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Move Beyond the Set Menu
After maintaining the status quo for nearly two years, the RBI has lowered the policy repo rate by 25 basis points, lowering it from 6.5 percent to 6.25 percent. The cut-first in five years- is justified, whether one looks at India’s own record over the last few years or its record relative to the rest of the world.
The top bank had raised the policy rate to 6.5 percent in February 2023, when GDP growth rate for the year was 7 percent and inflation had peaked at 6.7 percent. The rate did not seem too high the following year when growth accelerated to 8.2 percent and inflation moderated to 5.4 percent. However, the rate seemed unjustifiably high with growth projected to decelerate to 6.4 percent and inflation to moderate to 4.8 percent during the current year.
India’s inflation rate has moderated in comparison to other countries too. It was several times higher than those of the advanced economies (AE) or the global average prior to Inflation Targeting (IT). Currently it is close to the AE average and lower than the global average.
It would be erroneous to argue that an inflation rate of 4.8 percent would be inimical to growth, warranting continued tightening. No clear trade-off between growth and inflation has been established empirically at moderate levels of inflation. The trade-off is more evident at very low or very high levels of inflation. But an inflation rate of 4 percent, 4.5 percent, or 4.8 percent has little impact on growth.
A pertinent question to ask at this juncture, when the IT regime is in its 9th, and the RBI is in its 90th year of existence, is: how might IT, and RBI evolve, going forward?
Both warrant a fresh look.
After the initial success of IT, it was equally important to upgrade the necessary ingredients for a successful regime. These included a relevant and updated CPI basket; more accurate forecasts of inflation and growth to guide monetary policy; better measurement and management of the households’ inflationary expectations; and improvement in the transmission of monetary policy.
Disappointingly, most of these key ingredients have lagged: The RBI has been chasing an inflation target based on a dated price basket. It has often missed its forecasts by a large margin. The inflationary expectations of households have remained misaligned. And the transmission of monetary policy has not improved adequately.
An obvious way forward would be to address each of these key distortions to maintain the sanctity of the IT framework; as well as to minimize frictions with other constituents of the policy-making establishment. Some Suggestions are below.
Review Inflation Targeting
It would be prudent to conduct an independent review of IT to address the following questions: Is an inflation target of 4 percent still appropriate? Should India narrow down its inflation tolerance band of 2-6 percent? Should it move from targeting an inflation level within a band, to just targeting a narrower band of say 4-6 percent, with no explicit point target?
Revise CPI basket
This will help to better deliver the mandate of monetary policy. Other countries revise their baskets every 1-5 years, but India has not done so since 2011. This is despite the fact that in the interregnum, the per capita income has tripled and the shares of both agriculture and food in the GDP have declined substantially from their erstwhile levels. In view of such a transformation, its current weight of food in consumption is likely only 30-35 percent rather than 45.8 percent.
In fact, it would be sensible to commit to review and revise the IT framework and the CPI basket every 3-5 years.
Strengthen RBI’s technical expertise
The RBI’s technical expertise ought to be strengthened, especially its forecasting models; and the measurement and management of the inflationary expectations of households.
The RBI is often referred to as a full- menu central bank. Besides setting the monetary policy, it performs several other crucial tasks, such as, regulating the banks and parts of the NBFC segment; managing the debt of the Centre as well as of the states; and responding to the external conditions through the management of the exchange rate, foreign exchange reserves, macro prudential measures and capital flow measures. Each one of these functions matters for the health of the economy.
Yet, it is the setting of policy rates that attracts its maximum attention, especially since it adopted IT. Seemingly, the decision-making and engagement process around IT has partially crowded out its other functions.
RBI needs to strengthen all its functions to become the central bank of a ‘Viksit Bharat’. Among other things, the RBI ought to initiate a review of its supervision and regulatory frameworks, ensuring timely and gentler pre-emptive measures rather than reactive and excessive actions.
RBI has been a great institution and has evolved in keeping with the times. It needs to continue to evolve in accordance with the needs of a larger, faster-growing, a more internationally integrated, and a more complex economy.
