Employment to population ratio, 15+, total (%)

Source: worldbank.org, 19.12.2024

Year: 2023

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 76.8 +2.14% 2
Argentina Argentina 58.5 +2.03% 45
Australia Australia 64.7 +0.549% 17
Austria Austria 58.4 -0.132% 47
Belgium Belgium 51.9 -0.154% 69
Burkina Faso Burkina Faso 44.9 -35.1% 78
Bulgaria Bulgaria 53.3 -1.67% 64
Bahamas Bahamas 89.2 +27.1% 1
Bosnia & Herzegovina Bosnia & Herzegovina 46.3 +3.63% 75
Belarus Belarus 67.3 -0.57% 10
Bolivia Bolivia 76 +0.693% 3
Brazil Brazil 57.8 +0.468% 50
Brunei Brunei 60.5 +1.61% 35
Bhutan Bhutan 63 +6.1% 22
Botswana Botswana 52 +5.32% 68
Canada Canada 62.1 +0.126% 29
Switzerland Switzerland 64.9 +0.939% 16
Chile Chile 56 +1.45% 58
Colombia Colombia 57.6 +1.99% 52
Costa Rica Costa Rica 52.1 -4.45% 67
Cyprus Cyprus 61.4 +1.22% 31
Czechia Czechia 58.4 -0.217% 46
Germany Germany 59.6 +0.408% 40
Denmark Denmark 60.4 -0.253% 36
Dominican Republic Dominican Republic 60.7 +1.62% 34
Ecuador Ecuador 62.2 -0.505% 27
Spain Spain 51 +1.15% 70
Estonia Estonia 62.2 +0.374% 28
Finland Finland 56.7 -0.5% 56
France France 52.2 +0.221% 66
United Kingdom United Kingdom 59.7 -0.251% 39
Gambia Gambia 44.5 -23.8% 79
Greece Greece 46.4 +1.42% 74
Guatemala Guatemala 66.6 +14.1% 12
Hong Kong SAR China Hong Kong SAR China 55.6 -0.11% 60
Honduras Honduras 53.1 -0.815% 65
Croatia Croatia 49.1 +0.867% 71
Hungary Hungary 58.5 +0.643% 44
Indonesia Indonesia 65.7 +1.47% 15
India India 53.4 +7.22% 63
Ireland Ireland 62.7 +1.54% 25
Iceland Iceland 72.4 +0.867% 4
Israel Israel 62.9 +0.828% 24
Italy Italy 46.1 +2.06% 76
Jamaica Jamaica 64.4 +3.85% 18
Japan Japan 61.2 +0.548% 32
South Korea South Korea 62.9 +0.805% 23
Lithuania Lithuania 58.8 -0.58% 42
Luxembourg Luxembourg 59 +0.485% 41
Latvia Latvia 57.2 +0.434% 55
Moldova Moldova 70.3 -3.12% 6
Mexico Mexico 58.7 +1.58% 43
North Macedonia North Macedonia 45 -0.352% 77
Malta Malta 63.6 -7.24% 20
Mongolia Mongolia 57.3 -2.12% 53
Mauritius Mauritius 54.7 +6.43% 62
Netherlands Netherlands 66.1 +0.94% 14
Norway Norway 63.3 -0.428% 21
New Zealand New Zealand 69.4 +0.632% 7
Panama Panama 57.7 +0.0381% 51
Peru Peru 69.1 -3.02% 8
Poland Poland 57.2 +0.889% 54
Portugal Portugal 55.6 +0.88% 61
Paraguay Paraguay 66.7 +1.27% 11
Romania Romania 48.6 -0.666% 72
Russia Russia 60.8 +1.57% 33
Rwanda Rwanda 55.8 +9.22% 59
Saudi Arabia Saudi Arabia 63.9 +10.9% 19
Singapore Singapore 66.2 -1.87% 13
El Salvador El Salvador 60.1 +2.45% 38
Serbia Serbia 53.4 +1.94% 63
Slovakia Slovakia 58 +0.462% 48
Slovenia Slovenia 56.4 -0.339% 57
Sweden Sweden 62.4 +0.472% 26
Seychelles Seychelles 61.4 -2.59% 30
Thailand Thailand 67.7 +1.25% 9
Tunisia Tunisia 38.5 -0.691% 80
Turkey Turkey 48.3 +1.69% 73
Uruguay Uruguay 57.9 -0.56% 49
United States United States 60.3 +0.602% 37
Vietnam Vietnam 71 -2.06% 5
South Africa South Africa 37.4 +4.64% 81
Zimbabwe Zimbabwe 61.4 +4.8% 30 

                    
# 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 = 'SL.EMP.TOTL.SP.NE.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 <- 'SL.EMP.TOTL.SP.NE.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))