Country

Indicator

Value type

Values present

Service imports (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	8,492,283,242	-1.29%	63
Albania	3,828,555,761	+10.1%	76
Argentina	22,598,388,496	+0.319%	44
Armenia	3,847,153,472	+14.2%	75
Antigua & Barbuda	556,628,452	+5.11%	100
Australia	108,665,989,204	+7.28%	19
Austria	87,983,668,325	+4.25%	23
Azerbaijan	10,171,724,000	+18%	61
Belgium	158,993,405,816	-0.325%	14
Bangladesh	11,312,832,001	+2.72%	57
Bulgaria	8,204,780,000	+4.59%	65
Bahrain	12,373,936,170	+2.58%	56
Bahamas	2,472,835,226	+12.5%	85
Bosnia & Herzegovina	1,301,107,447	+15.7%	94
Belarus	6,481,623,115	+9.97%	67
Belize	362,314,446	+17.7%	103
Brazil	103,035,975,174	+16.2%	20
Brunei	1,746,204,731	+6.36%	91
Bhutan	227,833,100	-29.5%	108
Canada	160,061,443,575	+4.95%	13
Switzerland	214,617,321,506	+11.6%	9
Chile	21,106,235,969	+0.901%	46
China	612,978,060,796	+14.1%	2
Colombia	18,386,361,196	+8.16%	49
Cape Verde	299,810,294	+7.72%	105
Costa Rica	7,293,406,182	+14.5%	66
Cyprus	22,003,639,923	+11%	45
Czechia	38,017,590,524	+6.79%	34
Germany	551,800,989,819	+5.85%	3
Djibouti	729,630,114	-7.32%	98
Dominica	156,781,881	+3.35%	112
Denmark	121,197,813,816	+7.15%	17
Dominican Republic	6,336,500,000	+12.4%	69
Ecuador	6,083,957,026	-0.97%	71
Spain	111,558,518,699	+16.4%	18
Estonia	10,341,904,457	+4.91%	60
Finland	46,726,254,263	+2.31%	32
France	340,436,201,988	+2.7%	6
United Kingdom	401,774,108,284	+10.5%	4
Georgia	3,822,908,766	+5.64%	77
Gambia	160,904,229	+7.13%	111
Greece	31,213,224,333	+6.04%	38
Grenada	428,763,994	+9.92%	101
Guatemala	6,444,602,000	+14.3%	68
Hong Kong SAR China	90,279,136,204	+14.3%	22
Honduras	3,657,967,552	+2.02%	78
Croatia	8,348,970,061	+17.6%	64
Hungary	27,394,914,797	+1.6%	42
Indonesia	57,511,875,417	+12.1%	29
India	196,677,323,400	+10.2%	10
Iceland	5,002,202,594	+7.04%	74
Israel	43,908,500,000	-6.32%	33
Italy	162,797,965,156	+6.98%	11
Jamaica	3,457,421,854	-0.197%	79
Japan	246,310,848,083	+5.78%	8
Kazakhstan	13,049,384,357	+5.97%	55
Cambodia	3,081,773,248	+7.38%	81
St. Kitts & Nevis	238,860,228	-18.7%	107
South Korea	162,654,300,000	+6.67%	12
Kuwait	28,077,485,650	-7.65%	40
St. Lucia	597,609,900	+10.8%	99
Lesotho	414,996,556	+0.293%	102
Lithuania	14,682,632,251	+9.56%	53
Luxembourg	130,659,206,436	+2.72%	15
Latvia	5,914,082,602	+2.75%	72
Moldova	1,784,930,000	+15.5%	90
Maldives	1,885,374,975	+11.7%	89
Mexico	71,056,765,961	-5.98%	27
North Macedonia	2,009,290,851	-3.21%	88
Malta	17,199,789,582	+9.87%	51
Montenegro	1,190,414,521	+2.46%	95
Mozambique	2,112,555,385	+5.61%	87
Malaysia	56,370,777,860	+8%	30
Namibia	2,428,923,918	+8.59%	86
Nigeria	17,925,790,455	+1.41%	50
Nicaragua	1,306,000,000	+14.7%	93
Netherlands	268,041,519,257	+5.84%	7
Norway	64,210,804,465	+6.95%	28
Nepal	2,813,995,722	+37.2%	82
New Zealand	19,467,312,375	+5.56%	48
Pakistan	11,168,574,086	+14.8%	58
Panama	5,746,170,486	-2.72%	73
Peru	15,068,566,343	+9.05%	52
Philippines	37,398,270,378	+24%	35
Poland	75,176,000,000	+13.6%	25
Portugal	27,707,438,395	+6.9%	41
Paraguay	2,541,572,752	+1.46%	84
Palestinian Territories	1,390,509,522	-30.2%	92
Qatar	37,085,164,835	-13.2%	36
Romania	30,648,607,722	+7.42%	39
Russia	81,326,120,000	+6.21%	24
Saudi Arabia	101,664,647,256	+3.83%	21
Singapore	351,122,433,707	+7.62%	5
Solomon Islands	248,415,612	-2.26%	106
El Salvador	3,258,974,319	+22.5%	80
Suriname	920,532,199	+45.8%	97
Slovakia	13,063,999,667	+5.84%	54
Slovenia	9,590,558,749	+6.6%	62
Sweden	124,964,928,857	+9.35%	16
Thailand	73,643,648,418	+12.2%	26
Tajikistan	970,651,649	+29.7%	96
Timor-Leste	357,150,156	-9.9%	104
Tonga	161,065,222	+7.47%	110
Trinidad & Tobago	2,684,547,237	+3.14%	83
Turkey	53,257,000,000	+7.98%	31
Ukraine	22,734,000,000	-10.3%	43
Uruguay	6,205,519,842	-3.19%	70
United States	812,204,000,000	+8.56%	1
Uzbekistan	10,464,185,997	+28.1%	59
St. Vincent & Grenadines	196,315,273	+5.25%	109
Vietnam	36,183,000,000	+24.4%	37
Samoa	128,030,310	-2.63%	113
South Africa	19,761,492,503	+6.32%	47

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.NFSV.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.NFSV.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

Service imports (BoP, current US$)

Year: 2024

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