My Perspective on India’s Rice Export Prospects (31July 2025)

Study and Assumptions

In preparing this personal analysis, I reviewed publicly available trade statistics, news reports, and policy announcements through 31 July 2025. My aim was to understand how recent tariff actions by the United States affect India’s ability to export rice, particularly basmati, and to determine practical strategies that could mitigate any negative impact. The analysis draws heavily on Observatory of Economic Complexity (OEC) data for U.S. import shares, on reported tariff rates from U.S. government announcements, and on news accounts of currency movements and export taxes in competing countries. I assume that reported import values in the OEC represent the most current full‑year picture (June 2024 – May 2025) and that U.S. tariffs announced in April 2025 will remain in place at least through late 2025. I also assume typical export prices and currency exchange rates reported in early 2025 when calculating price differentials.

U.S. Rice Import Composition

The U.S. imports rice primarily to satisfy demand for aromatic varieties (jasmine and basmati) that domestic producers cannot supply. According to OEC data for June 2024 to May 2025, the U.S. bought roughly US $1.56 billion of rice. The pie chart below illustrates each supplier’s share (approximate percentages):

                        

This distribution shows that Thailand provides about half of all U.S. rice imports, while India supplies a quarter. China, Pakistan, and Vietnam collectively contribute less than 10 %, with the remainder coming from smaller producers. Because aromatic rice dominates U.S. imports, India’s basmati and Thailand’s jasmine are in direct competition.

Table: Illustrative Price Differential after Tariffs

To make sense of how tariffs affect competitiveness, I constructed a simple example using hypothetical export prices. Thai jasmine rice is assumed to sell at US $1,000 per tonne, while Indian basmati sells at a 25% discount, or US $750 per tonne. Applying the U.S. reciprocal tariffs announced in April 2025—36% on Thai rice and 26% on Indian rice—yields the following landed costs:

Item	Thai jasmine rice (US$/t)	Indian basmati rice (US$/t)
Base price per tonne	1,000 (assumed price)	750 (25 % less than Thai price)
Tariff rate	36%	26%
Duty amount	360	195
Landed price after tariff	1,360	945

Implication: Even after the higher U.S. duty, Indian basmati remains roughly 30% cheaper than Thai jasmine. This price gap, combined with a weaker rupee, can allow Indian exporters to retain or grow market share.

Factors Considered

Tariffs and quotas: In April 2025 the U.S. imposed reciprocal tariffs on major suppliers. Thai rice now faces a 36% duty, Vietnamese rice 46%, Pakistan 29%, China 54%, and India 26%. These rates supersede earlier tariff‑rate quotas that had allowed duty‑free access for basmati.

Quality and supply chain: India’s unique advantage lies in its ability to supply both basmati and non‑basmati rice. Between April 2024 and January 2025 India exported about 230,643 t of basmati and 51,334t of non‑basmati rice to the U.S., totaling 234,469t for fiscal year 2023‑24. Thailand, by contrast, shipped almost 850,000t of jasmine rice. Vietnam primarily sells fragrant rice and has introduced a 5 % value‑added tax on exports from July 2025. Pakistan supplies only about 1,500–3,000t per month.

Currency: The Indian rupee depreciated to around ₹89.5 per US$ in mid‑2025, making exports cheaper in dollar terms. Thailand’s baht strengthened, reducing its price competitiveness. Vietnam’s new export VAT effectively increases its selling price by 5 %.

Farmer dynamics and stocks: India’s rice production reached a record 151mt in 2024/25, leaving surplus stocks and prompting farmers to plant high‑yielding varieties such as Pusa 1509, which matures quickly. These stockpiles give India flexibility to price aggressively. Thailand, meanwhile, trimmed its export target to 7.5mt due to reduced competitiveness and strong currency.

Analysis and Insights

1. Price competitiveness favours India. Because Thai jasmine is significantly more expensive, the additional U.S. tariff amplifies the price gap. Even after absorbing part of the duty, Indian basmati remains cheaper.
2. Tariffs hurt competitors more than India. Vietnam and Pakistan face higher tariff rates (46% and 29%) and, in Vietnam’s case, an export tax. The U.S. market accounts for only ~5% of India’s basmati exports, so even a temporary slowdown does not threaten India’s overall export trajectory.
3. Currency and surplus stocks cushion the blow. The weak rupee and record inventories allow Indian exporters to price competitively and absorb part of the 26% duty. Thai exporters, facing a strong baht, have less room to reduce prices.
4. Supply chain reliability matters. Shipping times of 35–45 days and the need for long‑term contracts mean that exporters should invest in logistics and traceability to maintain buyer confidence.

