Secondary education, vocational pupils (% female)

The indicator of 'Secondary education, vocational pupils (% female)' plays a crucial role in evaluating gender parity in education, particularly within vocational training contexts. This measurement reflects the percentage of female students enrolled in secondary vocational programs, a valuable data point that indicates not only progress toward gender equity but also the potential impact on the labor market and economy. Understanding this metric helps various stakeholders gauge where improvements are needed to ensure equal educational opportunities for all genders.

The importance of this indicator cannot be understated. A higher percentage of female students in vocational education signifies a breaking down of traditional gender roles, which can lead to expanded career opportunities for women. Moreover, this indicator has significant implications for economic development; empowering women through vocational training often results in higher household incomes, improved living standards, and greater contributions to national economies. Countries with a balanced gender representation in vocational education often experience enhanced workforce diversity, stimulating innovation and improving organizational performance.

In terms of relation to other indicators, 'Secondary education, vocational pupils (% female)' interacts closely with several data points such as unemployment rates, economic participation by gender, and overall educational attainment. For instance, countries that exhibit higher enrollment rates for females in vocational programs often see a subsequent decrease in gender disparities in employment and income levels. As more women gain technical skills, they become better equipped to enter the workforce and compete effectively, thus contributing to reduced unemployment rates among women.

Several factors influence the attendance of females in vocational secondary education. Cultural norms and societal attitudes toward gender roles play a significant role; in many societies, vocational education may be undervalued for women due to stereotypes suggesting that their skills should be aligned with traditional roles. Furthermore, economic barriers such as poverty and the need for girls to contribute to household income may hinder access to schooling. Additionally, inadequate infrastructure, a lack of female educators, and safety concerns can act as deterrents, discouraging female pupils from pursuing vocational education.

To address the gaps in female enrollment in vocational secondary education, various strategies should be implemented. Raising awareness through community education campaigns can play a pivotal role in changing perceptions about women's roles in the workforce. Policies that incentivize families to invest in their daughters' education, such as conditional cash transfer programs, can significantly mitigate financial barriers. Additionally, improving the safety of educational environments and promoting a diversified curriculum that includes more female role models can have profound effects on female students' aspirations toward vocational careers.

While efforts to increase female participation in vocational education are vital, it is essential to note potential flaws in implementation. Sometimes interventions may emphasize enrollment numbers without ensuring quality in education, leading to female students graduating without adequate skills. This mismatch can exacerbate the very issues that vocational training aims to solve. Attention should be equally directed toward creating a curriculum that is relevant to current job market needs, enabling graduates to acquire skills that enhance their employability.

Analyzing the latest available data from the year 2019, the median value of female participation in vocational secondary education is recorded at 38.98%. Notably, the top areas where female enrollment exceeds this median include Nepal with 51.18%, Kazakhstan at 42.71%, Monaco at 38.98%, Djibouti at 38.87%, and Ghana at 26.72%. While Nepal stands out with more than half of its vocational pupils being female, this suggests a progressive stance towards gender equity in vocational education. In contrast, Ghana, occupying the bottom of the rankings at 26.72%, reflects a significant imbalance that warrants urgent attention.

Interestingly, Ghana and Djibouti also appear in the top five for the lowest female participation rates, alongside Monaco and Kazakhstan, which suggests complex socio-economic and cultural dimensions that affect gender education. In the case of Kazakhstan, while its percentage of 42.71% affirms that a significant portion of vocational students are female, it still illustrates that there may be challenges ahead in achieving full gender parity.

Globally, a historical review from 1970 showcases a tumultuous journey concerning female vocational enrollment, with numbers fluctuating from an initial figure of around 42.49% declining gradually over the decades to the current median. The data from these years, reflecting various socio-political changes, indicates that while some progress has been made since the early 2000s, females still face persistent challenges in securing equal participation. The long-term trend shows diminishing returns in gender equity as the figures drop from the 44% range observed in the late 1990s to the levels reported in the 2010s. This downward trend highlights the need for renewed commitment to advancing female access to vocational education as part of a broader framework of gender equality.

In conclusion, the indicator 'Secondary education, vocational pupils (% female)' serves as a vital barometer for measuring not only educational equity but also the broader economic implications for gender parity in society. By analyzing the interplay between this indicator and other socio-economic factors, stakeholders can better strategize interventions that promote female participation in vocational education. Ultimately, fostering a well-educated and diverse workforce benefits not only women and families but society and economies at large.

                    
# 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.SEC.ENRL.VO.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.SEC.ENRL.VO.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))