Sustaining India’s growing population?

Introduction:

In the present post we will look at the growing population of India along with the changes in the sex ratio between 2001 to 2026. The population of India in 2001 was 1.02 billion which is expected to rise to 1.39 billion in 2026. The population is expected to increase by 36% in 25 years. It should be noted that the projected population data was calculated based on 2001 census.

According to the 2011 census data the actual population was 1.21 billion whereas the projected population of India in 2011(based on 2001 census) is 1.19 billion. Hence, we should be aware that the projections are estimates and actual population in 2026 could be over or under the projected 1.39 billion.

Plot showing the population projection of India from 2001 to 2026.
Plot showing the population projection of India from 2001 to 2026.

 

The projection data provides us very good tool or starting point to plan 25 years ahead (or 6 years given it is 2018) and help us answer many questions for e.g.

  • what will be the demographic structure in 25 years?
  • How much should country grow to sustain its population?
  • How many jobs should be created?
  • What will be the sex ratio in 2025 and what policies can be implemented to change it? and the list continues.

Emergence of a New Challenge:

One major challenge that the countries all around the world are facing is process automation. A lot of jobs that have helped India grow its economy can slowly disappear given they can be automated due to new technologies involving use of machine learning and neural networks. Changes such as self driving cars,  placing orders online can be a major blow to many of the local industries ( for e.g. the local grocery store). Skeptics can always argue that these technologies will not impact India given its current infrastructure.

Well yes, but given that the country is truly pushing towards accepting change and adopting technology updating the infrastructure can only be seen as a hurdle waiting to be overcome.

India need to plan well in advance to sustain this growing population.

Growing Gender gap:

One more concerning issue revealed in the plot above is the widening gender gap observed between 2001 and 2026. The gender gap widens from 933 females per 1000 males in 2001 to 930 females per 1000 males in 2026.

Plot showing the rise in gender gap in India between 2001 to 2026.
Plot showing the rise in gender gap in India between 2001 to 2026.

Data:

The data for the projected population was sourced from Office of the Registrar General & Census Commissioner, INDIA.  The data is in a PDF format which i painfully extracted. The clean data is available here.

Code:

Both the plots can be generated using the following code. To reproduce the plot save the csv file on your local folder and use the setwd(<yourpath>) function to set your folder, with data, as a working directory.

#Load the necessary Libraries:
################################
library(ggplot2)
library(dplyr)
library(lubridate)
##################################
#import and transform data:
###############################
pop <- read.csv("population_2026.csv", stringsAsFactors = FALSE)
# convert to long format
pop_l <- melt(pop, id.vars = "year",variable.name = "state",value.name = "pop") 
# add a new column
pop_l %>% mutate(pop_t=pop/1000000) -> pop_l
#convert to Year format
pop_l$year= ymd(pop_l$year)
pop_l$state=as.character(pop_l$state)
sexratio = select(pop,c(year,INDIA_m,INDIA_f)) %>% mutate(ratio=(INDIA_f/INDIA_m)*1000)
sexratio$year= ymd(sexratio$year)
#####################################
#Projected population plot
#####################################
ggplot(data = filter(pop_l, state=="INDIA_t"|state=="INDIA_m"|state=="INDIA_f"),
 aes(x=year, y = pop_t,colour=state))+
 geom_line(size=1.05)+
 scale_colour_manual(values=c("firebrick","cyan","black"),name="Population", 
 limits=c("INDIA_t","INDIA_m","INDIA_f"),
 labels=c("Total","Male","Female"))+
 scale_x_date(date_breaks = "5 years", date_labels = "%Y")+
 scale_y_continuous(sec.axis = dup_axis())+
 theme_minimal()+
 theme(plot.caption=element_text(hjust=0),
 plot.subtitle=element_text(face="italic"),
 plot.title=element_text(size=16,face="bold"))+
 labs(x="year",y="population (in Billions)",title="Projected Population of India from 2001 to 2026",
 subtitle="Source: Office of the Registrar General & Census Commissioner, INDIA")
###################################################
# plot for the gender gap
###################################################
ggplot(sexratio)+
 geom_line(aes(x=year, y = ratio))+
 scale_x_date(date_breaks = "5 years", date_labels = "%Y")+
 theme_minimal()+
 theme(plot.caption=element_text(hjust=0),
 plot.subtitle=element_text(face="italic"),
 plot.title=element_text(size=16,face="bold"))+
 labs(x="year",y="Sex Ratio (# of Females per 1000 males)",title="Projected sex ratio of India from 2001 to 2026",
 subtitle="Source: Office of the Registrar General & Census Commissioner, INDIA",
 caption="Sex Ratio = (Number of Females/ Number of Males)*1000")

 

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