Gross fixed capital formation (constant 2015 US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 26,519,229,333 +7.22% 50
Albania Albania 4,251,184,609 +4.34% 92
Argentina Argentina 82,148,580,746 -17.4% 30
Armenia Armenia 3,927,216,608 +11.1% 96
Australia Australia 413,597,957,872 +4.51% 7
Austria Austria 96,967,694,824 -3.43% 26
Belgium Belgium 128,171,158,715 +1.43% 19
Benin Benin 5,336,328,782 +10.6% 90
Burkina Faso Burkina Faso 3,694,154,517 +3.41% 99
Bangladesh Bangladesh 105,571,257,263 +3.27% 23
Bulgaria Bulgaria 12,033,686,167 -1.12% 68
Bahamas Bahamas 3,586,245,464 +20.7% 101
Belarus Belarus 14,890,319,104 +7.79% 60
Bermuda Bermuda 872,167,190 +3.72% 115
Brazil Brazil 351,696,231,699 +7.29% 11
Brunei Brunei 3,999,159,489 -2.38% 95
Botswana Botswana 4,452,930,743 +5.06% 91
Central African Republic Central African Republic 544,778,063 +23% 118
Canada Canada 384,283,050,111 +0.109% 9
Switzerland Switzerland 198,816,362,701 -0.956% 16
Chile Chile 69,107,658,605 -1.45% 34
Côte d’Ivoire Côte d’Ivoire 22,100,615,343 +10.5% 53
Cameroon Cameroon 8,948,787,081 +10% 76
Congo - Kinshasa Congo - Kinshasa 74,203,523,825 +18.8% 33
Congo - Brazzaville Congo - Brazzaville 3,708,938,136 +5.6% 98
Colombia Colombia 62,616,218,522 +2.98% 38
Comoros Comoros 147,115,863 +3.35% 122
Costa Rica Costa Rica 12,703,425,003 +4.29% 65
Cyprus Cyprus 6,209,860,959 +0.0887% 82
Czechia Czechia 62,742,051,474 -1.16% 37
Germany Germany 713,710,410,837 -2.7% 3
Djibouti Djibouti 1,054,448,373 +9.39% 113
Denmark Denmark 78,431,678,764 +2.67% 31
Dominican Republic Dominican Republic 26,032,730,933 +2.87% 51
Ecuador Ecuador 18,750,643,459 -3.8% 58
Egypt Egypt 48,883,122,583 -6.55% 43
Spain Spain 272,581,264,873 +2.98% 14
Estonia Estonia 8,554,943,666 -6.93% 77
Ethiopia Ethiopia 41,867,426,444 +7.21% 45
Finland Finland 52,041,701,755 -7.09% 40
France France 590,195,464,229 -1.29% 4
Gabon Gabon 9,428,061,660 +7.61% 75
United Kingdom United Kingdom 585,093,929,932 +1.45% 5
Georgia Georgia 5,500,232,792 +15% 88
Ghana Ghana 13,546,388,037 +13.8% 64
Guinea Guinea 5,795,110,599 +45.6% 87
Gambia Gambia 762,021,286 +7.12% 116
Guinea-Bissau Guinea-Bissau 401,922,171 +9.31% 119
Equatorial Guinea Equatorial Guinea 940,027,778 -1.98% 114
Greece Greece 36,379,892,755 +4.5% 47
Guatemala Guatemala 14,168,887,440 +4.8% 62
Hong Kong SAR China Hong Kong SAR China 62,758,915,256 +2.42% 36
Honduras Honduras 6,691,783,901 +6.16% 81
Croatia Croatia 15,991,125,185 +9.9% 59
Haiti Haiti 663,318,395 -36.3% 117
Hungary Hungary 31,849,637,463 -11.1% 48
Indonesia Indonesia 388,248,546,814 +4.61% 8
India India 1,086,625,897,910 +6.13% 2
Ireland Ireland 77,759,854,850 -25.4% 32
Iran Iran 95,991,765,430 +1.86% 27
Iraq Iraq 21,802,865,259 +26.6% 56
Iceland Iceland 5,969,590,122 +7.46% 85
