Country

Indicator

Value type

Values present

Net secondary income (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	-287,430,469	-40.7%	74
Albania	1,408,216,906	+8.81%	38
Argentina	2,150,921,489	+13.2%	31
Armenia	410,669,376	+42.1%	50
Antigua & Barbuda	-103,091,227	+4.49%	71
Australia	-503,331,287	-54.6%	79
Austria	-3,997,511,782	+10.1%	95
Azerbaijan	631,744,000	-40.3%	45
Belgium	-11,458,480,660	+29.1%	103
Bangladesh	27,484,390,860	+21.9%	5
Bulgaria	1,072,470,000	-34.2%	41
Bahrain	-2,659,574,468	-0.14%	91
Bahamas	65,425,497	+25.2%	63
Bosnia & Herzegovina	2,972,906,399	+7.56%	27
Belarus	1,179,468,416	+2.86%	39
Belize	162,090,758	+26.1%	56
Brazil	2,925,294,288	+14.4%	28
Brunei	-633,685,082	-15.2%	82
Bhutan	80,603,861	-5.3%	60
Canada	-2,930,716,576	-50%	92
Switzerland	-15,442,491,339	+13%	106
Chile	263,686,257	-64.2%	52
China	14,990,125,928	+30.4%	11
Colombia	15,507,110,547	+20%	9
Cape Verde	467,877,377	+11%	48
Costa Rica	587,352,829	+4.08%	46
Cyprus	-413,476,554	-8.96%	77
Czechia	-1,685,043,361	-24.8%	86
Germany	-69,002,465,799	-5.56%	112
Djibouti	9,296,588	-15.1%	67
Dominica	11,110,099	+116%	66
Denmark	-5,173,017,110	+9.3%	97
Dominican Republic	10,137,100,000	+4.73%	14
Ecuador	5,920,705,688	+24.2%	21
Spain	-12,838,878,236	+0.0155%	104
Estonia	178,469,179	-18.7%	55
Finland	-2,135,760,238	-36.5%	88
France	-51,179,781,368	-9.52%	110
United Kingdom	-23,388,023,259	-6.56%	108
Georgia	3,305,765,709	-0.692%	25
Gambia	554,031,521	+2.59%	47
Greece	2,253,984,089	+60%	30
Grenada	-6,244,241	-27.2%	68
Guatemala	22,503,262,430	+7.89%	7
Hong Kong SAR China	-2,584,733,537	+18.2%	90
Honduras	10,057,107,050	+7.71%	15
Croatia	1,861,392,752	-30.4%	32
Hungary	-1,366,695,669	-39.4%	85
Indonesia	5,977,191,442	+11.1%	20
India	120,708,302,440	+18.4%	1
Iceland	-387,008,739	+7.46%	75
Israel	8,533,600,000	+11.4%	17
Italy	-17,775,907,494	+0.0918%	107
Jamaica	3,429,351,095	-0.564%	24
Japan	-30,476,316,595	+2.75%	109
Kazakhstan	-574,104,682	-44.6%	80
Cambodia	3,197,699,009	+0.75%	26
St. Kitts & Nevis	-18,273,014	+17.8%	69
South Korea	-4,001,700,000	-6.09%	96
Kuwait	-14,575,875,832	+12.7%	105
St. Lucia	34,072,935	+0.607%	64
Lesotho	679,294,126	+20.1%	44
Lithuania	-208,014,563	-201%	73
Luxembourg	-412,327,370	-58.2%	76
Latvia	925,413,557	+0.523%	42
Moldova	1,626,190,000	-10.4%	35
Maldives	-574,589,981	+6.66%	81
Mexico	64,283,933,672	+1.96%	2
North Macedonia	2,691,116,845	-7.74%	29
Malta	-93,189,955	-1.53%	70
Montenegro	466,200,392	-1.56%	49
Mozambique	1,151,901,773	-18.3%	40
Malaysia	-2,001,514,605	-23.7%	87
Namibia	1,699,167,906	+21.3%	34
Nigeria	24,041,406,547	+8.66%	6
Nicaragua	5,109,100,000	+12.5%	22
Netherlands	-9,324,569,385	+68.3%	100
Norway	-6,937,879,577	-13.3%	98
Nepal	15,281,622,963	+33.8%	10
New Zealand	-648,038,143	-1,525%	83
Pakistan	36,803,000,000	+31.6%	3
Panama	-178,725,595	+25.4%	72
Peru	7,603,732,791	+11.5%	19
Philippines	31,684,851,047	+1.8%	4
Poland	-3,964,000,000	+77.8%	94
Portugal	4,850,055,708	-0.0595%	23
Paraguay	809,611,005	+19.7%	43
Palestinian Territories	1,570,416,108	-24.9%	36
Qatar	-10,189,010,989	-27.3%	101
Romania	1,491,507,664	-20.4%	37
Russia	-3,053,870,000	-67.1%	93
Saudi Arabia	-55,737,216,237	+8.44%	111
Singapore	-7,571,581,810	+11.2%	99
Solomon Islands	146,259,809	+8.94%	59
El Salvador	8,391,945,989	+2.64%	18
Suriname	153,166,411	+10.3%	58
Slovakia	-933,980,112	+24.4%	84
Slovenia	-502,251,353	-11.4%	78
Sweden	-10,822,369,460	+16.1%	102
Thailand	9,219,190,993	-7.07%	16
Tajikistan	1,720,960,701	+29.7%	33
Timor-Leste	158,398,937	+80.6%	57
Tonga	197,295,302	-5.08%	54
Trinidad & Tobago	29,669,232	-74.7%	65
Turkey	72,000,000	-87.1%	61
Ukraine	21,832,000,000	-6.08%	8
Uruguay	202,751,321	+14.5%	53
United States	-206,897,000,000	+10.3%	113
Uzbekistan	10,578,217,988	+20.1%	13
St. Vincent & Grenadines	65,867,009	+7.84%	62
Vietnam	13,029,000,000	-0.199%	12
Samoa	281,018,377	+4.47%	51
South Africa	-2,512,082,797	+16.9%	89

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.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 <- 'BN.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

Net secondary income (BoP, current US$)

Year: 2024

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