Conclusions

In my view, India should approach the U.S. tariff challenge as an opportunity to improve its competitive position.

1. Use currency advantage to absorb tariffs. By leveraging the weak rupee and adjusting FOB prices, exporters can keep landed prices attractive. This is key to retaining market share over more expensive Thai jasmine.
2. Invest in value‑added branding. Premium packaging, organic certifications, and geographic‑indication labelling will justify higher retail prices and cushion tariffs. Offering ready‑to‑cook or fortified basmati products can capture new consumer segments.
3. Optimise logistics and contracts. Securing multi‑year supply agreements, consolidating shipments to reduce freight costs, and investing in U.S. warehousing will make Indian exports more reliable.
4. Diversify markets and pursue diplomacy. The US is high-visibility, but 84–85% of Indian rice exports go elsewhere. Since the U.S. takes only a small share of Indian basmati, expanding sales in the Middle East, Africa, and Europe will offset any U.S. slowdown. At the same time, engaging in trade negotiations could lead to reciprocal tariff reductions or a special status for basmati as a heritage product.

To conclude, while the US tariff hike is significant, it does not fundamentally undercut India’s capacity to export basmati rice profitably to the US. The most likely short-term impact is a squeeze on margins, but India’s exporters have enough buffer in price and scale—plus entrenched supply and brand presence, especially in the diaspora-driven segment

The smart path forward lies in combining this cost resilience with greater focus on branding, supply chain efficiency, and market diversification. If those are embraced, India can not only weather the US tariff storm but actually come out stronger, with broader markets and deeper roots in global rice trade.

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Research and reading sources:

Tariffs could reshape US rice prices | Food Business News https://www.foodbusinessnews.net/articles/28033-tariffs-could-reshape-us-rice-prices

Global Rice Market Divergence: India's Price Plunge vs. Bangladesh's Inflationary Surge- https://www.ainvest.com/news/global-rice-market-divergence-india-price-plunge-bangladesh-inflationary-surge-2507/

Indian rice market to face short-term disruption by US reciprocal tariffs | S&P Global-https://www.spglobal.com/commodity-insights/en/news-research/latest-news/agriculture/040325-indian-rice-market-to-face-short-term-disruption-by-us-reciprocal-tariffs

Vietnam fragrant rice prices surge as new tax weighs on export markets | S&P Global-https://www.spglobal.com/commodity-insights/en/news-research/latest-news/agriculture/080125-vietnam-fragrant-rice-prices-surge-as-new-tax-weighs-on-export-markets

Thai rice exports decline as India re-enters market | World Grain-https://www.world-grain.com/articles/21460-thai-rice-exports-decline-as-india-re-enters-market

Deconstructing the World Bank’s New Poverty and Inequality Numbers - Part 2

 Beyond the Metrics – The Policies, the People, and the Great Wealth Blind Spot

In the first part of this analysis, I deconstructed the "what" and "how" of the World Bank's recent, transformative update on Indian poverty and inequality. We established that the dramatic fall in the Gini index was overwhelmingly driven by a methodological recalibration, creating a new, lower baseline from which to measure progress. Now, I want to move beyond the statistical machinery and explore the "why." What does this new, albeit partial, picture tell us about the real-world policies that have shaped the lives of over a billion people? And, more importantly, what does its great blind spot—the complete absence of wealth data—mean for our understanding of India's economic trajectory? To do this, I will examine the report's implications through the four critical lenses of economic well-being: 1) access to income, 2) the policies that shape it, 3) access to wealth, and 4. the policies intended to foster it.

