The Leap Blog

Trust in the Era of the AI-Informed Customer

2026-06-07T22:31:29.399+05:30

by Maninder Singh Juneja.

A patient grappling with a longstanding eye problem was diagnosed with MGD (Meibomian Gland Dysfunction) by an eye specialist. To make sense of the medical jargon, she photographed her meibography report, the gland scan, and uploaded it to an AI, which confirmed the diagnosis and the line of treatment. A few weeks later, wanting to avoid buying shades of cosmetics she already owned, she uploaded a photograph of her stock and of the items she was about to buy to the same AI. Instead of commenting on the shades, the AI told her the waterproof eyeliner she had used for years was blocking the pores of the gland, like wax in a drain, and making her dry eyes worse. She stopped, and this one change brought immediate relief. The AI connected across domains, unasked. Neither the physician nor the cosmetics counter had connected the two. The ophthalmologist saw the eye. The counter saw the product. AI saw the person. Neither was wrong. Each was trained to look at one domain. Every institution serves its own; AI serves the customer. This is not a failure of individuals but a structural shift with strategic implications. In this article we examine these implications.

Verification in markets

For as long as markets have existed, buyers have had to trust sellers, because there was no way to verify their word, or the effort and cost were too high. The economist George Akerlof spent many days puzzling over the nature of the vegetable retail market in Delhi. In 1970, he offered new insights into the market failure that arises when the buyer cannot verify what the seller knows. He later won the Nobel prize in economics for this work which helps us understand asymmetric information.

The market economy combats asymmetric information using substitutes, brands, professional licensing, statutory audits, regulators, and guarantees. For example, the customer trusts the bank's brand name instead of reading the fine print, or the doctor's medical degree instead of evaluating the diagnosis. Half a century of institutional architecture in financial services is built on this logic.

The use of AI has brought about a foundational shift in trust. AI has made verification in many situations quite feasible. Customers who once accepted substitutes for trust in institutions can now check a claim, compare alternatives, or challenge a recommendation on an inkling of doubt. The barriers to specialist access, cost, time, language, jargon, have all collapsed at once. The result is not an end of trust, but the pillars on which trust rests have changed.

How it impacts the brand

The brand is an informational shortcut, one of Akerlof's substitutes for verification. It stood in for the customer's inability to verify. The bank's or insurer's reputation stood in place of the customer's understanding of provider soundness, and the terms and conditions of the contract. AI changes that, but the picture is complicated, because the brand connotes two things at once: A promise of what we will do for you, and an aspiration of who you become or which tribe you join by choosing us.

The promise of what the brand will do is now more verifiable. The customer's AI checks every promise before purchase, searching the wider internet, cross-referencing user reviews and triangulating from multiple public sources. The bank that claims to be customer-first has its complaint-resolution record extracted from annual reports or instantly summarised from X. The insurer that promises easy claims has its claim-rejection ratio surfaced against peers. Promises that survive verification strengthen the brand. Promises that do not are revealed in seconds.

The aspiration is not in the product, it lives in the customer. People buy Apple products partly because Apple-ness signals something about themselves. Customers bank with a private bank not for any major service benefit but for who else banks there. People ride Royal Enfield partly because owning one says something no specification sheet captures. AI audits the promise. It cannot interpret the tribe.

The split deepens in the AI-to-AI world, where the customer's AI transacts with the institution's AI. The customer only experiences the outcomes, settlement speed, dispute rate, complaint-resolution time and median application-to-approval. It does not watch advertisements. In this perspective, brands need to invest in advertising that bolsters tribal loyalty, but advertising that is supposed to bolster the promise is now less important.

How it impacts labour

Like the brand, the professional is going to be hit by the AI wave. The professional of the old world was, by training, organised around the domain. The cardiologist gave the right answer to the heart in front of her, not to the medicine cabinet or the financial situation at home. The mutual fund agent recommended within his manufacturer's product set, not against the seven existing funds in the customer's portfolio. The AI can see the landscape comprehensively and the professional has to now compete with it.

In most domains the gap between the bottom and the top decile of professionals has been wide. AI compresses the gap from below. The advisor who tests his recommendation with AI before delivering it catches the portability clause he had not considered. The relationship manager who has the model argue the customer's case against his own pitch will close more often. Every word the professional says can now be cross verified; the smartest professionals will go up against 'unsophisticated consumers' with more respect.

In recent research, Brynjolfsson, Li and Raymond (2023) studied 5,179 customer-support agents at a Fortune 500 software firm and found that average productivity rose 14% with access to a generative AI assistant. Within this overall average, novice and low-skilled workers improved by 34%, while experienced and highly skilled workers showed minimal gain. By this reasoning, AI-powered unskilled labour will be tough competition against skilled practitioners.

The pattern is visible in India also. At one NBFC, the productivity of fresh-college LAP underwriters rose 40% with AI assistance, while experienced underwriters showed minimal gain. The bottom of the labour quality distribution comes closer to the top.

As the floor rises, the implications are twofold. First, the customer's worst experience disappears, and with it the customer's reason to switch providers. Second, the professional's competence becomes the table stakes. They have to now provide what AI cannot supply: Trust earned over time, judgement under ambiguity, the willingness to take a customer-friendly call when things are not going well with the business. These are the qualities that live on the aspiration side of the brand, and they are the real moats of professional competence.

When the asymmetry reverses

So far the shift has run one way, AI in the customer's hand against the institution. The same architecture runs the other way too. Institutions have always known what customers did. But AI interactions reveal something deeper, what the customer considered doing. The questions asked, the scenarios tested, the decisions abandoned. These are cognitive traces, and they sit closer to intent than anything an institution has had access to before. If applied to underwriting, pricing or customer acquisition, they create a new informational advantage that did not exist a year ago.

The trust consequence runs deeper than the privacy one. Behaviour is what the customer did once it was done. Intent is what she rehearsed before she was ready to be seen. A customer can absorb the knowledge that her behaviour was logged. When she learns the institution priced her, or declined her, on the strength of her question, the breach is of a different order. The first asymmetry was about information she did not have. This one is about information she did not know she was giving. This may lead the customer to stop being candid with the one tool that was working for her, because she now suspects it is also working for the firms. The independence this piece began with is the first thing she loses when the architecture turns around. We will have to face a new world of consumer protection complexity, going beyond the simpler questions of data privacy.

What then survives in trust? Not the part that rested on the customer's inability to check. What survives is what AI cannot manufacture. Judgement under ambiguity, the call no model will take responsibility for. A relationship proven over time, the banker who backed the customer through a bad cycle and was proved right. The human presence in a hard moment, the advisor who delivers difficult news with care. None of this can be read off a document, so none of it can be verified, and so none of it can be commoditised. The trust that survives is the trust that was never about information in the first place.

The institutions that Akerlof described were built on substitutes for verification. Those substitutes served a purpose. They filled a gap the customer could not fill herself. That gap is closing. What remains when the substitute is no longer needed is the thing the substitute was always standing in for. Genuine expertise, honestly applied, in the customer's interest. The institutions that had that all along have nothing to fear from the informed customer. The institutions that were selling the substitute will find, quietly and permanently, that the customer has stopped calling back.

The scarce asset is the question

If one risk is that the institution reads the customer's question, the other is that she asks a wrong question. While AI can reduce the information asymmetry, friction has not entirely disappeared. It rests on arriving at the right question to ask. Speed and convenience applied to the wrong question produce a confident wrong answer faster. The customer who compares home loans on interest rate alone misses the prepayment clause. The customer who has already decided to switch insurers asks questions that confirm the decision. The informed customer is powerful. The misinformed customer with AI is powerfully wrong.

The question itself is the unclaimed opportunity. No bank will build a question set that surfaces its own prepayment clause weaknesses. No insurer will build a question set that exposes its own claim-rejection ratio. This needs independent actors: non-profits, researchers, consumer bodies, who can make a GitHub for the questions consumers should be asking financial institutions.

What the Boardroom should debate

Every earlier wave of technology was institution first. Computing, internet, mobile was adopted and absorbed by the organisation and then passed onto the customer on their terms. AI exploded in consumers' hand, 100 million users in two months. The Board is now governing businesses where customers will know as much if not more than the organisation.

The first institutional response to AI has been operational, adding chatbots, analytical tools, dashboards, voice bots, service automation, that are good cost-saving initiatives. The strategic question is the viability of the business model itself.

What happens when the customer arrives informed? Which elements of the value proposition survive verification? Which revenue streams depend on customer ignorance or high search costs? Which promises would survive an AI audit? Which parts of the sales process assume an information asymmetry that no longer exists? These are not technology questions for the CTO. They are business-model questions for the Board.

The institutions that emerge stronger will not be those that adopted AI fastest. They will be those whose value remains after verification becomes cheap.

References

Akerlof, George A. (1970). "The Market for 'Lemons': Quality Uncertainty and the Market Mechanism". Quarterly Journal of Economics, 84(3), 488-500. https://www.jstor.org/stable/1879431

Brynjolfsson, Erik, Danielle Li and Lindsey R. Raymond (2023). "Generative AI at Work". NBER Working Paper 31161. https://www.nber.org/papers/w31161

Reuters / Similarweb (2023). ChatGPT user-growth figures. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/

Reserve Bank of India. Annual Report of the Ombudsman Scheme. https://www.rbi.org.in/Scripts/AnnualReportPublications.aspx

Maninder Singh Juneja is a partner at True North. He serves on the boards of Pine Labs, Nivara Home Finance and Integrace, and has previously served on the boards of Niva Bupa Health Insurance, Federal Bank Financial Services and HomeFirst Finance. The author thanks participants at a talk at XKDR Forum for myriad good ideas, and Ajay Shah, Renuka Sane and Aditi Mascarenhas for comments on earlier drafts.

Chinks in market efficiency: A Melody story

2026-06-04T13:55:17.533+05:30

by Ajay Shah and Atibhi Sharma.

The main paradigm in finance is the efficient market hypothesis which suggests that as soon as there is new information, the news is rapidly incorporated in the price. Markets are generally quite efficient, it's hard to find opportunities for supernormal returns.

But there are chinks in the armour. There are clear examples, worldwide, where market prices have been clearly wrong.

In this article, we show a recent Indian story which should be added into the Mistakes of Markets catalog. The share price of Parle Industries soared following the video of Italian prime minister Giorgio Meloni receiving a packet of Parle Products' Melody, gifted by Indian prime minister Narendra Modi. Parle Products, the maker of Melody, is an unlisted private entity. We also show some interesting global examples and offer some thoughts on understanding finance.

The "Signal" Ticker Confusion (2021)

In January 2021, after WhatsApp changed its privacy policy, Elon Musk tweeted 'Use Signal', referring to the free, open-source, and heavily encrypted messaging app. This pushed noise traders to purchase shares of Signal Advance (SIGL). The actual Signal app is managed by the non-profit Signal Foundation and is not publicly traded. Signal Advance, on the other hand, is a tiny medical detection devices manufacturer in Texas.

SIGL was an illiquid penny stock. It surged from \$0.60 to \$70.85 within three days—an 11,700% surge within three days. It took the markets six to nine months to return to pre-tweet levels.

Clubhouse Media Group (2021)

In the same month, on January 31, 2021, Elon Musk tweeted that he would be joining a room on Clubhouse, the then-viral invite-only audio chat application. The next day, shares of Clubhouse Media Group (CMGR), a penny stock entirely unrelated to the privately held Clubhouse app, surged. CMGR had recently rebranded itself as a marketing agency from a healthcare firm. The stock opened at \$10.85 on February 1, 2021, up from \$1.50 - \$2.00 from the previous days. It reached an all-time high of \$28.43 on February 15, 2021 (a near 1,400% increase from its pre-tweet baseline) and it took roughly four months to return to its pre-tweet level.

Zoom Video vs Zoom Technologies (2019-2020)

When Zoom Video Communications (ZM) filed for its IPO in 2019, investors rushed to buy shares, accidentally purchasing the shares of a defunct mobile phone parts manufacturer called Zoom Technologies (Ticker symbol: ZOOM), a penny stock trading at \$0.005. The stock surged 54,000% to around \$5. This confusion was not a one-off incident, as a similar trend was observed during the lockdown in the pandemic of 2020 when ZOOM went up 1,800% to \$20.90 until the US Securities and Exchange Commission physically intervened, suspending trading for Zoom Technologies for 10 business days and forced a ticker symbol change to ZTNO.

Bombay Oxygen Investments (April 2021)

During the second wave of COVID-19, when there was a shortage of medical oxygen in India, noise traders started looking for oxygen in the market. They landed on Bombay Oxygen Investments, a Non-Banking Financial Company that had exited the oxygen manufacturing business in August 2019 and had received RBI's registration certificate on December 31, 2019 for the same. The stock surged 131.3% in under 12 trading sessions, rising from Rs 11,025 on March 31 to Rs 25,500 intraday on April 20 and then, fell by ~ 50% to 12,700 in August of the same year.

L. G. Balakrishnan (2025)

In 2024, shares of LG Balakrishnan & Bros (a manufacturer of automotive chains) surged to a 52-week high because noise traders mistook it for the upcoming IPO of the consumer electronics giant LG Electronics India. The mispricing lasted only a day as investors realised their mistake.

Pan-Homophonic Events

Zhang et al. (2026) documented a new phenomenon of pan-homophonic events, where confusion between linguistics, trending keywords and stock names triggers sudden market volatility, specifically for a Chinese technology firm named Chuan-da-zhi-sheng. Since "Chuan-pu" is a loose and phonetic translation of Trump in Mandarin, noise traders phonetically interpreted the company's name to mean "Trump wins big". This unrelated traffic software stock essentially became a trading proxy for US political events. In 2016, when Donald Trump won the US presidential election, shares of the company surged 7.6% in a single day and then again in 2024, following major turning points in the US election cycle, the same stock repeatedly hit its maximum 10% daily upper circuit limit.

Ticker Confusion and the Limits of Arbitrage

Balashov and Nikiforov (2019) documented the systematic nature of these mix-ups. Investigating 254 pairs of stocks, they found that erroneous trades account for roughly 5% of all trading turnover in the smaller "shadow" companies. A classic example is Ford Motor Company (Ticker: F). Investors systematically assume its ticker is 'FORD' - which is actually the trading symbol for Forward Industries, a micro-cap manufacturer of carrying cases for medical devices. Similarly, a paper by Rashes (2001) studied the mass confusion between MCI Communications and a completely unrelated fund with the ticker MCIC. They found that while the co-movement between the two similarly-named stocks is statistically significant, it is not something that arbitrageurs can easily exploit because shorting an illiquid shadow company is costly.

Parle Industries

The Parle Industries episode is the latest entry in this ledger. Parle Industries is not a confectionary giant; it is a micro-cap company involved in infrastructure development, real estate, and paper waste recycling. Prior to the viral Modi-Meloni video, its market capitalization hovered around Rs.360 million, the price of a few apartments in Bombay. It was a penny stock with a share price of about Rs.5 and an average trading volume of 20,000 to 60,000 shares daily in the 6 months prior window.

9:30 AM (IST) - The Baseline: Market opened in India. Parle Industries opened at INR 4.95.
10:00 AM (IST) - The Event: Italian Prime Minister Giorgia Meloni uploaded a video to her official Instagram account and then her X account with the caption, "Thank you for the gift."
10:00 AM to 3:35 PM (IST) - Amplification: The video went viral. On X, the hashtag #Melodi trended, and the reel became the most viewed reel on PM Meloni's Instagram account. Simultaneously, Google Trends intraday data showed a spike: searches for "Parle share" and "Melody".
3:30 PM (IST) - Noise trading: By the market close, Parle Industries was locked into a 5% upper circuit closing at INR 5.25. It closed at a volume of 857,248 shares.

The stock hit the upper circuit for five consecutive trading sessions. BSE historical data shows that from May 21 through the end of the month, the stock's delivery percentage reached exactly 100%. The noise traders were taking delivery, believing it was worth holding this for multi day horizons.

Market Efficiency and the Role of Liquidity

These are examples of how prices can go wrong, exposing failures in market efficiency.

A better interpretation of market efficiency comes from focus on how clever people could exploit the mistakes of the noise traders. Here, we see the problems of financial market completeness (can you take an opposing trade?) and financial market liquidity (is the size of your winning trade big enough to matter?). A market inefficiency that is not exploitable will not be readily solved by the market. With small cap penny stocks there are no single stock derivatives that rational traders can short. In India, stock lending does not work so it is not possible to short sell and profit from the mistakes in the price. To the extent that better financial economic policy increases liquidity, it will, in turn, increase access to the correct tools for trading (single stock derivatives and stock lending). As a result, these problems will be diminished.

These problems are a reminder of the difficulties of small capitalisation stocks. Financial market trading works extremely well for large firms. We may perhaps apply a thumb rule in India of a minimum point of a market capitalisation of Rs.10 billion. But we do wrong to assume it is equally useful and equally effective for small firms. There is a certain social justice instinct in India, where we like to bring the glory of stock market listing to small firms, thinking that we are giving a helping hand to a weak firm. We need to be more cautious in the usefulness of this approach.

Financial markets are a remarkable information processing system. It is easy to disrespect the drama that is ceaselessly afoot. What is going on is that millions of clever people have been harnessed to constantly look at the world and make prices. These prices are the commanding heights of the economy and shape the resource allocation. Markets are not perfect, they are the best aggregation of what humans can figure out based on their self-interest.

Bibliography

Parle Industries' upper circuit to Signal's 5,100% surge: 5 mistaken stocks that triggered market frenzy, Surabhi Pandey, Moneycontrol, 20 May 2026.

#Melodi trends on X as PM Modi gifts Melody toffees to Italian PM Giorgia Meloni, DH Online, Deccan Herald, 20 May 2026.

