Country

Indicator

Value type

Values present

Taxes on goods and services (% value added of industry and services)

Source: worldbank.org, 19.12.2024

Year: 2021

Country	Value	Value change, %	Rank
Albania	19.9	+9.31%	5
United Arab Emirates	0.54	-21.5%	117
Argentina	7.38	+3.19%	87
Armenia	15.6	+7.36%	20
Australia	5.75	+6.06%	99
Austria	13.3	+2.44%	35
Azerbaijan	8.36	-16.6%	79
Burundi	17.9	+1.45%	10
Belgium	11.5	+5.86%	52
Burkina Faso	11.1	+19.9%	57
Bangladesh	4.91	+10.8%	103
Bulgaria	17.2	-1.07%	13
Bahamas	11.2	-24.5%	54
Bosnia & Herzegovina	20.3	+2.73%	3
Belarus	10.9	+0.0154%	59
Brazil	6.82	+13.3%	90
Botswana	5.96	+14.9%	96
Central African Republic	6.74	-18.2%	91
Switzerland	5.31	-1.31%	101
Chile	13.1	+15.3%	37
China	5.03	-4.19%	102
Côte d’Ivoire	7.01	+3.19%	89
Cameroon	8.14	+8.2%	81
Congo - Kinshasa	4.67	+16.2%	105
Congo - Brazzaville	3.31	-16.7%	111
Colombia	8.76	+7.56%	73
Costa Rica	8.53	+13%	76
Cyprus	13.5	+2.02%	32
Czechia	9.37	-3.58%	69
Germany	6.68	+9.56%	92
Denmark	15.3	-6.05%	22
Dominican Republic	9.55	+14.9%	64
Ecuador	7.68	+1.53%	85
Spain	9.52	+6.53%	66
Estonia	15.3	+1.99%	23
Ethiopia	3.52	-22%	110
Finland	17	-0.796%	14
Fiji	11.1	-16.3%	58
France	10.4	-9.4%	62
Gabon	2.3	+7.92%	114
United Kingdom	12.9	+3.2%	40
Georgia	16.6	+6.09%	15
Ghana	7.27	+20.2%	88
Equatorial Guinea	0.946	-38.6%	116
Greece	18.6	+3.6%	8
Guatemala	7.93	+14.3%	82
Croatia	23.5	+3.04%	1
Hungary	18.3	-2.38%	9
Indonesia	5.33	+8.45%	100
Ireland	6.54	+3.23%	93
Iceland	13.2	+0.996%	36
Israel	12.5	+7.18%	43
Italy	11.2	+5.93%	55
Kazakhstan	3.93	-7.9%	108
Kenya	8.74	-0.814%	74
Kyrgyzstan	13.7	+32.1%	30
Cambodia	8.65	-13.9%	75
Kiribati	16.3	-2.98%	16
South Korea	6.34	+2.71%	95
Laos	10	+8.27%	63
Lebanon	2.54	-14.9%	113
Sri Lanka	4.25	+0.296%	106
Lesotho	18.7	-8.61%	7
Lithuania	13.5	+3.01%	31
Luxembourg	12.4	+4.67%	46
Latvia	23.5	-1.27%	2
Macao SAR China	15.6	-8.91%	19
Morocco	14.1	+6.78%	26
Moldova	18.9	+6.19%	6
Madagascar	8.5	+10.2%	77
Maldives	13.7	+31.2%	29
Mexico	6.41	-4.83%	94
North Macedonia	15	+10.9%	24
Malta	10.7	-4.01%	60
Mongolia	12.5	+17.5%	44
Mozambique	16	+22.6%	17
Mauritius	14	-14.4%	27
Malawi	7.68	-1.56%	84
Malaysia	3.27	-5.04%	112
Namibia	8.98	+27.7%	71
Nicaragua	13.5	+12.1%	33
Netherlands	12.4	+0.0739%	47
Norway	12.1	-16%	48
Nepal	12.8	+22.1%	41
New Zealand	11.6	+6.44%	51
Panama	3.95	-3.62%	107
Peru	9.12	+19.8%	70
Philippines	4.88	+1.44%	104
Papua New Guinea	5.83	+9.93%	98
Poland	15.7	+8.72%	18
Portugal	14.6	+3.98%	25
Paraguay	7.57	+2.72%	86
Palestinian Territories	13.3	-5.78%	34
Romania	11.8	+4.73%	49
Russia	9.54	-1.33%	65
Saudi Arabia	8.4	+31.4%	78
Senegal	12.4	-2.48%	45
Singapore	3.78	-2.14%	109
El Salvador	13.1	+10.5%	38
San Marino	8.77	+11.5%	72
Serbia	17.7	-12.2%	11
Slovakia	13	-0.45%	39
Slovenia	15.4	+3.66%	21
Sweden	14	-1.52%	28
Eswatini	7.75	-1.6%	83
Togo	11.2	+5.99%	56
Thailand	9.5	-0.492%	67
Tajikistan	11.7	+7.62%	50
Timor-Leste	2.18	-35.9%	115
Turkey	12.6	-6.48%	42
Tanzania	8.26	+0.641%	80
Uganda	9.43	+10.2%	68
Ukraine	17.5	+2.59%	12
United States	0.402	-6.59%	118
Uzbekistan	10.4	-6.75%	61
Samoa	20.2	-1.74%	4
South Africa	11.3	+7.46%	53
Zambia	5.95	-9.88%	97

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 = 'GC.TAX.GSRV.VA.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 <- 'GC.TAX.GSRV.VA.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

Taxes on goods and services (% value added of industry and services)

Year: 2021

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