1.     Let's begin with the most fundamental element: access to income generation. A sustainable reduction in poverty and inequality is built on the foundation of empowering people to earn a decent living. When I look for evidence of this in the report, I find an unsettling silence. The data, rooted in consumption, does not track wages, employment types, or business creation. It cannot tell us if the decline in poverty is due to a structural shift from low-paying agriculture to better-paying manufacturing or services, or if real wages have outpaced inflation. The report shows that the poorest households have more resources, but the origin of those resources remains ambiguous. This is where we must read between the lines. The primary evidence of improved household resources is the stunning drop in extreme poverty, which, by the new metric ($3.00/day), fell from 27.1% in 2011 to a mere 5.25% in 2022. A change of this magnitude is not trivial; it means tens of millions of families crossed a critical threshold of consumption. But was this because they were earning more or receiving more? The report’s own methodology, with its careful valuation of welfare transfers, points heavily towards the latter. The explicit accounting for PDS rations and other in-kind benefits as a form of non-cash income provides a clear, quantifiable source for this improved consumption. While real-world initiatives like the 'Make in India' campaign, the formalization of the economy post-GST, and various skilling missions are all aimed at boosting generated income, this report offers no data to measure their direct impact on household earnings. My conclusion, based strictly on the evidence presented, is that the report paints a picture of a population that is better supported by the state, rather than one that is necessarily more empowered in the labor market. The story of income generation remains largely an unwritten chapter.

 

2.     This leads directly to the second, and perhaps most compelling, aspect of the update: its ability to illuminate the power of policies that directly target consumption inequality. It is here that the new methodology truly shines, acting as a lens that brings the impact of India's social safety net into sharp focus. The most significant of these is the Public Distribution System (PDS), fortified by the National Food Security Act (NFSA) of 2013. For decades, the true value of this massive food transfer program was statistically invisible in welfare calculations. By now imputing the market value of this subsidized food, the World Bank isn't just making a technical tweak; it is statistically validating the role of food security as a cornerstone of India's poverty reduction and inequality compression. During the COVID-19 pandemic, this was amplified by the Pradhan Mantri Garib Kalyan Anna Yojana (PMGKAY), which provided additional free rations. This policy didn't just prevent a humanitarian crisis; it actively propped up the consumption floor, and the new methodology allows us to see that effect in the numbers.

Beyond food, the report hints at the impact of other policies. The mention of valuing "free school uniforms and footwear" points to the quiet but steady work of schemes under the Samagra Shiksha Abhiyan, which reduce the out-of-pocket expenditure for poor families, freeing up scarce cash for nutrition or other needs. Furthermore, while not named, the fingerprints of the Direct Benefit Transfer (DBT) system are all over these results. Since 2013, India has progressively shifted from inefficient in-kind subsidies to direct cash transfers for everything from LPG cylinders (PAHAL scheme) to farmer support (PM-KISAN). By plugging leaks and ensuring money reaches intended beneficiaries, DBT has likely been a powerful engine for boosting real consumption among the poor, an effect that is captured in the aggregate consumption data of the HCES. So, when we look at the 3.3-point decline in the Gini index between 2011 and 2022 under the new series, it is plausible to attribute a significant portion of it to the scaling-up and improved efficiency of these very policies. The new data doesn't just show a fall in inequality; it helps explain why, pointing directly to the success of a consumption-support model of governance.

3.     However, as we turn from consumption to wealth, the picture goes from high-definition to a complete blackout. This is the report’s great, and in my view, dangerous, blind spot. Access to wealth—owning a home, having savings, building a small business, possessing land—is the bedrock of intergenerational mobility and economic security. It is what separates a household that is one bad harvest away from poverty from one that has a buffer to withstand shocks. Yet, the report is methodologically designed to be blind to it. The exclusion of durable goods purchases removes a key indicator of asset accumulation. The exclusion of housing values removes the single largest asset for most families from the equation. The result is a paradox: we are analyzing inequality using a tool that ignores the most unequal and most critical component of a family's economic life.

This is not a trivial omission; it is a fundamental flaw in using this dataset alone to understand Indian inequality. The period from 2011 to 2022 was not just one of expanding welfare; it was also a period of explosive wealth creation at the top. The Indian stock market has seen historic booms, a new class of "unicorn" startups has emerged, and real estate values in major cities have soared. This creation of financial and physical wealth is, by its nature, highly concentrated. It is therefore entirely possible, and I would argue highly probable, that while consumption inequality was modestly declining (due to the welfare floor rising), wealth inequality was simultaneously increasing, perhaps significantly. The report offers us a comforting story about one trend while being structurally incapable of telling us the potentially alarming story of the other. For a policymaker, this is like flying a plane with the altimeter working but the radar turned off. You know your current altitude, but you have no idea if you are flying towards a mountain.

