Country

Indicator

Value type

Values present

Travel services (% of commercial service exports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	13.8	+39%	83
Albania	67.8	+6.03%	19
Argentina	29.4	-14.7%	57
Armenia	43.2	-18.8%	40
Antigua & Barbuda	84.7	+2.15%	9
Australia	62.6	+3.19%	22
Austria	28.2	+2.11%	60
Azerbaijan	25.3	+6.17%	65
Belgium	6.03	+1.74%	109
Bangladesh	8.72	-13.5%	100
Bulgaria	26.1	+2.54%	64
Bahrain	31.8	+6.6%	53
Bahamas	94.8	+1.54%	3
Bosnia & Herzegovina	52.4	-0.351%	30
Belize	73.3	-1.23%	16
Brazil	15.4	-0.979%	82
Brunei	32.6	+47.6%	52
Bhutan	77.5	+8.1%	12
Canada	31.6	+6.47%	55
Switzerland	12.4	-1.55%	89
Chile	28	+14.2%	61
China	10.4	+35.8%	96
Colombia	48.9	+2.31%	35
Cape Verde	80.3	+2.81%	11
Costa Rica	33.9	+5.01%	50
Cyprus	12.6	-1.99%	88
Czechia	21.5	+9.2%	69
Germany	8.62	+1.68%	101
Djibouti	6.16	+0.489%	107
Dominica	65.8	+6.54%	21
Denmark	8.31	-1.84%	102
Dominican Republic	76.4	-1.88%	14
Ecuador	48.7	-0.265%	36
Spain	48.4	+3.51%	37
Estonia	12.1	+2.87%	90
Finland	10.1	-8.5%	97
France	19.4	+0.0587%	75
United Kingdom	13.1	+4.07%	86
Georgia	58.3	-1.67%	26
Gambia	91.6	+9.85%	5
Greece	42.2	-0.0731%	43
Grenada	85.3	+0.637%	7
Guatemala	36.2	+10.9%	47
Hong Kong SAR China	20.8	-3.06%	71
Honduras	19.8	+11.3%	73
Croatia	66.1	+1.31%	20
Hungary	21.2	-0.724%	70
Indonesia	43.1	+2.39%	41
India	9.36	-2.07%	98
Iceland	47.1	+2.27%	39
Israel	2.68	-58.1%	111
Italy	38.1	-0.448%	46
Jamaica	82.6	+0.278%	10
Japan	24.3	+30.1%	66
Kazakhstan	22.3	+2.76%	68
Cambodia	75.7	+0.72%	15
St. Kitts & Nevis	86.3	+0.121%	6
South Korea	12.1	-1.56%	91
Kuwait	19.4	+19.9%	76
St. Lucia	94.9	+0.55%	2
Lesotho	68.1	-4.89%	18
Lithuania	7.94	-0.319%	103
Luxembourg	3.92	-2.75%	110
Latvia	16.1	+2.04%	81
Moldova	28.9	+4.25%	58
Maldives	95.4	+0.247%	1
Mexico	52.4	-4.39%	29
North Macedonia	19.8	-5.44%	74
Malta	11.7	+21.4%	93
Montenegro	54.6	-0.0952%	27
Mozambique	18.1	-7.92%	79
Malaysia	39.4	+12.8%	45
Namibia	34.8	+3.47%	49
Nigeria	7.39	-56.4%	104
Nicaragua	42.9	-15.8%	42
Netherlands	7.28	+1.6%	105
Norway	13.8	+8.62%	84
Nepal	50.2	-31.2%	32
New Zealand	52.3	+7.15%	31
Pakistan	10.7	-22%	94
Panama	33.1	+6.58%	51
Peru	52.8	+8.4%	28
Philippines	18.7	-0.994%	77
Poland	12.1	-13%	92
Portugal	48.3	+0.699%	38
Paraguay	30.9	+10.3%	56
Palestinian Territories	27.8	-41.5%	62
Qatar	28.4	-2.76%	59
Romania	13.4	+7.15%	85
Russia	18.4	+7.6%	78
Saudi Arabia	77.4	-0.338%	13
Singapore	6.03	+3.99%	108
Solomon Islands	22.8	+8%	67
El Salvador	59.5	+6.75%	24
Suriname	17.6	+1.06%	80
Slovakia	12.6	+4.86%	87
Slovenia	26.7	-3.22%	63
Sweden	9.27	-1.03%	99
Thailand	59.8	+13.1%	23
Tajikistan	10.5	+25.3%	95
Timor-Leste	91.8	+2.03%	4
Tonga	58.6	+7.32%	25
Trinidad & Tobago	35.2	-5.67%	48
Turkey	49	+3.77%	34
Ukraine	6.32	+17.6%	106
Uruguay	31.8	-3.65%	54
United States	20	+4.95%	72
Uzbekistan	49.1	+22.7%	33
St. Vincent & Grenadines	85.1	+3.24%	8
Samoa	70.5	-2.83%	17
South Africa	40.4	-0.468%	44

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.TRVL.ZS.WT'

# 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.TRVL.ZS.WT'

# 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

Travel services (% of commercial service exports)

Year: 2024

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