Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	10.2	-4.05%	45
Albania	0.4	-71.2%	109
Argentina	12.4	+0.325%	22
Australia	2.12	+3.41%	96
Austria	1.44	-13.8%	102
Azerbaijan	8.79	-2.33%	52
Burundi	11.9	+11.7%	29
Belgium	1.44	-13.8%	102
Benin	10.7	+0.188%	38
Burkina Faso	11.4	+1.06%	33
Bangladesh	12.6	+2.28%	18
Bulgaria	1.44	-13.8%	102
Bahrain	3.77	0%	79
Bahamas	27.9	+8.66%	1
Bosnia & Herzegovina	3.05	-1.61%	83
Belize	10	0%	46
Bermuda	19.7	-1.05%	2
Bolivia	9.97	0%	47
Brazil	13.8	+0.655%	12
Barbados	10.5	+30.3%	39
Brunei	0.19	+46.2%	112
Bhutan	6.36	+68.3%	63
Botswana	8.68	+7.29%	54
Central African Republic	18.1	+14.5%	7
Canada	1.46	-11.5%	101
Switzerland	0.7	-32%	107
Chile	1.01	-3.81%	105
China	5.21	+1.56%	69
Côte d’Ivoire	12.3	+5.66%	25
Cameroon	18.3	+0.882%	5
Congo - Brazzaville	18.6	+49.6%	4
Colombia	3.55	+12%	80
Cape Verde	13.8	+5.57%	11
Costa Rica	2.51	-4.2%	92
Cuba	10.4	+1.26%	41
Cyprus	1.44	-13.8%	102
Czechia	1.44	-13.8%	102
Germany	1.44	-13.8%	102
Denmark	1.44	-13.8%	102
Dominican Republic	3.97	-24.5%	76
Algeria	11	-13.1%	37
Ecuador	7.89	+1.15%	58
Spain	1.44	-13.8%	102
Estonia	1.44	-13.8%	102
Finland	1.44	-13.8%	102
France	1.44	-13.8%	102
Gabon	18.2	-1.14%	6
United Kingdom	0.69	-10.4%	108
Georgia	0.24	-20%	110
Ghana	12.4	+0.974%	20
Guinea	12.3	-0.646%	26
Gambia	13.2	-0.302%	13
Guinea-Bissau	12.7	+1.35%	17
Equatorial Guinea	18.9	+4.25%	3
Greece	1.44	-13.8%	102
Grenada	11.8	+27.2%	31
Guatemala	3.06	+5.15%	82
Guyana	8.05	-1.59%	56
Hong Kong SAR China	0		113
Honduras	1.54	-50.6%	99
Croatia	1.44	-13.8%	102
Hungary	1.44	-13.8%	102
Indonesia	5.78	-4.46%	65
India	9	+2.27%	50
Ireland	1.44	-13.8%	102
Iceland	0		113
Israel	2.79	-3.13%	89
Italy	1.44	-13.8%	102
Jamaica	8.72	+7.52%	53
Jordan	3.79	-1.81%	78
Japan	0.97	-11%	106
Kazakhstan	4.73	+5.82%	72
Kenya	15.1	+10.2%	9
Cambodia	12	+56.9%	28
South Korea	2.4	+12.1%	94
Kuwait	4.71	+19.2%	73
Laos	2.49	+0.81%	93
Liberia	12.5	-1.66%	19
Lesotho	4.5	-29%	74
Lithuania	1.44	-13.8%	102
Latvia	1.44	-13.8%	102
Macao SAR China	0		113
Moldova	2.08	+0.971%	97
Madagascar	9.69	+19.9%	48
Maldives	11.2	+2.93%	35
Mexico	2.62	-16.6%	91
North Macedonia	2.97	+4.21%	85
Mali	11.6	+4.77%	32
Malta	1.44	-13.8%	102
Myanmar (Burma)	2.84	-1.73%	88
Montenegro	1.52	-6.17%	100
Mongolia	4.85	-0.411%	71
Mauritania	12.3	+2.58%	23
Mauritius	1.06	-6.19%	104
Malaysia	5.54	-1.42%	66
Namibia	7.39	+5.12%	62
Niger	10.4	+1.17%	42
Nigeria	11.8	-4.54%	30
Nicaragua	2.89	-1.03%	87
Netherlands	1.44	-13.8%	102
Norway	0.22	-38.9%	111
Nepal	12.9	+4.81%	15
New Zealand	2.38	+17.8%	95
Oman	2.98	-0.334%	84
Pakistan	11.2	-6.1%	36
Panama	5.32	+0.377%	68
Peru	1.16	+1.75%	103
Philippines	3.82	-1.29%	77
Poland	1.44	-13.8%	102
Portugal	1.44	-13.8%	102
Paraguay	8.4	+1.82%	55
Qatar	4.09	-0.487%	75
Romania	1.44	-13.8%	102
Rwanda	10.4	-2.71%	40
Saudi Arabia	5.84	+24.8%	64
Senegal	12.4	-1.2%	21
Singapore	0		113
Solomon Islands	10.3	+14.9%	44
Sierra Leone	12.8	+0.627%	16
El Salvador	2.94	-5.77%	86
Suriname	8.94	-2.51%	51
Slovakia	1.44	-13.8%	102
Slovenia	1.44	-13.8%	102
Sweden	1.44	-13.8%	102
Eswatini	5.06	-32.5%	70
Seychelles	0.7	0%	107
Chad	17.8	-0.557%	8
Togo	12.3	+12.9%	24
Thailand	7.61	-1.81%	61
Tonga	10.3	+3.32%	43
Trinidad & Tobago	7.96	-1.49%	57
Turkey	5.48	+199%	67
Tanzania	12.1	+5.7%	27
Uganda	13.9	+2.35%	10
Ukraine	1.98	-2.46%	98
Uruguay	10.2	+0.394%	45
United States	2.78	-2.11%	90
St. Vincent & Grenadines	7.71	-4.1%	59
Venezuela	13	-1.81%	14
Vietnam	3.44	-13.1%	81
Vanuatu	9.34	+2.3%	49
South Africa	7.7	+2.67%	60
Zambia	11.3	-0.961%	34

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.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.MANF.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, manufactured products (%)

Year: 2022

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