Market Reaction to Insider Trading: Evidence from Regulatory Orders in India

by Arjun Gupta, Sonam Patel, and Renuka Sane.

Introduction

Market integrity depends on effective enforcement against market abuse. When regulators credibly sanction violations, they reinforce investor confidence and reduce the risk premiums that markets impose for uncertain governance. In developed markets, evidence suggests that enforcement achieves this objective: SEC enforcement actions in the United States produce abnormal stock price declines of $-0.5\%$ (Persons, 1997), and UK sanctions trigger reputational losses that far exceed the direct penalties (Armour et al., 2017). This is especially true for insider trading enforcement: Persons (1997) documents significant negative abnormal returns following the SEC's announcements of insider trading enforcement actions. (Engelen, 2012) finds that a clear negative abnormal return on the day of even newspaper coverage of the illegal insider trading practice of CEOs.

An open question, however, is whether this pattern extends to India. We investigate this by examining stock price movements around two types of insider trading enforcement actions in India: final SEBI adjudicatory orders and appellate decisions by the Securities Appellate Tribunal (SAT). We focus on insider trading orders as they can be a signal about the quality of the firm's internal governance. If insiders are trading on privileged information, it suggests that boards, compliance functions, and internal controls are weak, leading to investors discounting the stock accordingly. Further, when the firm and its executives face potential penalties, disgorgement, or other sanctions, these can impose direct costs on the firm and may affect its ability to attract capital and talent. The insider trading laws in India are quite expansive, and cover not only connected persons, but also those who just have access to unpublished price sensitive information, or if there have been some minor disclosure violations. All orders, therefore, may not signal governance issues within a firm. We therefore also look at orders by violation severity and type of insider relationship.

Empirical Strategy

We use an event-study methodology to test whether Indian stock markets react to SEBI enforcement actions and outcomes challenged before SAT. We compile a list of individuals and entities against whom an insider-trading order was issued, then identify the companies whose scrip was alleged to have been insider traded, map them to their corresponding order dates (event dates), and use these firm-event pairs to check for market reaction.

Estimation Procedure

We estimate each firm's normal return using the market model over an estimation window of 210 trading days ending 11 days before the event ($t = -210$ to $t = -11$):

\( R_{it} = \alpha_i + \beta_i R_{mt} + \varepsilon_{it} \)

where $R_{it}$ is the daily return of stock $i$ on day $t$ and $R_{mt}$ is the daily return on the Nifty~50 Index. The Abnormal Return (AR) on event day $t$ is the difference between the actual return and the predicted normal return:

\( AR_{it} = R_{it} - \left(\hat{\alpha}_i + \hat{\beta}_i R_{mt}\right) \)

Cumulative Abnormal Returns (CARs) are computed by summing $AR_{it}$ over a 21-day event window centred on the insider trading announcement date:

\( CAR_i = \sum_{t=-10}^{+10} AR_{it} \)

We test whether the cross-sectional average $\overline{CAR}$ is statistically different from zero using a $t$-test; a negative $\overline{CAR}$ indicates an adverse market reaction to the announcement.

Data and Sample

Our sample is drawn from the data set used by Aggarwal et al., (2025). It comprises two types of regulatory actions from 2009 to 2023, restricted to firms listed on the National Stock Exchange (NSE). After removing duplicates and cases with missing stock price data, our final sample contains:

1. SEBI Orders: Final adjudicatory orders; $N = 176$ firm-event pairs.
2. SAT Orders: Appellate Tribunal decisions; $N = 42$ firm-event pairs.

We further look for heterogeneity in market reactions by partitioning the sample along four dimensions of interest:

• Sanction status: Sanctioned ($N = 119$) vs. not sanctioned ($N = 57$). An order may or may not result in a sanction. Here, we examine a reaction based on whether an order resulted in a sanction.

• Violation severity: Major violations ($N = 74$) vs. minor violations ($N = 122$). We classify insider trading violations as Major (e.g., sharing or using unpublished price information for trading) or Minor (e.g., code-of-conduct breaches, delayed disclosures).

• Insider relationship: Connected persons ($N = 44$), deemed connected ($N = 21$), and those with access to UPSI ($N = 19$). Connected persons are loosely defined as those associated with a company (contractual, fiduciary, or employment), while those deemed to be connected persons include their relatives or cohabitants. UPSI access refers to knowledge of information materially impacting the stock price.

• Monetary outflow: Above-median ($N = 59$) vs. at-or-below-median ($N = 60$) alleged illegal gains. Monetary outflow is the total penalty and disgorgement paid to SEBI. We analyze market reaction based on the magnitude of this outflow to see if the amount paid affects the reaction.

Results

Baseline Event-Study Findings

Our event-study results indicate that Indian stock markets exhibit no statistically significant reaction to any type of insider trading enforcement announcement. CARs are indistinguishable from zero across all two regulatory action types at the 95% confidence level, with point estimates close to zero in magnitude. For comparison, SEC insider trading enforcement actions in the US produce average CARs of $-3.47\%$ (Muradoglu and Clark Huskey, 2008).

Figures display the CAR trajectories. In all two cases, the CARs fluctuate around zero with no discernible trend before, during, or after the announcement date.

Cumulative Abnormal Returns (CARs) around SEBI final order announcements ($N = 176$). The shaded region represents the 95% confidence interval.

The result for SEBI final orders is striking: these orders contain explicit findings of misconduct and penalties, yet markets do not react. We discuss four candidate explanations below: high appeal and reversal rates, long enforcement delays, low penalty amounts, and pre-existing credibility discount.

Cumulative Abnormal Returns (CARs) around SAT order announcements ($N = 42$). The shaded region represents the 95% confidence interval.

SAT orders, on the other hand, are more final in nature. They may affirm, modify, or overturn SEBI sanctions, and should lead to a market reaction. In our dataset, they also produce no detectable market reaction. However, this result should be interpreted with caution, given the small sample size. With only 42 events, our test has limited statistical power to detect abnormal returns. It is possible that these may be further appealed at the Supreme Court, but given the small sample size, we do not test for the impact of those decisions.

Subsample Analysis

We examine whether the aggregate result masks heterogeneous effects by partitioning the sample along the four dimensions described above. Across all subsample splits, CARs remain statistically and economically insignificant. Even for high-severity cases, directors trading on confidential information, monetary outflows above the median of Rs.~12.83 lakh, and third quartile of Rs.~5.27 crore, abnormal returns remain proximate to zero. Also, there is no evidence of a significant market reaction even in the subsample of cases where the insider relationship is more direct (connected persons). This suggests the null result is not an artefact of averaging across heterogeneous effects; rather, it is pervasive across subgroups.

One caveat to our analysis is if the true information release occurred earlier (e.g., via media leaks), our tests measure the reaction to information from informal sources rather than to the announcement itself.

