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Recently, the Supreme Court collegium reiterated its recommendations for the appointment of 11 judges to certain High Courts. It had first recommended these names earlier this year and in August last year, but these appointments were not made. The Indian judiciary faces high vacancies across all levels (the Supreme Court, High Courts, and subordinate courts). Vacancy of judges in courts is one of the reasons for delays and a rising number of pending cases, as there are not enough judges to hear and decide cases. As of today, more than four crore cases are pending across all courts in India. In this blog post, we discuss vacancies across courts over the years, delays in appointment of judges, and methods to determine the adequate judge strength required to handle the caseload courts face.
High vacancy of judges across courts
Vacancies in courts keep on arising periodically due to retirement, resignation, demise, or elevation of judges. Over the years, the sanctioned strength of judges in both High Courts and subordinate courts has been increased gradually. However, vacancies persist due to insufficient appointments (see Figures 1 and 2). Between 2010 and 2020, vacancies increased from 18% to 21% across all levels of courts (from 6% to 12% in the Supreme Court, from 33% to 38% in High Courts, and from 18% to 20% in subordinate courts).
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Figure 1: Vacancy of judges in High Courts |
Figure 2: Vacancy of judges in subordinate courts |
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Sources: Court News 2010-2018; Vacancy Statement, and Rajya Sabha replies, Part I, Budget Session (2021), Department of Justice; PRS. |
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As on November 1, 2021, the Supreme Court had a vacancy of one judge (out of a sanctioned strength of 34). Vacancy in High Courts stood at 37% (406 posts vacant out of a sanctioned strength of 1,098). Since May, 2021, the Supreme Court collegium has recommended more than 130 names for appointment as High Court judges. In three High Courts (Telangana, Patna, and Calcutta), at least half of the posts are vacant (see Figure 3). The Standing Committee on Personnel, Public Grievances, Law and Justice (2020) noted that every year, 35-40% of posts of High Court judges remain unfilled.
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Figure 3: Vacancy of judges across High Courts (in %) (as on November 1, 2021)
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Source: Vacancy Statement, Department of Justice; PRS. |
Appointments of High Court judges are guided by a memorandum of procedure. As per this memorandum, the appointment process is to be initiated by the concerned High Court at least six months before a vacancy occurs. However, the Standing Committee (2021) noted that this timeline is rarely adhered to by High Courts. Further, in the final stage of the process, after receiving recommendations from the Supreme Court collegium, the executive appoints judges to the High Court. No timeline is prescribed for this stage of the appointment process. In 2018 and 2019, the average time taken to appoint High Court judges after receiving the collegium’s recommendations was five to seven months.
As of today, over 3.6 crore cases are pending before subordinate courts in India. As on February 20, 2020, 21% posts for judges were vacant (5,146 posts out of the sanctioned strength of 24,018) in subordinate courts. Subordinate courts in Bihar, Haryana, and Jharkhand (among the states with high population) had a high proportion of vacancies of judges (see Figure 4). Note that the Supreme Court is monitoring the procedure for appointment of judges to subordinate courts.
For an analysis of the data on pendency and vacancies in the Indian judiciary, see here.
Figure 4: Vacancy of judges across subordinate courts (in %) (as on February 20, 2020)
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Source: Report No. 101, Standing Committee on Personnel, Public Grievances, Law and Justice (2020); PRS. |
How many judges do we need?
The Law Commission of India (1987) had noted the importance of manpower planning for the judiciary. Lack of adequate number of judges means a greater workload per judge. Thus, it becomes essential to arrive at an optimal judge strength to deal with pending and new cases in courts. Over the years, different methods of calculating the required judge strength for subordinate courts (where the backlog of cases in the Indian judiciary is concentrated) have been recommended (see Table 1).
Table 1: Methods recommended for calculating the required number of judges for subordinate courts
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Method of calculation |
Recommendation and its status |
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Judge-to-population ratio: optimum number of judges per million population |
The Law Commission of India (1987) had recommended increasing this ratio to 50 judges per million people. This was reiterated by the Supreme Court (2001) and the Standing Committee on Home Affairs (2002). For 2020, the judge-to-population ratio was 21 judges per million population. Note that this figure is calculated based on the sanctioned strength of judges in the Supreme Court, High Courts and subordinate courts. |
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Rate of disposal: number of additional judges required (to clear the existing backlog of cases and ensure that new backlog is not created) based on the average number of cases disposed per judge |
The Law Commission of India (2014) proposed this method. It rejected the judge-to-population ratio method, observing that filing of cases per capita varies substantially across geographic units depending on socio-economic conditions. |
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Weighted case load method: calculating judge strength based on the disposal by judges, taking into account the nature and complexity of cases in local conditions |
The National Court Management Systems Committee (NCMS) (2016) critiqued the rate of disposal method. It proposed, as an interim measure, the weighted case load method, which addresses the existing backlog of cases as well as the new flow of cases every year in subordinate courts. In 2017, the Supreme Court accepted this model. |
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Time-based weighted case load method: calculating the required judge strength taking into account the actual time spent by judges in different types of cases at varying stages based on an empirical study |
Used widely in the United States, this was the long-term method recommended by the NCMS (2016) to assess the required judge strength for subordinate courts. It involves determining the total number of ‘judicial hours’ required for disposing of the case load of each court. The Delhi High Court used this approach in a pilot project (January 2017- December 2018) to calculate the ideal judge strength for disposing of pending cases in certain courts in Delhi. |
Sources: Reports No. 120 (1987) and 245 (2014), Law Commission of India; Report No. 85, Standing Committee on Home Affairs (2002); Note for Calculating Required Judge Strength for Subordinate Courts, National Court Management Systems Committee (NCMS) (2016); Imtiyaz Ahmad vs. State of Uttar Pradesh, Supreme Court (2017); PRS.
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.
