Recently, the Cabinet Committee on Economic Affairs approved an increase in the Minimum Support Prices (MSPs) for Kharif crops for the 2018-19 marketing season. Subsequently, the Commission for Agricultural Costs and Prices (CACP) released its price policy report for Kharif crops for the marketing season 2018-19.
The central government notifies MSPs based on the recommendations of the CACP. These recommendations are made separately for the Kharif marketing season (KMS) and the Rabi marketing season (RMS). Post harvesting, the government procures crops from farmers at the MSP notified for that season, in order to ensure remunerative prices to farmers for their produce.
In this blog post, we look at how MSPs are determined, changes brought in them over time, and their effectiveness for farmers across different states.
How are Minimum Support Prices determined?
The CACP considers various factors such as the cost of cultivation and production, productivity of crops, and market prices for the determination of MSPs. The National Commission on Farmers(Chair: Prof. M. S. Swaminathan) in 2006 had recommended that MSPs must be at least 50% more than the cost of production. In this year’s budget speech, the Finance Minister said that MSPs would be fixed at least at 50% more than the cost of production.
The CACP calculates cost of production at three levels: (i) A2, which includes cost of inputs such as seeds, fertilizer, labour; (ii) A2+FL, which includes the implied cost of family labour (FL); and (iii) C2, which includes the implied rent on land and interest on capital assets over and above A2+FL.
Table 1 shows the cost of production as calculated by the CACP and the approved MSPs for KMS 2018-19. For paddy (common), the MSP was increased from Rs 1,550/quintal in 2017-18 to Rs 1,750/quintal in 2018-19. This price would give a farmer a profit of 50.1% on the cost of production A2+FL. However, the profit calculated on the cost of production C2 would be 12.2%. It has been argued that the cost of production should be taken as C2 for calculating MSPs. In such a scenario, this would have increased the MSP to Rs 2,340/quintal, much above the current MSP of Rs 1,750/quintal.
Which are the major crops that are procured at MSPs?
Every year, MSPs are announced for 23 crops. However, public procurement is limited to a few crops such as paddy, wheat and, to a limited extent, pulses as shown in Figure 1.
The procurement is also limited to a few states. Three states which produce 49% of the national wheat output account for 93% of procurement. For paddy, six states with 40% production share have 77% share of the procurement. As a result, in these states, farmers focus on cultivating these crops over other crops such as pulses, oilseeds, and coarse grains.
Due to limitations on the procurement side (both crop-wise and state-wise), all farmers do not receive benefits of increase in MSPs. The CACP has noted in its 2018-19 price policy report that the inability of farmers to sell at MSPs is one of the key areas of concern. Farmers who are unable to sell their produce at MSPs have to sell it at market prices, which may be much lower than the MSPs.
How have MSPs for major crops changed over time?
Higher procurement of paddy and wheat, as compared to other crops at MSPs tilts the production cycle towards these crops. In order to balance this and encourage the production of pulses, there is a larger proportional increase in the MSPs of pulses over the years as seen in Figure 2. In addition to this, it is also used as a measure to encourage farmers to shift from water-intensive crops such as paddy and wheat to pulses, which relatively require less water for irrigation.
What is the effectiveness of MSPs across states?
The MSP fixed for each crop is uniform for the entire country. However, the production cost of crops vary across states. Figure 3 highlights the MSP of paddy and the variation in its cost of production across states in 2018-19.
For example, production cost for paddy at the A2+FL level is Rs 702/quintal in Punjab and Rs 2,102/quintal in Maharashtra. Due to this differentiation, while the MSP of Rs 1,750/quintal of paddy will result in a profit of 149% to a farmer in Punjab, it will result in a loss of 17% to a farmer in Maharashtra. Similarly, at the C2 level, the production cost for paddy is Rs 1,174/quintal in Punjab and Rs 2,481/quintal in Maharashtra. In this scenario, a farmer in Punjab may get 49% return, while his counterpart in Maharashtra may make a loss of 29%.
Figure 4 highlights the MSP of wheat and the variation in its cost of production across states in 2017-18. In the case of wheat, the cost of production in Maharashtra and West Bengal is much more than the cost in rest of the states. At the A2+FL level, the cost of production in West Bengal is Rs 1,777/quintal. This is significantly higher than in states like Haryana and Punjab, where the cost is Rs 736/quintal and Rs 642/quintal, respectively. In this case, while a wheat growing farmer suffers a loss of 2% in West Bengal, a farmer in Haryana makes a profit of 136%. The return in Punjab is even higher at 1.5 times or more the cost of production.
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.
