Country

Indicator

Value type

Values present

Personal remittances, paid (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	419,534,482	-3.37%	63
Albania	205,314,881	+15.2%	76
Argentina	569,441,326	+9.61%	55
Armenia	526,486,132	-1.76%	58
Antigua & Barbuda	48,796,083	-13.8%	94
Australia	12,010,389,798	+16.4%	15
Austria	8,936,099,582	+9.93%	21
Azerbaijan	663,698,000	-1.64%	47
Belgium	13,213,597,803	+24.5%	10
Bangladesh	168,366,925	+13.2%	80
Bulgaria	422,870,000	+143%	62
Bahrain	2,659,574,468	-0.14%	31
Bahamas	320,921,379	+8.55%	68
Bosnia & Herzegovina	164,775,652	+20.4%	81
Belarus	228,697,861	+101%	74
Belize	47,224,551	+16%	96
Brazil	2,090,043,730	-8.59%	32
Bhutan	74,536,653	-4.2%	90
Canada	13,119,419,447	-3.81%	11
Switzerland	37,794,498,530	+2.04%	3
Chile	690,690,250	+1.32%	45
China	20,018,921,503	-1.13%	5
Colombia	406,846,514	-6.11%	64
Cape Verde	36,312,591	+21.8%	97
Costa Rica	647,344,642	+20.3%	48
Czechia	5,114,391,975	-5.88%	25
Germany	23,662,387,575	+5.64%	4
Djibouti	974,595	-74.6%	108
Dominica	10,009,164	-17.9%	104
Denmark	4,766,577,059	+10.4%	26
Dominican Republic	1,172,500,000	+10.5%	41
Ecuador	633,389,383	-10.4%	49
Spain	588,213,669	+4.42%	52
Estonia	264,609,461	-10.4%	72
Finland	1,553,352,318	+2.58%	36
France	19,671,612,649	-0.833%	6
United Kingdom	12,271,592,596	+6.06%	13
Georgia	312,756,323	-3.61%	70
Gambia	9,775,890	-72%	105
Greece	3,166,102,430	+6.13%	29
Grenada	54,711,707	-0.32%	92
Guatemala	34,242,900	+21%	98
Hong Kong SAR China	1,082,018,168	+11.9%	42
Honduras	346,096,593	+2.03%	67
Croatia	1,966,100,773	+52.4%	33
Hungary	1,568,292,261	+0.42%	35
Indonesia	11,128,256,369	+13.8%	17
India	12,072,300,700	-2.32%	14
Iceland	563,373,014	+25%	57
Israel	1,363,800,000	-50%	38
Italy	12,361,405,831	+1.1%	12
Jamaica	312,990,456	-9.14%	69
Japan	6,071,789,021	+2.3%	24
Kazakhstan	2,756,431,624	-1.86%	30
Cambodia	375,772,691	-12.6%	65
St. Kitts & Nevis	27,070,703	+6.84%	100
South Korea	10,228,700,000	+5.39%	19
Kuwait	14,168,984,683	+11.7%	9
St. Lucia	14,446,123	+5.04%	103
Lesotho	3,043,551	+0.729%	107
Lithuania	676,678,041	+24.1%	46
Luxembourg	17,633,955,042	+2.26%	8
Latvia	576,778,155	+20.7%	54
Moldova	512,440,000	+13.6%	60
Maldives	622,562,741	+5.13%	50
Mexico	1,308,403,827	+21.6%	39
North Macedonia	24,147,739	-0.308%	101
Malta	225,205,954	+12%	75
Montenegro	196,265,022	+35.7%	78
Mozambique	196,407,528	-1.33%	77
Malaysia	10,842,907,209	+12.4%	18
Namibia	111,060,171	+33.7%	86
Nigeria	92,301,298	+9.76%	87
Nicaragua	145,700,000	+9.55%	83
Netherlands	17,967,697,560	+5.45%	7
Norway	4,635,615,468	+1.96%	27
Nepal	63,404,725	+11.9%	91
New Zealand	1,185,375,790	+14.6%	40
Pakistan	352,000,000	+9.32%	66
Panama	596,169,824	+11.3%	51
Philippines	268,879,630	-13.5%	71
Poland	9,216,000,000	-19.8%	20
Portugal	445,337,389	+20.8%	61
Paraguay	79,507,130	-2.28%	89
Palestinian Territories	9,330,391	0%	106
Qatar	11,512,087,912	-2.22%	16
Romania	1,788,285,431	+27%	34
Russia	8,270,710,000	+11.4%	22
Saudi Arabia	46,564,898,867	+20.6%	2
Solomon Islands	51,778,698	-8.58%	93
El Salvador	256,055,247	-1.73%	73
Suriname	90,144,564	-7.26%	88
Slovakia	522,184,005	+0.175%	59
Slovenia	567,718,015	+8.32%	56
Sweden	4,034,440,424	-0.21%	28
Thailand	8,036,554,429	+13.5%	23
Tajikistan	586,128,284	+86.2%	53
Timor-Leste	118,507,877	-36.2%	85
Tonga	27,764,392	-5.11%	99
Trinidad & Tobago	150,105,444	+1.01%	82
Turkey	1,526,000,000	+53.5%	37
Ukraine	193,000,000	+105%	79
Uruguay	137,570,761	+1.69%	84
United States	98,418,000,000	+5.79%	1
Uzbekistan	736,979,579	+23.9%	44
St. Vincent & Grenadines	18,388,547	+7.76%	102
Samoa	47,900,675	+47.5%	95
South Africa	993,227,783	+6.52%	43

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.TRF.PWKR.CD.DT'

# 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.TRF.PWKR.CD.DT'

# 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 remittances, paid (current US$)

Year: 2024

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