Announcements

10 Years of the IBC: Conference

07^th - 08^th November, 2025

In 2026, it will be ten years since the Indian Insolvency and Bankruptcy Code (the IBC) was enacted. This milestone offers an opportunity to reflect on what the law has achieved - what was envisioned at its inception, and where we stand today.

Over the past decade, the IBC has reshaped India's credit landscape, influencing how firms, creditors, and markets respond to financial distress. This conference brings together policymakers, regulators, practitioners, and researchers who have engaged with the IBC and the broader credit market ecosystem, to take stock of its journey and discuss the road ahead.

The agenda can be found at https://www.xkdr.org/event/10-years-of-the-ibc.

If you are interested in attending, please contact outreach@xkdr.org.

Event Details

• Date: 07^th - 08^th November, 2025
• Location: Boundary Hall, MCA Bandra Club, Mumbai

How Indians rank their rights: what 26 interviews tell us about Article 19 and property

by Prashant Narang.

Citizens treat property as the material anchor that makes other freedoms meaningful; livelihood-enabling freedoms are prioritised, while free speech is cherished but policed asymmetrically.

In 1978, the Forty-Fourth Amendment removed the right to property from the Constitution's catalogue of fundamental rights. Lawyers and economists have debated the implications ever since. But do ordinary citizens internalise that demotion? This post introduces our Socio-Legal Review note - co-authored by Sehar Abdullah, Keerthana Satheesh, and Prashant Narang- which steps outside the courtroom to ask a simple question with big policy consequences: which rights do people treat as most important in their daily lives - and why? Drawing on 26 in-depth interviews across professions that are especially sensitive to rights restrictions (journalists, migrants, MSME owners, cab drivers, farmers, artists, street performers and more), we map how citizens rank the Article 19(1) freedoms alongside the right to property. The top-line finding: people continue to see property as foundational - often the precondition that makes other freedoms meaningful.

What we did

We used purposive and snowball sampling to reach respondents aged 19-65 whose livelihoods could be directly affected by limits on speech, association, movement, residence, profession, or on property. Most interviews were conducted in Delhi, with additional remote interviews in Kerala, Chennai, and Bengaluru. We piloted the instrument, then ran a three-part interview: background and demographics; general views on freedoms and "reasonable restrictions"; and case studies with graded constraints (for example, permits and bans; "public order" versus epidemic) to elicit trade-offs. Transcripts were thematically coded. This is qualitative research; insights are directional, not population estimates.

What we heard: the lived hierarchy

• Property as cornerstone - Across backgrounds, respondents described property as livelihood, security, and autonomy "a means to earn a living", as one farmer put it. People resisted permissions on buying and selling land and were most animated by compulsory acquisition scenarios. Support for acquisition often hinged on compensation: market-linked and predictable when the purpose was clearly public (for example, a metro), with sharper bargaining when it looked commercial (for example, a mall). The underlying intuition is economic: when property underwrites household security, the perceived risk of under-compensation looms large.
• Economic freedom as a gateway - Freedoms that enable livelihood - movement and residence for migrants (Article 19(1)(d) and (e)) and choice of occupation (Article 19(1)(g))- were consistently prioritised. A photojournalist linked movement directly to earning; an activist framed profession and property as part of a single "socio-economic" relationship that the state should ease rather than police. This fits a law-and-economics intuition: secure property and open markets reduce dependence and expand feasible choices, which then support speech and association.
• The free-speech asymmetry - Respondents valorised free expression for themselves - journalism as "the fourth pillar", bans as "the end of democracy" - but many were readier to restrict others, often using elastic notions of "harm" or "extremism". In short: pro-speech for me, pro-restriction for you. That asymmetry is a legitimacy warning: broad, vague grounds for curbing speech match the public's weakest intuitions and risk becoming catch-alls.
• Residence as identity - The right to reside and settle anywhere (Article 19(1)(e)) surfaced as a surprising anchor of national belonging. Several interviewees described the ability to live anywhere as central to Indian diversity - inking mobility to both opportunity and citizenship.

Why this matters for policy design

• Compensation design - Where acquisition feels commercial, citizens bargain harder and distrust adequacy; where purpose is plainly public, opposition is more about predictability than principle. Legislatures and agencies should therefore tighten "public purpose" definitions and commit to clear, market-linked compensation formulas (benchmarks, indexation, relocation assistance) and process timelines that reduce uncertainty rents and litigation.
• Targeted deregulation for livelihood rights -Frictions on movement, residence, and small-enterprise activity (permits, zoning that criminalises street vending, opaque lease and tenancy formalities) bite hardest on those who use these rights to earn. Policy wins lie in simplifying titles and transfers, digitising and time-bounding consents, rationalising vending, parking, and market rules, and reducing compliance steps for micro-businesses - exactly where our respondents located day-to-day pain points.
• Speech rules that travel well - The asymmetry we observed - tolerant for self, restrictive for others - suggests two drafting heuristics: (i) avoid vague grounds like "offence" without a tight harm standard, and (ii) pair restrictions with necessity-and-proportionality tests that officials must evidence ex ante. Narrow tailoring not only protects rights but also matches citizens' strongest defence of speech (for themselves) while tempering expansive instincts to curb others.

