Primary School Enrolment in India- 2013-16

In developing economies like India, the education system is a key indicator of economic growth. A hallmark of a good education system is the nature of inclusiveness- the enrolment across the demography of the country. India is a vast country with 28 states and 8 Union Territories. To study enrolment in the country, we will study the importance of enrolment in primary education across the length and breadth of the country, by gender, and the association with the facilities in the schools.

As previously mentioned, the document will address the enrolment in the schools by state. The study will also look into the disparity in school enrolment and facilities by gender. This is an accurate indicator of the socio-economic situation in the country with respect the inclusiveness of male and female enrolment in the schooling system.

Dataset

The dataset is published by the department of higher education in India. The dataset spans from 2012 to 2016, and it includes information of enrolment, drop out, and school facilities. For the puurposes of our study, we will look at primary school information, and dive deeper into the enrolment and school facilities in the country.

This document provides the R code that is executable, and the packages used in this exercise are dplyr, tidyverse, and ggplot2, and plotly.

Rpubs link:

Instructions: In case you want to run the file locally, set the directory using the provision provided in line #53.

Proposed Design

Enrolment Ratio by State

Enrolment Ratio by State

Growth/Decline of Enrolment Ratio

Growth/Decline of Enrolment Ratio

Parity in Facilities by Gender

Parity in Facilities by Gender

Load the data

setwd('C:\\Sashank\\Sashank\\SMU Fintech Courses\\Term 3\\Visual\\Assignment\\Assignment 5\\Submission')
input<- read.csv('gross-enrollment-ratio-2013-2016.csv')
enroled<-input%>%
  select(State_UT,Year,Primary_Boys,Primary_Girls, Primary_Total) 

input2<- read.csv('input.csv')
input3<- read.csv('schools-with-boys-toilet-2013-2016.csv')
input4<-read.csv('schools-with-girls-toilet-2013-2016.csv')

Enrolment in Primary school

In this segment, we will look at the primary school enrolment by state in India. We will look at this from a function of time, from 2012-2016. Essentially, this depicts the growth/decline by enrolment in primary schools by states. It provides us with a view of states and rank them to understand policies and initiatives to drive primary school enrolment.

Steps to create the data viz:

  1. Our objective is to create an interactive plot to visualize the change in enrolment rates by states. For this purpose, we will use the plotly function in R.
  2. Using ggplot, we will develop the visualization by using the x component as States, and y as Total Primary school enrolment.
  3. We will use geom_segment to connect two points, and use geom_point to visualize the growth/decline of the enrolment ratios across years.

Note: Ratios are based on projected population provided by the department of higher education, MHRD.

states<-enroled%>%
    

  ggplot(aes(x=State_UT, y=Primary_Total,color=Year))+geom_segment(aes(x=State_UT, xend=State_UT, y=0, yend=Primary_Total), color="grey")+geom_point(size=2)+coord_flip()+labs(title="Figure 1: Primary School Enrolment, 2013-2016", y="Total Enrolment Ratio",  caption="Source: Department of Higher Education: India")+theme(plot.caption = element_text(hjust = 1, face = "italic"))+scale_color_manual(values=c("#F13B17","#4472C4","#24C618"))

ggplotly(states)

From figure 1, we gather that there are a number of states that have regressed in ensuring growing enrolment ratios. States like Uttar Pradesh, Sikkim, Pondicherry, Nagaland, Manipur, Madhya Pradesh, Daman and Diu, and Andhra Pradesh display decreasing enrolment ratios.

This is an indication for a need to improve policy formulation in these states to improve the primary school enrolment ratio. However, it’s important to dive deeper to view the differences in enrolment amongst male and female students, and if there are any specific reasons to the decreasing enrolment ratios.

How do facilities affect enrolment ratio in Male students from 2012-2016?

Facilities in this context referes to the availability of bathrooms in primary school for boys and girls. We want to visualize if the availability of facilities affects the enrolment in the region. We have divided the study into one for schools with facilities for boys, and the other for school with facilities for girls.

Firstly, lets look at the facilities available in schools for boys by each state in the country.

Steps Involved: a) Plot enrolment of boys in primary school by state from 2012 to 2016 b) Plot the median change in boys enrolment from 2012 to 2016 c) Compare the facilities in primary schools for boys with facilities in primary schools for girls d) Growth/decline of facilities in primary schools for boys from 2012 to 2016 by state

states2<-enroled%>%
  ggplot(aes(x=State_UT, y=Primary_Boys,color=Year))+geom_segment(aes(x=State_UT, xend=State_UT, y=0, yend=Primary_Boys), color="grey")+geom_point(size=2)+coord_flip()+labs(title="Figure 2: Primary School Boys Enrolment, 2013-2016", y="Total Enrolment Ratio",  caption="Source: Department of Higher Education: India")+theme(plot.caption = element_text(hjust = 1, face = "italic"))+scale_color_manual(values=c("#F13B17","#4472C4","#24C618"))
ggplotly(states2)
boys<-enroled%>%
  ggplot(aes( x= Year,y=Primary_Boys),color=State_UT)+stat_summary(geom='point',color="green",fun='median',fill='lightgreen')+labs(title="Figure 3: Growth/Decline in Enrolment Ratio amongst boys",x="Enrolment Ratio", y="Year",  caption="Source: Department of Higher Education: India")
ggplotly(boys)

A good sign of the education facilities in India is that there is no disparity in the facilities available in primary schools for males and females. Primary school enrolement for boys follows a similar pattern to the overall enrolment of student in Primary school in the country.

