Country

Indicator

Value type

Values present

Services, value added (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	39.3	-1.06%	142
Albania	48.9	+1.93%	108
Andorra	77.6	-0.922%	4
Argentina	53.4	+0.389%	93
Armenia	61.5	+3.42%	53
Australia	65.5	+3.1%	30
Austria	65.3	+3.26%	33
Azerbaijan	42.3	+6.42%	135
Belgium	72.1	+1.42%	16
Benin	48.9	+2.43%	109
Burkina Faso	40.2	-7.79%	141
Bangladesh	51.4	+0.596%	99
Bulgaria	62.6	+0.125%	47
Bahamas	77.2	-1.75%	5
Bosnia & Herzegovina	58	+3.73%	73
Belarus	49.7	+1.79%	105
Brazil	59.3	+0.229%	64
Brunei	38.7	-0.344%	143
Botswana	63.5	+7.26%	41
Central African Republic	40.5	-0.1%	140
Switzerland	72	-0.185%	17
Chile	56.1	-2.65%	81
China	56.7	+0.728%	79
Côte d’Ivoire	53.9	+2.21%	91
Cameroon	49.9	-0.556%	103
Congo - Kinshasa	33	+1.36%	151
Congo - Brazzaville	45	+11.3%	125
Colombia	58.2	+2.56%	71
Comoros	50.1	-1%	102
Cape Verde	69.4	-0.376%	20
Costa Rica	68.8	+1.16%	23
Cyprus	76.9	+0.349%	6
Czechia	59.5	-0.291%	63
Germany	63.9	+1.2%	39
Djibouti	75.5	-1.57%	9
Dominica	56.9	-2.36%	77
Denmark	64	-0.599%	38
Dominican Republic	59.8	+0.371%	60
Ecuador	57.2	-2.5%	76
Egypt	48.9	-5.26%	107
Spain	69.1	+0.569%	21
Estonia	65.1	+0.833%	36
Ethiopia	37.6	+1.61%	145
Finland	62.9	+1.85%	44
Fiji	56.2	+1.98%	80
France	70.4	+1.06%	18
Gabon	37.5	+1.63%	146
United Kingdom	72.8	+0.403%	12
Georgia	62.8	+1.19%	45
Ghana	43.9	+1.73%	129
Guinea	37.5	+0.738%	147
Gambia	53.9	+0.554%	90
Guinea-Bissau	42.1	-8.2%	136
Equatorial Guinea	51.1	+1.43%	100
Greece	68	-0.939%	24
Grenada	65.2	+0.96%	35
Guatemala	61.8	+0.711%	52
Guyana	15.3	-18.2%	154
Honduras	58.4	+1.45%	68
Croatia	59.7	+1.13%	61
Haiti	48.3	+1.61%	111
Hungary	59.7	+2.7%	62
Indonesia	43.8	+2.08%	130
India	49.9	+0.698%	104
Ireland	61.8	+1.58%	51
Iran	47.9	-0.667%	113
Iraq	45.8	+4.8%	121
Iceland	65.5	+0.596%	31
Israel	72.5	+0.54%	14
Italy	65.6	+1.17%	29
Jamaica	60.3	+0.202%	57
Jordan	60.4	-0.634%	56
Kazakhstan	58.2	+3.31%	70
Kenya	55.9	+0.951%	82
Kyrgyzstan	52.1	+1.64%	95
Cambodia	35.6	-1.54%	149
St. Kitts & Nevis	65.5	+0.333%	32
Kuwait	55.9	+7.26%	83
Laos	43.5	-1.05%	131
Liberia	42.1	+20.1%	137
Libya	34.3	+35.1%	150
St. Lucia	75.9	-0.462%	8
Sri Lanka	57.5	-3.96%	75
Lesotho	48	-5.03%	112
Lithuania	63.6	+0.79%	40
Luxembourg	81.9	+1.11%	1
Latvia	63.1	+1.38%	43
Morocco	54.1	-0.24%	89
Moldova	62.3	+1.22%	49
Madagascar	46.4	-4.21%	119
Maldives	73.8	+4.55%	10
Mexico	58.2	+0.902%	72
North Macedonia	59.2	+3.43%	65
Mali	36.7	+1.47%	148
Malta	80.8	-0.398%	2
Myanmar (Burma)	41.4	+4.2%	139
Montenegro	62.1	-0.18%	50
Mongolia	44.2	+7.62%	128
Mozambique	38.4	-6%	144
Mauritania	43.2	-1.5%	132
Mauritius	64.4	-0.882%	37
Malawi	44.9	-5.52%	126
Malaysia	53.6	+0.306%	92
Namibia	54.5	+2.19%	88
Niger	45.4	-2.44%	123
Nigeria	47	+10%	116
Nicaragua	46.8	+0.71%	117
Netherlands	70.3	+1%	19
Norway	51.8	+3.6%	97
Nepal	55.2	-0.276%	85
Oman	46.5	+2.63%	118
Pakistan	50.5	-0.432%	101
Panama	68.8	+2.25%	22
Peru	52.7	+2.97%	94
Philippines	63.2	+1.32%	42
Papua New Guinea	41.5	-1.35%	138
Poland	59.9	+2.99%	59
Puerto Rico	51.5	-1.13%	98
Portugal	66.4	-0.44%	25
Paraguay	48.7	+1.26%	110
Qatar	45.9	+1.39%	120
Romania	62.5	+2.86%	48
Russia	57.5	+1.39%	74
Rwanda	47.6	+4.02%	114
Saudi Arabia	47.2	+5.37%	115
Sudan	54.9	+32.6%	87
Senegal	49.1	-0.98%	106
Singapore	73	+0.465%	11
Sierra Leone	44.8	+6.23%	127
El Salvador	61	+0.452%	54
Serbia	58.5	+1.67%	67
São Tomé & Príncipe	76.6	+0.99%	7
Slovakia	60	-0.222%	58
Slovenia	58.2	+0.823%	69
Sweden	65.9	+1.02%	27
Seychelles	65.8	-2.31%	28
Turks & Caicos Islands	72.6	-1.3%	13
Chad	31.6	+1.18%	152
Togo	52	+0.462%	96
Thailand	59.2	+1.19%	66
Turkey	56.8	+4.98%	78
Tanzania	28.4	-0.908%	153
Uganda	43.1	+1.29%	133
Ukraine	60.6	-0.974%	55
Uruguay	65.3	-0.593%	34
United States	79.7	+0.679%	3
Uzbekistan	45.2	+2.82%	124
St. Vincent & Grenadines	66.4	+0.0245%	26
Vietnam	42.4	+0.155%	134
Samoa	72.5	+7.02%	15
Kosovo	45.7	-1.79%	122
South Africa	62.7	+0.0962%	46
Zambia	55.1	-2.95%	86
Zimbabwe	55.8	-10.7%	84

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 = 'NV.SRV.TOTL.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 <- 'NV.SRV.TOTL.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

Services, value added (% of GDP)

Year: 2024

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