190 Million And Counting: Meloni's Melody Moment With PM Modi Is Mega Viral, Abhinav Singh, NDTV, 21 May 2026.

Publicly Listed Zoom Video Communications: Traders Buying Zoom Technologies, Jonathan Garber, Markets Insider, 18 April 2019.

Want to Invest in the Zoom IPO? Make Sure You Buy ZM, Not ZOOM, Minda Zetlin, Inc.com, 18 April 2019.

Traders mistakenly invest in Clubhouse Media Group after Elon Musk tweets about a separate, private app with the same name, Natasha Dailey, Business Insider, 2 February 2021.

COVID-19: Bombay Oxygen shares up 256%; it doesn't even make oxygen, Business Today, 20 April 2021.

Massively Confused Investors Making Conspicuously Ignorant Choices (MCI-MCIC), Michael S. Rashes, The Journal of Finance, Vol. 56, No. 5, 2001.

How much do investors trade because of name/ticker confusion? Vadim S. Balashov and Andrei Nikiforov, Journal of Financial Markets, Vol. 46, 2019.

Quantifying the Linguistic Complexity of Pan-Homophonic Events in Stock Market Volatility Dynamics, Yunfan Zhang, Jingqian Tian, Yutong Zou, Xu Zhang, and Xiao Cai, Entropy vol. 28, no. 1, 12 January 2026.

Mistaken Identity: LG Balakrishnan Shares Surge as Investors Confuse It for LG Electronics India, Nishanth Vasudevan, Economic Times, 15 October 2025.

The authors are researchers at XKDR Forum. The authors would like to thank Susan Thomas, Amrita Agarwal, Aditi Mascarenhas and Jay Kulkarni for their valuable feedback and discussions on this piece.

A Market Failure Framework for Evaluating Public Sector Undertakings

2026-05-28T21:53:27.362+05:30

by Arjun Krishnan.

Commentators and investors often judge companies, including state-owned firms, by their profitability. While profit is a useful metric for private firms focused on generating returns for owners, applying the same standard to state-owned enterprises is problematic. Many lament that India's Public Sector Undertakings (PSUs) incur losses, assuming that losses signal failure and profits signal success. However, this assumption misjudges the real purpose of PSUs. Although some evaluation frameworks expand beyond profit, few explicitly align performance criteria with the specific market failure that the enterprise was established to address. This article argues that assessing PSUs solely on profitability is misguided, and their success should be measured by how well they address the public purpose for which they were created.

This article proposes a two-part framework for evaluating PSUs. The first part poses an ex-ante question about purpose. A PSU is justified only when it aims to correct a market failure that less intrusive instruments cannot correct. The second part poses an ex-post question about performance. Evaluators should then judge a justified PSU on two dimensions: efficiency and effectiveness. Efficiency measures how productively an enterprise converts resources into outputs. Effectiveness captures whether the PSU actually corrects the failure it was created to address. Balance sheets cannot serve as a reliable proxy for either dimension on their own.

Ex-ante: when is a PSU justified?

The justification for any PSU must begin with market failure. When markets function well, they allocate resources efficiently, and the state has no grounds to intervene. Economists identify four situations where markets fail to do so. First, externalities arise when a cost or benefit of an economic activity falls on an unrelated third party. Positive externalities lead to underprovision, and negative externalities to overproduction. Second, public goods are non-excludable and non-rivalrous. Firms cannot easily charge users, and private markets typically underprovide them. Third, information asymmetry occurs when one party to a transaction knows more than the other, distorting decisions and reducing market efficiency. Fourth, market power arises when limited competition allows firms to raise prices or restrict output below socially optimal levels.

A market failure on its own does not justify a PSU. There are three additional tests. First, scale: how many people are affected, and by how much? A localised information asymmetry in a niche market is different from one that excludes millions from credit. Second, persistence: is the failure temporary and self-correcting, or structurally durable? Markets sometimes endogenously mitigate their own failures through competition, reputation, or contracting. Exogenous forces such as technological innovation or institutional adaptation can have the same effect. Non-state mechanisms such as industry associations, cooperatives, or third-party certifiers may emerge to address coordination problems or information asymmetries without government ownership. Even classical public-good cases have been addressed without state ownership. Coase's (1974) account of English lighthouses is a canonical illustration: what was treated in classical economics as a pure public good requiring state provision was, in fact, supplied for centuries by Trinity House, a private body that collected dues from ships at port. The presence of such adaptive mechanisms weakens the case for a PSU. By contrast, failures that persist despite opportunities for institutional adaptation present a stronger case for public intervention.

Even when a market failure is large-scale and persistent, the state has many ways to respond. The third test, then, is instrument choice: Is ownership the right way to address this failure? The state can regulate, tax, subsidise, or contract with private providers. Ownership is one of the most costly options. Ownership exposes the exchequer to operating losses, creates a vehicle vulnerable to political capture, and softens the budget constraint in ways regulation and subsidy do not. The case for ownership has weakened with experience. Publicly owned natural monopolies in many sectors turned out to deliver less output for a given level of inputs than the textbook treatment had suggested. Regulatory practice has grown more sophisticated, with sector-specific knowledge and administrative law tools that did not exist when many PSUs were created. Public-private partnerships have further narrowed the cases for state ownership, with private firms providing goods and services under contracts that set market structure, pricing, and quality. For every PSU, the central question is why the problem could not be addressed through one of these alternatives.

India operates 291 Central Public Sector Enterprises across sectors as varied as petroleum refining, power transmission, hotel management, and defence manufacturing. The policy debate about this universe has been conducted in terms of profitability. How many are loss-making? What do aggregate losses cost the exchequer? These questions are downstream of a prior one: which market failure, if any, justifies each enterprise. Those that address no market failure have no business existing and should be sold off to buyers or wound down, with assets liquidated. Those that aim to correct a market failure face a harder question: how well do they perform in addressing the failure they are expected to correct?

Ex-post: efficiency and effectiveness

Two questions emerge for judging how well a PSU is performing. The first is efficiency. Efficiency is the ability to derive the greatest possible output from a given quantity of financial, physical, and human resources. The second is effectiveness. Since a PSU is justified only for market failures, we need to evaluate whether it, in fact, corrects the failure it was created to address. A PSU can be efficient at producing what a competitive market would produce anyway, or effective at reaching its target population at three times the cost a regulated private operator would charge. Both outcomes are undesirable. In the first case, the PSU adds little social value. In the second, it imposes unnecessary costs to achieve a legitimate public objective.

Experience with direct public provision over the last half-century has weakened the case for PSUs on efficiency grounds. Publicly owned natural monopolies in many sectors exhibited poor x-efficiency, producing less output for a unit of input than private firms. The Ministry of Road Transport and Highways reports that the revenue-to-cost ratio for the 58 reporting undertakings fell to 63.6% in 2021-22, that state cabinets blocked fare revisions, and that the resulting losses reached Rs 30,192 crore in aggregate. The mobility problem the SRTUs were created to address is real, but the case for state ownership of the operator is much weaker than the case for state involvement in the sector through options like subsidies.

The regulatory and contracting alternatives to ownership have grown more capable over the same decades. The regulatory state now possesses sector-specific tariff and quality regulation, administrative law procedures for rule-making, and incentive-compatible contracting techniques (Laffont and Tirole, 1993). Public-Private Partnership (PPP) models have spread across sectors once considered the natural home of direct provision. Iossa and Martimort (2015) show that bundling construction and operation into a PPP can be efficient when build quality materially lowers operating costs. This structure is common in roads, water systems, and many public utilities. Even classical public goods, including urban streets and water supply, are now routinely constructed and operated through PPP agreements that specify quality and pricing. The Government of India's disinvestment policy lists market imperfections and public purpose as criteria for retaining a PSU in public hands. The government excludes profitability as a criterion.

For certain types of goods, state ownership may be the preferable option. When contract terms cannot specify aspects such as quality, the case for ownership over contracting strengthens (Hart, Shleifer, and Vishny, 1997). A private operator paid to deliver an output will cut costs along whatever margins the contract leaves unspecified. Where quality is one of those margins, the cost saving comes at the consumer's expense. A private prison contractor's contract may specify calorie counts and dietary variety, but regulating food quality or taste may prove impossible. These savings flow to the contractor while the welfare loss falls on inmates. Direct public ownership is preferable in such settings precisely because the public manager's weaker incentive to cut costs leaves the unspecified quality dimensions intact.

In addition to efficiency, effectiveness needs to be judged. Effectiveness measures whether the PSU is correcting the market failure it was created to address. To illustrate the difference, consider a state-owned bus operator tasked with providing transport connectivity to remote rural areas. An efficient operator minimises the resources needed to run the service. An effective operator ensures that the targeted rural communities are actually connected. A PSU may succeed on one dimension while failing on the other.

The effectiveness criteria take different forms across failure types because the welfare yardstick differs. The market power row needs some additional explanation. A monopolist with declining average costs cannot price at marginal cost without losses. Ramsey-Boiteux pricing sets the loss-minimising alternative: markups above marginal cost should rise as demand elasticity falls, placing the heaviest charges on users whose consumption is least price-sensitive. A markup on inelastic demand reduces output the least and destroys the least surplus per rupee of revenue. A political cross-subsidy structure follows a different logic, allocating markups across user groups by political weight rather than by elasticity. The effectiveness test for a PSU that disciplines market power, therefore, asks whether its markup structure approximates Ramsey-Boiteux rather than political cross-subsidy.

Table 1 sets out the effectiveness criterion for each market failure, with the less intrusive instrument serving as the comparator.

Table 1: Effectiveness criteria for PSUs by type of market failure

Market failure	Market problem	Role of PSU	Effectiveness criterion	Less intrusive instrument
Externalities (positive)	Producers cannot capture the full social benefit, so the private market undersupplies relative to the social optimum.	Produce at a level that accounts for spillovers private producers ignore, or finance investments whose social returns exceed appropriable private returns.	Is the targeted output being delivered, and is the additional supply above the private optimum sufficient to close the externality gap?	Production subsidies, tax credits, intellectual property protection, advance market commitments.
Externalities (negative)	Private producers impose costs on third parties they do not bear, so the market overproduces relative to the social optimum.	Produce at a level that internalises external costs private producers would otherwise externalise.	Has the targeted reduction in harm been achieved, and are emissions per unit of output below the unregulated counterfactual?	Pigouvian tax, tradable permits, command regulation.
Public goods	Private producers cannot exclude users from a non-rivalrous good, so the market undersupplies or fails to supply.	Provide the good where private cost recovery is impossible or inefficient.	Is the service reaching the target population, and is coverage approaching the welfare-maximising level?	Contracting with private providers under a public service obligation.
Information asymmetry (seller knows more)	Private sellers hold information about quality that buyers cannot observe, so low-quality goods crowd out high-quality ones.	Enter the market and disclose costs, quality, and pricing as a benchmark that private sellers would otherwise suppress.	Has the PSU's presence made quality observable to buyers and sustained transactions that would otherwise have unravelled?	Mandatory disclosure regulation, third-party certification, independent benchmarking authority.
Information asymmetry (buyer knows more)	Buyers hold private information about themselves that sellers cannot verify. Sellers respond by raising prices, rationing, or withdrawing supply.	Offer service to groups private firms avoid because they cannot distinguish high-risk from low-risk customers.	Is the PSU enrolling the high-risk groups private markets exclude, and is the share of high-risk individuals covered higher than under the private counterfactual?	Risk-pooling mandates, mandatory insurance schemes.
Market power	Private firms price above competitive levels or restrict output below the social optimum.	Compete to discipline private pricing. In a natural-monopoly case, price at the welfare-optimal level.	Is the PSU pricing closer to marginal cost than an unregulated monopolist would, and does the markup structure approximate Ramsey-Boiteux rather than political cross-subsidy?	Competition law and antitrust enforcement, sectoral price regulation, separation of monopoly network from competitive services.

A PSU can fail on either dimension or both, and each case calls for a different response. A state-owned bus operator that abandons remote routes for crowded urban corridors is efficiently delivering something the market can deliver. Efficient delivery of a service that the market would have provided is no justification for state ownership. A PSU that effectively corrects a market failure but does so at an unjustified cost may generate more welfare loss through waste than welfare gain from correcting the failure. Reform or contracting out may be the appropriate response.

Soft budget constraints

PSUs operate under what Kornai (1998) called a soft budget constraint. A private firm that runs at a persistent loss is likely to go bankrupt. A PSU does not, because the PSU expects the state to cover the shortfall. The expectation of rescue weakens the discipline that revenues and costs would otherwise impose on managers.

A PSU pursuing a legitimate mandate should not show sustained accounting losses on its own books. Where pricing reflects deliberate policy, including selling output below cost for welfare reasons, the resulting deficit is a transfer from the treasury to the consumer. The right place for that transfer is the government's expenditure account, recorded as an explicit subsidy and matched by income on the PSU's accounts. Accounting separation keeps the cost of social policy visible in the budget, where parliamentarians can scrutinise it, rather than in an enterprise's operating accounts.

The Food Corporation of India illustrates what happens when this discipline breaks down. The Corporation procures grain at minimum support prices set by the Cabinet, stores it, and supplies it to ration shops at prices well below procurement cost. The shortfall is intended to be transferred to FCI as a food subsidy from the union budget. However, for long stretches, the government did not transfer the full subsidy in time, and FCI raised debt, much of it from the National Small Savings Fund, to bridge the gap. This practice was especially prevalent between 2016 and 2021. The losses that appeared on FCI's books reflected the failure to transfer the subsidy on time. The government used FCI's balance sheet to delay recognition of expenditure that should have appeared in the budget.

The existence of PSUs can then soften budget constraints in two ways. First, if a PSU knows it can rely on transfers from the treasury to cover any shortfall, its managers have less incentive to contain costs than managers of a private firm would. As a result, the PSU's budget constraint is softened. Second, PSUs like FCI soften the budget constraint of the government that created them. Since the enterprise absorbs costs that should have appeared in the budget, the state's social spending is understated. Once this practice exists, the headline profit or loss of a PSU carries less information. A loss-making PSU may be one that delivered the mandate but did not receive the subsidy. A loss-making PSU may also be wasteful. Which case applies cannot be understood based on the profit and loss account. Persistent losses on a PSU's books are therefore a useful diagnostic. They suggest either operational inefficiency or off-budget accounting through the PSU's balance sheet.

The framework, so far, treats PSUs as faithfully pursuing their mandates. They often do not. Even an enterprise with a legitimate market failure justification is run by people with their own interests. Governments are themselves made up of self-interested actors, and political objectives can capture an enterprise created to address a market failure. These government failures show up in the performance criteria above: prices far from welfare-optimal levels, investments that do not deliver the promised social benefits, or operations that consume more inputs than the next-best instrument would have required.

Government failure also shapes how the state manages exit from enterprises that have outlived their justification. A framework that identifies when a PSU has no reason to exist is useful only if exit decisions follow honestly from that assessment. Chakrabarty (2023, page 9) finds that around 43% of India's disinvestment proceeds between 1991 and 2022 involved no actual transfer to private hands. Shares were transferred between public entities, and the proceeds were counted towards the disinvestment target without any change in underlying ownership. Even where the case for a PSU has lapsed, political incentives corrupt the exit process and sustain enterprises that should not exist.

Applying the framework

Consider three applications of the framework. First, the electricity transmission network exhibits natural-monopoly characteristics, with high fixed costs and declining average costs over the relevant output range. State intervention to address market power is justified ex-ante, whether through regulated private ownership or direct public ownership of the grid operator. Power Grid Corporation of India is a PSU that runs the inter-state grid. From the standpoint of allocative efficiency, tariffs should be set close to marginal cost. Because the marginal cost of transmission is below the long-run average cost, such pricing would generate a structural revenue deficit. A deficit of this kind would not necessarily signal operational inefficiency. It can reflect a deliberate tariff policy that expands access and maximises network use, and it would be justified where it represents the least-cost route to the social objective when compared with direct transfers or alternative subsidy mechanisms. India does not price transmission at marginal cost. Tariffs are set under the CERC Tariff Regulations, 2024, which use a cost-plus framework. For new transmission projects, Regulation 30(3) provides a base return on equity of 15%, along with recovery of interest costs, depreciation, interest on working capital, and operating and maintenance expenses. The PSU's operational record is strong: the transmission system was available 99.85% of the time in FY24 across 1,77,699 circuit kilometres carrying around half of India's inter-state electricity. Whether the cross-subsidy implicit in cost-plus regulation is the least-cost route to network expansion, or whether a direct transfer would deliver the same access at lower fiscal cost, is a question worth asking. Power Grid no longer builds new lines by default. The regulator auctions each new project to the lowest bidder, and Power Grid competes for these contracts alongside Adani, Sterlite and Tata Power. Private bidders win a growing share. Whether the legacy network would also be cheaper in private hands is a separate question. The framework's verdict on Power Grid therefore turns on an empirical question: whether the cost-plus regulation of the legacy network delivers cheaper transmission than competitive procurement would.

Second, the India Tourism Development Corporation (ITDC) runs the Ashok Group of Hotels. ITDC was established in 1966 to develop tourist infrastructure, including hotels. The Taj, Oberoi, ITC, Lemon Tree and Marriott chains, among others, operate across the segments and price points ITDC serves. Hotels are not a public good. Private operators can charge customers, competition is adequate, and there are no externalities, information failure, or market power problems that require the state to own a hotel chain. The market failure test fails at the first step, so no ex-post analysis is needed.

A third example concerns the post-independence wave of public investment in heavy industry and infrastructure. The standard defence of state ownership in this period rested on the absence of capital markets: long-term finance was scarce, and only the state could mobilise it. Bhagwati and Desai (1970) contested this claim, arguing that private capital existed and that the licensing regime was producing the shortages it purported to remedy. Whatever the merits of the original argument, India's capital markets have since deepened. A second defence rests on positive externalities through learning effects and supply-chain spillovers, where social returns exceed what a private investor can appropriate. Underinvestment results from this appropriability gap, even when capital is available. The defence still has limits. Where production subsidies, intellectual property protection, or advance market commitments can close the gap, ownership is the costlier instrument. A surviving PSU founded on these grounds must show that such alternatives remain inadequate.

