Electricity cost recovery and the political imagination: A comparison between private and public distribution in India's biggest cities

by Ajay Shah and Susan Thomas.

The problem

The Indian electricity system has major problems. It suffers from a central planning problem, where officials control the resource allocation, which undermines efficiency and innovation. It has a carbon emissions problem, with the lack of an effective path into the clean energy transition. It has a public finance problem, where debt sustainability in some states is materially affected by theft and by subsidies are paid for by the exchequer. As an example, in Mehta et al. 2024, we show that for Tamil Nadu, "A complete electricity sector reform versus business-as-usual translates into an FY 2028 outcome for the debt/GSDP ratio of 32.47% vs. 43.53%, and an IP/RR ratio outcome of 19.71% vs. 26.12%".

A root cause of these difficulties is subsidised and stolen electricity. As there is no free lunch, the lost revenues have to show up as a combination of explicit budgetary allocations for electricity subsidies, or distress for the distribution company. The precise mechanisms through which electricity is stolen are surprisingly subtle, e.g. as shown in Mahadevan 2024, which casts a shadow on conventional measures of AT&C losses. While there is some movement in favour of more transparent on-budget subsidies (Jaitly and Shah 2024) in states such as Karnataka, the overall problem reflects a combination of transparent on-budget subsidies, weak bill collection, and theft.

Figure 1: Feedback loops in the Indian electricity system

Source: Figure 10 (page 15) from Jaitly et al. 2025.

 

As Figure 1 shows, there are multiple positive feedback loops in operation in the Indian electricity system, which are grinding away, worsening the distress. Inadequate payment leads to SEB distress, which in turn forces high tariffs on paying C&I consumers, driving them to exit the grid and further worsening SEB finances. Inadequate payment for electricity, by many firms and households, is the core problem which then plays out in various ways.

The developments in Pakistan in recent years give us illustrations of how such causal forces could play out in the future in India (Economic Times, 2025). Conversely, the imposition of a single price for electricity applied to all buyers of electricity would materially change these feedback loops by alleviating financial distress in electricity distribution.

Household data as a research tool

A significant amount of theft of electricity is surely done by firms, who have high incentive to put in effort to obtain stolen electricity. But the overt political problems of subsidised electricity for households or agriculturists, and the political economy problems of a large number of persons stealing electricity, are uniquely present in the household sector. Household survey data offers valuable knowledge about the problems. Instead of starting from budget disclosures and the data as reported by distribution companies, we go bottom up by asking households what they pay for electricity. There are grounds for trusting the CMIE CPHS measurement of electricity expenditures at the household level (Das et al. 2024).

Figure 11 (page 19) from Jaitly et al. 2025 shows how electricity expenditures in the household data in Tamil Nadu are unusually low by Indian standards. While this appears out of line when we think that Tamil Nadu is richer than the overall Indian average, there is a need for more careful analysis which compares similar households and juxtaposes different arrangements for electricity distribution.

The gains from private distribution

In the present research, we focus on two groups of the biggest Indian cities with alternative electricity distribution arrangements. Bombay, Delhi and Calcutta have private distribution. Bangalore and Madras have public sector distribution. Using the CMIE CPHS data, we work out the median household expenditure on electricity in these two groups of cities. These are large datasets: In 2024, Bangalore and Madras add up to 1,641 households and Bombay, Calcutta and Delhi add up to 2,667 households. Given these large sample sizes, the median estimates are statistically robust. Weighted estimates are used, which can be interpreted as populated-weighting across the cities.

 

Figure 2: Median household electricity expenditure in large cities, private vs. public electricity distribution

Source: Authors' calculations using CMIE CPHS data

Figure 2 shows these facts. This shows much superior cost recovery for households in cities with private distribution. Further, it shows that over the years, the payment per urban household under public sector distribution has actually declined in nominal terms. With private distribution, it has risen. This rise is consistent with increased ownership of electricity-consuming appliances over these three years, by urban households, and the rising price of electricity in the context of overall inflation based on the inflation target of 4%. That expenditures by households under public sector distribution have not risen, in nominal terms over three years, is a remarkable finding.

While we may broadly think that household prosperity in Bombay, Delhi and Calcutta is similar to that seen in Bangalore and Madras, we control for this by placing households into nationwide urban consumption quartiles.

Quartile	Median Cons.	Median electricity (private)	Median electricity (public)
	(monthly)	(monthly spend)	(monthly spend)
Q1 (poor)	10,536	500	0
Q2	14,755	665	0
Q3	19,307	775	0
Q4 (rich)	29,522	1500	75

The quartiles are formed based on the all-India distribution of urban household consumption as seen in the CMIE CPHS data in 2024. The all-India median urban consumption runs from Rs.10,536 a month for the poorest quartile to Rs.29,522 a month for the richest quartile. With this in hand, all the urban households in Bombay, Delhi, Calcutta and then Bangalore and Madras are placed into the appropriate quartile bins. Since the bins are based on all-India urban consumption, households in each group (private distribution/public distribution) will not be equally split across the quartile bins. As we are studying the biggest cities in India, more households are likely to be slotted in higher consumption bins. We report the median value of the monthly electricity expenditure for the two groups.

These results show that after controlling for affluence of the households, public sector distribution obtains much lower payments for electricity when compared with private sector distribution. In the future, this work needs to be made statistically more rigorous by setting up a matching scheme where households in Bangalore/Madras are matched to households in Bombay/Delhi/Calcutta based on asset ownership. However, the magnitude of the difference suggests the core finding is robust.

The political economy of the Indian electricity sector

These results shed light upon questions of political economy. Politicians in states like Karnataka or Tamil Nadu are used to thinking that it is difficult to force households to pay for electricity. Politicians in Delhi, Maharashtra and West Bengal have successfully squared this circle: they are able to impose much higher expenditures upon urban households, with the consequential gains for the health of the electricity system and for state public finance.

The most striking finding here is the table organised by consumption quartiles. Why do top quartile households of one group pay Rs.1500 a month for electricity while the same kinds of households of the other group pay Rs.75? This table helps expand the political imagination across the country: What are the politicians of Maharashtra, Delhi and West Bengal getting right, that others are not? There should be a strong demonstration effect here: How are politicians in Maharashtra, Delhi and West Bengal doing the right thing and politically surviving? What is the political settlement in these states that has enabled their superior durable arrangement?

A striking feature of these results lies in the fact that Delhi, which is pooled with Bombay and Calcutta in this work, actually has a large on-budget electricity subsidy program. Even though Delhi has a big subsidy program going, we have a strong result where the group of cities with private distribution includes Delhi. This suggests that private distribution adds value even under a large on-budget subsidy. This brings a new nuance to the debates around the gains from a transparent on-budget subsidy (Jaitly and Shah, 2024). Private distribution, which brings gains in collection and theft reduction, seems to matter over and above the standard public finance gains from a transparent on-budget subsidy.

Conclusion

These results reflect a summary statistic of the working of the electricity system in the two groups of cities, bringing together all aspects that impact household payments for electricity, including overt subsidies, various mechanisms of theft, and the efficiency of bill collection. Future research is required on the sources of improvement through private distribution. Is it better metering and billing technology? A regulatory framework that insulates tariff-setting from short-term politics? Better enforcement against theft?

Section 9.7 of Jaitly et al. 2025 shows the elements of information that go into monitoring the electricity reform at the level of one state of India. The analysis presented here carries this objective one step forward.

This article emphasises the comparison between five cities and not states. Potentially, there can be a divide-and-conquer approach where urban distribution reforms are separated out from the remainder of the state. Once a successful distribution company is established, its footprint can be gradually enhanced, e.g. there is a ready path for the successful private distribution model in Bombay to cover the full footprint of the Mumbai Metropolitan Region (MMR).

