Country

Indicator

Value type

Values present

Net secondary income (Net current transfers from abroad) (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	-248,628,061	-45.3%	100
Albania	1,407,979,810	+8.83%	45
Andorra	-50,490,853	+6.68%	97
Argentina	2,150,921,489	+14.5%	37
Armenia	410,669,376	+42.1%	71
Antigua & Barbuda	-101,900,000	+18.9%	98
Australia	-1,041,747,251	-0.141%	105
Azerbaijan	631,744,000	-49.9%	62
Bangladesh	686,697,845	-19.4%	58
Bulgaria	1,074,001,900	-33.2%	50
Bosnia & Herzegovina	2,973,600,603	+7.63%	33
Belarus	1,179,500,000	+2.19%	47
Belize	162,090,750	+26.1%	81
Brazil	2,637,325,594	+17%	35
Barbados	122,000,000	-6,200%	85
Brunei	-632,697,811	-15.3%	103
Canada	-2,689,578,072	-54.3%	109
Chile	375,808,249	-55.9%	74
China	14,840,616,893	+29.2%	9
Côte d’Ivoire	-239,137,667	-5.26%	99
Cameroon	818,840,357	-8.16%	54
Congo - Kinshasa	1,346,036,696	-40%	46
Colombia	15,536,144,997	+20.5%	8
Cape Verde	468,247,199	+11.2%	67
Costa Rica	589,060,445	+0.596%	64
Germany	-64,029,299,009	+7.35%	119
Dominica	7,111,111	+38.1%	93
Denmark	-5,216,636,006	+10.5%	112
Dominican Republic	10,137,100,057	+4.73%	14
Ecuador	5,920,705,700	+24.2%	23
Egypt	21,949,200,000	+0.495%	6
Fiji	657,256,730	+17.8%	59
Micronesia (Federated States of)	162,800,000	+4.4%	80
Georgia	3,291,125,820	-1.2%	31
Guinea	955,151,683	+65.7%	51
Gambia	570,000,000	+2.9%	66
Guinea-Bissau	152,381,108	+12.5%	83
Grenada	-6,300,000	-25.9%	94
Guatemala	22,503,260,975	+7.89%	5
Guyana	892,250,000	-23.8%	53
Hong Kong SAR China	-2,584,371,039	+18.2%	108
Honduras	10,057,100,177	+7.71%	15
Croatia	1,861,427,924	-23.9%	38
Haiti	3,771,000,000	+7.08%	29
Indonesia	5,977,190,000	+11.1%	22
India	113,305,685,189	+6.21%	1
Iraq	357,400,000	-50.9%	75
Israel	8,632,735,186	-17.4%	17
Italy	-17,765,110,634	+0.121%	117
Jordan	5,831,690,141	+8.93%	24
Kazakhstan	-574,104,682	-44.6%	101
Cambodia	3,161,873,166	-0.37%	32
Kiribati	102,945,971	+23.6%	86
St. Kitts & Nevis	-18,300,000	+18.1%	95
Kuwait	-14,094,596,114	+9.05%	116
Laos	390,440,758	+24.1%	73
Liberia	413,000,000	-6.14%	70
St. Lucia	34,000,000	+0.592%	91
Sri Lanka	6,430,211,705	+10.5%	20
Morocco	13,572,504,302	+3.3%	10
Moldova	1,626,198,586	-10.4%	42
Madagascar	1,097,895,254	+21.1%	48
Maldives	-589,112,227	+8.94%	102
Mexico	67,136,498,206	+6.76%	2
Marshall Islands	91,650,058	+17.2%	87
North Macedonia	2,691,119,986	-7.74%	34
Mali	940,949,757	-24.8%	52
Myanmar (Burma)	1,408,000,000	-14.6%	44
Montenegro	467,100,405	-1.46%	68
Mongolia	447,304,250	+12.2%	69
Mozambique	1,087,954,427	-16.5%	49
Mauritania	335,306,641	+12.3%	76
Mauritius	-813,384,991	+42.7%	104
Malawi	582,383,963	+4.76%	65
Malaysia	-1,942,692,410	-25.1%	106
Namibia	1,697,218,853	+20.7%	41
Nigeria	24,040,999,994	+8.66%	4
Nicaragua	5,109,099,999	+12.5%	27
Nepal	11,810,759,775	+12.8%	12
Pakistan	32,201,000,000	+13.6%	3
Peru	7,193,176,617	+5.57%	19
Papua New Guinea	290,337,966	+201%	77
Poland	-3,955,252,020	+79.6%	111
Portugal	5,439,963,057	+5.97%	26
Paraguay	809,611,005	+19.7%	55
Qatar	-10,189,010,989	-27.3%	114
Romania	1,491,817,458	-38%	43
Russia	-3,053,836,997	-67.1%	110
Rwanda	802,346,747	-9.7%	56
Saudi Arabia	-55,737,200,000	+8.44%	118
Singapore	-7,571,583,698	+11.2%	113
Solomon Islands	146,258,567	+10.5%	84
Sierra Leone	408,400,008	-13.8%	72
El Salvador	8,391,950,000	+2.64%	18
Somalia	6,381,000,000	+13.1%	21
Serbia	5,711,178,730	-6.36%	25
São Tomé & Príncipe	82,700,000	+145%	88
Suriname	153,166,411	+10.3%	82
Slovakia	633,609,291	+10%	61
Sweden	-10,595,462,921	+15.9%	115
Eswatini	729,833,451	+22.5%	57
Seychelles	-21,172,663	+9.12%	96
Togo	635,858,241	-7.85%	60
Thailand	9,239,152,788	-6.92%	16
Tajikistan	1,720,519,780	-23.8%	40
Trinidad & Tobago	29,700,000	-75.2%	92
Tunisia	3,420,249,733	+10.8%	30
Turkey	72,000,000	-87.1%	89
Tanzania	606,718,003	-6.1%	63
Uganda	1,819,869,042	-11.8%	39
Ukraine	21,832,000,000	-6.08%	7
Uruguay	202,751,321	+14.5%	79
Uzbekistan	10,578,217,988	+20.1%	13
St. Vincent & Grenadines	64,807,407	+6.1%	90
Vietnam	13,029,000,000	-0.199%	11
Samoa	282,071,594	+1.8%	78
Kosovo	2,166,276,490	-1.84%	36
South Africa	-2,513,495,594	+16.2%	107
Zimbabwe	4,436,620,651	+49.7%	28

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 = 'NY.TRF.NCTR.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 <- 'NY.TRF.NCTR.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

Net secondary income (Net current transfers from abroad) (current US$)

Year: 2024

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