1
91 Deconstructing Delay: Analyses of Data from High Courts and Subordinate Courts
Harish Narasappa*
91 Deconstructing Delay: Analyses of Data from High Courts and Subordinate Courts
Harish Narasappa*
Abstract
DAKSH’s Rule of Law Project created a database containing case and hearing information from the Supreme Court, High Courts, and subordinate courts on a single analysable platform. The National Judicial Data Grid (NJDG) was set up by the government under its e-Courts Project to provide district court data. Both these databases have made aggregate data about the judicial system available for researchers, and analysing data to monitor judicial performance, as well as identify and understand the problem of pendency and delay in the judicial system, is now possible. This chapter uses data from the DAKSH database and NJDG to create metrics to measure pendency, efficiency, and workload of courts, as well as the progress of cases in the High Courts and subordinate courts.
. . . . .
Since the publication of the State of the Indian Judiciary in 2016 (SoJR 2016), the DAKSH database has been continuously updated and as on 11 August 2017, contains information for 1,13,80,155 cases filed in courts across India. Data is collected for both pending and disposed cases, along with information about hearings. All information about cases and hearings are sourced from the individual websites of the Supreme Court, each of the High Courts, and from the e-Courts website1 (for cases in the subordinate courts). Figure 1 provides a summary of the cases and hearings in the DAKSH database.
Figure 1. Summary of the DAKSH Database 92
Through an empirical analysis of High Court and subordinate court data from the DAKSH database, this chapter throws light on issues such as pendency, backlog, efficiency, and the manner in which courts are handling their case load.2
Part 1 of the chapter describes the present state of the Indian judiciary by providing a bird’s-eye view of pendency and workload of the courts across the country. A special analysis of courts in Delhi has also been carried out in Part 1. Part 2 of the chapter provides an in-depth analysis of factors that affect judicial efficiency, such as judge strength and disposal rates. Part 3 of the chapter contains detailed research methodology and sample descriptions for certain analyses.
Figure 2 shows the average pendency in all the High Courts and subordinate courts in the country.
Figure 2. Average Pendency in All High Courts and Subordinate Courts
93 Figure 3 shows the total number of cases pending in the subordinate courts in all states and union territories (UTs).3 Uttar Pradesh, which also has the highest population, has the highest number of pending cases in the country — 58,73,958 cases. The UT of Daman and Diu has the lowest number of pending cases in the country — 1,422 cases.
Figure 3. Number of Pending Cases in Subordinate Courts of the States and Union Territories
Notes: (i) Data, as on 7 August 2017, was collected from National Judicial Data Grid (NJDG) for subordinate courts. (ii) Data for pending cases was not available for the subordinate courts in Arunachal Pradesh, Nagaland, Puducherry, and Lakshadweep.
The volume of pending cases can also be looked at district-wise. Figure 4 presents the total number of cases pending in each district of Karnataka. Bengaluru and Belagavi have the highest number of pending cases, with 2,34,468 and 93,929 cases respectively.
Figure 4. Number of Pending Cases in Subordinate Courts of Districts in Karnataka 94
Note: Data collected from NJDG as on 29 March 2017.
Part 1
Figure 5 shows the average pendency of cases in all the 24 High Courts and their benches. Average pendency indicates the average age of the pending cases and the length of time for which they remain in the courts without being disposed.
Figure 5. Average Pendency in High Courts 95
Notes: (i) All values are expressed in years. (ii) For analysis, all the cases that were pending in the DAKSH database as on 19 September 2017 have been considered.
In the SoJR 2016, the High Court of Allahabad had the highest average pendency of approximately three years.4 From Figure 5, we see that the High Court of Rajasthan and the High Court of Allahabad have the highest average pendency of 4.32 years each. It is important to note that the number of cases in the DAKSH database over the last one year have increased substantially. To calculate average pendency, while the SoJR 2016 considered a total of 16,07,557 cases from the DAKSH database, the total number of pending cases considered this year are 45,51,453. A comparison between the number of pending cases considered for the SoJR 2016 and this year’s report is provided in the Annexure in Part 3. According to data released by Supreme Court News,5 the High Court of Allahabad has the highest number of pending cases in the country, with 9,25,084 cases while the High Court of Rajasthan is in the eighth position, with 2,50,824 cases.
Figure 6 compares the average pendency of cases in subordinate courts across all states and UTs. Gujarat has the highest average pendency at 9.51 years, followed by Dadar and Nagar Haveli with 8.79 years. However, in terms of the number of pending cases, Gujarat has 17,49,909 cases while Dadra and Nagar Haveli has 3,548 cases.6 Despite having very few pending cases, Dadra and Nagar Haveli has the second highest average pendency in the country. On the other hand, Meghalaya with 7,071 pending cases7 has the lowest average pendency of 2.74 years.
