Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	9.84	-51.4%	34
Albania	0.74	-67.5%	108
Argentina	1.08	-16.9%	103
Australia	0.8	+11.1%	107
Austria	0.84	-37.3%	106
Azerbaijan	3.89	-18.1%	67
Burundi	16	-1.23%	18
Belgium	0.84	-37.3%	106
Benin	12.3	-7.61%	26
Burkina Faso	5.64	+8.46%	58
Bangladesh	5.31	-7.65%	60
Bulgaria	0.84	-37.3%	106
Bahrain	4.79	+226%	63
Bahamas	5.85	-0.51%	56
Bosnia & Herzegovina	7.41	+71.1%	50
Belize	38.6	+4.55%	3
Bermuda	45.1	+47.5%	2
Bolivia	3.27	+10.8%	76
Brazil	1.93	-16.5%	94
Barbados	20.2	+0.0496%	8
Brunei	0		112
Bhutan	6.2	+66.2%	53
Botswana	7.68	+227%	48
Central African Republic	20.1	+16.4%	9
Canada	4.06	-21.2%	65
Switzerland	9.44	-22.6%	37
Chile	0.38	+11.8%	110
China	2	-1.96%	91
Côte d’Ivoire	5.64	-4.89%	58
Cameroon	19.9	+8.38%	10
Congo - Brazzaville	18.8	+10.5%	13
Colombia	2.26	-1.31%	86
Cape Verde	14.7	-0.406%	22
Costa Rica	3.34	-9.97%	74
Cuba	8	+24.8%	42
Cyprus	0.84	-37.3%	106
Czechia	0.84	-37.3%	106
Germany	0.84	-37.3%	106
Denmark	0.84	-37.3%	106
Dominican Republic	3.56	+41.8%	71
Algeria	7.95	-19.6%	44
Ecuador	1.99	+57.9%	92
Spain	0.84	-37.3%	106
Estonia	0.84	-37.3%	106
Finland	0.84	-37.3%	106
France	0.84	-37.3%	106
Gabon	19.3	+26.3%	11
United Kingdom	1.81	+77.5%	95
Georgia	0.86	+59.3%	105
Ghana	15.9	+27%	19
Guinea	15.2	-1.68%	20
Gambia	18.1	+4.62%	14
Guinea-Bissau	15	+13.3%	21
Equatorial Guinea	22.7	+6.13%	6
Greece	0.84	-37.3%	106
Grenada	14	-9.27%	24
Guatemala	3.51	+126%	72
Guyana	13.4	+28.4%	25
Hong Kong SAR China	0		112
Honduras	3.32	-1.19%	75
Croatia	0.84	-37.3%	106
Hungary	0.84	-37.3%	106
Indonesia	2.13	-7.39%	89
India	2.49	-29.3%	82
Ireland	0.84	-37.3%	106
Iceland	6.03	+4.69%	54
Israel	5.13	-17.5%	61
Italy	0.84	-37.3%	106
Jamaica	9.2	+19.6%	38
Jordan	3.71	-24.1%	70
Japan	2.53	-16.8%	81
Kazakhstan	2.15	+40.5%	88
Kenya	17.7	+28.3%	16
Cambodia	6.44	+165%	52
South Korea	10.9	-14%	30
Kuwait	3.89	+103%	67
Laos	1.67	+104%	97
Liberia	19.1	+7.53%	12
Lesotho	2.19	+85.6%	87
Lithuania	0.84	-37.3%	106
Latvia	0.84	-37.3%	106
Macao SAR China	0		112
Moldova	1.19	-25.6%	101
Madagascar	6.63	+7.28%	51
Maldives	7.98	+1.14%	43
Mexico	0.65	+16.1%	109
North Macedonia	3.84	-12.3%	69
Mali	9.72	+2.53%	35
Malta	0.84	-37.3%	106
Myanmar (Burma)	1.41	+11%	98
Montenegro	5.52	-20.1%	59
Mongolia	7.7	+4.76%	47
Mauritania	7.57	+22.9%	49
Mauritius	1.95	-7.14%	93
Malaysia	3.03	-3.81%	78
Namibia	3.05	+235%	77
Niger	8.85	-8.2%	39
Nigeria	9.62	-16.4%	36
Nicaragua	1.2	-22.6%	100
Netherlands	0.84	-37.3%	106
Norway	8.33	-31.7%	41
Nepal	10.8	-4.92%	31
New Zealand	1.24	+96.8%	99
Oman	2.3	+11.1%	85
Pakistan	4.95	-10.8%	62
Panama	11.2	-10.3%	27
Peru	0.11	+37.5%	111
Philippines	3.88	-14.2%	68
Poland	0.84	-37.3%	106
Portugal	0.84	-37.3%	106
Paraguay	2.62	+9.62%	80
Qatar	3.36	+15.5%	73
Romania	0.84	-37.3%	106
Rwanda	24.1	+2.21%	4
Saudi Arabia	5.76	+28.6%	57
Senegal	7.75	-2.88%	46
Singapore	0		112
Solomon Islands	48.8	+102%	1
Sierra Leone	17.7	+0.91%	15
El Salvador	1.77	+29.2%	96
Suriname	10.4	-5.44%	33
Slovakia	0.84	-37.3%	106
Slovenia	0.84	-37.3%	106
Sweden	0.84	-37.3%	106
Eswatini	2.31	+305%	84
Seychelles	5.97	+39.2%	55
Chad	23.8	+35.7%	5
Togo	11.2	-4.04%	28
Thailand	4.59	-23.9%	64
Tonga	4.03	0%	66
Trinidad & Tobago	10.8	-30.7%	32
Turkey	7.85	+10.7%	45
Tanzania	22.4	+7.97%	7
Uganda	14.2	+6.23%	23
Ukraine	1.24	+5.08%	99
Uruguay	1.18	-38.2%	102
United States	1	-2.91%	104
St. Vincent & Grenadines	8.8	+1.03%	40
Venezuela	11.1	+4.03%	29
Vietnam	2.03	-19.4%	90
Vanuatu	17.5	+17%	17
South Africa	2.4	-14%	83
Zambia	2.76	+21.6%	79

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.TCOM.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.TCOM.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, primary products (%)

Year: 2022

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