In April 2020, the International Labour Organisation (ILO) estimated that nearly 2.5 crore jobs could be lost worldwide due to the COVID-19 pandemic in 2020. Further, it observed that more than 40 crore informal workers in India may get pushed into deeper poverty due to the pandemic. In this blog post, we discuss the effect of COVID-19 on unemployment in urban areas as per the quarterly Periodic Labour Force Survey (PLFS) report released last week, and highlight some of the measures taken by the central government with regard to unemployment.
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Methodology for estimating unemployment in PLFS reports The National Statistics Office (NSO) released its latest quarterly PLFS report for the October-December 2020 quarter. The PLFS reports give estimates of labour force indicators including Labour Force Participation Rate (LFPR), Unemployment Rate, and distribution of workers across industries. The reports are released on a quarterly as well as annual basis. The quarterly reports cover only urban areas whereas the annual report covers both urban and rural areas. The latest annual report is available for the July 2019-June 2020 period. The quarterly PLFS reports provide estimates based on the Current Weekly Activity Status (CWS). The CWS of a person is the activity status obtained during a reference period of seven days preceding the date of the survey. As per CWS status, a person is considered as unemployed in a week if he did not work even for at least one hour on any day during the reference week but sought or was available for work. In contrast, the headline numbers on employment-unemployment in the annual PLFS reports are reported based on the usual activity status. Usual activity status relates to the activity status of a person during the reference period of the last 365 days preceding the date of the survey. |
To contain the spread of COVID-19, a nationwide lockdown was imposed from late March till May 2020. During the lockdown, severe restrictions were placed on the movement of individuals and economic activities were significantly halted barring the activities related to essential goods and services. Unemployment rate in urban areas rose to 20.9% during the April-June quarter of 2020, more than double the unemployment rate in the same quarter the previous year (8.9%). Unemployment rate refers to the percentage of unemployed persons in the labour force. Labour force includes persons who are either employed or unemployed but seeking work. The lockdown restrictions were gradually relaxed during the subsequent months. Unemployment rate also saw a decrease as compared to the levels seen in the April-June quarter of 2020. During the October-December quarter of 2020 (latest data available), unemployment rate had reduced to 10.3%. However, it was notably higher than the unemployment rate in the same quarter last year (7.9%).
Figure 1: Unemployment rate in urban areas across all age groups as per current weekly activity status (Figures in %)
Note: PLFS includes data for transgenders among males.
Sources: Quarterly Periodic Labour Force Survey Reports, Ministry of Statistics and Program Implementation; PRS.
Recovery post-national lockdown uneven in case of females
Pre-COVID-19 trends suggest that the female unemployment rate has generally been higher than the male unemployment rate in the country (7.3% vs 9.8% during the October-December quarter of 2019, respectively). Since the onset of the COVID-19 pandemic, this gap seems to have widened. During the October-December quarter of 2020, the unemployment rate for females was 13.1%, as compared to 9.5% for males.
The Standing Committee on Labour (April 2021) also noted that the pandemic led to large-scale unemployment for female workers, in both organised and unorganised sectors. It recommended: (i) increasing government procurement from women-led enterprises, (ii) training women in new technologies, (iii) providing women with access to capital, and (iv) investing in childcare and linked infrastructure.
Labour force participation
Persons dropping in and out of the labour force may also influence the unemployment rate. At a given point of time, there may be persons who are below the legal working age or may drop out of the labour force due to various socio-economic reasons, for instance, to pursue education. At the same time, there may also be discouraged workers who, while willing and able to be employed, have ceased to seek work. Labour Force Participation Rate (LFPR) is the indicator that denotes the percentage of the population which is part of the labour force. The LFPR saw only marginal changes throughout 2019 and 2020. During the April-June quarter (where COVID-19 restrictions were the most stringent), the LFPR was 35.9%, which was lower than same in the corresponding quarter in 2019 (36.2%). Note that female LFPR in India is significantly lower than male LFPR (16.6% and 56.7%, respectively, in the October-December quarter of 2019).
Figure 2: LFPR in urban areas across all groups as per current weekly activity status (Figures in %)
Note: PLFS includes data for transgenders among males.
Sources: Quarterly Periodic Labour Force Survey Reports, Ministry of Statistics and Program Implementation; PRS.