Jaitly and Shah, 2021, emphasise that the path to the Indian climate transition runs through the problems of the electricity sector. The political economy of electricity subsidies and theft is a key problem holding back the electricity sector. The empirical political economy results here help illuminate the questions and show pathways to progress.

Such natural experiments, with different parts of the country trying different things, represent the gains that come for the electricity system from the Constitutional scheme where electricity was largely made a state subject. When only one solution is used all through the country, there is reduced experimentation and inferior knowledge. There is much merit in the subsidiarity principle: problems should be placed at the lowest level of the government where they can possibly be placed (Shah and Varma, 2024).

Bibliography

The Price of Power: Costs of Political Corruption in Indian Electricity, Meera Mahadevan, American Economic Review vol. 114, no. 10, October 2024 (pp. 3314–44).

The usefulness of the CMIE household survey data for electricity research in India, Susan Das, Renuka Sane and Ajay Shah, The Leap Blog, 8 May 2024.

Pakistan's quiet solar rush puts pressure on national grid, Economic Times, 16 July 2025.

The lowest hanging fruit on the coconut tree: India’s climate transition through the price system in the power sector, Akshay Jaitly, Ajay Shah, XKDR Forum Working Paper 9, October 2021.

Electricity subsidies are getting better, Akshay Jaitly and Ajay Shah, Business Standard, 26 May 2024.

Electricity reforms in the economic strategy of Tamil Nadu, Akshay Jaitly, Renuka Sane, Ajay Shah, XKDR Forum Working Paper 38, February 2025.

The electricity chokepoint in Tamil Nadu public finance, Charmi Mehta, Radhika Pandey, Renuka Sane, Ajay Shah, XKDR Forum Working Paper 31, February 2024.

India needs decentralisation, Episode 47 of Everything is everything, 17 May 2024.

The economies of Russia and Ukraine in the war

by Rounak Hande, Ayush Patnaik, Ajay Shah, Susan Thomas.

The war that began in February 2022 had substantial implications for the economies and measurement systems of both countries. Long-running wars, or strategic wars, are wars of attrition. These are shaped to an important extent by the working of the economy. The outcome on the battlefield relies on the ability of the state to foster a well functioning economy and produce or obtain adequate resources including soldiers and their supporting civilian teams, their food and health care, and their materiel.

The traditional understanding of strategic war, with its focus on the functioning of the economy and the productive capacity in the defence industrial base, has evolved and changed in this war. A new age of standoff weapons has given attacks deep inside Russia, the likes of which did not happen even during World War II. The sanctions imposed upon Russia reflect a new level of capability in economic statecraft, which was not in play in any important conventional war prior to this. These developments in the conduct of war encourage us to observe the facts as we see them unfold, and not go by our traditional knowledge about strategic war.

Understanding the true state of the economy is thus an important element of understanding the Russian invasion of Ukraine. Conventional economic measurement faces difficulties in this environment, which has encouraged an alt data literature in bring pieces of the puzzle together. A new paper Shedding light on the Russia-Ukraine War by Rounak Hande, Ayush Patnaik, Ajay Shah, Susan Thomas contributes to this literature by carefully harnessing night time light data to obtain fresh insights into the war. The major ideas from this paper are summarised here.

The difficulties of measuring economic activity through nighttime lights data

Alas, the simple economists' dream, of using nighttime lights data as an easily observed GDP proxy, has been belied. In previous work (Patnaik et. al. 2021), we found important gains over the conventional NASA/NOAA or World Bank data, through a bias-correction algorithm that thinks better about clouds.

There are unique problems in working with nighttime lights data for Russia and Ukraine. These include the far longitudes, gas flaring, and aurora borealis. We carefully solve each of these questions and develop a sound methodology for the measurement of nighttime lights in places like Russia and Ukraine.

Aggregate economic impact

At an aggregate level, how are the economies of Russia and Ukraine faring, after the war started?

The aggregate nighttime lights for Russia shows roughly zero growth from 2022 to 2025. As the Russian economy has been turned into a war economy with a significant increase in military expenditures as a share of GDP, the stagnation of nighttime lights suggests a decline in the civilian economy.

Figure 1: Time series of aggregate nighttime lights of Russia (measured in January):

Most Russian gas production takes place in the Yamal-Nenets Autonomous Okrug, and economic activity there has been strong, which runs against the conventional understanding that Russian gas exports have declined sharply.

With Ukraine, there was a sharp decline from 2022 to 2025. For both countries, 2023 was a low and then there has been some recovery.

These measures focus on the boundaries of the two countries. Their interpretation for the military aspects of the war needs to reckon with the extent to which relevant production capacity extends beyond the border. In Russia's case, North Korea is an important site for war production. In the case of Ukraine, the defence industrial base and the economy of Europe is available, as long as relatively few voters in Europe support Russia.

The economy near the front

Close to the battlefront, we expect a combination of the impact of fighting, evacuations, blackouts, destruction of productive capacity, and the presence of troops and their logistics tail.

The oblasts where the war is taking place, and Crimea, have fared surprisingly well. Perhaps the nighttime lights associated with the logistics tail of armies in action -- which cannot quite be interpreted as economic activity in the way that nighttime lights data is normally interpreted -- exceeds the adverse impact of destruction of the productive economy.

The footprint of standoff weapons

Further away from the battlefront, there would be an adverse impact upon the economy through the new level of presence of stand off weapons. Modern war is unique in the extent to which a trench is hard to overcome, but it is not that difficult to hit a factory that is 200 kilometres in the rear. Hence, we expect to see a footprint of standoff weapons deep into the backfield.

Figure 2: Difference in pre-war and post-war growth rates:

A growth reversal is visible in locations within Russia that are hundreds of kilometres inside the Ukraine border. The radiance at the regions of Russia near Ukraine contracted by 10-58.8%, while eastern regions maintained growth of 8-18.56%.

Reversal of gains from trade

At various elements of the Russian international border, the natural economic geography had unfolded in response to the proximity to economic activity across the border. The adverse impact upon the local economy, the distortion away from the natural organisation reflecting proximity to the economy across the border, is likely to vary based on the intensity of new restrictions imposed by the bordering country.

A growth reversal is visible in the map above, at locations near the border with European countries -- which have imposed sanctions more completely -- as opposed to the border with other countries.

Changing economic geography

Over the many years of the ongoing war, the economic geography has been reshaped through government and private decisions. Understanding this map of shifting economic geography is important from the viewpoint of understanding regional economics, and for resource allocation in long range strikes and in air defence.

Figure 3: Shifting centre of gravity of the Russian economy (map):

The Ukrainian economy has shifted West, away from the war zone. The Russian economy has shifted East, away from Europe and the war. There was an eastward shift of the economic center of Russia by 245 kilometers between January 2019 and January 2025. We are able to see maps of levels and growth rates of subnational nighttime lights that yield fresh insights into the working of the Russian economy.

Conclusion

Nighttime lights data is one tool in the arsenal of economic measurement. In combination with other pathways to measurement, this gives fresh insights into the Russian and Ukrainian economies, and insights into this important strategic war. We are at the edge of the seat, waiting to do our January 2026 update.

Reproducible Research

For transparency and reproducibility, all data processing and analysis for this study can be replicated using our open Google Colab notebook. The notebook allows users to download satellite data, run the code, and generate all results and plots in a fully reproducible cloud-based environment—no manual installation of libraries or dependencies required.

The vector boundary data and the notebook will be made available here:

GitHub repository
Google Colab notebook

 

The authors are researchers at XKDR Forum, Bombay.  

Households that live within their means in India

by Jay Kulkarni and Susan Thomas.

The economic well-being of households is primarily about their ability to spend on consumption. Household consumption is dominated by what the income of the household is, but not limited by it. Households that spend less than they earn, build their savings. Households that spend more than they earn either borrow or draw down on earlier savings. There is a big difference in the life-cycle possibilities between households that manage to save versus those that do not. In this article, we analyse a panel dataset of Indian households to understand what differentiates households who live within, or beyond, their means.