India's early integrated steel plants at Rourkela, Bhilai, Durgapur, and Bokaro are concrete cases. The plants were justified on grounds that private firms could not raise the long-term capital required, and that they would generate large downstream spillovers through skilled labour, supplier networks, and engineering capabilities whose full social value private investors could not capture. The appropriability gap was real, and thin capital markets compounded the problem by raising the cost of private investment. Both conditions have since changed. India's capital markets have deepened, project finance has matured, and Tata Steel and JSW Steel have built modern integrated capacity at scale. The spillovers that public investment was meant to generate now flow through the private steel industry instead. SAIL, the operator of the original plants, produces around 15% of Indian steel and is profitable. Profitability does not save it from the framework's test. Where private operators produce the same steels at a comparable scale, the market failure has been resolved, and continued public ownership no longer has a justification.

The way forward

Profit is the wrong measure for judging a PSU. It speaks neither to whether the enterprise should exist nor to whether it is doing what it exists to do. For each of India's 291 central PSUs and more than a thousand at the state level, the question is whether a market failure persists, whether ownership is the cheapest way to address it, and whether the enterprise actually does so. Some profitable PSUs would fail this test. Some loss-making ones would pass.

References

India: Planning for Industrialization: Industrialization and Trade Policies Since 1951 by Bhagwati J and Desai P, 1970, Oxford University Press for OECD Development Centre.

On the Management of Public Monopolies Subject to Budgetary Constraints by Boiteux M, 1971, Journal of Economic Theory, 3(3), 219 to 240.

The Lighthouse in Economics by Coase R H, 1974, Journal of Law and Economics, 17(2), 357 to 376.

The Proper Scope of Government: Theory and an Application to Prisons by Hart O, Shleifer A and Vishny R W, 1997, Quarterly Journal of Economics, 112(4), 1127 to 1161.

The Simple Microeconomics of Public-Private Partnerships by Iossa E and Martimort D, 2015, Journal of Public Economic Theory, 17(1), 4 to 48.

In Service of the Republic: The Art and Science of Economic Policy by Kelkar V and Shah A, 2019, Penguin Allen Lane.

The Place of the Soft Budget Constraint Syndrome in Economic Theory by Kornai J, 1998, Journal of Comparative Economics, 26(1), 11 to 17.

A Theory of Incentives in Procurement and Regulation by Laffont J-J and Tirole J, 1993, MIT Press.

Allocative Efficiency vs. "X-Efficiency" by Leibenstein H, 1966, American Economic Review, 56(3), 392 to 415.

State versus Private Ownership by Shleifer A, 1998, Journal of Economic Perspectives, 12(4), 133 to 150.

Arjun Krishnan is a consultant at the Centre for Civil Society, a Delhi-based think tank. He thanks Sourya Banerjee for the early conversations that inspired this article, Jayana Bedi for her thoughtful feedback during its drafting, and an anonymous referee whose comments considerably sharpened the argument.

Words and deeds in the Indian exchange rate

2026-05-19T11:01:06.883+05:30

by Rajeswari Sengupta and Ajay Shah.

The most important price in any economy is the exchange rate. In India's case, this is the price of the Indian rupee against the US dollar. By default, the exchange rate is controlled by market forces. The policy stance of the government, towards the exchange rate, is termed 'the exchange rate regime'. This is one of the most consequential economic policy choices.

In most advanced economies, the answer is straightforward: the exchange rate is set by financial markets, and the government stays out. In India, it is more complicated. The RBI regularly intervenes in the foreign exchange market. India's exchange rate regime needs to be deciphered from the data using statistical tools.

At any point in time, to understand the Indian economy, knowing the present exchange rate regime is central. Looking back at economic history, knowing the dates and characteristics of the changing exchange rate regime is central.

Inferring the true exchange rate regime from the data

We now have mature tools for deciphering the exchange rate regime using exchange rate data, without requiring information about the actions taken by the government. This runs in two steps: the first is a sweet linear regression called 'the exchange rate regression' and the second is the econometrics of structural change through which structural breaks in the regression coefficients and the residual standard deviation are detected. This idea for structural breaks in linear regression models where the residual standard deviation can also change is taken from Zeileis, Shah and Patnaik (2010) and implemented in the R package fxregime which now has numerous applications into fields well beyond exchange rate regimes and structural change.

In this article we will first rev up this tool chain for the Indian rupee, offering measures of the present exchange rate regime and of the history of Indian macroeconomic policy. We will then turn to a comparison against RBI and IMF statements about the Indian exchange rate regime. Finally, we will offer ready access to reproducible research so that everyone can perform these calculations.

Reading the data: six distinct regimes since 2000

The exchange rate regression estimates how much of the movement in the rupee is explained by movements in the world's major floating currencies - the US dollar, the euro, the British pound, and the Japanese yen. The greater the role of these foreign currencies in explaining the rupee's movement, the less independently the rupee is floating. Alongside this, we get the residual standard deviation: the extent to which the movements of the rupee reflect none of the above. The dates of structural breaks mark the boundaries between different exchange rate regimes.

We apply this method to weekly exchange rate data from the BIS, covering the period from 1 January, 2000 to the most recent data available at the time of publication (15 May, 2026). The analysis shows six distinct exchange rate regimes. This gives us an updated version of the knowledge in Patnaik and Sengupta (2022) and Pandey, Patnaik and Sengupta (2024).

Figure 1: USD/INR exchange rate with structural breaks.

Figure 2: Annualised volatility of USD/INR (6 month rolling window) with structural breaks.

The six periods are as follows:

Regime 1 (14 January 2000 - 19 March 2004): This was a tight peg to the dollar. The rupee moved very little independently. The annualised INR-USD volatility averaged just 2.2%.

Regime 2 (26 March 2004 - 16 March 2007): This was a move towards greater flexibility. The rupee was moderately pegged to a basket of currencies. The volatility rose to 4.1%.

Regime 3 (23 March 2007 - 13 December 2013): This was the most flexible period in the 25 years under examination. This was the era when India came closest to a genuinely market-determined exchange rate. The USD/INR volatility was 8.7%. There were many months in this period where RBI trading on the currency market was 0. This gives us an interesting conjecture: If the rupee were to float, it would have an annualised vol of about 9%.

Regime 4 (20 December 2013 - 25 August 2023): This was a retreat to greater currency management. This was the longest single regime in our sample - nearly a decade. The INR-USD volatility fell back to 5%, and the RBI's interventions in the foreign exchange market grew steadily. It is ironic that inflation targeting came into India in February 2015, with the signing of the Monetary Policy Framework Agreement. This was roughly the same time that rupee flexibility was in retreat.

Regime 5 (1 September 2023 - 20 December 2024): A remarkable de-facto peg; the lowest volatility in 25 years. For this 15-month period, INR-USD volatility was just 1.5%: the lowest in our 25-year sample, lower even than Regime 1 which reflected the macroeconomics knowledge of long ago. The rupee barely moved against the dollar, even as other emerging market currencies fluctuated.

Regime 6 (27 December 2024 - 15 May 2026): Finally, we got a partial retreat from the peg. Volatility rose to 5%, comparable to Regime 4. In our analysis, this regime ends on 15 May 2026, because that is the latest available data.

What the RBI says

In 1993, India officially moved towards a "market-determined exchange rate". The RBI website states that its "exchange rate policy focuses on ensuring orderly conditions in the foreign exchange market" - implying that it intervenes only to prevent excessive volatility, not to target any particular level of the rupee.

The empirical evidence, however, shows that the Indian economy experienced six different exchange rate regimes without any changes in official statements, announcements, or rationale.

What the IMF says

The International Monetary Fund, which classifies every member country's exchange rate regime every year, had long described India's regime as "floating", noting that the rupee is "largely market determined" and that the RBI intervenes only to manage "excessive volatility". The IMF classification does not see the six regimes that the data reports.

Figure 3: USD/INR exchange rate with regime classification from IMF AREAER.

The econometrics of structural change shows the recent nearly-fixed exchange rate regime as running from 1 September 2023 - 20 December 2024. This event was so large and remarkable that the IMF picked it up. In its 2023 Annual Report on Exchange Arrangements and Exchange Restrictions (released in December 2024), the IMF reclassified India's de-facto exchange rate regime retroactively:

"Since December 2022, the exchange rate stabilized within a 2% band against the US dollar, with one realignment in August 2023. Therefore, the de facto exchange rate arrangement was reclassified retroactively to 'stabilized' from 'floating', effective December 6, 2022."

The structural change econometrics picks up different dates compared with these statements. The statistical techniques isolate precise dates for structural breaks down to the week, in contrast to the IMF classification, which is updated annually.

The centrality of the exchange rate regime in the Impossible Trinity

The Impossible Trinity is a foundational concept in economics. It states that a country can achieve at most two of the following three objectives simultaneously: an open capital account (allowing money to flow freely in and out of the country), a fixed or managed exchange rate, and an independent monetary policy. It is impossible to have all three at once.

India adopted inflation targeting in February 2015, which means it chose to have autonomy in domestic monetary policy with the legal mandate to keep CPI inflation at 4%. India also has a substantially open capital account after three decades of gradual liberalisation. According to the Trilemma, these two choices leave no room for a managed exchange rate. India cannot simultaneously target 4% inflation, maintain an open capital account, and stabilise the rupee against the dollar.

Yet the data show that from late 2022 to late 2024, that is what the RBI attempted to do. In this period, India's nominal anchor - what the monetary system was supposed to be anchored to - quietly shifted from the inflation target to the exchange rate. In effect, RBI's legal mandate under IT was temporarily displaced by an unannounced exchange rate objective. These attempts induce many difficulties; financial restrictions impeded economic growth, and inflation was excessively volatile owing to the pursuit of extraneous objectives.

Macroeconomic stability requires credibility of monetary policy. Under inflation targeting, the authorities must say what they will do, and then do what they just said. Even if all the right things are done immediately, it would take decades for private persons to learn to trust that there is a stable framework of macroeconomic policy.

It is easy to do these calculations

We have made this analysis a self-contained Google Colab notebook, which can be used by you to do runs or classroom teaching.

References

Radhika Pandey, Ila Patnaik and Rajeswari Sengupta (2024) "The journey of inflation targeting in India," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2024-022, Indira Gandhi Institute of Development Research, Mumbai, India.

Patnaik, Ila and Rajeswari Sengupta (2022) "Analyzing India's Exchange Rate Regime," India Policy Forum, National Council of Applied Economic Research, vol. 18(1), pages 53-85.

Zeileis, Achim, Ajay Shah and Ila Patnaik (2010) "Testing, monitoring, and dating structural changes in exchange rate regimes", Computational Statistics & Data Analysis, Volume 54, Issue 6.

The authors are researchers at IGIDR, Bombay and XKDR Forum, Bombay, respectively. The authors thank Rounak Hande for excellent research assistance with the data and analysis, and Anjali Sharma for valuable discussions and comments.

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

2026-05-11T13:40:32.463+05:30

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:

SEBI Orders: Final adjudicatory orders; $N = 176$ firm-event pairs.
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:

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).
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.
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.
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.

Announcements

2026-04-15T20:27:39.461+05:30

Call for papers: Alt Data Workshop 2026

Date: 21st & 22nd August 2026

Organisers: Chennai Mathematical Institute and XKDR Forum
Venue: XKDR Forum, Mumbai, India

Mode: In-Person

Overview

The Alt Data Workshop 2026 focuses on the use of "alternative data" -- the remarkable world of novel and clever data sources bubbling up outside the traditional official statistical machinery.

We seek to bridge the gap between STEM researchers (Computer Science, Statistics, Engineering) and domain experts in Economics, Public Policy, and Law. We will de-prioritise data from one-off field experiments and emphasise the use of widely available datasets that are predictably updated through time thus permitting long-term longitudinal research and the development of a research community surrounding each dataset. Examples of this include satellite imagery, digital footprints, high-frequency transaction data, and large-scale web-scraping to solve complex problems in the Indian context. Some examples are at https://www.xkdr.org/field/statistics-computer-science

The Scope of Alternative Data

For the purposes of this workshop, alternative data refers to non-traditional datasets that are, in principle, accessible to the broader research community on a sustained basis over the years, through which a literature can emerge on one dataset at a time.

We prioritise papers that address:

Methodological Rigor: Novel approaches to ground-truthing, signal extraction from noisy data, and bias correction.
Scalability: Techniques that can be applied across regions or time periods rather than isolated case studies.
Substantive Applications: Practical use in agricultural monitoring, urban heat stress, fiscal analytics, or market microstructure.

Themes and Topics

We welcome submissions from STEM and social science researchers on topics including, but not limited to:

Remote Sensing: Applications of satellite imagery for economic activity or environmental monitoring.
Digital Footprints: Analysis of transaction, payment, or transport data.
Automated Data Collection: Large-scale web scraping and crowdsourcing frameworks.
High-Frequency Proxies: Using alternative indicators to track fiscal or macroeconomic variables in real-time.
Computational Infrastructure: Engineering challenges in processing and storing large-scale alternative datasets for public policy.

Submission Guidelines

We invite both formal academic papers and industry-led technical talks.

Abstracts: Maximum 500 words. Must clearly state the research question, data sources, methodology, and results.
Full Papers: Maximum 10,000 words.
Format: Submissions should be in PDF. Include a separate cover page with author names, affiliations, and contact details.
Submission Portal: Submission Form

Review Process

Submissions will be reviewed for originality, technical rigor, and relevance. We value work that is reproducible and contributes to the public discourse on data-driven governance.

Review Committee

Sourish Das - Chennai Mathematical Institute
Rajeeva Karandikar - Chennai Mathematical Institute
Ayush Patnaik - XKDR Forum
Ajay Shah - XKDR Forum
Susan Thomas - XKDR Forum
Akhilesh Tilotia - Thurro
Mahesh Vyas - Centre for Monitoring Indian Economy

Travel Funding

We will fund authors and discussants to stay in Mumbai for two nights.

Important Dates

Submission Deadline: 15 May 2026
Notification of Acceptance: 15 June 2026
Full Paper/Presentation Submission: 31 July 2026
Workshop Date: 21 August 2026

Contact

Email: outreach@xkdr.org

Web: https://www.xkdr.org/event/alt-data-workshop-2026

Announcements

2026-04-14T15:13:23.984+05:30

Call for Papers: Cross-Border Flows and Frictions in India

Dates: 03-05 July, 2026
Venue: Goa, India
Mode: In-person

Overview

XKDR Forum invite submissions for its upcoming conference on â€œCross-Border Flows and Frictions in Indiaâ€ The conference aims to bring together academics, market participants, legal experts and policymakers to examine the empirical, institutional, and legal foundations shaping the case for a more open and resilient capital account in India. It proceeds from the view that cross-border flows are central to Indiaâ€™s financial development. The conference will feature original research papers, thematic talks, and panel discussions.

Submissions are encouraged that advance theoretical, empirical, legal, or policy-oriented debates on the nature, sources, and consequences of cross-border frictions. Interdisciplinary approaches and comparative perspectives are particularly welcome.

Themes and Topics

The conference will focus on, but is not limited to, the following themes:

International Capital & Investment: Capital flows, home bias, push/pull factors; EM flow maladies; capital controls; corporate finance, and internationalisation of high-productivity firms, cross-border payments.
Exchange Rates & Currency Markets: Currency markets, risk, and institutions; exchange rate regimes; costs and benefits of managed rates.
Financial Stability & Crisis: Contagion from international crises; the impossible trinity; conventional Indian crisis management; systemic risk under open capital accounts.
Regulation, Compliance & Governance: Laws, rule of law, and public administration for capital controls; tax policy and administration; CFT/PMLA/FATF.

Submissions beyond these themes that align with the overall scope of the conference will also be considered.

Submission Guidelines - Only Abstract Essential

Abstract:
- 300 - 500 words
- Should clearly state the research question, methodology, and expected key findings/arguments.
Full Paper (if available):
- No word limit.
Submission Portal:
- https://forms.gle/iPBmDBcCiidQXdQG6

Review Process

All submissions will undergo a review by the program committee of the conference. Selection will be based on originality, rigor, and relevance to the conference themes.

Program Committee

Shubho Roy, Shiv Nadar University
Renuka Sane, TrustBridge
Rajeswari Sengupta, IGIDR
Manish Singh, IIT Roorkee
Ajay Shah, XKDR Forum
Susan Thomas, XKDR Forum
Harsh Vardhan, Independent
Bhargavi Zaveri-Shah, The Professeer

Funding

Paper presenters and discussants will be provided with travel support.

Important Dates

Abstract submission deadline: 15 May, 2026
Notification of acceptance: 31 May, 2026
Full paper & presentation slides submission: 15 June, 2026
Conference dates: 3-5 July, 2026

Contact

For queries, please contact:
E-mail: outreach@xkdr.org
Web: xkdr.org

Announcements

2026-04-13T14:10:11.339+05:30

Call for Papers: 17^th Emerging Markets Conference

13^th - 16^th December, 2026

XKDR Forum in collaboration with Vanderbilt law School is inviting papers to be submitted for the 17^th Emerging Markets Conference, 2026. In the past, the audience for these events has comprised of academics, participants from the legal and financial industry, policy makers from government and regulators.

