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.
Last week, the Power Finance Corporation reported that state-owned power distribution companies across the country made financial losses amounting to Rs 68,832 crore in 2022-23. This is four times higher than the losses witnessed in 2021-22, and roughly equivalent to the annual budget of a state like Uttarakhand. This blog examines some of the causes and implications of such losses.
Overview of financial losses
For several years now, electricity distribution companies (discoms), which are mostly state-owned, have witnessed steep financial losses. Between 2017-18 and 2022-23, losses accumulated to over three lakh crore rupees. In 2021-22, discom witnessed substantial reduction in their losses, primarily because states released 1.54 lakh rupees in subsidies to clear pending dues. State governments provide discoms with subsidies, so that domestic and agricultural consumers receive affordable power. These payments are typically delayed which creates cash flow constraints, and leads to an accumulation of debt. In addition, costs incurred by discoms in 2021-22 remained unchanged.
Note: Data from 2020-21 onwards does not include Odisha, and Dadra & Nagar Haveli and Daman and Diu since their distribution function was privatised in 2020-21. Data for Ladakh is available from 2021-22 onwards. Data for Jammu and Kashmir is not available. The Delhi Municipal Council Distribution Utility has been included from 2020-21 onwards.
Sources: Power Finance Corporation reports for various years; PRS.
As of 2022-23, losses have increased again to reach Rs 68,832 crore. This increase has been driven by rising costs. At a per unit level, the cost of supplying one kilowatt of electricity rose from 7.6 rupees in 2021-22, to 8.6 rupees in 2022-23 (See Table 1).
Table 1: Financial details of state-owned power distribution companies
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Details |
2019-20 |
2020-21 |
2021-22 |
2022-23 |
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Average cost of supplying power (ACS) |
7.4 |
7.7 |
7.6 |
8.6 |
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Average revenue realised (ARR) |
6.8 |
7.1 |
7.3 |
7.8 |
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Per unit loss (ACS-ARR) |
0.6 |
0.6 |
0.3 |
0.7 |
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Total losses (in Rs crore) |
-60,231 |
-76,899 |
-16,579 |
-68,832 |
Note: Data from 2020-21 onwards does not include Odisha, and Dadra & Nagar Haveli and Daman and Diu since their distribution function was privatised in 2020-21. Data for Ladakh is available from 2021-22 onwards. Data for Jammu and Kashmir is not available. The Delhi Municipal Council Distribution Utility has been included from 2020-21 onwards.
Sources: Power Finance Corporation reports for various years; PRS.
Purchase of electricity from generation companies (gencos) forms about 70% of a discom’s total costs, and coal is the primary source for generating electricity. The following chain of events took place in 2022-23: (i) consumer demand for electricity rose by 10% over the previous year, as compared to a 6% year-on-year increase in the past 10 years, (ii) coal had to be imported to meet the increased demand, and (iii) global coal prices were elevated.
Coal imported at elevated prices to keep up with rising electricity demand
In 2022-23, demand for electricity increased by 10% over 2021-22. Between 2008-09 and 2018-19, demand increased at an annual growth rate (CAGR) of 6%. Electricity demand grew as the economy grew (at 7%), and largely came from domestic and agricultural consumers. These consumer categories account for 54% of the total electricity sales, and their demand rose by 7%.
Sources: Central Electricity Regulatory Commission; PRS.
Electricity cannot be stored at scale, which means that generation must be scheduled depending on anticipated demand. The Central Electricity Authority anticipates annual demand for each year. It estimated that demand in 2022-23 would be at 1,505 billion units. However, the actual demand was higher than anticipated in the first few months of 2022-23 (See Figure 3).
To meet this demand, electricity generation had to be ramped up. Coal stocks had already depleted from 29 million tonnes in June 2021 to eight million tonnes in September 2021, on account of high demand in 2021-22. To ensure uninterrupted supply of power, the Ministry of Power directed gencos to import coal. The Ministry noted that without imports, widespread power cuts and blackouts would have occurred.
Sources: Load Generation Balance Report 2022 and 2023, Central Electricity Authority; PRS.
Coal imports rose by about 27 million tonnes in 2022-23. While this constituted only 5% of the overall coal used in the sector, the price at which it was imported significantly impacted the sector. In 2021-22, India imported coal at an average price of Rs 8,300 per tonne. This rose to Rs 12,500 per tonne in 2022-23, a 51% increase. Coal was primarily imported from Indonesia, and prices shot up due to the Russia-Ukraine war, and demand surge by countries like India and China.
Sources: Ministry of Power; Ministry of Statistics and Programme Implementation; PRS.
