Chemicals (% of value added in manufacturing)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 19.7 +53.3% 40
United Arab Emirates United Arab Emirates 12.2 +36.4% 54
Argentina Argentina 12.8 -6.82% 53
Armenia Armenia 45.7 +84.4% 22
Australia Australia 18.7 +1.89% 44
Austria Austria 65.8 +5.41% 12
Azerbaijan Azerbaijan 33.8 +17.3% 28
Belgium Belgium 53.4 +28.8% 15
Bulgaria Bulgaria 19.8 -16.9% 39
Bosnia & Herzegovina Bosnia & Herzegovina 37.7 +119% 25
Belarus Belarus 22.5 +164% 38
Brazil Brazil 9.59 +43.2% 58
Botswana Botswana 11.1 +3.08% 56
Canada Canada 7.74 +48.5% 63
Switzerland Switzerland 19.4 +29.7% 41
China China 146 +24.3% 4
Colombia Colombia 18.8 +44.3% 43
Cyprus Cyprus 17.2 +49.9% 45
Czechia Czechia 27.1 +34.5% 33
Germany Germany 34.5 +16.3% 27
Denmark Denmark 9.64 -9.92% 57
Ecuador Ecuador 17 +52.3% 46
Spain Spain 26.1 +38.8% 35
Estonia Estonia 5.99 +31% 69
Finland Finland 47.4 +55.1% 18
Fiji Fiji 2.51 -39.2% 78
France France 16.5 +62.5% 47
United Kingdom United Kingdom 16 +24.5% 50
Georgia Georgia 45.9 +17.4% 21
Greece Greece 29.7 +37.1% 31
Croatia Croatia 9.08 +19% 59
Hungary Hungary 39.4 +40.2% 24
Indonesia Indonesia 8.6 +25.4% 61
India India 59.1 +37.2% 13
Ireland Ireland 3.81 -39.9% 74
Iraq Iraq 0.346 +360% 82
Iceland Iceland 78.4 +178% 10
Italy Italy 40.5 +48.5% 23
Jordan Jordan 15.1 -41.1% 52
Japan Japan 46.6 +66.4% 19
Kazakhstan Kazakhstan 356 +6.85% 3
Kyrgyzstan Kyrgyzstan 495 +48.2% 1
South Korea South Korea 6.6 +20.2% 65
Kuwait Kuwait 22.8 0.00000% 37
Lithuania Lithuania 1.49 +44.5% 80
Latvia Latvia 3.38 -26.6% 77
Macao SAR China Macao SAR China 6.06 -46.4% 68
Morocco Morocco 8.78 -59.5% 60
Moldova Moldova 1.65 +38% 79
North Macedonia North Macedonia 114 +178% 8
Malta Malta 3.93 -10.6% 73
Mongolia Mongolia 29.8 +27.5% 30
Mauritius Mauritius 8.31 +80.8% 62
Namibia Namibia 19.2 -56.6% 42
Nicaragua Nicaragua 0.483 -8.98% 81
Netherlands Netherlands 35.2 +80.4% 26
Norway Norway 15.2 -32.4% 51
New Zealand New Zealand 16.4 +79.9% 48
Panama Panama 4.82 +5.04% 71
Peru Peru 73.9 +100% 11
Philippines Philippines 16.2 -11.9% 49
Poland Poland 26.1 +84.7% 34
Puerto Rico Puerto Rico 3.38 -17.5% 76
Portugal Portugal 23.7 +133% 36
Paraguay Paraguay 3.49 +39.2% 75
Qatar Qatar 46.6 +14.5% 20
Romania Romania 33.7 +192% 29
Russia Russia 120 +6.17% 5
Rwanda Rwanda 47.9 -5.7% 17
Singapore Singapore 0.263 +32.2% 83
Serbia Serbia 6.2 +41.2% 66
Slovakia Slovakia 116 +275% 7
Slovenia Slovenia 86.7 +87.4% 9
Sweden Sweden 57.4 +24.5% 14
Thailand Thailand 6.1 +24.3% 67
Turkey Turkey 117 +106% 6
Tanzania Tanzania 12 -0.000532% 55
Ukraine Ukraine 48.7 +32.8% 16
Uruguay Uruguay 5.79 -34.5% 70
United States United States 4.52 +39.5% 72
Uzbekistan Uzbekistan 466 +132% 2
Vietnam Vietnam 27.5 +47.3% 32
South Africa South Africa 7 0.000000% 64 

                    
# 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.CHEM.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.CHEM.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))