Manufactures exports (% of merchandise exports)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Albania Albania 54.1 -4.51% 44
Argentina Argentina 13.3 -29.7% 74
Armenia Armenia 30.6 -36.5% 61
Antigua & Barbuda Antigua & Barbuda 14.8 -27.6% 72
Australia Australia 9.55 +14.7% 77
Azerbaijan Azerbaijan 4.8 +19.4% 82
Belgium Belgium 69.5 +0.714% 28
Burkina Faso Burkina Faso 2.71 -46% 84
Bulgaria Bulgaria 53.4 -2.94% 45
Bosnia & Herzegovina Bosnia & Herzegovina 74.2 +1.49% 20
Belize Belize 2.96 -1.16% 83
Bolivia Bolivia 6.52 +111% 80
Brazil Brazil 23.2 -3.63% 65
Barbados Barbados 39.9 +7.2% 52
Canada Canada 42.4 -4.03% 50
Switzerland Switzerland 68.5 -0.15% 29
Chile Chile 13.1 -27.8% 75
China China 91.2 -0.785% 1
Cyprus Cyprus 22.2 -2.39% 66
Czechia Czechia 90.2 +0.178% 3
Germany Germany 83.2 -1.4% 10
Denmark Denmark 73.1 +6.42% 23
Dominican Republic Dominican Republic 58.2 -7.84% 41
Ecuador Ecuador 4.89 +3.49% 81
Egypt Egypt 51.9 -0.497% 46
Spain Spain 65.2 -0.822% 34
Estonia Estonia 64.4 -2.46% 35
Finland Finland 66.7 +1.08% 32
Fiji Fiji 15.5 -4.37% 71
United Kingdom United Kingdom 61.3 +1.39% 38
Georgia Georgia 34 +14.6% 59
Greece Greece 37.1 +2.42% 56
Grenada Grenada 28.6 -21.9% 63
Guatemala Guatemala 45.9 +0.296% 48
Guyana Guyana 0.505 -27.2% 86
Hong Kong SAR China Hong Kong SAR China 87.4 -1.54% 6
Croatia Croatia 63.1 -1.22% 37
Hungary Hungary 85.4 -0.888% 9
India India 67.1 +5.59% 31
Ireland Ireland 90.7 +1.01% 2
Iceland Iceland 18.4 +19.8% 67
Israel Israel 86.3 -0.773% 8
Italy Italy 79.5 -0.346% 14
Japan Japan 82.5 -1.27% 11
Kyrgyzstan Kyrgyzstan 33.4 +5.11% 60
South Korea South Korea 87.3 +1.39% 7
Sri Lanka Sri Lanka 66 -1.84% 33
Lithuania Lithuania 60.5 -0.761% 39
Luxembourg Luxembourg 80.4 -1.18% 13
Latvia Latvia 54.8 -2.46% 43
Macao SAR China Macao SAR China 39.2 -23.5% 53
Moldova Moldova 38.7 +0.662% 55
Maldives Maldives 1.24 +489% 85
Mexico Mexico 79.1 +1.18% 16
North Macedonia North Macedonia 80.6 -2.19% 12
Malta Malta 76.4 -7.15% 19
Myanmar (Burma) Myanmar (Burma) 35.7 -18.6% 57
Montenegro Montenegro 35.1 +33.3% 58
Mauritius Mauritius 46.1 -2.19% 47
Malaysia Malaysia 69.8 +2.03% 27
Namibia Namibia 18.4 -43.7% 68
Netherlands Netherlands 63.4 +2.69% 36
Norway Norway 14.5 +10.7% 73
New Zealand New Zealand 15.8 -1.79% 70
Pakistan Pakistan 67.9 -4.26% 30
Panama Panama 10.9 +175% 76
Philippines Philippines 77.2 -2.88% 18
Poland Poland 78.1 -0.316% 17
Portugal Portugal 73.5 -1.79% 22
Paraguay Paraguay 16.2 +18.4% 69
French Polynesia French Polynesia 43.4 -36.4% 49
Romania Romania 79.3 +4.07% 15
El Salvador El Salvador 70 -1.77% 26
Suriname Suriname 9.08 +22.4% 78
Slovakia Slovakia 88.6 -1.16% 4
Slovenia Slovenia 87.4 +1.44% 5
Sweden Sweden 72.6 +0.482% 24
Togo Togo 42.2 +17.4% 51
Thailand Thailand 72.1 -1.15% 25
Trinidad & Tobago Trinidad & Tobago 59.1 -0.987% 40
Turkey Turkey 73.6 +0.513% 21
Ukraine Ukraine 30.1 +0.0336% 62
United States United States 55.7 -1.43% 42
Uzbekistan Uzbekistan 27.8 -20% 64
South Africa South Africa 39.1 -3.32% 54
Zimbabwe Zimbabwe 6.69 -11.4% 79 

                    
# 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 = 'TX.VAL.MANF.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 <- 'TX.VAL.MANF.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))