An often discussed measure of the household's income-consumption dynamic is the `marginal propensity to consume' or MPC, which is the marginal change in consumption for a marginal change in income. The MPC is a valuable part of the toolkit of macroeconomics. An equally important measure is the 'average propensity to consume' (which is abbreviated as APC). This is the fraction of disposable income that the household consumes. The APC shows the income-consumption dynamics of a household in a stated time period. When the APC is below 1, the household is saving, and on average, building up its wealth. There is a clear line between low APC households (i.e. those with APC below 1), who are building up wealth, vs. the households that are not.

In an advanced economy, we think of the APC as a part of life cycle optimisations. When an affluent and financial unconstrained household is young, it builds up savings (i.e. low APC), and then it dis-saves in old age (i.e. high APC). In a poor country, we see many households who are dis-saving even when they are young. Building up wealth versus drawing down wealth takes on a different character in the context of a low middle income economy (Badarinza et al, 2019).

Aggregate facts about household APC, and its covariates, are an important element of understanding India. This article aims to establish such facts. What is the average household APC in India? What fraction of households have a low APC? Do higher income households have a low APC? Do low APC households have lower income volatility? Are low APC households systematically older households? What is the connection between financial inclusion and household APC?

Data and Methodology

The measurement of consumption is an important feature of many government statistical systems. Aggregative statements are derived from the national accounts. The best information about households is found in advanced economies such as the US (Consumer Expenditure Surveys) and the UK (Family Resources Survey), which are observed at annual frequencies.

Less is known about Indian households. In recent times, better measurement of households has commenced in India. One such dataset is the Consumer Pyramids Household Survey (CPHS) published by CMIE, which began in 2014 and now surveys about 200,000 households every year, thrice a year. For this article, we focus on their 2023 and 2024 data.

Computing an APC can be done at different frequencies. In this article, we compute the APC at both monthly and annual data.

Average household APC in India using annual data

We start at the annual APC and establish basic facts. Household data is hard to measure, given difficulties in survey administration, in the interest of the household in offering information, and in the correct recollection by the household. Hence we show a robust estimator of the mean APC across the values obtained for each household. Table 1 reports this value along with other summary statistics. The big fact that we take away is that the (robust mean of the) APC was 0.64 in 2024. If (1 - APC) is the savings rate, this implies a savings rate of 0.33 percent in 2023 and 0.36 in 2024.

Table 1: Distribution of annual household APC in India, 2023, 2024

	25th	50th	75th	Mean	Std.Dev.	Fraction
						with APC<1
2024	0.50	0.65	0.80	0.64	0.33	94.23
2023	0.54	0.69	0.84	0.67	0.74	91.79

 

Going deeper into cross-sectional variation and higher frequency observation

Households may have an annual APC < 1, while having some months where APC > 1. For example, a farming family may be above the water when viewed at the level of the year, but it may earn income only at the Kharif harvest, and run with APC > 1 for all other months. We now define a `Low APC household' as one which lives strictly within its means, where every monthly APC (and therefore the annual APC) is less than 1. Using this definition, we partition the data into Low vs. High APC households.

Table 1 shows that 94.23 percent of Indian households in 2024 are at an annual APC < 1. But when we switch to this modified view of the APC within the year, the picture changes. In this perspective, 54 per cent of Indian households in 2024 are low APC. For 2023, this value was 48 per cent.

We also observe the age of the household head, as well as other household features such as the fraction of members who are dependents and the fraction who are employed. To make numbers comparable, we adjust prices for inflation using an all-India series re-based to December 2024, and use per-capita numbers to account for different household sizes in the sample.

We construct a measure of household income volatility, as the standard deviation of the percentage monthly changes in household income. We construct a financial participation score as in Palta et al (2022). This is the fraction of the number of financial assets households own, out of the 10 that dataset records. The debt status of each household is also separately observed.

We then explore cross-sectional variation by estimating a probit model to predict a low APC household based on the annual data. All explanatory variables are contemporaneous. Figure 1 presents the estimated coefficients from this regression. In this figure, the vertical dashed line indicates the 0 value of the null hypothesis. Any coefficient on the right is positive, and to the left the coefficient which can be used to answer the questions raised above. The distance of the error bars from the 0 value line shows that the coefficient is statistically significant, and influential in the probability of the household being a low-APC household.

Figure 1: Factors affecting the probability of being classified as a low APC household 

 

What do we see here?

• Do low APC households have higher income?
  The coefficient of log income is positive and significant. The higher the income, the higher the probability that the household is low APC.
• Do low APC households have higher income volatility?
  Income volatility has a negative and significant coefficient. This means that higher the volatility of income, the lower the chance of the household being low APC.
• Are low APC households older?
  The age of the head of the household is a proxy for the age of the household. The coefficient for this is close to zero (value of 0.0052) but is positive and significant. Households with older heads tend to be low APC. This income-consumption pattern is consistent with the life-cycle hypothesis of Modigliani and Brumberg (1950), or the permanent income hypothesis of Friedman (1957).
• Do low APC households have a better financial participation score?
  The household financial participation score is a useful way to think about the asset side of the household balance sheet (Ghosh and Thomas, 2022). This coefficient is positive and significant. In addition, the presence of borrowing tends to run in the opposite direction (borrower households are more likely to be high APC).

Discussion

We have a new fact about Indian households: About half of these have at least one month a year where they live beyond their means. Many of the results that we see here are consistent with empirical findings in other countries (Goodman and Webb, 1995, Blundell and Preston, 1998, Gorbachev, 2011, Fisher et al, 2020). Higher income, higher fraction of members employed, higher financial participation score, older households, lower income volatility, lower fraction of dependents, and not having debt, correlate with being a low APC household.

The trajectory of income, savings and wealth by an affluent, financially unconstrained household, operating in a well functioning macroeconomic and financial system is well-established. We expect households to save when they are young, and dis-save when they are old. In the Indian setting, such behaviour is perhaps the privilege of a small number of households who face more complex financial planning problems within the year.

In thinking about households in India, the distinction between households that are adding to their savings versus the households that are not, seems fundamental. It has far-reaching consequences for the life of a household. From the viewpoint of governments and firms, this is an interesting distinction which can be applied when thinking about households. This article is a first look, based on novel mechanisms of measurement, covering two years of data only. More research is needed to obtain insights into the causes and consequences of these phenomena. What is the dynamics of low APC across time? What kinds of households are able to achieve low APC on a sustained basis? How does the build-up of household wealth reshape the decisions of a low APC household?

References

1. Cristian Badarinza, Vimal Balasubramaniam and Tarun Ramadorai, The household finance landscape in emerging economies, Annual Review of Financial Economics, Volume 11, pages 109-129, 2019.
2. Richard Blundell and Ian Preston, Consumption Inequality and Income Uncertainty, The Quarterly Journal of Economics, Volume 113, Number 3, May 1998, pages 603-640.
3. Jonathan D. Fisher, David S. Johnson, Timothy M. Smeeding and Jeffrey P. Thompson, Estimating the marginal propensity to consume using distributions of income, consumption and wealthJournal of Macroeconomics, Volume 65, 2020.
4. Indradeep Ghosh and Susan Thomas, Financial inclusion measurement: Deepening the evidence, Chapter 9, Inclusive Finance India Report 2022, 17th edition, pages 117-125, January 2023.
5. Alissa Goodman and Steven Webb, The distribution of UK household expenditure, 1972-1992, Fiscal Studies, Volume 16, Number 3, pages 55-80, 1995.
6. Olga Gorbachev, Did household consumption become more volatile?, American Economic Review, Volume 101, Number 5, August 2011, pages 2248-70.
7. Geetika Palta, Mithila A. Sarah and Susan Thomas, Measuring financial inclusion: how much do households participate in the formal financial system?, The Leap Blog, 3 July 2022.