Why Earning Women Are More At Risk Of Domestic Violence
Unlike in most economies, employment doesn’t translate into protection from intimate partner violence for women in India. Perversely, they suffer a higher risk especially if they earn more.
The immense potential of India’s large population, particularly women, remains largely underutilised in its rapidly growing economy. Female labour force participation remains nearly 20 percentage points lower than in countries like US and China. One significant barrier women face is ironically at home — the high prevalence of intimate partner violence (IPV). IPV violates basic human rights and undermines their economic participation, reduces productivity in the economy, and negatively impacts future generations when children witness violence at home.
According to the latest National Family and Health Survey (NFHS-5) covering nearly 235k women respondents, one in three (31.8%) had experienced some form of violence from their husband or intimate partner in 2019-21. This rate is comparable to Tajikistan (30.8%), Mozambique (32.7%), and Pakistan (33.5%), and is significantly higher than in Philippines and Maldives (less than 18%). Physical violence alone in India was 28.2%.
Despite the enactment of Protection of Women against Domestic Violence Act (PWDVA 2005), deficiencies in implementation of the Act failed to materially reduce IPV incidence. Lifetime IPV incidence decreased by less than two percentage points during 2015-2021.
Equally concerning is the low rate of reporting among IPV victims. National Crime Records Bureau reveals a glaring mismatch between the reported incidence of IPV in surveys and the number of reported cases of domestic violence. Between 2015 and 2019, the crime rate for cases registered under PWDVA stagnated at just 0.1 per lakh population, and reports of “cruelty by husband or relatives” remained below 20 per lakh population. Survey data paints a similar picture.
NFHS-5 indicates that out of all victims of physical or sexual IPV, only about 1% sought help through formal channels such as police, doctors, lawyers, or social workers in the 2019-21 period. Cases of abuse of existing laws by women also need to be addressed, but their prevalence pales compared to unreported IPV against women.
Emancipation of women hasn’t kept pace with the economic advancement of India. In fact, some of the high-growth states like Tamil Nadu and Karnataka are among the worst offenders. Cultural factors play a big role in India, as many studies have indicated. The question Monique Newiak, Navya Srivastava and I examined in a recent paper is how such cultural factors interact with individual characteristics.
One key finding is that employed women remain at a higher risk of violence from their partners, especially if they earn more than their partners. This finding contrasts sharply with the experience in other countries where economic independence of women is viewed as a driving force in reducing IPV.
But there are mitigating factors. We find that the risk of violence is lower when the husband is more educated; when reporting by victims is more common; or societal acceptance of IPV is lower. At the state level, the presence of more women leaders, better reporting infra for victims, and higher charge-sheeting rates matter.
Focusing exclusively on economic independence of women is highly unlikely to materially reduce IPV in India, because social empowerment is also needed that requires challenging patriarchal norms. Violence is often normalised by the victim herself. The share of women who justify wife-beating is a mind-boggling 41%, even higher than those of men. What are some ways in which a society can break the shackles of prevalent cultural norms?
As a starter, signals from leadership matter. During his first Independence Day Speech in 2014, PM urged every parent to treat sons and daughters as equals during their formative years and reinforced the message in 2022 — to “get rid of everything in our behaviour, culture and everyday life that humiliates and demeans women”. Such messages must get more traction from media, and state and local leaders. But as recent elections campaigns in Delhi revealed, local politicians continue to use offensive remarks against women politicians.
Given the enormous cost of domestic violence for society and the economy, every Indian citizen is a stakeholder. In that regard, there are several steps that can be taken by different stakeholders.
First, schools and colleges should introduce mandatory courses on gender equality, as part of the larger goal of embracing diversity. Second, civil society organisations should launch systematic campaigns in every community. Third, all legal rights should be gender-equitable — women in India currently enjoy only 60% of the legal rights afforded to men. Fourth, the capacity of local institutions — such as judiciary, police stations, and support centres — needs overhauling. Finally, the victims themselves must rise against atrocities and find their voice. This can only happen if we prioritise the safety and dignity of every woman — not just in policy, but in practice. Only then can India move towards becoming an inclusive and developed economy.