Coal import situation going forward
In January 2023, the Ministry of Power advised gencos to import 6% of the required coal, to ensure sufficient stock until September 2023. It noted that due to floods and variable rainfall in various parts of the country, hydro generation capacity reduced by about 14%. This put additional burden on coal based thermal generation in 2023-24. Following this, in October 2023, the Ministry directed all gencos to continue using at least 6% imported coal until March 2024.
Sources: Ministry of Coal; PRS.
Structural issues in the power sector and its impact on state finances
Discoms witness persistent financial losses due to certain structural issues. Their costs are typically high because of old contracts with generation companies (gencos). Power purchase costs in these contracts do not account for production efficiencies over the years, and costs remain unchanged. Tariffs are only revised every few years, to ensure that consumers are protected from supply chain shocks. As a result, costs are carried forward for a few years. In addition, discoms sell electricity to certain consumers such as agricultural and residential consumers, below cost. This is supposed to primarily be recovered through subsidy grants provided by state governments. However, states often delay subsidy payments leading to cash flow issues, and accumulation of debt. In addition, tariff recovery from the power sold is not optimal.
Losses reported in the generation sector have also increased. In 2022-23, state-owned gencos reported losses worth Rs 7,175 crore, as compared to the Rs 4,245 crore in 2021-22. Rajasthan accounted for 87% of these, at Rs 6,278 crore. Note that under the Late Payment Surcharge Rules, 2022, discoms are required to make upfront payments to gencos.
Risk to state finances
Persistent financial losses, high debt and guarantees extended by states continue to pose a risk to state finances. These are contingent liabilities for state governments, i.e., in the event a discom is unable to repay its debt, the state would have to take it over.
Several such schemes have been introduced in the past to bail discoms out (See Table 2). As of 2022-23, discoms have an outstanding debt worth Rs 6.61 lakh crore, 2.4% of the national GDP. Debt is significantly high in states such as Tamil Nadu (6% of GSDP), Rajasthan (6% of GSDP), and Uttar Pradesh (3% of GSDP). Previous Finance Commissions have recognised that strengthening discom finances is key in minimising the risk to state finances.
Table 2: Key government schemes for the turnaround of the distribution sector over the years
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Year |
Scheme |
Details |
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2002 |
Bailout Package |
States take over the debt of state electricity boards worth Rs 35,000 crore, 50% waiver of interest payable by state electricity boards to central PSUs |
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2012 |
Financial Restructuring Package |
States take over 50% of the outstanding short-term liabilities worth Rs 56,908 crore |
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2015 |
Ujwal Discom Assurance Yojana (UDAY) |
States take over 75% of the debt of discoms worth Rs 2.3 lakh crore and also provide grants for any future losses |
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2020 |
Liquidity Infusion Scheme |
Discoms get loans worth Rs 1.35 lakh crore from Power Finance Corporation and REC Limited to settle outstanding dues of generators, state governments provide guarantee |
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2022 |
Revamped Distribution Sector Scheme |
Central government to provide result-linked financial assistance worth Rs 97,631 crore for strengthening of supply infrastructure |
Sources: NITI Aayog, Press Releases of the Ministry of Power; PRS.
For more details on the impact of discom finances on state finances, see here. For more details on structural issues in the power distribution sector, see here.
ANNEXURE
Table 3: Cost and revenue structure of discoms on energy sold basis (in Rs per kw)
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Details |
2019-20 |
2020-21 |
2021-22 |
2022-23 |
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Average cost of supplying power (ACS) |
7.4 |
7.7 |
7.6 |
8.6 |
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of which |
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Cost of procuring power |
5.8 |
5.9 |
5.8 |
6.6 |
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Average revenue realised (ARR) |
6.8 |
7.1 |
7.3 |
7.8 |
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of which |
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Revenue from sale of power |
5.0 |
4.9 |
5.1 |
5.5 |
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Tariff subsidy |
1.3 |
1.4 |
1.4 |
1.5 |
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Regulatory income and revenue grant under UDAY |
0.3 |
0.1 |
0.0 |
0.2 |
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Per unit loss |
0.6 |
0.6 |
0.3 |
0.7 |
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Total financial losses |
-60,231 |
-76,899 |
-16,579 |
-68,832 |
Sources: Power Finance Corporation reports for various years; PRS.