Acknowledgments

Jay has just wrapped up his masters in economics from Università Bocconi. Susan is senior research fellow at XKDR Forum. We thank Geetika Palta for help on working with CPHS, and Ajay Shah for positioning and inputs.

Get them to the court on time: bumps in the road to justice

by Mugdha Mohapatra, Siddarth Raman and Susan Thomas.

India's district courts currently face a staggering backlog of 4.6 crore pending cases (as of May 2025): 3.5 crore criminal and 1.1 crore civil. Proposals to solve this are familiar: hire more judges, build special courts, adopt new technology. But before rushing to solutions, it is important to understand where cases get stuck in their journey through courts. We hand-collect and analyse the life-cycle of a sample of cases from district courts, with some surprising observations. First, between 50-70 percent of cases are disposed before they get to trial, which is before the judge hears the substantive matter of the dispute. The time spent waiting for parties to appear is over a year. While criminal cases necessarily require strict adherence to due process, even civil cases face delays. These findings challenge conventional wisdom about judicial delays and point to a unexpected bottleneck. If getting people to show up in court is the core source of delays and pendency, strengthening the administrative processes of the court rather than the size of the bench, could lead to more speedy justice delivery from our courts.

The objective of building judicial capacity to achieve judicial efficiency requires an understanding of how cases move through courts, not just tracking pendency rates. This is because the journey of a case moves through deterministic stages, which vary in duration, and imposes varying resource demands from judges and staff. There have been few systematic studies of how a case moves through court. While some studies examine the total time for case disposal, few break this down by stage.

This study analyses stages for two common types of cases that represent a significant portion of the workload of courts: cheque bouncing cases (criminal matters under Section 138 of the Negotiable Instruments Act) and motor accident claims (civil matters under the Motor Vehicles Act).

Cheque bouncing cases account for 10-15% of criminal court workloads, while motor accident claims constitute over 10% of pending civil cases. Cheque bouncing cases happen when a cheque issued does not deliver payment as expected. Motor accident claims are filed to claim compensation for damages caused in the accident against the owner of the vehicle involved, with the vehicle insurance company as a co-respondent. Cheque bouncing cases are filed in a magistrate court, and the motor accident claims at the Motor Accident Claims Tribunals (MACT). Across these two types of cases, there are differences in procedures: whether it is for criminal and civil cases, and for different types of courts.

We use this analysis to answer the following questions:

1. What fraction of the cases go through the whole life-cycle?
2. How much time is spent in different stages of the case life-cycle? Is this different for civil and for criminal cases?

Methodology

The analysis examined 200 disposed cases randomly sampled from from the e-courts database for district courts from courts across Maharashtra, Kerala, Karnataka, Tamil Nadu, Delhi, Telangana and Rajasthan, filed between 2018-2022. After excluding transfers and circumstances where cases never went a court process, the final sample included 147 cases - 77 cheque bouncing cases and 70 motor accident claims cases.

Each case was tracked through its entire journey by analysing court orders and hearings. Cases go through different stages - filing, admission, summons, warrants, bail, written statements, framing of issues, evidence and others. We classify these different stages of 'Pre-Trial' and 'Trial'. Trial begins after both parties appear before the judge - in cheque bouncing cases, after the accused files for bail; in motor accident claims, after written statements are filed and issues are framed.

Results

1. The first finding relates to the stage at which the cases are disposed. Table 1 shows the number of cases disposed at each stage.

Table 1: Where cases end their journey

Stage	Case type: MV		Case type: S138
	No. of cases	Percentage	No. of cases	Percentage
Pre-trial	38	54 %	55	71 %
Trial	32	46 %	22	29 %

More than half of the cases analysed never reached trial. This is higher for the (criminal) cheque bouncing cases, where 70% of cases are disposed before they reach trial. For the (civil) motor accident claims cases, 54% of the cases are disposed before reaching trial.

2. The second finding relates to the time spent in the two stages

Table 2: Time taken by stage

Case type	Total no. of cases	Pre-trial	Trial
MV	70 (32 reached trial)	9.5 months	4 months
S138	77 (22 reached trial)	12 months	3.5 months

Once all parties are in present in court, cases resolve quickly - usually in 3-4 months. Most of the delay in matters is in the pre-trial stage where the court is waiting for parties to appear (usually the respondent). This takes between 9 months to a year.

These findings align with broader patterns visible in the National Judicial Data Grid for district courts (NJDG). The data shows that 72% of pending cases are stuck before trial: 48% are at the appearance stage, 14% are awaiting service of summons, and 10% are awaiting service of warrants. While the data from the NJDG is useful to know where cases are placed within the judicial system, it does not provide insights on the time spent in different stages. Our analysis quantifies the extent of the bottleneck.

Discussion

The analysis points to two key observations: Most cases that are filed in court do not reach trial, where judicial mind is applied to decide issues of the case. Further, the bulk of the time is spent in getting the parties to court. Once all parties are present, the time to resolution is much lower. The puzzle is in understanding what shapes these features, and how this understanding can be used to improve court efficiency in dealing with case workload.

• Are the delays in court cases inevitable?

The analysis points to the paradox of procedural protections for some cases. Cheque bouncing cases and other criminal matters demand the presence of the accused. This creates an inherent tension between speedy resolution of the matter and judicial procedure. The accused, facing potential imprisonment, has every incentive to delay appearing in court until forced by warrant. The very protections meant to ensure fair process become tools for delay.

In India, these procedures continue to evolve. Under Section 223 of the newly introduced Bharatiya Nagarik Suraksha Sanhita, magistrates must now offer the the accused an opportunity to be heard before admitting a complaint as a criminal case. This involves sending a notice by post, a process not unlike the current summons process. While intended to enhance due process, this additional step could further extend the timeline for cheque bouncing cases. The new code also allows for trial 'in absentia' under Section 356. If a person is declared as a 'proclaimed offender', and if the judge thinks that they are absconding to evade trial, the court can proceed without the accused. How these practices are implemented remains to be seen.

• Administrative and judicial functions of the court

The findings expose a fundamental blind spot in how courts actually work. The popular image of justice - a judge hearing arguments, weighing evidence and delivering verdicts - represents only one aspect of the judicial system. Behind every courtroom drama lies an extensive administrative operation of filing documents, scheduling hearings, maintaining records, and getting parties to court. These two systems complement each other, but our understanding of the administrative aspects of the court system is limited, because it is behind the scenes.

Current reform proposals focus heavily on expanding judicial capacity: hiring more judges, creating specialised courts, and implementing new technologies for case management. While these interventions have merit, they miss the core issue revealed by this analysis. The judicial system extends far beyond judges and courtrooms. Delivering summons and notices typically involves police officers, postal services, or process servers. When the simple act of getting parties to court becomes the biggest bottleneck, the solution requires rethinking the entire administrative infrastructure supporting the courts.

What does imply for potential solutions for institutional reforms of the judiciary? Some approaches that could address the summons/notices bottleneck include:

1. Digital service of summons and notices could reduce delays, though this requires updated legal frameworks and reliable technology infrastructure.
2. Police-court integration might improve warrant execution, though this raises questions about optimal resource allocation - should a capacity constrained police forces pursue cheque defaulters or focus on serious crimes?
3. Quicker escalation to warrants may secure attendance faster, but wielding state power to restrict liberty demands careful consideration. A judge's decision to issue an arrest warrant carries real consequences.
4. Penalties for non-appearance could be introduced to create stronger incentives for timely court attendance.
5. Private process servers, as used in U.S. courts, offer another model worth exploring.