Interpreting the results

One interpretation of this result is that markets may rationally discount the significance of SEBI enforcement actions. Several institutional features of Indian capital markets lend support to this interpretation:

1. High appeal and reversal rates: Aggarwal et al., (2025) find that a substantial fraction of SEBI orders (30-41%) are appealed to SAT, and around 50% result in modifications or reversals. Investors who have learned that sanctions are frequently overturned will rationally discount any announced penalty. This is probably compounded by the fact that several SEBI orders are not able to demonstrate the unfair gains or loss avoided, or provide reasons for imposing sanctions as debarment, reducing the credibility of its enforcement actions (Aggarwal et al., 2024).
2. Long enforcement delays: Damle and Zaveri (2022) find a median of over three years between violation and SCN, and a further 18 months to a final order. This is reinforced by the findings of Aggarwal et al., (2025), who find similar timelines for insider trading orders. By the time enforcement is announced, investors may have already moved on.
3. Low penalty amounts: The median penalty is Rs 12.83 lakhs, with approximately 40 cases involving amounts under Rs 10 lakhs. Such low penalties suggest a lower perceived severity of the offense, and consequently signal the market to treat this news as immaterial.
4. Pre-existing credibility discount: If years of weak or delayed sanctions have already led investors to assign a low probability to effective enforcement, individual announcements convey little new information, and markets have stopped paying attention.

Another possibility is that markets receive the enforcement information but do not regard insider trading as material to firm valuation. Under this view, it reflects an investor judgment that insider trading by management is not indicative of broader governance failure or future cash-flow risk.

Conclusion

Indian stock markets exhibit no statistically significant response to insider trading enforcement, in contrast to the negative abnormal returns documented in the US and UK. This result is robust across SEBI final orders and SAT appellate decisions, and persists even for high-severity violations involving senior insiders and large monetary outflows.

The functioning of SEBI entails considerable public expenditure, and the Board has, over time, sought progressively wider powers - including expanded surveillance capabilities. Given this, the question of what is actually being achieved warrants serious scrutiny. A stock price reaction to an enforcement order is one observable signal of whether the market believes the enforcement actions carry some significance. A null result across many orders suggests the market does not view these actions as conveying meaningful new information. It is, therefore, worth questioning if enforcement actions are advancing the goal that justified the expenditure in the first place.

The authors are researchers at Trustbridge Rule of Law Foundation.

What happens when arbitration deadlines are missed

by Prashant Narang and Renuka Sane.

Section 29A of the Arbitration and Conciliation Act 1996 was introduced to deal with delays in arbitration. It sets a time limit for making an award. If that time runs out, parties have to go to court to extend it. The court can also impose consequences for delay, such as reducing fees, awarding costs, or replacing the arbitrator.

Our new working paper studies how this works in practice. It looks at 202 reported orders of the Delhi High Court between 2015 and 2024.

It finds that the Court almost always grants extensions and almost never imposes sanctions.

What the data shows

Out of 202 cases, the court granted extensions in 198 (98%). Only 4 cases were dismissed, and those were on technical grounds. Sanctions were rarely imposed.

• Fee reduction: 0 out of 202 cases
• Adverse costs: 6 out of 202 cases (about 3%)
• Replacement of arbitrators: 4 out of 202 cases (about 2%)

Repeat extensions are not unusual. There are 30 cases where parties came back for a second or later extension. The court granted 29 of them (96.7%). There are no sanctions in these repeat cases.

These petitions also move quickly.

• Median time to decide: 3 days
• Median number of hearings: 1
• About 63% of cases are decided in a single hearing

So the delay is not in the court process. Courts dispose of these matters quickly. But they usually extend time without imposing any consequence.

Why extensions are common

Part of the answer lies in how Section 29A is structured.

For the Court, giving an extension is easy if both parties agree. The court can dispose of the case quickly.

Imposing a penalty is harder as the Court has to find out who caused the delay. It may have to look at the record in detail. It also has to hear the arbitrator before cutting fees. All this is likely to take more time and effort.

It is not surprising that consensual extensions are more common.

What this means for the law

Over time, this pattern shapes how the law works.

Section 29A was meant to push arbitrations to finish on time. It often works as a way to formally extend time after the deadline has passed.

If parties expect that extensions will be granted without much difficulty, the deadline may lose its force.

This does not mean the provision has no value. But it suggests that deadlines work best when consequences are easy to apply.

Looking ahead

If deadlines are not backed by predictable consequences, do they change behaviour?

The paper does not answer this fully. It focuses on what courts do once parties come for an extension. But the pattern is clear. Extensions are routine and sanctions are exceptional.

That may matter for how arbitration timelines are taken in practice.

You can read the working paper here.

The authors are researchers at TrustBridge Rule of Law Foundation.

Evaluating India's Energy Ambitions: Evidence from Electricity Generation Project-Level Data

by Upasa Borah, Akshay Jaitly and Renuka Sane.

India's electricity demand has been growing rapidly, at 9% per annum since 2021. Meeting this demand by 2030 would require around 777 GW of installed capacity, as estimated by the Central Electricity Authority (CEA). At the same time, India has committed to achieving 500 GW of installed non-fossil capacity by 2030. A study by CEEW (2025) finds that meeting this target would require adding around 56 GW of non-fossil capacity every year between 2025 and 2030, failing which India would need an additional 10 GW of coal-based capacity to meet future demand. There is little doubt that renewable energy in India has seen a sharp growth, with 74 GW in 2018 to 162 GW by the end of 2024 (excluding large hydro and nuclear projects), driven by falling renewable energy prices, and policy support like subsidies for developers, waivers on inter-state transmission charges, Green Energy Corridor investments, changes in Green Open Access Rules and various state-level initiatives that signal policy commitment to the sector. In 2025 alone, the country added 45 GW of renewable capacity.

However, the next phase of the transition is likely to be more complex. India is now facing new challenges regarding grid integration and transmission infrastructure, leading to delays in commissioning projects and curtailment of operational projects. As of June 2025, around 50 GW of awarded renewable capacity was stranded due to a lack of buyers, transmission constraints or disputes over land and environmental clearances. This results in time and cost overruns, dampening investor confidence.

In this backdrop, our paper Evaluating India's Energy Ambitions: Evidence from Electricity Generation Project-Level Data studies how electricity generation projects evolve from announcement to completion. Using project-level data from the Centre for Monitoring Indian Economy (CMIE) CapEx database, we analyse 8,540 projects announced between January 1957 and December 2024 to understand how project size, cost, ownership, energy technology and location influence project timelines. We ask,

1. How many projects have been announced and of them, how many have been implemented and completed? What is the time taken?
2. Given the projects currently in the pipeline, how likely is India to meet the 2030 targets?
3. How do factors like project size, geography and developer characteristics influence the completion timelines and probabilities?

From announcement to completion

We find a significant divergence between projects announced and completed: of the total announced conventional (CE) and renewable (RE) capacity, only 15% and 9% have been completed, respectively. Announcement here refers to events like signing of MoUs, inviting bids, seeking approvals or preparing feasibility reports and may differ from official statistics that use alternative definitions of project status (Borah et al., 2025). The next stage in a project lifecycle is beginning implementation, which includes events like awarding contracts, securing financing, obtaining approvals or beginning construction, indicating a deeper commitment of resources. Even among this set of projects that have been implemented, completion rates remain low: 30% of CE and 22% of RE capacity have been completed. The timelines from announcement to implementation and implementation to completion vary significantly among different technologies, with solar and wind having the shortest timelines.

