Country

Indicator

Value type

Values present

Secondary income, other sectors, payments (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	289,012,157	-41.8%	78
Albania	216,180,451	+20.1%	83
Argentina	1,172,326,761	+10.1%	49
Armenia	447,147,176	-2.59%	69
Antigua & Barbuda	149,070,712	-0.909%	89
Australia	7,136,517,188	+9.29%	28
Austria	7,705,508,171	+15.3%	27
Azerbaijan	572,711,000	-3.66%	63
Belgium	26,811,714,084	+12.2%	10
Bangladesh	327,645,083	+7.52%	76
Bulgaria	873,630,000	+6.95%	59
Bahrain	2,659,574,468	-0.14%	37
Bahamas	287,450,548	+10.8%	79
Bosnia & Herzegovina	172,972,558	+24.6%	86
Belarus	1,040,234,754	+19.7%	54
Belize	40,706,579	-23.4%	104
Brazil	10,499,104,899	+1.17%	21
Bhutan	82,943,245	-1.65%	95
Canada	12,908,787,831	-8.61%	16
Switzerland	67,682,717,788	+6.48%	3
Chile	4,453,230,413	-10.9%	32
China	22,949,387,164	+2.79%	11
Colombia	1,581,824,661	+8.71%	46
Cape Verde	48,075,369	+7.49%	99
Costa Rica	781,724,845	+15%	60
Cyprus	982,346,601	+0.759%	56
Czechia	4,611,670,109	-1.02%	31
Germany	143,801,040,528	+8.87%	2
Djibouti	18,158,765	+2.41%	108
Dominica	31,355,476	-3.87%	107
Denmark	3,789,759,985	+22.2%	34
Dominican Republic	1,760,100,000	+10.6%	44
Ecuador	1,153,724,474	-0.228%	50
Spain	30,361,045,809	+8.96%	9
Estonia	329,962,807	+5.36%	75
Finland	1,911,237,508	-18.9%	43
France	65,587,200,179	+3.88%	4
United Kingdom	43,712,060,178	+5.65%	7
Georgia	155,524,908	-18.1%	88
Gambia	12,863,682	-48%	109
Greece	2,087,500,240	+5.21%	41
Grenada	74,501,638	-1.83%	96
Guatemala	322,508,670	+32.1%	77
Hong Kong SAR China	4,373,242,822	+7.37%	33
Honduras	380,018,694	-0.536%	71
Croatia	1,939,439,355	+79.9%	42
Hungary	2,638,719,572	+0.608%	38
Indonesia	10,861,773,430	+10%	19
India	11,128,631,503	-11.2%	18
Iceland	550,985,970	+13.9%	66
Israel	7,813,300,000	+6.78%	25
Italy	20,937,072,297	+5.46%	12
Jamaica	229,502,840	-1.41%	82
Japan	63,054,836,300	+17%	5
Kazakhstan	1,068,344,613	-18.1%	52
Cambodia	179,909,977	-15.8%	85
St. Kitts & Nevis	44,564,433	+4.46%	101
South Korea	11,425,800,000	+2.65%	17
Kuwait	14,380,125,637	+13.1%	14
St. Lucia	42,985,028	+1.72%	102
Lesotho	382,111	+0.664%	110
Lithuania	754,995,910	+14.3%	61
Luxembourg	10,848,275,902	+5.86%	20
Latvia	568,233,055	+16.6%	65
Moldova	495,400,000	+16.4%	68
Maldives	669,471,020	+5.67%	62
Mexico	1,377,173,182	+20.3%	47
North Macedonia	184,510,611	+12.5%	84
Malta	890,753,125	-6.57%	58
Montenegro	121,962,845	+17.8%	91
Mozambique	136,208,545	+1.86%	90
Malaysia	10,208,466,252	+5.05%	22
Namibia	103,437,921	+40.4%	93
Nigeria	380,461,413	-6.08%	70
Nicaragua	162,600,000	+10.3%	87
Netherlands	15,110,385,734	+29%	13
Norway	9,306,019,615	+0.643%	23
Nepal	68,540,767	+0.559%	97
New Zealand	2,233,796,318	+14.6%	40
Pakistan	507,000,000	+34.8%	67
Panama	1,051,984,295	+0.852%	53
Peru	331,196,017	+3%	74
Philippines	1,153,346,398	+9.31%	51
Poland	7,733,000,000	+8.02%	26
Portugal	5,474,240,118	+5.14%	30
Paraguay	88,855,052	+2.14%	94
Palestinian Territories	230,731,344	-37.4%	81
Qatar	13,794,230,769	-9.97%	15
Romania	3,323,605,161	+1.63%	35
Saudi Arabia	50,675,996,201	+21.6%	6
Singapore	37,369,895,477	+3.37%	8
Solomon Islands	41,676,141	-13.3%	103
El Salvador	348,189,812	-1.87%	73
Suriname	66,056,481	+0.421%	98
Slovakia	1,175,413,380	+0.552%	48
Slovenia	1,602,706,438	+3.99%	45
Sweden	8,877,314,547	+7.28%	24
Thailand	6,827,789,393	+13.9%	29
Tajikistan	568,522,015	+91.9%	64
Timor-Leste	103,788,208	-43.9%	92
Tonga	37,638,781	-0.819%	105
Trinidad & Tobago	350,988,073	+3.32%	72
Turkey	3,032,000,000	+19.2%	36
Ukraine	960,000,000	-12.6%	57
Uruguay	249,847,836	-6.56%	80
United States	324,538,000,000	+9.27%	1
Uzbekistan	1,004,693,560	+15.4%	55
St. Vincent & Grenadines	35,870,075	+7.79%	106
Samoa	46,353,728	+43.5%	100
South Africa	2,348,010,687	-2.98%	39

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.TRF.PRVT.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.TRF.PRVT.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, other sectors, payments (BoP, current US$)

Year: 2024

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