Details of the previous conferences can be viewed at https://emergingmarketsconference.org/. The conference aims to cover presentations and discussions across the following set of research topics:

The sources of economic success or failure in EMs.
Finance in EMs (households, financial markets, financial intermediaries, firms and finance, finance and growth).
Political economy, law, public administration, regulation in EMs.
The impact of populism upon the possibility of sustained growth.
Insights into large EMs that matter in and of themselves.
Insights from narrow research projects that illuminate EMs in general.
The new phase of globalisation and its consequences for international trade, international finance and the nature of the EM firm.
Features of a society that enable or disable convergence into the 'normal' package of high levels of freedom and prosperity.
The puzzles faced by all kinds of decision makers: individuals, civil society actors, firms, all levels of government.
Grand challenges such as climate change: implications for EMs and ramifications of choices made in EMs.
State capability in EMs.
The interplay of military affairs, foreign policy and economics for EMs.

The ideal papers for EMC shed light on the great questions of the age, while being analytically sound and persuasive.

Conference design

For EMC 2026, we intend to bring on board a wider research papers, panels on contemporary policy and keynotes by experts in the area of finance, economics and law. The conference this year will be completely in - person mode.

Best Discussant Award

Each year, we award the Emerging Markets Conference discussant award for the best discussant and the first runner up discussant of the papers presented on each day of the EMC. The discussants are selected by an audience poll.

Program Committee

Adam Feibelman, Tulane University
Ajay Shah, XKDR Forum
Bidisha Chakraborty, Saint Louis University
Dan J Awrey, Cornell Law School
Harsh Vardhan, Independent
Indradeep Ghosh, Dvara Research
Joshua Felman, J. H. Consulting
Kose John, NYU Stern
Kumar Venkataraman, SMU â€“ Cox School of Business
Marios Panayides, The University of Oklahoma
N. Prabhala, Johns Hopkins University
Pab Jotikasthira, SMU - Edwin L Cox School of Business
Pradeep Yadav, The University of Oklahoma
Rambhadran Thirumalai, ISB
Rajeswari Sengupta, IGIDR
Renuka Sane, TrustBridge
Sanjay Kallapur, ISB
Susan Thomas, XKDR Forum
Tanika Chakraborty, IIM Calcutta
Vimal Balasubramaniam, Queen Mary University of London
Yesha Yadav, Vanderbilt University

Important dates

Paper submission deadline: 24^th August 2026.
Expected date for notification of acceptance: 30^th September 2026.
Dates of the conference: 13^th - 16^th December 2026.

Support

Academic authors whose papers have been accepted for the conference will be provided accommodation at the conference venue for three nights (13^th to 16^thDecember).

Registration and contact details

Submissions: Please submit your papers in pdf format by following this link here
For any clarifications, please reach out to Jyoti at announcements@emergingmarketsconference.org

Author: Natasha Aggarwal

2026-04-05T22:58:08.555+05:30

Natasha Aggarwal is a researcher at TrustBridge Rule of Law Foundation.

On this blog:

Comments on the Securities Market Code Bill, 2025, 1 April 2026.
Can technology augment order writing capacity at regulators?, 14 December 2025.
Return to sender: The misuse of remand orders by appellate tribunals, 2 October 2025.
Balancing Power and Accountability: An Evaluation of SEBI's Adjudication of Insider Trading, 2 April 2025.
A guide to writing good regulatory orders, 13 March 2025.
Mapping insider trading laws: A database for SEBI’s Prevention of Insider Trading Regulations, 14 February 2025.
Judicial overreach: Bypassing expert tribunals in the electricity sector, 9 December 2024.
The exercise of discretionary powers: The case of debarment and restraint from capital markets, 20 July 2024.

What happens when arbitration deadlines are missed

2026-04-02T10:50:00.007+05:30

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

2026-04-01T13:40:00.006+05:30

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,

How many projects have been announced and of them, how many have been implemented and completed? What is the time taken?
Given the projects currently in the pipeline, how likely is India to meet the 2030 targets?
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

2026-04-01T00:21:55.728+05:30

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 â€“ here's what's wrong with the Securities Markets Code' (ThePrint, 5 January 2026).

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

Gains from messy regulatory footprints

2026-03-27T09:06:00.002+05:30

by Amrita Agarwal and Ajay Shah.

The traditional view

Regulatory architecture is the design of regulatory agencies as a block diagram with a box for each agency, a clear problem statement for each agency, and a set of definitions about how the agencies interact. In the analysis of regulatory architecture in India, we generally think there should be full clarity on the regulatory perimeter (what activities are regulated) and on the state agency that is vested with the relevant regulatory power (who regulates what). It is believed that regulatory arbitrage is a bad thing. Firms should not be able to choose the regulator that they prefer, and firms should not be able to opt out of regulation by going to the edges of a poorly defined regulatory perimeter.

Consider the long journey to the Gold ETF (documented in Box 9.4 of Mistry, 2007). The Gold ETF was delayed by 5 years because all of the RBI, SEBI and FMC claimed jurisdiction over it. Reformers have long argued that regulatory architecture changes are required so as to eliminate such regulatory logjams.

In the conventional Indian discourse, a clean block diagram has been prized (Roy et. al. 2019). It is felt that there should be a simple diagram and then everyone knows where they stand. The firms then organise themselves to go to the right state agency and civil servants do not waste time fighting turf battles. State power is unambiguously defined, for any aspect of the coercive power of the state, private firms have clarity on who wields that power, private firms have no agency on these questions, there is no regulatory arbitrage. For the field of finance, FSLRC offered such a clean block diagram (FSLRC, 2013).

In this article, we explore limitations of this approach.

Dispersion of power

In the field of political science, the essential idea is that of dispersion of power, of checks and balances. The state performs better when power is contestable, when we `pit interest against interest' (Madison, 1788). Pure power becomes tyranny; checks and balances are the path to state capability.

This is the motivation for separation of powers (split the state vertically between the legislative, executive and judicial branches) and federalism (split the state between union, state government and city government). These give dispersion of power.

The checks and balances, the conflicts between these multiple elements of the state, is messy. But we get better outcomes out of this untidy mess than we would with concentration of power (and the associated clarity of who is in charge).

There is an interesting analogy in urban planning. Jane Jacobs (1961) and James C. Scott (1998) have emphasised that a highly legible, master-planned city is rarely a thriving one. A good city is teeming with a million kinds of thoughts and actions. It is a great city, but it's not easily understood. By pursuing simplicity of control and the ease of achieving state legibility (Scott, 1998) and state control, we don't get to a good society. Ultimately, we are after a great society, not a powerful state.

Three kinds of reasons favour epistemic pluralism in state building:

The world is complicated, and nobody knows what the correct state intervention is (Hayek, 1945). In this case, clearly handing over all the power to one state agency is less effective. It is better to have multiple different approaches by multiple agencies, which would yield more experimentation and diversity in the society. When one agency is doing something wrong, private persons benefit from having agency on going to another. Multiple agencies doing diverse things creates more knowledge as compared with one agency doing one thing.
Public choice theory shows us a causal pathway from greater power to reduced performance. When state personnel command more absolute power, there is a greater chance of going down pathways that suit the interests of the state and not the interests of the people.
Sometimes, there may not be a one size fits all regulatory strategy. There may be gains from having different government organisations approach things in diverse ways.

We should see the problem of agencies and their footprint in a more heterodox way. Instead of full clarity that all the power of X nature is to be wielded by Y agency only, and that all private persons must stand in line without flexibility or choice, would it help to have a greater blurring of the lines where multiple agencies overlap, including a role for fully unregulated arrangements? This could create better checks and balances.

If this approach is taken, there would be more experiments of alternative pathways to performing state functions and multiple government organisations would learn from these experiments. The people would be less controlled, they would have more choice on how to behave (Tiebout, 1956). This would generate better progress when compared with a monolithic approach.

Example: Hedge funds

The government believes it adds value by doing consumer protection for mutual funds. One could think of a single government regulatory system doing consumer protection that applies to all funds. But we don’t have to think like that. By the time a customer is bringing Rs.10M to a fund, there is no need for consumer protection.

This gives the `hedge fund' idea: The regulations must carve out a distinct industry, hedge funds, where the customer is obliged to bring in over Rs.10M. In India, we see this with the SEBI 2012 AIF regulations. This industry requires contract enforcement and prudential regulation (promises should be upheld, lying is not okay), and extremely large hedge funds can raise concerns about systemic risk regulation. But there is no case for consumer protection for hedge funds, which reduces the burden of regulation.

Once this is done, there will be competition in the eyes of some customers between the less regulated industry (hedge funds) vs. the more regulated industry (mutual funds). If regulation for consumer protection creates value, then all customers will be attracted to mutual funds. But if there are flaws in the regulation of mutual funds, the hedge fund industry will grow. The presence of less-regulated hedge funds is a constant counter-point to the consumer protection that’s sought to be done in mutual funds.

Example: Micro-prudential regulation vs. financial resolution

Consider the universal problem of a state-run financial resolution mechanism, the US FDIC and its ilk, vs the micro-prudential regulator. This is a messy world! On one hand, the micro-prudential regulator is charged with coercing the firm in ways that put a cap on its failure probability. But at the same time, the resolution corporation lurks over the firm, and unilaterally chooses when to force the closure of the firm.

One might think: Is it cleaner to have only one organisation that does these things? E.g. RBI that does micro-prudential regulation and also calls the shots on bankruptcy? This looks clean but it actually works poorly. The problems lie in public choice theory. The micro-prudential regulator does not like it when financial firms go bankrupt: these events show micro-prudential regulation in a poor light in public. So it has an incentive to ignore the problem and pretend things are okay. Only a fully arms length financial resolution, that does not consult the micro-prudential regulator when making the final decision, can get the job done right. The correct design inherently creates tension between two distinct government organisations.

Example: Company law in the US

Companies in the US can register in any state while operating in others. This creates competition between the company laws across states. This gives companies the ability to shop for their preferred legal regime. The state of Delaware has done well in modifying its laws and agencies so as to be more attractive to companies for incorporation, mergers & acquisitions, and exit. Some other states like Wyoming have followed this example and innovated on other aspects to provide a distinctive regime to attract companies.

This approach has created a rich tapestry of natural experiments which reveal the efficient frontier of how company law should work (Romano, 1993; Fisch, 2017). If there had been a single mechanism of company law for the full country, there would be less empirical learning.

Example: The US banking system

Banks in the USA have a choice between a National Charter, a State Charter and a State Non-Member status. They can switch between these when they so desire. A multi-state Bank may switch to a National Charter under the Office of the Comptroller of the Currency (OCC). An innovative local-oriented bank may choose a State charter under the state’s banking department and the Federal Reserve. The threat of banks switching also reshapes the incentives of each regulatory agency, where excesses of power will lead to flight of the regulated to a certain extent. This gives better flexibility and checks-and-balances when compared with a single national bank regulation system.

These benefits come with difficulties. Multiple alternative regulatory authorities (SEC vs. CFTC, OCC vs. Fed vs. FDIC) were part of a race to the bottom that led up to the crisis of 2008. In that period, Washington Mutual and AIG chose the under-resourced Office of Thrift Supervision (OTS) as its regulator. In response to the 2008 crisis, the Office of Thrift Supervision (OTS) was dismantled as part of the Dodd Frank Act (Granza et al, 2024).

Example: Recent thinking in Argentina

Javier Milei has emphasised that regulators are often captured by the existing players. Regulators obtain coercive power under the excuse of addressing market failure, but often use these powers in ways that hinder competition, creating a cosy profitable and less innovative equilibrium. One difficult pathway to address this is deeper regulatory reforms (akin to the FSLRC concepts of improving checks and balances and curbing the arbitrary power of regulators). Another pathway, that is being attempted in Argentina, is to subject the incumbent regulator and industry to the competitive pressure of alternative regulatory regimes.

Milei’s Minister of Deregulation, Federico Sturzenegger, has argued that economic reform needs to identify many points where a bureaucrat or a union has the power to say "No" and introduce an alternative pathway where the market can say "Yes" without them.

Towards these objectives, in 2023, the Amendment to the Civil and Commercial Code (Decree 70/2023) enforced a strict ‘freedom of contract’ settlement via their currency of choice - US dollars, cryptocurrency or even commodities. This gives choice back to the people and removes the monopoly of the local central bank for controlling local transactions or introducing cross border capital controls.

Similarly, the Fondo de Cese Laboral (Employment Severance Fund, Decree 847/2024) gives employers the choice to replace the traditional lump-sum expensive and uncertain litigation prone severance regulatory framework with a pre-funded predictable payout. This creates an alternative mechanism that firms in agreement with their labour union can choose to adopt if they feel it is superior in their context. This is playing out on the ground with each firm and labour union making their choice.

It is too early to tell whether Milei’s reforms will succeed in restoring high economic growth to Argentina. But they illustrate the ideas of the present article: When faced with a monolithic incumbent regime, there is value in creating ambiguity and flexibility about the regulatory perimeter and the power of each authority.

Example: Charter cities, Hong Kong and GIFT City

While nobody planned it this way, the fact that the British retained control of Hong Kong in 1949 created the possibility of forum shopping for private persons. If communist China did well in certain respects, individuals and firms could choose to locate in China. But if British style liberal democracy worked well in certain respects, individuals and firms could choose to locate in Hong Kong.

In the event, we know that Hong Kong worked out much better than China, to the point where it became an embarrassment for the CCP. But in the years where Hong Kong was a genuine alternative (roughly 1949-2015), it gave private persons a choice: to be ruled by one kind of government or another. The presence of this alternative made a major positive impact on China’s trajectory. The removal of this alternative has had an adverse impact on China’s trajectory.

Paul Romer has extended this idea into a more general possibility of establishing `Charter Cities’ where first world liberal democracies run enclaves in poor countries, and then individuals and firms get a choice about what kind of government they prefer.

Potentially, GIFT City can evolve towards a more first world governance style, and then it would become a counterpoint to conventional Indian thinking on how financial law and regulation works. From this perspective, the role that has been given to incumbent regulators in the governance of GIFT City represents a limitation to the possibilities of GIFT City emerging as a competitive rival to mainstream Indian state mechanisms on financial economic policy.

In each of these three settings -- Hong Kong vs. PRC, Charter Cities vs. developing country host, GIFT City vs. conventional Indian financial economic policy -- we see the gains from multiple choices being available to private persons as opposed to a simple monolithic state, where the people are crushed, where there is no possibility of forum shopping.

Epistemic pluralism as a consideration in agency architecture

Simplicity and clarity of a block diagram, a complete and unambiguous regulatory perimeter, the lack of turf battles: All these are appealing in the yearning of the state for more power. They fit well with a high-modernist desire for social engineering, to rearrange society in a way that increases state legibility and looks logical. But they come at the cost of epistemic pluralism.

State personnel and agencies perform better when their power is lower, when there are greater checks and balances. The society works better when there is more freedom, when the people are placed under weaker state power, when the people have more choice. It is better to envision a world where the people have more of a say in who will regulate them and how.

We fully recognise that this can be messy. Forum shopping can become a race to the bottom with private firms choosing the least burdensome regulation, creating incentives for government organisations to deregulate even when wise state action is required to address market failure. The messy arrangement will involve bigger payments to lawyers, and turf battles between government organisations. But we should simultaneously see the limitations of monolithic power. There is merit in careful choices that create some amount of a mess.

Practitioners do not see this in a strategic way. It is typical for an exasperated private firm CEO to say "Just tell me who's my feudal lord, and I will figure out how best to optimise under that structure of state coercion". But public policy thinkers need to play at a different level. We need to ask: How can state power be organised in a way that gives greater possibilities for private persons to innovate and obtain economic growth?

What we need is not a simple insistence on monolithic power. What we need is a sophisticated conversation on conditions under which we get a race to the top (more like Delaware) as opposed to a race to the bottom (more like the US Office for Thrift Supervision).

It is easy to criticise the US financial system as a sequence of disasters from LTCM to Lehman. But it is also important to see that the US has the highest per capita GDP in the world. There is a connection between the freedoms of the United States -- which come with difficulties -- and the immense success of the United States. The same financial system that failed with crises from LTCM to Lehman is the financial system that innovated, invented most of modern finance, and funded innovators and risk-takers that made the United States what it is today.

In India, we come from the other extreme: from the presumption of state power and low freedom. It would be useful to step back from the yearning for complete state power. A world with more checks and balances looks messier, but it is generally more conducive to a good society and economic success.

Introducing greater ambiguity around the regulatory perimeter is an important third pathway for economic reform, in addition to the traditional twin engines of (a) Deregulation, of removing state interference on problems where there is no market failure and (b) Putting the cladding of checks-and-balances and the rule of law, upon state agencies that do wield coercive power.

The field of regulation is far from figured out. On a global scale, regulators first came about less than 100 years ago in the United States. Here in India, it is only from the late 2000s that a conceptual understanding of regulators started emerging in the intellectual community. The world is complex, state capability is low, most state coercion in India is riddled with mistakes. A big journey lies ahead, both in India and elsewhere, to find the goldilocks zone, of addressing some market failure but avoiding the excesses of state power ranging from central planning (government control of products and processes) to corruption. One ingredient that we in India need to add to our regulatory philosophy is humility, the desire for epistemic pluralism.

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LRS TCS and Overseas Travel: A Policy Design Critique

2026-02-18T16:19:28.575+05:30

by Anirudh Burman.

Transaction taxes introduce frictions in transactions. Sometimes these frictions are in the larger public interest, for example, the interests of protecting government revenue ex ante because of the difficulty of ex post tax collection. In other cases, transaction taxes operate primarily to constrain transactions and their costs outweigh their benefits. A useful test is whether the tax (a) solves a genuine enforcement or information problem relative to ex post assessment, (b) is broadly designed and relatively neutral across comparable transactions, and (c) has stable, predictable parameters so that individuals can plan and comply without disproportionate costs. In addition, withholding taxes can be levied when there is a clear problem with ex post collection. However, the design of such taxes must still be proportionate to the objective and should not create large, avoidable liquidity and compliance frictions.