How much capacity will be added by 2030?

We used an accelerated failure time survival model to estimate the completion probabilities of projects currently in the pipeline (i.e. announced or under implementation as of December 2024). Applying a probability threshold of 0.5, i.e. excluding projects with less than 50% chance of completion by 2030, and scaling our dataset to match the capacities reported by the CEA, we find that India is likely to fall short of its capacity targets.

If the current completion trends continue, total installed capacity would fall short of the 777 GW target by around 56 GW for CE and 45 GW for RE. Similarly, for the 500 GW non-fossil target, the projected shortfall is around 77 GW. It is important to note that our analysis does not include new projects that may be announced after 2024. In that sense, our findings imply that meeting the 500 GW target would require announcing and completing 77 GW of projects within the next six years.

Explaining the capacity additions

We find that project characteristics play an important role in influencing implementation and completion timelines:

• Project size: Larger projects take longer to begin implementation and get completed.
• Ownership: Privately developed projects tend to be completed faster.
• Developer ranking: For RE projects, those developed by top firms (by market share) perform better.
• Location: RE projects in certain states such as Gujarat, Rajasthan and Andhra Pradesh complete faster than those in states with weaker RE ecosystems. Location is less important for CE projects.
• Year of announcement: RE projects announced after 2022 have longer implementation timelines compared to those announced before 2018.

These findings hold taking into account disruptions caused by the COVID-19 lockdown, which we explicitly model.

Finally, we compare completion timelines of large-scale solar and wind projects across states with benchmark timelines in the literature and find that even in RE-rich states, large projects face delays in commissioning.

Taken together, our findings suggest that the challenge is not just the announcement of new capacity but ensuring projects are implemented and completed on time. Bridging this gap will be critical to meeting India's future energy goals.

The authors are researchers at TrustBridge Rule of Law Foundation.

Comments on the Securities Market Code Bill, 2025

by Natasha Aggarwal, Pratik Datta, K. P. Krishnan, Bhavin Patel, M. S. Sahoo, Renuka Sane, Ajay Shah and Bhargavi Zaveri-Shah.

Finance is the brain of the economy. It dictates allocative efficiency. The financial system chooses which industries and firms receive capital. This efficiency determines the extent to which investment translates into GDP growth. Getting finance right is critical. The prioritisation of financial reform must be absolute.

The Securities Market Code Bill, 2025 (SMC) marks a substantial advance over the existing Securities and Exchange Board of India Act, 1992, particularly in strengthening governance arrangements and formalising the processes of regulation-making. Importantly, it makes a serious attempt to end the ''circular raj'' by confining the issuance of subsidiary instruments to the Chairperson or senior members of the Board, rather than dispersed internal authorities. Further, it has introduced timelines for investigations and attempted to separate the investigation function from the adjudication function, making the first effort towards a clearer separation of powers. That said, the SMC can make further strides if it focuses on the issues described below.

We now address the issues in relation to specific provisions drafted within the current SMC.

Separation of powers

The SMC raises three related concerns, which demonstrate a concentration of powers at SEBI.

Issue 1: Excessive delegation of essential legislative functions

Clause 96 prescribes imprisonment, a fine, or both as penalties for market abuse (an offence defined under Clause 93). However, Clause 93 also grants the regulatory authority to define new offences within the 'market abuse' category, which would carry the same criminal sanctions. This raises concerns around excessive delegation: the identification of criminal offences is a core legislative function and cannot be delegated. Moreover, such excessive delegation is subject to being struck down in judicial review.

Issue 2: Regulation-making on adjudication

Clause 146(2)(j), read with Clause 17(4), permits SEBI to make regulations on the manner of conducting adjudication proceedings. This should not be done by SEBI itself. SEBI is the agent, and the Parliament is the principal. The Parliament must define the checks and balances on the coercive power of the agent. Otherwise, the agent always has incentives to appropriate more arbitrary power.

Issue 3: Ineffective separation of investigative and adjudicatory functions

Clauses 17 and 27 introduce limited separation of investigative and adjudicatory functions for specific matters. Investigation is an executive function, and adjudication is a quasi-judicial function. A conflation of these two functions in the same individual raises concerns about the separation of powers.

In summary, there is no clear separation of power between the three functions of the regulator. The same regulator is empowered to define the scope of violations and offences, investigate them, enforce them, adjudicate upon them, and impose sanctions for their violations, all under regulations of its own design. This combination blurs the distinction between legislative, executive, and adjudicatory functions and concentrates powers in the same persons.

Proposal:

Remove Clauses 17(4), 92(f), 93(g), and 146(2)(j) from the SMC. Implement strong structural separation between the investigative and adjudicatory functions. One way to do this is to create a distinct career track for adjudicatory officers as Administrative Law Officers (ALO). One SEBI board member should also be designated as an Administrative Law Member, who oversees the functions of ALOs. These officers should be solely responsible for adjudication and must have no involvement in investigative or quasi-legislative functions. Introduce extraordinary safeguards to mandate arm's length operation between investigation and adjudication.

Timelines for investigation and adjudication

Issue: Clauses 13, 16, and 27 introduce timelines for investigation and interim orders. However, provisos allow these timelines to be extended (Clause 27(4), proviso to Clause 13(2)). Additionally, the SMC specifies no timelines for the completion of adjudication proceedings. This allows investigations and adjudications to continue indefinitely, rendering the statutory limits ineffective.

Proposal: Remove the power to extend timelines for investigation. If extensions are retained, mandate the publication of written reasons, subject to mandatory review by the SEBI governing board. Introduce a strict statutory timeline for the conclusion of adjudicatory proceedings. These timelines should be part of the Parliament-specified regulations on the manner of conducting adjudication proceedings that we recommend in our preceding suggestions.

Methodology for calculating unlawful gains

Issue: The SMC requires the determination of unlawful gains by an investigating officer under Clause 13(3), but provides no calculation methodology. This virtually guarantees arbitrary and inconsistent determinations. It defeats the rule of law.

Proposal: Codify standard methods or guidelines for calculating unlawful gains within the SMC. Operationalise these through detailed regulations. Reference the Competition Commission of India (Determination of Monetary Penalty) Guidelines, 2024, as a baseline.

Sanction determination factors

Issue: The SMC lists factors for adjudicating officers to consider while imposing sanctions. Some mirror Section 15J of the SEBI Act, which are unimplementable in practice. Terms like 'impact of the default or contravention on the integrity of the securities markets' (Clause 19(b)(v)) lack precision and invite arbitrariness.

Proposal: Base sanctions strictly on the quantifiable extent of harm caused to specific persons. Codify this methodology. Alternatively, publish binding guidelines detailing specific aggravating and mitigating factors, expanding upon the approach in the SEBI (Settlement Proceedings) Regulations 2018.