Figure 6. Average Pendency in Subordinate Courts 96
Notes: (i) All values are expressed in years. (ii) For this analysis, all the cases that were pending in the DAKSH database as on 29 August 2017 were considered. (iii) Due to paucity of data Chandigarh, Lakshadweep, and Arunachal Pradesh were not considered.
Figure 7 shows the average pendency of civil, criminal, and writ cases across the High Courts. We can see that civil cases in the High Court of Orissa have an average pendency of 6.9 years, which is higher than in all other High Courts, as well as the highest for any category of cases in all the High Courts. The High Court of Allahabad has the highest average pendency — 4.6 years — for writ cases. In general, civil cases have a higher average pendency than criminal and writ cases in most of the High Courts.
Figure 7. Average Pendency of Civil, Criminal, and Writ Cases in High Courts 97
Notes: (i) All values are expressed in years. (ii) For this analysis, all the cases that were pending in the DAKSH database as on 19 September 2017 were considered. (iii) Due to paucity of data, the High Court of Judicature at Patna, High Court of Gauhati at Aizawl, High Court of Gauhati at Naharlagun, High Court of Meghalaya, and the High Court of Jammu and Kashmir at Srinagar were not considered.
98 Figure 8 shows the average pendency of both civil and criminal cases in the subordinate courts.8 While Gujarat has similarly high average pendency for both civil (10.1 years) and criminal (9 years) cases, in Maharashtra, the average pendency of criminal cases (8.7 years) is nearly twice that of civil cases (4.7 years).
Figure 8. Average Pendency of Civil and Criminal Cases in Subordinate Courts
Notes: (i) All values are expressed in years. (ii) 3 per cent of the case types in subordinate courts could not be categorised. (iii) For analysis, all cases that were pending in the DAKSH database as on 29 August 2017 have been considered. (iv) Due to the paucity of data, Arunachal Pradesh, Chandigarh, and Lakshadweep were not considered.
Workload of Courts in India
In Figure 9, each differently coloured segment corresponds to the proportion of civil, criminal, and writ cases pending in the High Courts. The High Court of Bombay has the highest percentage (65.2 per cent) of pending civil cases. The High Court of Jharkhand has the highest proportion of criminal cases, constituting 72.5 per cent of its workload.
Figure 9. Proportion of Pending Cases in High Courts 99
Notes: (i) Data collected from NJDG as on 10 September 2017. (ii) Due to paucity of data, the High Courts of Allahabad, Gauhati, Sikkim, Manipur, and Jammu and Kashmir were not considered.
When we examine Figure 7 (average pendency of civil, writ, and criminal cases in High Courts) and Figure 9 together, we can see that civil cases in the High Court of Orissa constitute 26.4 per cent of the court’s workload, but have the highest average pendency in the country with 6.9 years. In the High Court of Rajasthan, criminal cases, which constitute 30.5 per cent of the workload, have the highest average pendency of 3.4 years (along with the High Court of Allahabad).
Figure 10 shows the proportion of pending civil and criminal cases in the subordinate courts of each of the states and UTs. The total number of criminal cases are nearly twice the number of civil cases in the country. The caseload of Uttarakhand is dominated by criminal cases, constituting 84.7 per cent of the total, and is the highest in the country. However, in Tamil Nadu, Andhra Pradesh, Goa, Manipur, and Himachal Pradesh, civil cases constitute more than 50 per cent of the courts’ workload.
Figure 10. Proportion of Pending Cases in Subordinate Courts 100
Notes: (i) Data collected from NJDG as on 7 August 2017. (ii) Data for pending cases was not available for the subordinate courts in Arunachal Pradesh, Nagaland, Puducherry, and Lakshadweep.
Workload of Subordinate Courts in the National Capital, Delhi
Delhi, the National Capital Territory (NCT) of India, is a hub of massive litigation. There are close to five lakh cases pending in all the subordinate courts in Delhi.9 In this chapter, we focus on the national capital, given the high number of pending cases, 30 per cent of which have been pending for more than two years.10
Figure 11 shows the proportion of pending civil and criminal cases in all the subordinate courts in Delhi.
Figure 11. Proportion of Civil and Criminal Cases in Subordinate Courts
Note: Data collected from NJDG as on 7 August 2017.
Figure 12 shows the types of cases that constitute a majority of the workload of subordinate courts in Delhi. The 10 case types highlighted in 101 the figure collectively represent 85 per cent of the courts’ workload. Cases registered by the police officials under Section 156 of the Code of Criminal Procedure (CrPC), 1973, which deal with cognisable offences are categorised as Criminal Cases (16.1 per cent of the workload). Individual complaints filed with a magistrate under Chapter 15 of the CrPC are categorised as Complaint Cases (18.2 per cent of the workload).