Israel Israel 92,864,770,637 -6.11% 28
Italy Italy 471,039,668,107 +0.519% 6
Kenya Kenya 19,601,985,464 +2.08% 57
Cambodia Cambodia 12,629,029,076 +0.933% 66
Libya Libya 7,321,342,813 -8.36% 79
Sri Lanka Sri Lanka 22,050,757,111 +19.4% 54
Lithuania Lithuania 13,889,817,398 -1.25% 63
Luxembourg Luxembourg 9,721,911,835 -7.25% 73
Latvia Latvia 7,257,092,880 -6.71% 80
Macao SAR China Macao SAR China 5,988,326,409 +4.61% 84
Moldova Moldova 2,701,719,114 +7.96% 107
Madagascar Madagascar 3,609,399,016 +17.5% 100
Mexico Mexico 320,399,696,838 +3.31% 13
Mali Mali 5,461,484,250 +4.8% 89
Malta Malta 3,383,233,153 +2.41% 102
Montenegro Montenegro 1,366,389,013 +9.3% 112
Mongolia Mongolia 5,843,327,704 +19.7% 86
Mauritius Mauritius 2,786,011,502 +8.27% 105
Malaysia Malaysia 90,221,397,600 +12% 29
Namibia Namibia 3,094,407,283 -7.9% 103
Niger Niger 4,046,091,340 -0.879% 94
Nicaragua Nicaragua 4,102,748,253 +17.3% 93
Netherlands Netherlands 187,859,157,293 -0.519% 17
Norway Norway 102,706,954,639 -1.91% 24
Nepal Nepal 9,711,165,325 +1.96% 74
Pakistan Pakistan 40,272,077,866 -3.62% 46
Peru Peru 51,038,242,446 +5.22% 42
Philippines Philippines 107,678,764,083 +6.32% 22
Poland Poland 123,544,554,981 -2.24% 20
Portugal Portugal 46,382,314,474 +2.96% 44
Paraguay Paraguay 9,806,878,898 +8.33% 72
Palestinian Territories Palestinian Territories 2,642,000,000 -30.7% 108
Romania Romania 62,387,117,800 -3.27% 39
Russia Russia 373,830,589,607 +6% 10
Rwanda Rwanda 3,778,091,867 +13.2% 97
Saudi Arabia Saudi Arabia 249,629,516,104 +1.56% 15
Senegal Senegal 10,562,714,819 +2.79% 71
Singapore Singapore 97,592,930,009 +2.94% 25
Sierra Leone Sierra Leone 1,869,180,535 +21.8% 111
El Salvador El Salvador 6,172,686,935 +4.67% 83
Somalia Somalia 2,225,193,346 +9.79% 110
Serbia Serbia 14,529,626,915 +6.52% 61
Slovakia Slovakia 22,437,455,495 +1.85% 52
Slovenia Slovenia 10,811,971,323 -3.74% 70
Sweden Sweden 137,831,711,356 -1.08% 18
Seychelles Seychelles 335,107,371 -16.7% 120
Chad Chad 2,788,989,824 +2.97% 104
Togo Togo 2,324,794,502 +2.65% 109
Thailand Thailand 111,078,597,112 -0.0221% 21
Tunisia Tunisia 7,788,109,989 +2% 78
Turkey Turkey 324,980,105,150 +3.87% 12
Tanzania Tanzania 31,783,798,495 +5.28% 49
Uganda Uganda 12,305,894,672 +5.54% 67
Ukraine Ukraine 21,948,134,472 +3.53% 55
Uruguay Uruguay 11,917,878,578 -1.33% 69
United States United States 4,942,841,850,570 +4.43% 1
Uzbekistan Uzbekistan 64,673,890,345 +27.6% 35
Samoa Samoa 278,981,836 -6.14% 121
South Africa South Africa 51,783,740,585 -3.69% 41
Zimbabwe Zimbabwe 2,750,907,318 +0.48% 106 

                    
# 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.GDI.FTOT.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.GDI.FTOT.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))