The writer is professor, National Council of Applied Economic Research. Views are personal.
New AI models risk economic amnesia
Artificial intelligence is now irreversibly out of Pandora’s box, as Martin Wolf of the Financial Times observed, and we must learn to live with machines that can think. The real concern, however, is not just the intelligence of these machines but what they might choose to forget.
For centuries, economic history has served as a foundation for policy-makers, investors and scholars to learn about financial cycles, crises and macroeconomic transformations. From the South Sea Bubble of 1720 to France’s Mississippi Company collapse and the Panic of 1873, history has repeatedly warned of the dangers of speculation, leverage and financial excess.
Contemporary challenges, from inflationary spirals to financial bubbles, have even more recent historical parallels from the Great Depression to the 2008 financial crisis, which reaffirm how history provides insight. Even our understanding of the global South’s economic trajectory today is incomplete without acknowledging the structural forces that shaped it over centuries.
As AI takes on a greater role in economic analysis and policy, an unsettling question arises: will its ability to recognise systemic risks with historical precedents weaken with a lack of immediate algorithmic reference? If AI models prioritise recent information, they may fail to detect the cyclical patterns that have long defined economic history.
The risk is not that AI will erase history, but that it will make historical knowledge seem less relevant and ultimately less studied. A world that sidelines history in favour of algorithmic optimisation risks repeating past mistakes under the illusion that AI-driven models are inherently forward-looking and rational.
Forgetting the past
Economic thought has never evolved in isolation. The Wealth of Nations by Adam Smith was shaped by the emergence of industrial capitalism, Keynesian economics was a response to the Great Depression and Milton Friedman’s monetarist theories were a reaction to the inflationary crises of the 1970s. More recently, post-2008 financial regulations, including stress testing, capital buffers and macroprudential measures, were crafted with past financial crises in mind.
Yet as central banks, financial institutions and regulatory bodies turn to AI models for predictive accuracy, historical analysis risks becoming secondary. AI excels at identifying correlations within a defined dataset, but economic and financial cycles do not always conform to short-term trends or linear progressions. For instance, Mexico’s vulnerability to short-term external debt before the 1994 peso crisis was crucially underestimated, as its immediate economic history had been one of high investor confidence. The failure of AI models to account for these deeper historical cycles could leave policy-makers unprepared for financial stress that follows long-established, often forgotten patterns.
Perils of financial amnesia
Financial markets, more than any other domain, have long suffered from a collective amnesia, repeatedly underestimating risks with clear historical precedents. AI-driven quantitative trading models, typically trained on just 10 to 20 years of data, could worsen this tendency by reinforcing short-termism. If AI-based trading strategies dominate decision-making, will human investors still be able to recognise these warning signs?
The 2008 financial crisis is a prime example: risk models failed to foresee a nationwide housing market collapse, largely because such an event had not occurred in modern financial history. If AI models trained primarily on post-2010 data reinforce similar blind spots, markets could once again be lulled into a false sense of security. The risk is not simply that financial crises will recur – that is inevitable –but that broader preparedness for systemic crises will erode as day-to-day algorithmic prediction becomes more precise.
Dangers of algorithmic curation
AI is not only transforming economic analysis, it is also shaping access to historical knowledge entirely. AI-powered search engines, large language models and financial news aggregators increasingly determine which historical narratives are visible and which fade from view. This algorithmic curation of history is not neutral; it reflects biases embedded in training data and prioritises dominant sources over others. Over time, this could narrow economic discourse, reinforcing widely accepted but potentially flawed interpretations, while marginalising alternative viewpoints.