Criminal enforcement

Issue: The SMC retains criminal liability, including imprisonment, for some offences. Establishing guilt in Indian criminal law requires proof beyond a reasonable doubt, typically coupled with the requirement to establish intention. This is an inefficient tool for complex financial markets. The boundary between aggressive trading and market manipulation is thin. The threat of criminal sanctions deters contrarian strategies. This reduces market liquidity and harms price discovery. Traditional fraud is adequately covered by the Bharatiya Nyaya Sanhita.

Proposal: Remove all criminal liabilities. Structure sanctions as punitive civil penalties or restorative remedies, scaling to a multiple of the illicit gains. Retain debarment for systemic misconduct.

Power to issue directions

Issue: Clause 23 vests SEBI with open-ended direction-making powers. Moreover, the requirement to record reasons in writing (currently included in Section 11(4) of the SEBI Act) has not been included in Clause 23.

Proposal: Delete Clause 23. Confine non-penal measures to specific, narrowly defined statutory triggers (e.g., immediate asset freezing powers under strict procedural safeguards). All adjudicatory actions must be justified by reasons in writing.

Nominee directors on the SEBI board

Issue: The SMC retains government nominee directors on the SEBI board. Nominee directors prioritise the perspective of their parent departments over market efficiency. They exercise disproportionate influence. Inter-agency coordination should not occur via board representation.

Proposal: Appoint mid-career professionals for fixed terms until a mandatory retirement age. Bind them statutorily to SEBI's specific objectives. Address inter-agency concerns externally through the Financial Stability and Development Council (FSDC).

Commodities markets

Issue: Clause 49 empowers the government to determine commodities eligible for trading. The market must decide which commodities warrant hedging instruments. State determination of eligible commodities is equivalent to the government deciding which firm is permitted to issue equity.

Proposal: Delete Clause 49. Empower SEBI to draft regulations defining objective eligibility criteria for commodity derivatives, identical to the framework for eligible scrips.

Ombudsperson

Issue: Clause 73 empowers SEBI to designate an Ombudsperson. This creates a conflict of interest. The SMC lacks an appeals mechanism for decisions made by the Ombudsperson.

Proposal: Mandate statutory independence for the Ombudsperson. Ensure job security separate from SEBI management. Define a clear appellate process.

Exemptions for PSUs

Issue: Clause 65(2) empowers the Central Government to exempt listed public sector companies from listing and disclosure requirements. This violates Article 14 of the Constitution. State-owned enterprises must face the identical market discipline applied to private enterprises.

Proposal: Delete Clause 65(2). Mandate equal treatment for all market participants.

References

Natasha Aggarwal and others, "'Balancing Power and Accountability: An Evaluation of SEBI's Adjudication of Insider Trading'" (Working Papers, TrustBridge Rule of Law Foundation, 2025).

In Re: The Delhi Laws Act, 1912 (AIR 1951 SC 332).

M S Sahoo and V Anantha Nageswaran, 'Regulatory architecture 2.0: Securities Markets Code marks a decisive shift' (Business Standard, 25 December 2025).

M.S. Sahoo and Sumit Agrawal, "Reimagining SEBI's Consent Settlement Framework" (Chartered Secretary, January 2026).

C.K. Takwani, Lectures on Administrative Law (7th edition, 2023) at page 100.

Bhargavi Zaveri-Shah, 'SEBI does not need unlimited powers – here's what's wrong with the Securities Markets Code' (ThePrint, 5 January 2026).

Bhargavi Zaveri-Shah and Harsh Vardhan, 'Ghost of the Commodities Controller—why India's new financial law feels like the 1970s' (ThePrint, 19 January 2026).

An Analysis of Electricity Outages in Delhi: 2024-25

by Upasa Borah and Renuka Sane.

Introduction

In a previous article, A Review of Outage Reporting by Indian DISCOMs, we examined the state of outage data reporting across India. We studied which distribution companies (DISCOMs) report such data and the variations in the way they do so. A natural next step is to thus look more closely at the available data to understand the kinds of analyses they enable.

This article focuses on the three privately owned DISCOMs operating in Delhi. Delhi's DISCOMs rank below the top 20 in the Ministry of Power's annual ranking of DISCOMs, all three graded B minus in the 13th Ranking exercise in 2025. They are similarly situated in terms of their billing and collection efficiency, power procurement portfolios and costs. There are, however, notable differences in the availability, structure and clarity of their reported outage data.

It is important to note that not all outages at a feeder level translate into outages for consumers due to the presence of redundancy in power systems. Most modern systems can re-route electricity through alternate feeders in case of faults. Understanding whether and how redundancy is accounted for is thus crucial to interpreting outage data. For instance, one of Delhi's DISCOMs, BSES Rajdhani Power Ltd., reports outages at the feeder level, but there is no information on which feeders have redundancy systems or how many outages were rerouted and thus did not cause interruptions for end consumers. On the other hand, Tata Power Delhi Distribution Ltd. reports outage data by zones and the number of consumers affected, allowing us to infer the extent of consumer impact. BSES Yamuna Power Ltd., however, reports outages by division and subdivisions and does not note the feeders or consumers impacted.

Given these data limitations, our analysis does not directly compare performance between DISCOMs. Instead, we study the available data to demonstrate the kinds of insights that can be drawn about the frequency, duration and spatial patterns of outages in Delhi. Specifically, we ask:

1. What is the pattern of outages on the following parameters:
1. Duration and frequency,
2. Intensity,
3. Geography,
4. Reasons for outages
2. What is the relationship between outages and electricity demand?

Methodology

There are four distribution companies operating in Delhi: i) BSES Rajdhani (BRPL) covering the southern and western areas, ii) BSES Yamuna (BYPL) covering the southeast and northeastern regions, iii) Tata Power (TPDDL) in the north and northwest areas, and iv) New Delhi Municipal Corporation (NDMC), which supplies to government buildings in central Delhi. Excluding NDMC, the first three DISCOMs are privately owned and supply to 93% of consumers in Delhi; BRPL supplies to 31 lakh consumers covering an area of approximately 700 sq km, TPDDL supplies to 20 lakh consumers in 510 sq km, and BYPL supplies to 19 lakh consumers in an area of around 200 sq km (Chitnis et al., 2025). In 2024-25, Delhi's electricity requirement stood at 38,287 MU, with peak demand hitting 8,685 MW.

We collected outage data from each DISCOM's website (see Data appendix). Lack of data for NDMC limited our analysis to the remaining three DISCOMs. The reported data includes date and time of outages, durations, areas affected, reasons for outages and measures taken to rectify the issue. However, there are inconsistencies in the data reported by the three. Table 1 summarises the variations in the availability of outage data for the three DISCOMs under study.