Figure 12. Judicial Workload of Subordinate Courts Categorised by Types of Cases
Figure 13 depicts the ageing profile of 10 case types, which collectively constitute 85 per cent of the workload of Delhi’s subordinate courts. A comparison is drawn between the average time for which a type of case is pending (average pendency) and the average time taken to dispose of that particular case type (average disposal). For 90,877 pending cases found in the DAKSH database, the overall average pendency is four years, while average disposal for 2,25,140 cases is two years.
Figure 13. Average Pendency and Disposal in Subordinate Courts
Note: All values are expressed in years.
102 The case type ‘Criminal Cases’ has the highest average pendency of 5.1 years, and a low average disposal of 3.4 years. Most case types exhibit a similar pattern, with average pendency being higher than average disposal. This shows that cases which are not disposed quickly tend to be pending for a longer duration. This finding is counter-intuitive, leading us to question why certain cases of a specific case type are disposed of quickly, while other cases of the same case type remain pending. These are complex questions that require further in-depth research and investigation.
Figure 14 represents the stage-wise distribution of hearings in the cases filed before the subordinate courts in Delhi. The stage related data provided on the e-Courts website is not uniform. To ascertain the stage-wise distribution of hearings, all the stages were manually standardised and categorised. The evidence stage forms the majority, with 36 per cent of hearings falling in this stage. During recording of evidence, the attendance of not only the parties, but also the witnesses is necessary, failing which proceedings tend to get delayed.
Figure 14. Stage-wise Distribution of Hearings in Cases in Subordinate Courts
Note: Both pending and disposed cases in the DAKSH database have been considered for this analysis. There were some stages, for example, ‘formality’, ‘applications’, etc. that could not be included under any of the categories and therefore these have not been included.
The amount of time cases spend at each stage plays a crucial role in determining the progress of the case. We have identified the stage of evidence as a significant milestone to determine the time spent by cases in the courts.
Figure 15 shows the amount of time taken from the first hearing of the case until the stage when evidence is recorded in the subordinate courts in Delhi. In 50 per cent of cases, it takes 331 days or less to reach the evidence stage from the day when they were first heard in court. The remaining cases take more than 331 days to reach the evidence stage, with one case going up to 4,486 days (12.3 years).
Figure 15. Time Taken from First Hearing to Evidence in Subordinate Courts 103
Figure 16 continues the analysis, and provides the complete picture of the life cycle of a case, by portraying the time taken from the evidence stage to the final judgment or order in all cases in Delhi’s subordinate courts. In 50 per cent of cases, there are 384 days or less between the evidence stage and until the final judgment or order is passed. However, in the remaining cases, it takes longer, with two cases going up to 4,350 days (11.9 years).
Figure 16. Time Taken from Evidence to Final Judgment in Subordinate Courts
One can note that only 50 per cent of the cases get disposed within two years in the subordinate courts in Delhi. Recommendations of the Malimath Committee11 and the Jagannadha Rao Committee12 have set a benchmark of two years within which cases should be disposed by the courts. Therefore, cases that take more than two years to be disposed should be considered as delayed.
Part 2 104
Understanding Measures to Reduce Pendency
High average pendency in the courts adversely affects the judicial system, as it results in the accumulation of cases and creation of backlog. In order to resolve these issues, courts must be able to identify causal factors which have a direct impact on pendency. Figure 17 depicts various factors that affect pendency.
Figure 17. Factors Affecting Pendency
Administrative inefficiency, as well as human resource and infrastructural deficits play a major role in contributing to pendency. Improving these factors will help in combating pendency in the Indian courts.
It is a common notion that courts will have a better rate of disposal if there is an increase in judges’ strength. Therefore, the question that arises is to what extent does an increase in judges’ strength lead to a better case clearance rate.
Figure 18 shows the relationship between the case clearance rates and the total number of judges in the subordinate courts across states and UTs. Case clearance rate is calculated by dividing the number of cases disposed during a specified period by the number of cases filed in the same period, and multiplying the result by 100. Case clearance rate thus indicates the rate at which courts dispose of cases. Courts that have a case clearance rate of less than 100 dispose fewer cases than those being filed, thus creating a backlog. In Figure 18, the blue line depicts the case clearance rate, while the orange bars indicate the total number of judges in each state and UTs.
