Country

Indicator

Value type

Values present

Trade in services (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	10.7	+4.87%	95
Albania	43.6	-2.98%	24
Argentina	6.27	+4.88%	108
Armenia	37	-1.75%	30
Antigua & Barbuda	80.8	-0.499%	8
Australia	11	+6.49%	93
Austria	34.9	+2.35%	33
Azerbaijan	24.6	+19.6%	53
Belgium	46.2	-4.52%	21
Bangladesh	3.99	+0.378%	112
Bulgaria	22.1	-6.34%	60
Bahrain	61.6	+3.1%	10
Bahamas	52.9	+9.89%	13
Bosnia & Herzegovina	17.8	+4.87%	72
Belarus	21.6	+7.21%	63
Belize	43.2	-2.19%	25
Brazil	6.95	+13.7%	105
Brunei	13.9	+6.2%	83
Bhutan	15.8	+5.85%	76
Canada	14.2	+0.827%	82
Switzerland	42.1	+5.33%	27
Chile	10	+7.66%	96
China	5.32	+12.2%	110
Colombia	8.64	-4.18%	99
Cape Verde	40.8	+4.32%	28
Costa Rica	24.5	+0.329%	54
Cyprus	145	+2.4%	3
Czechia	23.3	+6.61%	58
Germany	22	+2.06%	61
Djibouti	46.6	-3.62%	20
Dominica	50.4	+5.72%	15
Denmark	57.8	+2.7%	11
Dominican Republic	16.9	+10.3%	74
Ecuador	7.9	-7.47%	102
Spain	19.3	+6.56%	68
Estonia	55.8	+2.23%	12
Finland	29.3	+4.79%	41
France	23.4	+1.89%	57
United Kingdom	28.8	+2.29%	43
Georgia	34.1	-1.62%	34
Gambia	25.4	+12.1%	49
Greece	33.8	-0.195%	35
Grenada	87.5	+1.13%	6
Guatemala	9.82	+3.13%	97
Hong Kong SAR China	48.9	+5.66%	17
Honduras	19.8	-8.23%	66
Croatia	35.7	-4.02%	31
Hungary	29.5	-1.65%	40
Indonesia	6.91	+11.7%	106
India	14.6	+3.02%	79
Iceland	35.6	-2.89%	32
Israel	23.5	-6.38%	56
Italy	13.4	+3.16%	85
Jamaica	43.7	-2.81%	23
Japan	11.8	+12.4%	91
Kazakhstan	8.63	-1.75%	100
Cambodia	17.4	+4.07%	73
St. Kitts & Nevis	66.5	-16.8%	9
Kuwait	25.1	-0.267%	50
St. Lucia	80.8	+8.26%	7
Lesotho	18.9	-6.88%	70
Lithuania	45.8	+4.91%	22
Luxembourg	324	-3.01%	1
Latvia	32.8	+0.618%	36
Moldova	24.7	+3.4%	52
Maldives	99.1	+6.31%	5
Mexico	7.24	-1.44%	104
North Macedonia	31	-1.15%	38
Malta	172	+0.694%	2
Montenegro	50.9	-7.83%	14
Mozambique	14.5	-2.61%	81
Malaysia	26	+9.61%	48
Namibia	27.8	+4.3%	45
Nigeria	12	+97.1%	90
Nicaragua	13.2	-12.7%	86
Netherlands	47	-0.0958%	19
Norway	25	+6.67%	51
Nepal	10.8	+51.2%	94
New Zealand	14.6	+6.6%	80
Pakistan	5.16	+1.82%	111
Panama	27.8	-2.04%	44
Peru	7.68	+4.22%	103
Philippines	19.4	+7.82%	67
Poland	21.2	-1.59%	64
Portugal	29.2	+1.17%	42
Paraguay	11.7	+1.59%	92
Palestinian Territories	13.9	-14%	84
Qatar	30.9	-10.9%	39
Romania	19.2	-5.63%	69
Russia	5.68	+0.558%	109
Saudi Arabia	12.7	+5.54%	88
Singapore	136	+0.727%	4
Solomon Islands	21.6	-2.7%	62
El Salvador	26.2	+14.1%	47
Suriname	24	+3.09%	55
Slovakia	18.8	-2.17%	71
Slovenia	31.9	+0.794%	37
Sweden	39.6	+5.12%	29
Thailand	27.7	+16.8%	46
Tajikistan	8.2	+1.28%	101
Timor-Leste	23.3	+4.25%	59
Tonga	49.9	+9.27%	16
Trinidad & Tobago	14.9	+0.915%	77
Turkey	12.7	-8.67%	87
Ukraine	20.9	-9.49%	65
Uruguay	16.2	-4.67%	75
United States	6.58	+2.73%	107
Uzbekistan	14.8	+11.6%	78
St. Vincent & Grenadines	48.6	+8.06%	18
Vietnam	12.6	+10.8%	89
Samoa	42.7	-7.86%	26
South Africa	8.92	+3.4%	98

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 = 'BG.GSR.NFSV.GD.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 <- 'BG.GSR.NFSV.GD.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

Trade in services (% of GDP)

Year: 2024

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