Recently, the Indian Railways announced rationalisation of freight fares.  This rationalisation will result in an 8.75% increase in freight rates for major commodities such as coal, iron and steel, iron ore, and raw materials for steel plants. The freight rates were rationalised to ensure additional revenue generation across the network. An additional revenue of Rs 3,344 crore is expected from such rationalisation, which will be utilised to improve passenger amenities. In addition, the haulage charge of containers has been increased by 5% and the freight rates of other small goods have been increased by 8.75%. Freight rates have not been increased for goods such as food grains, flours, pulses, fertilisers, salt, and sugar, cement, petroleum, and diesel. In light of this, we discuss some issues around Railways’ freight pricing.

Railways’ sources of internal revenue

Railways earns its internal revenue primarily from passenger and freight traffic. In 2016-17 (latest actual figures available), freight and passenger traffic contributed to about 63% and 28% of the internal revenue, respectively. The remaining is earned from miscellaneous sources such as parcel service, coaching receipts, and platform tickets.

Freight traffic: Railways majorly transports bulk freight, and the freight basket has mostly been limited to include raw materials for certain industries such as power plants, and iron and steel plants. It generates most of its freight revenue from the transportation of coal (43%), followed by cement (8%), food-grains (7%), and iron and steel (7%). In 2018-19, Railways expects to earn Rs 1,21,950 crore from its freight traffic.

Railways fig1

Passenger traffic:  Passenger traffic is broadly divided into two categories: suburban and non-suburban traffic.  Suburban trains are passenger trains that cover short distances of up to 150 km, and help move passengers within cities and suburbs.  Majority of the passenger revenue (94% in 2017-18) comes from the non-suburban traffic (or the long-distance trains).

Within non-suburban traffic, second class (includes sleeper class) contributes to 67% of the non-suburban revenue.  AC class (includes AC 3-tier, AC Chair Car and AC sleeper) contributes to 32% of the non-suburban revenue.  The remaining 1% comes from AC First Class (includes Executive class and First Class).

Railways’ ability to generate its own revenue has been slowing

The growth rate of Railways’ earnings from its core business of running freight and passenger trains has been declining.  This is due to a decline in the growth of both freight and passenger traffic.  Some of the reasons for such decline include:

Freight traffic growth has been declining, and is limited to a few items

Growth of freight traffic has been declining over the last few years.  It has declined from around 8% in the mid-2000s to a 4% negative growth in mid-2010s, before an estimated recovery to about 5% now.

The National Transport Development Policy Committee (2014) had noted various issues with freight transportation on railways.  For example, Indian Railways does not have an institutional arrangement to attract and aggregate traffic of smaller parcel size.  Further, freight services are run with a focus on efficiency instead of customer satisfaction.  Consequently, it has not been able to capture high potential markets such as FMCGs, hazardous materials, or automobiles and containerised cargo.  Most of such freight is transported by roads.

Figure 2_Railways

The freight basket is also limited to a few commodities, most of which are bulk in nature.  For example, coal contributes to about 43% of freight revenue and 25% of the total internal revenue.  Therefore, any shift in transport patterns of any of these bulk commodities could affect Railways’ finances significantly.

For example, if new coal based power plants are set up at pit heads (source of coal), then the need for transporting coal through Railways would decrease.  If India’s coal usage decreases due to a shift to more non-renewable sources of energy, it will reduce the amount of coal being transported.  Such situations could have a significant adverse impact on Railways’ revenue.

Freight traffic cross-subsidises passenger traffic

In 2014-15, while Railways’ freight business made a profit of about Rs 44,500 crore, its passenger business incurred a net loss of about Rs 33,000 crore.17  The total passenger revenue during this period was Rs 49,000 crore.  This implies that losses in the passenger business are about 67% of its revenue.  Therefore, in 2014-15, for every one rupee earned in its passenger business, Indian Railways ended up spending Rs 1.67.

These losses occur across both suburban and non-suburban operations, and are primarily caused due to: (i) passenger fares being lower than the costs, and (ii) concessions to various categories of passengers.  According to the NITI Aayog (2016), about 77% to 80% of these losses are contributed by non-suburban operations (long-distance trains).  Concessions to various categories of passengers contribute to about 4% of these losses, and the remaining (73-76%) is due to fares being lower than the system costs.

The NITI Aayog (2016) had noted that Railways ends up using profits from its freight business to provide for such losses in the passenger segment, and also to manage its overall financial situation.  Such cross-subsidisation has resulted in high freight tariffs.  The NTDPC (2014) had noted that, in several countries, passenger fares are either higher or almost equal as freight rates.  However, in India, the ratio of passenger fare to freight rate is about 0.3.

Fig 3_Railways

Impact of increasing freight rates

The recent freight rationalisation further increases the freight rates for certain key commodities by 8.75%, with an intention to improve passenger amenities.  Higher freight tariffs could be counter-productive towards growth of traffic in the segment.  The NTDPC report had noted that due to such high tariffs, freight traffic has been moving to other modes of transport.  Further, the higher cost of freight segment is eventually passed on to the common public in the form of increased costs of electricity, steel, etc.  Various experts have recommended that Railways should consider ways to rationalise freight and passenger tariff distortions in a way to reduce such cross-subsidisation.

For a detailed analysis of Railways revenue and infrastructure, refer to our report on State of Indian Railways.

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). 

Figure 1: Vacancy of judges in High Courts

Figure 2: Vacancy of judges in subordinate courts



Sources: Court News 2010-2018; Vacancy Statement, and Rajya Sabha replies, Part I, Budget Session (2021), Department of Justice; PRS.

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. 

Figure 3: Vacancy of judges across High Courts (in %) (as on November 1, 2021)


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)


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

Method of calculation

Recommendation and its status

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.

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.

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.

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.