Persistence to grade 5, male (% of cohort)

Source: worldbank.org, 03.09.2025

Year: 2019

Flag Country Value Value change, % Rank
Aruba Aruba 85.7 -7.23% 60
Albania Albania 88.9 -0.61% 54
Argentina Argentina 94.4 -0.718% 45
Burundi Burundi 56.3 +13.9% 74
Belgium Belgium 96.1 -0.0247% 36
Benin Benin 50 +12.3% 75
Burkina Faso Burkina Faso 63.2 -2.69% 72
Bosnia & Herzegovina Bosnia & Herzegovina 93.2 -0.388% 48
Belize Belize 96 -0.284% 37
Bolivia Bolivia 96.4 -2% 32
Brunei Brunei 98.9 +2.35% 19
Switzerland Switzerland 100 +0.646% 1
Chile Chile 100 +0.262% 1
China China 100 +7.04% 1
Côte d’Ivoire Côte d’Ivoire 79 -4.9% 71
Colombia Colombia 93.8 +0.986% 46
Cape Verde Cape Verde 91.2 -0.899% 51
Cuba Cuba 96 -0.53% 38
Cyprus Cyprus 96 -2.59% 39
Czechia Czechia 99.6 +0.0787% 10
Djibouti Djibouti 88.1 +9.61% 56
Denmark Denmark 99.9 -0.00896% 6
Dominican Republic Dominican Republic 89.4 -4.61% 53
Ecuador Ecuador 98 +0.892% 25
Egypt Egypt 99.5 +0.467% 14
Spain Spain 99.9 +0.178% 3
Estonia Estonia 100 +0.773% 2
Finland Finland 99.7 -0.169% 8
Fiji Fiji 79 -14% 70
United Kingdom United Kingdom 99.9 -0.0313% 4
Georgia Georgia 98.8 -0.438% 21
Gambia Gambia 80.5 -3.85% 66
Greece Greece 99.6 -0.0384% 12
Guatemala Guatemala 79.9 +4.18% 67
Hong Kong SAR China Hong Kong SAR China 99.5 +1.79% 15
Honduras Honduras 79.3 -7.74% 69
India India 97.7 +10.6% 28
Iceland Iceland 99.6 +0.595% 13
Israel Israel 100 +0.542% 1
Italy Italy 99.8 +0.0657% 7
Jamaica Jamaica 86.5 -8.05% 59
Jordan Jordan 94.5 -1.45% 44
Cambodia Cambodia 80.7 -0.683% 65
South Korea South Korea 99.3 -0.053% 17
Laos Laos 79.8 -1.66% 68
Lebanon Lebanon 86.6 +0.466% 58
St. Lucia St. Lucia 93.7 -0.382% 47
Sri Lanka Sri Lanka 97.9 -0.0307% 26
Latvia Latvia 97.3 +0.15% 30
Macao SAR China Macao SAR China 99.9 +1.66% 5
Morocco Morocco 96.2 +0.408% 33
Mexico Mexico 97.5 +0.00561% 29
Marshall Islands Marshall Islands 81 -11.8% 64
North Macedonia North Macedonia 95.8 +9.28% 40
Malta Malta 98.8 +1.69% 20
Montenegro Montenegro 98.3 -0.793% 23
Mozambique Mozambique 56.9 -3.5% 73
Mauritius Mauritius 96.1 -1.65% 35
Malaysia Malaysia 95.3 +0.794% 41
Norway Norway 98.6 -0.974% 22
Philippines Philippines 96.9 -0.232% 31
Qatar Qatar 94.8 +4.75% 43
Romania Romania 94.8 +0.478% 42
Senegal Senegal 81.7 +16.2% 63
Singapore Singapore 99.4 +0.0327% 16
Sierra Leone Sierra Leone 48.4 +75.3% 76
El Salvador El Salvador 83.4 -2.16% 61
San Marino San Marino 98.1 -1.33% 24
Suriname Suriname 88.1 -1.41% 55
Slovenia Slovenia 99.6 -0.105% 9
Sweden Sweden 99.6 -0.0379% 11
Tonga Tonga 91.2 -0.315% 50
Tanzania Tanzania 81.9 -5.79% 62
Uruguay Uruguay 99.2 -0.179% 18
United States United States 96.2 34
Vanuatu Vanuatu 87 +12% 57
Samoa Samoa 92.3 -0.431% 49
South Africa South Africa 97.7 +3.64% 27
Zimbabwe Zimbabwe 90.8 +0.106% 52 

                    
# 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 = 'SE.PRM.PRS5.MA.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 <- 'SE.PRM.PRS5.MA.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))