Table 1: Availability of data on power outages

DISCOM	Days of data availability	Spatial unit of reporting data	Number of spatial units
TPDDL	April, May, July and August 2024	Zones	12 zones
BRPL	April 2024 to March 2025	Grid and feeder	428 grids, 2,951 feeders
BYPL	April 2024 to March 2025	Division and sub-division	28 divisions and 108 subdivisions

TPDDL data is available only for April, May, July and August 2024. It reports data on zone-wise outages and the number of consumers impacted. BRPL, on the other hand, provides data on grid and feeder levels, without noting how many consumers were affected. Since outages at the feeder level may not always indicate consumer-level interruptions, understanding redundancy systems is important, but data on these was not available. There is also no data on how many consumers are serviced by a grid or feeder. Finally, BYPL reports outage data at the division and sub-division level without specifying feeder details or the number of consumers affected.

Aside from these differences, we also noticed inconsistencies in the way data is recorded, in terms of structure, format and number formatting. We extracted outage data from PDFs, conducted thorough cleaning and reorganisation. Although the datasets included reported outage durations, we recalculated the duration of each outage for all three DISCOMs based on recorded start and end dates and times. In terms of reasons for outages, TPDDL lists six broad reasons, which we retained. In contrast, BRPL and BYPL record a wider and more open-ended set of reasons, which we analysed and classified into six broad categories using text search.

TPDDL: consumer-facing outages

Between April and August 2024 (excluding June), the parts of Delhi serviced by TPDDL recorded an average of around 87 outages per day. Across all zones and feeders, these outages cumulatively amounted to roughly 159 hours of interruptions per day, and affected around 46,000 consumers. Figure 1 shows the daily frequency and total cumulative hours of outages across all TPDDL zones. On most days, outages occurred in 11 of the 12 reported zones.

Figure 1: Aggregate frequency and duration of outages for TPDDL

Over the four months for which data is available, we analysed outage days and duration for each TDPPL zone, and then averaged the results across zones. The median and mean values are presented in Table 2.

Table 2: Average days of outages, intensity and number of consumers impacted in the four reported months

Total number of zones	Number of consumers facing outages (lakhs)		Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean	Median	Mean
12	4.17	4.75	121	116	8.39	13.59

* cumulative value across all feeders

On average, a TPDDL zone experienced outages on 116 days, affecting around 4.7 lakh consumers. It is important to note that these are aggregate zone-level values, i.e. they do not represent outages faced by an average consumer but rather the cumulative outages across all feeders within a zone, covering multiple subdivisions and localities. For instance, Narela, Badli, and Bawala zones have the highest number of outage days, with Narela having the highest intensity (40 hours cumulatively per outage day) across the various areas in the zone, affecting 9.15 lakh consumers. The total duration exceeds 24 hours because a single zone has several feeders whose outages are aggregated when they occur simultaneously.

Around 16% of all outages reported by TPDDL are due to planned events. Figure 2 shows the share of outages by reason. 71% of outages, accounting for 60% of total outage hours, are due to external factors where the specific cause is not reported. A more detailed classification of these categories would help identify the underlying causes of outages more accurately. It also remains unclear what is included under "EODB compliance" outages, which account for 12% of all outage hours, and "Industrial weekly off" that accounts for 3% of outage hours.

Figure 2: Reasons for outages for TPDDL

BRPL: Feeder-level outages

On an average day, around 48 feeders under BRPL experience outages, amounting to a cumulative total of 50 outages and 119 total hours of interruptions across all feeders. Figure 3 shows the daily frequency and duration of these outages. The highest number of outages occurred on 7 January 2025, when 104 feeders were affected, resulting in a combined total of 386 cumulative outage-hours.

Figure 3: Aggregate frequency and duration of outages for BRPL

Of the 428 BRPL grids, an average grid had around 8 feeders under outages, with a mean of 28 days of outages in a year. Cumulatively, this results in approximately 1.8 hours of interruption per outage day across its multiple feeders. Table 3 presents the median and mean values of feeders under outage, days of outages and intensity of outages across the grids. The median values are lower than the means, indicating that while most grids experience relatively fewer and shorter outages, a few grids have significantly higher levels of outages. For instance, in 2024-25, the most outages occurred in Jaffarpur grid (187 days of outages with a cumulative intensity of 9.9 hours per outage day), followed by Nilothi grid (247 days, 4.6 hours), Mitraon grid (182 days, 6 hours), Hastal grid (236 days, 4.4 hours) and C-Dot grid (185 days, 5.39 hours).

Table 3:Average days of outages, intensity and number of feeders impacted 2024-25

Total number of grids	Number of feeders under outages		Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean	Median	Mean
428	2	8	2	28	0.75	1.80

* cumulative value across all feeders

Figure 4 shows the share of outages by reason. Planned events account for 54% of all outages and 82% of total outage hours. Fault-related outages follow, making up 31% of outages and 9% of total outage hours. Most outages of BRPL are thus planned rather than caused by unforeseen circumstances.

Figure 4: Reasons for outages for BRPL

BYPL: Area-wise outages

On an average day, BYPL areas recorded 16 outages, with a cumulative duration of 12.5 hours across all affected feeders. Figure 5 shows the daily frequency and duration of these outages. The highest number of outages occurred on 28 June 2024, when 98 outages were recorded, lasting a combined total of about 100 hours.

Figure 5: Aggregate frequency and duration of outages for BYPL

For an average subdivision serviced by BYPL, outages occurred on about 22 days in a year, with a cumulative average of 58 minutes per outage day. The median values are lower at just three days of outages (Table 4), indicating that most subdivisions experienced fewer days of outages, while a few faced disproportionately higher outages. Sonia Vihar recorded the most outages (201 days with a cumulative intensity of 1.86 hours per outage day), followed by Nand Nagri (196 days, 1.84 hours) and Karawal Nagar (179 days, 1.62 hours).

Table 4: Days of outages, intensity per outage day during the year 2024-25

Total number of subdivisions	Days of outages		Intensity of outages per outage day* (hours)
	Median	Mean	Median	Mean
108	3	22	0.92	0.96

* cumulative value across all feeders

Figure 6 shows the share of outages by reason. BYPL has zero outages explicitly listed as "planned". 51% of outages accounting for 47% of outage duration were due to faults, followed by maintenance outages and outages due to infrastructure damage.

Figure 6: Reasons for outages for BYPL

Electricity demand and outages

The lack of consistent and comparable data makes it difficult to analyse the yearly correlation between Delhi's electricity demand and outages. However, looking at BRPL and BYPL's outage data reveals contrasting results. BRPL's daily outage hours show no correlation with Delhi's electricity demand (Figure 7), while BYPL outages are positively correlated, significant at the 1% level (Figure 8).

Figure 7: BRPL outages and Delhi's total electricity demand

Figure 8: BYPL outages and Delhi's total electricity demand

Moreover, when we look at the time when most outages occur, we find similar divergence. Most of the outages of TPDDL and BRPL were recorded to have occurred between 6am to 12pm, which is different from Delhi's peak demand hours which are generally from 2 pm to 5 pm, and 11 pm to 1 am. BYPL's outages, on the other hand, seem to mostly occur around 12pm to 6pm. A detailed share of total outages by time of day is given in Table 5.

