Country

Indicator

Value type

Values present

Personal transfers, receipts (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	13,980,811	+15.9%	88
Albania	1,555,198,485	+9.64%	35
Argentina	957,224,032	+3.44%	40
Armenia	691,329,426	-18.2%	46
Antigua & Barbuda	24,777,139	-26%	85
Austria	341,557,974	+12.8%	57
Azerbaijan	1,082,873,000	-34.6%	38
Belgium	1,815,288,527	+18.8%	34
Bangladesh	26,986,712,975	+22.3%	5
Bulgaria	1,535,920,000	-2.08%	36
Bahamas	65,929,000	+5.19%	81
Bosnia & Herzegovina	2,305,110,134	+10.1%	32
Belarus	399,421,599	+3.36%	53
Belize	151,792,093	+3.11%	69
Brazil	4,247,170,184	+6.27%	23
Bhutan	106,356,869	-0.158%	74
China	6,425,401,341	-6.76%	18
Colombia	11,848,222,292	+17.4%	10
Cape Verde	318,093,901	+6.11%	59
Costa Rica	650,206,707	+10.4%	47
Cyprus	562,962,262	+3.89%	48
Czechia	803,000,600	+10%	43
Germany	16,226,185	+0.278%	87
Dominica	36,237,037	+12.6%	84
Dominican Republic	10,756,000,000	+5.9%	12
Ecuador	6,539,835,109	+20.1%	17
Estonia	65,296,746	-0.202%	82
Finland	82,250,767	+0.087%	77
France	350,648,005	+0.087%	55
United Kingdom	2,552,793,718	+3.64%	30
Georgia	3,132,191,568	-0.361%	26
Gambia	520,646,127	+3.37%	49
Greece	309,977,848	-1.96%	60
Grenada	68,715,442	-0.0823%	80
Guatemala	21,452,862,460	+8.23%	6
Honduras	9,510,224,045	+6.3%	13
Croatia	3,109,324,378	+9.51%	27
Hungary	701,477,717	+9.3%	45
Indonesia	15,701,686,400	+10.4%	8
India	129,430,244,556	+15.3%	1
Iceland	80,330,954	+18.4%	78
Italy	2,512,297,741	+4.96%	31
Jamaica	3,357,945,830	-0.361%	25
Japan	4,369,056,576	-2.52%	22
Kazakhstan	236,511,763	-21.1%	62
Cambodia	2,704,043,463	+1.72%	29
St. Kitts & Nevis	21,179,333	-4.06%	86
South Korea	6,689,600,000	+6.49%	16
St. Lucia	63,678,543	+1.7%	83
Lesotho	6,569,491	-0.82%	89
Lithuania	931,737,256	+3.2%	41
Luxembourg	153,801,331	-39.2%	67
Latvia	763,326,525	+10.3%	44
Moldova	1,029,780,000	-8.27%	39
Mexico	64,746,381,615	+2.25%	2
North Macedonia	357,536,886	-2.72%	54
Montenegro	445,132,034	+5.56%	52
Mozambique	120,012,846	+11.5%	72
Namibia	117,304,129	+13.1%	73
Nigeria	20,977,029,788	+8.84%	7
Nicaragua	5,243,100,000	+12.5%	20
Netherlands	139,512,231	+10.3%	70
Nepal	14,109,947,134	+35.7%	9
Pakistan	34,662,000,000	+31.5%	3
Panama	467,577,274	+1.92%	51
Peru	4,933,771,110	+11%	21
Philippines	30,805,836,219	+3.35%	4
Poland	2,952,000,000	-10.3%	28
Paraguay	878,191,146	+17.8%	42
Palestinian Territories	152,563,038	0%	68
Qatar	1,443,131,868	+0.421%	37
Romania	5,292,720,720	+6.47%	19
Solomon Islands	77,766,328	+24.6%	79
El Salvador	8,461,154,438	+2.41%	15
Suriname	160,306,202	+9.13%	66
Slovakia	329,217,849	+0.725%	58
Slovenia	101,315,629	+0.087%	75
Sweden	343,834,067	+0.0721%	56
Thailand	8,777,476,137	-0.181%	14
Tajikistan	2,018,801,241	+46.8%	33
Timor-Leste	211,557,653	+12.5%	63
Tonga	191,882,614	-3.67%	64
Trinidad & Tobago	189,366,841	-1.89%	65
Turkey	473,000,000	-16.7%	50
Ukraine	4,200,000,000	+9.35%	24
Uruguay	132,280,231	+1.13%	71
Uzbekistan	11,426,911,864	+20.3%	11
St. Vincent & Grenadines	93,277,384	+3%	76
Samoa	270,387,890	+6.72%	61

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.TRF.PWKR.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 <- 'BX.TRF.PWKR.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

Personal transfers, receipts (BoP, current US$)

Year: 2024

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