Country

Indicator

Value type

Values present

Transport services (% of service exports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	22.5	-26.7%	37
Albania	10.7	-1.41%	80
Argentina	12.8	+4.49%	68
Armenia	20.2	+27.8%	45
Antigua & Barbuda	3.81	-30.4%	101
Australia	6.26	-1.01%	96
Austria	23.1	-1.07%	36
Azerbaijan	39	-15.6%	15
Belgium	21.4	-3.14%	40
Bangladesh	20.7	+70.9%	44
Bulgaria	15.9	-15.8%	57
Bahrain	14.1	+18.4%	63
Bahamas	1.39	-24.2%	109
Bosnia & Herzegovina	17.5	-4.12%	52
Belize	4.51	+27.3%	100
Brazil	15.6	+4.55%	58
Brunei	53.8	-0.307%	4
Bhutan	11	-25.2%	78
Canada	10.4	-4.5%	83
Switzerland	11.4	-6.72%	75
Chile	33.2	-9.66%	19
China	29.4	+10.9%	25
Colombia	16.4	-4.74%	53
Cape Verde	10.2	-14.1%	84
Costa Rica	3.07	-11.2%	104
Cyprus	17.5	-7.69%	51
Czechia	21.6	-2.11%	39
Germany	20.9	-1.2%	43
Djibouti	60.5	+6.49%	2
Dominica	1.04	-4.17%	110
Denmark	52.3	-0.222%	5
Dominican Republic	7.11	-3.56%	92
Ecuador	39.1	-3.46%	14
Spain	9.07	-17.2%	87
Estonia	20.9	-3.83%	42
Finland	13	-5.91%	66
France	16.4	-4.33%	54
United Kingdom	6.4	-2.34%	94
Georgia	22.2	+6.23%	38
Gambia	7.2	-53.7%	90
Greece	41.1	-3.72%	13
Grenada	2.44	-6.22%	106
Guatemala	14.5	+0.636%	61
Hong Kong SAR China	32.8	+6.67%	21
Honduras	5.02	+7.62%	98
Croatia	7.13	+2.26%	91
Hungary	24.8	-2.83%	33
Indonesia	14.1	+9.85%	64
India	8.9	+2.3%	88
Iceland	29.3	-2.07%	26
Israel	9.89	+37.4%	85
Italy	10.7	+6.38%	79
Jamaica	5.45	-5.41%	97
Japan	13.8	-4.84%	65
Kazakhstan	47	-1.48%	10
Cambodia	12.5	+11.8%	70
St. Kitts & Nevis	3.54	+21.8%	102
South Korea	29.1	+6.44%	27
Kuwait	15.4	+11.9%	59
St. Lucia	0.899	-6.49%	111
Lesotho	2.61	-2.68%	105
Lithuania	47.2	-0.444%	9
Luxembourg	11.9	-0.0722%	73
Latvia	32	+0.849%	23
Moldova	21	-5.34%	41
Maldives	3.34	-3.62%	103
Mexico	16.3	+7.96%	55
North Macedonia	19.7	-6.48%	48
Malta	19.9	+2.97%	46
Montenegro	25.1	-4.01%	32
Mozambique	78.4	+1.98%	1
Malaysia	14.5	-8.27%	62
Namibia	25.7	+17.7%	31
Nigeria	48.1	+11.7%	8
Nicaragua	7.33	+35.5%	89
Netherlands	15.1	-3.11%	60
Norway	37.7	-4.7%	16
Nepal	16.2	-25.4%	56
New Zealand	12.8	-10.1%	69
Pakistan	10.5	-9.78%	81
Panama	49.9	-4.99%	6
Peru	28.5	-6.47%	28
Philippines	6.86	+8.27%	93
Poland	27	-1.87%	30
Portugal	18.2	-5.83%	50
Paraguay	19.9	-13.9%	47
Palestinian Territories	1.66	+33.5%	107
Qatar	59.6	+3.27%	3
Romania	23.6	-5.53%	35
Russia	33	-5.75%	20
Saudi Arabia	12.4	-7.09%	71
Singapore	32.7	+5.26%	22
Solomon Islands	45.2	+5.73%	11
El Salvador	11.2	-12.9%	77
Suriname	19	+11%	49
Slovakia	30.8	+1.74%	24
Slovenia	28.1	+1.2%	29
Sweden	12.3	+2.52%	72
Thailand	10.5	-1.84%	82
Tajikistan	48.2	+14.9%	7
Timor-Leste	1.65	+0.176%	108
Tonga	12.9	-15.4%	67
Trinidad & Tobago	41.3	+10%	12
Turkey	34.6	-5.27%	17
Ukraine	23.9	+4.57%	34
Uruguay	11.3	+9.41%	76
United States	9.34	-1.96%	86
Uzbekistan	33.3	-12.6%	18
St. Vincent & Grenadines	4.51	-11.9%	99
Samoa	6.3	-0.0833%	95
South Africa	11.9	+0.721%	74

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.TRAN.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.TRAN.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

Transport services (% of service exports, BoP)

Year: 2024

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