Table 5: Proportion of total outages and duration by time of day

Time of day	Share of TPDDL's total outages (%)		Share of BRPL's total outages (%)		Share of BYPL's outages (%)
	By frequency	By duration	By frequency	By duration	By frequency	By duration
12am - 6am	8.4	6.4	7.2	2.3	21.1	22.3
6am - 12pm	38.7	51.5	53.1	73.2	22.0	21.9
12pm - 6pm	37.3	28.6	31.3	21.8	32.5	31.3
6pm - 12am	15.5	13.4	8.4	2.6	24.4	24.6

Conclusion

Our analysis finds that the lack of a common standard and clarity in reporting makes it difficult to draw definitive conclusions about the frequency, duration, and causes of outages in Delhi. There seems to be a substantial number and hours of outages, but in the case of BRPL and BYPL, we do not know how many of those lead to consumer-facing outages, and thereby cannot assess the reliability of supply.

Several other issues also stand out. For example, TPDDL's outage reasons are not clearly defined: what exactly counts as EODB and Industrial weekly off outages? Meanwhile, most of BRPL's outages are marked as "planned". It is unclear if they translate to interruptions for consumers, but it is worth asking why such a large share is planned. On the other hand, BYPL does not report a single planned outage, which seems equally puzzling.

There are also differences in the spatial units used for reporting. That TPDDL reports 12 zones, BRPL 428 grids and BYPL 108 subdivisions implies that TPDDL's higher outages could be due to its larger geographical units. Even between BSES's two DISCOMs, outage data are reported differently, with no information on how many consumers are connected to a feeder or fall under a subdivision, making it difficult to assess the real impact of outages.

While much attention is paid to the financial performance of DISCOMs, it is also important to study the reliability of the electricity they supply. Internationally, countries like the United States and the United Kingdom publish country-wide, disaggregated outage data that enable detailed analyses of reliability, causes and impacts. For instance, studies using US Department of Energy data examine reliability and causes across states (Ankit et al., 2022) and counties (Richards et al., 2024), while data from the UK's National Fault Interruption Reporting Scheme has been used to analyse trends in outages and weather data (Shouto et al., 2024). These highlight the potential of regular, consistent and transparent reporting, which is missing in India.

As we discussed in our previous article, several independent studies in India have tried to estimate outage data, largely through household surveys (Agrawal et al., 2020; Bigerna et al., 2024; Khanna & Rowe, 2024). However, DISCOMs are better positioned to provide granular, feeder-level data in an accessible and comparable form, but as of the writing of this article, they are not mandated to make this information public. There is also no command standard of reporting, which make it impossible to make meaningful assessments. While DISCOMs are investing in redundancy systems and infrastructure, they must also clarify which recorded outages translate into consumer-facing interruptions. Doing so would, in fact, allow for a more accurate evaluation of the measures undertaken to improve reliability.

Aklin et al. (2016) had conducted a household survey in six Indian states and found that not only are outages very frequent, but that increasing the reliability of supply has effects comparable to electrifying an unelectrified household. Improving reliability of supply, however, first requires an understanding of where, when and why outages occur, which in turn requires better data. We recommend adopting a common standard of reporting outage data that includes daily, consumer-facing feeder-level outages, with information on the outage start and end times, durations, reasons, the number of consumers and the localities impacted. A first-level reason can broadly indicate whether an outage is planned or unplanned, and then provide a detailed description of the underlying cause. The data should be updated regularly and historical archives should be publicly available. This would enable more accurate and regular analyses of outage patterns, across DISCOMs and states.

References

Factors affecting household satisfaction with electricity supply in rural India by Aklin, M., Cheng, C. Y., Urpelainen, J., Ganesan, K., & Jain, A., 2016, Nature Energy, 1(11), 1-6.

Stalemate - How Consumers are Losing in the Fight Between the Regulator and Discoms in Delhi by Chitnis, A., Dmonty, A. N., & Singh, D., 2025, CSEP.

Data appendix

The data on outages was extarcted from:

• BRPL, accessed on 2 June, 2025
• BYPL accessed 7 June, 2025
• TPDDL accessed on 7 July, 2025

Delhi's daily electricity demamd was accessed from Grid-India on 7 July, 2025

The cleaned datasets and code used in this analysis are available on our GitHub repository.

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

Anchor pension policy to its core design principles

by Renuka Sane.

Pensions policy, at its heart, stems from paternalism. When people stop working, they stop earning, and if they haven't saved enough, face the risk of destitution in old age. In most societies, the state feels compelled to step in with tax-payer funded cash transfers in the form of old age pensions. Over time, these commitments have expanded to cover entire populations. Yet financing the consumption of all elderly citizens through welfare transfers is fiscally unsustainable. As a result, pensions policy has focused on how to force individuals to build wealth during their working life. This coercion is justified on the grounds that people tend to underestimate their future needs, and discount the future too heavily. Assessment of pension policy, therefore, must recognise that it flows from the state's decision to compel individuals to save for their own future consumption. The legitimacy of this coercion depends on whether it contributes towards preventing poverty in old age. This article examines pension policy from such a perspective. The design of means-tested transfers for the already impoverished elderly, while significant, is not addressed here.

Four elements of a sustainable pension

This paternalistic foundation shapes the four defining features of a funded pension system. First, participation is mandatory. Second, the savings are illiquid. Third, the structure is low cost. Fourth, it converts savings into a stable stream of income in retirement. These features make a pension different from other forms of saving or investment. Take away one of them, and the system begins to resemble an ordinary investment account rather than a vehicle for old age income security.

Let us begin with the first feature: mandatory participation, where individuals are forced to save a proportion of their monthly income into a pension account. But, compulsion requires an employment relationship so that contributions can be enforced. Extending such schemes to the informal sector where workers move between jobs or remain outside formal payroll systems is a challenge. Further, there is the question of what contribution rate to mandate: if it is set too low, the accumulated savings will be inadequate for retirement; if set too high, it will unduly constrain consumption during working life.

The second feature, illiquidity, is put in place to provide income in old age, not to finance mid-life consumption. This design feature is always contested. Individuals argue that since it is their money, they should be able to access it when they need it. Such demands become greater when contribution rates are very high. Policymakers often give in, allowing partial withdrawals or loans against the accumulated corpus. But withdrawals can leave retirees with inadequate balances, defeating the entire purpose of the mandatory contribution.

The third feature, low costs, is crucial because of the long horizon of pension saving. Fees and commissions, even if small annually, compound heavily over decades. High costs can erode a significant portion of the final corpus. Keeping costs low is especially important because participation in a pension scheme is compulsory; having coerced individuals to save, policy cannot then channel their money into high-cost funds that primarily enrich fund managers. The global experience suggests that keeping costs low requires deliberate policy design. This can be achieved through two ways.

