Country

Indicator

Value type

Values present

Foreign direct investment, net outflows (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	0.0411	+4.64%	81
Albania	0.959	-14.1%	38
Argentina	0.425	-7.33%	61
Armenia	0.245	+9.35%	67
Antigua & Barbuda	0.803	-12.4%	46
Australia	0.894	+13.6%	41
Austria	2.07	+26.7%	15
Azerbaijan	0.999	-61.4%	37
Belgium	-0.914	-55.9%	97
Bangladesh	-0.000459	-2.62%	86
Bulgaria	0.922	-26%	39
Bahrain	0.576	-76.1%	57
Bahamas	1.03	-30.3%	35
Bosnia & Herzegovina	0.278	-23.9%	66
Belarus	0.195	+117%	72
Belize	0.0743	+1%	79
Brazil	1.12	-2.78%	30
Canada	3.76	-7.9%	8
Switzerland	-3.71	-256%	98
Chile	1.09	-58.4%	32
China	0.919	-25.6%	40
Colombia	1.1	+218%	31
Cape Verde	0.739	+151%	47
Costa Rica	1.05	-7.86%	33
Czechia	3.24	+31.6%	9
Germany	1.72	-24.6%	22
Dominica	0.0608	-0.000157%	80
Denmark	7.26	+173%	4
Dominican Republic	-0.0381	-113%	89
Spain	3.05	+24.4%	11
Estonia	-8.67	-262%	99
Finland	1.86	-612%	20
France	1.49	+15.3%	24
United Kingdom	1.87	+109%	18
Georgia	1.37	-10.9%	27
Greece	0.697	-58.1%	51
Grenada	0.195	-243%	71
Guatemala	0.729	+16.3%	48
Hong Kong SAR China	19.2	-24%	2
Honduras	1.86	+178%	19
Croatia	3.01	+86%	12
Hungary	-21.7	-36.5%	100
Indonesia	0.664	+27.9%	53
India	0.607	+59.3%	55
Iceland	1.36	+26.2%	28
Israel	1.94	+25.5%	16
Italy	1.44	+8.24%	26
Jamaica	0.00536	-127%	85
Japan	5.17	+11.5%	7
Kazakhstan	-0.656	-157%	96
Cambodia	0.371	+3.65%	63
St. Kitts & Nevis	0.225	-345%	69
Kuwait	6.44	-4.87%	5
St. Lucia	-0.319	-74.2%	93
Lithuania	0.184	-93.9%	74
Latvia	0.511	-78.7%	58
Moldova	0.492	+427%	60
Mexico	0.687	+1,532%	52
North Macedonia	-0.0179	-102%	87
Malta	127	+17.3%	1
Montenegro	0.84	+10.4%	43
Mozambique	-0.197	-124%	90
Malaysia	3.09	+55.1%	10
Namibia	0.306	-112%	64
Nigeria	0.217	+209%	70
Nicaragua	0.375	+58.5%	62
Netherlands	1.04	-104%	34
Norway	-0.613	-129%	95
New Zealand	0.58	-740%	56
Pakistan	0.0225	+138%	83
Panama	1	-16.6%	36
Philippines	0.622	-22.9%	54
Poland	0.849	-53.3%	42
Portugal	2.17	+11.6%	14
Paraguay	0.146	-75.1%	76
Palestinian Territories	0.078	+340%	78
Qatar	0.717	-898%	49
Romania	0.299	-42.6%	65
Russia	0.00776	-98.5%	84
Saudi Arabia	1.78	+25.2%	21
Singapore	10.1	+31%	3
Solomon Islands	3	+677%	13
El Salvador	0.814	-711%	44
Suriname	-0.234	-178%	92
Slovakia	1.63	-625%	23
Slovenia	1.93	+61.1%	17
Sweden	5.84	-15.4%	6
Thailand	1.48	-44.1%	25
Tajikistan	0.711	+118%	50
Timor-Leste	0.239	-107%	68
Trinidad & Tobago	0.813	-61%	45
Turkey	0.498	-6.52%	59
Ukraine	0.16	+125%	75
Uruguay	-0.201	-98.1%	91
United States	1.3	-20.7%	29
Uzbekistan	0.0319	+179%	82
St. Vincent & Grenadines	-0.024	-28.4%	88
Vietnam	0.126	-135%	77
Samoa	0.192	-421,901%	73
South Africa	-0.333	-55%	94

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 = 'BM.KLT.DINV.WD.GD.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 <- 'BM.KLT.DINV.WD.GD.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

Foreign direct investment, net outflows (% of GDP)

Year: 2024

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