What this does not claim

This is a qualitative, urban-skewed sample. We do not estimate a numeric hierarchy or claim causal links between income and preferences. Our aim is to surface design hypotheses and legitimacy risks that can be tested at scale and used now for better drafting and implementation. Rural and longitudinal work are obvious next steps.

The big picture

Constitutional amendments can change a right's formal rank without changing its everyday salience. In our interviews, property remains the backbone of autonomy and a hedge against shocks; livelihood-enabling freedoms are the everyday workhorses; and speech is cherished but policed asymmetrically. For policymakers and drafters, the take-away is practical: align legal categories and procedures with how citizens actually use and trade off rights. That means predictable compensation and acquisition processes; frictions-down reforms for movement, residence, and micro-enterprise; and narrowly tailored, evidence-based limits on expression. This is the path to a constitutional order that people recognise in their daily choices - not just in the statute book.

Read the paper

Rights in the Eyes of the Beholder: The Lived Hierarchy of Rights in India's Democracy - Socio-Legal Review, 21(1), 2025. Authors: Sehar Abdullah, Keerthana Satheesh, and Prashant Narang.

Prashant Narang is a researcher at TrustBridge Rule of Law Foundation.

Announcements

Researcher Position in Quantitative Research

Quantitative research: we integrate multiple datasets (household survey data, firm data, macroeconomic and financial time-series, satellite imagery, legal systems data, other custom datasets) to obtain insights into the world aiming for academic research and real world applications. Along the way, we innovate on methods. Here are some examples: self reported health, informational efficiency of credit ratings, the working of financial markets, improved methods for nighttime lights radiance satellite imagery. We build open source packages in Julia and R, partly to do better computation around existing methods, and partly to express our innovations in statistical and computational methods.

We are looking for someone with interest and experience in cross border-flows and global finance.

The right persons for quantitative research at XKDR Forum are those who have knowledge of mathematics, statistics, computer science and economics, and take interest in the world, in applying quantitative tools to obtaining insights into the world. Existing capabilities in Python, R, or Julia are a requirement, as well as some econometrics background. Of great importance is collaboration with the policy oriented researchers in XKDR Forum.

Please look us up at: website, youtube channel, open source releases, annual conference, newsletter on substack. The remuneration offered will be commensurate with your skill and experience and will be comparable with what is found in the Indian research ecosystem.

Interested candidates must email their resume with the subject line: Application for "Research Associate" at XKDR Forum, to Ms. Jyoti Manke at careers@xkdr.org by 30th November, 2025.

A frugal DIY air quality monitor

by Ayush Patnaik.

Particulate matter pollution creates a public health crisis in India and other emerging economies. Yet individuals lack access to immediate, localised air quality data. This data matters for personal decisions: whether to exercise outdoors, wear a mask, or run an air purifier. It also helps test whether air cleaning devices work by comparing inlet and outlet readings.

Air quality varies dramatically across short distances, making hyperlocal measurement essential. Government monitoring stations are sparse and report delayed data. In Haryana, all state AQI stations went offline in April 2025 and remained down for months (Times of India, 2025). Even where stations exist, authorities sometimes relocate them to cleaner areas (Hindustan Times, 2023). Portable commercial devices like the Laser Egg offer an alternative but present barriers to widespread adoption. They cost around Rs. 10,000, are heavy and bulky, and require regular charging.

The Aqui project addresses these gaps. It demonstrates that a DIY product which can enable citizen-led data collection and improve public understanding of local air quality dynamics.

This article updates an earlier version published at LEAP Blog.

Project website: https://ayushpatnaikgit.github.io/aqui/

Design approach

The design follows radical frugality through functional delegation to the host device. It centres on the SDS011 sensor, which costs around Rs. 1,200, for real-time measurement of PM2.5 and PM10 concentrations.

This class of low-cost optical sensor has limitations. It degrades within weeks under continuous operation as particles trap in its mechanism (World Air Quality Index, 2025). But the design objective focuses on intermittent monitoring driven by citizen curiosity and spot measurements. This mitigates the degradation issue.

