Country

Indicator

Value type

Values present

Gross capital formation (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	20,100,497,840	-0.407%	61
Argentina	99,571,356,365	-20.4%	30
Armenia	5,717,376,315	+11.5%	94
Australia	427,382,546,112	+3.16%	11
Austria	116,332,850,016	-10.5%	25
Azerbaijan	15,667,588,235	+21.9%	66
Belgium	159,662,159,421	-2.35%	20
Benin	7,532,176,827	+10.9%	86
Burkina Faso	6,299,924,109	+37.5%	93
Bangladesh	138,188,871,817	+2.07%	22
Bulgaria	22,839,711,656	+12.9%	56
Bahamas	4,244,200,000	+21.6%	101
Bosnia & Herzegovina	8,720,739,625	+20.1%	80
Belarus	19,625,394,924	+9.57%	62
Bermuda	1,013,300,000	+5.46%	121
Brazil	368,532,645,337	+6.72%	13
Brunei	4,385,711,083	-1.94%	100
Botswana	7,021,613,823	+19.4%	90
Central African Republic	423,984,487	-30.1%	125
Canada	521,502,104,640	+0.421%	8
Switzerland	252,757,426,251	+8.74%	16
Chile	76,752,978,609	-2.08%	36
Côte d’Ivoire	21,181,432,955	+1.94%	59
Cameroon	10,973,360,192	+14.1%	75
Congo - Kinshasa	23,650,998,045	+10.3%	54
Congo - Brazzaville	4,219,971,933	+9.25%	102
Colombia	71,518,396,925	+18.8%	37
Comoros	181,426,032	-2.55%	128
Cape Verde	441,674,794	-10.1%	124
Costa Rica	14,940,481,178	+13.2%	68
Cyprus	6,882,219,312	-1.61%	92
Czechia	89,849,893,396	-6.56%	34
Germany	980,817,462,226	-0.00179%	3
Djibouti	-154,441,289	+21.7%	129
Denmark	92,912,275,382	-0.0136%	32
Dominican Republic	33,553,778,958	+0.147%	50
Ecuador	23,043,140,000	-7.89%	55
Egypt	50,565,105,807	-22.6%	44
Spain	352,727,229,537	+3.65%	14
Estonia	11,196,918,553	-2.09%	74
Finland	64,946,075,266	-4.22%	38
France	693,522,513,262	-1.85%	4
Gabon	3,780,342,008	+7.5%	105
United Kingdom	652,109,524,029	+12.4%	5
Georgia	7,706,204,376	+0.157%	85
Ghana	8,246,545,828	+3.42%	82
Guinea	7,899,968,986	+50.5%	84
Gambia	977,561,341	+49.5%	122
Guinea-Bissau	443,704,074	+12.6%	123
Equatorial Guinea	1,143,278,272	-0.997%	120
Greece	46,744,404,871	+14.7%	46
Guatemala	18,885,780,349	+9.83%	63
Hong Kong SAR China	64,099,398,334	+4.23%	39
Honduras	8,343,209,291	+9.46%	81
Croatia	21,747,654,494	+10.7%	58
Haiti	2,507,120,060	-8.83%	114
Hungary	52,653,124,566	-5%	43
Indonesia	438,387,324,293	+5.37%	10
India	1,275,962,564,854	+5.11%	2
Ireland	100,540,763,114	-30.8%	29
Iran	174,807,142,782	+11.3%	18
Iraq	82,181,561,649	+26.9%	35
Iceland	8,890,111,513	+10.8%	79
Israel	126,593,278,774	-5.34%	23
Italy	530,397,602,245	+0.382%	7
Kenya	20,897,192,859	+18.3%	60
Cambodia	14,919,123,975	+5.84%	69
Libya	6,919,569,687	-6.9%	91
Sri Lanka	26,679,452,150	+29.6%	53
Lithuania	17,323,870,659	-1.01%	65
Luxembourg	14,122,496,628	-7.22%	70
Latvia	9,204,884,973	-12%	78
Macao SAR China	7,254,974,007	+2.52%	87
Morocco	46,197,988,630	+11%	47
Moldova	3,845,955,631	+14.6%	104
Madagascar	3,938,159,103	+24.6%	103
Mexico	447,632,471,063	+4.24%	9
North Macedonia	4,742,492,804	+1.52%	98
Mali	5,544,527,037	+0.954%	95
Malta	4,573,445,462	+3.81%	99
Montenegro	2,302,596,263	+10.8%	115
Mongolia	8,171,156,579	+18.6%	83
Mozambique	5,409,126,259	+29.8%	96
Mauritius	3,168,351,389	+13%	112
Malaysia	92,211,804,974	+2.67%	33
Namibia	3,425,125,875	-1.46%	110
Niger	3,649,705,545	+0.792%	109
Nicaragua	4,863,242,560	+23.3%	97
Netherlands	236,768,562,778	+2.76%	17
Norway	116,168,561,458	-0.827%	26
Nepal	13,045,676,220	+2.03%	71
Pakistan	48,260,540,405	+1.85%	45
Peru	56,166,045,516	+9.67%	41
Philippines	109,332,466,585	+6.88%	28
Poland	161,486,320,656	+12.5%	19
Puerto Rico	18,599,200,000	+9.21%	64
Portugal	61,997,752,048	+4.51%	40
Paraguay	10,152,331,419	+16.8%	77
Palestinian Territories	3,218,000,000	-29.6%	111
Romania	93,012,199,896	+2.88%	31
Russia	571,693,958,658	+2.19%	6
Rwanda	3,693,191,920	+9.26%	107
Saudi Arabia	372,881,333,333	+4.79%	12
Sudan	1,433,914,068	+65.4%	119
Senegal	10,612,045,718	-17.5%	76
Singapore	121,521,246,669	+13.2%	24
Sierra Leone	2,226,282,571	+76.1%	116
El Salvador	7,194,730,000	+2.74%	88
Somalia	2,750,440,440	+12.1%	113
Serbia	22,773,597,763	+13.7%	57
Slovakia	28,460,640,247	+7.3%	52
Slovenia	15,333,620,842	-0.231%	67
Sweden	150,266,051,434	+3.23%	21
Seychelles	372,099,665	-17.3%	126
Chad	3,672,564,776	+4.35%	108
Togo	2,212,641,880	+5.1%	117
Thailand	113,558,890,016	-2%	27
Tunisia	7,132,384,812	+90.4%	89
Turkey	338,008,576,373	+0.875%	15
Tanzania	31,326,473,002	-3.37%	51
Uganda	11,968,878,074	+7.56%	73
Ukraine	35,559,948,655	+8.59%	49
Uruguay	12,627,301,858	-7.67%	72
United States	6,345,913,000,000	+6.27%	1
Uzbekistan	38,279,643,948	+7.74%	48
Samoa	350,162,228	+0.658%	127
Kosovo	3,767,886,673	+6.03%	106
South Africa	55,805,163,094	-5.66%	42
Zimbabwe	1,974,196,498	-65.6%	118

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.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.GDI.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 capital formation (current US$)

Year: 2024

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