Figure 18. Relationship between Case Clearance Rate and Judges’ Strength 105
Notes: (i) Case clearance rate is calculated on number of cases filed and disposed between 9 April 2017 and 9 May 2017, and 7 July 2017 and 7 August 2017. An average of the two rates has been taken. (ii) Data for the pending cases and the number of judges (including vacant courts) in the subordinate courts was taken from NJDG. (iii) Data for pending cases was not available for the subordinate courts in Arunachal Pradesh, Nagaland, Puducherry, and Lakshadweep.
In Figure 18, all the states and UTs have been arranged in the decreasing order of the number of pending cases, with Uttar Pradesh having the highest number of pending cases in the country. It can be seen that states such as Telangana13 and Andhra Pradesh,14 which have a similar number of pending cases, have different case clearance rates. Telangana has 341 judges, however it has a higher case clearance rate than Andhra Pradesh which has 539 judges. Also, Karnataka15 and Madhya Pradesh,16 which have a similar number of pending cases but varying judge strength, have an equal case clearance rate of 97. While Madhya Pradesh has 1,547 judges, Karnataka has 819 judges.
There is a weak correlation of 0.05 between case clearance rate and judges’ strength in the country. A weak correlation indicates that there is a lower likelihood of a relationship between the two variables.
Although, more judges means a better case clearance rate, it is not a proportionate increase. For instance, in Figure 18, consider Odisha17 and Tamil Nadu:18 Odisha, with 497 judges, has a case clearance rate of 117, while Tamil Nadu, with 812 judges, has only a slightly higher case clearance rate of 127. Thus, the number of judges does not have a significant impact on the case clearance rate.
Day-to-day efficiency primarily determines the rate at which courts clear cases. Increasing judges’ strength cannot not be viewed as a standalone measure to address the problem of pendency. Attention must be given to improving systemic efficiency, such as the effective implementation of case flow management rules, in order to reduce pendency in the courts.
There is also a need to improve infrastructure in courts. Apart from improving the physical infrastructure of the courts, governments now realise the importance of providing information and communications technology (ICT) to re-engineer current judicial processes. ICT infrastructure aims at digitisation of services, for example, setting up of kiosks in court halls for efficient dissemination of information or providing an integrated payment gateway for depositing court fees.19 The National 106 and Policy Action Plan to implement and improve ICT infrastructure was proposed in 2005.20 As a part of this plan, the High Court of Judicature at Hyderabad was chosen to implement the integrated knowledge management information system. Since the implementation of the plan, there has been a positive impact of ICT infrastructure on the judicial system.21
Trends of Civil and Criminal Cases
Figure 19 compares the case clearance rates of civil and criminal cases in the subordinate courts in all the states and UTs. In the analysis, the civil and criminal cases categorisation has not been carried out by DAKSH. The NJDG specifically provides data on the number of civil and criminal pending cases in the subordinate courts, therefore, all the numbers have been taken from the NJDG.
Overall, the case clearance rate of civil cases is higher than the case clearance rate of criminal cases across all states, leading to the accumulation of a higher number of criminal cases than civil cases in the country.
Figure 19. Case Clearance Rates in Civil and Criminal Cases
Notes: (i) Case clearance rate is calculated on number of cases filed and disposed between 9 April 2017 and 9 May 2017, and 7 July 2017 and 7 August 2017. An average of the two rates has been taken. (ii) Data for the pending cases in the subordinate courts was taken from NJDG. (iii) Data on pending cases was not available for subordinate courts in Arunachal Pradesh, Nagaland, Puducherry, and Lakshadweep.
Figure 20 shows the quantum of the population currently fighting a case in court. The percentage of population involved in civil litigation is compared to the percentage in criminal litigation in subordinate courts in all the states.22 The percentage is calculated as: number of pending cases *2(assuming each case has a minimum of two parties)/population *100.
Figure 20. Percentage of Population Involved in Civil and Criminal Litigation in India 107
Notes: (i) Data for pending cases in the subordinate courts is taken from NJDG as on 7 August 2017. (ii) Data for pending cases was not available for the subordinate courts in Arunachal Pradesh, Nagaland, Puducherry, and Lakshadweep. (iii) Data for population is taken as per census 2011.
108 In Delhi 4.7 per cent of the population is involved in criminal litigation, which is the highest in the country. As per the data released by National Crime Records Bureau (NCRB) on crime rates in India in 2015, the rate of cognisable crimes reported in Delhi was 916.8 for every 1,00,000 people, which is also the highest in the country.23 In Chandigarh and Goa, nearly three per cent of the population is involved in civil litigation, which is much higher than most other states. Barring Goa, Tamil Nadu, Andhra Pradesh, and Manipur, the percentage of people involved in criminal litigation is higher than those involved in civil litigation.