The sensor interfaces directly with Android devices via USB Serial Cable and USB-OTG adapter. This eliminates the need for microcontrollers, displays, batteries, and wireless modules. These components drive cost and bulk in commercial devices. Delegating processing and power to the user's smartphone cuts both.

The Aqui Android companion app is open-source on GitHub. It connects to the sensor over USB serial and streams the live particulate data (PM2.5 / PM10) to your phone.

The physical enclosure uses 3D printing for low-cost replication. The entire project is released under Creative Commons CC0 (Public Domain) dedication. All design files and code are freely accessible for scrutiny, replication, and modification.

Results

Total component cost for a complete Aqui sensor is approximately Rs. 2,000. This includes the SDS011, cables, and 3D-printed enclosure. The cost represents an order-of-magnitude reduction compared to commercial-grade monitors.

The sensor streams live PM data via the companion Android application. Response time is seconds. In controlled demonstrations, the device measured clear differences in PM2.5 readings between a busy road and a park 50 metres away. This immediate visualisation captures real-time pollution gradients missing from centralised data.

People understand air quality differently when they see their own numbers. Abstract government statistics become personal, actionable information that influences health decisions.

Discussion

Aqui addresses practical gaps in air quality monitoring by providing an accessible, data-generating tool. Delegating key functions to smartphones achieves the frugality needed for mass citizen adoption. This fills data gaps created by sparse and delayed government monitoring.

The SDS011 sensor choice remains optimal for intermittent, curiosity-driven spot-checking despite its continuous operation constraints. The policy impact lies in making citizens aware by turning abstract statistics into personal information that directly influences health decisions.

The hands-on nature provides a pathway for scientific curiosity and learning. Users transition from passive news consumers to active environmental investigators. This citizen science mindset, driven by curiosity and enabled by affordable technology, anchors a more responsive and engaged air quality strategy.

Dense networks of distributed Aqui sensors could create curiosity-driven air quality maps. Future work should focus on methodologies for crowdsourcing and aggregating such data.

Resources and build guide

The Aqui project is open-source. All files, documentation, and videos are available:

• Demo video: Aqui in action
• Assembly tutorial: Step-by-step assembly
• Android app: Download APK for sideloading
• Source code: Android app on GitHub
• SDS011 sensor: Example parts link
• 3D print files: Top shell, Base shell
• Full assembly model: STEP file for remixing
• Complete build guide: Parts list and instructions

References

Hindustan Times (2023). "Now, BMC Seeks to Shift Air Quality Monitors to 'Cleaner' Areas." 5 February 2023. https://www.hindustantimes.com/cities/mumbai-news/now-bmc-seeks-to-shift-air-quality-monitors-to-cleaner-areas-101675537753172.html.

Times of India (2025). "No Pollution Data from Haryana as All State AQI Stations Offline since April." 29 July 2025. https://timesofindia.indiatimes.com/india/no-pollution-data-from-haryana-as-all-state-aqi-stations-offline-since-april/articleshow/122963786.cms.

World Air Quality Index (2025). "The SDS011 Air Quality Sensor Experiment." Accessed 7 October 2025. https://aqicn.org/sensor/sds011/.

Ayush Patnaik is a Senior Research Associate at XKDR Forum and Founder AccioPix. Special thanks to Saurabh Nandedkar for help building the app.

Beyond Pendency: Counting Cases Correctly

by Pavithra Manivannan, Siddarth Raman and Bhargavi Zaveri-Shah.

The discourse on Indian judicial reform is dominated by questions of pendency and the workload of courts. However, official sources of caseload estimates in India have been found to be deficient in terms of both the methodology used, and the quality of underlying data (Jain and Reddy, 2025; Damle and Anand 2020). This leads to miscalculation of the caseload of courts and renders it unamenable for comparison across courts. In this article, we propose a new approach for estimating the caseload at Indian courts. We apply this to analyse the caseload at the Original Side of the Bombay High Court, which accounts for 35% of the total caseload of the Court. Our analysis yields three main findings. First, the caseload at the Original Side of the Bombay High Court (the Court) is being overcounted by 66%. Second, the caseload composition of the Court has remained largely stable over the 7-year period of our study with two case-types, namely, inheritance cases, and writ petitions filed against the government, accounting for half the cases filed in the Court. Third, suits as a case-type generates the most number of sub or interim cases.

