Country

Indicator

Value type

Values present

Households and NPISHs Final consumption expenditure per capita (constant 2015 US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	1,307	-2.7%	75
Albania	4,053	+4.67%	38
Argentina	8,276	-4.57%	19
Armenia	3,718	+4.36%	42
Australia	33,506	-0.946%	3
Benin	853	+2.48%	82
Burkina Faso	508	+2.8%	97
Bangladesh	1,322	+4.71%	74
Bulgaria	6,673	+4.25%	24
Bahamas	20,957	+2.76%	6
Bosnia & Herzegovina	4,709	+2.73%	33
Belarus	4,709	+13%	34
Bermuda	49,083	+3.19%	1
Brazil	6,198	+4.34%	27
Brunei	10,124	+5.09%	15
Botswana	3,245	+0.222%	50
Central African Republic	540	+15.7%	95
Chile	9,256	+0.506%	16
Côte d’Ivoire	1,466	+3.76%	70
Cameroon	1,116	+1.23%	78
Congo - Kinshasa	339	-0.188%	100
Congo - Brazzaville	847	+3.98%	83
Colombia	5,211	+0.499%	29
Comoros	1,527	+2.79%	69
Cape Verde	3,387	+4.5%	48
Costa Rica	9,160	+3.46%	17
Cyprus	20,663	+2.41%	7
Djibouti	2,551	+3.98%	57
Dominican Republic	6,323	+3.68%	25
Ecuador	3,913	-2.13%	40
Egypt	3,485	+6.17%	47
Ethiopia	674	+7.29%	87
Gabon	2,233	+0.374%	62
Georgia	5,116	+12.3%	31
Ghana	1,730	+2.65%	67
Guinea	704	+1.83%	85
Gambia	554	+2.05%	93
Guinea-Bissau	644	-1.18%	90
Equatorial Guinea	3,080	-4.36%	51
Guatemala	4,074	+3.98%	37
Hong Kong SAR China	30,102	-0.413%	4
Honduras	2,263	+2.6%	61
Croatia	10,677	+5.43%	13
Haiti	1,267	-6.25%	76
Indonesia	2,445	+4.26%	59
India	1,429	+6.67%	71
Iran	2,868	+1.48%	53
Iraq	2,311	+1.81%	60
Kenya	1,347	+2.06%	72
Cambodia	1,335	+1.15%	73
Libya	2,516	+1.25%	58
Sri Lanka	2,758	+4.58%	55
Macao SAR China	19,580	+3.65%	8
Morocco	2,026	+2.61%	64
Moldova	3,552	+8.18%	44
Madagascar	328	+0.132%	101
Mexico	7,474	+1.94%	22
North Macedonia	4,990	+3.23%	32
Mali	671	+1.11%	89
Malta	16,342	+1.74%	9
Montenegro	7,654	+8.69%	20
Mongolia	3,295	+11.5%	49
Mozambique	418	-8.71%	98
Mauritius	7,558	+3.25%	21
Malaysia	7,205	+3.83%	23
Namibia	3,713	+10.8%	43
Niger	381	-0.228%	99
Nicaragua	1,807	+7.19%	66
Nepal	1,006	+1.22%	79
Pakistan	1,569	+4.73%	68
Peru	4,467	+1.65%	35
Philippines	2,801	+4%	54
Poland	10,290	+3.38%	14
Paraguay	4,368	+3.93%	36
Palestinian Territories	1,891	-34.1%	65
Romania	9,101	+5.94%	18
Russia	6,211	+5.61%	26
Rwanda	769	+1.99%	84
Saudi Arabia	11,866	-1.93%	11
Sudan	519	-16.9%	96
Senegal	995	+0.634%	80
Singapore	24,294	+2.76%	5
Sierra Leone	1,210	+2%	77
El Salvador	3,520	+2.75%	46
Somalia	605	+2.32%	91
Serbia	5,563	+4.75%	28
Seychelles	12,531	+10.1%	10
Chad	546	-2%	94
Togo	672	+3.06%	88
Thailand	3,920	+4.46%	39
Tunisia	3,010	+3.43%	52
Turkey	11,366	+3.43%	12
Tanzania	599	+0.196%	92
Uganda	691	-2.08%	86
Ukraine	2,078	+6.28%	63
United States	45,923	+1.76%	2
Uzbekistan	2,719	+5.36%	56
Samoa	3,533	+10.1%	45
Kosovo	5,126	+16.5%	30
South Africa	3,835	-0.222%	41
Zimbabwe	915	+1.16%	81

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.PC.KD'

# 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.PC.KD'

# 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 per capita (constant 2015 US$)

Year: 2024

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