Economic growth can have a direct impact on the litigation activity in a state. Figure 21 compares the percentage of gross domestic product (GDP)24 contributed by the state or UT to the national GDP and the proportion of civil cases pending in the subordinate courts in each state or UT. The correlation between the contribution to GDP and civil cases is 0.9. A positive correlation value, which is close to one, indicates a strong relationship between two data points. In other words, states that contribute more towards the GDP of the country have a higher percentage of civil cases pending in the country.25 GDP plays a role in determining the growth of civil litigation in the country.26 With the growth in the economic sphere, more people are aware of their rights and approach the courts, hence increasing the overall percentage of civil litigation.
Figure 21. Impact of Gross Domestic Product on Civil Litigation
Notes: (i) The data for the number of cases pending in the subordinate courts is taken from NJDG as on 7 August 2017. (ii) Data for pending cases was not available for the subordinate courts in Arunachal Pradesh, Nagaland, Puducherry and Lakshadweep. (iii) Due to paucity of GDP data, West Bengal, Daman and Diu, Dadar and Nagar Haveli, and Tripura were not considered.
109 Court Order Compliance — Execution Cases
Execution cases are filed to implement the final order or judgment of the courts. Even after fighting a case for several years, parties who win may have to approach the court again if they are denied the benefit of the award passed in their favour.27 Figure 22 highlights the average pendency and disposal of execution cases in subordinate courts in the DAKSH database.
Figure 22. Average Pendency and Disposal of Execution Cases
Note: To calculate average pendency and disposal, all the cases in the DAKSH database that were filed between 2010 and 2017 were taken into consideration.
The average time taken to dispose of execution cases is four years. That means that parties spend four years in court, after the decision in their favour in the primary case, before they can get the benefit of the final order or judgment. Additionally, execution cases are a strong indicator for determining public trust and confidence in the judiciary. Compliance and non-compliance with court orders indicate the seriousness with which parties acknowledge the decisions of the courts.
Of the total number of execution cases in the subordinate courts in the DAKSH database, 63 per cent of the cases originate from five states, namely, Delhi, Haryana, Kerala, Punjab, and Tamil Nadu.
Figure 23 highlights the average pendency of execution cases in each of these states.
Figure 23. Average Pendency of Execution Cases
Notes: (i) All values are expressed in years. (ii) All the execution cases in the DAKSH database filed between 2010 and 2017 in the subordinate courts have been considered.
110 Part 3
Research Methodology and Sample Description
Figure 5. Average Pendency in High Courts
All cases from the DAKSH database whose current status were not marked as ‘disposed’ and ‘dismissed’ were deemed to be pending. All such cases were considered for the calculation of average pendency of cases in the High Courts. Cases whose status was ‘null or blank’ were also considered to be pending and included in the sample. The sample size is therefore 45,51,453 cases.
Average pendency is calculated by finding the difference (in days) between the current date, which in this case was taken to be 19 September 2017 and the date on which the case was filed.
The date of filing is a key piece of data, required to calculate average pendency. There were 32,50,565 cases in which date of filing was not provided. For such cases, we considered the date of filing to be 1 July (middle of the calendar year) of the year of filing provided in the case number. For instance, the filing date for case number SA/1/2015, where the date of filing was not provided, was considered to be 1 July 2015.
Figure 6. Average Pendency in Subordinate Courts
All cases from the DAKSH database whose current status was denoted as ‘pending’ were considered for the analysis. Since there is a uniformity in the current status of cases on the e-Courts website, identifying pending cases was simple. For this analysis, we had a sample of 17,10,605 cases from 3,364 subordinate courts across the country, based on which average pendency as on 29 August 2017 was calculated.
Average pendency for cases in subordinate courts was calculated by finding the difference in days between the filing date and the current date (similar to the method used for High Courts and described under Figure 5). For the 4,695 cases which did not have a date of filing, average pendency has been calculated based on the registration date.
Figure 7. Average Pendency of Civil, Criminal, and Writ Cases in High Courts
To arrive at the average pendency of civil, criminal, and writ cases separately (from the data set used in Figure 5), it was essential to classify cases based on their case type. Chapter 1 of the State of the Indian Judiciary: A Report by DAKSH, published in 2016 (SoJR 2016), titled, ‘Decoding Delay: Analysis of Court Data’, provides details about the manner of classification of cases in the High Courts by DAKSH. Once the classification of cases from the DAKSH database was completed, average pendency for civil, criminal, and writ cases was computed using the same method described in respect of Figure 5. The analysis was carried out on a sample of 45,51,453 cases pending as on 19 September 2017. Less than one per cent of the case types could not be classified and hence, were not considered.
