Country

Indicator

Value type

Values present

Goods and services expense (% of expense)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Albania	8.74	+6.33%	60
United Arab Emirates	28.9	-14%	7
Argentina	3.74	+4.72%	83
Armenia	10	-16.5%	51
Austria	6.63	-0.575%	75
Burkina Faso	9.69	+0.667%	53
Bulgaria	8.08	-0.601%	63
Bahamas	21.6	+5.02%	18
Bosnia & Herzegovina	17	-1.67%	29
Belarus	13.9	+4.56%	40
Brazil	2.41	+13%	86
Botswana	22.4	0%	14
Canada	6.78	-13%	72
Switzerland	6.96	+0.554%	71
Chile	7.88	-3.5%	67
Côte d’Ivoire	16.2	-8.62%	32
Colombia	8.04	-4.33%	65
Costa Rica	20.8	-4.92%	22
Cyprus	8.73	-12.8%	61
Denmark	7.96	+1.14%	66
Dominican Republic	18.3	+8.44%	26
Spain	2.86	+11.5%	85
Estonia	9.29	-2.84%	57
Ethiopia	20.9	+6.25%	21
Finland	7.76	-11.1%	68
Fiji	17.4	+5.81%	28
France	6.74	+5.68%	73
United Kingdom	13.7	-4.57%	41
Georgia	14	+2.68%	39
Guinea-Bissau	32.4	+18.6%	4
Greece	8.07	+2.61%	64
Guatemala	14	-12.8%	38
Croatia	10.6	-3.97%	50
Iceland	21.5	+4.11%	19
Israel	13.1	-0.544%	43
Italy	2.9	-0.842%	84
Jordan	9.79	-21.1%	52
Kazakhstan	16.1	+1.7%	33
Kenya	21.3	+4.47%	20
Kyrgyzstan	11.5	-2.06%	47
Cambodia	15	-5.96%	36
Kiribati	29.4	+9.53%	6
South Korea	12.5	+14.8%	45
Sri Lanka	6.03	+26.1%	79
Lithuania	6.41	-6.93%	77
Luxembourg	7.43	-6.18%	70
Latvia	10.6	-12.6%	49
Macao SAR China	18	-16.5%	27
Morocco	8.97	-11.1%	58
Moldova	15.9	-1.16%	34
Madagascar	6.56	-7.08%	76
Mexico	5.92	-10.1%	80
North Macedonia	5.77	-13.5%	81
Malta	19.3	+3.2%	25
Mauritius	9.56	-3.66%	54
Malaysia	11.6	+7.14%	46
Namibia	12.6	+4.3%	44
Nicaragua	17	+5.39%	30
Netherlands	6.72	+9.07%	74
Norway	9.34	-0.738%	56
Philippines	22.3	+4.1%	15
Papua New Guinea	23.1	-17.2%	12
Poland	7.44	-5.09%	69
Portugal	8.79	-3.73%	59
Paraguay	15	+6.57%	37
Rwanda	27.8	+5.17%	9
Saudi Arabia	27.4	+8.42%	10
Senegal	21.9	-0.00352%	16
Singapore	27.9	-13%	8
El Salvador	15.6	-4.41%	35
San Marino	22.9	-27.8%	13
Somalia	19.9	+17%	23
Slovenia	10.7	-0.17%	48
Togo	16.7	-18.8%	31
Thailand	19.4	-15.7%	24
Tajikistan	21.7	+21%	17
Tonga	46.5	+6.11%	1
Turkey	8.59	-8.81%	62
Tanzania	13.4	+9.48%	42
Uganda	25.6	-16.6%	11
Ukraine	35.7	+44%	3
Uruguay	9.47	-12.5%	55
United States	6.23	-7.92%	78
Uzbekistan	4.95	-11.1%	82
Vanuatu	29.8	+1.66%	5
Samoa	44.8	+3.71%	2

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 = 'GC.XPN.GSRV.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 <- 'GC.XPN.GSRV.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

Goods and services expense (% of expense)

Year: 2023

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