Country

Indicator

Value type

Values present

Commercial service exports (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	126,557,613	+66.9%	109
Albania	7,980,624,733	+13%	65
Argentina	16,877,752,679	+6.02%	50
Armenia	5,648,280,814	-0.141%	74
Antigua & Barbuda	1,238,858,857	+12.9%	92
Australia	82,711,587,736	+8.96%	24
Austria	93,427,331,331	+4.46%	22
Azerbaijan	8,084,308,000	+29.2%	64
Belgium	145,429,261,782	-3.23%	15
Bangladesh	5,052,258,329	+12.3%	76
Bulgaria	16,564,830,000	+1.85%	51
Bahrain	17,025,000,000	+9.62%	49
Bahamas	5,838,371,692	+14.7%	73
Bosnia & Herzegovina	3,733,854,835	+5.19%	81
Belarus	9,889,470,470	+14%	62
Belize	1,109,753,559	+12.7%	95
Brazil	47,707,681,688	+7.33%	33
Brunei	390,567,995	+21.7%	102
Bhutan	257,086,032	+106%	105
Canada	157,869,070,950	+3.18%	13
Switzerland	177,191,141,365	+8.92%	11
Chile	11,534,147,813	+16.8%	61
China	382,416,592,213	+16.9%	6
Colombia	17,541,865,560	+11.1%	48
Cape Verde	806,157,755	+16.4%	97
Costa Rica	16,079,204,566	+8.91%	53
Cyprus	30,573,251,784	+9.05%	40
Czechia	42,518,273,319	+7.53%	35
Germany	465,752,036,693	+4.21%	3
Djibouti	842,191,646	+8.05%	96
Dominica	178,468,833	+11.5%	108
Denmark	126,626,636,881	+9.55%	17
Dominican Republic	14,362,000,000	+14.7%	55
Ecuador	3,652,560,135	-10.3%	82
Spain	220,371,720,132	+11.8%	10
Estonia	13,443,910,157	+6.52%	58
Finland	41,090,839,317	+11.8%	37
France	398,371,908,855	+8.22%	4
United Kingdom	644,864,382,280	+10.5%	2
Georgia	7,588,049,436	+9.1%	66
Gambia	475,091,067	+21.2%	99
Greece	55,655,373,320	+5.04%	29
Grenada	786,868,619	+4.4%	98
Guatemala	4,572,167,170	+8.73%	79
Hong Kong SAR China	108,762,377,425	+11.8%	21
Honduras	3,612,864,280	-3.71%	83
Croatia	24,694,192,905	+1.61%	42
Hungary	38,113,070,761	+3.08%	39
Indonesia	38,763,687,828	+16.5%	38
India	374,274,753,904	+11.1%	7
Iceland	6,873,823,339	+0.762%	70
Israel	83,012,500,000	+1.7%	23
Italy	154,301,744,900	+5.46%	14
Jamaica	5,224,620,934	-0.324%	75
Japan	224,752,178,150	+9.52%	9
Kazakhstan	11,550,111,750	+10.9%	60
Cambodia	4,809,616,709	+17.2%	78
St. Kitts & Nevis	411,045,147	+0.354%	101
South Korea	138,166,700,000	+11%	16
Kuwait	11,663,856,590	+8.52%	59
St. Lucia	1,460,228,570	+14.8%	89
Lesotho	13,422,942	-11.6%	113
Lithuania	24,126,363,371	+12.9%	44
Luxembourg	170,392,936,440	+3.62%	12
Latvia	8,328,285,928	+2.96%	63
Moldova	2,645,340,000	+10.7%	86
Maldives	5,018,481,649	+12.8%	77
Mexico	62,846,093,917	+12.3%	26
North Macedonia	3,158,564,551	+10.3%	84
Malta	24,628,524,836	+10.6%	43
Montenegro	2,915,212,988	-2.67%	85
Mozambique	1,146,959,526	+1.66%	94
Malaysia	53,326,261,491	+25.2%	30
Namibia	1,250,928,130	+21.4%	90
Nigeria	4,067,698,384	+2.62%	80
Nicaragua	1,190,818,290	-17.9%	93
Netherlands	307,795,457,738	+6.5%	8
Norway	56,737,177,844	+6.73%	28
Nepal	1,520,125,204	+97.3%	88
New Zealand	18,308,070,461	+12.3%	46
Pakistan	7,045,794,534	+12.2%	67
Panama	18,131,693,115	+2.66%	47
Peru	6,964,892,414	+22.6%	68
Philippines	51,948,847,893	+7.54%	32
Poland	118,281,000,000	+9.07%	18
Portugal	62,071,227,963	+8.19%	27
Paraguay	2,448,527,574	+8.63%	87
Palestinian Territories	474,610,426	-43.7%	100
Qatar	29,653,846,154	-2.08%	41
Romania	42,868,559,749	-0.00687%	34
Russia	41,217,700,000	+3.86%	36
Saudi Arabia	52,898,234,393	+14.2%	31
Singapore	395,208,671,140	+10.4%	5
Solomon Islands	122,732,387	+14.8%	110
El Salvador	5,840,241,737	+18.2%	72
Suriname	202,324,122	+20.6%	106
Slovakia	13,492,309,942	+1.28%	57
Slovenia	13,504,775,483	+4.95%	56
Sweden	115,595,458,588	+9.42%	19
Thailand	71,355,542,222	+27.2%	25
Tajikistan	192,083,110	-19.6%	107
Timor-Leste	69,310,981	+22.4%	112
Tonga	105,179,951	+31.7%	111
Trinidad & Tobago	1,242,742,887	+7.99%	91
Turkey	114,947,000,000	+8.39%	20
Ukraine	16,554,000,000	+3.85%	52
Uruguay	6,889,213,666	+0.977%	69
United States	1,076,649,000,000	+8.33%	1
Uzbekistan	6,524,314,232	+20.5%	71
St. Vincent & Grenadines	364,568,798	+23.9%	103
Vietnam	23,851,000,000	+17.7%	45
Samoa	327,496,076	+8.15%	104
South Africa	15,762,029,646	+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 = 'TX.VAL.SERV.CD.WT'

# 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 <- 'TX.VAL.SERV.CD.WT'

# 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

Commercial service exports (current US$)

Year: 2024

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