1. Auction-based system for selecting limited fund managers (as was the case of India's National Pension System (NPS)). The larger the corpus with a fund manager, the lower the fees. For example, Vanguard S&P500 ETF has assets of about US$1.5 trillion, and an expense ratio of about 0.03% (3 bps). When fund managers are given a specific mandate to manage a large corpus, costs can be negotiated down. Pension policy, especially when the market is very small, must then decide between the competing trade-offs of multiple managers and choice vs. limited fund managers and low costs.

2. Limit investment options to low-cost passive index funds. Over long periods, index funds typically outperform most actively managed funds, net of costs. Critics argue that restricting investment options stifles innovation and deprives individuals from exercising choice. But the idea of unfettered choice has also been questioned, given that most individuals may not be equipped to make complex financial decisions. These trade-offs become relevant because of the forced nature of savings.

A reasonable middle ground lies in offering a limited number of fund managers who provide a restricted menu of low-cost index fund options. Index funds with equity exposure provide for an upside and international exposure can help reduce risk through diversification.

The final feature, a retirement income, is what completes the cycle. This can be achieved through an annuity, which converts the accumulated balance into a guaranteed stream of payments for life. In the case of an inflation-indexed annuity, the payments are adjusted for inflation throughout retirement. While annuities provide longevity insurance (and can sometimes provide inflation protection), they are often unpopular because they appear to offer poor returns and lack flexibility. Pricing of annuities may be a challenge when the bond market is itself underdeveloped. The compromise in many systems is to mandate partial annuitisation requiring that a fraction of the corpus be used to buy an annuity while allowing flexibility for the rest. The other alternative is to design systematic withdrawal plans that allow for gradual withdrawals from the corpus. This doesn't insure against longevity risk, but is often preferred for its simplicity and flexibility.

It is important to emphasise the word funded when outlining the four features. If taxpayer resources are used to finance retirement, features such as guaranteed returns can be built in. However, such arrangements are often vulnerable to funding pressures over time. If the system is to remain self-sustaining, the investment risk must rest with the individual unless that risk is explicitly priced and paid for.

Pensions in India

The central question for any government, then, is how to achieve these four features. Let's consider how the EPF, NPS fare on these parameters, especially relative to a mutual fund that is not a pensions product.

Feature	Mutual fund	EPF	NPS
Mandatory	No	Yes (for formal sector)	Somewhat
Illiquidity	No	Cannot withdraw 25% of corpus	Yes
Low cost	No	Somewhat	Yes
Retirement income	No	No	Yes

A mutual fund is not mandatory, or illiquid, or low cost. It makes no promises of a retirement income. These features are not expected from a mutual fund, as it is not a pensions product.

EPF

The Employees Provident Fund (EPF) functions effectively on the issue of mandatory contributions, as it is meant for formal sector workers, where employment is defined and contributions are linked to payroll. However, its contribution rate, around 24%, is high, making it burdensome, particularly for low-income workers. Early withdrawals from the EPF have been a persistent concern. The EPFO has recently restricted withdrawals to 75% of the corpus (and hence 25% of the corpus is illiquid till retirement), which is an improvement, but still undermines the purpose of a pension product. The EPFO needs to consider a calibration of the contribution rates. The administrative costs, borne implicitly through an employer levy of about 0.5% of wages, make it relatively expensive. Moreover, it offers no choice in investments and provides a guaranteed rate of return, which limits both flexibility and upside potential. Finally, by paying out a lump sum at retirement, the EPF exposes individuals to longevity risk. From a pension design perspective, the EPF would benefit from reforms across all four foundational elements of a pension system.

NPS

The National Pension System (NPS) did not encounter challenges of coverage when participation was mandatory for government employees. The total AUM of NPS in September 2025 was about US$178 billion (Rs. 15.8 lakh crore), of which 85% was with the three public sector fund managers (SBI Pension Funds Pvt Ltd, LIC Pension Fund Ltd, UTI Retirement Solutions Ltd). The Pension Fund Regulatory and Development Agendy (PFRDA) has capped investment management fees for all pension fund managers, which continue to be some of the lowest in the world (between 3bps - 9bps). As the total corpus grows these may further come down. The NPS permits equity exposure but limits international investments, thereby constraining diversification opportunities. The scheme allows only three partial withdrawals during the entire subscription period and limits the amount that can be withdrawn. It would do well to reserve these safeguards, which reinforce the principle of lliquidity that underpins any pension scheme. It requires artial annuitisation at retirement and offers a systematic ithdrawal plan, with ongoing efforts to design additional tructures that can strengthen income security in old age. These are steps in the right direction.

Despite these advantages, the transition to the Unified Pension Scheme (UPS) has brought forth a fundamental challenge for the NPS: building a base of contributors for whom saving is compulsory. The natural tendency will be to compete in the market for voluntary savings. More recently, under the Multiple Scheme Framework (MSF) fund managers are permitted to design and offer multiple schemes tailored for different subscriber segments. While this will allow more choice for subscribers, it runs the risk of diluting what makes the NPS a pension product. A mature mutual fund industry already caters to voluntary investors, and an excessive focus on marketing voluntary contributions risks undermining the NPS's defining advantage - its low-cost, low investment options structure.

Conclusion

Retirement schemes, whether the EPF or the NPS, are only one component of an individual's broader savings portfolio. Yet, for the portion that is locked into a dedicated pension scheme, fidelity to the four core design principles is crucial. This is especially important as both schemes, and particularly the NPS, consider various reforms related to the design of different schemes, valuation models and withdrawal options. The focus of a pension system should remain on expanding wholesale participation through large-scale group subscriptions, rather than competing directly in the retail savings market.

The author is a researcher at 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.

References

State of Electricity Access in India: Insights from the India Residential Energy Survey (IRES) by Agrawal, S., Mani, S., Jain, A., & Ganesan, K., October 2020, CEEW Report.

India's electric grid reliability and its importance in the clean energy transition by Athawale, R., May 2021, Regulatory Assistance Project.

Election cycles and electricity provision: Evidence from a quasi-experiment with Indian special elections by Baskaran, T., Min, B., & Uppal, Y, June 2015, Journal of Public Economics.

India's Statistical System: Past, Present, Future by Bhattacharya, P., June 2023, Carnegie Working Paper.

An empirical investigation of the Indian households' willingness to pay to avoid power outages by Bigerna, S., Choudhary, P., Jain, N. K., Micheli, S., & Polinori, P., November 2024, Energy Policy.

Assessing reliability of electricity grid services from space: The case of Uttar Pradesh, India by Dugoua, E., Kennedy, R., Shiran, M., & Urpelainen, J., June 2022, Energy for Sustainable Development.

Blackouts: The Role of India's Wholesale Electricity Market by Jha, A., Preonas, L., & Burlig, F., December 2021, NBER Working Paper.

The long-run value of electricity reliability in India by Khanna, S., & Rowe, K., April 2024, Resource and Energy Economics.

Five Stitches in Time: Regulatory and policy actions to ensure effective electricity service by Mandal, M., Nhalur, S., Pandey, A., & Josey, A., May 2019, Prayas (Energy Group).

