Foreign direct investment, net inflows (BoP, current US$)

Source: worldbank.org, 01.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola -135,788,883 -93.6% 101
Albania Albania 1,714,282,845 +5.76% 58
Argentina Argentina 11,430,969,650 -52.1% 29
Armenia Armenia 138,515,325 -76.1% 86
Antigua & Barbuda Antigua & Barbuda 270,634,323 -17.5% 76
Australia Australia 55,229,644,654 +70% 7
Austria Austria 9,305,312,183 +218% 32
Azerbaijan Azerbaijan 231,276,000 -8.53% 81
Belgium Belgium -35,568,962,017 +1,181% 109
Bangladesh Bangladesh 1,506,325,966 +8.75% 59
Bulgaria Bulgaria 3,500,390,000 -37.2% 46
Bahrain Bahrain 2,702,659,574 -62.6% 50
Bahamas Bahamas 240,551,037 -25.4% 77
Bosnia & Herzegovina Bosnia & Herzegovina 1,127,282,091 +8.9% 65
Belarus Belarus 1,726,513,429 -13.3% 57
Belize Belize 127,880,237 +697% 87
Brazil Brazil 71,069,846,845 +13.8% 4
Brunei Brunei 29,063,019 -152% 94
Bhutan Bhutan 22,778,262 +91.2% 95
Canada Canada 62,144,528,141 +47.7% 5
Switzerland Switzerland -112,057,950,069 +26.1% 110
Chile Chile 12,521,388,616 -31.9% 28
China China 18,556,141,173 -63.9% 17
Colombia Colombia 14,234,479,242 -15.2% 25
Cape Verde Cape Verde 107,665,271 -32.2% 88
Costa Rica Costa Rica 5,277,358,749 +12.6% 36
Czechia Czechia 13,051,379,573 +9.75% 27
Germany Germany 47,599,143,489 -38.5% 8
Djibouti Djibouti 67,819,948 -50.5% 91
Dominica Dominica 36,796,264 -22.7% 92
Denmark Denmark 18,099,950,524 +295% 19
Dominican Republic Dominican Republic 4,475,900,000 -5.78% 38
Ecuador Ecuador 232,114,172 -51.1% 80
Spain Spain 32,546,805,334 -24.2% 11
Estonia Estonia -3,499,236,298 -165% 105
Finland Finland 1,981,247,324 -681% 54
France France 55,436,761,139 +530% 6
United Kingdom United Kingdom 3,504,720,459 -74.6% 45
Georgia Georgia 1,367,720,692 -34.5% 60
Gambia Gambia 232,357,064 +13% 78
Greece Greece 6,453,966,685 +36.7% 35
Grenada Grenada 225,731,499 +2.6% 82
Guatemala Guatemala 1,828,129,500 +10.8% 56
Hong Kong SAR China Hong Kong SAR China 117,026,502,147 -4.07% 3
Honduras Honduras 1,309,045,847 +20.5% 62
Croatia Croatia 4,665,323,979 +39.6% 37
Hungary Hungary -32,786,402,181 -53.7% 108
Indonesia Indonesia 24,130,025,026 +12% 14
India India 27,609,339,739 -1.67% 12
Iceland Iceland 523,503,302 -67.9% 71
Israel Israel 16,808,500,000 +4.17% 21
Italy Italy 21,778,302,761 -48.2% 15
Jamaica Jamaica 164,600,000 -56.3% 84
Japan Japan 17,174,260,939 -14.9% 20
Kazakhstan Kazakhstan -681,517,542 -112% 102
Cambodia Cambodia 4,394,647,334 +11% 39
St. Kitts & Nevis St. Kitts & Nevis 21,865,650 -29.3% 96
South Korea South Korea 15,225,800,000 -20% 24
Kuwait Kuwait 614,580,246 -70.9% 69
St. Lucia St. Lucia 179,352,899 +45.3% 83
Lesotho Lesotho -12,520,535 -51.2% 99
Lithuania Lithuania 3,595,625,406 -6.15% 42
Latvia Latvia 1,287,374,738 -23.8% 63
Moldova Moldova 333,342,854 -6.68% 74
Maldives Maldives 806,204,044 +5.13% 68
Mexico Mexico 43,856,854,622 +43.1% 9
North Macedonia North Macedonia 1,172,324,293 +82.7% 64
Malta Malta 42,521,601,949 +69.1% 10
Montenegro Montenegro 598,128,701 +13.7% 70
Mozambique Mozambique 3,508,626,212 +30.7% 44
Malaysia Malaysia 15,593,246,087 +96.9% 23
Namibia Namibia 2,060,784,122 -10.2% 53
Nigeria Nigeria 1,080,310,701 -42.3% 66
Nicaragua Nicaragua 1,352,300,000 +21.4% 61
Netherlands Netherlands -13,501,813,799 -95.6% 107
Norway Norway 4,113,646,139 -70.2% 40
Nepal Nepal 72,719,828 -2.81% 90
New Zealand New Zealand 3,147,488,248 -12.4% 48
Pakistan Pakistan 2,568,000,000 +25.4% 51
Panama Panama 3,240,411,694 +35.7% 47
Philippines Philippines 8,929,837,510 +0.0528% 33
Poland Poland 18,456,000,000 -46.2% 18
Portugal Portugal 13,182,630,098 +15.2% 26
Paraguay Paraguay 399,928,219 -30.6% 73
Palestinian Territories Palestinian Territories 162,176,714 +56.6% 85
Qatar Qatar 460,164,835 -197% 72
Romania Romania 7,351,846,707 -15.6% 34
Russia Russia -8,175,980,000 -18.6% 106
Saudi Arabia Saudi Arabia 15,737,488,788 -31% 22
Singapore Singapore 151,941,202,884 +14.7% 2
Solomon Islands Solomon Islands 32,966,761 -58.2% 93
El Salvador El Salvador 923,880,785 +43.7% 67
Suriname Suriname -37,555,190 -29.3% 100
Slovakia Slovakia 3,581,368,042 -1,193% 43
Slovenia Slovenia 1,951,667,370 +34.8% 55
Sweden Sweden 26,690,532,879 +48.1% 13
Thailand Thailand 10,230,221,359 +57% 31
Tajikistan Tajikistan 291,312,573 +107% 75
Timor-Leste Timor-Leste 232,205,779 +266% 79
Tonga Tonga -12,079,633 -345% 98
Trinidad & Tobago Trinidad & Tobago -1,040,628,200 -33.1% 103
Turkey Turkey 11,257,000,000 +5.63% 30
Ukraine Ukraine 3,796,000,000 -17% 41
Uruguay Uruguay -2,540,494,767 -52.7% 104
United States United States 387,990,000,000 +11.2% 1
Uzbekistan Uzbekistan 2,836,124,398 +31.5% 49
St. Vincent & Grenadines St. Vincent & Grenadines 73,769,595 -0.322% 89
Vietnam Vietnam 20,170,000,000 +9.03% 16
Samoa Samoa 3,741,016 +56.1% 97
South Africa South Africa 2,424,808,366 -29.6% 52 

                    
# 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 = 'BX.KLT.DINV.CD.WD'

# 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 <- 'BX.KLT.DINV.CD.WD'

# 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))