Country

Indicator

Value type

Values present

Gross national expenditure (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	69,567,822,364	-3.95%	68
Argentina	626,026,233,120	-4.73%	20
Armenia	25,643,726,479	+5.61%	94
Australia	1,714,836,604,992	+3.77%	11
Austria	505,915,117,196	+0.777%	25
Azerbaijan	67,541,823,529	+9.01%	70
Belgium	664,651,421,353	+2.49%	19
Benin	22,118,255,147	+7.43%	100
Burkina Faso	24,750,712,116	+13.7%	95
Bangladesh	480,472,385,072	+4.42%	26
Bulgaria	109,602,478,535	+11.6%	56
Bahamas	16,468,700,000	+8.52%	110
Bosnia & Herzegovina	33,835,144,426	+7.77%	89
Belarus	77,206,585,439	+10.4%	65
Bermuda	6,097,300,000	+4.47%	121
Brazil	2,168,555,563,558	+1.27%	8
Brunei	12,354,756,233	+2.19%	116
Botswana	22,039,463,910	+10.8%	101
Central African Republic	3,294,499,039	-0.409%	123
Canada	2,246,311,069,072	+3.43%	7
Switzerland	840,821,666,798	+6.04%	18
Chile	318,421,164,894	-4.24%	40
Côte d’Ivoire	86,080,375,177	+4.46%	61
Cameroon	54,617,731,956	+6.58%	73
Congo - Kinshasa	73,756,985,961	+7.13%	67
Congo - Brazzaville	13,775,240,513	+9.09%	114
Colombia	439,230,805,223	+14.5%	28
Comoros	1,925,669,138	+6.62%	128
Cape Verde	3,082,558,451	+2.94%	125
Costa Rica	89,884,389,291	+9.8%	60
Cyprus	35,030,916,668	+4.47%	87
Czechia	322,390,822,751	-1.12%	39
Germany	4,480,248,767,176	+3.13%	2
Djibouti	3,595,988,714	+8.65%	122
Denmark	383,028,977,439	+2.44%	36
Dominican Republic	132,046,977,290	+2.26%	51
Ecuador	120,494,362,000	-0.405%	55
Egypt	415,703,215,403	+2.7%	30
Spain	1,649,003,396,999	+5.94%	12
Estonia	42,992,552,700	+2.24%	82
Finland	296,678,321,723	+0.465%	42
France	3,185,314,102,066	+2.33%	5
Gabon	13,340,548,967	+6.15%	115
United Kingdom	3,680,432,433,110	+8.19%	4
Georgia	36,345,135,617	+8.72%	86
Ghana	81,871,888,909	+0.792%	62
Guinea	28,378,891,398	+17.7%	91
Gambia	3,275,714,989	+13.4%	124
Guinea-Bissau	2,452,892,644	+3.33%	127
Equatorial Guinea	11,516,384,116	+5.79%	117
Greece	270,922,067,526	+6.23%	44
Guatemala	130,811,689,496	+8.91%	52
Hong Kong SAR China	390,595,108,839	+3.11%	35
Honduras	46,000,456,787	+7.93%	78
Croatia	95,410,774,533	+11%	58
Haiti	29,120,362,599	+22%	90
Hungary	210,533,188,098	+3.07%	47
Indonesia	1,319,887,337,223	+4.38%	13
India	4,078,669,980,687	+7.94%	3
Ireland	337,671,836,759	-8.22%	38
Iran	451,828,261,304	+9.47%	27
Iraq	254,253,953,226	+16.2%	45
Iceland	33,845,747,675	+7.78%	88
Israel	526,997,222,385	+5.89%	23
Italy	2,318,446,841,401	+2.06%	6
Kenya	129,209,329,266	+13.7%	53
Cambodia	45,324,272,475	+7.38%	80
Libya	39,302,198,912	+3.52%	84
Sri Lanka	101,560,830,003	+18.8%	57
Lithuania	80,473,354,539	+4.95%	63
Luxembourg	62,650,151,464	+3.79%	71
Latvia	44,635,067,226	+1.09%	81
Macao SAR China	27,910,379,103	+2.77%	93
Morocco	168,616,778,599	+7.82%	48
Moldova	22,903,130,785	+10.7%	99
Madagascar	18,765,940,308	+10.4%	105
Mexico	1,957,047,486,957	+4.02%	10
North Macedonia	18,874,773,204	+5.93%	104
Mali	28,147,685,412	+5.99%	92
Malta	20,093,898,557	+8.63%	102
Montenegro	9,899,408,676	+10.8%	119
Mongolia	23,758,382,435	+27.8%	97
Mozambique	24,703,967,078	+2.46%	96
Mauritius	15,624,024,083	+6.5%	111
Malaysia	399,426,578,420	+5.3%	32
Namibia	16,894,736,904	+10.2%	109
Niger	17,517,533,501	+5.9%	108
Nicaragua	23,160,067,398	+14.8%	98
Netherlands	1,079,066,219,755	+5.23%	16
Norway	416,523,489,725	+2.02%	29
Nepal	53,289,515,922	+4.76%	74
Pakistan	397,965,373,511	+9.42%	33
Peru	273,027,580,994	+5.97%	43
Philippines	527,806,952,888	+5.8%	22
Poland	878,037,561,957	+14.7%	17
Puerto Rico	124,556,000,000	+5.34%	54
Portugal	303,070,622,648	+5.83%	41
Paraguay	45,537,469,755	+7.46%	79
Palestinian Territories	19,151,100,000	-25.9%	103
Romania	406,114,322,373	+10.5%	31
Russia	2,048,223,746,599	+4.94%	9
Rwanda	15,370,582,755	-6.26%	112
Saudi Arabia	1,193,922,933,333	+4.58%	15
Sudan	49,947,413,108	+24.9%	76
Senegal	37,135,017,432	-3.36%	85
Singapore	351,877,928,475	+10.8%	37
Sierra Leone	9,253,514,457	+29.5%	120
El Salvador	42,133,210,000	+4.65%	83
Somalia	18,686,100,660	+11.2%	106
Serbia	94,496,091,291	+11.3%	59
Slovakia	141,489,949,258	+7.3%	49
Slovenia	67,710,506,373	+4.62%	69
Sweden	583,323,720,325	+3.81%	21
Seychelles	2,558,047,317	+2.57%	126
Chad	18,118,881,142	+4.44%	107
Togo	11,286,342,984	+8.31%	118
Thailand	507,903,514,802	+1.96%	24
Tunisia	57,746,676,636	+13.5%	72
Turkey	1,319,703,802,088	+15.2%	14
Tanzania	80,238,990,068	-2.3%	64
Uganda	52,870,526,474	+4.25%	75
Ukraine	226,853,512,504	+3.53%	46
Uruguay	76,867,564,539	+2%	66
United States	30,087,941,000,000	+5.5%	1
Uzbekistan	132,435,255,509	+9.76%	50
Samoa	1,406,871,805	+4.91%	129
Kosovo	14,539,881,995	+6.29%	113
South Africa	392,340,264,331	+3.8%	34
Zimbabwe	47,926,717,087	+26.4%	77

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.DAB.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.DAB.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

Gross national expenditure (current US$)

Year: 2024

Acquire full dataset with Python or R from source