The unemployment rate in India jumped to 29% since the country went into lockdown from March 2020, says the report of CMIE – Centre For Monitoring Indian Economy. The lockdown to contain the coronavirus outbreak has forced many industries to shut down thus increasing unemployment across the country.

The unemployment rate in India rose to 7.2 percent in February 2019, the highest since September 2016, and also up from 5.9 percent in February 2018, according to the latest data compiled by the Centre for Monitoring Indian Economy (CMIE).

What is Unemployment?

Unemployment occurs when a person who is actively searching for employment is unable to find work. Unemployment is often used as a measure of the health of the economy. The most frequent measure of unemployment is the unemployment rate, which is the number of unemployed people divided by the number of people in the labor force.

National Sample Survey Organization (NSSO) defines employment and unemployment on the following activity statuses of an individual:

• Working (engaged in an economic activity) i.e. ‘Employed’.
• Seeking or available for work i.e. ‘Unemployed’.
• Neither seeking nor available for work.

The first two constitutes labour force and unemployment rate is the percent of the labour force that is without work.

Unemployment rate = (Unemployed Workers / Total labour force) × 100

Types of Unemployment in India

• Disguised Unemployment:
  • It is a phenomenon wherein more people are employed than actually needed.
  • It is primarily traced in the agricultural and the unorganised sectors of India.
• Seasonal Unemployment:
  • It is an unemployment that occurs during certain seasons of the year.
  • Agricultural labourers in India rarely have work throughout the year.
• Structural Unemployment:
  • It is a category of unemployment arising from the mismatch between the jobs available in the market and the skills of the available workers in the market.
  • Many people in India do not get job due to lack of requisite skills and due to poor education level, it becomes difficult to train them.
• Cyclical Unemployment:
  • It is result of the business cycle, where unemployment rises during recessions and declines with economic growth.
  • Cyclical unemployment figures in India are negligible. It is a phenomenon that is mostly found in capitalist economies.
• Technological Unemployment:
  • It is loss of jobs due to changes in technology.
  • In 2016, World Bank data predicted that the proportion of jobs threatened by automation in India is 69% year-on-year.
• Frictional Unemployment:
  • The Frictional Unemployment also called as Search Unemployment, refers to the time lag between the jobs when an individual is searching for a new job or is switching between the jobs.
  • In other words, an employee requires time for searching a new job or shifting from the existing to a new job, this inevitable time delay causes the frictional unemployment. It is often considered as a voluntary unemployment because it is not caused due to the shortage of job, but in fact, the workers themselves quit their jobs in search of better opportunities.
• Vulnerable Employment:
  • This means, people working informally, without proper job contracts and thus sans any legal protection. These persons are deemed ‘unemployed’ since records of their work are never maintained.
  • It is one of the main types of unemployment in India.

Related Terms

• Unemployment trap is a situation when unemployment benefits discourage the unemployed to go to work. People find the opportunity cost of going to work too high when one can simply enjoy the benefits by doing nothing.
  • Description: While the purpose of social security and welfare systems is to provide relief to the unemployed, they end up providing them with an incentive not to return to work. An unemployment trap arises when opportunity cost of going to work is higher than the income received, discouraging people from returning to work and being productive.
• Harmonised unemployment rates define the unemployed as people of working age who are without work, are available for work, and have taken specific steps to find work. The uniform application of this definition results in estimates of unemployment rates that are more internationally comparable than estimates based on national definitions of unemployment.
  • This indicator is measured in numbers of unemployed people as a percentage of the labour force and it is seasonally adjusted. The labour force is defined as the total number of unemployed people plus those in civilian employment.

Measurement of Unemployment in India

National Sample Survey Office (NSSO), an organization under Ministry of Statistics and Programme Implementation (MoSPI) measures unemployment in India on following approaches:

• Usual Status Approach: This approach estimates only those persons as unemployed who had no gainful work for a major time during the 365 days preceding the date of survey.
• Weekly Status Approach: This approach records only those persons as unemployed who did not have gainful work even for an hour on any day of the week preceding the date of survey.
• Daily Status Approach: Under this approach, unemployment status of a person is measured for each day in a reference week. A person having no gainful work even for 1 hour in a day is described as unemployed for that day.

Unemployment stats (based on findings from CMIE’s latest data):

• The unemployment rate in India rose to 7.2 percent in February 2019, the highest since September 2016, and up from 5.9 percent in February 2018.
• The total number of employed persons in February 2019 is estimated at 400 million against 406 million in the year-ago period and 407.5 million employed in February 2017.
• The labour participation rate fell from 43.2% in January 2019 to 42.7% in February 2019.
  • Labour Participation Rate defines that section of working population in the economy which is currently employed or seeking employment.