4.     This brings me to the final area of analysis: policies designed to reduce wealth inequality. Here, the report’s silence is deafening, as it offers no way to evaluate the success or failure of the government's own flagship programs. Consider the Pradhan Mantri Awas Yojana (PMAY), a massive mission to provide "Housing for All." Has it succeeded in creating housing assets for the poor and reducing the housing gap? Or has its implementation been uneven? We cannot know from this report. Think of the Pradhan Mantri Jan Dhan Yojana (PMJDY), which brought over 500 million people into the formal banking system. Has this financial inclusion translated into actual savings and wealth creation for the poor, or are many of these accounts dormant? The report offers no clue. What about the MUDRA Yojana for micro-enterprise loans, or state-level efforts to grant land titles? These are all policies explicitly aimed at building an asset base for the economically vulnerable. Their impact is arguably more important for long-term security than annual consumption support. Yet, the primary tool used by the World Bank to monitor India's progress cannot say a single word about whether they are working. This is a critical failure of the data ecosystem. It means that trillions of rupees are being spent on wealth-creation policies with no corresponding metric in this global benchmark to track their distributional impact.

In my final analysis, the World Bank’s June 2025 update is a profound and necessary revision that nonetheless comes with a heavy burden of responsibility for anyone who uses it. It is an invaluable tool for understanding the success of India's consumption-support model. It provides, for the first time, a statistical validation of the immense role that food security and welfare transfers have played in lifting millions from abject poverty and compressing consumption gaps. We should celebrate this clarity. However, we must resist the temptation to accept this partial picture as the whole truth. The report’s silence on income generation and its structural blindness to wealth inequality are not minor details; they are gaping holes in the narrative. The risk is that we celebrate a battle won on consumption support while ignoring a potential war being lost on wealth concentration and opportunity. The true challenge for India, and for those of us who analyze it, is to build upon this new baseline: to continue strengthening the safety net it so clearly validates, while urgently developing and using better tools to measure and address the deeper, structural inequalities of income and wealth that it currently leaves shrouded in darkness.

Deconstructing the World Bank’s New Poverty and Inequality Numbers - Part 1

 Part 1: The Recalibration of India: Deconstructing the World Bank’s New Poverty and Inequality Numbers

When the World Bank releases a major update to its Poverty and Inequality Platform (PIP), it’s a significant event for global development watchers. But when that update involves India, a country home to one-sixth of humanity, the implications are monumental. The "June 2025 Update" is just such an event. At first glance, the numbers are startling, suggesting a dramatic and rapid decline in both poverty and, most strikingly, inequality in India. It’s a narrative that aligns with a story of broad-based progress. Yet, as I began to delve into the accompanying technical note, a far more complex and layered picture emerged. The story of India's improved metrics is not a simple tale of policy triumph; it is an intricate interplay of genuine progress, profound methodological shifts, and critical data omissions. This update is less a photograph of India and more a recalibration of the camera itself. Understanding the nature of this recalibration is the most important task for anyone seeking to grasp the reality of poverty and inequality in India today.

The significance of this update lies not just in the new numbers it presents, but in the fundamental break it creates with all previously published data. To comprehend the shift, we must first understand the machinery. The Gini index, our primary metric for inequality, measures the distribution of a resource—in this case, consumption—across a population. A score of 0 represents perfect equality (everyone has the same), while a score of 100 represents perfect inequality (one person has everything). For years, India’s Gini hovered in the mid-to-high 30s, a figure that, while not extreme, pointed to significant disparities. The new report, however, presents a Gini of just 28.8 for 2011, plummeting to 25.5 by 2022. This is a seismic shift. To put it in perspective, the previous World Bank vintage, published just months earlier in September 2024, had reported a Gini of 35.4 for the very same year, 2011. How can a country’s inequality metric for a year in the past fall by nearly 7 points overnight? The answer has nothing to do with history and everything to do with methodology. The Bank has, in essence, re-engineered its entire approach to measuring Indian consumption, and this re-engineering accounts for the lion’s share of the “improvement.”

Let's break down the four key factors driving this change.

-        The single most dominant factor, which the report’s own analysis suggests accounts for a staggering 80% of the reduction in inequality, is the shift in the survey's recall period. Previously, India’s surveys used a Uniform Reference Period (URP), asking households to recall all their expenditures over the last 30 days. The new methodology, applied retroactively to 2011, uses a Modified Mixed Reference Period (MMRP). Under MMRP, families report frequent purchases like food and groceries over a short period (7 or 30 days) but recall infrequent, lumpy expenditures like clothing, footwear, or small appliances over a full year (365 days). The impact of this is profound. A low-income family might not buy a new sari or a pair of school shoes every month, so a 30-day survey could easily miss this expenditure, understating their annual consumption. A 365-day recall for these items captures this spending far more accurately. This systematically lifts the measured consumption of lower and middle-income households, who spend a larger portion of their non-food budget on such lumpy items. It has a powerful compressive effect on the entire distribution, closing the gap between the bottom and the top and, consequently, causing the Gini index to fall dramatically.

