Net intake rate in grade 1, female (% of official school-age population)

The net intake rate in grade 1 for females, expressed as a percentage of the official school-age population, stands as a vital indicator within the realm of educational assessment. It explicitly measures the proportion of girls who are entering the first grade of primary school for the first time, relative to the total number of girls who are of the official school age for enrollment. This metric is essential as it reflects not only educational access but also gender equity in education systems.

The importance of monitoring this indicator cannot be overstated, as it has direct implications for broader societal development. A high net intake rate suggests that a significant number of girls are being afforded the opportunity to receive an education, which can lead to improved literacy rates and enhanced economic opportunities for women in the long term. Furthermore, education is a critical player in promoting gender equality, empowering women to participate fully in society, and fostering sustainable development. Conversely, low net intake rates raise alarms about barriers that girls may face in accessing education—whether those barriers are economic, cultural, or infrastructural.

This indicator does not stand alone; it is interlinked with various other educational and social indicators. For instance, gains in female net intake rates can correlate with improvements in overall school enrollment rates, educational attainment levels, and ultimately, even employment rates for women. Similarly, it can be juxtaposed against adolescent pregnancy rates, poverty levels, and access to health services. The relationships between these metrics can paint a clearer picture of the educational landscape and its impact on female empowerment.

Several factors can affect the net intake rate for females in grade 1. Socio-economic conditions play a crucial role; families that experience financial hardships may prioritize education less, especially for girls who might be seen as less valuable in terms of economic prospects. Cultural attitudes towards female education can also significantly influence these rates. In regions where traditional norms discourage female schooling, parents may opt to keep girls at home. Additionally, the availability and quality of schools dictate whether families believe sending their daughters to school is a viable option. Infrastructure challenges further compound these issues, particularly in rural areas where schools may be distant or poorly equipped.

Addressing the challenges reflected by the net intake rate in grade 1 for females requires a multi-faceted approach. First and foremost, governments and educational authorities must prioritize gender-responsive education policies to ensure that girls have equal access to enrollment opportunities. Investing in community awareness programs can help shift cultural perceptions concerning girls' education. Financial incentives, such as scholarships or conditional cash transfers for families that send their daughters to school, can also be effective in increasing enrollment rates. Furthermore, improving the infrastructure surrounding education—building more schools in underserved areas, training more female teachers, and ensuring that schools are safe environments—can significantly affect girls’ education.

However, there are inherent flaws in relying solely on the net intake rate as an indicator of educational success. While it provides valuable insights, it does not account for the quality of education or retention rates. A high net intake rate could be misleading if there are high dropout rates later in the schooling system, which are often overlooked in discussions focused solely on initial enrollment. It is vital to complement this indicator with others that measure educational quality, such as results from standardized assessments, to gain a holistic understanding of educational outcomes for girls.

Analyzing recent data provides a clearer picture of the current landscape of female net intake rates. In 2019, the median value stood at 54.87%, reflecting significant disparities across various regions and countries. Among the top areas, Djibouti emerged with a notable net intake rate of 64.31%, suggesting that particular interventions may have successfully increased school access for girls. Conversely, Kazakhstan also had a figure of 45.43%, but this placement highlights the challenges that persist within the region; particularly in a country which exhibited both figures, indicating that various regions in Kazakhstan might be experiencing disparate conditions leading to lower female enrollment in initial grades.

World values between 2005 and 2018 illustrate a gradual upward trend in net intake rates, demonstrating some progress made in increasing female access to primary education. The progression from 64.18% in 2005 to 69.48% by 2018 indicates an encouraging improvement of roughly five percentage points over a decade. However, this rise also underscores the necessity of continuous focus on amplifying efforts to ensure that this progress is sustained and universally applied, particularly as these percentages still reflect the global disparities faced by different regions and communities.

In summary, the net intake rate in grade 1 for females serves as a significant indicator of educational equity and access. While strides have been made over the years to improve female enrollment in primary education globally, numerous factors conditional on socio-economic and cultural climates continue to influence these numbers. Emphasizing comprehensive strategies that tackle both the root barriers to education for girls and the qualitative aspects of education is paramount to building a more equitable future where every girl has the opportunity to learn and thrive within educational institutions.

                    
# 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.NINT.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.NINT.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))