Conclusion

The clamour for court reform has been dominated by traditional solutions: more judges, rewritten procedures, and new technology. But when the relatively simple task of getting parties to court becomes the system's biggest bottleneck, a more nuanced approach is essential. Court reform must recognise that efficient justice delivery requires strengthening both judicial and administrative capacity in parallel. Separating court administration from judicial functions, as some countries have done, could allow specialised focus on each component while maintaining their complementary relationship.

The invisible administrative machinery of courts deserves as much attention as the visible judicial functions. Until administrative capacity matches judicial capacity, Indian courts will continue struggling with delays that have less to do with complex legal reasoning and more to do with basic case management. The path to speedier justice may lie not in the courtroom, but in the clerk's office, the process server's route, and the administrative systems that bring cases to life. Only by addressing both aspects of the judicial system can India's courts deliver the swift justice that 4.6 crore pending cases demand.

Siddarth and Susan are senior research lead and senior research fellow at XKDR Forum. Mugdha was a research associate at XKDR Forum. We thank Pavithra Manivannan for insights, Shubho Roy for help with the interpretation, and Ajay Shah for inputs.

Who lends to the Indian state?

by Aneesha Chitgupi, Ajay Shah, Manish Kumar Singh, Susan Thomas and Harsh Vardhan.

Public finance researchers in India have paid great attention to debt and deficits. By now, the main messages of the field have started sinking into common knowledge: that it is good to run primary deficits in most years, so as to create space to surge the deficit once in a while when faced with a crisis. There is an adjacent field of public debt management that is equally important. Here, the strategic question is: How should the government borrow? From whom? Debt management strategy has not received the required level of interest.

Strategic thinking in debt management

A sound public debt management strategy must cater to three objectives:

• The mechanism for borrowing must not induce economic distortions upon the domestic economy.
• It must create strategic depth of being able to borrow on a very large scale when faced with great challenges, once every few decades.
• It must induce sustainable mechanisms for reasonably low cost borrowing, at reasonably predictable rates, for the long term.

There are four main pathways to choose from in debt issuance:

1. Monetisation of the deficit. Here, the central bank distorts the monetary base with `fiscal dominance’ where it buys the bonds issued by the government.
2. Coerced borrowing from financial firms. These are typically regulated firms, who are coerced using the tools of financial regulation.
3. Borrowing from voluntary participants (domestic or foreign). This is done through local currency bonds issued domestically, possibly nominal and possibly inflation indexed.
4. Borrowing abroad using foreign currency denominated bonds. As an example, this could involve Yen denominated bonds issued in London.

As with many other countries, we started out in India with the first method (monetisation of the deficit). This induces an economic distortion: the loss of monetary policy autonomy. A long journey of monetary policy reform took place, from the Ways and means agreement of 1993, to the Monetary policy framework agreement in 2015 that ushered in inflation targeting. This freed up monetary policy from the limitations imposed by debt management. In 2015, there was an attempt at institutional reform, in the form of the establishment of the Public Debt Management Agency (freeing up the Reserve Bank of India of the responsibility of issuing public debt), but this did not come to pass.

From 1993 onward, the main strategy for public debt management in India has involved method 2 in the list: a system of `financial repression’ where the government borrows from coerced financial firms. This is a tax upon financial intermediation. The interest rates discovered through government borrowing are important prices that impinge upon the economy. But these rates are distorted owing to the presence of coerced buyers of government debt. The lack of voluntary lenders creates the lack of strategic depth. The government is limited in how it can expand its borrowing when faced with special situations.

From the late 1990s onwards, economists and thinkers have sought to enhance fiscal prudence in India through the mechanism of fiscal responsibility law. It is increasingly clear that this does not work. In recent work, Datta et. al. 2023 show that the Indian constitutional arrangements frustrate the possibility of Parliamentary law imposing fiscal discipline upon the union government. Once this idea is internalised, there is one main path towards fiscal responsibility: market discipline. This requires removing the system of financial repression.

Who lends to the Indian state?

In this context, the question Who lends to the Indian state? attains importance. A recent paper by Aneesha Chitgupi, Ajay Shah, Manish Singh, Susan Thomas and Harsh Vardhan examines this question. For a period of 10 years, we assemble information from multiple sources, which were all available in the public domain, to examine the nature of lenders to the Indian state. Some discoveries that we make are:

• The SLR went down in the last decade. This meant that the extent of bank funds mandated for the government decreased. However, the actual investments by banks in government debt securities was higher than what was mandated.
• Simultaneously, there was major growth in the role of insurance and pension funds lending to the government. While de jure financial repression of banks declined, there has been no such retreat with pensions and insurance.
• All the three groups of financial firms bought a lot more government bonds as compared with the de jure requirements. Excess ownership went from about 0 in 2011 to Rs.30 trillion in 2021.
• How did the government increase borrowing over the last decade, while simultaneously elongating the maturity profile? The answer lies in (a) Strong growth in insurance and pensions industries, and (b) Excess ownership of government bonds by coerced industries.
• The voluntary lenders are the private firms, MFs and FIIs, who are 4.8% of investors in the government debt market for 2021. India (along with China) remains an outlier in having very low borrowing from international debt markets.

Important questions for the future

This field is target rich with interesting questions, some of which are:

1. Why do financial firms lend so much to the government?
2. What will the structure of lenders to the government look like, 10 years out into the future?
3. If a big surge in borrowing is required, where will it come from?
4. How are households and firms changing their behaviour in response to the financial repression tax?
5. What is the path to fiscal responsibility?

Conclusion

The field of public finance in India has studied deficits and debt. There has been work on the institutional arrangements for debt management (i.e. the establishment of the Public Debt Management Agency). There has been relatively little work on the economic reasoning, the strategic thinking for debt management. In this paper, we offer novel insights and facts for this journey. More research is required, at the interfaces between public finance, finance and public administration, to grow knowledge on the important field of debt management strategy.

Survey-based measurement of Indian courts

by Pavithra Manivannan, Susan Thomas, and Bhargavi Zaveri-Shah.

Public institutions do not face a market test. Achieving state capacity is about establishing checks and balances. The traditional idea is to instrument the operations, and construct an operational MIS, which is released into the public domain. Through this, deficiencies of the working of the organisation are visible to researchers and the public. The other pathway is to ask the persons who interact with the state institution about what they feel, to elicit their perceptions. This is an important pathway to obtain evidence and thus create feedback loops. For instance, citizen surveys are commonly used to assess the quality and impact of public services such as health and education (UNDP 2021, Clifton et al, 2020, OECD-ADB 2019).

In the legal system, perception surveys of court users can generate useful knowledge about how well courts function in their delivery of justice (National Center for State Courts, 2005). Ongoing surveys of user experience of courts can help measure the performance of a component of the entire legal system, and in assessing the impact of interventions made for reforming the legal system.

Surveys of court users and the public on their perception of the judiciary have been prevalent in developed countries from the 1990s, and are gaining currency in India (eg., Dougherty et al, 2006; Rottman and Tyler, 2014; Staats et al, 2005; Daksh 2016). Such surveys seek to capture the perceptions of court users on qualitative metrics (Manivannan et al, 2022). Such metrics can be used to evaluate the functioning of a single court, or compare alternative courts.

On one hand, perceptions are not reality. On the other hand, the views of end-users of the justice system are particularly important because, ultimately, the justice system exists to serve end-users whose interests and preferences may differ from those of judges and lawyers. We can readily discern certain difficulties in survey-based measurement of perceptions:

1. There are many different users of a court, who differ in their extent of knowledge. Litigants who see a court case as a disruption of their daily lives, may see things differently when compared with lawyers, for whom courts are part of their professional lives.
2. A person who loses a case is likely to be unhappy with his experience of the court and vice versa.
3. Different individuals might be working on non-comparable cases, and their subjective experience of the court is then not comparable.
4. It is not clear what is an objective benchmark of sound performance. A perfect court may be prohibitively expensive. Users of courts may have normalised a variety of difficulties; their `satisfaction' may only flow from learned helplessness.
5. It is important to narrowly measure a court or a group of courts, and make claims about the narrow unit of observation, as opposed to bigger claims about the Indian legal system.

