Country

Indicator

Value type

Values present

Service exports (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	128,874,452	+69.9%	110
Albania	8,031,895,336	+12.9%	66
Argentina	17,122,406,906	+6.28%	50
Armenia	5,682,935,856	-0.133%	75
Antigua & Barbuda	1,241,419,419	+12.9%	93
Australia	83,386,076,110	+8.9%	23
Austria	94,303,390,217	+4.43%	22
Azerbaijan	8,120,879,000	+29.2%	64
Belgium	148,182,016,221	-2.88%	15
Bangladesh	6,652,036,784	+4.29%	71
Bulgaria	16,626,460,000	+1.7%	52
Bahrain	17,025,000,000	+9.62%	51
Bahamas	5,896,984,361	+14.5%	74
Bosnia & Herzegovina	3,740,016,887	+5.21%	82
Belarus	9,889,470,470	+14%	62
Belize	1,157,918,437	+10.5%	95
Brazil	48,476,770,634	+6.84%	33
Brunei	409,655,983	+20.5%	102
Bhutan	288,136,244	+127%	105
Canada	159,075,382,630	+3.02%	13
Switzerland	179,834,839,845	+8.81%	11
Chile	11,957,320,639	+16.3%	60
China	383,959,880,583	+16.8%	6
Colombia	17,780,007,721	+10.9%	48
Cape Verde	830,391,553	+16%	97
Costa Rica	16,113,784,566	+8.9%	53
Cyprus	30,725,167,862	+8.94%	40
Czechia	42,539,017,982	+7.53%	35
Germany	471,582,221,930	+4.2%	3
Djibouti	1,173,078,423	+6.15%	94
Dominica	190,714,016	+17.1%	108
Denmark	127,108,619,356	+9.51%	17
Dominican Republic	14,691,100,000	+14.4%	55
Ecuador	3,769,129,569	-10.3%	81
Spain	220,371,720,132	+11.8%	10
Estonia	13,514,263,609	+6.64%	58
Finland	41,218,546,444	+11.7%	37
France	400,103,952,726	+8.14%	4
United Kingdom	649,278,415,070	+10.7%	2
Georgia	7,699,911,956	+9.16%	67
Gambia	475,091,067	+21.2%	100
Greece	55,809,407,774	+5.04%	29
Grenada	788,534,108	+4.4%	98
Guatemala	4,667,670,440	+8.7%	79
Hong Kong SAR China	108,762,377,425	+11.7%	21
Honduras	3,678,944,612	-3.68%	83
Croatia	24,694,192,905	+1.61%	42
Hungary	38,303,865,515	+3.04%	39
Indonesia	39,029,764,328	+16.1%	38
India	374,879,313,857	+11.1%	7
Iceland	6,898,128,381	+0.778%	70
Israel	83,041,600,000	+1.7%	24
Italy	155,270,705,616	+5.41%	14
Jamaica	5,256,028,511	-0.322%	76
Japan	227,964,593,806	+9.24%	9
Kazakhstan	11,828,321,750	+10.8%	61
Cambodia	4,960,590,978	+18.4%	78
St. Kitts & Nevis	470,693,038	-14.6%	101
South Korea	138,952,700,000	+10.6%	16
Kuwait	12,218,417,787	+8.11%	59
St. Lucia	1,463,166,469	+14.7%	89
Lesotho	15,405,551	-10.2%	113
Lithuania	24,196,653,152	+12.9%	44
Luxembourg	171,242,194,406	+3.6%	12
Latvia	8,362,883,643	+2.94%	63
Moldova	2,703,730,000	+10.8%	86
Maldives	5,029,804,432	+12.8%	77
Mexico	63,034,084,919	+12.2%	26
North Macedonia	3,163,088,401	+10.3%	84
Malta	24,663,225,864	+10.5%	43
Montenegro	2,915,081,529	-2.67%	85
Mozambique	1,146,959,526	+1.66%	96
Malaysia	53,402,361,578	+25.1%	31
Namibia	1,287,847,419	+20.4%	91
Nigeria	4,565,221,443	+2.81%	80
Nicaragua	1,298,118,290	-16.7%	90
Netherlands	309,268,136,500	+6.58%	8
Norway	56,850,553,617	+6.71%	28
Nepal	1,842,069,167	+106%	88
New Zealand	18,504,852,880	+12.2%	46
Pakistan	8,096,794,534	+9.33%	65
Panama	18,267,678,139	+2.79%	47
Peru	7,152,907,339	+22%	68
Philippines	51,977,999,883	+7.54%	32
Poland	118,714,000,000	+9.09%	18
Portugal	62,298,595,014	+8.2%	27
Paraguay	2,654,147,409	+8.1%	87
Palestinian Territories	514,284,473	-42.2%	99
Qatar	30,172,252,747	-2.59%	41
Romania	42,933,131,922	+0.0289%	34
Russia	42,185,550,000	+4.23%	36
Saudi Arabia	55,277,135,153	+13.9%	30
Singapore	395,565,869,234	+10.4%	5
Solomon Islands	132,632,606	+15.1%	109
El Salvador	5,999,510,604	+17.4%	73
Suriname	211,424,414	+21.9%	106
Slovakia	13,562,516,851	+1.5%	56
Slovenia	13,522,501,850	+5%	57
Sweden	116,828,243,175	+9.75%	19
Thailand	72,142,163,405	+27.2%	25
Tajikistan	194,650,900	-20.1%	107
Timor-Leste	82,067,349	+18.2%	112
Tonga	108,678,754	+31.9%	111
Trinidad & Tobago	1,259,365,501	+7.98%	92
Turkey	115,249,000,000	+8.12%	20
Ukraine	17,226,000,000	+3.76%	49
Uruguay	6,948,322,538	+0.968%	69
United States	1,107,359,000,000	+7.87%	1
Uzbekistan	6,547,114,232	+20.5%	72
St. Vincent & Grenadines	365,989,474	+23.8%	103
Vietnam	23,851,000,000	+17.7%	45
Samoa	327,496,076	+8.15%	104
South Africa	15,939,059,525	+11.8%	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.GSR.NFSV.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.GSR.NFSV.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

Service exports (BoP, current US$)

Year: 2024

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