This post argues that the Union Government's Tax Collection at Source (TCS) on outward remittances under the Liberalised Remittance Scheme (LRS) and on purchase of an overseas tour programme packages fails these tests. It introduces a large, transaction-specific friction on outward remittances under LRS and on the domestic purchase of overseas tour programme packages. It does so without a clear statement of objective or a stable instrument design.

Introduction: TCS on LRS and overseas travel

TCS on LRS and on overseas tour packages was first introduced in the 2020 Union budget and the Finance Act, 2020. The Finance Act, 2020 inserted section 206C(1G) into the Income Tax Act, 1961 ( See Finance Act 2020 amendment here). The legal architecture in section 206C(1G) has two components. One is tied to an authorised dealer who "receives an amount for remittance from a buyer...", who intends to remit money out of India under the LRS. The other is a receipt-trigger tied to a seller of an overseas tour programme package. The second trigger is not a "remittance", which is conceptually confusing, since the move is primarily aimed at taxing cross-border movement of capital: LRS, a scheme under the Foreign Exchange and Management Act, 2000, allows overseas remittances up to USD 250,000 per annum, and the first component introduces a TCS on this, whereas the second component imposes TCS on overseas tour packages, independent of LRS. The second component introduces frictions for domestic purchases routed through Indian sellers.

Since the 2020 Finance Act, the Union government has tweaked this provision multiple times, adjusting tax rates, thresholds and exemptions. This years Union budget proposes to rationalise these further, but leaves the basic architecture intact.

Brief chronology of events

The table below shows that the instrument has been repeatedly redesigned along: (i) rates/thresholds and (ii) scope/coverage/definitions. This makes compliance and planning difficult for affected transactions.

Title of document (1)	Type of instrument (2)	Date (3)	What Changed (4)	Provision in the regulatory instrument (5)	Change type (6)
Finance Act, 2020	Law	March 2020	(a) introduced TCS on LRS remittances above INR 7 lakh per financial year (general rate 5%) (b) set concessional TCS rate of 0.5% for education remittance financed by an education loan (c) introduced TCS at 5% on sale of an overseas tour programme package (no threshold in the section text) (d) created exemptions where buyer is Government/embassy etc., or where buyer deducts TDS on the amount (as specified in the provisos)	Income-tax Act, 1961: s.206C(1G)	Rate/ threshold; Scope/ definitions; Exemptions
Notification No. 20/2022 (S.O. 1432(E))	Notification	March 2022	(a) created exemption from TCS for an individual who is non-resident and visiting India	Income-tax Act, 1961: s.206C(1G) (Notification No. 20/2022)	Exemptions
Notification No. 99/2022 (S.O. 3878(E))	Notification	August 2022	(a) superseded previous notification and replaced the exemption category: TCS not applicable to a non-resident buyer who does not have a permanent establishment in India	Income-tax Act, 1961: s.206C(1G) (Notification No. 99/2022)	Exemptions
Finance Act, 2023	Law	February 2023	(a) continued TCS at 5% on LRS remittances for education and medical treatment in excess of INR 7 lakh (b) continued concessional TCS at 0.5% on education remittances financed by an education loan in excess of INR 7 lakh (c) proposed increasing TCS rates from 5% to 20% for other LRS purposes and purchase of overseas tour programme packages	Income-tax Act, 1961: s.206C(1G)	Rate/ threshold
Foreign Exchange Management (Current Account Transactions) (Amendment) Rules, 2023 (G.S.R. 369(E))	Regulation	May 2023	(a) removed exemption in FEMA Current Account Transactions Rules, bringing international credit card usage while outside India within Rule 5 (and therefore within LRS accounting), which can expand the practical ambit for TCS.	FEMA CAT Rules, 2000: Rules 5, 7	Scope/ definitions
Press Release (Ministry of Finance): Clarification regarding applicability of TCS to small Debit/Credit Transactions under LRS	Press Release	May 2023	(a) clarified that international debit/credit card payments by an individual up to INR 7 lakh per financial year are excluded from LRS limits and will not attract TCS	MoF Press Release	Scope/ definitions
Press Release (Ministry of Finance): Important changes w.r.t LRS and TCS (deferral and thresholds)	Press Release	June 2023	(a) superseded the 19 May 2023 clarification and postponed implementation of the 16 May 2023 FEMA amendment, keeping overseas international credit card spends outside LRS (and outside TCS) until further order (b) restored INR 7 lakh annual threshold for TCS across all LRS categories irrespective of purpose (c) specified post-threshold LRS TCS rates: 0.5% for education loan, 5% for education/medical, 20% for other purposes (d) specified overseas tour programme package TCS: 5% up to INR 7 lakh and 20% above, INR 7 lakh (e) deferred the increased TCS rates to October 1, 2023.	MoF Press Release: LRS/TCS	Implementation/ deferral; Rate/ threshold
CBDT Circular No. 10 of 2023 (Guidelines to remove difficulty in implementation of changes relating to TCS on LRS and overseas tour packages)	Circular	June 2023	(a) clarified that overseas international credit card spending is not treated as LRS for now, so no TCS on such spends till further order (b) clarified that the INR 7 lakh LRS threshold for TCS applies per remitter (not separately per purpose or per authorised dealer) (c) clarified category boundary for overseas tour programme package, standalone international air ticket or standalone hotel booking is not a "package" (package must include at least two specified components)	CBDT Circular 10/2023	Scope/ definitions
Circular No. 11 of 2023	Circular	July 2023	(a) no change to TCS rates/thresholds/categories/exemptions.	CBDT Circular 11/2023	Scope/ definitions
Foreign Exchange Management (Current Account Transactions) Amendment Rules, 2023 (re-insertion of Rule 7)	Regulation	June 2023	(a) reinstated exemption in FEMA Current Account Transactions Rules, excluding overseas international credit card use from Rule 5 (and therefore from LRS accounting), reversing the 16 May 2023 omission	FEMA CAT Rules, 2000: Rule 7	Scope/ definitions
Finance Act, 2024	Law	February 2024	(a) Inserted a sixth proviso to s.206C(1G) governing TCS collection based on the pre-amendment position (as on 01-04-2023.	Income-tax Act, 1961: s.206C(1G) (sixth proviso)	Implementation/ deferral
Finance Act, 2025	Law	March 2025	TCS thresholds increased.	Income-tax Act, 1961: s.206C(1G) (threshold amendment)	Rate/ threshold
Finance Bill, 2026	Law	February 2026	(a) proposed reducing TCS rate for LRS remittances for education/medical treatment (above INR 10 lakh) from 5% to 2% (b) proposed reducing TCS on sale of overseas tour programme package to 2% and removing the threshold/slab so 2% applies irrespective of amount (c) retained 20% TCS rate for LRS purposes other than education/medical.	Finance Bill, 2026: s.206C(1G)	Rate/ threshold

Regulatory uncertainty

A predictable, stable regime is important for economic freedom. Economic freedom implies the ability to plan properly, and planning requires foreseeability and predictability. The table above discusses frequent regulatory changes to the LRS and overseas travel framework since 2020. The government has revised thresholds, exemptions, rates frequently, set different TCS rates for different categories of spends and revised them, exempted non-residents, included or excluded foreign credit and debit-card spends, clarified what purchasing an overseas tour package means, and so on.

This matters for economic freedom: individuals cannot reliably forecast the cost of lawful foreign transactions, intermediaries cannot standardise compliance processes, and the effective burden depends on the tax rate, on exclusions and exemptions, as well as whether refunds are timely.

TCS on LRS and overseas travel as a hindrance to economic freedom

LRS was introduced in 2004 as part of a broader liberalisation of Indian finance in 2004. Since then, the LRS limit has been increased gradually from USD 25,000 to USD 250,000. This liberalisation reduced frictions in the ability of Indians to transact abroad, purchase foreign goods and services, and contributed to India's global integration.

In 2020, the Finance Act introduced a friction of a 5 percent TCS, which it increased to 20 percent in 2023 for purposes other than education and medical expenses. The budget speeches of the Finance Minister in Parliament ( 2020-21 and 2023-24 ) do not provide any reasons for introducing this friction. TCS is collected at the time of transaction, regardless of eventual tax liability. TCS imposes a significant friction on such activity. By doing this, the TCS changes the set of choices individuals have by making certain specific uses and transactions costlier from the perspective of both compliance and financial liquidity. In addition, if refunds are delayed, the private cost is not 20%, it is the aggregated cost of the time value of money, the opportunity cost of having made other choices had this liquidity constraint not been imposed, as well as the friction and uncertainty of Indian tax compliance.

Finally, the effect of the TCS is distributional regressive. Individuals facing the highest liquidity constraints are hit the hardest (young professionals, small business owners, families with recurring expenses, etc.). While lower frictions for educational and medical purposes alleviate some of this, the remaining frictions impose invisible opportunity costs on many other types of potential activities.

Paying advance TCS on overseas travel

While the TCS on LRS taxes foreign remittances, the TCS on overseas travel taxes even domestic transactions.

It is important not to equate foreign remittances with domestic transactions. The state regulates cross-border outflows under FEMA and other regulations and has articulated some objectives for this (e.g., managing outflows, monitoring, national security), even if one disagrees with the choice of objective or the proportionality of the instrument. By contrast, domestic transactions already sit within a general indirect tax architecture (GST), whose design objective was precisely to subsume transaction taxes into a broad-based system.

The issue here is not whether cross-border remittances can ever be regulated or taxed, but whether a narrow, high-variance transaction friction outside the GST framework, where the tax is collected upfront regardless of eventual tax liability, represents a coherent and proportionate policy design.

CBDT Circular 10/2023, Question 8, states that "overseas tour program package" includes expenses for "travel" or "hotel stay" etc., and then clarifies that purchase of only an international travel ticket or only hotel accommodation "by in itself is not covered."

Though two of three conditions need to be fulfilled for this to be triggered, it effectively brings domestic payments within its ambit. If one buys an international flight ticket from a domestic airline or a domestic travel aggregator as part of an overseas tour, such domestic expenditure in INR will also be included within the threshold of the TCS. As drafted, this requirement seems to collect an advance tax on individuals spending within India's domestic economy for foreign travel, as well as any spends outside India. This is novel, as earlier prohibitions, even in the license-raj era focused on foreign transactions and remittances, not domestic consumption of goods and services for foreign travel. The use of tax-based frictions serves to reduce the average Indian individual's integration with the globalised economy. In addition, the continual changes discussed above also affect the predictability and forecasting of decisions within the domestic economy because domestic spends on foreign travel are also included within the ambit of TCS.

One possible defence of this TCS is that it is intended to reduce certain outflows, analogous to a Tobin-tax style tax on foreign exchange transactions. The analogy is limited. Tobin's proposal was to cushion exchange-rate fluctuations. It was also designed to disincentivise very short-term speculative round-tripping transactions through a small uniform charge on foreign exchange conversions. The TCS regime is significantly broader in coverage (household remittances and even a domestic purchase trigger for overseas tour packages) and has been set at rates (e.g., 20 percent for many categories) that are far from marginal.

The Finance Minister in her budget speech of 2026 has proposed to rationalise many of the tax rates under the TCS regime. The proposal to reduce TCS to 2 percent for certain categories moves the rate closer to the range conceptually associated with a low-rate transaction tax. The current budget proposal is welcome. However, the deeper concern is the instrument design: a transaction-specific levy that is collected upfront irrespective of final liability, without sectoral neutrality and predictability. A better course of action will be to only pursue those cross-border transactions where the Indian state has clearly articulated objectives in a neutral, low-friction, predictable manner. Absent this, the core concerns remain about the design, and the consequent inability for households to plan and execute their economic activities.

Anirudh Burman is a research at XKDR Forum.

Announcements

2026-02-14T13:35:27.717+05:30

IIHS University

The IIHS (Institution Deemed to be) University is now accepting applications from interested candidates for the second batch of their Master’s programmes. The University offers a set of five transformative interdisciplinary programmes for the 2026-27 intake.

Master of Science in Sustainability Science and Practice.
Master of Science in Climate Change Science and Practice.
Master of Science in Urban Economic and Infrastructure Development.
Master of Arts in Urban Studies and Practice.
Master of Arts in Human Development Policy and Practice.

The attached brochure gives an overview of the five programmes. Interested candidates can apply at www.iihs.ac.in; applications are open until 20 March 2026.

The University also offers a PhD in Urban Studies and Practice, applications for which will open on 13 March 2026.

As India undergoes the largest rural-to-urban transition in human history, urbanisation is fundamentally reshaping its economy, society, culture, and environment. Recognising this defining moment, the University is premised on the need to transform the current nature of urban education, one of the most important drivers for India’s national development and sustainable global futures.

Recent graduates and young professionals from any educational background are eligible to apply to any of the five Master’s programmes. For example, even bachelor’s degree holders in non-science subjects can apply to IIHS University’s Master of Science programmes.

For queries, you can write to admissions@iihs.ac.in or contact +91 99012 55788, 96325 20741 (10 am to 6 pm India Time, Monday to Friday). IIHS, Sadashivanagar Campus, Bengaluru 560 080, India and IIHS, Kengeri Campus, Bengaluru 560 060, India.

Author: Siddarth Raman

2026-02-09T00:21:38.751+05:30

Siddarth Raman is a senior research lead at XKDR Forum.

On this blog:

The MACT Litigation Overload: How India's Regulatory Trifecta Forces Cases into Court, 8 February 2026.
High-Voltage Treatment for a Comatose Elephant, 28 December 2025.
Establishing the baseline for the Kollam district court reform, 21 November 2025.
Beyond Pendency: Counting Cases Correctly, 5 October 2025.
Get them to the court on time: bumps in the road to justice, 12 June 2025.
Who is litigating cheque bounce cases?, 5 June 2023.

The MACT Litigation Overload: How India's Regulatory Trifecta Forces Cases into Court

2026-02-08T23:56:59.167+05:30

by Siddarth Raman and Maya Ramesh.

Indian courts are drowning in third-party motor accident claims. More than a million claims, worth over INR 80,000 crore await resolution. These disputes account for a tenth of all civil pendency. In this article, we argue that this explosion in litigation is a consequence of poor regulatory design. India's third-party motor insurance market operates under a unique set of rules that dismantle the foundational economics of insurance. Coverage is mandatory, insurers cannot select customers, premiums are fixed by the regulator, and liability for injury or death is uncapped. These rules distort incentives: they encourage claimants to pursue bigger awards through courts, while leaving insurers with only one strategy - delay. This behaviour is often narrated as a simple story of bad firms harming consumers, when it is the inevitable consequence of a certain arrangement of incentives. The system effectively guarantees that most accidents end up in litigation.

Insurance economics - A short introduction

Insurance operates on an elegant economic principle -individual risks aggregate across large populations to convert unpredictable events into manageable outcomes at the group level.

By pooling risks, insurers use the premiums of many to pay the claims of the few. Risk-based pricing is key: older people pay higher health insurance premiums than younger people, smokers pay more than non-smokers for life insurance, homeowners in flood or earthquake zones pay more for property insurance. In the UK, younger drivers pay higher premiums to get behind the wheel, compared to drivers over 30.

In most insurance transactions, the interests of the insurer and the policyholder align. When you buy health or comprehensive car insurance, your insurer wants to pay valid claims promptly to keep customers satisfied, build loyalty and ensure recurring revenue. This alignment breaks down in third-party (TP) liability. The insurer has no customer relationship to maintain with the victim, creating a financial incentive to minimize and delay payouts.

Unique distortions in the Indian market

The Indian regulatory framework distorts conventional TP insurance dynamics through three specific interventions:

Mandatory Purchase and Mandatory Offer: Section 146 of The Motor Vehicles Act (MVA) mandates that every vehicle owner buy third-party insurance. Section 32D of the Insurance Act, 1938 mandates that general insurers underwrite minimum percentages of motor TP business. IRDAI's 2015 regulations explicitly forbid insurers from refusing liability-only policies. This dual compulsion creates a captive market where neither buyer nor seller has meaningful choice.
Regulated, Non-Risk-Based Pricing: IRDAI sets the premium for this mandatory TP insurance. These premiums are based on vehicle categories and historical aggregate claims data. They do not factor in the individual driver's risk profile - their driving record, age, experience, location, or their history of insurance claims. A safe or good driver with no history of accidents pays the same TP premium as a high-risk driver for the same vehicle class who may have chalked up a record. This decouples price from individual risk, preventing insurers from charging premiums commensurate with perceived risk.
Uncapped Liability for Injury / Death: The MVA imposes unlimited liability on the insurer for death or bodily injury. The 2019 amendment has a mechanism to link premiums and liability (Section 147(2)). When notified, the Motor Vehicles (Third Party Insurance Base Premium and Liability) Rules, 2022 schedule continues to specify base premiums for unlimited liability.

As of December 2025, the premium for a 1-year TP insurance for a 4 wheeler of less than 1000 cc is INR 2094. Prices were last raised in 2020. The industry faces persistently high claims ratios (claims paid out as a ratio of premiums collected). In 2023-24, the industry-wide incurred claims ratio for motor insurance reached 78%. For PSUs like New India Assurance, the net incurred claims ratio hit 108% in FY 2024-25, meaning they paid out more in claims than they collected in premiums. The problem isn't new. The industry was discussing similar problems a decade ago.

The litigation funnel

Parties negotiate liability compensation in specialized Motor Accidents Claims Tribunals (MACT). A 2019 amendment automatically converts police DARs into claim petitions. Bargaining now begins under the direct oversight of a court. Litigation is the default, institutionalised starting point.