In 2023, we conducted two pilot surveys to evaluate their utility as feedback loops for courts.

One survey was administered to understand the functioning of five alternative forums that can be approached to adjudicate matters of debt disputes: the Bombay benches of the National Company Law Tribunal (NCLT), the Debt Recovery Tribunal (DRT), the Bombay High Court (Bom HC), the Metropolitan Magistrate (MM) courts (which adjudicates criminal proceedings for cheque bouncing cases), and the Alternative Dispute Resolution (ADR) process.

To help improve data quality, the survey was conducted on practitioners who had multiple instances of interacting with the five courts. By selecting practitioners that have had repeated instances of approaching these forums to resolve disputes, the survey results are less vulnerable to the 'loser' effect. To obtain comparability, we presented a hypothetical, canonical problem of debt dispute resolution to each survey respondent. We then asked them to rank the five forums on five dimensions of court performance, namely, efficiency, effectiveness, predictability, independence, cost and convenience, and calculated the average rank for each forum on each of these dimensions.

The second survey was conducted with litigants at the DRT, with the objective of understanding the functioning of this court. For this, we deployed a team of four, who visited the premises of the Bombay bench of the DRT. The team administered a survey questionnaire on individuals, in order to evaluate the performance of the DRT on the above mentioned five dimensions. The participants were asked to rate their experience at the DRT on a five-point scale.

Method

Survey design

We used a combination of qualitative (in-depth expert interviews and open-ended comments) and quantitative surveys (multiple choice and scaled questions). Qualitative surveys with experts provide more contextual insights, enable comprehensive analysis. They helped validate our founding conjecture, the idea that there was a class of disputes which could go to multiple different forums. However, these surveys were time-intensive and it was difficult to obtain the interest and involvement of experts.

Survey mode

We administered the survey in both online and offline formats. Surveying litigants on court premises was challenging in two ways. First, litigants do not always accompany their lawyers to courts, especially in disputes of larger sizes involving firms. Second, one forum may deal with multiple type of disputes (civil v. criminal; mergers v. insolvency). This poses difficulty in identifying a litigant with a desired case-type.

The questionnaire used for the surveys and the responses collected can be found here.

Results: The perceptions of practitioners

The practitioner survey involved eliciting their choice of forum for the following hypothetical, canonical problem:

Q is a large public listed company. It has availed of a working capital loan of Rs. 7 crores from N, a small sized NBFC, repayable within three years with simple interest @16% p.a. Q and N are 100% domestically owned. As collateral for the loan, Q has granted N a floating charge over some of its movable assets, for example, its machinery or its inventory. One year into the loan, Q defaults on its loan to N. The outstanding amount exceeds Rs.1 crore. Post-dated cheques issued by Q towards interest payment bounce due to insufficient funds. The collateral is not sufficient to cover the outstanding amount. You are advising N.

The survey respondents were asked to make two assumptions, namely, that the limitation period is the same across all the courts; and that all courts have jurisdiction.

We collected responses from 18 respondents, of which 16 were lawyers and two were key managerial personnel at an asset reconstruction company and a debt restructuring advisory firm. Six of our respondents had between 20 to 30 years of experience in this area, eight of them had experience of less than 20 years, and two of them had more than 30 years experience in this field. They had significant experience with many of the venues of interest: 14 had experience with the NCLT and the Bom HC, 11 with the DRT and ADR process, and 5 with the MM Courts.

We aggregated the ranks assigned by the respondents to each of these forums on the parameters of independence, efficiency, effectiveness, predictability and access, and averaged them to arrive at an overall rank for each forum. The specific statements on which the respondents ranked the forums and their ranks are presented in Table 1. The forums are arranged in increasing order of the average rankings on each parameter. The NCLT was ranked the highest on the parameter of Efficiency, followed by ADR, the Bom HC, the DRT and the Metropolitan Magistrate. On the other hand, the Bom HC was ranked as the most preferred forum of choice on the parameter of independence.

Table 1: Preference ordering of five debt enforcement forums

Metric	Survey Statement	Ranking
		1	2	3	4	5
Efficiency	Most likely to dispose of my matter in a timely manner	NCLT	ADR	Bom HC	DRT	MM Courts
Effectiveness	Easiest to recover the amount awarded in the judgement decree.	NCLT	Bom HC	DRT, ADR	MM Courts
Predictability	(i) Expected sequence of stages in my matter was clear.	NCLT	ADR	Bom HC	DRT	MM Courts
	(ii) Hearings are most likely to be held as scheduled.	ADR	NCLT	Bom HC	MM Courts	DRT
Independence	Decisions are most likely made based on the merits of the case.	Bom HC	ADR	NCLT	MM Courts	DRT
Access	(i) Can afford to take my case to this forum.	MM Courts	DRT	NCLT	Bom HC	ADR
	(ii) Ease of navigation; staff helpfulness; website; ease of filing process	ADR	Bom HC	NCLT	DRT	MM Courts

Table 1 contains new insights on a specific court on each attribute. For example, while the Bom HC and the ADR process are perceived to be most unbiased, they are perceived as more expensive to access. ADR is perceived to be most predictable, but less effective on actually getting the relief. The NCLT, on the other hand, is perceived to be more efficient and effective, when compared to the other forums, but less likely to also be unbiased. The DRT and the Metropolitan Magistrate courts are perceived unfavourably on all aspects, except affordability.

Results: The perceptions of litigants

The in-person survey conducted at the DRT observed 55 persons, who were presently a party to a dispute at the DRT. Among these, 24 were debtors, 19 were creditors, and 12 belonged to the residual category, such as court/privately appointed receivers and auction awardees. Of these, 30.6% were at early stages (admission), 28.6% were at advanced stages (such as post-admission or pending last hearing), and 22.4% were awaiting a final hearing or pronouncement of judgement.

Litigants at the DRT had more positive perceptions than practitioners. Litigants ranked the DRT the highest on predictability of the hearing: most litigants agreed that when a hearing for their case is scheduled at the DRT, it will be held on the scheduled date. About 67-69% of litigants perceived the DRT to be an affordable and unbiased forum to resolve their dispute. More creditors ranked it higher (85-89%) on these two metrics than debtors (58-62%). However, 52% of litigants did not think that the DRT resolves cases in a timely manner.

Discussion

Good performance by the judicial branch in a country is essential. As with all aspects of public policy, this requires the loop of evidence, identification of difficulties, creative policy proposals, policy reforms, and measurement of the gains. In the legal system, generally, evidence and measurement involves quantitative measures. In this article, we have shown a case study where survey-based evidence was useful. This constitutes a useful additional pathway to measurement of the legal system.

Litigants are the ultimate end-users of courts, so their views matter greatly, but their information set may be limited. Legal practitioners have better information through repeated interactions and potentially observation of multiple venues, but their views may not capture the views of the litigants themselves. In the future, it would be useful to go further, by way of surveying the general public, measuring the view of persons who have not experienced litigation at a given location.

References

Shaun Bowler, Joseph L. Staats, and Jonathan T. Hiskey (2005). Measuring Judicial Performance in Latin America, Latin American Politics and Society.

Judith Cliftona, Marcos Fernandez-Gutierrez and Michael Howlett (2020). Assessing public services from the citizen perspective: What can we learn from surveys?, Journal of Economic Policy Reform.

Daksh (2016). Access to Justice Survey, A DAKSH report.

David B. Rottman and Tom R. Tyler (2014). Thinking about judges and judicial performance: Perspective of the Public and Court users, Onati Socio-legal Series.