Causes of Unemployment

• Large population.
• Low or no educational levels and vocational skills of working population.
• Inadequate state support, legal complexities and low infrastructural, financial and market linkages to small/ cottage industries or small businesses, making such enterprises unviable with cost and compliance overruns.
• Huge workforce associated with informal sector due to lack of required education/ skills, which is not captured in any employment data. For ex: domestic helpers, construction workers etc.
• The syllabus taught in schools and colleges, being not as per the current requirements of the industries. This is the main cause of structural unemployment.
• Inadequate growth of infrastructure and low investments in manufacturing sector, hence restricting employment potential of secondary sector.
• Low productivity in agriculture sector combined with lack of alternative opportunities for agricultural worker which makes transition from primary to secondary and tertiary sectors difficult.
• Regressive social norms that deter women from taking/continuing employment.

Impact

• The problem of unemployment gives rise to the problem of poverty.
• Young people after a long time of unemployment indulge in illegal and wrong activities for earning money. This also leads to increase in crime in the country.
• Unemployed persons can easily be enticed by antisocial elements. This makes them lose faith in democratic values of the country.
• It is often seen that unemployed people end up getting addicted to drugs and alcohol or attempts suicide, leading losses to the human resources of the country.
• It also affects economy of the country as the workforce that could have been gainfully employed to generate resources actually gets dependent on the remaining working population, thus escalating socioeconomic costs for the State. For instance, 1 percent increase in unemployment reduces the GDP by 2 percent

Steps Taken by Government

• Integrated Rural Development Programme (IRDP) was launched in 1980 to create full employment opportunities in rural areas.
• Training of Rural Youth for Self-Employment (TRYSEM): This scheme was started in 1979 with objective to help unemployed rural youth between the age of 18 and 35 years to acquire skills for self-employment. Priority was given to SC/ST Youth and Women.
• RSETI/RUDSETI: With the aim of mitigating the unemployment problem among the youth, a new initiative was tried jointly by Sri Dharmasthala Manjunatheshwara Educational Trust, Syndicate Bank and Canara Bank in 1982 which was the setting up of the “RURAL DEVELOPMENT AND SELF EMPLOYMENT TRAINING INSTITUTE” with its acronym RUDSETI near Dharmasthala in Karnataka. Rural Self Employment Training Institutes/ RSETIs are now managed by Banks with active co-operation from the Government of India and State Government.
• By merging the two erstwhile wage employment programme – National Rural Employment programme (NREP) and Rural Landless Employment Guarantee Programme (RLEGP) the Jawahar Rozgar Yojana (JRY) was started with effect from April, 1, 1989 on 80:20 cost sharing basis between the centre and the States.
• Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA):
  • It is an employment scheme that was launched in 2005 to provide social security by guaranteeing a minimum of 100 days paid work per year to all the families whose adult members opt for unskilled labour-intensive work.
  • This act provides Right to Work to people.
• Pradhan Mantri Kaushal Vikas Yojana (PMKVY), launched in 2015 has an objective of enabling a large number of Indian youth to take up industry-relevant skill training that will help them in securing a better livelihood.
• Start Up India Scheme, launched in 2016 aims at developing an ecosystem that promotes and nurtures entrepreneurship across the country.
• Stand Up India Scheme, launched in 2016 aims to facilitate bank loans between Rs 10 lakh and Rs. 1 crore to at least one SC or ST borrower and at least one women borrower per bank branch for setting up a greenfield enterprise.

Way Forward

• There are number of labour intensive manufacturing sectors in India such as food processing, leather and footwear, wood manufacturers and furniture, textiles and apparel and garments. Special packages, individually designed for each industry are needed to create jobs.
• Public investment in sectors like health, education, police and judiciary can create many government jobs.
• Decentralisation of Industrial activities is necessary so that people of every region get employment.
• Development of the rural areas will help mitigate the migration of the rural people to the urban areas thus decreasing the pressure on the urban area jobs.
• Entrepreneurs generate employments to many in a country; therefore government needs to encourage entrepreneurship among the youth.
• Concrete measures aimed at removing the social barriers for women’s entry and their continuous participation in the job market is needed.
• Government needs to keep a strict watch on the education system and should try to implement new ways to generate skilled labour force.
• Effective implementation of present programs like Make in India, Skill India, Start up and Stand-Up India.
• There is a need for National Employment Policy (NEP) that would encompass a set of multidimensional interventions covering a whole range of social and economic issues affecting many policy spheres and not just the areas of labour and employment. The policy would be a critical tool to contribute significantly to achieve the goals of the 2030 Agenda for Sustainable Development.
• The underlying principles for the National Employment Policy may include
  • enhancing human capital through skill development;
  • creating sufficient number of decent quality jobs for all citizens in the formal and informal sectors to absorb those who are available and willing to work;
  • strengthening social cohesion and equity in the labour market;
  • coherence and convergence in various initiatives taken by the government;
  • supporting the private sector to become the major investor in productive enterprises;
  • supporting self-employed persons by strengthening their capabilities to improve their earnings;
  • ensuring employees’ basic rights and developing an education training and skill development system aligned with the changing requirements of the labour market.