In India, there are two official sources that publish information on caseloads - the annual report of the Supreme Court and the National Judicial Data Grid (NJDG). Apart from the quality of the data, there are two specific problems with the estimation methodology used by these sources. First, as a case progresses in a court of law, it generates multiple sub-cases. For instance, if a case is filed as a "Suit" for recovery of money, several interlocutory applications may be filed through which the main money suit (the 'main case') progresses. Such sub-cases could range from simple applications seeking the addition of a new party to the proceedings to an interim injunction seeking a stay on the transfer of assets of the respondent. Currently, the NJDG counts such sub-cases as distinct cases. This leads to overestimation of the caseload, inflating pendency and disposal rates. This is because the hearings for sub-cases are held as part of the main case proceedings. Further, a reading of the orders of cases suggests that more often than not, the final disposal order is common for both the main case and its sub-cases. Second, the taxonomy for case-type categorisation is inconsistent across official sources. The Bombay High Court's website lists 142 case-types on its Original Side. On the other hand, the NJDG reports only 19 case-types for the Original Side of the Bombay High Court. This includes an 'Original' and an 'Other' category, which provides little to no information on the case-type filed in the court. Further, the annual report of the Supreme Court has an altogether different classification system, which cannot be readily mapped to the other two official sources. It has a large bucket under 'Other' which does not have a clear definition. Our approach attempts to address these problems.

We count sub-cases as part of its corresponding main case. That is, we adopt the 'family of cases' as the unit of analysis for caseload estimation. This involves collapsing the 142 case-types on the Original Side of the Court into 17 main case-types and two sub case-types, based on their subject. For example, of the 142 case-types, 80 case-types are in the nature of sub-cases such as "Interim Applications", "Leave Petitions", "Chamber Order Lodging" and "Notice of Motion". We classify these as "Interim Applications" and count these as sub-cases. Similarly, "Arbitration Petitions" and "Arbitration Applications" are categorised as "Arbitration cases". This standardisation of case-types makes the caseload estimation exercise scalable and amenable to comparison across similar courts. The list of the 142 case-types and the classification assigned by us can be accessed here.

Data and Methodology

We collect the life-cycle data of 2,36,953 cases filed at the Original Side of the Court between the period January 2017 to December 2024 (Study Period). The Bombay High Court exercises original jurisdiction or jurisdiction over first time civil cases, and appellate jurisdiction or jurisdiction over cases that come before it as appeals from lower courts. We source the information on the life-cycle of cases filed at the Court's original jurisdiction from its website, and it is comprehensive to the extent the Court has made the data available.

As on the date of our data collection exercise (February 2025), the Court's website reported 1,43,514 (61%) cases as disposed of and 93,254 (39%) cases as pending. If the status of a case was unknown or marked as transferred, we classify it into the 'Other' category (185 cases).

We tag each case in our dataset as a main case or a sub-case. Next, we create a family of cases using the CNR number assigned by the Court as the unique identifier. This family of cases becomes our unit of analysis. Finally, each family of case is classified into one of the 17 case types.

Finding 1: Official sources overestimate caseload

The Court's website shows that about 2.5 lakh cases are filed before its Original Side during our Study Period. That is, on an average, about 30,000 cases are filed every year. However, we find that about 40% of these cases are sub-cases (Table 1 below). Viewed in this light, the 30,000 new cases per year can be understood as an overestimate. The annual average of new main cases filed before the Original Side of the Court is about 18,000, almost half of the original estimate.

Table 1: No. of filings

Nature	Count	Average per-year	% of total
Main cases	1,41,608	17,850	60
Sub-cases	95,435	11,769	40
Total	2,36,953	29,619	100

Finding 2: Six case-types dominate caseload

On applying our categorisation framework, we find that six case-types contribute to about 95% of the caseload at the Original Side of the Court (Table 2). In that, Inheritance cases and Writ petitions constitute half the caseload. We also find that the share of case filings across years for these categories do not vary significantly.

Table 2: No. of filings per case-type

Case Category	Count	% of Total
Writs	36,145	25.5
Inheritance and Succession cases	32,979	23.3
Execution cases	19,779	14.0
Tax cases	19,665	13.9
Arbitration cases	16,529	11.7
Suits	8,652	6.1
Other	7,859	5.6
Total	1,41,608	100.0

Finding 3: Suits generate the most sub-cases

We take a closer look at the number of sub-cases per main case in Table 4 for the top six case-types. We find that, while Inheritance cases and Writ petitions are the highest contributor to the caseload of the Court, Suits that is at the bottom of Table 2, has the highest number of sub-cases per main case. 50% of Suits have upto two sub-cases, suggesting that on a per-case basis, Suits may generate more workload for judges compared to Writ petitions and Inheritance cases.