Consider the incentives this structure creates for rational actors. Insurers are forced to accept uncapped liability at a fixed, non-risk-adjusted price. Any large claim, involving injury or death is riddled with subjectivity, making it impossible to anticipate the potential payout. While an objective formula has been proposed, the deviations and exceptions are many. These formulae usually involve compensation of potential future income. In a poor country, this may involve pedestrians and drivers whose income isn't reported formally.

From the perspective of an insurance firm, each policy brings with it the prospect of potentially unbounded losses. There is no upside to paying higher amounts or doing it quicker - the insurer has no relationship to forge or salvage, and there are no reputational costs to delays or denial, unlike in own damage insurance. The legal costs in this kind of bulk litigation that insurance firms go through are comparably trivial to an uncapped liability. This is evident in the data from the IIB Motor Annual Report 2019-20: while 93% of OD claims settle for under INR 50,000, TP payouts run into lakhs.

In this context -

Challenging the quantum of compensation is standard practice. It offers a chance to reduce the payout on appeal.
Delaying the payout allows them more float.
Signalling intransigence prevents future claims from using past allowances as precedent.

Insurers are also expected to make an offer within 30 days of the DAR being filed. This rarely happens. Any offer without a claim request will act as the floor for future bargaining. It is game theory optimal to lowball, or not make an offer. Contesting the claim amount, or challenging the facts surrounding income or extent of disability is perceived as unnecessary adversarial obstruction. It is a rational response to managing uncapped, subjectively determined liabilities against inadequate, fixed premiums, especially when bargaining in public.

Claimants pursue a different calculus. Under the standards established in Sarla Verma vs DTC (2009) and National Insurance Co. Ltd. vs Pranay Sethi (2017), the judiciary interprets 'just compensation' liberally. Any discussion between claimant and insurance firm is also intermediated through lawyers from the get-go. Lawyers acting to maximise their client's outcome advise the client on the potential for higher awards through the MACT process, leveraging the subjective elements in compensation calculation and the pro-claimant judicial stance. Accepting an early, potentially lower, out-of-court offer is less rational than pursuing the claim through the tribunal. If the initial award seems insufficient, claimants are also incentivised to appeal for enhancement in the High Court.

Why cases take decades

On paper, the system has avenues for settlement. In practice, they are largely an illusion. The regulatory architecture systematically discourages private resolution and co-opts settlement into the formal court process.

The moment a police officer files a Detailed Accident Report (DAR), the MVA mandates that the MACT must treat it as a claim petition. The clock starts ticking, and the case is officially in the judicial system, often before the claimant has even hired a lawyer. Even if the parties wish to settle, an offer to the claimant must be made within 30 days of receiving information of the accident, and recorded with the tribunal.

While mechanisms like Lok Adalats settle many cases, they function as an adjunct to the courts, handling cases referred by the MACT. The resulting settlement becomes a binding award, stamped with judicial finality. The system doesn't prevent compromise, but it demands that compromise happens under its watch, contributing to the docket load and reinforcing the MACT as the inescapable center of the universe for accident claims.

The MACT isn't necessarily efficient at disposing these claims. Cases last over a year, and the MACT often struggles with just getting parties to court. Even when an award is passed - both insurers and claimants have reasons to challenge it. For insurers, every MACT award above their initial assessment is worth appealing. The potential reduction in payout, combined with years of additional float on unpaid claims, makes the appeal economically viable even with low success rates. For claimants, the judicial system's pro-welfare stance and the subjective nature of compensation calculations mean enhancement petitions often succeed. Their lawyers, working on contingency, have every reason to encourage appeals.

The result is that cases often take decades to complete. We get a rough dipstick by examining a random sample of three judgements in MACT appeals that were delivered in January 2025 in the Delhi High Court.

Case 1 - MAC APPL. 314/2021 (10 years): 2015 Accident → 2025 HC Judgment.
Case 2 - MAC. APP. 94/2020 (12 years): 2013 Accident → 2025 HC Judgment.
Case 3 - MAC. APP. 139/2021 (11 years): 2014 Accident → 2025 HC Judgment.

Each of these took over a decade to resolve. These are not outliers. Data from Delhi, Kerala, and Odisha shows that High Court MACT appeal pendency runs at 25-30% of district court pendency - a staggering appeal rate that reflects both parties' incentives to keep fighting.

Global parallels: The logic of trade-offs

India's regulatory framework is a global anomaly. While mandatory TP insurance is common worldwide, no other major economy imposes the same rigid combination of constraints. Other systems balance the mandate to purchase with trade-offs in pricing or liability.

The UK and Singapore, like India, have uncapped liability for personal injury to ensure victims are fully compensated. However, they balance this enormous potential payout by allowing competitive, risk-based pricing. Insurers can charge a high-risk driver more than a safe one, using the price mechanism to manage their exposure. China takes the opposite approach. It has regulated pricing for its compulsory insurance (CTALI), but it balances this by imposing strict statutory caps on the insurer's liability for death, injury, and property damage. The insurer's risk is known and finite. Most other systems, like those in the US and Australia, also mix and match, but they consistently avoid the trifecta. They pair mandatory insurance with risk-based pricing and various liability caps through tort reform.

Country	Regulated Pricing	Uncapped Liability
India	✔	✔
UK	✖	✔
Singapore	✖	✔
China	✔	✖
USA	✖	✖
Australia	Varies	✖

India stands alone in forcing insurers to take on unknown risks (uncapped liability) for a fixed, non-risk-based price. Insurance firms have no competitive edge - they cannot differentiate on offering, on price, on customer selection or ability to underwrite. This leaves them with no lever except optimising operational costs, or resorting to delay and dispute, making litigation the inevitable outcome.

Conclusion

The million-plus pendency overwhelming India's MACTs isn't a bug - the system is working as designed. When regulation simultaneously mandates purchase, fixes prices without regard to risk, and imposes uncapped liability, it makes fighting every claim the most sensible financial strategy for insurers. Claimants, guided by lawyers who understand the rules, have every reason to push for higher awards. Both sides are responding to incentives.

More judges won't solve this. Neither will faster procedures, or better technology. These are bandages to a structural problem. The solution demands fundamental regulatory reform: keep compulsory purchase (Section 146), but free the other levers - allow insurers to price for risk, and replace the unlimited liability by a capped liability schedule linked to base premiums (Section 147(2)). These are difficult changes with second order effects - poor drivers can get priced out and the burden of large claims will shift from firms to individuals. They may require complementary policies like higher penetration of personal accident insurance, a large public fund for the uninsured, top-up options to increase the liability cap for commercial vehicle owners. The fix is conceptually straightforward; the transition is unlikely to be. A time-bound expert committee should draft the amendments and phase them in.

India needs more functional insurance markets, not more courts. Markets need release valves - one cannot fix every variable and expect the system to work. Until our policies reflect this understanding, the litigation assembly line will keep running. Processing human tragedy through a decade-long judicial machinery serves no one's interests - not the victims waiting for compensation, not the insurers bleeding money, not the courts drowning in cases. In this case, the road to this dysfunction was paved with the best social welfare intentions.

References

Department of Justice, Government of India. n.d. National Judicial Data Grid (District Courts).

Economic Times. 2024. “10.46 lakh motor accident claims worth Rs 80,455 crore pending nationwide: RTI.” May 26.

Wood, Zoe. 2024. “They quoted £7,000-£8,000’: young drivers face huge car insurance rises.” The Guardian, January 27.

Insurance Regulatory and Development Authority of India. 2021. Motor Insurance Handbook. Hyderabad: IRDAI.

Insurance Regulatory and Development Authority of India. 2015. IRDAI (Obligation of Insurer in respect of Motor Third Party Insurance Business) Regulations, 2015. Gazette notification.

Ministry of Road Transport and Highways. 2022. Motor Vehicles (Third Party Insurance Base Premium and Liability) Rules, 2022. G.S.R. 394(E), May 25.

General Insurance Council. 2024. General Insurance Council Yearbook 2023-24. Mumbai: General Insurance Council.

The New India Assurance Company Limited. 2025. Annual Report 2024-25.

Saraswathy, M. 2013. “Third-party motor segment a burden on insurers.” Business Standard, September 14.

Mohapatra, Mugdha, Siddarth Raman, and Susan Thomas. 2025. “Get them to the court on time: bumps in the road to justice.” The Leap Blog, June 12.

Sarla Verma v. Delhi Transport Corporation. 2009. AIR 2009 SC 3104. Supreme Court of India.

National Insurance Co. Ltd. v. Pranay Sethi. 2017. AIR 2017 SC 5157. Supreme Court of India.

Rajesh Tyagi v. Jaibir Singh. 2009. Delhi High Court.

Insurance Information Bureau of India. 2020. IIB Motor Annual Report 2019-20.

Financial Times. 2021. “General insurance pricing practices.”.

Land Transport Authority. 2025. “Buying insurance.” OneMotoring.

LawinfoChina. 2022. “Compulsory Traffic Accident Liability Insurance (CTALI) Regulations.”.

National Association of Insurance Commissioners. 2024. Product Filing Handbook.

State Insurance Regulatory Authority. 2021. CTP Premium and Market Supervision: Review of the Risk Equalisation Mechanism (REM).

Siddarth Raman is senior research lead at XKDR Forum. Maya Ramesh is Counsel at Solaris Legal. The authors thank Shubho Roy, Ajay Shah and Susan Thomas for useful inputs and discussions.

Beyond Syndication: Unlocking the Power of Single-Asset Securitisation

2026-02-02T17:34:00.001+05:30

by Amrita Agarwal and Harsh Vardhan.

An overlooked yet transformative pillar of India’s evolving securitisation framework is the newfound empowerment of lenders to undertake the securitisation of a single, standard asset (Master Direction – Reserve Bank of India (Securitisation of Standard Assets) Directions, 2021. RBI/DOR/2021-22/85, September 24, 2021). To appreciate the magnitude of this shift, one must first look at the historical contours of the Indian market. Traditionally, securitisation has been synonymous with "pass-through certificates" (PTCs) backed by granular pools of consumer debt—typically vehicle and personal loans or microfinance receivables. The market has also frequently, if somewhat inaccurately, applied the term to "direct assignments," where loan portfolios are sold outright between lenders.

Indian securitisation has long been a predictable, if somewhat narrow, affair. Historically, the market has been the domain of "pass-through certificates" (PTCs)—complex bundles of granular consumer debt, ranging from tractor loans in Punjab to microfinance receivables in Tamil Nadu. The logic was simple: there is safety in numbers. By pooling thousands of small loans, lenders could hedge against individual defaults, creating a diversified product for investors.

In September 2021, the Reserve Bank of India (RBI) issued a comprehensive new framework. The impact on traditional volumes was immediate. PTC transactions, which languished at a modest ₹0.33tn in the 2021 financial year, surged beyond ₹1tn by 2024. Data from the first half of 2025 suggests the market is reaching critical mass, with volumes already beyond ₹0.7tn.

Yet the true significance of the 2021 guidelines lies not in the scaling of the old model, but in the birth of a new one. In a radical departure from the "diversified pool" orthodoxy, the new regime permits a lender to carve out a single, standard project loan, house it in a bankruptcy-remote trust, and issue securities against that solitary asset.

It is the "securitisation of one" -- a sweet insight that is one pioneer away from becoming a market reality.

Breaking the CLO Mould

Globally, the securitisation of commercial credit is a mature, if occasionally notorious, discipline dominated by Collateralised Loan Obligations (CLOs). But the Indian iteration is a distinct breed. A typical European or American CLO might bundle 150 different corporate loans to achieve the "granularity" required for a triple-A rating.

India’s provision, by contrast, allows for the atomisation of a single, massive exposure. This offers banks unprecedented agility in managing the long-tenure, high-ticket exposures inherent in infrastructure finance.

Until now, such projects were managed through the consortium model: a cumbersome, 20th-century process where a lead bank manages a gaggle of lenders or "down-sells" portions of the debt to stay within internal risk limits. Crucially, under syndication, the loan remains an illiquid fixture on the balance sheet. Single-asset securitisation changes the chemistry of the transaction, turning a private contract into a tradable financial instrument.

Efficiency by design

The single-asset model introduces a surgical approach to capital efficiency. Under RBI rules, a fully disbursed project loan becomes eligible for securitisation after a six-month "minimum holding period." This timing is strategic.

In the volatile world of infrastructure, early-stage risks -- regulatory bottlenecks, environmental clearances, and land acquisition disputes -- are at their most acute during the first shovel-load of dirt. By the time a loan is fully disbursed and has survived its first six months, these foundational uncertainties have typically receded.

Securitising at this juncture allows a bank to capture the higher yields associated with the high-risk inception phase, only to exit and free up capital just as the asset settles into the boring, predictable cash flows of a stable utility.

Beyond individual relief, this mechanism addresses two systemic vulnerabilities that have long haunted the Indian subcontinent:

The Asset-Liability Mismatch: Indian banks are perennially caught in the "borrow short, lend long" trap: using three-year deposits to fund 20-year power plants. Securitisation provides a vital exit valve, moving long-dated assets off the books.
Broadening the Investor Base: By transmuting a private loan into a security, the industry can tap into the deep pockets of institutional liquidity. Insurance companies and pension funds, which crave long-duration cash flows, have historically lacked a direct route to project-level credit without taking on the broader, often messy, corporate risk of the developer.

The friction points

If the advantages are so compelling, why has the market not yet ignited? The answer lies in a combination of dormant private capital expenditure and significant fiscal friction.

For the better part of the last decade, India’s infrastructure story has been a public sector affair, financed largely through the bond markets. As the private sector begins to re-engage, two primary hurdles remain:

The stamp duty trap: The primary hurdle is the prohibitive cost of registration. In many Indian states, transferring a loan to a trust can incur charges of between 3% and 4%. While some states have capped this at 1% for pooled assets (creating regional hubs for securitisation) the single-asset model remains burdened by archaic fees. Analysts argue that state governments must act in their own enlightened self-interest to reduce these frictions if they wish to see infrastructure projects in their backyard funded efficiently.
The Regulatory Glass Ceiling: Current Securities and Exchange Board of India (SEBI) regulations present a barrier. Rules for listed securitised debt instruments require that no single obligor represents more than 25% of the asset pool. This effectively bans single-asset securities from being listed on recognized exchanges.
This lack of listing creates a domino effect. Insurance companies, governed by strict IRDAI mandates, are generally restricted to "approved investments" that must be listed. Without a SEBI carve-out, the senior, high-quality tranches of these deals (precisely what insurers want to buy) remain off-limits. Furthermore, under Minimum Retention Ratio (MRR) rules, the originating bank must keep a 10% "skin in the game," usually the junior "equity" tranche. This leaves the "Senior Tranche" looking for a home that current listing rules effectively block.

How this fits into Indian economic growth

As the private capex cycle begins its long-awaited ascent, the demand for sophisticated financing tools will be immense. The "securitisation of one" is no longer a mere regulatory curiosity or an academic exercise. It is a vital instrument for a banking sector that must fund a nation's growth without choking on the resulting long-term risk. For the pioneer who manages to navigate the stamp duty and the listing hurdles, the rewards and the market share will be substantial.

The framework is there. The assets are coming. All that remains is for the market to move beyond the safety of the pool and embrace the power of the one.

The authors are experts on finance and public policy.

Are all regulators equal? The Supreme Court doesn't think so

2026-02-02T13:35:15.846+05:30

by Chitrakshi Jain and Bhavin Patel.

Introduction

The judiciary has played a very important role in the development of the regulatory state in India. Thiruvengadam and Joshi (2013) argue that this is dissimilar to the evolution of the regulatory state in the global North, where courts have been marginal actors. In contrast, the Indian Supreme Court, they reveal, has on multiple occasions mediated conflicts that arose during the reform of telecom regulation and in this process imparted institutional credibility to the sectoral regulator. Consequently, the Supreme Court's role in designing the regulatory state should be studied more closely. In this article, we examine the implications of select Supreme Court decisions which shape the characterisation of regulators' functions and their appellatory rights.

It bears repeating that regulators in India accumulate executive, legislative, and quasi-judicial functions. This accumulation of functions combined with ambiguous laws that outline the powers of regulators has led to a scenario where the courts have been asked to clarify the character and nature of functions and the corresponding appellate remedies.

Regulators in India are not similarly situated vis-a-vis the powers that they have been bestowed with. This complicates matters even further. In the case of utilities, regulators are tasked with the crucial function of determining tariffs (amongst others). The allocation of this function to regulators was critical in ensuring that these sectors are depoliticised, for state governments perceivably manipulate rational determination of tariff to advance their political interests. Regulators are instead expected to rely on their expert knowledge and make tariff determination a rational and efficient exercise which protects consumer interest.

In three decisions, PTC v CERC (2010), GRIDCO v Western Electricity Supply Company of Orissa Ltd and Ors. (2023), and AERA v Delhi International Airport Ltd. (2024), the Supreme Court has been asked to rule on the nature of tariff determination in regulators, especially the various Electricity Regulatory Commissions (ERCs). In this article, we examine how these decisions have shaped regulatory practices and study whether the interventions by the Supreme Court have:

been consistent in identifying the nature of tariff determination across regulators;
led to inconsistency in the substantive appellate rights of regulated entities in sectors governed by different regulators.

We first outline the reasoning from the recent decisions of the Supreme Court on tariff determination. We proceed to identify the challenges they create for the development of regulatory theory in India generally. Lastly, we conclude with the suggestion to legislate based on principles that should apply uniformly across regulators.

Judicial decisions

The PTC, GRIDCO, and AERA decisions have a bearing on the following issues:

What is the nature of tariff determination? Is it a quasi-judicial function?
Can a regulator prefer an appeal against an adverse appellate order which modified or overruled the regulator's tariff order?

