Country

Indicator

Value type

Values present

ICT service exports (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	28,750,304	-16.5%	90
Albania	239,701,718	+11.8%	74
Argentina	2,810,026,345	+14.7%	39
Armenia	1,180,569,273	+9.93%	56
Antigua & Barbuda	5,445,185	+0.8%	99
Australia	2,524,994,963	+1.73%	42
Austria	11,905,971,838	+3.88%	18
Azerbaijan	111,256,000	-19.8%	81
Belgium	23,324,634,780	+7.96%	11
Bangladesh	701,758,915	+10.2%	61
Bulgaria	4,366,290,000	+11.2%	34
Bahrain	1,406,648,936	+5.4%	52
Bosnia & Herzegovina	484,323,092	+4.38%	69
Belize	115,848,331	+10.2%	79
Brazil	6,173,496,274	+6.75%	27
Brunei	14,779,730	-56.9%	94
Bhutan	1,288,128	+43.7%	108
Canada	19,147,136,069	-6.91%	15
Switzerland	20,447,355,967	+10.4%	13
Chile	677,077,571	+12.6%	63
China	65,033,312,106	+12.2%	3
Colombia	1,995,346,147	+13.5%	46
Cape Verde	12,375,087	+15%	95
Costa Rica	2,387,424,580	+12.1%	44
Cyprus	9,123,349,271	+8.89%	23
Czechia	7,619,332,949	+6.78%	25
Germany	53,942,494,153	+8.14%	5
Djibouti	80,518,862	-22.4%	85
Dominica	3,146,523	+1.5%	103
Denmark	9,647,637,803	+8.59%	21
Dominican Republic	52,500,000	-9.95%	86
Ecuador	98,496,360	+1.72%	82
Spain	22,725,917,758	+3.19%	12
Estonia	3,470,380,306	+16.7%	38
Finland	13,622,835,651	+24.1%	17
France	29,722,649,504	+5.84%	8
United Kingdom	58,841,894,239	+5.83%	4
Georgia	842,273,110	-5.57%	59
Gambia	1,479,038	+0.411%	107
Greece	1,777,124,296	+18.9%	48
Grenada	5,237,262	-1.64%	101
Guatemala	576,648,200	-4.59%	65
Honduras	82,617,112	-24.1%	84
Croatia	1,897,379,560	+1.56%	47
Hungary	4,319,443,950	+14.5%	35
Indonesia	3,760,052,545	+35.6%	36
India	177,745,449,131	+9.32%	1
Iceland	535,520,206	-1.31%	67
Israel	53,815,900,000	+6.7%	6
Italy	9,580,908,452	+1.03%	22
Jamaica	127,565,623	+0.00124%	78
Japan	11,012,590,447	-6.36%	19
Kazakhstan	880,992,944	+20.3%	58
Cambodia	313,904,470	+31.5%	72
St. Kitts & Nevis	4,009,121	+0.503%	102
South Korea	14,511,000,000	+9.06%	16
Kuwait	5,471,545,644	+5.78%	28
St. Lucia	10,922,207	+1.5%	97
Lesotho	15,284	+0.664%	109
Lithuania	2,770,712,699	+19.6%	40
Luxembourg	5,253,098,105	-17.9%	29
Latvia	1,470,240,275	+6.98%	51
Moldova	721,380,000	+14.6%	60
Maldives	46,894,839	+7.48%	87
Mexico	2,397,516,378	+46.1%	43
North Macedonia	685,342,866	+14%	62
Malta	417,917,778	+13.4%	70
Montenegro	218,809,811	+4.06%	75
Mozambique	9,116,548	-31.5%	98
Malaysia	4,623,972,785	+14.7%	33
Namibia	26,028,083	+23.8%	91
Nigeria	184,351,734	-3.11%	76
Nicaragua	252,300,000	-10%	73
Netherlands	27,721,070,452	+4.92%	9
Norway	4,741,971,391	+11.4%	32
Nepal	112,632,972	-9.29%	80
New Zealand	1,182,002,720	-1.6%	55
Pakistan	3,632,000,000	+33.6%	37
Panama	537,030,328	+1.37%	66
Peru	154,467,015	+18%	77
Philippines	8,085,429,150	+13.9%	24
Poland	19,604,000,000	+16.3%	14
Portugal	5,083,949,749	+5.22%	30
Paraguay	17,900,000	+16.2%	93
Palestinian Territories	91,019,192	-16.8%	83
Qatar	1,039,560,440	-21.9%	57
Romania	11,002,034,690	-0.43%	20
Russia	2,385,990,000	-17.3%	45
Saudi Arabia	1,719,231,346	+1.44%	49
Singapore	30,795,370,070	+5.2%	7
Solomon Islands	2,541,793	-18.8%	104
El Salvador	414,653,137	-1.9%	71
Suriname	22,355,497	+15.7%	92
Slovakia	2,657,331,039	+22.7%	41
Slovenia	1,199,848,583	+11.4%	54
Sweden	23,947,556,757	+6.64%	10
Thailand	512,398,070	+10.4%	68
Tajikistan	2,015,810	-70.3%	105
Timor-Leste	2,000,060	+50.3%	106
Tonga	11,049,760	+1.97%	96
Trinidad & Tobago	33,489,136	-47.1%	88
Turkey	4,856,000,000	+14.2%	31
Ukraine	6,610,000,000	-3.98%	26
Uruguay	1,354,037,394	-0.00383%	53
United States	79,265,000,000	+12.2%	2
Uzbekistan	619,736,903	+40.5%	64
St. Vincent & Grenadines	5,387,948	+3.02%	100
Samoa	28,815,115	+41.1%	89
South Africa	1,527,453,631	+14.3%	50

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.CCIS.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.CCIS.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

ICT service exports (BoP, current US$)

Year: 2024

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