Country

Indicator

Value type

Values present

Final consumption expenditure (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	49,467,324,524	-5.32%	70
Albania	22,394,284,153	+15.6%	92
Argentina	526,454,876,755	-1.03%	19
Armenia	19,926,350,164	+4.04%	95
Australia	1,287,454,058,881	+3.98%	12
Austria	389,582,267,180	+4.72%	25
Azerbaijan	51,874,235,294	+5.64%	67
Belgium	504,989,261,931	+4.12%	20
Benin	14,586,078,320	+5.73%	106
Burkina Faso	18,450,788,007	+7.36%	98
Bangladesh	342,283,513,255	+5.4%	29
Bulgaria	86,762,766,879	+11.3%	56
Bahamas	12,224,500,000	+4.63%	112
Bosnia & Herzegovina	25,114,404,801	+4.07%	89
Belarus	57,581,190,515	+10.7%	65
Bermuda	5,084,000,000	+4.28%	122
Brazil	1,800,022,918,220	+0.224%	6
Brunei	7,969,045,150	+4.62%	119
Botswana	15,017,850,086	+7.18%	104
Central African Republic	2,870,514,551	+6.27%	124
Canada	1,724,808,964,432	+4.38%	8
Switzerland	588,064,240,546	+4.92%	18
Chile	241,668,186,285	-4.9%	37
Côte d’Ivoire	64,898,942,222	+5.31%	62
Cameroon	43,644,371,763	+4.84%	75
Congo - Kinshasa	50,105,987,916	+5.69%	69
Congo - Brazzaville	9,555,268,581	+9.02%	117
Colombia	367,712,408,298	+13.7%	26
Comoros	1,744,243,107	+7.68%	129
Cape Verde	2,640,883,658	+5.49%	125
Costa Rica	74,943,908,113	+9.14%	57
Cyprus	28,148,697,356	+6.07%	86
Czechia	232,540,929,354	+1.15%	40
Germany	3,499,431,304,950	+4.04%	2
Djibouti	3,750,430,003	+9.13%	123
Denmark	290,116,702,057	+3.25%	35
Dominican Republic	98,493,198,331	+3%	53
Ecuador	97,451,222,000	+1.55%	54
Egypt	365,138,109,596	+7.55%	27
Spain	1,296,276,167,463	+6.59%	11
Estonia	31,795,634,146	+3.86%	83
Finland	231,732,246,457	+1.86%	41
France	2,491,791,588,804	+3.55%	5
Gabon	9,560,206,959	+5.63%	116
United Kingdom	3,028,322,909,081	+7.32%	3
Georgia	28,638,931,240	+11.3%	85
Ghana	73,625,343,082	+0.506%	60
Guinea	20,478,922,412	+8.62%	94
Gambia	2,298,153,648	+2.81%	126
Guinea-Bissau	2,009,188,570	+1.49%	128
Equatorial Guinea	10,373,105,844	+6.59%	115
Greece	224,177,662,655	+4.61%	43
Guatemala	111,925,909,147	+8.75%	50
Hong Kong SAR China	326,495,710,505	+2.89%	31
Honduras	37,657,247,496	+7.6%	78
Croatia	73,663,120,039	+11.1%	59
Haiti	26,613,242,539	+26%	87
Hungary	157,880,063,533	+6.08%	47
Indonesia	881,500,012,930	+3.89%	14
India	2,802,707,415,833	+9.27%	4
Ireland	237,131,073,645	+6.48%	39
Iran	277,021,118,522	+8.37%	36
Iraq	172,072,391,576	+11.7%	46
Iceland	24,955,636,162	+6.73%	90
Israel	400,403,943,611	+10%	23
Italy	1,788,049,239,156	+2.57%	7
Kenya	108,312,136,407	+12.8%	51
Cambodia	30,405,148,500	+8.15%	84
Libya	32,382,629,225	+6.06%	82
Sri Lanka	74,881,377,853	+15.3%	58
Lithuania	63,149,483,881	+6.72%	64
Luxembourg	48,527,654,835	+7.51%	72
Latvia	35,430,182,252	+5.16%	79
Macao SAR China	20,655,405,097	+2.86%	93
Morocco	122,418,789,969	+6.67%	48
Moldova	19,057,175,154	+9.98%	97
Madagascar	14,827,781,205	+7.13%	105
Mexico	1,509,415,015,894	+3.96%	9
North Macedonia	14,132,280,399	+7.49%	108
Mali	22,603,158,375	+7.3%	91
Malta	15,520,453,095	+10.1%	103
Montenegro	7,596,812,413	+10.8%	120
Mongolia	15,587,225,856	+33.3%	102
Mozambique	19,294,840,818	-3.26%	96
Mauritius	12,455,672,694	+4.96%	111
Malaysia	307,214,773,447	+6.12%	33
Namibia	13,469,611,029	+13.7%	110
Niger	13,867,827,955	+7.33%	109
Nicaragua	18,296,824,838	+12.7%	99
Netherlands	842,297,656,977	+5.95%	15
Norway	300,354,928,267	+3.16%	34
Nepal	40,243,839,702	+5.67%	77
Pakistan	349,704,833,107	+10.6%	28
Peru	216,861,535,478	+5.05%	44
Philippines	418,474,486,303	+5.52%	22
Poland	716,551,241,301	+15.1%	17
Puerto Rico	105,956,800,000	+4.68%	52
Portugal	241,072,870,600	+6.18%	38
Paraguay	35,385,138,337	+5.05%	80
Palestinian Territories	15,933,100,000	-25.1%	101
Romania	313,102,122,477	+13%	32
Russia	1,476,529,787,941	+6.04%	10
Rwanda	11,677,390,834	-10.3%	113
Saudi Arabia	821,041,600,000	+4.49%	16
Sudan	48,513,499,040	+24%	73
Senegal	26,522,971,714	+3.78%	88
Singapore	230,356,681,806	+9.59%	42
Sierra Leone	7,027,231,885	+19.4%	121
El Salvador	34,938,480,000	+5.06%	81
Somalia	15,935,660,220	+11%	100
Serbia	71,722,493,528	+10.6%	61
Slovakia	113,029,309,011	+7.3%	49
Slovenia	52,376,885,531	+6.13%	66
Sweden	433,057,668,891	+4.01%	21
Seychelles	2,185,947,652	+6.95%	127
Chad	14,446,316,366	+4.46%	107
Togo	9,073,701,104	+9.13%	118
Thailand	394,344,624,787	+3.16%	24
Tunisia	50,614,291,824	+7.38%	68
Turkey	981,695,225,715	+21.1%	13
Tanzania	48,912,517,066	-1.61%	71
Uganda	40,901,648,400	+3.32%	76
Ukraine	191,293,563,849	+2.64%	45
Uruguay	64,240,262,681	+4.14%	63
United States	23,742,028,000,000	+5.3%	1
Uzbekistan	94,155,611,561	+10.6%	55
Samoa	1,056,709,577	+6.4%	130
Kosovo	10,771,995,322	+6.39%	114
South Africa	336,535,101,237	+5.56%	30
Zimbabwe	45,952,520,590	+42.8%	74

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.TOTL.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.TOTL.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

Final consumption expenditure (current US$)

Year: 2024

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