Country

Indicator

Value type

Values present

Gross national expenditure (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	86.5	+1.4%	120
Argentina	98.9	-2.8%	79
Armenia	99.4	-1.35%	76
Australia	97.9	+2.34%	84
Austria	97	-1.15%	89
Azerbaijan	90.9	+6.24%	111
Belgium	100	-0.583%	71
Benin	103	-1.62%	58
Burkina Faso	106	-0.601%	40
Bangladesh	107	+1.48%	38
Bulgaria	97.7	+1.85%	87
Bahamas	104	+4.67%	53
Bosnia & Herzegovina	119	+4.92%	14
Belarus	102	+5.33%	63
Bermuda	67.9	-0.193%	125
Brazil	99.5	+1.82%	74
Brunei	79.9	-0.241%	124
Botswana	114	+10.9%	26
Central African Republic	120	-7.51%	13
Canada	100	+0.295%	69
Switzerland	89.8	+1.27%	113
Chile	96.4	-2.71%	94
Côte d’Ivoire	99.5	-3.89%	75
Cameroon	106	+2.33%	41
Congo - Kinshasa	104	+1.47%	51
Congo - Brazzaville	87.6	+6.32%	119
Colombia	105	+0.208%	49
Comoros	125	-1.35%	8
Cape Verde	111	-5.56%	29
Costa Rica	94.3	-0.394%	106
Cyprus	96.4	-2.56%	93
Czechia	93.4	-1.65%	108
Germany	96.1	+0.156%	95
Djibouti	88	+4.15%	116
Denmark	89.2	-2.9%	115
Dominican Republic	106	-0.886%	42
Ecuador	96.6	-3.22%	90
Egypt	107	+4.51%	37
Spain	95.7	-0.368%	98
Estonia	101	-1.28%	68
Ethiopia	106	-1.12%	43
Finland	98.9	-1.17%	78
France	101	-1.24%	66
Gabon	63.9	+2.03%	128
United Kingdom	101	+0.0523%	65
Georgia	108	-0.931%	36
Ghana	98.8	-1.98%	80
Guinea	112	+4.14%	28
Gambia	131	+8.34%	4
Guinea-Bissau	116	+1.28%	19
Equatorial Guinea	90.2	+2.24%	112
Greece	105	+0.59%	48
Guatemala	116	+0.412%	20
Hong Kong SAR China	95.9	-3.49%	97
Honduras	124	-0.0344%	10
Croatia	103	+1.26%	56
Haiti	115	-3.99%	21
Hungary	94.4	-1.03%	104
Indonesia	94.5	+2.5%	103
India	104	+0.372%	52
Ireland	58.5	-12.3%	129
Iran	103	+1.38%	55
Iraq	90.9	+11.7%	110
Iceland	101	+1.3%	64
Israel	97.5	+0.364%	88
Italy	97.7	-0.873%	86
Kenya	104	-1.37%	54
Cambodia	97.8	-1.92%	85
Libya	84.3	+0.104%	122
Sri Lanka	103	+0.457%	59
Lithuania	94.8	-1.33%	101
Luxembourg	67.2	-2.47%	126
Latvia	103	-1.11%	60
Macao SAR China	55.6	-6.2%	130
Morocco	109	+0.83%	31
Moldova	126	+1.67%	7
Madagascar	108	+0.555%	35
Mexico	106	+0.715%	47
North Macedonia	113	+0.0763%	27
Mali	106	-1.85%	46
Malta	82.6	-0.795%	123
Montenegro	123	+3.4%	11
Mongolia	101	+10.2%	67
Mozambique	110	-4.23%	30
Mauritius	104	+0.435%	50
Malaysia	94.7	-0.256%	102
Namibia	126	+2.29%	6
Niger	89.7	-9.49%	114
Nicaragua	118	+3.79%	18
Netherlands	87.9	-1.04%	117
Norway	86.1	+1.85%	121
Nepal	124	+0.201%	9
Pakistan	107	-0.9%	39
Peru	94.4	-2.19%	105
Philippines	114	+0.168%	24
Poland	96	+1.84%	96
Puerto Rico	99	-0.915%	77
Portugal	98.2	-0.677%	82
Paraguay	102	+4.22%	61
Palestinian Territories	140	-3.56%	2
Romania	106	+1.25%	44
Russia	94.2	-0.00405%	107
Rwanda	108	-5.73%	34
Saudi Arabia	96.5	+2.98%	92
Sudan	100	-0.182%	70
Senegal	115	-8.06%	23
Singapore	64.3	+2.31%	127
Sierra Leone	123	+9.98%	12
El Salvador	119	+0.183%	15
Somalia	154	+0.72%	1
Serbia	106	+1.66%	45
Slovakia	99.8	+1.34%	72
Slovenia	93.4	-0.196%	109
Sweden	95.6	-0.383%	99
Seychelles	118	+3.53%	17
Chad	87.8	-3.25%	118
Togo	114	+0.0801%	25
Thailand	96.5	-0.0769%	91
Tunisia	108	+2.42%	33
Turkey	99.7	-2.65%	73
Tanzania	102	-1.95%	62
Uganda	98.5	-5.24%	81
Ukraine	119	-1.63%	16
Uruguay	94.9	-1.75%	100
United States	103	+0.212%	57
Uzbekistan	115	-2%	22
Samoa	132	-7.84%	3
Kosovo	130	-0.193%	5
South Africa	98	-1.27%	83
Zimbabwe	108	+0.767%	32

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 = 'NE.DAB.TOTL.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 <- 'NE.DAB.TOTL.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

Gross national expenditure (% of GDP)

Year: 2024

Acquire full dataset with Python or R from source