Devendra Damle and Tushar Anand (2020). Problems with the e-Courts data, NIPFP Working Paper Series 314.

George W. Dougherty, Stephanie A. Lindquist and Mark D. Bradbury (2006). Evaluating Performance in State Judicial Institutions: Trust and Confidence in the Georgia Judiciary, State and Local Government Review.

Institute of Social Studies and Analysis (2021). Satisfaction with Public Services in Georgia, United Nations Development Programme.

National Center for State Courts (2005). CourTools: Trial Court Performance Measures.

Pavithra Manivannan, Susan Thomas and Bhargavi Zaveri-Shah (2022). Evaluating contract enforcement by courts in India: a litigant's lens, XKDR Working Paper No. 16.

Pavithra Manivannan, Susan Thomas and Bhargavi Zaveri-Shah (2023). Helping litigants make informed choices in resolving debt disputes, The Leap Blog.

OECD-ADB (2019). Government at a Glance Southeast Asia, Serving Citizens: Citizen satisfaction with public services and institutions, OECD Publishing, Paris.

Pavithra Manivannan and Susan Thomas are researchers at XKDR Forum, Mumbai. Bhargavi Zaveri-Shah is a doctoral candidate at the National University of Singapore. We thank Surya Prakash B.S., Renuka Sane, and Anjali Sharma for their suggestions on the design of the surveys. We acknowledge the very diligent assistance by Nell Crasto and Balveer Godara, students at Kirit P. Mehta School of Law, NMIMS Mumbai, on conducting the litigant survey. We are grateful to all the survey respondents for their generous participation, and thank Mahesh Krishnamurthy, K.P. Krishnan, Sachin Malhan, Harish Narsappa, Rashika Narain, Geetika Palta, Siddarth Raman, Ajay Shah, and Arun Thiruvengadam for their comments and suggestions on this work.

How substantial are non-substantive hearings in Indian courts: some estimates from Bombay

by Pavithra Manivannan, Karthik Suresh, Susan Thomas, and Bhargavi Zaveri-Shah.

The problem

If we think about court as a services production organisation, then the number of staff, technology and other resources would be inputs to deliver well-defined outcomes of litigants' cases being decided satisfactorily. In between these inputs and outcome are hearings as the output of courts. Hearings are where the matter of the dispute is presented in front of a judge. When hearings are substantive, progress is made in resolving the dispute.

Not all hearings are substantive. Some non-substantive hearings are inevitable, involving procedural matters such as the filing of documents. When a hearing is non-substantive because the matter is rescheduled to a later date, this imposes a burden of time and cost upon litigants and the court.

Such unexpected non-substantive hearings are an important problem in the Indian legal system. The Civil Procedure Code (1909) prescribes a limit of three adjournments per case, but reality often diverges from this stated limit. In 2021, the e-committee of the Supreme Court has proposed an alert for judges to be informed about breaches in this 3-adjournment rule within its case management system. There is thus a recognition of the presence of this problem.

What is not, at present, known is a quantitative sense of the improtance of the problem. In this article, we estimate the magnitude of non-substantive hearings for one group of situations. The working of the Indian legal system varies widely by venue and case type. In order to measure the phenomenon of non-substantive hearings, we pick one relatively homogeneous class of disputes --- debt dispute resolution --- which are heard at three courts in Bombay. They are the National Company Law Tribunal (NCLT), the Debt Recovery Tribunal (DRT), and the Bombay High Court (Bombay HC). For these three venues, we seek to estimate four quantities:

1. What is the fraction of substantive hearings in these courts?
2. Out of the hearings in a case, how many are substantive?
3. How much time elapses till a first substantive hearing?
4. How likely is the first hearing to be a substantive hearing?

Definitions and estimates

An understanding of the number and likelihood of such hearings is important to set litigant expectations about the time and costs spent when seeking redress from the court. Regy and Roy (2015) use the term 'failed hearing' in their work on understanding what causes delays at the Delhi Debt Recovery Tribunal (DRT). They classify failed hearings as those hearings that satisfy three criteria: the hearing resulted in an adjournment without any judicial business, the adjournment was avoidable and the adjournment was not penalised. Khaitan et al. (2017) record hearings as 'inefficient' in their study on court efficiency, where the definition of an efficient court is based on whether the court meets set deadlines or not. In their work on cases from the Delhi High Court, they record hearings as 'inefficient' when there is a failure, either because of the court ('insufficient time to hear the case', 'absent judge') or because of either party ('counsel sought time', 'Absent counsel', 'Delay condoned', 'Restoration'). These papers present us with the earliest estimates of non-substantive hearings. Regy and Roy (2017) record 58% of hearings at the DRT as failed hearings. Khaitan et al (2017) record 48% of hearings at the Delhi HC as inefficient. Both suggest that attempts to reduce adjournments could reduce court delays by up to 50-60%, based on these estimates.

The Ministry of Law, Justice and Company Affairs, in the context of fees payable to government counsel, refer to 'Effective hearings' and 'Substantial work'. Effective hearings are where either one or both parties are heard by the court, while 'non-effective' hearings are where 'the case is mentioned and adjourned or only directions are given or only judgement is delivered by the court'. The same memorandum refers to substantial work as 'when the case has been admitted by the Court after hearing of preliminary objections or filing of the affidavits/counter-affidavits etc. by the Counsel'. These definitions guide a distinction between adjournments and non-substantive hearings.

In this article, we broaden the notion of differentiating non-substantive hearings beyond adjournments. Only hearings where there is application of judicial mind to the resolution of the dispute, are classified as 'substantive'. Thus hearings that involve disposals, withdrawal, admission, reporting settlement, are classified as substantive hearings. Adjournments are classified within non-substantive hearings. A reading of the order for an adjourned hearing may simply have a next date given for a hearing. These may be adjournments on account of paucity of time, time sought by parties, non-appearance of parties, wrongly listed or technical glitches. We also classify hearings as non-substantive when orders in which the court gives directions to file pleadings or take on record pleadings. Hearings that involve matters of procedure, without a substantial impact on the resolution of the dispute itself, are taken as non-substantive for a litigant.

The dataset

We hand-constructed a novel dataset, where for a sample of cases, we built the existing case life-cycle by collating all the hearings for a given case. We then read and classified each hearing in the case life-cycle as a substantive or a non-substantive hearing using the approach listed in the previous section. Since each judge records what transpired at the hearing in her own style, parsing and classifying every order necessarily involved a subjective judgement about whether it is a substantive hearing, or not. Therefore, once we had classified orders, we then subjected the classification to a double-blind peer review.

We built this dataset for cases of debt dispute resolution, using orders collected from the websites of the High Court (HC), DRT and NCLT in Bombay. The analysis was done for a random sample of 200 matters from each of the three courts. In these samples, we selected 100 disposed cases and 100 pending cases for each court. The hearing dates ran between 2018 and 2022.

One difference in how orders are uploaded on the Bombay HC versus the two tribunals is important to take note of: each court follows a different timeline for uploading case life-cycle data. On the Bombay HC website, the case appears from the date of filing. For the tribunal courts, the case appear on their respective websites only from the first hearing date, irrespective of the filing date of the case. Since the sample of cases from each court was drawn at random, there could be cases in the Bombay HC without a hearing, while this is not possible with cases in the sample from the tribunal courts. Further, this makes a strict comparison of hearing characteristics at the Bombay HC and the tribunal courts difficult. These differences impose constraints on how various measures are calculated for each court, in order to enable a balanced comparison across the courts.

Findings: What is the fraction of substantive hearings in the three courts?

