Country

Indicator

Value type

Values present

ICT goods exports (% of total goods exports)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	0.02	+100%	83
Albania	0.01	-87.5%	84
Andorra	12.6	-40.4%	11
Argentina	0.03	0%	82
Armenia	9.78	+1,818%	14
Australia	0.7	-19.5%	58
Austria	4.88	-1.41%	22
Azerbaijan	0.07	+250%	79
Burundi	0.1	-78.3%	77
Belgium	1.64	-11.4%	44
Benin	0.05	+25%	81
Bulgaria	3.31	+0.608%	28
Bahrain	0.68	-24.4%	59
Bosnia & Herzegovina	0.24	+50%	70
Belize	0.14	-48.1%	75
Bermuda	2.5	-44.8%	35
Bolivia	0.03	-50%	82
Brazil	0.27	+3.85%	68
Barbados	1.1	+108%	50
Brunei	0.03	-25%	82
Botswana	0.16	+45.5%	74
Canada	1.37	-7.43%	47
Chile	0.29	+7.41%	66
China	22.6	-11.2%	6
Cyprus	1.66	-21%	43
Czechia	16.1	+3.54%	8
Germany	4.77	-6.84%	23
Denmark	3.71	-5.84%	25
Dominican Republic	0.56	-76.4%	61
Egypt	2.38	-17.4%	36
Spain	1.37	-13.8%	47
Estonia	7.28	-25.7%	19
Finland	2.6	+1.56%	34
Fiji	0.5	-75.6%	63
France	3.65	+0.275%	26
United Kingdom	3.39	-7.88%	27
Georgia	1.26	-16%	48
Greece	2.82	-7.24%	32
Grenada	1.79	+348%	42
Guatemala	0.26	-13.3%	69
Guyana	0.01	-66.7%	84
Hong Kong SAR China	62.5	+2.11%	1
Croatia	1.6	-25.2%	45
Hungary	10.3	-15.9%	13
India	2.77	+24.2%	33
Ireland	8.59	-6.02%	15
Iceland	0.19	-40.6%	72
Israel	13.5	-4%	10
Italy	1.99	-0.995%	40
Jamaica	0.28	-17.6%	67
Jordan	0.68	-43.8%	59
Japan	8.16	-5.23%	16
Kazakhstan	1.85	+65.2%	41
Kyrgyzstan	4.67	+30.4%	24
South Korea	27.7	-5.21%	4
Lebanon	0.96	+74.5%	52
Sri Lanka	0.19	-5%	72
Lithuania	3.2	-6.71%	30
Luxembourg	1.24	-26.2%	49
Latvia	7.28	-18.1%	19
Macao SAR China	19	-14%	7
Morocco	2.36	+10.8%	37
Moldova	0.27	+50%	68
Mexico	13.7	-4.46%	9
North Macedonia	0.63	+5%	60
Malta	25.7	+59%	5
Myanmar (Burma)	0.17	-70.7%	73
Montenegro	0.79	-32.5%	56
Mozambique	0.03	+50%	82
Mauritius	0.81	-8.99%	55
Namibia	0.21	-16%	71
Niger	0.14	+40%	75
Nicaragua	0.03	-25%	82
Netherlands	7.49	-24.6%	18
Norway	0.47	-36.5%	64
New Zealand	1.08	+8%	51
Philippines	50.7	+4.6%	2
Poland	6.47	-4.57%	20
Portugal	3.28	-6.55%	29
Paraguay	0.08	-11.1%	78
French Polynesia	2.01	+73.3%	39
Qatar	0		85
Romania	2.83	-6.91%	31
Rwanda	0.87	+4.82%	54
Singapore	32.7	-5.85%	3
El Salvador	0.4	+8.11%	65
Serbia	1.43	-13.9%	46
São Tomé & Príncipe	0.06	-72.7%	80
Suriname	0.08	-27.3%	78
Slovakia	10.5	-12.7%	12
Slovenia	2.17	-13.9%	38
Sweden	5.27	-11.1%	21
Togo	0.13	+85.7%	76
Turkey	0.7	-20.5%	58
Tanzania	0.13	+85.7%	76
Ukraine	0.91	+33.8%	53
Uruguay	0.07	+16.7%	79
United States	7.85	-13.5%	17
Uzbekistan	0.53	+231%	62
South Africa	0.71	+4.41%	57
Zimbabwe	0.02	+100%	83

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.ICTG.ZS.UN'

# 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.ICTG.ZS.UN'

# 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 goods exports (% of total goods exports)

Year: 2022

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