Table 3: Sub-cases per case-type

Case category	Sub-cases per main case (in %)
	0	1-2	3-5	6-10	>10
Writs	86	13	1	0	0
Inheritance and Succession cases	77	21	2	0	0
Execution cases	86	13	1	0	0
Tax cases	84	16	0	0	0
Arbitration cases	84	15	1	0	0
Suits	31	52	14	3	0

Conclusion

Our finding that the caseload of the Bombay High Court is overestimated by about 66% likely means other courts across India are overreporting caseloads as well. When official sources like the NJDG count sub-cases as distinct new filings, it exaggerates the problem of pendency. This prompts the policymakers to focus on solutions like increasing the number of judges, and creating more courts or courtrooms. Such a sole focus on this metric not only neglects the underlying data quality issues leading to inefficient resource allocation but also ignores the unique challenges that each type of case filed in the court face.

Measures of the economy such as GDP, inflation, and employment rate, took decades to be built and continue to be challenged and improved, by researchers and policy-makers alike. Similar sound systems for the measurement of court metrics, of which caseload is only one part, need to be developed. Such systems are imperative for any meaningful discussion on court reform.

References

Chitrakshi Jain and Prashant Reddy T. Tareekh Pe Justice: Reforms for India's District Courts. Simon and Schuster India, 2025.

Devendra Damle and Tushar Anand. Problems with the e-Courts data. NIPFP WP Series, 314, 2020.

Mugdha Mohapatra, Siddarth Raman and Susan Thomas. Get them to the court on time: bumps in the road to justice. The Leap Blog, 2025.

The authors are researchers at XKDR Forum, Bombay.

Return to sender: The misuse of remand orders by appellate tribunals

by Natasha Aggarwal and Bhavin Patel.

Appellate tribunals were established to ensure speedy and expert adjudication of appeals from regulators' orders. A defining feature of their institutional design is the inclusion of both judicial and technical members. Judicial members possess legal training and skills and can ensure the application of, and compliance with, judicial principles, such as the principles of natural justice. Technical members are expected to bring sector-specific knowledge relevant to the domain of the tribunal's work. As such, tribunals are 'expert' bodies that are well-equipped to provide speedy and efficient resolution and it would be incorrect to assume that they lack the technical expertise necessary to decide a matter on merits.

However, tribunals in India frequently remand matters to regulators. Our analysis of 764 cases disposed of by the Securities Appellate Tribunal (SAT) from 2009 - 23 reveals that 76 cases (10%) resulted in a remand (Aggarwal and others, 2025). Further, our analysis of 919 cases disposed of by the the Appellate Tribunal for Electricity (APTEL) from 2013 - 22 reveals that 200 cases (21.7%) resulted in a remand (Jain, Patel and Sane, 2025). The fact that one in ten instances before the SAT and a staggering one in five instances before the APTEL are remanded is a matter of concern. Remands result in additional time and resources being spent on resolving a matter, since the dispute is first heard by the regulator, then in challenge by the tribunal, and then, once again by the regulator. Therefore, it is important to study the reasons provided by the tribunal when it remands a matter.

In a recent paper, titled "Return to sender: The misuse of remand orders by appellate tribunals", we study orders issued by the SAT and the APTEL in 2024, evaluate the instances in which the SAT and the APTEL remanded matters, and examine whether these remands are consistent with recognised legal principles regarding when a remand may be ordered. The paper was also included in Daksh's "The State of Tribunals Report", which was released on 25 September 2025.

Our paper addresses three questions:

• What costs do remands impose on the parties and the adjudicatory infrastructure of regulators?

  Our examination of two matters, one originating at SEBI and the other originating at the Kerala Electricity Regulatory Commission, shows that remands cause delays that span up to 14 years. This defeats one of the primary reasons for establishing tribunals, that is, to ensure speedy justice.

• What reasons does the law recognise as valid for remands by tribunals?

  Our earlier studies demonstrate frequent remands by SAT and APTEL. Indian law circumscribes the instances in which courts may order remands. These boundaries are set in the Code of Civil Procedure, 1908 (CPC) and in judicial decisions of superior courts. The CPC, parent statutes that establish tribunals, and rules of procedure framed by tribunals for themselves do not, however, clearly identify when tribunals may remand matters. There is limited guidance on this in superior court decisions. We argue that the reasons for which courts can order remands should also limit the discretion of tribunals in remanding matters. We call these reasons "Permissible Reasons" for remand, and classify any other reasons provided by tribunals when remanding matters as "Other Reasons".

• How many matters were remanded by SAT and APTEL in 2024? What reasons were provided? Are the reasons permissible under law?

  We study the orders of SAT and APTEL for 2024, in which there are 13/228 (5.7%) remands ordered by SAT and 28/171 (16.4%) remands ordered by APTEL. We find that the SAT ordered a remand for Permissible Reasons in 21 appeals and for Other Reasons in four appeals. The APTEL remanded the matter for Permissible Reasons in 26 appeals and for Other Reasons in 37 appeals.