Machine learning is a trendy topic in this age of Artificial Intelligence. The fields of computer vision and Natural Language Processing (NLP) are making breakthroughs that no one could’ve predicted. We see both of them in our lives more and more, facial recognition in your smartphones, language translation software, self-driving cars and so on. What might seem sci-fi is becoming a reality, and it is only a matter of time before we attain Artificial General Intelligence.

In this article, I will be covering Jeff Dean’s keynote on the advancements of computer vision and language models and how ML will progress towards the future from the perspective of model building.

The field of Machine learning is experiencing exponential growth today, especially in the subject of computer vision. Today, the error rate in humans is only 3% in computer vision. This means computers are already better at recognizing and analyzing images than humans. What an amazing feat! Decades ago, computers were hunks of machinery the size of a room; today, they can perceive the world around us in ways that we never thought possible.

The progress we’ve made from 26% error in 2011 to 3% error in 2016 is hugely impactful. The way I like to think is, computers have now evolved eyes that work. — Jeff Dean

Now this achievement — made possible with advancements in machine learning — isn’t just a celebration for computer geeks and AI experts, it has real-world applications that save lives and make the world a better place. Before I blab about a life-saving application of computer vision, let me illustrate to you the power of computer vision.

Let’s say I give you 10,000 pictures of dogs and I ask you to classify them into their respective species, are you able to do that? Well, you can, but you have to be a dog expert and it’ll take days by the time you’re done. But for a computer (with a GPU), this takes mere minutes. This incredible capability of computer vision opens up a profusion of applications.

Application of computer vision

One quintessential application for computer vision given by Jeff Dean is in diabetic retinopathy — which is a diabetes complication that affects the eye. Now to diagnose it, an extensive eye exam is required. In third-world countries and rural villages where there is a paucity of doctors, a machine learning model that uses computer vision to make a diagnosis will be extremely beneficial. As with all medical imaging fields, this computer vision can also be a second opinion for the domain experts, ensuring the credibility of their diagnosis. Generally, the purpose of computer vision in the medical field is to replicate the expertise of specialists and deploy it in places where people need it the most.

NLP and Transformers

 Language models are algorithms that help machines understand the text and perform all kinds of operations such as translating text. According to Jeff Dean, a lot of progress has been made in language models.

Today, computers can understand paragraphs of text at a much deeper level than they could before. Even though they aren’t at the level of reading an entire book and understanding it the way we humans do, the ability to understand a few paragraphs of text is fundamental to things such as improving the Google search system.

The BERT model, the latest Natural Language Processing (NLP) model that Google announced has been put to use in their search ranking algorithms, This helped enhance the search results for myriads of different kinds of queries that were previously very difficult. In other words, the search system can now better understand different kinds of searches done by users and help provide better and more accurate answers.

“Deep learning and machine learning architectures are going to change a lot in the next few years. You can see a lot of this already, where now with NLP, the only game in town basically is Transformer networks,” — Yann LeCun

These Transformer-based models for translation are showing spectacular gains in the BLEU score, which is a measurement of translation quality. So, Machine Learning architectures that utilize transformers such as BERT are increasing in popularity and functionality.

The problem with ML today

In the keynote, the Google Senior Fellow mentioned atomic models that Machine Learning developers use today to perform all kinds of unit tasks. He believes these models are inefficient and computationally expensive, and more effort is required to achieve good results in those tasks.

To elaborate, in the ML world today, experts find a problem that they want to solve and they focus on finding the right dataset to train the model and perform that particular task. Dean argues that by doing so, they basically start from zero — they initialize the parameter of the model with random floating points and then try to learn about everything that tasks from the dataset.

To elaborate on this matter, he gives an excellent comparison that goes like this:

“It’s akin to when you want to learn something new, you forget all your education and you go back to being an infant, and now you try to learn everything about this task”

He compares this methodology with humans becoming infants every time we want to learn something new and taking a brain out and putting in a different one in. Not only is this method computationally expensive, but more effort is also required to achieve good outcomes in those tasks. And Jeff Dean proposes a solution.

Epitome

Computer vision and NLP will continue to play a significant role in our lives. But there are adverse implications to this advancement as well, such as China using facial recognition to implement a rating system on the people (straight out of an episode from the TV show black mirror) and the proliferation of fake news. We must progress in Machine Learning while taking into account of algorithmic biases and ethics that remind us of our place, a creation of God and not creators.

As for the uber model, there is much evidence proving we are inching closer and closer towards it. For example, transfer learning — a way of reusing the model for a different purpose achieves good results with fewer data and multi-task learning — a model that operates at small scales of five or six related things all tend to make things work well.

Thus, it’s logical to say that the realization of an uber model is plausible by extending those ideas — transfer learning and multi-task learning — out and developing on them, it’s only a matter of when and not how.

Thanks for reading my excerpt on the future of ML and my synopsis of Jeff Dean’s keynote. I hope you got a glimpse of what is to come in Machine Learning and AI.