In PTC, the Supreme Court was asked to clarify whether APTEL has the power of judicial review and the authority to decide on the validity of regulations made by the Central Electricity Regulatory Commission (CERC). In the course of its reasoning, the Court observed that tariff determination, while being legislative in character, is made quasi-judicial in the context of the CERC because the Electricity Act, 2003 (Electricity Act) has made tariff orders appealable to APTEL. The judgment was rendered by a constitution bench (5 judge) of the Court and limited the scope of its application to the statutory scheme in the Electricity Act. This has not stopped parties from invoking the reasoning given in PTC in matters where other regulators are involved. For example, counsels for Delhi International Airport Ltd., in AERA placed reliance on PTC to argue that tariff determination is a quasi-judicial function.

In GRIDCO, a division bench of the Supreme Court developed upon the reasoning in PTC (that tariff determination is a quasi-judicial function) and extended it to argue that it would be improper for regulators to prefer appeals against orders of the APTEL to the Supreme Court. While the Court did not sufficiently explain its reasoning, it implies that regulators cannot be parties to appeals against orders which are passed while discharging a quasi-judicial function.

Finally, in AERA, a full bench of the Court clarified the law on impleadment of regulators as parties in appeals. It held that while an authority discharging a quasi-judicial function should not be impleaded as a party to an appeal, such an authority must be impleaded as a respondent in an appeal against its order if it was issued in exercise of its regulatory role or if its participation can further effective adjudication. The Court said that the PTC judgment should be treated as authoritative only to argue that classifying tariff determination as legislative or an adjudicatory function should depend on the statute which distributes the powers to the relevant authority. To hold that AERA is allowed to appeal, the Court after reading the statute argues that tariff determination for aeronautical services is a 'regulatory' function.

Collectively read, these judgments fall short of clearly articulating an objective criteria for demarcating the different functions exercised by regulators. Consequently, these determinations have to be located in the laws that create each individual regulator, and post facto by judicial interpretation. The lack of a well-defined objective criteria for identification of different functions has serious implications for Indian regulatory theory.

Problems for regulatory theory

These cases raise three major problems for Indian regulatory theory:

SRAs are treated differently

While PTC and GRIDCO hold that tariff determination by ERCs is a quasi-judicial function, AERA holds that a similar exercise by the regulator would be a 'regulatory' function. All three cases rely on interpretations of the SRAs' parent statutes to arrive at this conclusion. The judicial discussion hinges on points such as whether the statute provides an appellate mechanism against tariff determination orders. But this does not answer the question of what the core characteristics of quasi-judicial or regulatory functions are. This is ad hoc and intricate statutory interpretation which does not illuminate any underlying principles of Indian regulatory jurisprudence.

While GRIDCO casts doubts on an ERC's ability to file an appeal against an adverse APTEL order on an appeal from its quasi-judicial orders (which, as we have noted above, include tariff determination orders), AERA can file appeals against TDSAT orders arising out of its tariff determination orders. This creates an arbitrary distinction between these two SRAs. The court decisions turn on intricate statutory interpretation, but do not tell us anything about the distinguishing features of the underlying nature of tariff determination at these two SRAs.

No ex ante certainty on nature and appealability of orders

Since no clear rules emerge from these decisions regarding how to distinguish between quasi-judicial and regulatory orders, or whether SRAs can file appeals against adverse appellate tribunal decisions in appeals against their quasi-judicial orders, there is no clarity on these points as regards other SRAs. Whether a particular order passed by an SRA is quasi-judicial or regulatory, or whether it can file an appeal to defend such orders, has to be decided on a case-by-case basis. The answers to these questions can only obtain certainty when the Court pronounces on them.

This means that there is no ex ante conclusive answer to these questions in Indian regulatory jurisprudence. SRAs, regulated entities, and other concerned parties have a tough enough job navigating the maze of Indian regulatory case law as matters stand - they do not need the added uncertainty created by these decisions to make things worse. It is also deeply concerning that fundamental questions about character of functions and right to appeal are being decided decades after these laws were enacted.

The lack of ex ante clarity on these matters also complicates the job of the appellate tribunals, who have to justify choosing between precedents that are not consistent with each other. For example, very recently APTEL had to carefully navigate its way through PTC, GRIDCO and AERA to justify placing reliance on AERA and argue that tariff orders are also legislative and regulatory in character and that regulators should be allowed to defend their orders.

APTEL becomes the final authority on tariff determination

Tariff determination is delegated to ERCs via legislative mandate. The reasons traditionally offered for this include sectoral expertise and independence from the political executive. In addition, we may note that ERCs are much closer to the ground, as it were, and have (or should have) access to much more information about factors for tariff determination. As such it is ERCs, and nobody else, who should determine tariffs.

But if an ERC passes a tariff determination order, and APTEL sets it aside or modifies it, then the ERC cannot file a second appeal before the Supreme Court, even when they are the only contesting parties. This effectively means that APTEL's decision on tariff determination automatically trumps the decision of the expert body specifically identified by Parliament to take such decisions. Additionally, if APTEL makes an erroneous decision, the possibility of it being corrected is diluted unless regulated entities challenge it before the Supreme Court.

This amounts to an egregious violation of the principle of separation of powers across Parliament and the judiciary.

Conclusion

The problems identified above all point to the need for a better theory of institutional design for Indian regulators. One can take the view that this is still a relatively new area of Indian jurisprudence, since regulators are (other than the RBI) a fairly modern addition to the structure of the Indian government. That said, the ERCs were created in 1998, and it has been 28 years since. Similarly, it has now been well over three decades that SEBI was established, and over a quarter of a century since TRAI was set up. Surely enough jurisprudential mileage has been accumulated now to warrant some stock-taking:

We need a clear articulation of the different types of functions that regulators may discharge, and a categorisation of these functions into the three broad heads of executive, quasi-legislative, and quasi-judicial. Such a theoretical categorisation is necessary because judicial interpretation has led to varying, ad hoc categorisation across SRAs. Without a strong justification - which cannot be based on statutory hair-splitting - this is arbitrary, since it means that regulated entities in different sectors are treated differently. Calling a function 'quasi-judicial' means something: appellate rights exist, and certain judicial procedures have to be followed in discharging them; calling it 'executive' means limited grounds of judicial review are available through writ proceedings, and fewer procedural safeguards apply.
The Tinbergen Rule may help here - for each policy target a regulator is expected to influence (tariffs, enforcing regulation, contractual disputes, etc.), they must use a distinct instrument. This way each policy target stands a better chance of being achieved, and no conflicts arise from attempts to use the same instrument to achieve multiple targets. Law makers will have to think harder about the specific targets they want regulators to achieve, and for each, describe the instrument they must use. This legislative prophylaxis eliminates the risk of contradictory judicial interpretations later.
Pertinently, in its 31st report, the Parliamentary Standing Committee on Energy reviewed the Electricity Bill 2002 and acknowledged that assigning all the three functions to the same institution can create challenges and had asked for a reconsideration of the clauses assigning functions to the regulators. It is unclear whether a careful examination of the relevant provisions has since been undertaken; however, the implications of not delineating the functions in the statute itself can be seen in the repeated litigations before the courts.
Tariff rationalisation is listed as an objective in the long title of the Act. Regulators have expertise and independence from the executive, hence they are entrusted with such functions in infrastructure industries like electricity. Regulators are also obliged to ensure costs are efficiently allocated and consumer interest is protected at the same time. If we expect regulators to be accountable, they should also have the right to defend their decisions on tariff determination in a second appeal; not allowing this prevents them from discharging their function effectively.
Given the need for a uniform theory across regulators, and the need to avoid ex post determination through the judicial machinery, the categorisation of functions we propose should be done through a statutory instrument. The American Administrative Procedure Act is one example of how this can be done. It is possible that new functions emerge with changing circumstances; these can be addressed through amendment, rather than judicial review.

These measures will help achieve the goals of uniformity across regulators, non-arbitrariness in classification of functions, and greater certainty and predictability for regulated entities. The current proliferation of judicially-determined, ad hoc rules is undesirable, and helps with none of these objectives.

References

Judiciaries as Crucial Actors in Regulatory Systems of the Global South: The Indian Judiciary and Telecom Regulation (1991-2012) by Arun K Thiruvengadam and Piyush Joshi [2013] Oxford University Press.

Electricity Tariffs by Javier Reneses, MarÃa PÃa RodrÃguez and Ignacio J.P. Arriaga [2013] Springer.

Chitrakshi Jain and Bhavin Patel are researchers at the TrustBridge Rule of Law Foundation and would like to thank Renuka Sane and Ajay Shah for useful feedback.

Cholesterol in the Indian GST

2026-01-14T12:36:00.000+05:30

by Arbind Modi and Ajay Shah

In 2017, the Goods and Services Tax (GST) replaced the fragmented indirect tax structure of excise duties, service tax and state-level value added tax (VAT) with a unified, destination-based value-added tax. GST was conceived in Kelkar 2003, as an Indian adaptation of the global idea of the value added tax, which has the desirable features of having no cascading effects and being neutral to international trade.

Input tax credit is the beating heart of the GST: the tax paid on inputs must be credited against output tax liability. In a new XKDR Forum Working Paper, Input Tax Credit and refunds under GST in India: Conceptual and legal framework, we show that many features of the modern Indian GST interfere with input tax credit. In this, we combine the strategic sense of modern public economics coupled with a detailed reading of the law.

We worry that the Indian GST is a case of isomorphic mimicry: it resembles the external form of a modern VAT in form but lacks its underlying function. While Section 16 of the CGST Act establishes foundation of ITC entitlement, Section 17 narrows the scope through apportionment and blocked credits. Section 17(5) explicitly denies ITC for business inputs such as motor vehicles and works contracts. Furthermore, the refund framework under Rule 89 limits refunds to Net ITC and embedding revenue safeguards at the expense of neutrality. Critically, the law excludes capital goods and services from refund calculations in these cases. This breaks the credit chain.

When credit is denied, the tax on inputs becomes a cost. This creates a cascading tax through the supply chain. The tax system functions as a tax on intermediate production rather than on consumption. By blocking input tax credit for capital goods, the GST acts as an incentive for reduced investment.

This is not how the value added tax works internationally. India stands as an outlier relying on a narrow refund design, heavy compliance preconditions, and extensive exclusion. The current system minimizes administrative risk by denying refunds ex-ante, rather than managing risk through ex-post verification. The recent movements on `GST 2.0' do not deliver an improved GST as they actually worsen the blockage of input tax credit.

These flaws in tax policy have harmful effects for India: (a) Reduced capital deepening and reduced investment that hampers the emergence of high productivity and high wage firms; (b) Unfair competition from imports against domestic producers; (c) Reduced export competitiveness. The paper proposes a reform path focused on three pillars: guaranteeing full flow-based ITC across all inputs, rationalising rates to a single band, and automating refund administration.

The authors are researchers at XKDR Forum.

High-Voltage Treatment for a Comatose Elephant

2025-12-28T10:20:00.006+05:30

Unshackling the Elephant by Anand Prasad: A Review

by Siddarth Raman.

Anand Prasad's Unshackling the Elephant provides an interesting clean-sheet critique of the Indian legal system. India's development story into the 21st century is shackled by a judicial system that is stuck in the past. The book succeeds incredibly well at applying economic logic and management principles to the judiciary. Many of the suggested reforms have the weight of common sense - the kind that becomes visible once someone has taken the trouble to articulate it well. It stumbles into uncertain territory when suggesting we succumb to indigenous instinct, especially given the fragility of India's current state capacity and my own preference for cautious reforms that avoid the treacherous currents of populism.

This book matters. It is precise in its diagnosis and bolder in its questions than most will venture. Systems rest on assumptions we stop interrogating; surfacing these debates is important because knowing why we got here tells us what needs to be fixed and what doesn't need relitigating.

The Low-Hanging Fruit of Process Modernisation

On several fronts, Prasad's prescriptions will invite broad agreement - particularly from those who have witnessed the impact of process modernisation in other fields and wondered why the legal system has resisted it for so long.

The law is both judicial reasoning and process management. Other fields have walked the journey of process improvement. IT services, consulting, accounting, even corporate law in some aspects. Templatised pleadings and standardised procedures need not be recreated from first principles for every matter; translation tools chip away at language barriers; and the knowledge management systems Prasad envisions - his "legal knowledge grid" - would make legal corpora accessible to law graduates and large language models alike. Ideas of virtual hearings and asynchronous proceedings push this further, reducing the friction of physical presence without necessarily compromising procedural integrity.

These are safe bets. They enhance capacity without demanding discretionary overreach. Early efforts have begun and should be embraced (see here, here, here); many of these ideas deserve serious piloting as part of the eCourts Phase III modernisation effort.

Prasad also questions the figure of the "all-purpose judge" -the expectation that foundational legal education alone equips one to adjudicate matters spanning technology, finance, and specialised commercial arrangements. The assumption may have held in a simpler era. Whether it holds today is less certain.

A note of caution on artificial intelligence. The justice system functions because society accepts the state's monopoly on coercive power and consents to have it applied through due process. This is a social contract grounded in human accountability. AI may assist judges. But the decision must remain with someone who can be questioned, overruled, and held responsible. As much as the engineer in me might wish law were code, it is not.

Fixing the Incentive Systems

Courts are not just forums for dispute resolution. They are markets where incentives shape behaviour.

Consider the interest rate regime applied to legal disputes. Courts award simple interest at rates that don't match any reasonable cost of capital. The effect is predictable: delay becomes a strategy. A defendant who owes money has every reason to stretch proceedings; time works in their favour. Prasad is correct to argue that judges need to understand the time value of money. Aligning judicial interest rates with economic reality would shift the calculus against strategic delay.

On damages, Prasad makes the case for stronger deterrence -particularly through punitive awards for corporate fraud. On costs, he argues that allowing successful litigants to recover expenses would reduce barriers to access, enabling those confident in their position could invest in quality representation without bearing the full risk.

He also correctly identifies information asymmetry and power imbalance as distortions in the current system. Those with deeper pockets hire better lawyers, and in an adversarial system, this matters considerably. His proposals - removing restrictions on contingency payments, allowing lawyers to advertise, and litigation funding - would make legal representation function more like a market, creating a more level playing field.

Like Chief Justice Suryakant, Prasad echoes the need for judicial performance reviews. This deserves serious consideration, though any implementation must balance accountability against judicial independence. For the bar, he calls for high penalties for malpractice and stricter consequences for perjury. Whether the profession will police itself remains an open question. Prasad's experience understanding lawyers and litigants shines through in this section - he dissects how courts, like any market, respond to incentives.

The Temptations of an Overhaul

Anand Prasad's frustration with the legal system is understandable. Stepping into a courtroom feels like walking back through time.

While some structural reforms like splitting the Supreme Court into a constitutional court and court of appeals or curbing judicial legislation merit serious debate, some of the more radical proposals warrant caution. Moving towards an inquisitorial system would transform judges from neutral adjudicators into active investigators. In a country where discretionary power is prone to abuse, such a shift is a dangerous gamble.

The most troubling temptation is the urge to decolonise the system. Weakening foundational principles like the presumption of innocence in favour of indigenous traditions risks legitimising majoritarian sentiment as law, especially when honour killings persist in the 21st century and extrajudicial encounters meet public approval. The philosophical questions in attempting an Indic reinterpretation are formidable - attempting to balance ideas of karma against retributive justice, uniform civil codes against community specific practices, or the contextual obligations of Raja Dharma against the common law tradition that all stand equal before the law.

The Contradictions

There is an internal tension in the book's vision. It praises codification for its clarity and bemoans lack of consistency in judgements while simultaneously advocating for inquisitorial discretion and culturally-responsive justice. Codification demands predictability. Instinctive justice invites its opposite. A similar tension runs through the hope that algorithms will fix what humans could not. AI may detect patterns across thousands of judgements, but it cannot bear responsibility for any one of them.

The deepest contradiction is one of trust. The book hopes to build a high-trust society where precedent holds and contracts are sacred. This is hard to reconcile with privileging cultural instincts that are fluid and contested. One cannot ask people to trust in precedent while empowering judges to override it.

Conclusion

The boldness of the project deserves recognition. Unshackling the Elephant is a precise diagnosis of a system that has resisted reform for too long. Many treatments align with modernity. But there is a rebel streak - perhaps born of frustration - that carries risk. Giant shocks may end up killing the elephant rather than reviving it.

References

Prasad, Anand. 2025. Unshackling the Elephant: Transforming Indian Law, Culture and Economy. Bloomsbury India.

Siddarth Raman is Senior Research Lead at XKDR Forum.

Can technology augment order writing capacity at regulators?

2025-12-14T22:45:00.823+05:30

by Natasha Aggarwal, Satyavrat Bondre, Amrutha Desikan, Bhavin Patel and Dipyaman Sanyal.

Indian regulators have extensive quasi-judicial powers that they express through adjudicatory orders. It is critical that these powers are exercised in a proportionate, legitimate, and well-reasoned manner, as they not only impact the persons directly involved, but also the wider ecosystem in which they operate. Arbitrary actions, unsubstantiated by clearly articulated reasoning, can raise serious concerns around the legitimacy of regulatory actions and lead to a loss of confidence in the regulator. Such actions may also be set aside by appellate and review fora. Clearly written, well-researched, and reasoned orders help provide clarity, predictability, and knowability of the law, which are key indicators of a rule of law system (Aggarwal, Patel and Singh, 2025). Our study of the state of Indian regulatory order writing shows there is room for improvement in this regard.

We notice a growing interest in the use of Generative Artificial Intelligence (Gen AI) to resolve procedural inefficiencies at quasi-judicial and judicial authorities in India (Supreme Court Committee on AI, 2025; Kerala High Court, 2025), coupled with concerns around the potential dangers of using such technologies without adequate safeguards. Against this background, in a new working paper titled, 'Can technology augment order writing capacity at regulators?' we critically examine the opportunities and challenges of using technology, in particular Large Language Models (LLMs), to assist regulatory order writing in quasi-judicial settings.