We find that remands are often ordered for reasons that are not included in the CPC and applicable common law. Unnecessary remands add time and cost to regulatory proceedings. They undermine investor confidence in regulated sectors, since it is difficult to take business decisions in the face of uncertainty about whether a regulator's orders will have to be reconsidered and modified. This also adversely affects the rule of law requirements of predicability and certainty in regulatory proceedings.

There may be several ways to reduce this problem, including, possibly, by improving order writing practices at the regulators whose orders are challenged in appeal before tribunals. We suggest that it is also useful to consider how this problem can be addressed at tribunals, and therefore recommend that:

• Clear rules determining the scope of the power of ordering a remand should be made applicable to all tribunals. These rules should apply in a consistent manner across tribunals, rather than being framed by each tribunal for itself. This will help ensure consistency and predictability, and limit the discretion of tribunals in this matter.
• These rules should be incorporated in the parent statutes of tribunals.
• In the absence of any reason to deviate from the rules on remand provided in the CPC and applicable common law, the scope of remanding power for tribunals should be the same as that available to courts.

Providing this clarity would help avoid unnecessary and unreasonable orders of remand, and help ensure that the core rule of law principles of consistency, predictability, and clarity are satisfied in the procedural aspects of the functioning of tribunals.

References

Natasha Aggarwal, Amol Kulkarni, Bhavin Patel, Sonam Patel, and Renuka Sane, "Balancing Power and Accountability: An Evaluation of SEBI's Adjudication of Insider Trading" [2025] (13) Trustbridge Rule of Law Foundation Working Papers.

Chitrakshi Jain, Bhavin Patel, and Renuka Sane, "Examining the performance of ERCs at APTEL" [2025] The Leap Blog.

The authors are researchers at the TrustBridge Rule of Law Foundation.

A Review of Outage Reporting by Indian DISCOMs

by Upasa Borah and Renuka Sane.

In 2023, 99.5% of India's population had access to electricity. This statistic, however, should be measured along with the data on consistency and quality of electricity supply. Frequent power outages and low and fluctuating voltage can adversely affect appliances, reduce productivity, increase the cost of production and reduce standards of living (Jha et al., 2021). It adds a financial burden on both households and firms, who are forced to invest in costly backup options like inverters and diesel generators (Pargal and Banerjee, 2014). As India expands its electricity access, it is useful to measure how it is faring on the quality of its electricity supply. The aggregate data does not appear promising. According to the 2019 Global Competitiveness Index by the World Economic Forum, India ranked 108 out of 141 countries in electricity supply quality.

A response to the question of quality should first begin with assessing its measurement. In this article, we examine the availability of outage data in India. Outages refer to any interruptions in the supply of electricity to end consumers, and are classified into three types, depending on the location of the interruption in either generation, transmission or the distribution segments of the electricity system. We focus on outages happening in the distribution system, as it captures the final impact on consumers and takes into account upstream interruptions. These fall under the purview of distribution companies (DISCOMs), so we study all the DISCOMs in the country, and ask:

1. How many DISCOMs report data on outages?
2. Is the format of available data consistent across DISCOMs on
1. methodology,
2. period of data availability, and
3. the spatial unit of reporting?
3. Is there consistency in the reporting of outage data across states?
4. Is there a relationship between DISCOM characteristics like fiscal health and ranking, ownership and location and the availability of outage data?

Measuring outages

The quality and reliability of electricity supply are estimated by relying on a measure of either frequency or duration, or a combination of both, of interruptions faced by consumers. Feeders, which could be underground or overhead wires connecting substations to service areas, are the backbone of the distribution network, and distribution outages are typically measured using data from these feeders.

Two of the most widely used reliability indices are the System Average Interruption Frequency Index (SAIFI), and the System Average Interruption Duration Index (SAIDI). The former measures how often an average customer experiences an interruption, while the latter denotes the total minutes (or hours) of interruption an average customer faces. For example, as an illustration, if a distribution network serves 1,000 customers and experiences 200 supply interruptions in a given year, SAIFI would be 200/1000, or 0.2, interruptions per customer. If the total duration of interruptions in the same network were 3,000 minutes, then SAIDI would be 3000/1000, i.e. 3 minutes of interruptions per customer.

Supply interruptions or outages can be planned or unplanned, where planned outages are those that have been scheduled in advance, like maintenance work, which the DISCOM is supposed to disclose to customers in advance. Unforeseen outages due to disruptions, faults in the distribution system, extreme weather events, etc., are unplanned outages. From the perspective of the consumer, however, both planned and unplanned outages disrupt daily consumption and production activities and thereby have costs associated with them. Moreover, many households report not receiving prior information on planned outages (Agrawal et al., 2020), and it is often unclear which specific events are categorised as planned.

