Firms offering formal training (% of firms)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 30.2 +28.3% 28
Armenia Armenia 16 -41.9% 42
Azerbaijan Azerbaijan 34 +0.216% 25
Belgium Belgium 61.4 +6.27% 8
Benin Benin 19.4 -2.93% 36
Burkina Faso Burkina Faso 12 -51.7% 48
Bahrain Bahrain 40.6 22
Bhutan Bhutan 14.9 -42.8% 44
Canada Canada 55.9 11
China China 79.1 -0.0574% 1
Cameroon Cameroon 24 -36% 33
Congo - Kinshasa Congo - Kinshasa 17.3 +2.3% 39
Congo - Brazzaville Congo - Brazzaville 25.4 -32.2% 32
Cape Verde Cape Verde 14.2 -14.4% 46
Cyprus Cyprus 43.1 +8.64% 17
Czechia Czechia 76.2 +74.8% 2
Ecuador Ecuador 54.6 -26% 12
Spain Spain 73.4 +32.8% 4
United Kingdom United Kingdom 57.5 10
Equatorial Guinea Equatorial Guinea 45.1 15
Ireland Ireland 62.4 +4.35% 7
Iceland Iceland 47.7 14
Israel Israel 5.92 -68.2% 50
Italy Italy 42.3 +236% 19
Jamaica Jamaica 23 -11.4% 34
Jordan Jordan 42.2 +150% 20
Kazakhstan Kazakhstan 12.1 -44.5% 47
South Korea South Korea 16.2 -59.1% 40
Laos Laos 34.1 +39.6% 24
Latvia Latvia 41.2 -22.2% 21
Moldova Moldova 31.3 -17.6% 27
Mali Mali 11.6 -34.3% 49
Malta Malta 68.8 +37.9% 5
Malaysia Malaysia 37 +53.9% 23
Namibia Namibia 60.2 +137% 9
Papua New Guinea Papua New Guinea 75.1 +1.91% 3
Senegal Senegal 20.9 +20.4% 35
Serbia Serbia 15.3 -59.9% 43
South Sudan South Sudan 64.9 +279% 6
Slovenia Slovenia 44.9 +2.09% 16
Sweden Sweden 49.2 -20.5% 13
Eswatini Eswatini 27.7 -23.3% 29
Tajikistan Tajikistan 16 -34.2% 41
Turkmenistan Turkmenistan 26.5 31
Tonga Tonga 18.9 +70.3% 38
Tunisia Tunisia 26.7 +40.3% 30
Turkey Turkey 14.6 -52.5% 45
Uruguay Uruguay 32.1 -39.9% 26
United States United States 43 18
Uzbekistan Uzbekistan 19.3 +14.1% 37 

                    
# 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 = 'IC.FRM.TRNG.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 <- 'IC.FRM.TRNG.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))