Understanding Youth Unemployment In India: Using PLFS Data

Policy Update
Kirti Ranjan

Background

With a population of 1.4 billion, India is the world’s most populous country, home to 17% of the global population. Yet, with an average age of 29, it also has one of the youngest populations in the world. This youthful demographic offers a potential “demographic dividend” for the nation. However, addressing employment and unemployment challenges is essential to fully realising one’s potential. High unemployment stifles economic growth and imposes substantial social and psychological burdens. Surprisingly, the overall unemployment figure published in the Periodic Labour Force Survey (2022-23) is 3.2 % (15 years and above) according to the Usual status (ps+ss).

The definition plays a considerable role in what we see as the data point. If we tweak the definition, the number changes dramatically.

Periodic Labour Force Survey

PLFS (Periodic Labour Force Survey) provides employment and unemployment data estimates using usual status, which includes principal and subsidiary activity (US(ps+ss)). This article estimates unemployment rates using the usual principal activity using unit-level PLFS data from 2017-18 to 2022-23. With the recent focus on youth employment schemes in India, the article analyses the trends in unemployment among youth. Also, are higher-educated people less unemployed?

Before exploring data, let’s look at the key definitions. A person is regarded as unemployed when he/she is not working but seeking or available for work. The activity status on which a person spent a relatively long time (major time criterion) during the 365 days preceding the survey date is considered the person’s usual principal activity status. In subsidiary economic activity status, persons may have also pursued, in addition to his/her usual principal status, some economic activity for 30 days or more during the reference period of 365 days preceding the survey date.

So, what does it mean to be unemployed according to the usual principal activity status? Suppose that in the last 365 days before the survey, you were outside the labour force for three months; for four months, you were employed; next five months, you were unemployed. Thus, out of 9 months in which you were in the labour force, you were unemployed the majority of the time. Thus, you will be regarded as unemployed according to your usual principal status.

But in subsidiary status, you will be employed as it considers ”any work in which you are engaged with more than 30 days in a year. When we take usual status(ps+ss), it will regard this person as ”employed”. Usual status (ps+ss) regards a person as unemployed when he/she cannot find employment for even 30 days in the last 365 days. On the other hand, if we consider the definition of only the usual principal status, we look into the percentage of people who cannot find employment most of the year.

PLFS Unemployment Data

Table 1 shows the Unemployment rate between the age group of 15-59 years using the usual principal status. The major trends that can be observed are : (1) The unemployment rate, in single digits, has fallen over the last six years from 6.9 % in 2017 to 4.3 % in 2022. (2) The unemployment rate is higher in urban than rural areas. (3) The unemployment rate among women in urban areas is higher than their male counterparts.

However, the participation of women in the labour force has remained low, though it has risen from 22.9% in 2017 to 31.1% in 2022 (according to usual status,15-59 age). The overall employment data doesn’t show much concern as unemployment is in the single digits and declining. Thus, there is a need to look into different age categories further.

According to National Youth Policy 2014, people between 15 and 29 years of age are considered youth in India (MoSPI,2022). Table 2 shows the unemployment rate among youth in India. The key takeaways are: (1) The double-digit unemployment rate is rampant among the ‘15-24’ age category. The unemployed female in rural areas in the 15-19 age category has fallen over time in the rural areas but in urban areas it has seen a recent increase to 23 % in 2022 (2) The unemployment rate among women (20-24 years old) is higher than that of their male counterparts in urban and rural areas. The gap is highest among urban women (32.9% in 2022-23) and males (20.4% in 2022-23).

The next question arises: who are these unemployed? Table 3 looks at the unemployment rate with different educational attainment for 2022-23. As we can see, unemployment rates are highest among graduates and above. One reason for the higher unemployment rate among the educated is their reluctance to accept low-skilled informal jobs. At the same time, there is also a shortage of adequate regular salaried positions to meet their qualifications (Bairagya,2018).

As we can see from the data also, among 15-59 years, there is 0.3 % unemployment among those not literate and 15.79% unemployment among graduates. The unemployment rate among youth who have passed high secondary is double-digit (i.e. 15.7 % in Females and 12.6% in Males). Educated women face an exorbitantly high rate of unemployment, 39.51 % among graduates for the 15-29 age category.

The higher unemployment rate involves both demand and supply side problems. Firstly, on the demand side, there is capital-intensive growth in India, thus creating fewer employment opportunities. ’India Development Update’, a World Bank report, states, ”Over the past decade, India’s high-tech exports, such as mobile devices and services, have seen substantial growth. However, the country has fallen behind in low-skilled industries like apparel, leather, and textiles. In contrast, Bangladesh and Vietnam have gained an advantage due to China’s retreat from labour-intensive manufacturing.” There has been jobless growth in India. Secondly, growth has been driven by the service sector, which is skill-intensive. There is a problem of skill mismatch while hiring.

On the supply side, let’s focus on the issue of a higher unemployment rate among urban women. Firstly, there is a higher level of unemployment among highly educated women, as they tend to wait longer for the ‘right opportunity’. The right opportunity consists of many factors like job profile, security, distance to the office, and working hours, as women have the double burden of work. Secondly, the marriage market now demands educated women who are associated with the family’s status.

Thus, the type of job a woman takes also determines the family’s status. Hence, there is a search for more feminine job profiles. Thirdly, many women withdraw from the labour force due to a mismatch between labour supply and demand (Thomas, 2020). This also means that there is an underestimation of women’s unemployment rates.

Way Forward

To address the issue of rising unemployment, the government has launched three “Em- ployment Linked Incentive” schemes in Budget 2024-25. The schemes include one-month wage support for first-time workers and EPFO contributions to encourage employment in manufacturing. Additionally, an internship program that aims to provide one-year-long internship opportunities in Top 500 companies to 1 crore youth (aged 21-24 years) spanning over five years. There is a need to boost the labour-intensive sectors. Efforts need to be made to get urban women into the labour force. To fully capitalize on its demographic dividend, India must prioritize addressing unemployment.

References

Ministry of Statistics & Programme Implementation.Government of India. (2022). Youth in India 2022.
Thomas, J. J. (2020, Aug 22). Labour Market Changes in India, 2005–18 Missing the Demographic Window of Opportunity? Economic & Political Weekly.
World bank. (2024, Sep 3). India Development Update. Link to the article
Bairagya, I. (2018, Feb 17). Why Is Unemployment Higher among the Educated? Economic & Political Weekly, 53(7).

About the contributor– Kirti Ranjan is currently an Intern at IMPRI and a PhD scholar in Economics at the Centre for Economic Studies and Planning (CESP), Jawaharlal Nehru University (JNU).

Acknowledgment– The author extends sincere thanks to Dr. Arjun Kumar for the invaluable opportunity, and to Ishita Deb and Sana for their informative inputs.