Country

Indicator

Value type

Values present

Imports of goods, services and primary income (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	30,997,912,088	-5.71%	73
Albania	12,940,759,076	+12.4%	85
Argentina	98,923,797,191	-11.8%	47
Armenia	20,749,660,115	+29.6%	78
Antigua & Barbuda	1,416,191,038	+2.65%	106
Australia	530,674,445,261	+2.86%	19
Austria	333,138,182,779	-1.21%	28
Azerbaijan	32,667,281,000	+8.22%	72
Belgium	629,627,715,025	-2.3%	17
Bangladesh	80,055,105,884	+3.29%	51
Bulgaria	69,050,340,000	+0.255%	53
Bahrain	41,923,138,298	+5%	64
Bahamas	7,989,761,929	+10.6%	93
Bosnia & Herzegovina	17,596,407,988	+5.97%	81
Belarus	54,111,113,588	+5.23%	60
Belize	1,875,580,820	+9.99%	100
Brazil	487,706,709,635	+8.08%	21
Brunei	9,618,836,424	-2.24%	91
Bhutan	1,723,788,065	-11.7%	102
Canada	916,853,633,485	+2.32%	11
Switzerland	825,915,172,350	+3.36%	13
Chile	128,353,459,100	+0.983%	43
China	3,693,275,518,541	+3.85%	2
Colombia	101,696,145,239	+2.88%	44
Cape Verde	1,571,549,943	+4.96%	104
Costa Rica	39,901,385,927	+9.26%	65
Cyprus	68,643,728,917	+2.92%	54
Czechia	249,546,206,180	-0.544%	32
Germany	2,079,221,589,601	+0.246%	3
Djibouti	4,923,082,738	-9.77%	97
Dominica	395,392,592	-7.19%	113
Denmark	288,139,957,352	+4.44%	31
Dominican Republic	44,295,600,000	+7.22%	63
Ecuador	37,599,680,039	-2.41%	68
Spain	704,954,402,780	+4.51%	16
Estonia	36,655,104,400	+1.8%	69
Finland	154,288,537,176	-1.66%	39
France	1,425,685,948,895	+1.78%	5
United Kingdom	1,716,188,959,616	+1.83%	4
Georgia	22,888,250,048	+3.58%	75
Gambia	1,575,531,353	+12.7%	103
Greece	139,110,488,969	+3.39%	40
Grenada	1,155,712,604	+6.29%	107
Guatemala	39,636,629,440	+7.35%	67
Hong Kong SAR China	967,272,743,090	+7.75%	10
Honduras	21,627,972,273	+2.9%	76
Croatia	56,683,932,947	+7.48%	59
Hungary	192,313,725,300	-4.11%	34
Indonesia	325,327,306,051	+6.1%	29
India	1,026,045,621,699	+8.58%	9
Iceland	15,618,354,230	+9.87%	82
Israel	163,772,500,000	+0.96%	37
Italy	871,599,289,088	-1.25%	12
Jamaica	10,444,589,505	-2.48%	89
Japan	1,131,817,054,393	-1.52%	7
Kazakhstan	100,068,861,101	-3.04%	46
Cambodia	35,296,638,490	+14.4%	70
St. Kitts & Nevis	700,620,687	-3.36%	109
South Korea	803,427,900,000	+0.225%	14
Kuwait	68,009,503,086	-1.28%	56
St. Lucia	1,755,162,943	+13.9%	101
Lesotho	2,192,773,871	+0.579%	99
Lithuania	64,064,132,793	+1.06%	57
Luxembourg	561,572,002,657	+4.27%	18
Latvia	33,215,388,094	-1.82%	71
Moldova	11,395,400,000	+6.82%	87
Maldives	6,142,398,614	+5.98%	95
Mexico	773,014,872,155	+4.45%	15
North Macedonia	13,766,307,233	+0.0584%	83
Malta	48,383,100,216	+2.81%	61
Montenegro	6,018,981,276	+8.52%	96
Mozambique	13,409,983,076	+0.425%	84
Malaysia	313,386,341,751	+9.97%	30
Namibia	10,307,450,533	+9.34%	90
Nigeria	68,239,131,461	-13%	55
Nicaragua	12,803,500,000	+9.87%	86
Netherlands	1,305,241,673,790	-0.548%	6
Norway	213,935,082,238	+3.42%	33
Nepal	18,039,470,762	+28.1%	80
New Zealand	84,999,678,626	+1.25%	50
Pakistan	77,342,574,086	+17.4%	52
Panama	39,784,570,220	-9.48%	66
Peru	90,208,393,677	+7.87%	49
Philippines	173,931,632,493	+6.4%	36
Poland	497,877,000,000	+3.99%	20
Portugal	161,890,642,532	+2.18%	38
Paraguay	20,353,303,484	+2.77%	79
Palestinian Territories	8,355,361,322	-29%	92
Qatar	96,346,978,022	+0.964%	48
Romania	179,406,738,978	+4.21%	35
Russia	434,852,960,000	-1.34%	22
Saudi Arabia	351,012,272,357	+10.9%	27
Singapore	1,074,610,746,626	+8.97%	8
Solomon Islands	924,309,317	-0.0234%	108
El Salvador	21,059,021,983	+7.35%	77
Suriname	3,010,032,778	+17.1%	98
Slovakia	128,411,457,189	+0.834%	42
Slovenia	58,788,091,338	+2.62%	58
Sweden	379,439,453,583	+2.86%	26
Thailand	382,138,268,317	+7.38%	25
Tajikistan	7,378,891,142	+19%	94
Timor-Leste	1,542,253,280	+3.76%	105
Tonga	411,094,557	+3.55%	112
Trinidad & Tobago	11,081,176,719	+11.6%	88
Turkey	392,343,000,000	-3.37%	24
Ukraine	100,877,000,000	+4.64%	45
Uruguay	26,866,524,326	-0.522%	74
United States	5,552,042,000,000	+7.46%	1
Uzbekistan	48,156,759,424	+2.52%	62
St. Vincent & Grenadines	659,971,410	+10.4%	110
Vietnam	419,907,000,000	+14.5%	23
Samoa	621,474,813	+2.42%	111
South Africa	138,181,494,620	-1.59%	41

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.GSR.TOTL.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 <- 'BM.GSR.TOTL.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

Imports of goods, services and primary income (BoP, current US$)

Year: 2024

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