Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	11.6	-3.66%	35
Albania	0.57	-64.6%	107
Argentina	12	+0.42%	32
Australia	2.01	+4.15%	95
Austria	1.95	-12.9%	97
Azerbaijan	8.89	-4%	54
Burundi	12.6	+11.9%	28
Belgium	1.95	-12.9%	97
Benin	11.3	-0.177%	39
Burkina Faso	11.4	+0.971%	38
Bangladesh	12.9	+2.55%	22
Bulgaria	1.95	-12.9%	97
Bahrain	3.65	+1.39%	79
Bahamas	26.3	+11.2%	1
Bosnia & Herzegovina	3.39	+7.28%	81
Belize	12	+1.26%	31
Bermuda	22.3	+0.225%	2
Bolivia	9.83	0%	49
Brazil	13.3	+0.682%	17
Barbados	11.9	+24.6%	34
Brunei	0.18	+50%	109
Bhutan	6.77	+66.7%	62
Botswana	8.25	+5.91%	57
Central African Republic	19.3	+21%	4
Canada	1.83	-11.2%	100
Switzerland	3.16	-23.5%	83
Chile	1.03	-3.74%	106
China	5.36	+0.942%	69
Côte d’Ivoire	12.7	+4.86%	27
Cameroon	18.9	+1.56%	6
Congo - Brazzaville	19.2	+48.7%	5
Colombia	3.55	+11.3%	80
Cape Verde	14.3	+5.3%	11
Costa Rica	2.63	-5.05%	89
Cuba	10.6	+1.53%	43
Cyprus	1.95	-12.9%	97
Czechia	1.95	-12.9%	97
Germany	1.95	-12.9%	97
Denmark	1.95	-12.9%	97
Dominican Republic	4.48	-19%	74
Algeria	11.2	-12.7%	40
Ecuador	7.82	+1.56%	58
Spain	1.95	-12.9%	97
Estonia	1.95	-12.9%	97
Finland	1.95	-12.9%	97
France	1.95	-12.9%	97
Gabon	18.8	+0.965%	7
United Kingdom	1.13	+13%	103
Georgia	0.4	-6.98%	108
Ghana	13	+0.935%	18
Guinea	12.9	-1.83%	21
Gambia	14	-0.357%	12
Guinea-Bissau	13.6	+2.42%	16
Equatorial Guinea	20	+7.08%	3
Greece	1.95	-12.9%	97
Grenada	12.9	+22.5%	19
Guatemala	3.12	+4%	84
Guyana	9.16	-1.29%	52
Hong Kong SAR China	0		111
Honduras	1.63	-51.5%	101
Croatia	1.95	-12.9%	97
Hungary	1.95	-12.9%	97
Indonesia	5.82	-3.32%	65
India	10.1	+2.03%	44
Ireland	1.95	-12.9%	97
Iceland	1.32	-13.7%	102
Israel	3.16	-2.17%	83
Italy	1.95	-12.9%	97
Jamaica	9.72	+8.6%	51
Jordan	5.59	+6.88%	67
Japan	1.99	-8.72%	96
Kazakhstan	4.75	+6.03%	71
Kenya	15.8	+12.1%	9
Cambodia	12.3	+59.5%	29
South Korea	5.73	+14.6%	66
Kuwait	4.64	+20.2%	73
Laos	2.49	0%	91
Liberia	13.6	-3.48%	15
Lesotho	4.19	-30.3%	75
Lithuania	1.95	-12.9%	97
Latvia	1.95	-12.9%	97
Macao SAR China	0		111
Moldova	2.37	+1.28%	92
Madagascar	10	+17.6%	45
Maldives	9.73	-6.71%	50
Mexico	2.69	-18%	88
North Macedonia	3.65	+5.49%	79
Mali	12.2	+6.91%	30
Malta	1.95	-12.9%	97
Myanmar (Burma)	2.98	-2.93%	86
Montenegro	1.88	-4.57%	99
Mongolia	5.04	0%	70
Mauritania	12.9	+3.03%	20
Mauritius	1.07	-7.76%	105
Malaysia	5.43	-3.21%	68
Namibia	7.09	+5.35%	61
Niger	10.7	+2.48%	42
Nigeria	11.9	-6.3%	33
Nicaragua	2.98	-1.97%	86
Netherlands	1.95	-12.9%	97
Norway	2.57	-26.4%	90
Nepal	13.6	+4.12%	13
New Zealand	2.27	+18.8%	93
Oman	3.18	+1.92%	82
Pakistan	11	-5.84%	41
Panama	5.93	-1%	64
Peru	1.1	+1.85%	104
Philippines	3.94	-1.75%	77
Poland	1.95	-12.9%	97
Portugal	1.95	-12.9%	97
Paraguay	8.27	+1.97%	55
Qatar	3.98	+0.759%	76
Romania	1.95	-12.9%	97
Rwanda	11.9	-0.252%	34
Saudi Arabia	5.96	+26.8%	63
Senegal	12.8	-1.01%	25
Singapore	0.05	-37.5%	110
Solomon Islands	11.9	+17.7%	33
Sierra Leone	13.6	-0.946%	14
El Salvador	3.09	-5.79%	85
Suriname	9.91	-2.36%	46
Slovakia	1.95	-12.9%	97
Slovenia	1.95	-12.9%	97
Sweden	1.95	-12.9%	97
Eswatini	4.74	-34%	72
Seychelles	1.91	+9.14%	98
Chad	18.6	+2.15%	8
Togo	12.9	+13.4%	23
Thailand	8.26	-2.59%	56
Tonga	9.84	+3.25%	48
Trinidad & Tobago	9.02	-13.4%	53
Turkey	7.48	+68.5%	59
Tanzania	12.8	+6.07%	24
Uganda	15.1	+3.14%	10
Ukraine	2.09	-3.24%	94
Uruguay	9.9	+0.202%	47
United States	2.72	-2.51%	87
St. Vincent & Grenadines	9.72	-0.613%	51
Venezuela	12.7	-2.38%	26
Vietnam	3.71	-12.1%	78
Vanuatu	11.5	+5.81%	37
South Africa	7.31	+2.09%	60
Zambia	11.5	-1.37%	36

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.SM.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.SM.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, simple mean, all products (%)

Year: 2022

Acquire full dataset with Python or R from source