Country

Indicator

Value type

Values present

Tariff rate, applied, weighted mean, all products (%)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	8.85	-21.9%	34
Albania	0.26	-77%	109
Argentina	6.45	-0.922%	47
Australia	0.99	+22.2%	104
Austria	1.33	-4.32%	97
Azerbaijan	5.45	-9.32%	52
Burundi	8.96	+5.04%	32
Belgium	1.33	-4.32%	97
Benin	10.2	-6.7%	25
Burkina Faso	7.25	-0.685%	45
Bangladesh	10.6	-3.56%	24
Bulgaria	1.33	-4.32%	97
Bahrain	3.66	+75.1%	69
Bahamas	16.3	-4.16%	9
Bosnia & Herzegovina	4.04	+44.3%	64
Belize	18.1	+1.46%	5
Bermuda	29.5	+23.8%	1
Bolivia	5.08	-0.781%	56
Brazil	7.26	-6.44%	44
Barbados	14.6	+19.1%	10
Brunei	0.01	-50%	110
Bhutan	4.79	+43.8%	58
Botswana	3.23	+240%	73
Central African Republic	14.5	-11.9%	11
Canada	1.37	-41.7%	96
Switzerland	1.3	-7.14%	98
Chile	0.46	+6.98%	107
China	2.18	-5.63%	82
Côte d’Ivoire	7.25	-4.98%	45
Cameroon	18.1	+16.9%	4
Congo - Brazzaville	18.2	+57.4%	3
Colombia	2.83	+9.69%	77
Cape Verde	12.6	+26.1%	17
Costa Rica	1.3	-9.09%	98
Cuba	9.16	+2.81%	31
Cyprus	1.33	-4.32%	97
Czechia	1.33	-4.32%	97
Germany	1.33	-4.32%	97
Denmark	1.33	-4.32%	97
Dominican Republic	3.97	+4.47%	66
Algeria	9.25	-10.1%	28
Ecuador	5.19	+12.1%	55
Spain	1.33	-4.32%	97
Estonia	1.33	-4.32%	97
Finland	1.33	-4.32%	97
France	1.33	-4.32%	97
Gabon	16.6	+14.3%	7
United Kingdom	1	+38.9%	103
Georgia	0.42	+23.5%	108
Ghana	12.5	+19%	18
Guinea	12.6	+2.77%	16
Gambia	16.4	-7.07%	8
Guinea-Bissau	11.7	-1.02%	19
Equatorial Guinea	18.2	+16.3%	3
Greece	1.33	-4.32%	97
Grenada	13	+20.3%	14
Guatemala	2.39	+39%	79
Guyana	9.27	+96%	27
Hong Kong SAR China	0		111
Honduras	2.11	-26%	85
Croatia	1.33	-4.32%	97
Hungary	1.33	-4.32%	97
Indonesia	1.83	0%	88
India	4.59	-21.8%	61
Ireland	1.33	-4.32%	97
Iceland	1.63	+7.95%	92
Israel	2.62	-9.03%	78
Italy	1.33	-4.32%	97
Jamaica	9.24	+7.32%	29
Jordan	3.2	-19.6%	74
Japan	1.64	-10.9%	91
Kazakhstan	2.9	+33.6%	76
Kenya	11.7	+25.8%	19
Cambodia	7.2	+34.1%	46
South Korea	5.66	+16.7%	51
Kuwait	4.01	+40.7%	65
Laos	1.41	+25.9%	95
Liberia	6.22	-6.33%	49
Lesotho	3.36	+0.299%	72
Lithuania	1.33	-4.32%	97
Latvia	1.33	-4.32%	97
Macao SAR China	0		111
Moldova	1.16	-3.33%	101
Madagascar	8.74	+16.4%	35
Maldives	11.1	+5.4%	22
Mexico	1.62	+33.9%	93
North Macedonia	2.34	-1.68%	80
Mali	8.9	+12.9%	33
Malta	1.33	-4.32%	97
Myanmar (Burma)	0.82	-21.2%	105
Montenegro	2.9	-9.09%	76
Mongolia	5.35	+1.33%	54
Mauritania	8.68	+4.33%	36
Mauritius	1.2	-5.51%	100
Malaysia	3.42	-5.52%	71
Namibia	3.58	+171%	70
Niger	8.36	-1.18%	39
Nigeria	9.34	-23.4%	26
Nicaragua	2.04	+4.08%	86
Netherlands	1.33	-4.32%	97
Norway	2.31	-23%	81
Nepal	11.2	-6.59%	21
New Zealand	1.7	+97.7%	90
Oman	2.14	+7%	84
Pakistan	7.61	-15.7%	42
Panama	5.93	-7.78%	50
Peru	0.66	+1.54%	106
Philippines	1.76	+1.73%	89
Poland	1.33	-4.32%	97
Portugal	1.33	-4.32%	97
Paraguay	4.63	+3.12%	60
Qatar	3.69	+4.53%	67
Romania	1.33	-4.32%	97
Rwanda	11.1	-7.18%	23
Saudi Arabia	5.4	+27.7%	53
Senegal	8.4	-5.41%	38
Singapore	0		111
Solomon Islands	20.7	+44.4%	2
Sierra Leone	13.7	-4.01%	12
El Salvador	2.16	+14.9%	83
Suriname	7.8	-7.47%	41
Slovakia	1.33	-4.32%	97
Slovenia	1.33	-4.32%	97
Sweden	1.33	-4.32%	97
Eswatini	2.98	+39.3%	75
Seychelles	1.27	+18.7%	99
Chad	16.8	+2.69%	6
Togo	13.6	+26.4%	13
Thailand	3.67	+16.5%	68
Tonga	6.44	-2.28%	48
Trinidad & Tobago	7.51	-15.3%	43
Turkey	4.31	+33.9%	62
Tanzania	8.64	-0.346%	37
Uganda	7.86	-9.34%	40
Ukraine	1.86	+8.77%	87
Uruguay	4.85	-7.62%	57
United States	1.49	+1.36%	94
St. Vincent & Grenadines	9.17	+1.21%	30
Venezuela	12.8	-5.66%	15
Vietnam	1.07	-8.55%	102
Vanuatu	11.5	+3.61%	20
South Africa	4.66	+4.48%	59
Zambia	4.08	-15.2%	63

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 = 'TM.TAX.MRCH.WM.AR.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 <- 'TM.TAX.MRCH.WM.AR.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

Tariff rate, applied, weighted mean, all products (%)

Year: 2022

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