Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	1,142,726,312	-46.9%	22
Albania	-1,453,695,575	+7.03%	68
Argentina	-8,741,212,671	-58.2%	100
Armenia	-75,466,085	-85.7%	38
Antigua & Barbuda	-252,758,909	-18.3%	52
Australia	-39,560,149,067	+110%	111
Austria	1,503,040,780	-72.4%	20
Azerbaijan	511,163,000	-68.5%	24
Belgium	29,497,212,208	-379%	7
Bangladesh	-1,508,392,908	+8.74%	69
Bulgaria	-2,466,340,000	-42.7%	78
Bahrain	-2,427,659,574	-60.3%	76
Bahamas	-77,610,341	-20.1%	39
Bosnia & Herzegovina	-1,048,410,395	+12.2%	62
Belarus	-1,578,705,129	-18.1%	70
Belize	-125,269,203	+809%	41
Brazil	-46,751,213,310	+25.4%	112
Brunei	-29,063,019	-152%	32
Bhutan	-22,778,262	+91.2%	31
Canada	27,783,990,499	-40.5%	8
Switzerland	77,315,997,225	-29.8%	3
Chile	-8,929,504,285	-7.1%	102
China	153,687,573,951	-11.8%	2
Colombia	-9,623,205,709	-38%	103
Cape Verde	-87,202,866	-42.4%	40
Costa Rica	-4,275,037,259	+15.5%	88
Cyprus	-5,579,767,747	-54.4%	92
Czechia	-1,872,787,885	-45.6%	72
Germany	32,564,762,200	+25.9%	6
Djibouti	-67,819,948	-50.5%	34
Dominica	-36,377,088	-22.9%	33
Denmark	13,097,627,142	+110%	12
Dominican Republic	-4,523,200,000	+3.03%	89
Ecuador	-232,114,172	-51.1%	49
Spain	19,921,319,178	-708%	11
Estonia	-210,021,997	-93.3%	46
Finland	3,584,762,780	-591%	18
France	-8,476,049,387	-128%	99
United Kingdom	64,801,535,028	+295%	4
Georgia	-905,907,582	-43.9%	60
Gambia	-232,357,064	+13%	50
Greece	-4,660,779,369	+594%	90
Grenada	-223,011,683	+0.54%	47
Guatemala	-1,003,001,490	+0.704%	61
Hong Kong SAR China	-38,925,262,501	+50.9%	110
Honduras	-620,111,972	-27.6%	56
Croatia	-1,877,761,691	-4.91%	73
Hungary	-15,584,385,847	+584%	107
Indonesia	-14,851,819,437	+3.01%	106
India	-3,840,512,082	-73%	86
Iceland	-69,048,385	-94.7%	35
Israel	-6,330,800,000	-23%	96
Italy	12,295,104,563	-207%	13
Jamaica	-163,531,495	-57%	43
Japan	191,046,455,351	+9.01%	1
Kazakhstan	-1,209,811,102	-56.5%	65
Cambodia	-4,222,818,310	+10.9%	87
St. Kitts & Nevis	-19,466,205	-38.9%	30
South Korea	33,363,000,000	+154%	5
Kuwait	9,708,694,798	+6.85%	14
St. Lucia	-187,496,461	+22.2%	44
Lesotho	12,572,108	-51%	28
Lithuania	-3,439,469,848	+141%	82
Luxembourg	25,520,673,997	-62%	10
Latvia	-1,062,847,222	+58.6%	63
Moldova	-243,792,854	-28.6%	51
Maldives	-806,204,044	+5.13%	59
Mexico	-31,136,136,421	+4.17%	109
North Macedonia	-1,175,312,723	+123%	64
Malta	-11,612,796,582	+980%	105
Montenegro	-531,703,988	+13.5%	54
Mozambique	-3,552,735,848	+41.6%	84
Malaysia	-2,554,721,616	-5,552%	80
Namibia	-2,019,872,268	-22.4%	74
Nigeria	-672,356,993	-58.4%	58
Nicaragua	-1,278,500,000	+19.3%	67
Netherlands	26,294,799,663	-212%	9
Norway	-7,080,352,865	+104%	98
Nepal	-72,719,828	-2.81%	36
New Zealand	-1,637,357,196	-57.2%	71
Pakistan	-2,484,000,000	+23.2%	79
Panama	-2,375,883,191	+71.4%	75
Peru	-5,625,860,695	+96.5%	93
Philippines	-6,058,204,033	+12.2%	94
Poland	-10,692,000,000	-45.3%	104
Portugal	-6,472,214,696	+11.7%	97
Paraguay	-335,122,088	+3.46%	53
Palestinian Territories	-151,487,835	+50.9%	42
Qatar	1,102,747,253	+290%	23
Romania	-6,206,049,640	-9.8%	95
Russia	8,344,750,000	-59.8%	16
Saudi Arabia	6,308,211,620	-216%	17
Singapore	-96,683,787,762	+3.35%	113
Solomon Islands	19,910,801	-127%	26
El Salvador	-636,071,996	-7.55%	57
Suriname	26,504,991	-58.2%	25
Slovakia	-1,272,768,800	+1,347%	66
Slovenia	-551,347,152	-10.9%	55
Sweden	8,957,021,406	-60.1%	15
Thailand	-2,429,233,515	-134%	77
Tajikistan	-190,288,018	+89.3%	45
Timor-Leste	-227,705,779	+73.2%	48
Tonga	13,333,126	-452%	27
Trinidad & Tobago	1,255,394,919	-39.8%	21
Turkey	-4,661,000,000	-0.703%	91
Ukraine	-3,491,000,000	-21.4%	83
Uruguay	2,377,725,683	-184%	19
United States	-8,918,000,000	-108%	101
Uzbekistan	-2,799,430,876	+30.5%	81
St. Vincent & Grenadines	-74,046,902	-0.429%	37
Vietnam	-19,570,000,000	-2.39%	108
Samoa	-1,691,134	-29.4%	29
South Africa	-3,755,876,264	-39.9%	85

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 = 'BN.KLT.DINV.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 <- 'BN.KLT.DINV.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

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

Year: 2024

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