Methods

As per the Electricity Act 2003 and the National Electricity Policy 2005, the Central Electricity Authority (CEA) is tasked with collecting and publishing reliability indices for DISCOMs. However, this is not a statutory mandate, and compliance remains voluntary (Sekhar et al., 2016). The State Electricity Regulation Commissions have Standards of Performance regulations that outline metrics for reliable supply and guidelines such as time taken to restore supply, penalties, etc. (Athawale, 2021) Further, the Electricity (Rights of Consumers) Rules, 2020 mandate that DISCOMs should supply power 24x7 as the norm, with the State Commissions specifying the acceptable levels of SAIDI and SAIFI values for unavoidable interruptions. It also states that DISCOMs should have a mechanism to monitor and restore outages and disclose feeder-wise outage data and efforts made to minimise outages. The Service Rating of DISCOMs by the Ministry of Power & Rural Electrification Corporation Limited factors outages in its rating of DISCOMs; however, this data on actual outages is not publicly available.

We compiled a list of all the DISCOMs in the country using the annual ranking of DISCOMs by the Ministry of Power. While the CEA publishes annual reliability indices, not all DISCOMs are included in their lists. Moreover, such annual data masks the granular, day-to-day variations needed to meaningfully study the reliability of electricity supply.

We reviewed each DISCOMs official website to assess their current reporting practices. We restricted our search to official websites, and on encountering broken or unsafe links, we considered the data to be unavailable.

Findings: Availability of data

As per the Ministry of Power, there are a total of 72 DISCOMs in the country, all of which are included in our dataset. Among them, 36 (50%) have some form of outage data available on their websites, although irregular. The remaining 36 DICSOMs have no mention of outage or interruption data anywhere on their websites. Among the DISCOMs for which data is available, a closer look reveals the inconsistency and sporadic nature of the reported data. Broadly, there are three types of inconsistencies: i) the type of data reported and the methodology used, ii) the time period for which data is available, and iii) the spatial unit of measurement.

Reporting of data

The first inconsistency lies in the way outage data is reported. 17 out of the 36 DISCOMs use the SAIFI and SAIDI indices. The rest report interruptions by date and time, without noting how many customers were affected. Among the ones that report SAIFI and SAIDI, there is an inconsistency in the way the reliability indices are calculated. For instance, the Delhi Standard of Supply Code states that planned outages and outages less than five minutes shall not be included in calculating the reliability indices. On the other hand, the Haryana Standard of Supply Code includes planned outages in the calculation of the indices, while excluding outages of less than three minutes. DISCOMs like Karnataka's Chamundeshwari Electricity Supply Corporation (CESC) reference a "Reliability Index" without specifying which one. Other Karnataka DISCOMs provide feeder or area-wise frequency and duration of interruptions without calculating the SAIFI and SAIDI indices. Yet others, like Adani Electricity Mumbai Limited (AEML), report only the number of complaints registered and the duration taken to resolve them. Additionally, six of the 36 DISCOMs only reported scheduled or planned outages, and there was no data on unplanned outages.

Time period for which data is available

The second inconsistency concerns the time for which the data is reported. Only eight out of the 36 DISCOMs had data going back at least five years. For the rest, data availability was patchy and lacked any clear patterns. Some have data only for the past year, while others have data for sporadic years like 2022, or 2019 to 2024 and so on. Five DISCOMs had outage data only for the current date (as of visiting the website), and past archives were not available. In another instance, like that of West Bengal State Electricity Distribution Company (WBSEDCL), viewing outage data was allowed only for 60 days prior to the current date. Table 1 summarises the time period covered by the DISCOMs. There are also variations in the frequency of reporting outage data; some publish daily figures, others have data weekly, monthly or quarterly. 15 DISCOMs reported monthly SAIDI and SAIFI data, while two reported them daily.

Table 1: Coverage period of outage data

Time period covered	No. of DISCOMs
Last five years	8
Sporadic years	23
Current day	5

Spatial unit of measurement

The final inconsistency relates to the spatial unit of reporting, summarised in Table 2. 16 DISCOMs report interruptions both by feeders and areas, while four reported only feeder-wise data. Among these, some report outages in 33kV and 11kV feeders separately, while others club them together. The distinction is important because 33kV feeders carry electricity from high-voltage substations to 33/11kV substations where voltage is stepped down, and 11kV feeders then deliver power to local service areas through distribution transformers that further reduce voltage for end-users. 16 DISCOMs report outages in terms of geographic area, like zones, divisions or areas affected. However, it is unclear if these area lists are comprehensive; for instance, Assam Power Distribution Company (APDCL) reported district-wise data, but did not include all districts.