Table 4: State-wise profit/loss of power distribution companies (in Rs crore)
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State/UT |
2017-18 |
2018-19 |
2019-20 |
2020-21 |
2021-22 |
2022-23 |
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Andaman and Nicobar Islands |
-605 |
-645 |
-678 |
-757 |
-86 |
-76 |
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Andhra Pradesh |
-546 |
-16,831 |
1,103 |
-6,894 |
-2,595 |
1,211 |
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Arunachal Pradesh |
-429 |
-420 |
NA |
NA |
NA |
NA |
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Assam |
-259 |
311 |
1,141 |
-107 |
357 |
-800 |
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Bihar |
-1,872 |
-1,845 |
-2,913 |
-2,966 |
-2,546 |
-10 |
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Chandigarh |
321 |
131 |
59 |
79 |
-101 |
NA |
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Chhattisgarh |
-739 |
-814 |
-571 |
-713 |
-807 |
-1,015 |
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Dadra & Nagar Haveli and Daman & Diu |
312 |
-149 |
-125 |
NA |
NA |
NA |
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Delhi |
NA |
NA |
NA |
98 |
57 |
-141 |
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Goa |
26 |
-121 |
-276 |
78 |
117 |
69 |
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Gujarat |
426 |
184 |
314 |
429 |
371 |
147 |
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Haryana |
412 |
281 |
331 |
637 |
849 |
975 |
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Himachal Pradesh |
-44 |
132 |
43 |
-153 |
-141 |
-1,340 |
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Jharkhand |
-212 |
-730 |
-1,111 |
-2,556 |
-1,721 |
-3,545 |
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Karnataka |
-2,439 |
-4,889 |
-2,501 |
-5,382 |
4,719 |
-2,414 |
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Kerala |
-784 |
-135 |
-270 |
-483 |
98 |
-1,022 |
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Ladakh |
NA |
NA |
NA |
NA |
-11 |
-57 |
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Lakshadweep |
-98 |
-120 |
-115 |
-117 |
NA |
NA |
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Madhya Pradesh |
-5,802 |
-9,713 |
-5,034 |
-9,884 |
-2,354 |
1,842 |
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Maharashtra |
-3,927 |
2,549 |
-5,011 |
-7,129 |
-1,147 |
-19,846 |
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Manipur |
-8 |
-42 |
-15 |
-15 |
-22 |
-146 |
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Meghalaya |
-287 |
-202 |
-443 |
-101 |
-157 |
-193 |
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Mizoram |
87 |
-260 |
-291 |
-115 |
-59 |
-158 |
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Nagaland |
-62 |
-94 |
-477 |
-17 |
24 |
33 |
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Puducherry |
5 |
-39 |
-306 |
-23 |
84 |
-131 |
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Punjab |
-2,760 |
363 |
-975 |
49 |
1,680 |
-1,375 |
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Rajasthan |
-11,314 |
-12,524 |
-12,277 |
-5,994 |
2,374 |
-2,024 |
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Sikkim |
-29 |
-3 |
-179 |
-34 |
NA |
71 |
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Tamil Nadu |
-12,541 |
-17,186 |
-16,528 |
-13,066 |
-9,130 |
-9,192 |
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Telangana |
-6,697 |
-9,525 |
-6,966 |
-6,686 |
-831 |
-11,103 |
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Tripura |
28 |
38 |
-104 |
-4 |
-127 |
-193 |
|
Uttar Pradesh |
-5,269 |
-5,902 |
-3,866 |
-10,660 |
-6,498 |
-15,512 |
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Uttarakhand |
-229 |
-808 |
-323 |
-152 |
-21 |
-1,224 |
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West Bengal |
-871 |
-1,171 |
-1,867 |
-4,261 |
1,045 |
-1,663 |
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State Sector |
-56,206 |
-80,179 |
-60,231 |
-76,899 |
-16,579 |
-68,832 |
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Dadra & Nagar Haveli and Daman & Diu |
NA |
NA |
NA |
242 |
148 |
104 |
|
Delhi |
109 |
657 |
-975 |
1,876 |
521 |
-76 |
|
Gujarat |
574 |
307 |
612 |
655 |
522 |
627 |
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Odisha |
NA |
NA |
-842 |
-853 |
940 |
746 |
|
Maharashtra |
NA |
590 |
1,696 |
-375 |
360 |
42 |
|
Uttar Pradesh |
182 |
126 |
172 |
333 |
256 |
212 |
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West Bengal |
658 |
377 |
379 |
398 |
66 |
-12 |
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Private Sector |
1,523 |
2,057 |
1,042 |
2,276 |
2,813 |
1,643 |
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All-India |
-54,683 |
-78,122 |
-59,189 |
-77,896 |
-13,766 |
-67,189 |
Note: Minus sign (-) indicates loss; Dadra & Nagar Haveli and Daman & Diu discom was privatised on April 1, 2022; New Delhi Municipal Council Distribution utility has been added from 2020-21 onwards.
Sources: Power Finance Corporation reports for various years; PRS.