-        The second factor is the construction of a new welfare aggregate. This is not one change but a collection of crucial adjustments that collectively lift the bottom of the distribution while lowering the top. To lift the bottom, the Bank now imputes a market-equivalent price for goods received through the Public Distribution System (PDS). For decades, a poor family’s consumption was valued at the one or two rupees they paid for subsidized grain, or zero if it was free. This was a massive understatement of their real welfare. By valuing that 35kg of grain at its market price, the new methodology correctly captures the enormous impact of India’s food security net, boosting the measured welfare of millions. Simultaneously, the methodology lowers the top by excluding certain types of expenditure. Purchases of durable goods, jewelry, and even wristwatches are no longer counted. The justification is that these are often stores of value, not pure consumption. Furthermore, and critically, the value of housing services has been completely excluded for both renters and homeowners due to challenges in consistent data capture. Since wealthier, urban households spend disproportionately more on high-value items and housing, excluding these components clips the top of the consumption distribution, further compressing the Gini index.

-        The third and fourth factors, the revision of price deflators and the adoption of new 2021 Purchasing Power Parities (PPPs), are more technical but important. Using more granular state-specific price indices instead of simple urban-rural ones makes the data far more accurate in a country as diverse as India. However, its impact on the Gini index is minimal compared to the MMRP and welfare aggregate changes. The new PPPs, meanwhile, primarily affect the international poverty headcount—for instance, changing the extreme poverty line from $2.15 to $3.00 per day. They have no direct impact on the Gini index, which is calculated on the domestic currency distribution before any international conversion. Together, these four changes create the "structural break." The India of 2011 in this report is a statistically different entity from the India of 2011 in all previous reports. The fall in inequality from 35.4 to 28.8 for that year is not a reflection of a newly discovered reality, but the result of applying a new measurement ruler. The subsequent, more modest decline from 28.8 in 2011 to 25.5 in 2022 is the trend we are now invited to analyze, a trend that is itself shaped by these new methodological choices.

What this new, re-engineered camera fails to capture, however, is as important as what it now brings into focus. The report, based on the Household Consumption Expenditure Survey (HCES), is silent on the crucial questions of income and wealth. It tells us what households spend, but not how they earn. There are no metrics here on access to better jobs, rising wages, or entrepreneurial income. Has inequality of opportunity in the labor market improved? We cannot say from this data. The improved consumption at the bottom could be from better wages, or it could be, as the methodology itself suggests, overwhelmingly from government transfers. The report shows a population that is better supported, but not necessarily one that is more empowered through income generation. The omission of wealth is even more stark. The methodology explicitly excludes the primary channels of wealth creation and accumulation for ordinary families: the purchase of durables, the value of housing, and other assets. In a period where India has seen a booming stock market and significant real estate activity, this is a colossal blind spot. We are being shown a picture of declining consumption inequality while being left completely in the dark about what might be a very different, and potentially diverging, trend in wealth inequality. The report cannot tell us if the bottom 40% are accumulating assets or falling further behind in the race for wealth.

In conclusion, the World Bank’s June 2025 update is a landmark revision that must be handled with extreme care. The good is that the new methodology is undeniably more robust. It is far better at capturing the real-world impact of India’s massive social safety net, particularly the PDS, providing a more accurate measure of the consumption floor in the country. This is a welcome and long-overdue improvement. The bad, however, is the high potential for misinterpretation. The headline-grabbing drop in the Gini index is primarily a statistical artifact of this methodological change, not a simple continuation of a past trend. Comparing the new Gini of 25.5 to the old figures in the mid-30s is a comparison of apples and oranges. The most significant shortcoming, however, lies in its omissions. By focusing exclusively on a narrow definition of consumption and actively excluding proxies for wealth, the report presents a partial and potentially sanitised view of inequality. For policymakers, this is perilous. It risks fostering complacency by highlighting success in consumption support while masking potential failures in promoting equitable income opportunities and preventing the concentration of wealth. The public, in turn, may be led to believe that the challenge of inequality is far smaller than it actually is. This update has given us a better tool to measure one part of the elephant, but we must not forget that it leaves the other, perhaps larger, parts entirely in the dark.

