Country

Indicator

Value type

Values present

Personal remittances, received (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	0.0175	+23.4%	102
Albania	8.37	-3.22%	22
Argentina	0.165	+5.55%	87
Armenia	4.58	-24%	33
Antigua & Barbuda	1.18	-32.1%	57
Australia	0.108	+7.54%	92
Austria	0.689	+8%	68
Azerbaijan	1.82	-31.1%	52
Belgium	2.29	-0.731%	47
Bangladesh	6.03	+18.9%	27
Bulgaria	2.38	+6.65%	45
Bahamas	0.416	+1.46%	74
Bosnia & Herzegovina	11	+7.72%	16
Belarus	1.82	+4.65%	51
Belize	4.38	-10.1%	34
Brazil	0.225	+11.2%	81
Canada	0.038	-2.54%	98
Switzerland	0.374	-2.55%	76
Chile	0.0303	+4.81%	99
China	0.168	+3.84%	86
Colombia	2.84	+2.76%	40
Cape Verde	12.1	-2.52%	12
Costa Rica	0.76	-0.695%	65
Czechia	1.23	-1.18%	55
Germany	0.455	-1.94%	73
Djibouti	1.37	-8.39%	53
Dominica	5.61	+7.23%	28
Denmark	0.355	+1.13%	78
Dominican Republic	9.05	+2.65%	20
Ecuador	5.25	+16.6%	31
Spain	0.364	+10.6%	77
Estonia	1.16	-1.66%	60
Finland	0.25	-1.54%	80
France	1.17	-3.61%	59
United Kingdom	0.133	-0.355%	88
Georgia	11.8	-13.3%	13
Gambia	21.1	-1.87%	8
Greece	0.218	-1.9%	82
Grenada	5.05	-4.87%	32
Guatemala	19.1	-0.103%	9
Hong Kong SAR China	0.114	-4.61%	91
Honduras	25.7	-1.55%	5
Croatia	7.28	+1.17%	24
Hungary	2.35	-2.16%	46
Indonesia	1.15	+8.87%	61
India	3.52	+7.11%	36
Iceland	0.72	+3.89%	67
Israel	0.176	-8.05%	84
Italy	0.47	-10.5%	72
Jamaica	17.9	-3.53%	10
Japan	0.115	+3.13%	90
Kazakhstan	0.0834	-28.1%	94
Cambodia	6.1	-7.15%	26
St. Kitts & Nevis	3.42	-2.94%	37
Kuwait	0.0127	-1.11%	103
St. Lucia	2.55	-2.15%	43
Lesotho	22	-3.89%	7
Lithuania	1.24	+1.36%	54
Luxembourg	2.61	-8.09%	42
Latvia	3.07	+5.83%	39
Moldova	10.5	-12.5%	18
Maldives	0.081	-0.784%	95
Mexico	3.65	-1.13%	35
North Macedonia	2.75	-6.4%	41
Malta	0.0633	-8.49%	97
Montenegro	10.6	-0.853%	17
Mozambique	1.19	-2.02%	56
Malaysia	0.38	-10.8%	75
Namibia	1.07	+5.82%	62
Nigeria	11.3	+111%	15
Nicaragua	26.6	+1.72%	3
Netherlands	0.355	+0.919%	79
Norway	0.128	+3.45%	89
Nepal	33.1	+30.5%	2
Pakistan	9.36	+19.1%	19
Panama	0.616	-0.431%	70
Philippines	8.73	-2.46%	21
Poland	0.95	-9.65%	63
Portugal	0.585	-0.601%	71
Paraguay	1.98	+14.2%	48
Palestinian Territories	5.37	-70.5%	30
Qatar	0.662	-1.87%	69
Romania	2.49	-12.4%	44
Russia	0.0878	-30.5%	93
Saudi Arabia	0.026	+1.47%	101
Solomon Islands	5.41	+6.18%	29
El Salvador	24	-1.94%	6
Suriname	3.4	-20%	38
Slovakia	1.93	-4.87%	49
Slovenia	1.18	-12.1%	58
Sweden	0.771	+10.8%	64
Thailand	1.82	-3.08%	50
Tajikistan	47.9	+26.5%	1
Timor-Leste	11.7	+25.8%	14
Trinidad & Tobago	0.754	-5.06%	66
Turkey	0.0742	-19.4%	96
Ukraine	6.27	-24%	25
Uruguay	0.168	-2.61%	85
United States	0.027	-3.36%	100
Uzbekistan	14.4	+4.47%	11
St. Vincent & Grenadines	8.16	-4.51%	23
Samoa	26.4	-6.41%	4
South Africa	0.214	+1.27%	83

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

Personal remittances, received (% of GDP)

Year: 2024

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