Figure 8. Average Pendency of Civil and Criminal Cases in Subordinate Courts
To determine the average pendency of civil and criminal cases in the subordinate courts, all the cases from the DAKSH database whose current 111 status was ‘pending’ were considered, constituting a sample of 17,10,605 cases as on 29 August 2017. For these cases, the case type was extracted from the DAKSH database. Since there was no readily available list of case types with their full forms, a repository of case types and full forms was manually created. Case types were then classified into two categories, namely, civil and criminal. Thereafter, average pendency for civil and criminal cases was calculated in a manner similar to that described for Figure 6.
Around three per cent of the case types could not be classified and hence, they were not considered.
Figure 12. Judicial Workload of Subordinate Courts Categorised by Types of Cases
Case details of all the cases in the DAKSH database for selected subordinate courts in Delhi were extracted. The courts that were considered are:
1. Chief Metropolitan Magistrate, North, RHC (Rohini).
2. Chief Metropolitan Magistrate, North-East, KKD (Karkardooma).
3. Chief Metropolitan Magistrate, South, Saket.
4. Chief Metropolitan Magistrate, South-West, DWK (Dwarka).
5. Chief Metropolitan Magistrate, West, THC (Tis Hazari).
6. District and Sessions Judge, North, RHC (Rohini).
7. District and Sessions Judge, North-West, RHC (Rohini).
8. District and Sessions Judge, South, Saket.
9. District and Sessions Judge, New Delhi, PHC (Patiala House).
10. Senior Civil Judge-cum-RC, East KKD (Karkardooma).
11. Senior Civil Judge-cum-RC, New Delhi, PHC (Patiala House).
12. Senior Civil Judge-cum-RC, North, RHC (Rohini).
13. Senior Civil Judge-cum-RC, North-West, RHC (Rohini).
14. Senior Civil Judge-cum-RC, South, Saket.
The sample consisted of 3,53,461 cases. The cases were categorised based on case type, and the percentage they constituted of the total sample were calculated accordingly.
Figure 13. Average Pendency and Disposal in Subordinate Courts
To calculate average pendency, all cases in the 14 courts chosen for analysis (see Figure 12) whose current status was indicated as ‘pending’ in the DAKSH database as on 15 September 2017 were considered.
To calculate the average disposal, all cases in the 14 courts chosen for analysis (see Figure 12) whose status was ‘disposed’ in the DAKSH as on 15 September 2017 were considered. Average days to disposal was calculated by finding the average of the difference (in days) between the date of disposal and the date of filing for all disposed cases.
Figure 14. Stage-wise Distribution of Hearings in Cases in Subordinate Courts
To calculate the stage-wise proportion of hearings in the studied subordinate courts in Delhi, 112 information from the column titled ‘purpose of hearing’ was extracted from the DAKSH database in all cases, which resulted in a sample of 23,93,905 hearings. Due to paucity of data at the hearings level, data from the court of Senior Civil Judge-cum-RC, East KKD was not taken into consideration.
The column titled ‘purpose of hearing’ records the stages of a case’s progress in the courts. Since there are numerous stages that are recorded on the e-Court’s website that have been collated by DAKSH in the database, the stages were standardised and categorised into five main stages for the purposes of this analysis, namely, appearance, framing of issues or charges, evidence, arguments, and final order or judgment. 0.8 per cent of stages that could not be classified were categorised under ‘others’ and have not been considered in the analysis.
Figures 15 and 16. Time Taken from First Hearing to Evidence and Time Taken from Evidence to Final Judgment, in Subordinate Courts
All cases whose current status was indicated as ‘disposed’ in the DAKSH database for the subordinate courts in Delhi were considered. For this analysis, the time taken was calculated in two parts. First, the number of days between the first hearing of the case and the first hearing on evidence was computed. Second, the number of days between the first hearing on evidence and the disposal date was calculated. Thereafter, all cases were ranked on the basis of time spent between the two events.
Figure 18. Relationship between Case Clearance Rate and Judges’ Strength
The National Judicial Data Grid (NJDG) provides a summary of cases filed and disposed in the previous month for each state and union territory. Data on the number of cases filed and disposed in the subordinate courts was collected at two different intervals — between 9 April 2017 and 9 May 2017 (extracted from the DAKSH database) and between 7 July 2017 and 7 August 2017 (manually entered from NJDG). The case clearance rate for both time periods was calculated separately.
Case clearance rate is calculated by dividing the number of cases disposed during a specified period by the number of cases filed in the same period, and multiplying the result by 100. Thereafter, an average of the two clearance rates was used to carry out the analysis.
The data for the number of judges (including vacant courts) in each of the states and union territories was manually collected from the NJDG on 7 August 2017.