The paper proposes augmenting rather than replacing human decision-makers, aiming to improve regulatory order writing practice through responsible use of LLMs. It identifies the core principles of administrative law that must be upheld in these settings - such as application of mind, reasoned orders, non-arbitrariness, rules against bias, and transparency - and analyses how inherent limitations of LLMs, including their probabilistic reasoning, opacity, potential for bias, confabulation, and lack of metacognition, may undermine these principles.

While the available Indian literature on the topic focuses largely on these limitations, and on critiquing proposals based on an over-reliance on technocratic means to improve state capacity, this paper's contribution lies in its integrative work: we draw upon the design principles articulated in frameworks developed in other jurisdictions and relate them to the applicable principles of Indian administrative law. We use this synthesis to develop a Problem-Solution-Evaluation (PSE) framework that is attentive to international practice, the legal principles underpinning quasi-judicial decision-making in India, and problems and limitations inherent to GenAI and LLMs.

The PSE framework proposed in the paper maps specific technical, design, and systemic solutions to each identified risk, and outlines evaluation strategies - end-to-end, component-wise, human-in-the-loop, and automated - to ensure ongoing alignment with legal standards. An overview of the framework is set out in the table below:

Table 1: Applying the Problem-Solution-Evaluation framework. This table illustrates how the PSE framework can be operationalised to align the design, development and use of LLMs for order writing assistance with the requirements of Applicable Law.
Problem	Applicable law	Solution	Evaluation
Non-application of mind	Non-application of mind; Failure to provide reasons; Arbitrariness	Interface Checkpoints; Confidence Score Display; Dual-Prompt Pipelines; Functionality Limitation; Constraint Enforcement; Workflow Design for; Review Role-Based Access	Edit Rate; Turnaround Time (TAT); Prompt Divergence Rate; Coherence Score
Black-box problem	Failure to provide reasons; Transparency	Chain-of-thought prompting; Input Token Influence Identification Symbolic Reasoning Systems Traceability tools; Visualisation; Simplified model explanations	Clarity rating; Audit Trail Incidence Document Traceability Rate
Potential for bias	Rules against bias; Arbitrariness	Data Preprocessing; Bias penalisation; Domain-specific content filters; Automated Bias Flagging Tools; Establishment of Legal Fairness Criteria; Mandatory Periodic Benchmarking	Bias Flag Rate Override Percentage; Fairness Benchmark Scores
Confabulation problem	Non-application of mind; Failure to provide reasons; Arbitrariness	Retrieval Augmented Generation; Post-Generation Verification; Legal Knowledge Graph Integration; Mandatory reviewer verification; Watermarking for traceability; Communicate technical limitations	Secondary LLM ''Judge'' for Fact-Checking; End-to-End Evaluation Tools Hallucination Rate; Retrieval Precision@k/ MRR NLI Coherence Checks; Self-Consistency Rate
Lack of metacognition	Non-application of mind; Arbitrariness	Prompt engineering; LLM as a judge; Iterative improvement from feedback	Closeness Metric; Human evaluation on overconfidence in output
Training corpus	NA	Adaptive Scraping Frameworks; Sector-specific pre-training; Structured Entity; Extraction and Legal Knowledge Graphs; Isolated Model Containers; Source inclusion; Perplexity tracking; Legal Retrieval Benchmarking; Curate sector-specific legal databases	Crawl coverage; OCR Error Reduction; Validation perplexity; Retrieval lift
Data security and privacy	NA	Stringent access control; Synthetic supervision-based PII detectors; NLP filters for information masking; Isolated Model Containers; On-premise infrastructure	Unauthorised access attempts; Mean Time To Remediation (MTTR); Penetration Test Pass Rate; PII Detection Accuracy

By itself the framework may be insufficient. It must be supplemented with systemic measures taken at the regulatory level. We offer stage-wise recommendations on how LLM-based order review tools can be built for and used in regulatory adjudication.

References

Natasha Aggarwal, Bhavin Patel, and Karan Singh, "A Guide to Writing Good Regulatory Orders" [2025] Trustbridge Rule of Law Foundation Working Papers.

Anurag Bhaskar and others, "White Paper on Artificial Intelligence and Judiciary" Centre for Research and Planning, Supreme Court of India, 2025.

High Court of Kerala, "Policy Regarding the Use of Artificial Intelligence (AI) Tools in District Judiciary" Official Memorandum HCKL/7490/2025-DI-3-HC Kerala, 2025.

Natasha Aggarwal, Amrutha Desikan and Bhavin Patel are researchers at the TrustBridge Rule of Law Foundation. Satyavrat Bondre and Dipyaman Sanyal work on AI and technology at dōnō consulting.

An Analysis of Electricity Outages in Delhi: 2024-25

2025-12-06T11:13:13.452+05:30

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:

What is the pattern of outages on the following parameters:

Duration and frequency,
Intensity,
Geography,
Reasons for outages

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)
Total number of zones	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)
Total number of grids	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)
Total number of subdivisions	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 (%)
Time of day	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.

Emerging Markets Conference, 2025

2025-11-28T16:52:00.006+05:30

We are now on the 16th run of the Emerging Markets Conference. This is in Bombay, 14-17 December. It brings together the important questions of the age with an inter-disciplinary mix of pathways to insight. It is a feast of good knowledge and good community. Here is the conference website. Buy tickets! See you there!

Establishing the baseline for the Kollam district court reform

2025-11-21T00:19:06.002+05:30

by Siddarth Raman.

An important milestone in Indian legal system reform has been underway in Kollam district for the last year. The High Court of Kerala has launched a court for cheque dishonour cases under Section 138 of the Negotiable Instruments Act as its first pilot in the 24x7 ONCourts initiative. PUCAR - of which XKDR Forum is a part - is a knowledge partner to the High Court of Kerala in this.

24x7 ONCourts aims to make the litigant experience efficient, predictable and seamless. Before assessing the intervention's impact, we need a baseline. We estimate five measures of court performance using a random sample of 100 disposed cases filed in 2015-2024. In Kollam, the median case takes 609 days over 12 hearings. Only 19% of hearings are substantive. The first substantive hearing occurs after 240 days. The gap between hearings is 61 days. Stage-wise analysis shows that the most time is spent in getting the litigants to court, and most cases do not reach trial. We compute the same metrics for Thrissur to enable future difference-in-differences analysis.

Motivation

Changing a system is hard. Public policy interventions in India are often guided by conjecture, anecdote and assumption. Too many reforms fail because they are not grounded in data, and their outcomes are not measured. A data-driven process with clear hypotheses on the problem, the solution and the expected outcomes, backed by rigorous measurement and empirical evidence, is needed to distinguish success from failure.

Early in the 24x7 ONCourts journey, we planned for continuous assessment. The first step is to establish a baseline. It fixes the pre-intervention state so later changes can be attributed to the intervention rather than unrelated trends. Publishing a compact, replicable baseline also lets other researchers repeat the measurement after go-live and compare the site of implementation against unaffected locations.

Approach to measurement

Courts and court processes should be judged by their impact on litigants; they are the central stakeholders in the system. In this, there are two types of measures one can develop. There are coarse black-box metrics that give a broad picture of outcomes - like the time to disposal. Alongside this, there can be fine-grained process metrics like the time taken to make payments, extent of delay attributable to postal services or police processes, or the time taken by an accused person to file for bail. These are necessary for continuous improvement of processes. Existing court systems do not capture or publish this type of process data.

We use coarse metrics for our baseline, relying purely on public data, so that anyone can reproduce the numbers without privileged access to court systems. As courts modernize and publish more data in the public domain, we may be able to develop more sophisticated metrics.

Using this approach, we pick five measures of court performance:

Time to disposal is the number of days from filing to the court's final order.
Hearings to disposal is the number of hearings a case goes through before it ends.
The share of substantive hearings is the fraction of hearings that move the case forward towards resolution. For instance, a hearing where the judge is on leave or a summons is re-issued is non-substantive; hearings where evidence is recorded or arguments are heard count as substantive.
Time to the first substantive hearing is the wait before meaningful progress begins.
Time between hearings is the within-case median gap between consecutive hearings. Lower the time, quicker the next hearing. The inter-quartile range (IQR) across cases is a measure of scheduling predictability. A lower IQR implies less variation in the time between hearings across cases.

A more detailed discussion of these measures and how to use them in evaluating court reforms is available here.

We conducted data collection and analysis in November 2024. Our approach, methodology and data are public, so that others can repeat the measurement for different samples, periods, or districts using e-Courts records.

Methodology

This article reports the pre-reform numerical values for Kollam, using a simple empirical strategy. We construct a baseline for Kollam before the 24x7 ONCourts intervention and compare it with Thrissur, which serves as the control district for future difference-in-differences analysis. We selected Thrissur as the control district after eliminating regions with existing special NI courts. Thrissur mirrors Kollam in judicial capacity (approx. 2,500-2,900 pending cases per judge), economic activity (GDVA per capita ratio of 0.9), and administrative composition (both have municipal corporations).

While Thrissur shares structural similarities with Kollam, direct cross-sectional comparisons of absolute performance levels between the two districts are methodologically unsound. Districts differ in idiosyncratic ways that are difficult to quantify -caseload volume, caseload mix, allocation rules. Consequently, our evaluation strategy avoids asking 'Is Kollam faster than Thrissur?' Instead, we focus on within-district changes over time.

We draw a random sample of 100 disposed Section 138 matters filed in 2015â€“2024 from Kollam and Thrissur using the public e-Courts database. Cheque dishonour cases are filed before the magistrate's court. In Kerala, these are filed as private complaints (criminal miscellaneous petitions) before becoming criminal cases. For each case, we reconstruct the case lifecycle from hearing dates and order text, tagging hearings by stage and classifying them as substantive or non-substantive.

For the baseline, we report medians and means. Medians describe the typical case; means indicate the influence of long tails. By sampling only disposed cases, we measure the speed of the 'successful' cohort. We acknowledge this introduces survival bias: this metric underestimates the time for difficult cases still stuck in pendency. We aim to address this in future work by using survival analysis to estimate the time to disposal for ongoing cases.

Results

The tables below report the pre-reform baseline values for Kollam and, for comparison, Thrissur. They describe how long cases took, how many hearings they required, and how much of that time and effort was substantive.

Time to Disposal (days)

District	Median	Mean
Kollam	609	782
Thrissur	792	1003

Hearings to disposal

District	Median	Mean
Kollam	12	15
Thrissur	7	10

Share of Substantive Hearings

District	Median	Mean
Kollam	19%	26%
Thrissur	25%	28%

Note: The dataset spans 2015-2024; COVID-19 lockdowns (2020-2021) introduce exogenous shocks to the metric; it has not been adjusted for here.

Time to First Substantive Hearing

District	Median	Mean
Kollam	240	446
Thrissur	276	447

Note: Changes in procedure resulting from the shift from Criminal Procedure Code (CrPC) to the Bharatiya Nagarik Suraksha Sanhita (BNSS) may affect this metric.

Time Between Hearings

District	Median	Mean	IQR
Kollam	61	74	48
Thrissur	123	121	62

Stage-wise Analysis

We analyse how time and hearings are distributed across case stages, as part of understanding the pre-reform baseline. In S138 cases, the case trajectory is:

A complainant files a case.
Post scrutiny and error correction, the case is registered as a miscellaneous petition.
The judge takes cognizance and the case is now a criminal case.
On cognizance, a summons is issued to the accused, followed by warrants and other methods to compel appearance of the accused.
On appearance, the accused pleads not guilty and takes bail.
Trial follows with evidence and arguments.
The court delivers a judgement.

Not all cases go through all stages. For cases that reach each stage, we compute the median and mean time and hearings spent at that stage.

Kollam	Time		Hearings		Cases
	Median	Mean	Median	Mean
Filing - Registration	0	4	-	-	99*
Cognizance	149	268	2	4	63
Appearance	312	533	5	7	74
Trial	337	454	11	15	41
Judgement	27	38	1	1	100

Note: *An error on e-Courts for one case reports the registration before the filing, which has been excluded.

Thrissur	Time		Hearings		Cases
	Median	Mean	Median	Mean
Filing - Registration	0	1	-	-	100
Cognizance	235	317	2	2	52
Appearance	721	878	5	8	81
Trial	335	451	8	12	13
Judgement	42	69	1	1	100

In both districts, getting the litigants to court takes the most time. We also observe that most cases do not reach trial. This reaffirms earlier observations in other district courts.

Way Forward

24x7 ONCourts has been running for about a year. It is too early to compare outcomes - only a fifth of cases filed have been disposed. But this benchmark will enable comparison when the court reaches its full load. We aim to track the court through this journey and continue to share updates on the court's performance.

We will continue to refine our methodology. The baseline analysis provides simple statistics from a set of 100 disposed cases. This suffers from the problem of censoring. We are only observing cases where the event of interest has occurred and we do not take into account ongoing cases. In systems like the court, where pendency is common, we should be using methods like survival analysis to account for ongoing cases. Other methods like time series analysis could identify trends. With Thrissur as a control, a difference-in-differences design can attribute average changes to the 24x7 ONCourts intervention. The present benchmark is the starting point; as post-intervention data accumulate, we can apply more demanding econometric checks using these tools.

Data and metrics form the backbone of evidence-based reform. The 24x7 ONCourts dashboard reports case statistics on a public dashboard in near real-time. This openness to measurement and public accountability sets a precedent for judicial reform initiatives across India. Public reporting supports continuous monitoring, mid-course correction and iterative refinement. A steady flow of transparent data, paired with simple, repeatable measures, is the practical route to learning what works.

References

24x7 ONCourts website.

Vision, ONCourts 24x7.

Evolution and Implementation of the 24x7 ONCourts: A Journey of Innovation Through Collaboration, PUCAR, 2024.

In Service of the Republic: The art and science of public policy, Vijay Kelkar and Ajay Shah, Penguin Allen Lane, 2022.

Approach to evaluate court reforms, Siddarth Raman, Seminar #9 of the series 'Indian Legal System Reform' by XKDR Forum, 2024.

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

How substantial are non-substantive hearings in Indian courts: some estimates from Bombay, Pavithra Manivannan, Karthik Suresh, Susan Thomas, and Bhargavi Zaveri-Shah, The Leap Blog, 6 December 2023.

Get them to the court on time: bumps in the road to justice, Mugdha Mohapatra, Siddarth Raman, and Susan Thomas, The Leap Blog, 12 June 2025.

Understanding Judicial Delays in Debt Tribunals, Prasanth Regy and Shubho Roy, Working Paper 195, National Institute of Public Finance and Policy, 2017.

Making courts transparent: What every litigant should know before filing a case, Pavithra Manivannan, Siddarth Raman, Gokul Sunoj, and Bhargavi Zaveri-Shah, Bar and Bench, 2025.

A litigant's guide to courts: How efficient is your court?, Pavithra Manivannan, Siddarth Raman, Gokul Sunoj, and Bhargavi Zaveri-Shah, Bar and Bench, 2025.

A litigant's guide to courts: Understanding predictability in India's courts, Pavithra Manivannan, Siddarth Raman, Gokul Sunoj, and Bhargavi Zaveri-Shah, Bar and Bench, 2025.

A litigant's guide to courts: Towards a more measurable future, Pavithra Manivannan, Siddarth Raman, Gokul Sunoj, and Bhargavi Zaveri-Shah, Bar and Bench, 2025.

The Long Road to Change, Ajay Shah and Amit Varma, Episode #36 of the podcast 'Everything is Everything', 1 March 2024.

Siddarth Raman is senior research lead at XKDR Forum. XKDR Forum is part of PUCAR - Public Collective for Avoidance and Resolution of Disputes. PUCAR is a knowledge partner to the High Court of Kerala in their 24x7 ONCourts initiative.

The Leap Blog

Trust in the Era of the AI-Informed Customer

Verification in markets

How it impacts the brand

How it impacts labour

When the asymmetry reverses

The scarce asset is the question

What the Boardroom should debate

References

Chinks in market efficiency: A Melody story

The "Signal" Ticker Confusion (2021)

Clubhouse Media Group (2021)

Zoom Video vs Zoom Technologies (2019-2020)

Bombay Oxygen Investments (April 2021)

L. G. Balakrishnan (2025)

Pan-Homophonic Events

Ticker Confusion and the Limits of Arbitrage

Parle Industries

Market Efficiency and the Role of Liquidity

Bibliography

A Market Failure Framework for Evaluating Public Sector Undertakings

Ex-ante: when is a PSU justified?

Ex-post: efficiency and effectiveness

Soft budget constraints

Applying the framework

The way forward

References

Words and deeds in the Indian exchange rate

Inferring the true exchange rate regime from the data

Reading the data: six distinct regimes since 2000

What the RBI says

What the IMF says

The centrality of the exchange rate regime in the Impossible Trinity

It is easy to do these calculations

References

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

Introduction

Empirical Strategy

Estimation Procedure

Data and Sample

Results

Baseline Event-Study Findings

Subsample Analysis

Interpreting the results

Conclusion

Announcements

Call for papers: Alt Data Workshop 2026

Overview

The Scope of Alternative Data

Themes and Topics

Submission Guidelines

Review Process

Review Committee

Travel Funding

Important Dates

Contact

Announcements

Call for Papers: Cross-Border Flows and Frictions in India

Overview

Themes and Topics

Submission Guidelines - Only Abstract Essential

Review Process

Program Committee

Funding

Important Dates

Contact

Announcements

Call for Papers: 17th Emerging Markets Conference

13th - 16th December, 2026

Conference design

Best Discussant Award

Program Committee

Important dates

Support

Registration and contact details

Author: Natasha Aggarwal

What happens when arbitration deadlines are missed

What the data shows

Why extensions are common

What this means for the law

Call for Papers: 17^th Emerging Markets Conference

13^th - 16^th December, 2026