boysvgirls<-input2%>%
   
  ggplot(aes(x = Facilities_Primary_Only_Girls, y = Facilities_Primary_Only_Girls)) + 
  geom_line(alpha=0.7, colour = "#51A0D5") + 
  labs(x = "Facilities in Girls Primary Schools", 
       y = "Facilities in Boys Primary Schools",
       title = "Figure 4: Parity in facilities by Gender") +
  theme_classic()

ggplotly(boysvgirls)
boys2<-input3%>%
  ggplot(aes(x=State_UT, y=Primary_Only,color=year))+geom_segment(aes(x=State_UT, xend=State_UT, y=0, yend=Primary_Only), color="grey")+geom_point(size=2)+coord_flip()+labs(title="Figure 5: Primary School Facilities for Boys, 2013-2016", y="Ratio of schools with facilities",  caption="Source: Department of Higher Education: India")+theme(plot.caption = element_text(hjust = 1, face = "italic"))


ggplotly(boys2)

However, the enrolment ratios have a seen a consistent drop from 2013 to 2016. Moreover, there is a significant drop in the enrolment ratios from 2013 to 2014. Almost all states have ensured continuous increase in the facilities from 2013-2016. Interestingly, there is no association between enrolment and the facilities available at the schools.

How do facilities affect enrolment ratio in FeMale students from 2012-2016?

In India, its well documented that there are disparities between males and females, and its manifestation in the education system. In view of the same, we will look at the availability of facilities in primary schools for girls.

Steps Involved: a) Plot enrolment of girls in primary school by state from 2012 to 2016 b) Plot the median change in girls enrolment from 2012 to 2016 c) Compare the facilities in primary schools for girls with facilities in primary schools for girls d) Growth/decline of facilities in primary schools for girls from 2012 to 2016 by state

states3<-enroled%>%

  ggplot(aes(x=State_UT, y=Primary_Boys,color=Year))+geom_segment(aes(x=State_UT, xend=State_UT, y=0, yend=Primary_Boys), color="grey")+geom_point(size=2)+coord_flip()+labs(title="Figure 6: Primary School Enrolment for Girls, 2013-2016", y="Total Enrolment",  caption="Source: Department of Higher Education: India")+theme(plot.caption = element_text(hjust = 1, face = "italic"))+scale_color_manual(values=c("#F13B17","#4472C4","#24C618"))


girls<-enroled%>%
  ggplot(aes( x= reorder(Year,-Primary_Girls),y=Primary_Girls))+stat_summary(geom='point',fun='median',color='red',fill='lightblue')+ labs(title="Figure 7: Growth/Decline of Primary School Enrolment for Girls, 2013-2016",x="Year", y="Primary Girls Enrolment Ratio",  caption="Source: SingStat")+scale_color_manual(values=c("#F13B17","#4472C4","#24C618"))




girls2<-input4%>%
  ggplot(aes(x=State_UT, y=Primary_Only,color=year))+geom_segment(aes(x=State_UT, xend=State_UT, y=0, yend=Primary_Only), color="grey")+geom_point(size=2)+coord_flip()+labs(title="Figure 8: Primary School Facilities for Girls, 2013-2016", y="Ratio of schools with facilities",  caption="Source: Department of Higher Education: India")+theme(plot.caption = element_text(hjust = 1, face = "italic"))+scale_color_manual(values=c("#F13B17","#4472C4","#24C618"))

ggplotly(states3)
ggplotly(girls)
ggplotly(girls2)

The decline in enrolment ratios from 2013 to 2016 are fairly consistent compared to the decline in the enrolment for boys. As was evident in the facility availability for preimary school for boys, there is a conistent increase in the availability of facilities for girls in most states across the country. As withnessed in the chart above, some states exhibit higher increase in the availability of facilities in the primary school for girls. States like Maharastra, and Goa are those that exhibit slow growth in availability of facilities. However, these states indicate a full availability of facilities in primary schools for girls.

Data Viz

The final visulaization combines the Figures 1-8. Insights and observations can be derived from the following visulaizations:

Insights

From the combined data visualization:

  1. The enrolment ratio in India from 2013 to 2016 has seen a consistent drop from 2013 to 2016. Diving deeper into the difference in enrolment ratios by gender, we observe that the drop in enrolment ratio in boys experiences a steep drop in 2014, as compared to a more consistent drop in the enrolment ratio in girls of primary schools.

  2. A good sign of the education facilities in India is that there is no disparity in the facilities available in primary schools for males and females. Primary school enrolement for boys follows a similar pattern to the overall enrolment of student in Primary school in the country.

However, the enrolment ratios have a seen a consistent drop from 2013 to 2016. Moreover, there is a significant drop in the enrolment ratios from 2013 to 2014. Almost all states have ensured continuous increase in the facilities from 2013-2016. Interestingly, there is no association between enrolment and the facilities available at the schools.

  1. From figure 1, we gather that there are a number of states that have regressed in ensuring growing enrolment ratios. States like Uttar Pradesh, Sikkim, Pondicherry, Nagaland, Manipur, Madhya Pradesh, Daman and Diu, and Andhra Pradesh display decreasing enrolment ratios.

This is an indication for a need to improve policy formulation in these states to improve the primary school enrolment ratio. However, it’s important to dive deeper to view the differences in enrolment amongst male and female students, and if there are any specific reasons to the decreasing enrolment ratios. For instance, increasing the facilities doesn’t necessarily increase the enrolment ratios. Hence, different policies should be implemented.

Moreover, India is a vast country and other soci-economic indicators must be studied to better understand the reasons behind the enrolment in primary schools in different regions in the country.