54.1%	48
Turkey	790,753,241	+142%	2
Tanzania	30,468,881	-58.3%	27
Uganda	-25,611,907	-7.34%	72
Ukraine	-15,147,612	-124%	67
Uzbekistan	58,697,003	+14%	20
St. Vincent & Grenadines	0		49
Vietnam	379,518,427	+158%	5
Vanuatu	0		49
Samoa	0		49
Kosovo	13,251,644	+5,098%	39
Yemen	-18,320,984	+58.3%	68
South Africa	538,883,426	+328%	3
Zambia	3,828,101	-86.8%	46
Zimbabwe	0		49

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.NIFC.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.NIFC.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

IFC, private nonguaranteed (NFL, US$)

Year: 2023

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