Country

Indicator

Value type

Values present

Travel services (% of commercial service imports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	18.4	+13.1%	66
Albania	77.7	+5.69%	1
Argentina	34.9	+6.29%	17
Armenia	44.9	-8.68%	8
Antigua & Barbuda	16.2	+3.33%	76
Australia	42.5	+0.665%	10
Austria	19.4	+6.83%	62
Azerbaijan	15.9	+17.5%	79
Belgium	16.6	+14.6%	73
Bangladesh	14.9	-4.76%	81
Bulgaria	24.7	+9.48%	41
Bahrain	10.7	+13.3%	95
Bahamas	12.3	+13.8%	90
Bosnia & Herzegovina	36.9	+9.13%	13
Belize	25.1	-0.0893%	39
Brazil	19.4	-6.58%	61
Brunei	32.2	+27%	22
Bhutan	47	-10.1%	6
Canada	27.4	+5.2%	33
Switzerland	11.3	-3.33%	92
Chile	16.2	+7.36%	75
China	41.1	+13%	12
Colombia	32	+8.93%	24
Cape Verde	29.8	+7.67%	28
Costa Rica	26.2	-0.822%	38
Cyprus	9.1	-1.83%	99
Czechia	23.2	+10.5%	48
Germany	21.9	-1.62%	56
Djibouti	3.05	-3.87%	110
Dominica	17.2	+20.5%	72
Denmark	9.43	-0.101%	98
Dominican Republic	18.5	-16.9%	64
Ecuador	24.6	+8.87%	42
Spain	29.2	-1.6%	31
Estonia	14.5	+5.63%	82
Finland	13.2	+4.04%	86
France	17.7	+4.46%	71
United Kingdom	29.9	+7.23%	27
Georgia	12.9	-8.75%	89
Gambia	7.01	+39.8%	104
Greece	9.85	+9.32%	97
Grenada	6.9	+11.3%	105
Guatemala	24.6	+8.52%	43
Hong Kong SAR China	32	+11.1%	23
Honduras	16.1	+2.18%	78
Croatia	31.1	+12.9%	25
Hungary	14.5	+4.15%	83
Indonesia	23.4	+2.74%	47
India	17.9	-4.81%	70
Iceland	35.2	+0.0476%	16
Israel	19.1	-8.59%	63
Italy	22.2	-2.13%	55
Jamaica	8.83	-1.13%	101
Japan	5.87	+6.95%	106
Kazakhstan	30.5	+8.92%	26
Cambodia	26.5	-6.26%	35
St. Kitts & Nevis	18.5	+35.8%	65
South Korea	18.1	-0.89%	68
Kuwait	46	-10.6%	7
St. Lucia	13	+12.1%	88
Lesotho	75.2	+0.512%	2
Lithuania	13.4	+7.04%	85
Luxembourg	4.02	+0.333%	109
Latvia	24	+9.2%	44
Moldova	33.9	-2.08%	21
Maldives	27.2	+9.06%	34
Mexico	16.1	+30.3%	77
North Macedonia	21	+1.5%	58
Malta	4.16	+8.84%	108
Montenegro	7.92	+19.3%	102
Mozambique	7.63	-20.7%	103
Malaysia	22.8	+6.53%	51
Namibia	4.71	+29.3%	107
Nigeria	26.3	+9.05%	37
Nicaragua	16.2	+11.1%	74
Netherlands	10.5	+7.23%	96
Norway	28.4	-0.978%	32
Nepal	68.3	+2.09%	3
New Zealand	23.1	+4.77%	50
Pakistan	22.8	-3.37%	52
Panama	22.7	+7.24%	53
Peru	23.6	-2.59%	46
Philippines	34.4	+54.2%	19
Poland	15.6	-0.443%	80
Portugal	26.4	-0.0639%	36
Paraguay	24.9	-28.5%	40
Palestinian Territories	23.2	-38.6%	49
Qatar	44.1	+1.14%	9
Romania	34.5	+4.53%	18
Russia	48.4	+5.29%	5
Saudi Arabia	29.6	+10.4%	29
Singapore	8.91	+12.3%	100
Solomon Islands	18.2	-12%	67
El Salvador	35.6	+17%	15
Suriname	11.7	-17.6%	91
Slovakia	19.9	+5.58%	59
Slovenia	29.5	-0.887%	30
Sweden	11.2	-6.77%	93
Thailand	21.4	+14.7%	57
Tajikistan	1.96	-21.4%	111
Timor-Leste	34.3	+2.18%	20
Tonga	36.7	+16.1%	14
Trinidad & Tobago	19.8	-3.46%	60
Turkey	14.1	-13.9%	84
Ukraine	65.8	-11.3%	4
Uruguay	23.7	-10.8%	45
United States	22.6	+2.92%	54
Uzbekistan	41.9	+11.5%	11
St. Vincent & Grenadines	17.9	+4.16%	69
Samoa	11.1	+26.5%	94
South Africa	13	-1.95%	87

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 = 'TM.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 <- 'TM.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 imports)

Year: 2024

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