Report No. 245
Computing Judge Strength
A. Overview of Data and its Limitations
Lack of complete data was a great handicap in making critical analysis and more meaningful suggestions as responses to questionnaires received from many High Courts16 were incomplete. However, data supplied by High Courts of Andhra Pradesh, Bihar, Delhi, Gujarat, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Punjab & Haryana, Sikkim, and Uttarakhand proved very useful in furnishing the basis for the present work. The analysis in this report is based on the data received from these High Courts.
16. See Annexure I and II.
High Courts have provided data for the period 2002 to 2012. All the data received has been computed on an annual basis. Therefore, for example, each High Court has provided data as on 31st December of each year, under the categories of institution, disposal, pendency, etc.
Some High Courts provided data that was disaggregated into two categories: Higher Judicial Service and Subordinate Judicial Service. Other High Courts provided data disaggregated by cadre, i.e., Higher Judicial Service, Civil Judge (Senior) Division, and Civil Judge (Junior) Division. For uniformity of analysis, all the data has been analysed in the two broad categories of Higher Judicial Service and Subordinate Judicial Service.
It is important to note that the data on institution, disposal and pendency does not indicate the actual number of cases in the system. High Courts count data in various ways. Some High Courts such as those of Himachal Pradesh, Jammu & Kashmir, Orissa and Sikkim count interlocutory applications before Subordinate Courts as separate institutions, disposals and pendencies. Kerala even counts committal proceedings as separate for purposes of institution, disposal and pendency.
Therefore, a single case may be counted multiple times in some High Courts. Thus, the number of cases pending, instituted or disposed of by the Courts is significantly smaller than the overall pendency, institution or disposal figures would suggest.
Further, the multiplicity of approaches in tabulating data make a cross-comparison between different High Courts problematic. For example, in the High Courts of Delhi, Andhra Pradesh, Bombay, Karnataka and Madhya Pradesh, interlocutory applications are not counted separately. In Punjab and Haryana, Jharkhand and West Bengal, the practice of counting or not counting differs from district to district.
Similarly, while Karnataka does not count traffic and police challans as part of the institution, disposal and pendency figures, most other High Courts do. Given this variance, in the Commission's view a cross-comparison of States for making pan-India recommendations especially in view of the data currently available may not be very appropriate.
Besides gaining access to appropriate data from all High Courts, a major challenge was determining its accuracy. Potential errors could be seen upon close scrutiny of the data. For example, data received from the Delhi High Court indicates that in 2010.-40054 Negotiable Instrument Act, matters were instituted in the Delhi Subordinate Courts and 111517 were disposed of.
Since a negative number of institutions is patently impossible, this number appears to have been inserted to balance the backlog tally and make up for a previous mistake in the number of pending negotiable instrument act matters.17 It is not known how many other errors like this have not occurred. Also, such adjusting of the statistics to get a correct backlog tally then misrepresents the number of institutions in a given year, distorting the overall institution rate.
17. At the end of 2009 there were 416700 pending, while at the end of 2010 there were 265129.
Similarly, the data on institution, disposal and pendency for many High Courts did not tally from year to year.18 There were also inconsistencies between data sources. In some cases, the data received in response to the first Questionnaire (Annexure I) and the second Questionnaire (Annexure II) did not match. However, given these errors and unexplained inconsistencies, the Commission approached these data with caution used only for a broad trends analysis, in order to understand general and approximate patterns.
18. For example, the Pendency (PN) in any given year (N) should be equal to Pendency in the Previous Year (PN-1) + Institution in N (IN.- Disposal in N (DN). This formula can be represented as: (PN) = (PN-1) + I.- DN.
However, in the absence of any uniformity in data collection presently and certain lack of quality of data of various High Courts, the Commission strongly recommends that urgent steps be taken to evolve uniform data collection and data management methods. Such steps, if taken in earnest, would ensure transparency and more importantly facilitate policy prescriptions for the judicial system. At this stage, a caveat may be added, that so far as the present work is concerned, it relies largely on the latest information supplied by the High Courts.