Figure 19. Case Clearance Rates in Civil and Criminal Cases
Data on number of cases filed and disposed in the subordinate courts was collected at two different intervals — between 9 April 2017 and 9 May 2017 (extracted from the DAKSH database) and between 7 July 2017 and 7 August 2017 (manually collected from NJDG). The NJDG provides a summary of civil and criminal cases filed and disposed in the previous month for each state and union territory. The case clearance rate for both time periods 113 was calculated (as described in the methodology for Figure 18) separately for civil and criminal cases. Thereafter, an average of clearance rates obtained for the two selected periods was used to carry out the analysis.
Figure 20. Percentage of Population Involved in Civil and Criminal Litigation in India
To calculate the percentage of population involved in litigation, two components are necessary: the total number of pending cases in a particular court and the population of the territorial jurisdiction of that court. The total number of cases pending in subordinate courts in each of the states and union territories was obtained from the NJDG on 7 August 2017. The NJDG also provides a distribution of criminal and civil cases pending in each of the states and union territories. Population figures as per Census 2011 were manually collected from the website of the government of India (http://www.censusindia.gov.in/).
The percentage of population involved in litigation for civil and criminal cases was calculated using the following formula: number of pending cases multiplied by 2 (assuming each case has a minimum of two parties) divided by total population, multiplied into 100.
Figure 22. Average Pendency and Disposal of Execution Cases
To identify the number of execution cases in the subordinate courts, all subordinate court case types, which began with ‘exe’ were extracted from the DAKSH database and manually checked. This method threw up 1,23,766 cases. To calculate average pendency and disposal, only those execution cases that were filed between 2010 and 2017 were included, thus constituting a sample of 96,556 cases.
Figure 23. Average Pendency of Execution Cases
The total number of execution cases filed between 2010 and 2017 in the DAKSH database was a sample of 96,556 cases, of which 60,678 cases were found to be concentrated in five states and union territories, namely, Delhi, Haryana, Kerala, Punjab, and Tamil Nadu.
The average pendency of execution cases in these states and union territories was calculated based on a sample of 16,774 cases in the DAKSH database.
Annexure 1
Number of Cases Considered to Calculate Average Pendency
|
Sl No. |
High Court |
2016 Report (number of cases) |
2017 Report (number of cases) |
|
1. |
High Court of Allahabad |
1,46,585 |
6,160,72 |
|
2. |
High Court of Bombay |
68,653 |
3,06,200 |
|
3. |
High Court of Bombay at Goa |
N/A* |
3,788 |
|
4. |
High Court of Calcutta |
69,384 |
2,57,578 |
|
5. |
High Court of Chhattisgarh |
N/A* |
15,680 |
|
6. |
High Court of Delhi |
87,731 |
1,20,304 |
|
7. |
High Court of Gauhati |
N/A* |
28,787 |
|
8. |
High Court of Gauhati at Aizawl |
N/A* |
456 |
|
9. |
High Court of Gauhati at Naharlagun |
N/A* |
1,856 |
|
10. |
High Court of Gujarat |
99,883 |
2,15,845 |
|
11. |
High Court of Himachal Pradesh |
9,149 |
37,789 |
|
12. |
High Court of Jammu and Kashmir at Jammu |
N/A* |
5,569 |
|
13. |
High Court of Jammu and Kashmir at Kashmir |
N/A* |
2,484 |
|
14. |
High Court of Jharkhand |
53,227 |
1,46,515 |
|
15. |
High Court of Judicature at Hyderabad |
2,26,997 |
6,18,495 |
|
16. |
High Court of Judicature at Patna |
97,223 |
2,31,718 |
|
17. |
High Court of Karnataka |
2,55,973 |
1,91,861 |
|
18. |
High Court of Kerala |
1,33,538 |
3,44,287 |
|
19. |
High Court of Madhya Pradesh |
71,678 |
2,78,438 |
|
20. |
High Court of Madras |
84,570 |
4,50,834 |
|
21. |
High Court of Manipur |
N/A* |
5,341 |
|
22. |
High Court of Meghalaya |
N/A* |
1,278 |
|
23. |
High Court of Orissa |
49,280 |
1,39,471 |
|
24. |
High Court of Punjab and Haryana |
96,736 |
3,04,609 |
|
25. |
High Court of Rajasthan |
44,950 |
1,62,700 |
|
26. |
High Court of Sikkim |
90 |
410 |
|
27. |
High Court of Tripura |
2,247 |
11,080 |
|
28. |
High Court of Uttarakhand |
9,663 |
52,008 |
* Data from these courts were not available on the DAKSH database last year, and hence were not considered in the 2016 report.
114 Notes
* The authors wish to thank Surya Prakash B.S. for his comments.
