Country

Indicator

Value type

Values present

Final consumption expenditure (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	61.5	-0.047%	123
Albania	82.4	+0.178%	48
Argentina	83.1	+0.967%	45
Armenia	77.3	-2.82%	76
Australia	73.5	+2.55%	96
Austria	74.7	+2.73%	90
Azerbaijan	69.8	+2.95%	106
Belgium	76	+0.997%	81
Benin	67.9	-3.18%	110
Burkina Faso	79.4	-6.15%	63
Bangladesh	76	+2.43%	80
Bulgaria	77.3	+1.54%	74
Bahamas	77.2	+0.917%	77
Bosnia & Herzegovina	88.6	+1.32%	31
Belarus	75.8	+5.6%	82
Bermuda	56.6	-0.38%	125
Brazil	82.6	+0.763%	47
Brunei	51.5	+2.13%	127
Botswana	77.4	+7.24%	73
Central African Republic	104	-1.3%	6
Canada	77	+1.21%	78
Switzerland	62.8	+0.196%	119
Chile	73.2	-3.39%	97
Côte d’Ivoire	75	-3.11%	87
Cameroon	85	+0.663%	38
Congo - Kinshasa	70.8	+0.0982%	104
Congo - Brazzaville	60.8	+6.25%	124
Colombia	87.9	-0.487%	32
Comoros	113	-0.378%	3
Cape Verde	95.4	-3.21%	17
Costa Rica	78.6	-0.99%	68
Cyprus	77.5	-1.07%	72
Czechia	67.4	+0.615%	112
Germany	75.1	+1.04%	86
Djibouti	91.8	+4.61%	26
Denmark	67.6	-2.13%	111
Dominican Republic	79.2	-0.167%	66
Ecuador	78.2	-1.33%	70
Egypt	93.9	+9.45%	21
Spain	75.2	+0.235%	85
Estonia	74.3	+0.278%	93
Ethiopia	85.7	+0.552%	36
Finland	77.3	+0.201%	75
France	78.8	-0.0556%	67
Gabon	45.8	+1.53%	128
United Kingdom	83.1	-0.746%	46
Georgia	84.8	+1.4%	40
Ghana	88.9	-2.26%	30
Guinea	80.8	-3.93%	57
Gambia	91.7	-1.76%	27
Guinea-Bissau	94.8	-0.527%	18
Equatorial Guinea	81.3	+3.02%	56
Greece	87.2	-0.942%	33
Guatemala	98.9	+0.269%	13
Hong Kong SAR China	80.2	-3.69%	59
Honduras	102	-0.342%	8
Croatia	79.6	+1.35%	61
Haiti	106	-0.83%	4
Hungary	70.8	+1.85%	103
Indonesia	63.1	+2.02%	118
India	71.6	+1.61%	100
Ireland	41.1	+1.68%	131
Iran	63.4	+0.366%	117
Iraq	61.5	+7.36%	122
Iceland	74.6	+0.32%	91
Israel	74.1	+4.27%	95
Italy	75.4	-0.38%	84
Kenya	87	-2.1%	34
Cambodia	65.6	-1.22%	115
Libya	69.4	+2.56%	107
Sri Lanka	75.7	-2.46%	83
Lithuania	74.4	+0.332%	92
Luxembourg	52.1	+1.02%	126
Latvia	81.4	+2.87%	54
Macao SAR China	41.2	-6.12%	130
Morocco	79.3	-0.248%	65
Moldova	105	+0.987%	5
Madagascar	85.1	-2.41%	37
Mexico	81.5	+0.652%	53
North Macedonia	84.7	+1.56%	41
Mali	85	-0.636%	39
Malta	63.8	+0.583%	116
Montenegro	94.1	+3.4%	20
Mongolia	66.1	+14.9%	114
Mozambique	86.1	-9.57%	35
Mauritius	83.3	-1.02%	44
Malaysia	72.8	+0.517%	98
Namibia	101	+5.47%	10
Niger	71	-8.27%	101
Nicaragua	92.9	+1.92%	25
Netherlands	68.6	-0.37%	108
Norway	62.1	+3%	120
Nepal	93.8	+1.08%	22
Pakistan	93.7	+0.128%	23
Peru	75	-3.04%	88
Philippines	90.7	-0.095%	29
Poland	78.3	+2.28%	69
Puerto Rico	84.2	-1.53%	42
Portugal	78.1	-0.352%	71
Paraguay	79.6	+1.89%	62
Palestinian Territories	116	-2.53%	2
Romania	81.8	+3.53%	52
Russia	67.9	+1.05%	109
Rwanda	81.9	-9.78%	50
Saudi Arabia	66.3	+2.89%	113
Sudan	97.2	-0.899%	15
Senegal	82.2	-1.27%	49
Singapore	42.1	+1.19%	129
Sierra Leone	93.1	+1.47%	24
El Salvador	98.8	+0.568%	14
Somalia	132	+0.586%	1
Serbia	80.5	+0.984%	58
Slovakia	79.7	+1.34%	60
Slovenia	72.3	+1.25%	99
Sweden	71	-0.19%	102
Seychelles	101	+7.95%	9
Chad	70	-3.23%	105
Togo	91.4	+0.831%	28
Thailand	74.9	+1.1%	89
Tunisia	94.8	-3.1%	19
Turkey	74.2	+2.36%	94
Tanzania	62.1	-1.26%	121
Uganda	76.2	-6.09%	79
Ukraine	100	-2.48%	11
Uruguay	79.3	+0.318%	64
United States	81.4	+0.0186%	55
Uzbekistan	81.9	-1.25%	51
Samoa	98.9	-6.53%	12
Kosovo	96.6	-0.107%	16
South Africa	84.1	+0.398%	43
Zimbabwe	104	+13.8%	7

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.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.TOTL.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

Final consumption expenditure (% of GDP)

Year: 2024

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