Table 1: Fraction of substantive hearings to total hearings in three debt dispute resolution courts

Court	Hearings in full sample			Hearings in disposed cases
	Total	Substantive	Fraction		Total	Substantive	Fraction
Bombay HC*	399	192	0.34*		208	139	0.48*
DRT	575	229	0.40		267	116	0.43
NCLT	1135	258	0.23		365	145	0.40

^{*57 cases at the Bombay HC had zero hearings. The fractions reported for the Bombay HC have been adjusted to account for this.}

Table 1 shows the total number of hearings, the number of substantive hearings and the ratio of substantive to total hearings in the three courts. The number of non-substantive hearings can be calculated as (Total hearings - Substantive hearings). This table shows that the NCLT generates the lowest ratio of substantive hearings among the three courts, while the Bombay HC outputs the highest ratio.

Table 1 also shows the data on the ratio of substantive hearings for disposed cases in the three courts. This indicates two features: first, the court generates a higher fraction of substantive hearings in the case of disposed cases. This means that there is a higher number of substantive hearings among hearings for cases that have been disposed. But, there are still fewer substantive hearings than non-substantive hearings. Less than than 50% of all hearings for disposed cases are substantive hearings. This observation holds for all three courts. This suggests that process improvements that simplify administrative hearings or reduce the incidence of adjournments will have a significant improvement in the experience of the litigant in these courts.

The above finding relates to the outputs generated by the courts as a whole, in relation to each other. The litigant focus will be more on what we observe about hearings per case. We examine these questions next.

Findings: What is the fraction of substantive hearings per case in the three courts?

Table 2 shows the summary statistics of hearings per case in the sample. The values presented include the minimum, median, maximum and average number of hearings per case.

Table 2: Number of hearings per case for three debt dispute resolution courts

Court	Hearings			Substantive hearings
	Median	Average		Median	Average
Bombay HC	1	1.21*		1	0.43*
DRT	3	2.88		1	0.82*
NCLT	5	5.68		1	0.91*

^{*Each court has a different number of cases for which substantive hearings could be observed. The counts are 79 cases in the Bombay HC, 57 cases in the DRT and 60 cases in the NCLT with no substantive hearings}

Table 2 shows two values for each court: the average number of hearings per case, and the average number of substantive hearings per case. We see that the Bombay HC has the lowest average number of hearings per case (1.21). The NCLT has the largest number of hearings per case (5.68). This indicates that NCLT has more than 3 times the hearings per case compared to the Bombay HC. It holds more than 2 times the average number of hearings at the DRT which has 2.88 hearings per case, on average.

When comparing the values of the average number of hearings per case to the average number of substantive hearings per case, Table 2 shows that all courts have less than 1 substantive hearing per case, on average. The NCLT has the highest average number of substantive hearings per case (0.91) but it is less than one. The average number of substantive hearings per case for the DRT is almost the same as the NCLT, despite the number of hearings per case being double at the NCLT. This suggests that for every 6 hearings at the NCLT, one is likely to be substantive, while for every 3 hearings at the DRT, one is likely to be substantive. If the number of hearings can be used as a proxy for the cost of filing a case in court, then NCLT is likely to be the lowest benefit to cost for the litigant.

But, the hearing or substantive hearing per case is often not the sole objective for a litigant who approaches court for the resolution of debt. What is also important is the time within which the substantive hearing can be reached. For this, we next examine what is the expected time to the first substantive hearing.

Finding: Time to first substantive hearing

When the case gets a first substantive hearing is an important milestone for a litigant. It is likely to be a hearing in which substantive oral arguments will be made on questions such as the admission of the matter before the court, questions of interim relief that will operate pending the final disposal of the matter, the impleadment of new parties to the matter, the time schedule for the filing of replies and counter-replies, and so on. Setting an expectation on when such a hearing is likely to be conducted after the case is filed, is therefore an important input to preparing for the case.

We use a survival analysis approach to estimate the time to a first substantive hearing after the filing date (Manivannan et al, 2023). Figure 1 shows two survivor functions for each court. The survivor function can be represented as a curve on a graph, which shows the chances of not getting a first hearing / substantive hearing (on the y-axis) against time from filing the case in court (on the x-axis). When the case is first filed, the chance of not getting a substantive hearing is 1 or 100%. I.e., at the outset, all cases experience no hearing / substantive hearing. As time progresses, this number starts to become lower than 1. The `faster' the curve drops from 1, the higher the chances that the case had a first hearing / substantive hearing. On each graph, the darker line shows the chances of a first substantive hearing, while the lighter line shows the chances of a first hearing.

The graph for the Bombay HC (in red) shows that at the end of one year, 40% of the cases have not obtained one hearing. When we focus on substantive hearings only, 60% of the cases have not achieved this milestone. The dark and light line are clearly separated, which indicates that these two values are distinctly different from each other.

The graph for the DRT (in green) shows that 77% of the cases have not got one hearing at the end of the first year after filing. When we focus on substantive hearings only, this is true for 80% of the cases. This means that only 20% of the cases can be expected to get a substantive hearing by the end of the first year from filing.

The graph for the NCLT (in blue) shows that at the end of one year, a little less than 50% of the cases have not got one hearing. When we focus on substantive hearings only, this fraction goes up to 70%. This means that 30% of the cases are likely to have achieved a first substantial hearing in the first year from filing. The gap between the curves for the first hearing and the first substantive hearing is the largest for the NCLT, among the three venues.

These graphs show that the litigant is most likely to get a first substantive hearing within one year of filing from the BHC.

We have chosen to estimate the chances of getting a first hearing and a first substantive hearing in one year after the case has been filed. But these same graphs can be equally used to estimate the chance of a first substantive hearing for shorter or longer periods of time also. For example, the chance of a first hearing within one month of filing the case is the highest at the NCLT, followed by the DRT, and last, at the Bombay HC. Similarly, the graphs show that the chances of getting a first substantive hearing within one month of filing is the highest at the NCLT, up to three months after filing. But if the case is not heard within this time, the chances of getting either a first hearing or a first substantive hearing are higher for a case which is filed at the Bombay HC.

Conclusion

Unpredictable non-substantive hearings constitute a process failure. In this article, we show that for one kind of matter (debt dispute resolution), at three venues, the fraction of non-substantive hearings is 64%, 60% and 77%. From the litigants' perspective of measuring the performance of courts, if a good measure is the fraction of matters that get to a substantive hearing within the first year after filing, we find that this value stands below 50% for all the courts studied.

There is merit in establishing systematic mechanisms for computing such performance metrics. These findings can help litigants estimate the possibilities of events and expenditures, after a case begins. Such information systems would help improve decision-making about suing, about settling, and the choice of venue, for the litigant. A regular estimation of these metrics can also be a useful guide for changes made in court processes, with the understanding that a change in performance metric will be some complex combination of the process change, along with the change in the response of the people who both make up the legal system, and those who use it.

Finally, this work highlights the difference in objectives for which performance metrics need to be designed. While the producer (court) will find it optimal to use the ratio of aggregate substantive to total hearings, the litigant will optimise based on the metric of substantive hearings per case which can lead to a different choice relative to what the court might expect.

References

Nitika Khaitan, Shalini Seetharam and Sumathi Chandrashekaran (2017), Inefficiency and Judicial Delay: New Insights from the Delhi High Court , Vidhi, March 2017.

Pavithra Manivannan, Susan Thomas and Bhargavi Zaveri-Shah (2023), Helping litigants make informed choices in resolving debt disputes, The Leap Blog, 15 June 2023.

Prasanth Regy and Shubho Roy (2017), Understanding Judicial delays in debt tribunals, NIPFP Working Paper 195, May 2017.

Pavithra Manivannan, Karthik Suresh, and Susan Thomas are researchers at XKDR Forum, Mumbai. Bhargavi Zaveri-Shah is a doctoral candidate at the National University of Singapore. We thank Geetika Palta for data support, and Purbasha Panda for her support in reading through the case orders. We also thank two anonymous referees and Ajay Shah for useful feedback and comments.