1. See http://ecourts.gov.in/ecourts_home/ (accessed on 17 October 2017).
2. Data for the analyses carried out here was collected from the NJDG and the DAKSH database between 29 March 2017 and 21 September 2017.
3. The NJDG’s website is available online at http://njdg.ecourts.gov.in/njdg_public/main.php (accessed on 17 October 2017).
4. For detailed methodology of calculation, refer to Part 3 of this chapter.
5. Supreme Court of India. ‘July to September, 2016’, Court News, 11(3): 8. Available online at http://supremecourtofindia.nic.in/pdf/CourtNews/2016_issue_3.pdf (accessed on 19 October 2017).
6. Data collected from NJDG as on 7 August 2017.
7. Data collected from NJDG as on 7 August 2017.
8. Kishore Mandyam, Harish Narasappa, Ramya Sridhar Tirumalai, and Kavya Murthy. 2016. ‘Decoding Delay: Analysis of Court Data’, in Harish Narasappa and Shruti Vidyasagar (eds.), State of the Indian Judiciary: A Report by DAKSH, pp. 3–24. Bengaluru: DAKSH and EBC. Available online at http://dakshindia.org/state-of-the-indian-judiciary/11_chapter_01.html#_idTextAnchor009 (accessed on 17 October 2017) .
9. 115 Data collected from NJDG as on 12 August 2017.
10. Data collected from NJDG as on 12 August 2017.
11. Committee on Reforms of Criminal Justice System (Malimath Committee). 2003. Committee on Reforms of Criminal Justice System Report. New Delhi: Government of India, Ministry of Home Affairs, p. 164. Available online at http://www.mha.nic.in/hindi/sites/upload_files/mhahindi/files/pdf/criminal_justice_system.pdf. (accessed on 17 October 2017).
12. Jagannadha Rao Committee. 2005. Consultation Paper on Case Management. New Delhi: Law Commission of India, p. 16. Available online at http://lawcommissionofindia.nic.in/adr_conf/casemgmt%20draft%20rules.pdf (accessed on 17 October 2017).
13. As per data collected from NJDG, Telangana had 3,89,686 pending cases on 7 August 2017.
14. As per data collected from NJDG, Andhra Pradesh had 4,47,531 pending cases on 7 August 2017.
15. As per data collected from NJDG, Karnataka had 13,32,792 pending cases on 7 August 2017.
16. As per data collected from NJDG, Madhya Pradesh had 13,39,716 pending cases on 7 August 2017.
17. As per data collected from NJDG, Odisha has 9,93,112 pending cases on 7 August 2017.
18. As per data collected from NJDG, Tamil Nadu has 9,24,194 pending cases on 7 August 2017.
19. Ranjeet Rane and Ahmed Pathan. 2017. ‘Courts Say No to Paper Trail, Go Digital From the Top Down’, Hindustan Times, 10 July, available online at http://www.hindustantimes.com/tech/courts-say-no-to-paper-trail-go-digital-from-the-top-down-opinion/story-iyuMYWfLj48ugbuZE1vhEI.html (accessed on 17 October 2017).
20. E-committee of the Supreme Court of India. 2005. National Policy and Action Plan for Implementation of Information and Communication Technology in the Indian Judiciary. New Delhi: Supreme Court of India. Available online at http://hcraj.nic.in/action-plan-ecourt.pdf (accessed on 17 October 2017).
21. Rane and Pathan, ‘Courts Say No to Paper Trail’.
22. Population figures were manually collected from the Census India website http://www.censusindia.gov.in/, which contains official statistics on census.
23. National Crime Records Bureau. 2015. Crime in India. New Delhi: Ministry of Home Affairs, p. 29. Available online at http://ncrb.gov.in/StatPublications/CII/CII2015/FILES/CrimeInIndia2015.pdf (accessed on 17 October 2017).
24. 2015–2016 GSDP on Current Prices. Data collected from Directorate of Economics and Statistics of respective State Governments, and from All-India-Central Statistics Office, available online at http://mospi.nic.in/data (accessed on 9 October 2017).
25. It must be noted that Uttar Pradesh is an outlier. The percentage of pending civil cases in Uttar Pradesh is much higher than the percentage of GDP it contributes to the country. To understand the causal factors that affect the GDP and the civil cases in this state, a more detailed study needs to be carried out.
26. Sital Kalantry, Theodore Eisenberg, and Nick Robinson. 2013. ‘Litigation as a Measure of Well-Being’, DePaul Law Review, 62(2): 247–292.
27. According to Section 38 of the Code of Civil Procedure, 1908, courts that have passed the decree or to whom the case is sent for execution, have the right to execute the decree.
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