Country

Indicator

Value type

Values present

Personal remittances, received (current US$)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	14,106,590	+16.9%	105
Albania	2,274,440,594	+11.7%	47
Argentina	1,044,565,331	+3.45%	60
Armenia	1,181,642,798	-18.6%	58
Antigua & Barbuda	26,278,876	-24.7%	102
Australia	1,883,610,723	+9.04%	50
Austria	3,591,781,613	+10.1%	39
Azerbaijan	1,351,678,000	-29.3%	56
Belgium	15,208,330,615	+2.34%	13
Bangladesh	27,122,365,385	+22.3%	7
Bulgaria	2,672,220,000	+16.9%	45
Bahamas	65,929,000	+5.19%	97
Bosnia & Herzegovina	3,124,315,488	+10.7%	42
Belarus	1,382,627,658	+9.68%	55
Belize	154,149,603	+3.06%	89
Brazil	4,902,400,448	+10.6%	32
Bhutan	109,604,204	+1.54%	92
Canada	851,437,898	+0.501%	67
Switzerland	3,501,719,973	+2.04%	41
Chile	100,140,400	+3.17%	93
China	31,410,598,200	+6.53%	6
Colombia	11,873,232,976	+17.4%	16
Cape Verde	336,131,379	+6.24%	79
Costa Rica	724,796,055	+9.47%	70
Czechia	4,247,984,188	-0.652%	37
Germany	21,225,006,284	+0.969%	10
Djibouti	55,997,202	-4.43%	99
Dominica	38,629,993	+12%	100
Denmark	1,525,855,426	+6.69%	53
Dominican Republic	11,247,000,000	+5.91%	17
Ecuador	6,544,355,901	+20%	28
Spain	6,265,012,172	+17.6%	29
Estonia	496,257,592	+1.85%	76
Finland	748,914,875	+0.087%	68
France	36,860,723,169	-0.131%	4
United Kingdom	4,833,962,372	+7.75%	33
Georgia	3,999,181,897	-4.81%	38
Gambia	528,765,281	+2.69%	74
Greece	561,447,512	+3.6%	72
Grenada	70,237,558	-0.0245%	96
Guatemala	21,649,327,000	+8.35%	8
Hong Kong SAR China	462,446,995	+1.91%	77
Honduras	9,532,724,045	+6.29%	20
Croatia	6,739,238,332	+10.9%	27
Hungary	5,237,273,919	+1.9%	31
Indonesia	16,038,366,972	+10.9%	12
India	137,674,533,896	+15.2%	1
Iceland	240,772,257	+10.5%	84
Israel	952,100,000	-2.99%	62
Italy	11,151,789,911	-7.86%	18
Jamaica	3,564,377,716	-1.01%	40
Japan	4,644,199,356	-1.44%	35
Kazakhstan	240,391,763	-20.9%	85
Cambodia	2,827,917,512	+1.65%	43
St. Kitts & Nevis	36,437,261	-2.03%	101
South Korea	7,449,400,000	+5.89%	25
Kuwait	20,390,350	-4.2%	103
St. Lucia	64,958,539	+2.64%	98
Lesotho	499,297,840	+3.08%	75
Lithuania	1,053,040,838	+7.82%	59
Luxembourg	2,430,239,294	-2.18%	46
Latvia	1,335,826,281	+8.19%	57
Moldova	1,917,700,000	-4.7%	48
Maldives	5,652,307	+5%	106
Mexico	67,637,913,797	+2.11%	2
North Macedonia	458,150,266	-0.932%	78
Malta	15,404,229	+0.209%	104
Montenegro	855,216,059	+6.24%	65
Mozambique	266,914,842	+4.82%	82
Malaysia	1,605,279,752	-5.82%	52
Namibia	142,746,932	+14%	90
Nigeria	21,292,957,333	+8.92%	9
Nicaragua	5,245,600,000	+12.5%	30
Netherlands	4,358,133,100	+7.32%	36
Norway	621,067,525	+3.62%	71
Nepal	14,185,362,324	+36.4%	14
Pakistan	34,914,000,000	+31.5%	5
Panama	531,591,167	+3.09%	73
Philippines	40,279,405,932	+3.02%	3
Poland	8,688,000,000	+1.72%	22
Portugal	1,804,893,649	+5.91%	51
Paraguay	878,191,146	+17.8%	63
Palestinian Territories	735,607,752	-77.3%	69
Qatar	1,443,131,868	+0.421%	54
Romania	9,523,551,276	-4.43%	21
Russia	1,907,830,000	-27.1%	49
Saudi Arabia	321,870,198	+3.05%	80
Solomon Islands	95,178,838	+12.6%	94
El Salvador	8,488,402,967	+2.43%	23
Suriname	160,306,202	+9.13%	88
Slovakia	2,741,066,434	+0.725%	44
Slovenia	852,252,884	-7.82%	66
Sweden	4,701,879,680	+15.5%	34
Thailand	9,584,457,475	-1.11%	19
Tajikistan	6,801,890,973	+46.8%	26
Timor-Leste	219,567,997	+13.8%	86
Tonga	256,868,087	+1.03%	83
Trinidad & Tobago	199,168,367	-1.57%	87
Turkey	982,000,000	-4.57%	61
Ukraine	11,969,000,000	-20%	15
Uruguay	136,080,579	+1.09%	91
United States	7,870,000,000	+1.75%	24
Uzbekistan	16,579,258,955	+17%	11
St. Vincent & Grenadines	94,443,874	+3.06%	95
Samoa	282,254,313	+6.54%	81
South Africa	855,374,671	+6.47%	64

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.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 <- 'BX.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, received (current US$)

Year: 2024

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