Country

Indicator

Value type

Values present

Net primary income (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	-7,644,110,001	-11.1%	82
Albania	-206,259,464	-31.4%	42
Argentina	-12,766,376,338	-5.45%	90
Armenia	-939,052,546	+44.1%	54
Antigua & Barbuda	-109,593,425	+29.8%	38
Australia	-53,722,458,606	-15.2%	109
Austria	1,741,604,866	+800%	14
Azerbaijan	-2,734,735,000	-14.5%	64
Belgium	11,350,534,506	+37%	10
Bangladesh	-4,502,575,818	+16.4%	71
Bulgaria	-5,718,170,000	-15.7%	77
Bahrain	-3,317,553,191	+27.2%	68
Bahamas	-820,216,647	-4.53%	50
Bosnia & Herzegovina	-87,778,145	-44.7%	37
Belarus	-1,811,139,562	-27.7%	59
Belize	-129,932,839	+16.9%	39
Brazil	-75,402,651,242	-5.14%	111
Brunei	490,941,187	+152%	20
Bhutan	-181,779,102	+24.6%	41
Canada	-1,471,099,831	-84.1%	56
Switzerland	-29,900,340,612	-18.5%	104
Chile	-17,000,019,201	+18%	97
China	-130,028,929,456	-2.74%	113
Colombia	-13,152,759,381	-2.08%	91
Cape Verde	-51,810,545	+72.8%	35
Costa Rica	-8,188,860,221	+15.8%	84
Cyprus	-3,953,859,313	+5.65%	70
Czechia	-14,786,147,554	-2.76%	93
Germany	161,259,769,833	+9.15%	2
Djibouti	115,928,624	+12.6%	26
Dominica	3,549,943	-917%	29
Denmark	14,623,448,214	+25.8%	9
Dominican Republic	-6,723,000,000	+23.6%	80
Ecuador	-3,336,591,146	+18.5%	69
Spain	-8,836,481,292	+12.6%	85
Estonia	-930,274,875	-28%	53
Finland	1,176,949,938	+36.6%	15
France	66,873,547,316	-4.57%	3
United Kingdom	-32,022,045,223	-43.9%	106
Georgia	-2,204,097,100	-7.95%	61
Gambia	82,413,865	-437%	27
Greece	-4,668,118,018	+6.11%	74
Grenada	-132,887,973	-4.22%	40
Guatemala	-1,591,237,240	-17.6%	58
Hong Kong SAR China	38,541,228,970	+19.4%	4
Honduras	-2,885,543,055	+11.7%	65
Croatia	346,489,791	-218%	21
Hungary	-5,985,626,658	-12.4%	78
Indonesia	-35,896,808,407	-0.327%	107
India	-52,101,901,142	+9.6%	108
Iceland	-76,060,890	-113%	36
Israel	-4,630,900,000	+42%	72
Italy	-17,080,877,824	+25.2%	98
Jamaica	-349,770,129	+21%	44
Japan	267,333,607,604	+3.17%	1
Kazakhstan	-20,790,541,110	-21.9%	101
Cambodia	-358,822,546	-62.4%	45
St. Kitts & Nevis	-12,404,986	-28.2%	33
South Korea	26,619,300,000	+1.41%	7
Kuwait	33,089,591,463	+2.51%	5
St. Lucia	-252,427,714	+26.8%	43
Lesotho	504,797,684	+6.48%	19
Lithuania	-2,095,793,326	-14.4%	60
Luxembourg	-28,882,159,897	+4.53%	103
Latvia	-732,544,024	-27.1%	48
Moldova	157,930,000	-40.8%	25
Maldives	-750,084,472	-0.894%	49
Mexico	-54,001,157,208	+23.6%	110
North Macedonia	-866,837,435	+7.98%	52
Malta	-3,263,718,076	+31.1%	67
Montenegro	-23,110,074	-132%	34
Mozambique	-2,520,639,307	+36.8%	63
Malaysia	-13,546,685,524	+17.3%	92
Namibia	-441,711,236	-23.9%	47
Nigeria	-6,631,751,525	-37.1%	79
Nicaragua	-988,900,000	+22.7%	55
Netherlands	-17,164,556,302	+77.2%	99
Norway	22,710,592,768	+31%	8
Nepal	704,749,302	+103%	18
New Zealand	-9,129,177,778	+18.1%	87
Pakistan	-9,479,000,000	+32.7%	88
Panama	-4,638,419,188	+5.02%	73
Peru	-17,378,669,608	+14.9%	100
Philippines	4,965,503,325	+14.4%	12
Poland	-30,881,000,000	-0.416%	105
Portugal	-5,400,533,229	-28.4%	76
Paraguay	-1,494,179,539	-5.81%	57
Palestinian Territories	909,992,454	-71.9%	17
Qatar	-7,218,131,868	+18.8%	81
Romania	-10,157,424,589	+8.11%	89
Russia	-28,486,140,000	+6.1%	102
Saudi Arabia	6,166,803,128	-20%	11
Singapore	-88,990,071,594	-4.28%	112
Solomon Islands	1,760,788	-93%	30
El Salvador	-2,256,261,420	+5.48%	62
Suriname	-366,076,511	+14.2%	46
Slovakia	-3,025,748,115	+11.3%	66
Slovenia	-842,794,472	+21.8%	51
Sweden	26,771,131,409	+7.07%	6
Thailand	-15,901,792,730	+22.8%	96
Tajikistan	4,455,403,495	+44.5%	13
Timor-Leste	231,123,067	+14.2%	23
Tonga	54,916,001	+9.43%	28
Trinidad & Tobago	190,999,320	-62.2%	24
Turkey	-15,779,000,000	+38.2%	95
Ukraine	330,000,000	-93.5%	22
Uruguay	-5,236,018,127	-4.86%	75
United States	-8,884,000,000	-113%	86
Uzbekistan	1,134,794,800	+14.4%	16
St. Vincent & Grenadines	-6,459,199	-386%	31
Vietnam	-15,693,000,000	-30.1%	94
Samoa	-10,383,270	-27%	32
South Africa	-7,911,053,013	+51.8%	83

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 = 'BN.GSR.FCTY.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 <- 'BN.GSR.FCTY.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 primary income (BoP, current US$)

Year: 2024

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