Textiles and clothing (% of value added in manufacturing)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 22.8 +3.77% 51
United Arab Emirates United Arab Emirates 6.08 +26.4% 79
Argentina Argentina 5.15 +22.2% 81
Armenia Armenia 16 +3.92% 64
Australia Australia 10.6 -17.4% 71
Austria Austria 53.5 +12.7% 21
Azerbaijan Azerbaijan 198 +25% 3
Belgium Belgium 22.8 -54.7% 50
Bulgaria Bulgaria 23 +43.9% 48
Bosnia & Herzegovina Bosnia & Herzegovina 26.5 +46.3% 43
Belarus Belarus 18.9 -71.1% 59
Brazil Brazil 23.7 +15.7% 46
Botswana Botswana 10.9 -3.52% 70
Canada Canada 10.5 +16.2% 72
Switzerland Switzerland 25.9 +17.8% 44
China China 135 +24.3% 5
Côte d’Ivoire Côte d’Ivoire 161 +58.2% 4
Colombia Colombia 41.1 +15.5% 29
Cyprus Cyprus 17.8 +3.75% 61
Czechia Czechia 20 -19.4% 55
Germany Germany 39.7 +8.85% 33
Denmark Denmark 15.5 -35.4% 66
Ecuador Ecuador 29.9 -141% 40
Spain Spain 47.7 +79.4% 26
Estonia Estonia 23.6 -22.7% 47
Finland Finland 64.2 +1.56% 18
Fiji Fiji 40 -24.5% 31
France France 25.4 +25.3% 45
United Kingdom United Kingdom 38.2 +32.3% 35
Georgia Georgia 7.65 -36.9% 77
Greece Greece 15.7 -34.7% 65
Hong Kong SAR China Hong Kong SAR China 9.85 +2.72% 74
Croatia Croatia 19 -31.7% 58
Hungary Hungary 115 +44.4% 6
Indonesia Indonesia 9.21 -3.6% 75
India India 29.1 +47.9% 42
Ireland Ireland 22 +377% 53
Iraq Iraq 40.4 -15.7% 30
Iceland Iceland 3.59 -4.07% 84
Italy Italy 36.8 +22.1% 36
Jordan Jordan 223 +68.3% 2
Japan Japan 41.3 +24.1% 28
Kazakhstan Kazakhstan 85.5 +7.77% 11
Kyrgyzstan Kyrgyzstan 22.9 +400% 49
South Korea South Korea 4.88 +8.56% 82
Kuwait Kuwait 3.15 0.00000% 85
Lithuania Lithuania 36.4 +6.58% 37
Luxembourg Luxembourg 0 88
Latvia Latvia 19.8 -9.18% 56
Macao SAR China Macao SAR China 6.77 -31.9% 78
Morocco Morocco 8.95 +1,704% 76
Moldova Moldova 11.5 -19.4% 69
Madagascar Madagascar 18.8 +58.4% 60
North Macedonia North Macedonia 32 +32.3% 39
Malta Malta 86.8 -48.5% 9
Mongolia Mongolia 19.6 +5.66% 57
Mauritius Mauritius 74 +2.83% 15
Namibia Namibia 21.3 -3.25% 54
Nicaragua Nicaragua 17.5 +52.2% 63
Netherlands Netherlands 53.3 +30.8% 22
Norway Norway 4.73 -45.6% 83
New Zealand New Zealand 53.2 -12.2% 23
Panama Panama 1.44 +1.34% 87
Peru Peru 82.5 -4.44% 12
Philippines Philippines 86.1 +147% 10
Poland Poland 53 +22.4% 24
Puerto Rico Puerto Rico 1,967 -14.6% 1
Portugal Portugal 64.4 +152% 17
Paraguay Paraguay 55.2 +18.2% 19
Qatar Qatar 39.6 +109% 34
Romania Romania 33.5 +25.2% 38
Russia Russia 75.8 -13% 14
Rwanda Rwanda 78.5 +6.75% 13
Senegal Senegal 15 -156% 68
Singapore Singapore 5.16 +86% 80
Serbia Serbia 52.8 +23.1% 25
Slovakia Slovakia 29.5 +51.9% 41
Slovenia Slovenia 42.1 +78.9% 27
Sweden Sweden 89.6 +52.3% 8
Thailand Thailand 17.6 +472% 62
Turkey Turkey 53.6 +90.1% 20
Tanzania Tanzania 71.8 +0.00007% 16
Ukraine Ukraine 15.2 -6.69% 67
Uruguay Uruguay 111 +23.3% 7
United States United States 10.1 +26.4% 73
Uzbekistan Uzbekistan 39.9 +117% 32
Vietnam Vietnam 22.1 +30.6% 52
South Africa South Africa 1.79 0.00000% 86 

                    
# 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.TXTL.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.TXTL.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))