Country

Indicator

Value type

Values present

Portfolio equity, net inflows (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	0	-100%	73
Angola	0		73
Albania	-4,191,680	-172%	83
Andorra	48,885,189	-204%	36
Argentina	-90,116,137	-67.7%	96
Armenia	801,437	-75.3%	66
Antigua & Barbuda	-1,503,128	-20%	81
Australia	-19,126,986,334	-141%	120
Austria	-1,189,046,396	-44.4%	107
Azerbaijan	-1,197,000	-126%	77
Burundi	8,605,166	+22.8%	49
Belgium	342,091,630	-171%	26
Burkina Faso	34,424,750	+7.98%	39
Bangladesh	-134,026,399	-20.8%	97
Bulgaria	-49,420,000	-303%	94
Bahrain	6,119,414,894	+334%	10
Bahamas	0		73
Bosnia & Herzegovina	-1,459,129	-83.1%	80
Belarus	-422,337	+55%	76
Belize	0		73
Bolivia	14,063,732	-850%	45
Brazil	799,496,038	-93%	21
Bhutan	0		73
Botswana	40,077,911	+30.1%	38
Canada	-35,807,204,430	+289%	123
Switzerland	9,358,357,384	-143%	7
Chile	-1,416,208,506	-118%	108
China	7,446,111,759	-80.6%	8
Cameroon	0		73
Congo - Kinshasa	433,220	-34.9%	68
Colombia	19,923,914	-104%	43
Cape Verde	0		73
Costa Rica	172,006,756	+32.9%	31
Curaçao	-2,058,468	-434%	82
Cyprus	-1,005,470,548	-171%	105
Czechia	991,665,706	-822%	20
Germany	-16,226,619,550	+51.9%	119
Djibouti	-20,015,361	+1,778,475%	90
Dominica	0		73
Denmark	-7,003,044,354	+17.3%	116
Dominican Republic	0		73
Ecuador	5,635,304	+200%	53
Egypt	-252,900,000	-81.7%	101
Spain	-10,624,491,399	-609%	117
Estonia	201,583,310	-70.8%	30
Finland	-3,832,303,220	-223%	112
France	-11,874,613,153	-40.8%	118
United Kingdom	-21,096,643,281	-44.5%	121
Georgia	2,496,093	+9.19%	59
Ghana	-9,279,759	-56.6%	88
Guinea	3,120,000	+97.7%	58
Greece	1,649,013,632	+2,188%	14
Grenada	0		73
Guatemala	0		73
Guyana	90,259,580	+26.9%	33
Hong Kong SAR China	-1,036,863,279	-120%	106
Honduras	0		73
Croatia	-20,217,534	-38%	91
Hungary	277,853,894	-117%	28
Indonesia	264,695,146	-60.5%	29
India	21,427,093,557	-229%	5
Ireland	165,860,971,208	+63.2%	1
Iraq	-6,100,000	-6,200%	84
Iceland	-75,796,157	-310%	95
Israel	-213,800,000	-130%	100
Italy	3,893,302,522	-131%	12
Jamaica	4,091,929	-113%	57
Jordan	-42,253,521	-55.8%	93
Japan	24,251,033,610	-393%	4
Kazakhstan	313,986,051	+149%	27
Kenya	-152,219,253	-26.5%	99
Cambodia	0		73
St. Kitts & Nevis	233,719	-52.5%	70
South Korea	11,622,500,000	-328%	6
Kuwait	-374,807,343	-131%	103
Laos	-175,607	-230%	74
Lebanon	76,066,304	+384%	34
St. Lucia	626,056	+14.5%	67
Sri Lanka	8,089,240	-94.6%	50
Lesotho	188,059	-9.87%	71
Lithuania	-8,304,808	-33.8%	86
Luxembourg	97,125,155,424	-388%	3
Latvia	-8,724,514	-79.8%	87
Macao SAR China	5,582,764	-374%	54
Morocco	139,238,056	-48.8%	32
Moldova	-1,280,000	-612%	78
Maldives	6,103,595	-206%	52
Mexico	-5,162,478,344	+10.8%	114
North Macedonia	-375,669	-117%	75
Mali	2,468,438	-209%	60
Malta	1,329,107,826	-396%	17
Montenegro	9,624,565	-7,524%	47
Mongolia	12,734,931	+437%	46
Mozambique	0		73
Mauritius	3,008,138,824	-388%	13
Malaysia	484,310,361	-88.4%	23
Namibia	1,829,647	-24%	63
Niger	1,631,898	-83.3%	64
Nigeria	1,588,279,928	+441%	15
Nicaragua	0		73
Netherlands	-26,389,946,053	+1,576%	122
Norway	-3,057,763,384	-180%	111
Nepal	0		73
Nauru	0		73
New Zealand	1,135,906,722	-21.3%	18
Oman	-964,629,389	-435%	104
Pakistan	69,000,000	-174%	35
Panama	0		73
Peru	-23,612,452	-78.5%	92
Philippines	396,304,534	-181%	25
Papua New Guinea	0		73
Poland	706,000,000	-157%	22
Portugal	1,112,055,200	+1,814%	19
Paraguay	0		73
Palestinian Territories	-141,871,822	+168%	98
Qatar	483,516,484	-89.9%	24
Romania	-276,785,586	-8.28%	102
Russia	-2,427,182,696	-85.3%	110
Rwanda	857,395	+10%	65
Saudi Arabia	4,223,413,333	-60.9%	11
Senegal	8,652,160	-43.1%	48
Singapore	7,025,196,733	-18.1%	9
Solomon Islands	0		73
Sierra Leone	0		73
El Salvador	0		73
Serbia	6,474,047	-324%	51
Suriname	0		73
Slovakia	0		73
Slovenia	-7,760,162	-78.1%	85
Sweden	-2,380,163,752	-88.2%	109
Sint Maarten	358,135	-109%	69
Seychelles	15,888,223	-55.1%	44
Thailand	-5,657,548,885	-188%	115
Tajikistan	-1,298,310	-698%	79
Timor-Leste	0		73
Tonga	0		73
Trinidad & Tobago	0		73
Tunisia	20,803,638	-233%	42
Turkey	1,387,000,000	-134%	16
Tanzania	5,500,000	0%	55
Uganda	4,800,000	+3%	56
Ukraine	2,000,000	-118%	61
Uruguay	41,250,618	-61.1%	37
United States	133,015,000,000	+5,046%	2
Uzbekistan	23,154,516	-9.46%	40
St. Vincent & Grenadines	4,770	-93.9%	72
Samoa	1,948,623	+15.4%	62
Kosovo	0		73
South Africa	-4,382,755,472	-526%	113
Zambia	-18,966,705	+26,556%	89
Zimbabwe	22,800,000	-75.7%	41

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 = 'BX.PEF.TOTL.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.PEF.TOTL.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))

Compare

Portfolio equity, net inflows (BoP, current US$)

Year: 2023

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