Four MegaVakkyas that transformed my perspective: Lessons from Dr. S. Jaishankar

Every now and then, I stumble across a quote or idea that sticks with me for days. These are moments in life when a single statement can shift your perspective and inspire deep self-reflection. In recent times, I found myself thinking about some of the bold statements made by Dr. S. Jaishankar, India’s Foreign Minister. Even though he’s talking about world affairs, I couldn’t help but see how his words could apply to my own life.

From the many profound statements, I choose four “MegaVakkyas” – profound statements. Though rooted in international relations, I found that each one held a mirror to my own life, offering lessons that could elevate not just in professional journey, but personal happiness as well. Here’s how each of these “MegaVakkyas” got me thinking—and potentially changing my perspective.

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1. "Europe has to grow out of the mindset that Europe's problems are the world's problems, but the world's problems are not Europe's problems."

Meaning:
Dr. Jaishankar’s words call out the double standards that sometimes exist in global affairs. He reminds us that solidarity must be mutual—if you expect others to care about your challenges, you must also care about theirs.

Empowering Thought:
At first, this sounded like pure geopolitics. But the more I thought about it, the more I realized I’ve sometimes expected others to drop everything for my problems, even when I haven’t always been as attentive to theirs. I realized that in my relationships—whether at work or at home—I can’t expect support and empathy if I’m not willing to offer the same. True connection is a two-way street. It made me think about a relationship and how much I value the ones where support goes both ways. For me, this became a lesson in reciprocity. I started making a conscious effort to check in on people, not just when I needed something, but because I genuinely cared. It’s funny how much closer I’ve grown to people just by being there for them, too.

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2. "The era of uninterrupted dialogue is over. Actions have consequences."

Meaning:
This statement is about setting boundaries and holding others accountable. Dr. Jaishankar makes it clear that endless engagement is not sustainable in the face of repeated hostility.

Empowering Thought:
This one hit home in a different way. I’ve definitely been guilty of giving endless chances—to people, to projects, in shares, to situations that just weren’t working. Often found myself stuck in cycles where I kept giving chances to people or situations that drained me. Reading this, I realized it’s okay to draw a line. I don’t have to keep engaging with things (or people) that leave me frustrated or drained. Setting boundaries felt awkward at first, but it’s honestly been freeing. I have more energy for the things—and the people—that matter. I learned that it’s okay to walk away from relationships or commitments that no longer serve me, and that my well-being matters. By valuing my time and energy, I created space for more meaningful connections and opportunities—both professionally and personally.

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3. "We are not part of any club that excludes others."

Meaning:
Here, Dr. Jaishankar emphasizes inclusivity and the importance of not building walls that shut others out.

Empowering Thought:
This made me think about the groups I’ve been a part of, both at work and outside. I’ve never liked the feeling of being left out, but I hadn’t really considered whether I was unintentionally doing the same to others. Was I unconsciously excluding others? Was I seeking validation by being “in” with certain people? Since then, I’ve tried to be more open and welcoming, inviting new people into conversations or projects. It’s made my world a bit bigger and a lot more interesting. By embracing diversity and making others feel included, I not only enriched my own experience but also contributed to a more positive environment at work and in my social life.

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4. "History is not a spectator sport."

Meaning:
A bold reminder that we must actively participate in shaping our destiny, rather than passively watching events unfold.

Empowering Thought:
This one really got me. I’ve spent a lot of time waiting—waiting for the right opportunity, the right moment, someone else to take the lead. But life isn’t something you can just watch from the sidelines. I started saying yes to more things, speaking up more often, and taking a few risks. Not everything has worked out perfectly, but I feel more in control and, honestly, more alive. I started taking ownership of my choices, setting goals, and pursuing them with intention. The more proactive I became, the more empowered and fulfilled I felt. I realized that happiness and success are not accidental—they are created by those who dare to participate.

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How These Lessons Elevate My Life

I didn’t expect to get so much out of a handful of diplomatic soundbites, but these four  MegaVakkyas really did nudge me out of my comfort zone. I’m not saying I’ve got it all figured out, but I do feel like I’m moving in the right direction—more connected, more assertive, and a lot more open to the world around me. And that feels pretty good, both at work and at home. They’ve helped me build stronger relationships, set healthier boundaries, foster inclusivity, and become an active participant in my own life story. Professionally, I’ve become a better team player and leader. Personally, I feel more connected, confident, and content.

