Persistence to grade 5, female (% of cohort)

Source: worldbank.org, 03.09.2025

Year: 2019

Flag Country Value Value change, % Rank
Aruba Aruba 94.3 -5.47% 49
Albania Albania 90.1 +1.61% 56
Argentina Argentina 96 +1.1% 41
Burundi Burundi 63.6 +11% 74
Belgium Belgium 98.1 +0.0733% 32
Benin Benin 47.7 +13% 78
Burkina Faso Burkina Faso 73.2 -1.2% 73
Bosnia & Herzegovina Bosnia & Herzegovina 94 -0.401% 50
Belize Belize 89.9 -6.61% 57
Bolivia Bolivia 97.8 -1.18% 33
Brunei Brunei 99.5 +1.46% 16
Switzerland Switzerland 99.5 -0.18% 18
Chile Chile 98.6 -1.34% 26
China China 99.8 +6.45% 8
Côte d’Ivoire Côte d’Ivoire 80.5 -4.23% 70
Colombia Colombia 97.5 +0.105% 36
Cape Verde Cape Verde 91.9 -1.64% 52
Cuba Cuba 96.5 -0.644% 39
Cyprus Cyprus 99.1 -0.0766% 22
Czechia Czechia 99.6 +0.0215% 15
Djibouti Djibouti 88.5 +25% 61
Denmark Denmark 99.9 +0.0295% 5
Dominican Republic Dominican Republic 89.2 -7.14% 59
Ecuador Ecuador 95.9 -1.02% 44
Egypt Egypt 100 +0.223% 2
Spain Spain 99.8 -0.214% 9
Estonia Estonia 99.7 -0.28% 13
Finland Finland 99.7 +0.209% 12
Fiji Fiji 80.1 -14% 71
United Kingdom United Kingdom 99.8 +0.0596% 10
Georgia Georgia 99.1 +0.0781% 24
Gambia Gambia 88.7 -1.66% 60
Greece Greece 99.7 +0.303% 11
Guatemala Guatemala 82.8 +5.06% 69
Hong Kong SAR China Hong Kong SAR China 98.5 -0.45% 28
Honduras Honduras 83.6 -5.76% 68
India India 99.4 +14.1% 19
Iceland Iceland 98.5 -1.38% 29
Israel Israel 98.2 -0.409% 30
Italy Italy 99.9 +0.0364% 3
Jamaica Jamaica 59.1 -37.7% 75
Jordan Jordan 96.2 -0.921% 40
Cambodia Cambodia 87.8 +0.642% 63
South Korea South Korea 99.2 -0.137% 21
Laos Laos 84.5 -0.458% 67
Lebanon Lebanon 89.5 +0.376% 58
St. Lucia St. Lucia 96 +3.59% 42
Sri Lanka Sri Lanka 99.6 -0.208% 14
Latvia Latvia 97.5 +0.226% 37
Macao SAR China Macao SAR China 99.5 -0.284% 17
Morocco Morocco 97.5 +0.502% 38
Mexico Mexico 97.7 -0.408% 35
Marshall Islands Marshall Islands 77 -5.91% 72
North Macedonia North Macedonia 93.4 +2.69% 51
Malta Malta 99 +1.28% 25
Montenegro Montenegro 99.8 +0.778% 6
Mozambique Mozambique 56 -1.6% 76
Mauritius Mauritius 98.1 +1.43% 31
Malaysia Malaysia 99.2 +6.22% 20
Norway Norway 100 +0.274% 1
Philippines Philippines 98.5 -0.886% 27
Qatar Qatar 95.9 +7% 43
Romania Romania 95.4 +1.1% 46
Senegal Senegal 87.2 +18.7% 65
Singapore Singapore 99.9 +0.0781% 4
Sierra Leone Sierra Leone 49.7 +76.2% 77
El Salvador El Salvador 87.1 -0.984% 66
San Marino San Marino 97.8 +0.963% 34
Suriname Suriname 94.7 -2.55% 48
Slovenia Slovenia 99.8 -0.0326% 7
Sweden Sweden 100 0% 1
Tonga Tonga 91.5 -0.0952% 54
Tanzania Tanzania 87.2 -3.42% 64
Uruguay Uruguay 99.1 -0.781% 23
United States United States 90.7 55
Vanuatu Vanuatu 91.7 +23.2% 53
Samoa Samoa 88.2 -8.11% 62
South Africa South Africa 95.6 -4.02% 45
Zimbabwe Zimbabwe 95.1 +0.684% 47 

                    
# 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.FE.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.FE.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))