Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	8.5	+3.79%	38
Albania	0.15	-80.8%	108
Argentina	7.8	+1.04%	43
Australia	1.04	+22.4%	94
Austria	1.64	+14.7%	89
Azerbaijan	6.03	-3.98%	54
Burundi	6.78	+7.45%	46
Belgium	1.64	+14.7%	89
Benin	8.51	-0.468%	37
Burkina Faso	7.81	-1.88%	42
Bangladesh	12.9	-3%	11
Bulgaria	1.64	+14.7%	89
Bahrain	2.95	+3.51%	75
Bahamas	18.6	-24.3%	2
Bosnia & Herzegovina	2.29	+7.01%	79
Belize	10.9	+5.4%	20
Bermuda	21.9	+5%	1
Bolivia	5.33	-2.02%	57
Brazil	8.68	-4.72%	36
Barbados	11.7	+36.1%	16
Brunei	0.05	+66.7%	110
Bhutan	4.17	+32%	65
Botswana	2.16	+209%	80
Central African Republic	12.6	-24.7%	12
Canada	0.76	-57.1%	101
Switzerland	0.12	-36.8%	109
Chile	0.48	+4.35%	104
China	2.45	-4.67%	76
Côte d’Ivoire	8.33	-6.09%	39
Cameroon	17.5	+27.2%	4
Congo - Brazzaville	17.9	+80.1%	3
Colombia	2.98	+12.5%	74
Cape Verde	10.7	+27.6%	21
Costa Rica	0.83	-6.74%	98
Cuba	10.1	-2.71%	23
Cyprus	1.64	+14.7%	89
Czechia	1.64	+14.7%	89
Germany	1.64	+14.7%	89
Denmark	1.64	+14.7%	89
Dominican Republic	4.2	-4.11%	63
Algeria	9.95	-4.51%	24
Ecuador	6.59	+10.9%	48
Spain	1.64	+14.7%	89
Estonia	1.64	+14.7%	89
Finland	1.64	+14.7%	89
France	1.64	+14.7%	89
Gabon	15.6	+10.5%	6
United Kingdom	0.71	+2.9%	102
Georgia	0.19	-24%	106
Ghana	11.7	+18.3%	18
Guinea	11.7	+3.81%	17
Gambia	15.3	-14.3%	8
Guinea-Bissau	9.12	-11.8%	32
Equatorial Guinea	16.3	+12.2%	5
Greece	1.64	+14.7%	89
Grenada	12	+32.5%	14
Guatemala	1.98	+11.2%	83
Guyana	8.25	+108%	40
Hong Kong SAR China	0		111
Honduras	1.71	-36.2%	88
Croatia	1.64	+14.7%	89
Hungary	1.64	+14.7%	89
Indonesia	1.74	+3.57%	87
India	6.22	-8.39%	50
Ireland	1.64	+14.7%	89
Iceland	0		111
Israel	1.78	-8.72%	86
Italy	1.64	+14.7%	89
Jamaica	9.28	+1.64%	28
Jordan	3.4	-2.58%	70
Japan	0.85	-17.5%	97
Kazakhstan	3.09	+33.2%	73
Kenya	9.07	+17%	34
Cambodia	9.16	+44.9%	31
South Korea	1.79	+55.7%	85
Kuwait	4.21	+32%	62
Laos	1.03	-16.3%	95
Liberia	5.78	-5.09%	55
Lesotho	4.19	-6.05%	64
Lithuania	1.64	+14.7%	89
Latvia	1.64	+14.7%	89
Macao SAR China	0		111
Moldova	1.14	+6.54%	93
Madagascar	9.8	+21.9%	25
Maldives	12.6	+5.9%	12
Mexico	1.78	+29.9%	86
North Macedonia	1.48	+7.25%	91
Mali	9.19	+25.5%	30
Malta	1.64	+14.7%	89
Myanmar (Burma)	0.66	-31.3%	103
Montenegro	1.24	+1.64%	92
Mongolia	4.7	+0.213%	60
Mauritania	9.5	-5.66%	26
Mauritius	0.79	-8.14%	100
Malaysia	3.64	-6.19%	69
Namibia	3.81	+123%	68
Niger	7.79	+1.96%	44
Nigeria	9.21	-25.4%	29
Nicaragua	2.34	+12%	77
Netherlands	1.64	+14.7%	89
Norway	0.23	-11.5%	105
Nepal	11.1	-9.41%	19
New Zealand	1.81	+96.7%	84
Oman	2.14	+4.9%	81
Pakistan	10.5	-9.79%	22
Panama	4.12	-6.36%	66
Peru	0.83	+2.47%	98
Philippines	0.9	+3.45%	96
Poland	1.64	+14.7%	89
Portugal	1.64	+14.7%	89
Paraguay	4.93	+2.71%	59
Qatar	3.85	+2.39%	67
Romania	1.64	+14.7%	89
Rwanda	6.23	-11.9%	49
Saudi Arabia	5.56	+32.4%	56
Senegal	8.83	-6.76%	35
Singapore	0		111
Solomon Islands	12.2	+24.6%	13
Sierra Leone	12	-4.09%	15
El Salvador	2.3	+11.1%	78
Suriname	6.86	-8.04%	45
Slovakia	1.64	+14.7%	89
Slovenia	1.64	+14.7%	89
Sweden	1.64	+14.7%	89
Eswatini	3.36	+16.7%	71
Seychelles	0.17	+21.4%	107
Chad	15.5	-3.6%	7
Togo	14.7	+41.9%	9
Thailand	3.4	+52.5%	70
Tonga	7.85	-4.85%	41
Trinidad & Tobago	6.1	+1.16%	53
Turkey	3.33	+79%	72
Tanzania	6.16	-3.45%	52
Uganda	6.17	-14.8%	51
Ukraine	2.08	+8.33%	82
Uruguay	6.62	-1.63%	47
United States	1.61	+2.55%	90
St. Vincent & Grenadines	9.46	+1.5%	27
Venezuela	13.8	-10.1%	10
Vietnam	0.82	-6.82%	99
Vanuatu	9.11	-2.25%	33
South Africa	5.31	+4.94%	58
Zambia	4.43	-17%	61

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.MANF.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.MANF.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, manufactured products (%)

Year: 2022

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