Country

Indicator

Value type

Values present

Gross fixed capital formation (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	20,100,497,840	-0.407%	60
Albania	6,356,004,202	+12.2%	90
Argentina	100,288,864,685	-16.4%	29
Armenia	5,593,807,450	+11.3%	93
Australia	425,205,956,513	+5.64%	10
Austria	126,929,779,098	-0.382%	22
Azerbaijan	12,482,764,706	-0.954%	71
Belgium	162,141,027,171	+2.88%	18
Benin	7,447,076,802	+10.9%	86
Burkina Faso	3,835,269,390	+9.85%	102
Bangladesh	138,188,871,817	+2.07%	21
Bulgaria	20,125,083,940	+5.14%	59
Bahamas	4,076,900,000	+22.6%	100
Belarus	18,079,060,019	+10.6%	65
Bermuda	1,013,300,000	+5.46%	119
Brazil	371,217,219,098	+3.23%	13
Brunei	4,354,399,069	-1.97%	97
Botswana	5,527,869,480	+11.1%	94
Central African Republic	423,984,487	+13.7%	122
Canada	507,892,094,720	+2.26%	7
Switzerland	234,652,590,132	+1.59%	17
Chile	77,577,608,120	-4.63%	35
Côte d’Ivoire	21,186,737,759	+15%	57
Cameroon	10,973,360,192	+16.4%	74
Congo - Kinshasa	23,304,564,460	+10.2%	53
Congo - Brazzaville	4,171,980,388	+9.34%	98
Colombia	69,137,943,688	+14.3%	36
Comoros	181,426,032	+8.11%	125
Costa Rica	15,089,704,083	+7.82%	66
Cyprus	7,454,518,395	+2.56%	84
Czechia	90,283,802,701	-3.64%	33
Germany	971,923,541,914	-0.112%	3
Djibouti	1,074,855,525	+11.9%	118
Denmark	95,381,315,614	+3.81%	32
Dominican Republic	32,463,879,925	-7.01%	51
Ecuador	22,953,050,700	-4.56%	54
Egypt	45,512,764,932	-24%	44
Spain	336,528,323,441	+5.39%	15
Estonia	11,164,260,970	-3.18%	73
Finland	64,347,518,867	-6.62%	38
France	705,805,366,821	+0.148%	4
Gabon	3,780,342,008	+7.5%	103
United Kingdom	634,121,617,947	+6.72%	5
Georgia	7,447,418,926	+12.8%	85
Ghana	8,113,911,061	+4.38%	82
Guinea	8,139,967,068	+48.3%	81
Gambia	977,561,341	+49.5%	120
Guinea-Bissau	484,005,138	+8.03%	121
Equatorial Guinea	1,158,087,491	+0.814%	117
Greece	39,279,327,440	+6.37%	48
Guatemala	18,240,788,445	+6.95%	64
Hong Kong SAR China	65,990,904,726	+2.21%	37
Honduras	8,875,192,435	+8.74%	79
Croatia	21,894,759,020	+15.5%	56
Haiti	2,507,120,060	-8.83%	111
Hungary	52,111,801,180	-4.86%	43
Indonesia	406,959,842,018	+1.81%	12
India	1,158,447,492,084	+4.64%	2
Ireland	99,264,463,361	-22.4%	30
Iran	116,796,555,069	+7.64%	26
Iraq	57,554,450,995	+40.1%	42
Iceland	8,838,337,127	+13.2%	80
Israel	122,614,875,707	-1.73%	23
Italy	521,154,505,994	+0.427%	6
Kenya	21,989,457,231	+18.1%	55
Cambodia	14,640,645,704	+5.82%	67
Libya	6,919,569,687	-6.9%	88
Sri Lanka	18,598,173,511	+24.5%	62
Lithuania	19,096,388,821	+1.06%	61
Luxembourg	13,361,394,825	-5.2%	69
Latvia	10,053,926,943	-4.31%	77
Macao SAR China	6,903,716,154	+3.24%	89
Morocco	40,377,376,256	+9.99%	47
Moldova	3,637,428,703	+12%	105
Madagascar	3,938,159,103	+25%	101
Mexico	447,709,317,111	+4.29%	9
Mali	5,745,725,502	+7.61%	92
Malta	4,360,356,646	+3.71%	96
Montenegro	1,633,089,347	+10.5%	114
Mongolia	6,321,609,387	+22.8%	91
Mauritius	3,133,340,910	+9.88%	107
Malaysia	86,741,858,441	+12.8%	34
Namibia	3,163,826,307	-2.3%	106
Niger	3,649,703,592	+2.78%	104
Nicaragua	4,512,773,661	+18.2%	95
Netherlands	242,233,501,765	+4.42%	16
Norway	105,783,326,871	+1.16%	28
Nepal	10,440,548,230	+3.44%	75
Pakistan	41,881,011,672	+0.658%	46
Peru	60,096,278,717	+6.22%	40
Philippines	108,805,587,779	+5.33%	27
Poland	154,448,006,837	+6.19%	19
Puerto Rico	18,316,600,000	+10.5%	63
Portugal	61,185,179,806	+5.22%	39
Paraguay	9,321,413,308	+4.73%	78
Palestinian Territories	2,995,600,000	-30.2%	108
Romania	98,231,732,777	+3.8%	31
Russia	481,132,998,139	+5.58%	8
Rwanda	4,149,120,963	+8.58%	99
Saudi Arabia	354,934,666,667	+4.46%	14
Sudan	1,433,914,068	+65.4%	116
Senegal	10,347,308,126	-4.74%	76
Singapore	119,858,512,700	+5.89%	24
Sierra Leone	2,226,282,571	+75.2%	112
El Salvador	7,846,880,000	+5.53%	83
Somalia	2,750,440,440	+12.1%	110
Serbia	21,047,098,466	+10.7%	58
Slovakia	28,832,469,327	+2.12%	52
Slovenia	14,537,887,193	-1.46%	68
Sweden	148,813,881,869	+1.62%	20
Seychelles	372,099,665	-17.3%	123
Chad	2,968,824,362	+4.35%	109
Togo	2,212,640,231	+5.1%	113
Thailand	117,113,620,063	-1.01%	25
Tunisia	7,132,384,812	-5.9%	87
Turkey	410,189,478,682	+15.1%	11
Tanzania	32,575,570,126	-3.57%	50
Uganda	11,561,741,788	+7.35%	72
Ukraine	36,012,353,580	-0.0797%	49
Uruguay	13,146,489,946	-1.98%	70
United States	6,293,963,000,000	+6.14%	1
Uzbekistan	42,601,435,126	+28%	45
Samoa	325,966,364	+0.65%	124
South Africa	58,116,496,527	+2.22%	41
Zimbabwe	1,578,761,190	-63.3%	115

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.GDI.FTOT.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.GDI.FTOT.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 fixed capital formation (current US$)

Year: 2024

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