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Last week, the Planning Commission filed an affidavit in the Supreme Court updating the official poverty line to Rs 965 per month in urban areas and Rs 781 in rural areas. This works out to Rs 32 and and Rs 26 per day, respectively. The perceived inadequacy of these figures has led to widespread discussion and criticism in the media. In light of the controversy, it may be worth looking at where the numbers come from in the first place. Two Measures of the BPL Population The official poverty line is determined by the Planning Commission, on the basis of data provided by the National Sample Survey Organisation (NSSO). NSSO data is based on a survey of consumer expenditure which takes place every five years. The most recent Planning Commission poverty estimates are for the year 2004-05. In addition to Planning Commission efforts to determine the poverty line, the Ministry of Rural Development has conducted a BPL Census in 1992, 1997, 2002, and 2011 to identify poor households. The BPL Census is used to target families for assistance through various schemes of the central government. The 2011 BPL Census is being conducted along with a caste census, and is dubbed the Socio-Economic & Caste Census (SECC) 2011. Details on the methodology of SECC 2011 are available in this short Ministry of Rural Development circular. Planning Commission Methodology Rural and urban poverty lines were first defined in 1973-74 in terms of Per Capita Total Expenditure (PCTE). Consumption is measured in terms of a collection of goods and services known as reference Poverty Line Baskets (PLB). These PLB were determined separately for urban and rural areas and based on a per-day calorie intake of 2400 (rural) and 2100 (urban), each containing items such as food, clothing, fuel, rent, conveyance and entertainment, among others. The official poverty line is the national average expenditure per person incurred to obtain the goods in the PLB. Since 1973-74, prices for goods in the PLB have been periodically adjusted over time and across states to deduce the official poverty line. Uniform Reference Period (URP) vs Mixed Reference Period (MRP) Until 1993-94, consumption information collected by the NSSO was based on the Uniform Reference Period (URP), which measured consumption across a 30-day recall period. That is, survey respondents were asked about their consumption in the previous 30 days. From 1999-2000 onwards, the NSSO switched to a method known as the Mixed Reference Period (MRP). The MRP measures consumption of five low-frequency items (clothing, footwear, durables, education and institutional health expenditure) over the previous year, and all other items over the previous 30 days. That is to say, for the five items, survey respondents are asked about consumption in the previous one year. For the remaining items, they are asked about consumption in the previous 30 days. Tendulkar Committee Report In 2009, the Tendulkar Committee Report suggested several changes to the way poverty is measured. First, it recommended a shift away from basing the PLB in caloric intake and towards target nutritional outcomes instead. Second, it recommended that a uniform PLB be used for both rural and urban areas. In addition, it recommended a change in the way prices are adjusted, and called for an explicit provision in the PLB to account for private expenditure in health and education. For these reasons, the Tendulkar estimate of poverty for the years 1993-94 and 2004-05 is higher than the official estimate, regardless of whether one looks at URP or MRP figures. For example, while the official 1993-94 All-India poverty figure is 36% (URP), applying the Tendulkar methodology yields a rate of 45.3%. Similarly, the official 2004-05 poverty rate is 21.8% (MRP) or 27.5% (URP), while applying the the Tendulkar methodology brings the number to 37.2%. A Planning Commission table of poverty rates by state comparing the two methodologies by is available here.
Sources: Court News, 2006, Supreme Court of India; National Data Judicial Grid accessed on August 7, 2019; PRS.
Overall, the pendency of cases has increased significantly at every level of the judicial hierarchy in the last decade. Between 2006 and now, there has been an overall increase of 22% (64 lakh cases) in the pendency of cases across all courts. As of August 2019, there are over 3.5 crore cases pending across the Supreme Court, the High Courts, and the subordinate courts. Of these, subordinate courts account for over 87.3% pendency of cases, followed by 12.5% pendency before the 24 High Courts. The remaining 0.2% of cases are pending with the Supreme Court. The primary reason for growing pendency of cases is that the number of new cases filed every year has outpaced the number of disposed of cases. This has resulted in a growing backlog of cases.
In High Courts and subordinate courts, over 32 lakh cases pending for over 10 years
Sources: National Data Judicial Grid accessed on August 7, 2019; Court News, 2006-17, Supreme Court of India; PRS.
In the High Courts, over 8.3 lakh cases have been pending for over 10 years. This constitutes 19% of all pending High Court cases. Similarly, in the subordinate courts, over 24 lakh cases (8%) have been pending for over 10 years. Overall, Allahabad High Court had the highest pendency, with over seven lakh cases pending as of 2017.
Despite high pendency, some High Courts have managed to reduce their backlog. Between 2006 and 2017, pendency of cases reduced the most in Madras High Court at a rate of 26%, followed by Bombay High Court at 24%. Conversely, during the same period, the pendency of cases doubled in the Andhra Pradesh High Court, and increased by 2.5 times in Karnataka High Court.
As a result of pendency, number of under-trials in prison is more than double that of convicts
Sources: Prison Statistics in India, 2015, National Crime Record Bureau; PRS.
Over the years, as a result of growing pendency of cases for long periods, the number of undertrials (accused awaiting trial) in prisons has increased. Prisons are running at an over-capacity of 114%. As of 2015, there were over four lakh prisoners in jails. Of these, two-thirds were undertrials (2.8 lakh) and the remaining one-third were convicts.
The highest proportion of undertrials (where the number of inmates was at least over 1,000) were in J&K (85%), followed by Bihar (82%). A total of 3,599 undertrials were detained in jails for more than five years. Uttar Pradesh had the highest number of such undertrials (1,364) followed by West Bengal (294).
One interesting factor to note is that more criminal cases are filed in subordinate courts than in High Courts and Supreme Court. Of the cases pending in the subordinate courts (which constitute 87% of all pending cases), 70% of cases were related to criminal matters. This increase in the pendency of cases for long periods over the years may have directly resulted in an increase in the number of undertrials in prisons. In a statement last year, the Chief Justice of India commented that the accused in criminal cases are getting heard after serving out their sentence.
Vacancies in High Courts and Subordinate Courts affect the disposal of cases
Sources: Court News, 2006-17, Supreme Court of India; PRS.
Vacancy of judges across courts in India has affected the functioning of the judiciary, particularly in relation to the disposal of cases. Between 2006 and 2017, the number of vacancies in the High Courts has increased from 16% to 37%, and in the subordinate courts from 19% to 25%. As of 2017, High Courts have 403 vacancies against a sanctioned strength of 1,079 judges, and subordinate courts have 5,676 vacancies against a sanctioned strength of 22,704 judges. As of 2017, among the major High Courts (with sanctioned strength over 10 judges), the highest proportion of vacancies was in Karnataka High Court at 60% (37 vacancies), followed by Calcutta High Court at 54% (39 vacancies). Similarly, in major subordinate courts (with sanctioned strength over 100 judges), the highest proportion of vacancies was in Bihar High Court at 46% (835 vacancies), followed by Uttar Pradesh High Court at 42% (1,348 vacancies).
