Country

Indicator

Value type

Values present

Travel services (% of service imports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	17.9	+13.7%	67
Albania	76.4	+5.16%	1
Argentina	34.5	+6.49%	17
Armenia	44.4	-8.79%	7
Antigua & Barbuda	16.2	+3.33%	73
Australia	42	+0.725%	9
Austria	19.3	+6.83%	60
Azerbaijan	15.7	+17.7%	79
Belgium	16.6	+14.6%	72
Bangladesh	14.4	-5.49%	82
Bulgaria	24.7	+9.47%	40
Bahrain	10.7	+13.3%	94
Bahamas	11	+12.7%	93
Bosnia & Herzegovina	36.7	+9.28%	13
Belize	23	-0.283%	48
Brazil	19.1	-5.7%	62
Brunei	31.7	+27.1%	24
Bhutan	45.5	-10.6%	6
Canada	27.1	+5.39%	33
Switzerland	11.3	-3.32%	91
Chile	15.8	+7.4%	78
China	40.9	+13%	11
Colombia	31.7	+8.85%	23
Cape Verde	27.9	+7.28%	31
Costa Rica	26.1	-1.02%	37
Cyprus	9.06	-1.79%	99
Czechia	23.2	+10.5%	47
Germany	21.8	-1.61%	54
Djibouti	2.9	-4.48%	110
Dominica	17.2	+20.5%	71
Denmark	9.42	-0.0968%	98
Dominican Republic	18	-17.2%	65
Ecuador	24.2	+8.51%	41
Spain	29.2	-1.6%	28
Estonia	14.4	+5.68%	81
Finland	13.2	+4.04%	86
France	17.7	+4.47%	70
United Kingdom	29.7	+7.77%	27
Georgia	12.7	-8.69%	89
Gambia	7.01	+39.8%	104
Greece	9.72	+8.74%	97
Grenada	6.83	+10.8%	105
Guatemala	24	+8.7%	42
Hong Kong SAR China	32	+11.3%	21
Honduras	15.9	+2.26%	76
Croatia	31	+12.9%	25
Hungary	14.4	+4.3%	83
Indonesia	23.4	+2.72%	45
India	17.8	-4.84%	68
Iceland	35	+0.0528%	14
Israel	19	-8.63%	63
Italy	22	-2.18%	52
Jamaica	8.67	-1.14%	101
Japan	5.8	+6.77%	106
Kazakhstan	30.1	+8.81%	26
Cambodia	26.2	-6.14%	36
St. Kitts & Nevis	18.3	+35.5%	64
South Korea	18	-0.871%	66
Kuwait	44.2	-6.08%	8
St. Lucia	13	+12.1%	87
Lesotho	73.9	+0.505%	2
Lithuania	13.3	+7.02%	85
Luxembourg	4.01	+0.293%	109
Latvia	24	+9.19%	43
Moldova	33.1	-2.19%	20
Maldives	27	+8.55%	34
Mexico	15.9	+30.2%	75
North Macedonia	20.6	+1.37%	57
Malta	4.16	+8.83%	108
Montenegro	7.81	+19.2%	102
Mozambique	7.37	-21.4%	103
Malaysia	22.8	+6.57%	50
Namibia	4.64	+29.9%	107
Nigeria	25.3	+7.78%	38
Nicaragua	15.9	+12.2%	77
Netherlands	10.5	+7.23%	95
Norway	28.4	-0.966%	30
Nepal	67.5	+1.8%	3
New Zealand	23	+4.82%	49
Pakistan	21.5	-5.16%	55
Panama	22.3	+7.16%	51
Peru	23.3	-2.52%	46
Philippines	34.1	+54.4%	19
Poland	15.6	-0.376%	80
Portugal	26.3	-0.017%	35
Paraguay	24.7	-28.5%	39
Palestinian Territories	20.5	-41.1%	58
Qatar	38.2	-0.892%	12
Romania	34.3	+4.42%	18
Russia	47.7	+5.12%	5
Saudi Arabia	27.2	+12.6%	32
Singapore	8.91	+12.3%	100
Solomon Islands	16.1	-13%	74
El Salvador	35	+17.2%	15
Suriname	11.7	-17.5%	90
Slovakia	19.9	+5.59%	59
Slovenia	29.2	-0.455%	29
Sweden	11.2	-6.76%	92
Thailand	21.3	+14.7%	56
Tajikistan	1.94	-21.2%	111
Timor-Leste	31.9	-3.06%	22
Tonga	34.8	+16.5%	16
Trinidad & Tobago	19.3	-4.4%	61
Turkey	13.8	-13.8%	84
Ukraine	62.4	-8.08%	4
Uruguay	23.5	-10.8%	44
United States	21.9	+3.2%	53
Uzbekistan	41.7	+11.6%	10
St. Vincent & Grenadines	17.7	+4.13%	69
Samoa	10.3	+25.8%	96
South Africa	12.9	-1.63%	88

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 = 'BM.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 <- 'BM.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 imports, BoP)

Year: 2024

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