Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	38.1	-35.9%	35
Albania	14.5	-19.9%	82
Argentina	35.9	+12.3%	38
Armenia	26.6	+2.5%	58
Antigua & Barbuda	5.32	-5.79%	104
Australia	19	-2.3%	71
Austria	35.9	-0.81%	39
Azerbaijan	33.1	+15.8%	45
Belgium	35.3	+9.08%	40
Bangladesh	59.4	-2.11%	9
Bulgaria	40.8	+2.32%	26
Bahrain	22.8	-6.65%	68
Bahamas	1.79	-18.9%	108
Bosnia & Herzegovina	29.1	+3.47%	52
Belize	21.8	-4.92%	69
Brazil	46.8	-6.26%	20
Brunei	13.2	-46.5%	85
Bhutan	11.5	+312%	89
Canada	30.4	-4.96%	49
Switzerland	44.5	+4.19%	22
Chile	10.7	-4.95%	92
China	26.7	-20.8%	57
Colombia	28.2	-1.25%	54
Cape Verde	7.85	-5.3%	99
Costa Rica	24.4	-4.52%	63
Cyprus	38.5	-5.22%	33
Czechia	39.3	-2.76%	29
Germany	41.7	-1.31%	25
Djibouti	35.1	-9.74%	41
Dominica	25.3	+3.84%	61
Denmark	23.1	-17.5%	65
Dominican Republic	11.5	-5.43%	90
Ecuador	9.98	+14.2%	95
Spain	25.4	+1.8%	60
Estonia	55.6	+5.05%	13
Finland	65.2	+3.73%	6
France	35.9	-3.4%	37
United Kingdom	18.4	-2.99%	72
Georgia	15	-6.6%	81
Gambia	0.311	-69.4%	110
Greece	9.18	+13.3%	96
Grenada	10.7	-3.69%	93
Guatemala	41.9	-7.51%	24
Honduras	74.6	-2.69%	1
Croatia	18.3	-3.98%	73
Hungary	38.7	+2.52%	32
Indonesia	16	+3.11%	79
India	55.8	-1.38%	12
Iceland	15.3	-6.06%	80
Israel	70.5	+4.71%	5
Italy	31.7	-4.88%	47
Jamaica	12.1	+0.932%	87
Japan	44.7	-14.5%	21
Kazakhstan	18.1	-2.69%	74
Cambodia	12.6	+7.25%	86
St. Kitts & Nevis	16.5	-43.2%	78
South Korea	48.1	-1.85%	18
Kuwait	49.5	-2.66%	17
St. Lucia	3.66	-8.9%	106
Lesotho	36.5	+12.1%	36
Lithuania	29.2	-2.32%	51
Luxembourg	14.3	-13.5%	83
Latvia	38.9	+2.8%	31
Moldova	44.4	+2.05%	23
Maldives	1.16	-29.8%	109
Mexico	7.45	+0.196%	100
North Macedonia	40.5	-2%	27
Malta	48	-4.99%	19
Montenegro	11.7	+12.9%	88
Mozambique	1.96	+5.1%	107
Malaysia	33.4	-4.47%	44
Namibia	23.7	-14.9%	64
Nigeria	16.9	+1.36%	77
Nicaragua	52.3	+12.9%	14
Netherlands	64.1	+0.919%	7
Norway	34.7	+3.68%	43
Nepal	30.6	+108%	48
New Zealand	21.4	-3.7%	70
Pakistan	74	+6.17%	2
Panama	5.29	+17.9%	105
Peru	5.59	-11%	103
Philippines	72.8	-0.953%	3
Poland	39.8	+2.75%	28
Portugal	26	+1.12%	59
Paraguay	50.9	+0.892%	15
Palestinian Territories	72.7	+35.1%	4
Qatar	6.83	-10.5%	102
Romania	49.5	-2.23%	16
Russia	34.9	+10.2%	42
Saudi Arabia	7.41	-7.49%	101
Singapore	23.1	-4.68%	66
Solomon Islands	23	-9.44%	67
El Salvador	27.8	-6.45%	55
Suriname	57.6	-2.48%	10
Slovakia	38.5	-0.961%	34
Slovenia	27.4	+6.48%	56
Sweden	56.1	+3.08%	11
Thailand	28.7	-17.7%	53
Tajikistan	17.5	-8.96%	76
Timor-Leste	18	-11.5%	75
Tonga	30.4	-4.4%	50
Trinidad & Tobago	9.15	-21%	97
Turkey	11.4	+0.236%	91
Ukraine	60.7	-3.79%	8
Uruguay	24.7	+0.509%	62
United States	31.9	-1.93%	46
Uzbekistan	14.2	-23.6%	84
St. Vincent & Grenadines	7.99	-16.5%	98
Samoa	10.3	-12.2%	94
South Africa	39.2	+0.396%	30

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.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 <- 'BX.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 exports, BoP)

Year: 2024

Acquire full dataset with Python or R from source