Country

Indicator

Value type

Values present

Secondary income receipts (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	22,365,933	+42.9%	110
Albania	1,631,157,094	+10.3%	76
Argentina	3,713,140,288	+12.1%	55
Armenia	1,192,388,993	-3.26%	80
Antigua & Barbuda	51,410,237	-9.58%	106
Australia	9,855,115,364	+9.25%	38
Austria	9,339,962,560	+8.64%	39
Azerbaijan	1,246,543,000	-29.6%	79
Belgium	24,193,974,617	+2.65%	17
Bangladesh	27,812,035,942	+21.7%	14
Bulgaria	2,865,940,000	-19.1%	64
Bahamas	368,568,336	+14.8%	92
Bosnia & Herzegovina	3,369,335,465	+8.34%	60
Belarus	3,045,074,328	+8.78%	62
Belize	209,501,995	+7.66%	98
Brazil	13,964,368,206	+0.303%	29
Brunei	66,966,191	+16.5%	105
Bhutan	163,547,106	-3.48%	100
Canada	15,100,040,057	+9%	28
Switzerland	65,182,770,332	+4.15%	5
Chile	4,904,264,504	-18.5%	53
China	37,939,513,092	+12.2%	9
Colombia	17,125,912,712	+18.8%	21
Cape Verde	520,909,474	+10.8%	89
Costa Rica	1,373,814,241	+10.1%	77
Cyprus	858,317,385	+2.55%	85
Czechia	5,803,233,036	+3.92%	47
Germany	135,274,097,551	+10.4%	2
Djibouti	31,675,670	+2.26%	109
Dominica	45,481,684	+7.92%	107
Denmark	5,719,417,795	-0.59%	48
Dominican Republic	11,899,600,000	+5.57%	32
Ecuador	7,082,746,359	+19.4%	46
Spain	32,194,562,297	+8.18%	12
Estonia	1,082,723,814	+0.861%	81
Finland	3,046,365,160	+21.2%	61
France	59,452,893,000	+9.04%	6
United Kingdom	38,474,577,158	+5.78%	8
Georgia	3,424,279,481	-0.903%	58
Gambia	520,646,127	+3.37%	90
Greece	7,388,057,634	+23.8%	44
Grenada	76,523,226	+1.53%	104
Guatemala	22,860,713,100	+8.26%	18
Hong Kong SAR China	2,189,080,367	-3.29%	67
Honduras	10,441,042,362	+7.4%	36
Croatia	5,353,184,044	+4.21%	49
Hungary	3,509,435,893	+23%	57
Indonesia	16,847,635,497	+10.4%	24
India	132,961,766,118	+15.2%	3
Iceland	332,976,354	+10.3%	93
Israel	16,903,400,000	+8.49%	23
Italy	25,300,380,397	+1.94%	15
Jamaica	3,663,952,751	-0.617%	56
Japan	38,882,254,369	+22.6%	7
Kazakhstan	2,012,874,281	-1.91%	70
Cambodia	3,403,145,909	-0.301%	59
St. Kitts & Nevis	31,900,940	-2.46%	108
South Korea	10,598,100,000	+12.7%	35
Kuwait	7,806,195	+29.8%	111
St. Lucia	82,444,980	+1.48%	103
Lesotho	705,925,314	+21.8%	86
Lithuania	1,830,992,478	+11.8%	73
Luxembourg	15,943,267,914	+6.34%	26
Latvia	2,056,316,241	+4.78%	69
Moldova	2,131,470,000	-5.51%	68
Maldives	96,178,464	-0.344%	102
Mexico	65,695,106,854	+2.27%	4
North Macedonia	2,899,061,802	-6.69%	63
Malta	1,055,147,486	+4.69%	82
Montenegro	618,669,479	+2.93%	87
Mozambique	1,320,491,944	-16.4%	78
Malaysia	8,507,975,458	+16.2%	41
Namibia	1,896,583,872	+21.2%	72
Nigeria	24,421,867,960	+8.4%	16
Nicaragua	5,271,700,000	+12.4%	51
Netherlands	19,140,341,160	-0.633%	20
Norway	9,966,178,122	+13.6%	37
Nepal	15,350,163,730	+33.6%	27
New Zealand	2,370,607,525	-14.1%	65
Pakistan	37,387,000,000	+31.9%	10
Panama	879,734,141	-3.62%	84
Peru	7,942,927,207	+11.1%	43
Philippines	32,876,852,142	+2.05%	11
Poland	11,953,000,000	-2.06%	31
Portugal	13,601,314,517	+3.94%	30
Paraguay	899,866,057	+17.7%	83
Palestinian Territories	1,801,275,767	-26.8%	74
Qatar	4,991,483,516	+41.6%	52
Romania	8,067,385,879	-2.72%	42
Russia	11,431,150,000	+1.08%	34
Singapore	29,953,975,104	+1.55%	13
Solomon Islands	193,540,717	+1.87%	99
El Salvador	8,740,202,335	+2.45%	40
Suriname	224,277,648	+8.19%	97
Slovakia	1,794,710,689	-5.81%	75
Slovenia	1,957,855,704	+4.47%	71
Sweden	7,338,588,700	-3.46%	45
Thailand	16,048,171,041	+0.538%	25
Tajikistan	2,289,482,736	+41%	66
Timor-Leste	262,187,145	-3.84%	95
Tonga	237,084,499	-4.23%	96
Trinidad & Tobago	412,163,953	-15%	91
Turkey	5,296,000,000	+3.1%	50
Ukraine	22,807,000,000	-6.38%	19
Uruguay	560,290,229	+2.5%	88
United States	201,683,000,000	+2.56%	1
Uzbekistan	11,603,653,580	+19.8%	33
St. Vincent & Grenadines	109,578,294	+7.85%	101
Vietnam	17,122,000,000	+6.27%	22
Samoa	327,704,822	+8.47%	94
South Africa	4,608,784,990	+12.7%	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 = 'BX.TRF.CURR.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 <- 'BX.TRF.CURR.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

Secondary income receipts (BoP, current US$)

Year: 2024

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