Table 2: Spatial units used in reporting outages

Unit of reporting	No. of DISCOMs
Area and feeder	16
Only feeder	4
Circles, divisions, towns, cities	13
Zones	2
Areas affected	1

Findings: Does DISCOM ranking, ownership, or state matter?

Next, we examined whether a DISCOM's characteristics, like the state where it is located, its ownership and ranking are correlated with the availability of outage data.

There were no visible patterns of data availability observed across states. In states with multiple DISCOMs like Uttar Pradesh, Gujarat and Maharashtra, most did not report outage data. In contrast, all four DISCOMs of Odisha and all three of Andhra Pradesh had outage data available on their websites. The three inconsistencies discussed earlier were also evident within states. For instance, among the four DISCOMs in Delhi, only three had data available, and among them, there were variations in the spatial units used (area vs feeder) and the time period for which data were reported.

We used the 13th DISCOM ranking by the Ministry of Power to see if better-performing DISCOMs tended to have better data availability. However, there was no clear correlation; both high-ranking and low-ranking DICSOMs seemed equally likely or unlikely to make outage data available. There was also no correlation between ownership and data availability.

Finally, we compared the DISCOMs that publish outage data on their websites to those for whom CEA has compiled annual reliability indices. Of the 49 DISCOMs included in CEA's 2021-22 list, only 26 had data available on their websites. Alternatively, among the 23 DISCOMs not included in the CEA list, 10 had outage data available on their websites. It is worth noting that for eight of these 10 DISCOMs, the data was available for sporadic years, which may explain their exclusion from the CEA's lists. Nonetheless, these findings point to the disconnect between the CEA and DISCOMs reporting practices.

Accuracy of reported data

The availability of data does not guarantee its accuracy. Several studies have raised concerns about the unreliability of outage data reporting, particularly in developing countries (Min et al., 2017). For instance, in January 2017, the National Load Dispatch Centre reported only a 0.9% shortfall in power supply in Uttar Pradesh, while Prayas Energy Group recorded a daily average of nine hours of power outages in rural areas and two hours in urban areas. Other studies point to similar discrepancies: scheduled power cuts in India often last longer than officially noted (Baskaran et al., 2015), there are logical inaccuracies in reported data (Mandal et al., 2019), and household survey data do not align with government-reported outage statistics (Agrawal et al., 2020).

These findings suggest that simple reporting of outage data is not sufficient. There is an urgent need for independent and transparent monitoring systems that complement official reporting and allow for verification of accuracy. Independent studies have attempted to fill this gap, using surveys (Agrawal et al., 2020; Bigerna et al., 2024; Khanna & Rowe, 2024), satellite night-light data (Min et al., 2017; Dugoua et al., 2022) or initiatives like the Supply Monitoring Initiative (ESMI) by Prayas Energy Group, but these efforts are usually restricted to specific regions and limited time periods. The lack of a single agency reporting outage data, combined with the inconsistencies in reporting practices by DISCOMs further complicates the process of data verification.

Conclusion

In July 2024, the National Feeder Monitoring System was inaugurated, which has data on around 2.5 lakh 11kV feeders across the country. Its dashboard provides data on hours of supply in rural and urban areas by state and DISCOM. However, to the best of our knowledge, it does not offer access to historical, granular data on daily hours of supply by feeders, state or DISCOM. The CEA reports annual reliability indices, but it should cover all DISCOMs in its list and augment it by including more granular data. A logical next step, however, is to ensure that the available data is accurate, which requires independent monitoring systems. Prayas Energy Group's ESMI has minute-wise data on supply from November 2014 to December 2018, recording not just outages but voltage fluctuations. Such efforts should be scaled up and maintained on an ongoing basis.

Having accurate and accessible data on hours of supply and areas of outage is crucial not only for consumers to understand and plan their production and consumption but also for a thorough review of DISCOMs' performance. While much of the discussion on DISCOMs centres around their financial health, it is also important to assess their ability to supply reliable power to their customers. Standard of performance indicators should include data on feeder-wise outages, distribution transformer failure rates and SAIDI, SAIFI (Pargal & Banerjee, 2014; Mandal et al., 2019). Reliability indices are valuable for providing consistent, comparable measures of service quality over time, but daily reporting of feeder-wise data that includes time, duration, cause of outage and measures taken to resolve the issue, also has its benefits in allowing for spatial, minute-by-minute analysis to pinpoint weak links in the network. Whichever approach is used, however, it should be standardised across DISCOMs and reported collectively, with a common format agreed upon by all stakeholders. If the sector moves towards reliability indices, their calculation methods should be consistent and published more frequently to ensure meaningful assessments, comparisons and verifications.

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The authors are researchers at TrustBridge Rule of Law Foundation. They thank an anonymous referee for useful comments.