Whose Power Gets Cut? Using High-Frequency Satellite Images to Measure Power Supply Irregularity by Min, B., O'Keeffe, Z., & Zhang, F., June 2017, World Bank Research Working Paper 8131.

More Power to India: The Challenge of Electricity Distribution by Pargal, S., & Banerjee, S. G., 2014, World Bank Directions in Development.

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

Powering AI with Reliable Grids and Networks

by Renuka Sane.

There is much action and anticipation related to the AI decade, and especially about the potential of building large data centres, in India. As of April 2025, at least five hyperscale data centres were in the works. It is expected that India's data centre capacity will surpass 4,500 MW by 2030, backed by $25 billion in investments. Recently, OpenAI has indicated its interest in setting up a data center in India. Before we celebrate these investments, we should ask if the economics of locating them in India adds up? Even leaving aside issues such as land and taxes, do power prices, network quality, and the reliability of both electricity and the internet make us globally competitive? Some investment will come anyway because of data-localisation rules. But that is compulsion, not strategy. Real scale will only occur when a rational firm would choose India even without a localisation mandate, because the numbers and the policy risk both make sense. The AI story has two prerequisites: abundant, reliable electricity and reliable connectivity.

Reliable electricity

Let's start with electricity consumption. A small data center requires about 1-5 MW of power, while a "hyperscale" data centre draws about 100 MW of power at full load. Assuming a power usage effectiveness (PUE) of a data center of 1.2, one hyperscale data center will require 100 MW * 1.2 * 8,760 h = 1.0512 TWh. over a year.

Where is this power going to be sourced from, and how much will it cost? There are three supply options: (1) grid supply,(2) round-the-clock renewable energy plus storage (RTC-RE+storage) contracted from a developer under green open access or (3) a captive plant powering the data centre.

1. Grid supply: If we assume an industrial tariff of Rs. 7.5 per kWh (which is close to the tariffs in Maharashtra and Tamil Nadu,the two states at the forefront of the data center business), the electricity bill for uninterrupted grid power is roughly Rs. 7.9 billion (US$ 90 million) per year. Except that power supply is not guaranteed, and significant power outages imply that data centres have to build alternatives to ride through grid outages. At Rs. 25-30/kWh, diesel generators routinely cost an order of magnitude more to almost US 236-300 million.

2. Round-the-clock renewables with storage (RTC-RE + storage) via green open access: Here we have to consider three cost items. The first is the cost of the RTC power purchase agreement (PPA), which will be higher than plain solar/wind because it includes storage and portfolio diversity. The second are the network charges and losses that include intra-state transmission, wheeling (distribution) charges, and transmission + wheeling losses before it reaches the meter. If this is inter-state then one has to start considering the prevailing ISTS regime. The third is the cross-subsidy surcharge (CSS) and additional surcharge (AS) to the DISCOM. In Maharashtra, for example, these are quite high, potentially making the final price above the grid, even if the landed cost of the RTC PPA is significantly lower than the grid price. An approx overall price of Rs.9/kWh hour, gives us a total cost of around US$110 million.

3. Group-captive RTC: In this case, CSS and AS charges wouldn't apply and the price may come close to (or be lower than) the grid price. The data centre, however, would need to hold the required equity and off-take, and would have its own governance challenges. It is these that can become the binding constraints, not just price. A group-captive cost of Rs.7/kWh leads to a cost of US$83 million.

How do these numbers compare to say the US or Germany? The table below gives some indicative answers.

Country	Unit cost (US$)	Annual costs (US$)
India (grid average)	0.085	89.4 m
India (RTC, group captive)	0.076-0.080	80-85m
India, (RTC, third-party)	0.104-0.112	109-118 m
Germany, (grid average)	0.196	205.5 m
United Kingdom (grid average)	0.249	261.6 m
US, (grid average)	0.0886	93.1 m
US (Texas)	0.063	66.6m

As the table shows, despite the difficulties in India, it remains competitive vis-a-vis countries such as Germany and the UK as far as electricity prices are considered. However, India is not competitive vis-a-vis the US, where prices in places such as Texas and Virginia (US$0.091) are lower than the third-party open access option in India. The economics of electricity will further change based on the amount of cooling required, which will be relatively higher in India, requiring more energy than in countries with ambient temperatures much lower than India.

Reliable connectivity

There is much more awareness regarding the impediments of the electricity sector for our AI ambition. The issue of reliability of connectivity is less talked about - reliability that gets compromised because of our world-leading record of internet shutdowns. India sees routine network blackouts for reasons related to mobile data bans during exams, to internet suspensions in conflict for months on end. In 2024 India accounted for 28% of all government ordered shutdowns globally - the highest by any country. There have already been 28 shutdowns in 2025. One study estimates the economic cost of shutdowns to the Indian economy at $968 million.

One could argue that data centres used leased lines, and a mobile only shutdown would not matter much. Except that mobile only bans knock out end-user access potentially affecting any product whose customers are on these networks. Shutdowns can affect logistics, field engineering, and remote operations. A shutdown freezes the demand for AI inference in that region. Such vanishing demand equals loss of demand of electricity, leading to idle capacity, another indirect cost for an entrepreneur to handle. One could also argue that shutdowns primarily occur in conflict zones, or districts that are on the periphery of economic activity, and therefore not likely to materially affect the AI story. While that may be true at the moment, routine ad-hoc shutdowns undermine trust in India as a location for latency-critical workloads and cross-border data partnerships. They can lead to a sovereign reliability discount, raising the hurdle rate for capital. Even without shutdowns, India routinely has to deal with connectivity problems owing to frequent power cuts causing internet outages.

Way forward

There are two ways to look at the issue of data centres. The first is whether India can become the regional hub and service clients across Asia and the Middle East. On this question, the answer is clear. Firms will make rational decisions - unless it makes economics sense, firms will prefer to rent equipment or make API calls to the cheapest data centres elsewhere in the world. The second is what it costs firms in India to be forced to place data centres here owing to data localisation mandates. If the cost of training and inference is lower in the US (or other overseas markets) than in India, then firms in India will be at a considerable disadvantage if forced to use local facilities.

Blackouts and shutdowns are not compatible with the way in which AI services evolve and deploy. Foundation models, hyperscale data centres, and exportable AI services demand 24x7 supply and connectivity. India needs to get its grid electricity to world-class levels. It needs to reform its charge structure that makes firm green more expensive than grid making exit difficult. While nuclear energy remains an option, there is no clarity yet on the resolution of supplier-liability laws, making its future still uncertain. Additionally, India needs a radical overhaul of its policy on shutdowns. They should be the absolute last instrument; a rule-of-law state first exhausts narrower tools such as content take-downs, site-specific throttling, geofenced blocks, and targeted law enforcement, and only then even contemplates turning off the network. Our AI strategy should focus on building on two pillars: dependable power and dependable networks.

The author is a researcher at the TrustBridge Rule of Law Foundation. I thank Ajay Shah and Anand Venkatanarayanan for useful comments.