Measures taken by the government for workers
The Standing Committee on Labour in its report released in August 2021 noted that 90% of workers in India are from the informal sector. These workers include: (i) migrant workers, (ii) contract labourers, (iii) construction workers, and (iv) street vendors. The Committee observed that these workers were worst impacted by the pandemic due to seasonality of employment and lack of employer-employee relationship in unorganised sectors. The Committee recommended central and state governments to: (i) encourage entrepreneurial opportunities, (ii) attract investment in traditional manufacturing sectors and developing industrial clusters, (iii) strengthen social security measures, (iv) maintain a database of workers in the informal sector, and (v) promote vocational training. It took note of the various steps taken by the central government to support workers and address the challenges and threats posed by the COVID-19 pandemic (applicable to urban areas):
The central and state governments have also taken various other measures, such as increasing spending on infrastructure creation and enabling access to cheaper lending for businesses, to sustain economic activity and boost employment generation.
Sources: Court News, 2006, Supreme Court of India; National Data Judicial Grid accessed on August 7, 2019; PRS.
Overall, the pendency of cases has increased significantly at every level of the judicial hierarchy in the last decade. Between 2006 and now, there has been an overall increase of 22% (64 lakh cases) in the pendency of cases across all courts. As of August 2019, there are over 3.5 crore cases pending across the Supreme Court, the High Courts, and the subordinate courts. Of these, subordinate courts account for over 87.3% pendency of cases, followed by 12.5% pendency before the 24 High Courts. The remaining 0.2% of cases are pending with the Supreme Court. The primary reason for growing pendency of cases is that the number of new cases filed every year has outpaced the number of disposed of cases. This has resulted in a growing backlog of cases.
In High Courts and subordinate courts, over 32 lakh cases pending for over 10 years
Sources: National Data Judicial Grid accessed on August 7, 2019; Court News, 2006-17, Supreme Court of India; PRS.
In the High Courts, over 8.3 lakh cases have been pending for over 10 years. This constitutes 19% of all pending High Court cases. Similarly, in the subordinate courts, over 24 lakh cases (8%) have been pending for over 10 years. Overall, Allahabad High Court had the highest pendency, with over seven lakh cases pending as of 2017.
Despite high pendency, some High Courts have managed to reduce their backlog. Between 2006 and 2017, pendency of cases reduced the most in Madras High Court at a rate of 26%, followed by Bombay High Court at 24%. Conversely, during the same period, the pendency of cases doubled in the Andhra Pradesh High Court, and increased by 2.5 times in Karnataka High Court.
As a result of pendency, number of under-trials in prison is more than double that of convicts
Sources: Prison Statistics in India, 2015, National Crime Record Bureau; PRS.
Over the years, as a result of growing pendency of cases for long periods, the number of undertrials (accused awaiting trial) in prisons has increased. Prisons are running at an over-capacity of 114%. As of 2015, there were over four lakh prisoners in jails. Of these, two-thirds were undertrials (2.8 lakh) and the remaining one-third were convicts.
The highest proportion of undertrials (where the number of inmates was at least over 1,000) were in J&K (85%), followed by Bihar (82%). A total of 3,599 undertrials were detained in jails for more than five years. Uttar Pradesh had the highest number of such undertrials (1,364) followed by West Bengal (294).
One interesting factor to note is that more criminal cases are filed in subordinate courts than in High Courts and Supreme Court. Of the cases pending in the subordinate courts (which constitute 87% of all pending cases), 70% of cases were related to criminal matters. This increase in the pendency of cases for long periods over the years may have directly resulted in an increase in the number of undertrials in prisons. In a statement last year, the Chief Justice of India commented that the accused in criminal cases are getting heard after serving out their sentence.
Vacancies in High Courts and Subordinate Courts affect the disposal of cases
Sources: Court News, 2006-17, Supreme Court of India; PRS.
Vacancy of judges across courts in India has affected the functioning of the judiciary, particularly in relation to the disposal of cases. Between 2006 and 2017, the number of vacancies in the High Courts has increased from 16% to 37%, and in the subordinate courts from 19% to 25%. As of 2017, High Courts have 403 vacancies against a sanctioned strength of 1,079 judges, and subordinate courts have 5,676 vacancies against a sanctioned strength of 22,704 judges. As of 2017, among the major High Courts (with sanctioned strength over 10 judges), the highest proportion of vacancies was in Karnataka High Court at 60% (37 vacancies), followed by Calcutta High Court at 54% (39 vacancies). Similarly, in major subordinate courts (with sanctioned strength over 100 judges), the highest proportion of vacancies was in Bihar High Court at 46% (835 vacancies), followed by Uttar Pradesh High Court at 42% (1,348 vacancies).
