Country

Indicator

Value type

Values present

ICT service exports (% of service exports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	22.3	-50.9%	12
Albania	2.98	-0.982%	82
Argentina	16.4	+7.96%	24
Armenia	20.8	+10.1%	14
Antigua & Barbuda	0.439	-10.7%	106
Australia	3.03	-6.59%	81
Austria	12.6	-0.526%	30
Azerbaijan	1.37	-37.9%	96
Belgium	15.7	+11.2%	25
Bangladesh	10.5	+5.68%	40
Bulgaria	26.3	+9.3%	9
Bahrain	8.26	-3.85%	54
Bosnia & Herzegovina	12.9	-0.787%	28
Belize	10	-0.221%	44
Brazil	12.7	-0.0854%	29
Brunei	3.61	-64.3%	76
Bhutan	0.447	-36.6%	105
Canada	12	-9.64%	32
Switzerland	11.4	+1.47%	35
Chile	5.66	-3.18%	70
China	16.9	-3.95%	22
Colombia	11.2	+2.33%	37
Cape Verde	1.49	-0.903%	94
Costa Rica	14.8	+2.9%	27
Cyprus	29.7	-0.0407%	7
Czechia	17.9	-0.695%	19
Germany	11.4	+3.78%	34
Djibouti	6.86	-26.9%	65
Dominica	1.65	-13.3%	93
Denmark	7.59	-0.845%	59
Dominican Republic	0.357	-21.3%	107
Ecuador	2.61	+13.4%	85
Spain	10.3	-7.73%	42
Estonia	25.7	+9.48%	10
Finland	33.1	+11%	6
France	7.43	-2.13%	62
United Kingdom	9.06	-4.4%	48
Georgia	10.9	-13.5%	38
Gambia	0.311	-17.2%	108
Greece	3.18	+13.2%	78
Grenada	0.664	-5.78%	104
Guatemala	12.4	-12.2%	31
Honduras	2.25	-21.2%	88
Croatia	7.68	-0.0558%	58
Hungary	11.3	+11.1%	36
Indonesia	9.63	+16.7%	45
India	47.4	-1.57%	2
Iceland	7.76	-2.07%	57
Israel	64.8	+4.92%	1
Italy	6.17	-4.16%	68
Jamaica	2.43	+0.325%	87
Japan	4.83	-14.3%	72
Kazakhstan	7.45	+8.55%	61
Cambodia	6.33	+11.1%	67
St. Kitts & Nevis	0.852	+17.6%	99
South Korea	10.4	-1.37%	41
Kuwait	44.8	-2.15%	4
St. Lucia	0.746	-11.5%	101
Lesotho	0.0992	+12.1%	109
Lithuania	11.5	+5.95%	33
Luxembourg	3.07	-20.7%	80
Latvia	17.6	+3.92%	21
Moldova	26.7	+3.46%	8
Maldives	0.932	-4.73%	98
Mexico	3.8	+30.2%	75
North Macedonia	21.7	+3.35%	13
Malta	1.69	+2.54%	92
Montenegro	7.51	+6.91%	60
Mozambique	0.795	-32.6%	100
Malaysia	8.66	-8.35%	52
Namibia	2.02	+2.87%	90
Nigeria	4.04	-5.75%	74
Nicaragua	19.4	+8.08%	18
Netherlands	8.96	-1.56%	49
Norway	8.34	+4.42%	53
Nepal	6.11	-60.7%	69
New Zealand	6.39	-12.3%	66
Pakistan	44.9	+22.2%	3
Panama	2.94	-1.38%	83
Peru	2.16	-3.27%	89
Philippines	15.6	+5.92%	26
Poland	16.5	+6.63%	23
Portugal	8.16	-2.76%	55
Paraguay	0.674	+7.53%	103
Palestinian Territories	17.7	+44%	20
Qatar	3.45	-19.8%	77
Romania	25.6	-0.458%	11
Russia	5.66	-20.6%	71
Saudi Arabia	3.11	-11%	79
Singapore	7.79	-4.73%	56
Solomon Islands	1.92	-29.4%	91
El Salvador	6.91	-16.5%	64
Suriname	10.6	-5.05%	39
Slovakia	19.6	+20.9%	16
Slovenia	8.87	+6.13%	50
Sweden	20.5	-2.83%	15
Thailand	0.71	-13.2%	102
Tajikistan	1.04	-62.9%	97
Timor-Leste	2.44	+27.1%	86
Tonga	10.2	-22.7%	43
Trinidad & Tobago	2.66	-51%	84
Turkey	4.21	+5.6%	73
Ukraine	38.4	-7.46%	5
Uruguay	19.5	-0.963%	17
United States	7.16	+4.04%	63
Uzbekistan	9.47	+16.6%	47
St. Vincent & Grenadines	1.47	-16.8%	95
Samoa	8.8	+30.5%	51
South Africa	9.58	+2.2%	46

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.ZS'

# 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.ZS'

# 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 (% of service exports, BoP)

Year: 2024

Acquire full dataset with Python or R from source