If you’re looking for a way to grow and find greater happiness, I invite you to reflect on these  MegaVakkyas. Let them guide you toward a life of empathy, empowerment, inclusivity, and purposeful action. The world—and your own journey—will be richer for it.

Know your consumer

                                                     

RBI has been conducting consumer confidence survey and publishes results on consumer perception  about the prevailing economic conditions in the country(Current Situation Index -CSI).

RBI expects the survey to provide evidence of consumer confidence and a predictor of consumer spending. They expect economic agents that influence aggregate supply – manufacturers; retailers; investors; builders; government agencies - rely on measures of consumer confidence to make an assessment of consumption expenditure which, in turn, forms their production decisions.

The indicators are expected to  also provide useful insights to wielders of monetary and fiscal policies who have to make decisions on modulating aggregate demand in the economy around the path of productive capacity in the economy in order to ensure macroeconomic stability.

The survey first started in 2010, is conducted quarterly and summarized into yearly report and released in the 3rd quarter every year.

Recently, RBI released its 16-17 report. 

The survey is done through opinions collected from 5400 respondents in 6 cities - Bengaluru;. Chennai; Hyderabad; Kolkata; Mumbai; and New Delhi.

The CSI is a summation of five parameters - general economic situation, employment scenario, price level, household income & household's spending.

While, the results has a lot of charts and statistics, what is perplexing is that RBI has stuck to the six cities till date.

Two of fastest growing states, in terms of GDP growth %, are not represented at all (Gujarat & Madhaya Pradesh). Also missing out is UP, Bihar, and so 35% of  population has remained unrepresented. Kerala, the most literate state is missing. And finally, NE is completely neglected. Kolkata cannot be by any stretch of imagination represent NE? 

The second key aspect of the report is its relevance. The report by its obvious nature should be central part of discussions, debate and should have seen fair representation in the media  and economic forums

Save for an article, there is no murmur about this survey. The yearly economic survey by the Government of India also does not seem to bother too much on the results of this survey.

Analyst and other institutions seems to be either unaware or not keen to look at this report. Not to mention, manufacturers, retailers or other economic agents have no reason to use this report.

Surely there is enough evidence for RBI to rethink its main assumptions of this survey and its relevance.

In my opinion, RBI should stop releasing reports that does not represent India in its current avatar. The "metro is a representation of India", is no longer an assumption that RBI should lean into. 

RBI, do you know your consumers?

RBI vs. ROB (Rest of the Banks)

RBI on 4^th Oct 2017 released a report of the Internal Study Group, to Review the Working of the Marginal Cost of Funds Based Lending Rate System (MCLR). This rate is applicable to borrowers when they apply for loan. RBI through the banking system (I will call them - Rest of Banks (ROB)) manages the transmission of interest rate policy.

The Executive summary started off with a very damning note on monetary policy transmission by the banks (under “Key findings”). The extract below is an indication that the committee was categorical in ascribing malafide intention, and was short of using the word “cheating”, in the manner in which, the Banks avoided policy rates transmission, to its respective customers.

Extract from report -Key findings 

“Analysis conducted by the Study Group suggests that banks deviated in an ad hoc manner from the specified methodologies for calculating the base rate and the MCLR, to either inflate the base rate or prevent the base rate from falling in line with the cost of funds.

These ad hoc adjustments included, inter alia,

(i)            inappropriate calculation of the cost of funds;

(ii)           no change in the base rate even as the cost of deposits declined significantly;

(iii)          sharp increase in the return on net worth out of tune with past track record or future prospects to offset the impact of reduction in the cost of deposits on the lending rate; and

(iv)         Inclusion of new components in the base rate formula to adjust the rate to a desired level. The slow transmission to the base rate loan portfolio was further accentuated by the long (annual) reset periods”. (para 3 of executive summary) -       End of extract

The report cited that the major factor that impeded the transmission, was the large portion of deposits with fixed rates and 1 year and above maturity (53% of deposits held were in this category). While this was in the liability side, the other factor was Banks were competing with  savings instruments like mutual funds and small savings schemes and hence were reluctant to reduce the interest rates (would have led to loss of deposits, though tax adjusted returns are much lower)

The other factor quoted for non-transmission is large NPA problem that the banks are facing and the banks thought it prudent to cover themselves with unexpected loan losses in credit portfolios by retaining higher spreads.

