Country

Indicator

Value type

Values present

Transport services (% of service imports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	36.2	+11.5%	27
Albania	10.3	-15.6%	110
Argentina	20.9	-12.9%	76
Armenia	32.3	+2.43%	37
Antigua & Barbuda	21.5	-5.2%	71
Australia	16.8	-5%	99
Austria	24.3	-3.36%	61
Azerbaijan	18.4	-8.9%	88
Belgium	18.9	-2.59%	85
Bangladesh	61.7	+0.957%	5
Bulgaria	28.8	-2.03%	45
Bahrain	33.4	-1.15%	35
Bahamas	21.7	-7.27%	69
Bosnia & Herzegovina	33.6	-7.82%	34
Belize	35.9	+0.24%	28
Brazil	21.9	+0.115%	68
Brunei	17	-4.32%	94
Bhutan	20.9	-19.6%	75
Canada	17.2	+1.67%	93
Switzerland	15.9	+3.87%	102
Chile	35.2	+0.963%	30
China	27.5	-7.61%	47
Colombia	24.9	+1.24%	60
Cape Verde	27.1	-10.4%	48
Costa Rica	23.3	-2.13%	63
Cyprus	17	-6.62%	95
Czechia	22	-6.76%	67
Germany	20	-2.69%	79
Djibouti	87.4	+0.416%	1
Dominica	23	-9.51%	64
Denmark	40.6	-1.29%	20
Dominican Republic	44.5	-4.46%	16
Ecuador	34.9	+2.42%	31
Spain	12.7	+9.89%	107
Estonia	26.4	-1.99%	53
Finland	16.5	-3.8%	101
France	18	-3.39%	91
United Kingdom	9.06	-15.7%	111
Georgia	59.5	-3.58%	6
Gambia	66.7	-9.96%	3
Greece	63	-3.73%	4
Grenada	21.5	-3.69%	70
Guatemala	45.4	+2.67%	14
Hong Kong SAR China	22.9	+0.926%	65
Honduras	53.9	-2.68%	8
Croatia	14.5	+0.306%	105
Hungary	26.9	-3.18%	49
Indonesia	24.9	-1.89%	59
India	17.5	+5.93%	92
Iceland	18.1	-3.1%	90
Israel	16.7	-9.92%	100
Italy	21.1	+4.16%	73
Jamaica	46.2	-1.33%	11
Japan	14.7	-1.64%	103
Kazakhstan	25.5	-9.22%	58
Cambodia	49	+2.91%	10
St. Kitts & Nevis	28.9	+29.6%	44
South Korea	22.2	-0.859%	66
Kuwait	20	+9.82%	78
St. Lucia	26.6	+2.78%	51
Lesotho	12.3	+1.47%	109
Lithuania	46.1	+0.386%	12
Luxembourg	16.8	-1.4%	97
Latvia	24.1	-4.64%	62
Moldova	38.9	+1.19%	22
Maldives	26.4	-10.7%	55
Mexico	34.8	-8.22%	32
North Macedonia	26.4	+8.22%	54
Malta	18.5	+2.5%	87
Montenegro	52.4	-9.55%	9
Mozambique	36.8	-12.9%	26
Malaysia	25.6	-1.93%	56
Namibia	13.3	+1.9%	106
Nigeria	35.6	-11.4%	29
Nicaragua	56.7	+0.634%	7
Netherlands	19.2	-3.52%	84
Norway	19.4	+0.593%	83
Nepal	19.5	-31.3%	82
New Zealand	19.7	-12.1%	81
Pakistan	41.8	-6.12%	19
Panama	44.6	-6.57%	15
Peru	32.6	+3.19%	36
Philippines	19.9	-19.5%	80
Poland	21	+0.429%	74
Portugal	21.2	-9.07%	72
Paraguay	45.4	+19.9%	13
Palestinian Territories	37.1	+19.2%	25
Qatar	32.2	+4.05%	38
Romania	16.8	+2.05%	98
Russia	18.3	-8.22%	89
Saudi Arabia	27.7	+2.96%	46
Singapore	34.5	+1.17%	33
Solomon Islands	29	-3.54%	43
El Salvador	31	-11.7%	40
Suriname	16.9	-10.1%	96
Slovakia	30.2	-2.59%	41
Slovenia	20	+4.81%	77
Sweden	12.5	-0.634%	108
Thailand	31.2	-5.9%	39
Tajikistan	69.5	-5.88%	2
Timor-Leste	25.5	+22.5%	57
Tonga	29.5	-10.1%	42
Trinidad & Tobago	26.6	+7.8%	52
Turkey	39	+3.96%	21
Ukraine	14.7	+26.4%	104
Uruguay	26.7	+10.2%	50
United States	18.9	-0.932%	86
Uzbekistan	44.2	-12.5%	17
St. Vincent & Grenadines	42.2	+2.21%	18
Samoa	37.2	-10.5%	24
South Africa	37.5	-6.94%	23

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.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 <- 'BM.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 imports, BoP)

Year: 2024

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