Country

Indicator

Value type

Values present

General government final consumption expenditure (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	5,038,306,976	+14.8%	58
Albania	3,484,984,889	+22.9%	68
Argentina	95,185,136,278	-10.1%	11
Armenia	2,767,747,053	-18.8%	75
Australia	389,492,841,511	+5.75%	5
Azerbaijan	10,676,764,706	+6.7%	40
Benin	1,936,520,337	+3.57%	85
Burkina Faso	4,361,912,988	+7.44%	60
Bangladesh	26,593,391,221	+7.32%	27
Bulgaria	22,084,636,268	+17.1%	29
Bahamas	2,044,200,000	+2.08%	84
Belarus	14,434,974,887	+11.6%	35
Bermuda	989,800,000	+1.48%	91
Brazil	410,160,842,541	-1.7%	2
Brunei	3,561,827,945	+3.18%	66
Botswana	6,230,128,240	+9.58%	49
Central African Republic	266,118,993	+24.9%	98
Chile	49,762,496,203	-3.06%	23
Côte d’Ivoire	7,757,053,928	+8.11%	44
Cameroon	5,412,427,720	+3.77%	55
Congo - Kinshasa	5,745,302,239	+9.09%	52
Congo - Brazzaville	2,103,511,701	+4.81%	83
Colombia	61,590,392,608	+14.7%	17
Comoros	141,978,721	+5.11%	101
Cape Verde	572,739,774	+3.91%	94
Costa Rica	13,996,009,436	+8.79%	38
Cyprus	6,755,944,972	+6.99%	48
Djibouti	766,751,971	+15.1%	93
Dominican Republic	14,301,332,961	+5.27%	37
Ecuador	16,569,731,000	+0.69%	32
Egypt	24,395,300,951	-9.24%	28
Gabon	2,537,066,725	+7.26%	78
Georgia	4,541,828,787	+21.5%	59
Ghana	3,999,669,268	-9.71%	62
Guinea	3,397,400,021	+12.4%	69
Gambia	212,083,321	+20.1%	99
Guinea-Bissau	376,928,583	+12.7%	97
Equatorial Guinea	3,618,229,047	+11.9%	65
Guatemala	12,337,063,864	+5.15%	39
Hong Kong SAR China	52,256,628,249	+3.05%	21
Honduras	5,759,662,719	+12.3%	51
Croatia	20,940,560,878	+16.6%	30
Haiti	1,434,217,740	+16.2%	88
Indonesia	107,947,174,941	+5.6%	10
India	396,911,857,971	+5.85%	4
Iran	56,441,225,556	+8.31%	20
Iraq	56,806,978,312	+20.5%	19
Kenya	14,311,962,999	+11.2%	36
Cambodia	2,696,427,981	+4.79%	76
Libya	17,127,677,451	+7.64%	31
Sri Lanka	6,887,315,633	+21.4%	46
Macao SAR China	6,125,548,739	-2.89%	50
Morocco	27,742,106,681	+6.09%	26
Moldova	3,253,842,658	+8.59%	70
Madagascar	2,665,227,713	+7.32%	77
Mexico	207,033,132,352	+3.97%	7
North Macedonia	2,800,815,566	+19.1%	74
Mali	3,491,502,311	+8.58%	67
Malta	4,176,579,463	+12.6%	61
Montenegro	1,441,418,506	+6.91%	87
Mongolia	3,845,752,233	+45.5%	63
Mozambique	3,825,895,498	+6.25%	64
Mauritius	2,193,207,213	+8.54%	82
Malaysia	50,706,260,801	+6.16%	22
Namibia	2,871,924,528	+6.39%	72
Niger	2,305,240,218	+2.72%	81
Nicaragua	2,414,896,735	+5.17%	80
Nepal	3,192,434,123	+17.4%	71
Pakistan	31,707,025,236	-8.96%	25
Peru	38,789,864,282	+7.12%	24
Philippines	66,974,017,380	+7.75%	16
Poland	190,015,130,189	+23.4%	9
Puerto Rico	10,330,500,000	+3.7%	41
Paraguay	5,608,979,102	+7.27%	53
Palestinian Territories	2,835,900,000	-21.5%	73
Romania	70,108,081,072	+16.6%	15
Russia	403,319,887,716	+8.42%	3
Rwanda	2,434,107,408	+4.2%	79
Saudi Arabia	264,356,533,333	+3.63%	6
Sudan	8,241,416,949	+18.5%	43
Senegal	5,276,372,088	+8.74%	57
Singapore	57,991,233,565	+14.7%	18
Sierra Leone	415,422,745	+17.7%	96
El Salvador	6,780,140,000	+5.06%	47
Somalia	921,924,910	+14.9%	92
Serbia	15,827,039,173	+13.6%	34
Seychelles	569,545,083	+6.93%	95
Chad	1,799,377,757	+4.35%	86
Togo	1,304,205,710	+13%	90
Thailand	87,863,421,344	+2.49%	12
Tunisia	9,912,016,356	+5.75%	42
Turkey	195,101,395,937	+33.2%	8
Tanzania	7,236,879,360	+4.5%	45
Uganda	5,353,199,656	+14.7%	56
Ukraine	72,333,483,173	-4.64%	14
United States	3,916,690,000,000	+5.18%	1
Uzbekistan	16,033,830,414	+7.85%	33
Samoa	194,068,287	+13.9%	100
Kosovo	1,376,744,518	+4.38%	89
South Africa	76,974,156,286	+4.49%	13
Zimbabwe	5,509,430,409	+3.64%	54

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.CD'

# 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.CD'

# 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 (current US$)

Year: 2024

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