The report indicated two key elements on the transmission of spread changes

1.    The report dwelt that there was a “lag in spreads changes” both in private and public banks. While the spread in private banks was around 6 months, the public banks did not change the spreads even after 6 months.

2.    The second aspect was that for the existing/outstanding loan, the transmission was lower than for new loans. This means, the existing borrowers continued to pay higher interest rates than what RBI’s monetary policy rates indicated (since mostly the report covered period when the interest rates were coming down).

Finally, the report cites, how banks tended to charge the spreads over Marginal Cost of Funds Based Lending Rate (MCLR) arbitrarily (evidence gathered from a special study of select banks).

These arbitrary actions were

1)    Large reduction in MCLR (that could have led to lower interest rates) was partly offset by simultaneous  increase in spreads in the form of  “business strategy premium”, ostensibly to reduce  the pass through to lending rates

2)    There were no documentation of the rationale for fixing business strategy premium for various sectors (sector-wise adjustment, without adequate documentation is a clear indication that the banks did not want the RBI or for that matter anybody else to see what and how this was calculated)

3)    Many of the banks cited, did not get these premiums approved its Board

4)    Some of the banks, just arrived at this premium as the “residual” by subtracting the existing rate from the arrived MCLR, thus in fact retaining the rates (hence no transmission)

From the above its clear and unambiguous that Banks in India have played a big role in thwarting transmitting policy rates to the customers through erroneous as well as with malafide intentions. Moreover, indication of opaqueness, lack of internal governance and rank opportunism is revealed.

This seems very similar to the LIBOR gate (rate manipulation) that saw few unscrupulous bankers decide LIBOR rates to suit their needs than through normal economic considerations.

Recommendations by study group-

1.    Banks to be advised to recalculate the base rate immediately by removing/adjusting arbitrary and discretionary components added to the formula. The intent is correct, but who and how this will be executed and who in RBI will take responsibility and how much time will the banks be given to make this changes is not clear. Without recommendation that specifies deadline, this seems improbable at this moment

2.    RBI had not specified sunset clause for Bank rate (the earlier avatar of MCLR) leading to very slow migration to MCLR by banks (contrast this with GST migration). The study group has recommended that Banks be asked to migrate the existing customers to MCLR without paying any conversion fee or switchover fee. Banks have been given time between April18 till March 2019.

3.     Banks are recommended to show in each branch and their sites, the MCLR rate for each tenor across various industries. They have also recommended that IBA or such other organization may be asked to show the rates across various banks for easy comparison.

4.    The study group recommended that both the Bank rate and MCLR have not been effective in transmitting the policy rates to the final customer/in the market, and has given alternative “external benchmark rates” that may be adopted after taking into account feedback and suggestion from the stakeholders. Out of possible 13 alternatives, the study group has identified 3 as benchmark rates. These are

a.    a risk-free curve involving T-Bill rates

b.    CD (Certificates of Deposit) rates

c.    Reserve Bank’s policy repo rate         

RBI is expected to take action on the above recommendation.

In the end, the issue is not which benchmark rate will RBI use to monitor and transmit its policy direction, but how will it ensure that Banks do follow on and pass it the customers in reasonable time, as it intended to and do not rob the customers. With the expected increase in accessing formal banking (JAM) by retail customers, integration of large no of formal SME into financial system and opening up of digital banking (payments bank, P2P lending, etc), RBI’s role is cut out in its most important function of setting the right interest rate and transmission into the financial system and for, RBI this is “too big to fail”.

Annexure:

 Key charts that support the issues surrounding transmission of policy rates

 

How to read the above tables.

1.    Repo rate – Policy rate reduction by RBI (reduction indicated in negative).

2.    Changes in Base rate (the earlier avatar of MCLR (prior to April’16) against the repo rate is lower and with wide margins.

3.    Compare this with the reduction in Deposit rates (liabilities side) and WALR –outstanding/fresh rupee loan.

4.    The transmission in outstanding loan is far lower than fresh loans

5.    Impact of demonetization seems to be positive in driving transmission of policy rates.

6.    The higher cut in the MCR post demonetizations is due to high liquidity (deposit of high value note) and weak credit demand

7.    Private banks transmission was better than public sector banks

8.    In the sector-wise lending rates, the personal vehicle, housing and agri loans were least adjusted while, export credit loans and large corporates loan, the transmission was higher. The bias towards large corporates is very visible, while retail loans have not benefitted (in the reducing interest regime)