Country

Indicator

Value type

Values present

Households and NPISHs Final consumption expenditure (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	44,429,017,547	-7.16%	42
Albania	18,909,299,264	+14.4%	63
Argentina	431,269,740,477	+1.22%	11
Armenia	17,158,603,112	+8.98%	64
Australia	897,961,217,369	+3.23%	6
Azerbaijan	41,197,470,588	+5.36%	46
Benin	12,649,557,983	+6.07%	74
Burkina Faso	14,088,875,019	+7.34%	72
Bangladesh	315,690,122,033	+5.24%	15
Bulgaria	64,678,130,611	+9.42%	37
Bahamas	10,180,300,000	+5.15%	83
Bosnia & Herzegovina	19,558,142,340	+3.74%	61
Belarus	43,146,215,627	+10.4%	44
Bermuda	4,094,200,000	+4.98%	94
Brazil	1,389,862,075,679	+0.806%	3
Brunei	4,407,217,205	+5.8%	93
Botswana	8,787,721,846	+5.54%	86
Central African Republic	2,604,393,909	+4.67%	96
Chile	191,905,690,082	-5.37%	23
Côte d’Ivoire	57,141,888,294	+4.94%	39
Cameroon	38,231,944,043	+5%	50
Congo - Kinshasa	44,360,685,677	+5.26%	43
Congo - Brazzaville	7,451,756,880	+10.3%	88
Colombia	306,122,261,123	+13.5%	17
Comoros	1,602,264,386	+7.91%	101
Cape Verde	2,068,143,884	+5.94%	98
Costa Rica	60,947,898,678	+9.22%	38
Cyprus	21,392,752,384	+5.79%	59
Djibouti	2,984,071,951	+7.72%	95
Dominican Republic	84,191,865,369	+2.63%	32
Ecuador	80,881,491,000	+1.72%	33
Egypt	340,742,808,644	+8.99%	13
Gabon	7,023,140,234	+5.06%	89
Georgia	24,097,102,453	+9.55%	58
Ghana	69,625,673,821	+1.16%	35
Guinea	17,081,522,392	+7.9%	65
Gambia	2,086,070,327	+1.33%	97
Guinea-Bissau	1,632,259,986	-0.801%	99
Equatorial Guinea	6,754,876,797	+3.94%	90
Guatemala	99,588,845,282	+9.22%	28
Hong Kong SAR China	274,239,082,256	+2.86%	18
Honduras	31,897,584,777	+6.79%	53
Croatia	52,722,559,161	+9.09%	41
Haiti	25,179,024,800	+26.6%	57
Indonesia	773,552,837,989	+3.66%	8
India	2,405,795,557,862	+9.86%	2
Iran	220,579,892,966	+8.39%	22
Iraq	115,265,413,265	+7.75%	27
Kenya	94,000,173,408	+13.1%	31
Cambodia	27,708,720,519	+8.49%	56
Libya	15,254,951,774	+4.33%	69
Sri Lanka	67,994,062,220	+14.7%	36
Macao SAR China	14,529,856,358	+5.49%	71
Morocco	94,676,683,288	+6.84%	30
Moldova	15,803,332,496	+10.3%	67
Madagascar	12,162,553,491	+7.08%	76
Mexico	1,302,381,883,542	+3.96%	4
North Macedonia	11,331,464,833	+4.97%	80
Mali	19,111,656,064	+7.07%	62
Malta	11,343,882,930	+9.26%	79
Montenegro	6,155,393,907	+11.8%	92
Mongolia	11,741,473,623	+29.8%	77
Mozambique	15,468,945,320	-5.36%	68
Mauritius	10,262,465,481	+4.23%	82
Malaysia	256,508,512,645	+6.11%	20
Namibia	10,597,686,501	+15.8%	81
Niger	11,562,587,737	+8.3%	78
Nicaragua	15,881,928,102	+14%	66
Nepal	37,051,405,579	+4.77%	51
Pakistan	317,997,807,871	+13%	14
Peru	178,071,671,196	+4.61%	24
Philippines	351,500,468,925	+5.1%	12
Poland	526,536,111,112	+12.4%	10
Puerto Rico	95,626,300,000	+4.79%	29
Paraguay	29,776,159,234	+4.65%	54
Palestinian Territories	13,097,200,000	-25.9%	73
Romania	242,994,041,406	+12%	21
Russia	1,073,209,900,225	+5.17%	5
Rwanda	9,243,283,426	-13.5%	85
Saudi Arabia	556,685,333,333	+4.9%	9
Sudan	40,272,082,092	+25.1%	49
Senegal	21,246,599,626	+2.62%	60
Singapore	172,365,448,241	+7.96%	25
Sierra Leone	6,611,809,140	+19.6%	91
El Salvador	28,158,340,000	+5.06%	55
Somalia	15,013,735,310	+10.8%	70
Serbia	55,895,454,355	+9.77%	40
Seychelles	1,616,402,570	+6.96%	100
Chad	12,646,938,609	+4.48%	75
Togo	7,769,495,394	+8.5%	87
Thailand	306,481,203,443	+3.35%	16
Tunisia	40,702,596,775	+7.79%	47
Turkey	786,593,829,778	+18.5%	7
Tanzania	41,675,637,707	-2.6%	45
Uganda	35,548,448,744	+1.79%	52
Ukraine	118,960,080,676	+7.64%	26
United States	19,825,338,000,000	+5.33%	1
Uzbekistan	78,121,781,147	+11.2%	34
Samoa	862,641,289	+4.84%	102
Kosovo	9,395,250,804	+6.69%	84
South Africa	259,560,944,951	+5.88%	19
Zimbabwe	40,443,090,181	+50.5%	48

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.PRVT.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.PRVT.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

Households and NPISHs Final consumption expenditure (current US$)

Year: 2024

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