Food, beverages and tobacco (% of value added in manufacturing)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 43.1 +3.89% 18
United Arab Emirates United Arab Emirates 2.03 +13.3% 79
Argentina Argentina 29.1 -12% 28
Armenia Armenia 1.22 +27% 83
Australia Australia 9.63 -2.35% 56
Austria Austria 52.8 +23.9% 11
Azerbaijan Azerbaijan 2.59 -24.9% 78
Belgium Belgium 17.4 +2.35% 43
Bulgaria Bulgaria 10.7 +8.91% 54
Bosnia & Herzegovina Bosnia & Herzegovina 42.4 -4.35% 19
Belarus Belarus 63 +44.6% 8
Brazil Brazil 2.78 +4.03% 74
Botswana Botswana 30.7 -8.36% 26
Canada Canada 9.47 +42.5% 57
Switzerland Switzerland 39.1 +2.84% 21
China China 160 +24.3% 3
Colombia Colombia 4.33 +23.8% 65
Cyprus Cyprus 27.9 +13.8% 29
Czechia Czechia 20.6 -0.766% 36
Germany Germany 17.8 +3.88% 41
Denmark Denmark 15.3 -3.41% 46
Ecuador Ecuador 7.89 -16.9% 60
Spain Spain 15.5 +8.79% 45
Estonia Estonia 149 +37.8% 5
Finland Finland 56.7 +65.6% 10
Fiji Fiji 38.2 -42.3% 22
France France 18.5 +109% 40
United Kingdom United Kingdom 17.4 +18.8% 42
Georgia Georgia 4.11 +13.5% 68
Greece Greece 3.9 +17% 70
Hong Kong SAR China Hong Kong SAR China 9.42 +17.4% 58
Croatia Croatia 46.7 +29.2% 17
Hungary Hungary 22 +20% 35
Indonesia Indonesia 4.02 -2.94% 69
India India 3.63 +12% 71
Ireland Ireland 13.6 +51% 49
Iraq Iraq 0.204 -9.72% 86
Iceland Iceland 1.46 +16.1% 82
Italy Italy 38.2 +23.9% 23
Jordan Jordan 1.2 -48.9% 84
Japan Japan 7.65 +36% 62
Kazakhstan Kazakhstan 2.62 -0.49% 77
Kyrgyzstan Kyrgyzstan 4.18 +219% 67
South Korea South Korea 0.588 -3.66% 85
Kuwait Kuwait 7.7 0.00000% 61
Lithuania Lithuania 64.3 +40.5% 7
Luxembourg Luxembourg 0 -100% 89
Latvia Latvia 152 +38.4% 4
Macao SAR China Macao SAR China 41.6 +14.8% 20
Morocco Morocco 5.62 -64.9% 64
Moldova Moldova 11.5 -1.05% 53
Madagascar Madagascar 49.9 -12.5% 14
Marshall Islands Marshall Islands 98.1 0.00000% 6
North Macedonia North Macedonia 23.5 +83.5% 33
Malta Malta 2.67 -63% 76
Myanmar (Burma) Myanmar (Burma) 3.17 +99% 72
Mongolia Mongolia 14.6 +6.22% 48
Mauritius Mauritius 8.79 +42.8% 59
Namibia Namibia 34.1 +3.9% 24
Nicaragua Nicaragua 1.5 +1.79% 81
Netherlands Netherlands 19.1 +17.7% 39
Norway Norway 11.8 -24.8% 51
New Zealand New Zealand 210 +5.41% 2
Panama Panama 59.4 +5.04% 9
Peru Peru 11.8 +14.4% 52
Philippines Philippines 4.21 -50% 66
Poland Poland 24.9 +34.6% 32
Puerto Rico Puerto Rico 34 -21% 25
Portugal Portugal 51.1 +35.5% 13
Paraguay Paraguay 10.3 +17% 55
Qatar Qatar 2.69 -5.54% 75
Romania Romania 47.2 +54.4% 16
Russia Russia 16.4 +33.2% 44
Rwanda Rwanda 555 +8.05% 1
Senegal Senegal 0.157 +231% 87
Singapore Singapore 0.153 -3.52% 88
Serbia Serbia 15.1 +16.2% 47
Slovakia Slovakia 30 +71.6% 27
Slovenia Slovenia 48.3 +37.5% 15
Sweden Sweden 52.4 +73.8% 12
Thailand Thailand 1.56 -12.6% 80
Turkey Turkey 19.3 +48.4% 38
Tanzania Tanzania 27.5 +0.00024% 30
Ukraine Ukraine 7.41 -20.9% 63
Uruguay Uruguay 20 +28.4% 37
United States United States 2.9 +19.9% 73
Uzbekistan Uzbekistan 12 +137% 50
Vietnam Vietnam 25.2 +11.1% 31
South Africa South Africa 22.2 0.00000% 34 

                    
# 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 = 'NV.MNF.FBTO.ZS.UN'

# 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 <- 'NV.MNF.FBTO.ZS.UN'

# 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))