Country

Indicator

Value type

Values present

Gross capital formation (% of GDP)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	25	+5.14%	41
Argentina	15.7	-18.8%	111
Armenia	22.2	+4.12%	68
Australia	24.4	+1.74%	45
Austria	22.3	-12.2%	65
Azerbaijan	21.1	+18.8%	80
Belgium	24	-5.27%	49
Benin	35.1	+1.54%	5
Burkina Faso	27.1	+20.2%	26
Bangladesh	30.7	-0.81%	17
Bulgaria	20.4	+3.06%	87
Bahamas	26.8	+17.2%	31
Bosnia & Herzegovina	30.8	+16.9%	16
Belarus	25.8	+4.55%	37
Bermuda	11.3	+0.754%	124
Brazil	16.9	+7.29%	106
Brunei	28.4	-4.27%	25
Botswana	36.2	+19.5%	4
Central African Republic	15.4	-35.1%	114
Canada	23.3	-2.62%	56
Switzerland	27	+3.85%	28
Chile	23.2	-0.524%	57
Côte d’Ivoire	24.5	-6.21%	44
Cameroon	21.4	+9.54%	75
Congo - Kinshasa	33.4	+4.5%	8
Congo - Brazzaville	26.8	+6.48%	30
Colombia	17.1	+3.94%	105
Comoros	11.7	-9.84%	123
Cape Verde	16	-17.5%	109
Costa Rica	15.7	+2.7%	112
Cyprus	18.9	-8.23%	93
Czechia	26	-7.05%	35
Germany	21	-2.88%	81
Djibouti	-3.78	+16.6%	130
Denmark	21.6	-5.22%	73
Dominican Republic	27	-2.94%	27
Ecuador	18.5	-10.5%	97
Egypt	13	-21.2%	121
Spain	20.5	-2.52%	85
Estonia	26.2	-5.46%	34
Ethiopia	20.5	-7.54%	84
Finland	21.7	-5.78%	72
France	21.9	-5.27%	69
Gabon	18.1	+3.32%	99
United Kingdom	17.9	+3.94%	100
Georgia	22.8	-8.73%	59
Ghana	9.96	+0.573%	125
Guinea	31.2	+33.1%	15
Gambia	39	+42.9%	3
Guinea-Bissau	20.9	+10.4%	82
Equatorial Guinea	8.96	-4.32%	127
Greece	18.2	+8.65%	98
Guatemala	16.7	+1.26%	108
Hong Kong SAR China	15.7	-2.44%	110
Honduras	22.5	+1.38%	62
Croatia	23.5	+0.956%	53
Haiti	9.94	-28.3%	126
Hungary	23.6	-8.79%	52
Indonesia	31.4	+3.47%	14
India	32.6	-2.25%	12
Ireland	17.4	-33.9%	103
Iran	40	+3.03%	1
Iraq	29.4	+22%	22
Iceland	26.6	+4.16%	32
Israel	23.4	-10.3%	55
Italy	22.4	-2.5%	63
Kenya	16.8	+2.62%	107
Cambodia	32.2	-3.33%	13
Libya	14.8	-9.97%	116
Sri Lanka	27	+9.66%	29
Lithuania	20.4	-6.94%	86
Luxembourg	15.2	-12.8%	115
Latvia	21.2	-13.9%	78
Macao SAR China	14.5	-6.42%	118
Morocco	29.9	+3.8%	20
Moldova	21.1	+5.21%	79
Madagascar	22.6	+13.6%	61
Mexico	24.2	+0.927%	47
North Macedonia	28.4	-4.09%	24
Mali	20.9	-6.51%	83
Malta	18.8	-5.2%	94
Montenegro	28.5	+3.4%	23
Mongolia	34.6	+2.19%	6
Mozambique	24.1	+21.4%	48
Mauritius	21.2	+6.57%	76
Malaysia	21.9	-2.75%	70
Namibia	25.6	-8.56%	38
Niger	18.7	-13.9%	95
Nicaragua	24.7	+11.5%	42
Netherlands	19.3	-3.37%	92
Norway	24	-0.986%	50
Nepal	30.4	-2.41%	18
Pakistan	12.9	-7.76%	122
Peru	19.4	+1.23%	91
Philippines	23.7	+1.19%	51
Poland	17.7	-0.0782%	102
Puerto Rico	14.8	+2.73%	117
Portugal	20.1	-1.92%	89
Paraguay	22.8	+13.3%	58
Palestinian Territories	23.5	-8.31%	54
Romania	24.3	-5.72%	46
Russia	26.3	-2.62%	33
Rwanda	25.9	+9.87%	36
Saudi Arabia	30.1	+3.18%	19
Sudan	2.87	+32.2%	129
Senegal	32.9	-21.6%	10
Singapore	22.2	+4.5%	67
Sierra Leone	29.5	+49.6%	21
El Salvador	20.3	-1.65%	88
Somalia	22.7	+1.5%	60
Serbia	25.6	+3.85%	39
Slovakia	20.1	+1.34%	90
Slovenia	21.2	-4.82%	77
Sweden	24.6	-0.937%	43
Seychelles	17.2	-16.5%	104
Chad	17.8	-3.33%	101
Togo	22.3	-2.89%	66
Thailand	21.6	-3.95%	74
Tunisia	13.4	+71.8%	120
Turkey	25.5	-14.8%	40
Tanzania	39.8	-3.02%	2
Uganda	22.3	-2.22%	64
Ukraine	18.6	+3.17%	96
Uruguay	15.6	-11.1%	113
United States	21.7	+0.941%	71
Uzbekistan	33.3	-3.81%	9
Samoa	32.8	-11.6%	11
Kosovo	33.8	-0.438%	7
South Africa	13.9	-10.3%	119
Zimbabwe	4.47	-72.5%	128

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.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.GDI.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

Gross capital formation (% of GDP)

Year: 2024

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