This is particularly concerning in cases where economic history is complex and contested. There is continuous debate over whether the Great Depression was primarily a market failure or a policy failure, whether the collapse of the Bretton Woods system in the 1970s was inevitable or the result of avoidable policy missteps, and whether the 2008 financial crisis stemmed more from excessive leverage or from decades of financial deregulation. There are various interpretations of colonialism’s economic consequences and the extent to which today’s economic development problems go back to taxation and trade policies of those times.
These are not simple questions, nor do they have singular, prepackaged answers. Yet if AI-driven knowledge systems prioritise specific interpretations over others, they risk flattening historical complexity into deterministic narratives, narrowing the scope of economic debate.
AI’s impact on policy-making
The most immediate danger of AI-driven historical amnesia lies in economic policy-making. Central banks and financial regulators have traditionally studied past crises to inform present decisions. Even post-pandemic monetary policies reflected a deep reading of past inflationary episodes. As AI-driven models take on a larger role in policy analysis, we will see historical case studies being supplanted by algorithmic forecasting and real-time risk modelling. While AI can improve short-term predictive accuracy, it remains unclear whether it will detect vulnerabilities rooted in past financial crises.
OMFIF’s Future of payments notes that institutions such as Bank Indonesia and Banque de France are deploying AI to enhance predictive accuracy. Would AI models trained primarily on post-2010 data have flagged the structural weaknesses that led to the Asian financial crisis, the 1994 Mexican peso crisis or the 2008 collapse? The danger is not simply a failure to predict the next financial shock, but a weakening of institutional memory that makes crisis response less effective.
Despite these risks, AI could serve as a tool for historical preservation rather than amnesia, and, if used properly, could enhance economic history rather than erode it. AI has the potential to digitalise and reconstruct lost economic records, making archives more accessible as well as help identify long-term economic patterns that can span multiple centuries and improve our understanding of financial cycles. AI has the potential to democratise access to historical economic knowledge, ensuring that insights once buried in academic journals or central bank archives are available to a broader audience.
The challenge, then, is not merely to ensure AI’s accuracy in short-term forecasting, but to guard against the erosion of historical context in its analysis. The machines are here, but whether they deepen our understanding of economic history or contribute to its gradual neglect remains a choice still within human hands.
Udaibir Das is a visiting professor at the National Council of Applied Economic Research, senior non-resident adviser at the Bank of England, senior adviser of the International Forum for Sovereign Wealth Funds, and distinguished fellow at the Observer Research Foundation America. He was previously at the Bank for International Settlements, the International Monetary Fund and the Reserve Bank of India.
Reduce, reuse, and recycle
Waste management is the responsibility of municipal bodies in urban India. But these local bodies have inadequate waste-disposal facilities and poor resources.
The excess dumping of waste at the Dhapa ground has been a cause of concern for the Kolkata Municipal Corporation. The civic body is looking for an alternative dumping ground as Dhapa is gradually becoming inadequate to cater to the vast amount of waste that the city produces every day. This is a problem for almost all major cities in India and is likely to aggravate in the future since by the middle of this century, more than 60% of India’s habitants will reside in urban India. India ranks seventh globally in solid waste generation, with the present rate of SWG being 0.34 kg per capita per day; this is expected to increase to 0.7 kg per day by 2025. If this trend continues, India will generate 165 million tonnes of waste by 2030. Why do we generate so much waste? Do other countries also throw so much waste to their landfills?
Several countries have achieved progress in waste management, even reducing waste generation over the years. Sweden, for example, has drastically reduced the amount of waste dumped into landfills, from around 22% of municipal solid waste in 2001 to 1% in 2010. This means that only 42,000 tonnes of solid waste were ending up in landfills in 2010 in Sweden compared to 8,80,000 tonnes in 2001. The recycling rate of electrical and electronic equipment waste hovers near 75% in Sweden, whereas India recycled only 32.9% of the e-waste generated in 2021-2022. If Sweden can achieve this level of recycling, why are we lagging behind? Recycling is a labour-intensive activity. Given the low cost of labour in India, unlike that of a developed country like Sweden, it is surprising that India has such a low recycling rate.
