Country

Indicator

Value type

Values present

Households and NPISHs final consumption expenditure (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	55.3	-1.99%	86
Albania	69.6	-0.897%	40
Argentina	68.1	+3.26%	45
Armenia	66.5	+1.79%	53
Australia	51.2	+1.81%	89
Azerbaijan	55.4	+2.69%	84
Benin	58.9	-2.87%	76
Burkina Faso	60.6	-6.17%	72
Bangladesh	70.1	+2.27%	38
Bulgaria	57.6	-0.153%	80
Bahamas	64.3	+1.42%	60
Bosnia & Herzegovina	69	+0.987%	42
Belarus	56.8	+5.32%	83
Bermuda	45.6	+0.288%	95
Brazil	63.8	+1.35%	62
Brunei	28.5	+3.29%	103
Botswana	45.3	+5.6%	96
Central African Republic	94.7	-2.79%	5
Chile	58.1	-3.86%	79
Côte d’Ivoire	66	-3.45%	55
Cameroon	74.5	+0.811%	32
Congo - Kinshasa	62.7	-0.305%	65
Congo - Brazzaville	47.4	+7.47%	93
Colombia	73.1	-0.667%	33
Comoros	104	-0.162%	2
Cape Verde	74.7	-2.8%	30
Costa Rica	63.9	-0.916%	61
Cyprus	58.9	-1.34%	77
Djibouti	73	+3.26%	34
Dominican Republic	67.7	-0.53%	49
Ecuador	64.9	-1.16%	57
Egypt	87.6	+10.9%	9
Ethiopia	80.2	+1.59%	20
Gabon	33.7	+0.972%	99
Georgia	71.3	-0.177%	36
Ghana	84.1	-1.62%	15
Guinea	67.4	-4.56%	50
Gambia	83.2	-3.17%	16
Guinea-Bissau	77	-2.77%	24
Equatorial Guinea	52.9	+0.453%	87
Guatemala	88	+0.697%	7
Hong Kong SAR China	67.4	-3.72%	51
Honduras	86	-1.09%	12
Croatia	57	-0.495%	82
Haiti	99.8	-0.352%	3
Indonesia	55.4	+1.79%	85
India	61.5	+2.16%	68
Iran	50.5	+0.381%	90
Iraq	41.2	+3.6%	98
Kenya	75.5	-1.88%	29
Cambodia	59.8	-0.91%	73
Libya	32.7	+0.888%	100
Sri Lanka	68.7	-2.95%	43
Macao SAR China	29	-3.72%	102
Morocco	61.3	-0.09%	70
Moldova	86.8	+1.25%	10
Madagascar	69.8	-2.45%	39
Mexico	70.3	+0.65%	37
North Macedonia	67.9	-0.824%	48
Mali	71.9	-0.85%	35
Malta	46.6	-0.222%	94
Montenegro	76.3	+4.29%	25
Mongolia	49.8	+11.8%	91
Mozambique	69	-11.5%	41
Mauritius	68.6	-1.71%	44
Malaysia	60.8	+0.508%	71
Namibia	79.3	+7.46%	22
Niger	59.2	-7.44%	75
Nicaragua	80.6	+3.05%	19
Nepal	86.3	+0.214%	11
Pakistan	85.2	+2.31%	13
Peru	61.6	-3.45%	67
Philippines	76.1	-0.488%	27
Poland	57.6	-0.142%	81
Puerto Rico	76	-1.43%	28
Paraguay	67	+1.49%	52
Palestinian Territories	95.5	-3.49%	4
Romania	63.5	+2.6%	63
Russia	49.4	+0.223%	92
Rwanda	64.9	-13%	58
Saudi Arabia	45	+3.3%	97
Sudan	80.7	+0.0368%	18
Senegal	65.8	-2.37%	56
Singapore	31.5	-0.317%	101
Sierra Leone	87.6	+1.56%	8
El Salvador	79.6	+0.568%	21
Somalia	124	+0.376%	1
Serbia	62.7	+0.231%	64
Seychelles	74.6	+7.96%	31
Chad	61.3	-3.21%	69
Togo	78.3	+0.25%	23
Thailand	58.2	+1.29%	78
Tunisia	76.2	-2.73%	26
Turkey	59.4	+0.103%	74
Tanzania	52.9	-2.25%	88
Uganda	66.3	-7.47%	54
Ukraine	62.4	+2.27%	66
United States	67.9	+0.0422%	47
Uzbekistan	68	-0.724%	46
Samoa	80.8	-7.9%	17
Kosovo	84.3	+0.175%	14
South Africa	64.8	+0.701%	59
Zimbabwe	91.5	+20%	6

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.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.PRVT.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

Households and NPISHs final consumption expenditure (% of GDP)

Year: 2024

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