Country

Indicator

Value type

Values present

Primary income payments (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	8,315,386,810	-9.48%	58
Albania	1,244,229,160	+9.61%	83
Argentina	18,925,274,409	-4.48%	46
Armenia	1,662,779,506	+12.5%	79
Antigua & Barbuda	133,960,724	+26.1%	103
Australia	125,338,088,035	-1.08%	15
Austria	48,670,874,184	+11.8%	24
Azerbaijan	5,328,473,000	+3.1%	65
Belgium	98,598,274,774	+0.596%	18
Bangladesh	5,095,333,449	+17.7%	66
Bulgaria	9,021,170,000	-7.16%	54
Bahrain	8,878,723,404	+17.5%	56
Bahamas	921,114,503	-2.96%	88
Bosnia & Herzegovina	1,394,373,035	+12.8%	80
Belarus	3,432,290,473	-13.4%	74
Belize	151,263,877	+14.6%	102
Brazil	110,656,629,155	-0.365%	16
Brunei	508,340,886	-33.4%	93
Bhutan	210,806,328	+15.7%	99
Canada	183,075,912,299	+6%	11
Switzerland	243,360,815,782	+0.255%	10
Chile	29,114,502,033	+7.74%	35
China	439,282,819,517	+1.21%	3
Colombia	23,063,327,777	+2.95%	42
Cape Verde	98,324,180	+42.7%	105
Costa Rica	9,442,167,253	+16.5%	53
Cyprus	34,841,903,914	+2.05%	29
Czechia	32,805,190,129	+3.09%	32
Germany	304,920,258,469	+4.07%	7
Djibouti	158,377,655	-15.4%	101
Dominica	7,860,130	-11.2%	113
Denmark	35,186,144,398	+8.54%	28
Dominican Republic	8,151,100,000	+18.8%	59
Ecuador	3,629,732,841	+16.8%	73
Spain	136,452,710,167	+12.2%	14
Estonia	4,280,453,942	+1.63%	68
Finland	31,644,692,905	+3%	33
France	351,251,040,147	+14.3%	6
United Kingdom	558,340,261,578	-2.25%	2
Georgia	3,973,602,995	-7.21%	72
Gambia	26,302,384	-41.7%	110
Greece	16,702,141,374	+9.09%	49
Grenada	165,125,203	+1.51%	100
Guatemala	4,060,524,940	+4.97%	70
Hong Kong SAR China	243,876,061,136	+7.8%	9
Honduras	3,393,422,752	+9.71%	75
Croatia	6,824,040,450	+15.1%	62
Hungary	38,236,775,095	+2.36%	27
Indonesia	45,908,462,905	+4.53%	25
India	102,965,024,299	+20.5%	17
Iceland	1,320,296,886	+126%	82
Israel	23,334,700,000	+7.12%	41
Italy	154,321,759,516	+7.91%	13
Jamaica	920,143,726	+8.9%	89
Japan	166,769,707,814	+9.09%	12
Kazakhstan	25,823,219,212	-15.3%	37
Cambodia	967,606,096	-32.9%	87
St. Kitts & Nevis	57,686,385	+3.39%	108
South Korea	44,704,400,000	+3.46%	26
Kuwait	6,488,611,900	+18.8%	63
St. Lucia	309,485,819	+24.4%	97
Lesotho	110,048,145	+6.57%	104
Lithuania	5,572,786,951	+1.43%	64
Luxembourg	401,539,915,339	+5.18%	5
Latvia	3,981,303,586	+0.644%	71
Moldova	977,030,000	+18.1%	86
Maldives	797,910,982	-1.72%	91
Mexico	75,948,148,771	+16.2%	19
North Macedonia	1,122,589,731	+11.1%	84
Malta	23,878,188,978	-2.23%	40
Montenegro	541,257,132	+42.7%	92
Mozambique	2,922,280,435	+34.5%	76
Malaysia	34,295,994,786	+9.53%	30
Namibia	1,108,025,436	+12.6%	85
Nigeria	10,508,681,838	-19.1%	51
Nicaragua	1,366,200,000	+20.4%	81
Netherlands	421,087,303,415	+0.426%	4
Norway	51,467,935,215	+1.42%	23
Nepal	262,429,708	+30.5%	98
New Zealand	17,001,186,141	+9.39%	48
Pakistan	10,499,000,000	+34%	52
Panama	8,897,779,286	+10.9%	55
Peru	23,048,473,681	+16.1%	43
Philippines	12,777,389,487	+6.25%	50
Poland	55,932,000,000	+1.71%	21
Portugal	24,914,181,599	+0.372%	39
Paraguay	1,976,396,810	+0.967%	78
Palestinian Territories	91,105,641	-28.4%	106
Qatar	26,654,945,055	+14.6%	36
Romania	19,831,644,257	+5.91%	45
Russia	53,402,480,000	-12.6%	22
Saudi Arabia	34,000,619,327	+28.2%	31
Singapore	288,590,316,063	+12%	8
Solomon Islands	67,181,056	+64.2%	107
El Salvador	2,704,981,316	+4.77%	77
Suriname	438,847,664	+19.5%	95
Slovakia	8,121,541,107	+6.72%	60
Slovenia	4,205,245,458	+5.76%	69
Sweden	69,913,770,639	+8.07%	20
Thailand	30,719,593,961	+6.4%	34
Tajikistan	471,971,753	+75.1%	94
Timor-Leste	344,946,348	+8.76%	96
Tonga	18,206,716	+34.7%	112
Trinidad & Tobago	891,213,051	+25.6%	90
Turkey	25,321,000,000	+30.3%	38
Ukraine	8,852,000,000	+22.2%	57
Uruguay	7,749,273,437	+0.0171%	61
United States	1,443,600,000,000	+10.2%	1
Uzbekistan	4,532,608,182	+4.79%	67
St. Vincent & Grenadines	18,792,622	+108%	111
Vietnam	21,235,000,000	-21.4%	44
Samoa	45,725,701	-0.575%	109
South Africa	18,591,512,619	+9.58%	47

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 = 'BM.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 <- 'BM.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

Primary income payments (BoP, current US$)

Year: 2024

Acquire full dataset with Python or R from source