Presently, India generates more than 62 million tonnes of waste in a year, of which only 43 MT gets collected and 31 MT is discarded in wasteyards because only 12 MT can be treated before disposal. With increasing populations and urbanisation, waste management is now a serious problem in India. Waste management is generally considered the responsibility of local municipal bodies in urban India. But these local bodies have inadequate waste-disposal facilities, poor resources, and shoddy technologies for treatment. In India, the informal sector is primarily involved in waste collection and disposal, unlike some developed countries where it is an organised industry, and this leads to inefficient waste collection and segregation. This not only causes inefficiency in the waste management process but is also harmful to the people who are at risk of exposure to dangerous chemicals and neurotoxicants, including lead and mercury, from e-waste disposal sites. India generates over 5.6 MT of plastic waste a year. There is little infrastructure to accommodate the recycling of the plastic waste generated every day in the country. Public awareness and participation in waste management are also abysmally low. Additionally, policies concerning waste management in India broadly focus on end-of-life waste management and there is little focus on reducing waste and maintaining the value proposition of material and components. The business environment is also not very conducive to developing businesses for goods and services that are dependent on recycled material since the supply chain is not developed yet and there is also a lack of incentives for downcycling.
India has launched Mission Lifestyle for Environment, which encourages individuals to adopt environmentally-friendly, sustainable lifestyles. This calls for nudging individuals to practise simple yet effective environment-friendly actions in their daily lives, gradually influencing industries and markets to supply environment-friendly commodities and generate demand for them. The Union budget of 2024-25 announced that water supply, sewage treatment and solid waste management projects and services for 100 large cities would be promoted through bankable projects in partnership with state governments and multilateral development banks. These projects will also promote the use of treated water for irrigation and for filling up tanks in nearby areas. On the regulation front, there are certain stipulations regarding the waste management of cities, including rules on plastic waste management, e-waste management, construction and demolition waste management, and policies on metals recycling. NITI Aayog, in consultation with the ministry of environment and forest and climate change, identified 11 areas to facilitate transitioning from linear to circular economy and to give an impetus to India’s Atmanirbhar Bharat Abhiyaan. The identified areas were Municipal Solid Waste and Liquid Waste, Scrap Metal (ferrous and non-ferrous), Lithium Ion batteries, Tyre and Rubber Recycling, Gypsum Waste, End-of-life Vehicles, Electronic Waste, Toxic and Hazardous Industrial Waste, Used Oil Waste, Agriculture Waste and Solar Panels. Municipal solid and liquid waste management has received a significant push with the launch of the Swachh Bharat Mission-Urban in 2014. Significant progress has been made in utilising fly ash and slag generated in the steel industry and other sectors. The ministry of consumer affairs has set up a committee to come up with a Right to Repair framework under which farming equipment, mobile phones and tablets, consumer durables, automobiles and automobile equipment and so on are covered.
There is an urgent need to increase our policy focus on ‘reuse or recycle’, which would gradually reduce the generation of waste commodities. In this circular economy approach, products need to be designed in such a way that waste and pollution are minimised. This involves considering the full lifecycle of a product through strategies like reuse, repair, remanufacturing, and recycling. The goal is to keep materials and products circulating in the economy for as long as possible.
There is a significant need for research and development focussing on reuse and recycling. Waste-to-energy programmes must be encouraged where different methods of incineration, gasification, pyrolysis, anaerobic digestion and others are explored. It is often found that manufacturers retain proprietary control of spare parts, including their design. Warranty cards of several products mention that getting them repaired from an outfit not recognised by the makers would lead to customers losing their warranty benefit. Regulations are needed to make repairing easy and cost-effective. Publication of manuals for complex electronic and electrical goods must be made mandatory to help users or local repair shops make repairs easily.
India needs to focus on the mantra of ‘Reduce, Reuse, and Recycle’ to meet the challenge of urban waste management.
Chetana Chaudhuri is a Fellow and Sanjib Pohit a Professor at the National Council of Applied Economic Research. Views are personal.