Court Data

Do increases in court congestion decrease future institution?

Whether there is a correlation between time lag in the disposal of pending cases and number of new cases filed.

Does the amount of time a court takes to dispose of pending cases influence the total number of cases being filed? Do fewer litigants approach the court when it takes longer to dispose of all pending cases? We found that, historically, the time taken by the court to dispose of cases has no obvious effect on the number of cases instituted in the court in subsequent years.

 

Figure 1 graphs both congestion (the time taken to dispose of cases) and institution (number of cases filed) over the period 1950 – 2018.  The two follow different trends over time. The time taken to dispose of cases increases until the mid-90s and subsequently flattens out. By contrast, the number of cases instituted continuously increases throughout the court’s history and does not  correspondingly drop in the mid-90s.

Figure 1. Our graph uses  a logarithmic y-axis to meaningfully display both congestion and institution on the same graph. On average, congestion is significantly less than institution. It has a range of 4.66, whereas institution has a rang of 87,949 cases.

 

How can we test if changes in congestion correspond to changes in institution? One method is to examine their linear correlation coefficient which checks if an increase in congestion corresponds to a statistically significant decrease in institution. Figure 2 captures the linear correlation coefficient between congestion and institution.

 

We also tested the correlation between the two with various time offsets, to account for possible time lags between increases in congestion and decreases in institution. For example, we examined if an increase in congestion caused a decrease in institution two years later? A correlation coefficient between -0.8 and -1 would have emerged in such case.

 

Figure 1. Our graph uses  a logarithmic y-axis to meaningfully display both congestion and institution on the same graph. On average, congestion is significantly less than institution. It has a range of 4.66, whereas institution has a rang of 87,949 cases.

 

How can we test if changes in congestion correspond to changes in institution? One method is to examine their linear correlation coefficient which checks if an increase in congestion corresponds to a statistically significant decrease in institution. Figure 2 captures the linear correlation coefficient between congestion and institution.

 

We also tested the correlation between the two with various time offsets, to account for possible time lags between increases in congestion and decreases in institution. For example, we examined if an increase in congestion caused a decrease in institution two years later? A correlation coefficient between -0.8 and -1 would have emerged in such case.

Figure 2

 

Congestion and institution don't appear to affect each other, even after accounting for various time offsets.

 

More work needs to be done to see if people's perceptions of congestion affect their likelihood of approaching the court.  After all, there is a difference between perceived congestion and congestion. Perhaps we can more insight by factoring in changes in the number of practicing Supreme Court advocates.
Data sourced: Supreme Court of India, 'Indian Judiciary: Annual Report 2017-18 (May 2018) <https://www.sci.gov.in/pdf/AnnualReports/Annual%20Report%202018-light.pdf> accessed on 16 July 2019.