Country

Indicator

Value type

Values present

Travel services (% of service exports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	13.5	+36.5%	84
Albania	67.4	+6.08%	18
Argentina	29	-14.9%	56
Armenia	43	-18.8%	39
Antigua & Barbuda	84.5	+2.16%	7
Australia	62.1	+3.25%	20
Austria	27.9	+2.14%	60
Azerbaijan	25.2	+6.18%	65
Belgium	5.92	+1.38%	108
Bangladesh	6.62	-6.91%	104
Bulgaria	26	+2.69%	63
Bahrain	31.8	+6.6%	52
Bahamas	93.9	+1.67%	3
Bosnia & Herzegovina	52.3	-0.376%	28
Belize	70.2	+0.757%	16
Brazil	15.1	-0.526%	82
Brunei	31.1	+49%	55
Bhutan	69.1	-1.6%	17
Canada	31.4	+6.64%	54
Switzerland	12.2	-1.45%	89
Chile	27	+14.7%	61
China	10.4	+35.9%	94
Colombia	48.3	+2.45%	35
Cape Verde	78	+3.17%	9
Costa Rica	33.8	+5.02%	50
Cyprus	12.5	-1.89%	88
Czechia	21.5	+9.2%	68
Germany	8.51	+1.69%	100
Djibouti	4.42	+2.29%	109
Dominica	61.6	+1.41%	21
Denmark	8.27	-1.81%	101
Dominican Republic	74.7	-1.65%	12
Ecuador	47.2	-0.19%	37
Spain	48.4	+3.51%	34
Estonia	12.1	+2.75%	90
Finland	10.1	-8.47%	96
France	19.3	+0.13%	75
United Kingdom	13	+3.9%	86
Georgia	57.5	-1.73%	25
Gambia	91.6	+9.85%	4
Greece	42.1	-0.0716%	41
Grenada	85.2	+0.641%	5
Guatemala	35.5	+10.9%	47
Hong Kong SAR China	20.8	-2.97%	71
Honduras	19.4	+11.3%	74
Croatia	66.1	+1.31%	19
Hungary	21.1	-0.685%	70
Indonesia	42.8	+2.74%	40
India	9.34	-2.05%	97
Iceland	47	+2.25%	38
Israel	2.68	-58.1%	111
Italy	37.9	-0.401%	46
Jamaica	82.1	+0.276%	8
Japan	24	+30.4%	66
Kazakhstan	21.7	+2.84%	67
Cambodia	73.4	-0.353%	14
St. Kitts & Nevis	75.3	+17.6%	11
South Korea	12	-1.15%	91
Kuwait	18.5	+20.3%	77
St. Lucia	94.7	+0.568%	2
Lesotho	59.3	-6.39%	22
Lithuania	7.92	-0.282%	102
Luxembourg	3.9	-2.73%	110
Latvia	16.1	+2.06%	81
Moldova	28.2	+4.2%	58
Maldives	95.1	+0.264%	1
Mexico	52.3	-4.35%	29
North Macedonia	19.7	-5.42%	72
Malta	11.7	+21.4%	93
Montenegro	54.6	-0.1%	27
Mozambique	18.1	-7.92%	78
Malaysia	39.3	+12.8%	45
Namibia	33.8	+4.4%	49
Nigeria	6.58	-56.5%	105
Nicaragua	39.4	-17%	44
Netherlands	7.24	+1.52%	103
Norway	13.7	+8.63%	83
Nepal	41.4	-34.1%	42
New Zealand	51.7	+7.21%	30
Pakistan	9.29	-19.9%	98
Panama	32.9	+6.44%	51
Peru	51.4	+8.95%	31
Philippines	18.7	-0.996%	76
Poland	12	-13%	92
Portugal	48.2	+0.683%	36
Paraguay	28.5	+10.9%	57
Palestinian Territories	25.7	-42.9%	64
Qatar	27.9	-2.26%	59
Romania	13.4	+7.11%	85
Russia	18	+7.21%	79
Saudi Arabia	74.1	-0.112%	13
Singapore	6.03	+3.99%	107
Solomon Islands	21.1	+7.75%	69
El Salvador	57.9	+7.43%	24
Suriname	16.9	-0.0061%	80
Slovakia	12.5	+4.63%	87
Slovenia	26.6	-3.26%	62
Sweden	9.17	-1.33%	99
Thailand	59.2	+13.1%	23
Tajikistan	10.3	+26%	95
Timor-Leste	77.5	+5.59%	10
Tonga	56.7	+7.1%	26
Trinidad & Tobago	34.7	-5.66%	48
Turkey	48.9	+4.03%	33
Ukraine	6.08	+17.7%	106
Uruguay	31.5	-3.64%	53
United States	19.4	+5.4%	73
Uzbekistan	48.9	+22.7%	32
St. Vincent & Grenadines	84.8	+3.3%	6
Samoa	70.5	-2.83%	15
South Africa	40	+0.313%	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 = 'BX.GSR.TRVL.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.TRVL.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

Travel services (% of service exports, BoP)

Year: 2024

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