Country

Indicator

Value type

Values present

General government final consumption expenditure (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	6.27	+21.2%	96
Albania	12.8	+6.45%	62
Argentina	15	-8.28%	43
Armenia	10.7	-24.1%	76
Australia	22.2	+4.29%	8
Azerbaijan	14.4	+3.99%	49
Benin	9.01	-5.15%	84
Burkina Faso	18.8	-6.08%	21
Bangladesh	5.91	+4.29%	97
Bulgaria	19.7	+6.84%	15
Bahamas	12.9	-1.54%	60
Belarus	19	+6.45%	18
Bermuda	11	-3.05%	74
Brazil	18.8	-1.17%	19
Brunei	23	+0.726%	6
Botswana	32.1	+9.64%	3
Central African Republic	9.67	+16%	81
Chile	15.1	-1.52%	42
Côte d’Ivoire	8.96	-0.538%	85
Cameroon	10.5	-0.371%	78
Congo - Kinshasa	8.12	+3.32%	90
Congo - Brazzaville	13.4	+2.15%	55
Colombia	14.7	+0.419%	45
Comoros	9.18	-2.75%	83
Cape Verde	20.7	-4.67%	12
Costa Rica	14.7	-1.31%	46
Cyprus	18.6	-0.217%	22
Djibouti	18.8	+10.3%	20
Dominican Republic	11.5	+2.03%	71
Ecuador	13.3	-2.16%	56
Egypt	6.27	-7.63%	95
Ethiopia	5.53	-12.5%	100
Gabon	12.2	+3.09%	68
Georgia	13.4	+10.7%	51
Ghana	4.83	-12.2%	102
Guinea	13.4	-0.603%	54
Gambia	8.46	+14.7%	88
Guinea-Bissau	17.8	+10.5%	30
Equatorial Guinea	28.3	+8.17%	4
Guatemala	10.9	-3.05%	75
Hong Kong SAR China	12.8	-3.55%	61
Honduras	15.5	+4.01%	40
Croatia	22.6	+6.32%	7
Haiti	5.69	-8.54%	99
Indonesia	7.73	+3.7%	91
India	10.1	-1.56%	79
Iran	12.9	+0.304%	59
Iraq	20.3	+15.9%	14
Kenya	11.5	-3.52%	72
Cambodia	5.82	-4.29%	98
Libya	36.7	+4.09%	2
Sri Lanka	6.96	+2.69%	94
Macao SAR China	12.2	-11.4%	67
Morocco	18	-0.785%	27
Moldova	17.9	-0.292%	28
Madagascar	15.3	-2.24%	41
Mexico	11.2	+0.661%	73
North Macedonia	16.8	+12.5%	35
Mali	13.1	+0.553%	58
Malta	17.2	+2.84%	32
Montenegro	17.9	-0.231%	29
Mongolia	16.3	+25.4%	39
Mozambique	17.1	-0.683%	34
Mauritius	14.7	+2.36%	47
Malaysia	12	+0.563%	69
Namibia	21.5	-1.28%	9
Niger	11.8	-12.2%	70
Nicaragua	12.3	-4.91%	66
Nepal	7.44	+12.3%	93
Pakistan	8.5	-17.5%	87
Peru	13.4	-1.12%	53
Philippines	14.5	+2.02%	48
Poland	20.8	+9.63%	11
Puerto Rico	8.21	-2.45%	89
Paraguay	12.6	+4.04%	63
Palestinian Territories	20.7	+2.15%	13
Romania	18.3	+6.87%	25
Russia	18.6	+3.31%	24
Rwanda	17.1	+4.79%	33
Saudi Arabia	21.4	+2.04%	10
Sudan	16.5	-5.23%	37
Senegal	16.4	+3.45%	38
Singapore	10.6	+5.95%	77
Sierra Leone	5.5	0.00000%	101
El Salvador	19.2	+0.568%	17
Somalia	7.61	+4.13%	92
Serbia	17.8	+3.73%	31
Seychelles	26.3	+7.92%	5
Chad	8.72	-3.33%	86
Togo	13.1	+4.44%	57
Thailand	16.7	+0.449%	36
Tunisia	18.6	-4.57%	23
Turkey	14.7	+12.6%	44
Tanzania	9.19	+4.87%	82
Uganda	9.98	+4.3%	80
Ukraine	37.9	-9.4%	1
United States	13.4	-0.101%	52
Uzbekistan	13.9	-3.71%	50
Samoa	18.2	+0.0962%	26
Kosovo	12.3	-1.99%	65
South Africa	19.2	-0.613%	16
Zimbabwe	12.5	-17.4%	64

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.CON.GOVT.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.CON.GOVT.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

General government final consumption expenditure (% of GDP)

Year: 2024

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