Country

Indicator

Value type

Values present

Communications, computer, etc. (% of service imports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	12.3	+13.4%	99
Albania	10	-10.2%	105
Argentina	40.8	+2.38%	50
Armenia	10.9	+6.7%	104
Antigua & Barbuda	47.8	-6.25%	29
Australia	38.6	+1.45%	55
Austria	51	+0.145%	21
Azerbaijan	37.1	+26.8%	62
Belgium	52.2	-3.46%	19
Bangladesh	16.3	+11.2%	95
Bulgaria	37.9	-5.66%	59
Bahrain	20.9	-2.2%	88
Bahamas	50.4	+11.3%	23
Bosnia & Herzegovina	11.8	+23.7%	101
Belize	28.9	-6.74%	77
Brazil	54.3	+1.64%	16
Brunei	44.1	-18.3%	40
Bhutan	8.36	-530%	107
Canada	46.8	-3.85%	34
Switzerland	69.1	-0.357%	4
Chile	37.7	-1.13%	60
China	27.3	-10.1%	80
Colombia	33	-8.42%	69
Cape Verde	38.9	-1.14%	54
Costa Rica	39.5	+4.18%	53
Cyprus	54.2	+1.39%	17
Czechia	47.6	+0.633%	30
Germany	49.8	+1.9%	26
Djibouti	7.12	+8.88%	109
Dominica	40.1	-7.26%	51
Denmark	45	-5.43%	37
Dominican Republic	25.7	+36.8%	83
Ecuador	28	-12.6%	78
Spain	47.8	-2.01%	28
Estonia	54.9	-1.23%	15
Finland	64.5	-1.11%	5
France	53.4	-1.77%	18
United Kingdom	50.2	-2.65%	24
Georgia	19.6	+14.7%	91
Gambia	-1.45	-307%	110
Greece	18	+7.12%	93
Grenada	58.3	-6.06%	11
Guatemala	19.4	-9.39%	92
Hong Kong SAR China	12.3	-65.8%	98
Honduras	23.6	+0.452%	84
Croatia	47.1	-9.55%	32
Hungary	52	+0.0639%	20
Indonesia	41.5	-0.402%	49
India	60.5	+1.13%	9
Iceland	41.6	+0.712%	48
Israel	62.7	+5.67%	8
Italy	47.3	-0.639%	31
Jamaica	38.1	+1.1%	57
Japan	63.7	-5.61%	6
Kazakhstan	33	-6.49%	70
Cambodia	12.2	-21.5%	100
St. Kitts & Nevis	34.9	-30.6%	66
South Korea	56.3	+1.46%	13
Kuwait	13.6	+20.1%	97
St. Lucia	50.7	-6.8%	22
Lesotho	11.2	-4.67%	103
Lithuania	35.1	-3.19%	65
Luxembourg	34.5	-2.46%	67
Latvia	46.3	-2.01%	35
Moldova	23.3	-2.51%	86
Maldives	32.5	+1.79%	72
Mexico	32	-4.22%	74
North Macedonia	50	+75.3%	25
Malta	74.8	-0.601%	2
Montenegro	38.5	+9.01%	56
Mozambique	46.8	+26%	33
Malaysia	42.6	-1.1%	45
Namibia	79.7	-1.77%	1
Nigeria	30.6	+18.4%	76
Nicaragua	11.5	-15.2%	102
Netherlands	63	+0.0244%	7
Norway	46.3	-0.775%	36
Nepal	8.25	+732%	108
New Zealand	43.4	+6.61%	44
Pakistan	26.8	+15.3%	81
Panama	14	-0.266%	96
Peru	33.8	-1.65%	68
Philippines	32.5	-15.3%	73
Poland	56.3	+0.992%	12
Portugal	45	+3.49%	38
Paraguay	23.5	+15.8%	85
Palestinian Territories	36.8	+21.8%	64
Qatar	26.1	-5.16%	82
Romania	44.9	-2.92%	39
Russia	20.7	-7.33%	89
Saudi Arabia	27.9	-34.4%	79
Singapore	49.4	-3.44%	27
Solomon Islands	55.1	+8.43%	14
El Salvador	16.6	-12.1%	94
Suriname	58.6	+7.73%	10
Slovakia	42.4	-3.03%	46
Slovenia	43.9	-2.39%	41
Sweden	71.3	+0.624%	3
Thailand	40	-0.593%	52
Tajikistan	-2.18	-129%	111
Timor-Leste	32.7	-12.8%	71
Tonga	31.5	-9.73%	75
Trinidad & Tobago	37.5	-3.05%	61
Turkey	36.8	-1.88%	63
Ukraine	20.1	+12.1%	90
Uruguay	43.7	+0.319%	42
United States	41.9	-0.889%	47
Uzbekistan	9.08	+20.3%	106
St. Vincent & Grenadines	21.1	-25.5%	87
Samoa	37.9	-8.06%	58
South Africa	43.6	+3.15%	43

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 = 'BM.GSR.CMCP.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 <- 'BM.GSR.CMCP.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

Communications, computer, etc. (% of service imports, BoP)

Year: 2024

Acquire full dataset with Python or R from source