Urbanomics

The demand side constraint of the Indian economy

noreply@blogger.com (Urbanomics) — Sun, 26 Jul 2026 23:30:00 +0000

I blogged here, arguing that Indian businesses face a structural cost constraint similar to that of developed economies that erode their global competitiveness. This cost constraint on the supply side must be seen alongside a constraint of a consumption class that is far smaller and shallower than imagined. In other words, a developed country's cost structure in a low-income country's market size.

This point has been a consistent argument of this blog and was documented in great detail in Can India Grow? in 2016, much before it became more widely recognised.

India’s demand-side problem is twofold. For a start, it already has a low per capita income, the lowest among its emerging market peers. Second, this itself conceals a low base of the consumption class that is also not widening proportionately with economic growth. The nature of economic growth is such that it is not only not broad-based enough but is also widening inequality.

The recently released PRICE - Tata Sons Many Urban Indias report is a confirmation of the demand-side constraint. It finds that the top 100 cities, with just under a fifth of the population, capture almost half of all household surplus, the closest measure to genuine discretionary spending capacity. What sits outside, the other 81% of India, holds only about 53% of surplus combined, and most of that is in the top slices of tier-2/3 towns and the productive rural belt. The surplus-generating layer of the economy is essentially urban and already fully contained. There is no large pool of "middle India" outside urban India waiting to broaden the base.

PRICE's top-100 city-tier framework (Big Six, Boomtowns, Breakout, Frontier) is the empirical proof that "urban India" is not a homogeneous consuming class. The gap in savings between the top and bottom tiers is nearly 3.5×, and one in six Frontier-city households is already financially overstretched. This is what “narrow” looks like at the city level.

The top 15 cities have two-thirds of all top-100 consumption, and the Delhi NCR alone has 15% of all urban consumption. The consuming class is a stratum within the Big Six, not the Big Six themselves. Even within the six megacities, PRICE reports that 24% of households are low-income. The consuming class is not "urban India" (~500M) or even "top-100 city India" (282M), but a stratum of prosperous households scattered across the Big Six and the top of the Boomtown tier.

The Blume Ventures categorisation of India’s consumption class finds three groups, with the first comparable in size to the population of Mexico at 30 million households (the Blume Rule of 30, or 10% of Indian households). A worrying finding is that the consuming class that anchors private demand is small and getting deeper, not significantly wider. A vast base has almost no discretionary spending power at all. This is the inverse of the broad, upwardly mobile middle that sustains private investment.

The PRICE report's optimistic headline (~715M "middle income" people by 2031) conflates a household that shares a scooter with one that services a car EMI. Once you apply a real surplus filter - savings above ₹4 lakh a year, which is roughly what it takes to run a car, insure a family, and take one flight - the number lands where Blume said it did. In simple terms, PRICE's 715M by 2031 measures how many Indians will earn enough not to be poor, a significant achievement. Blume's 140M measures how many can act as a consuming class today - the market a brand can actually sell a car, an AC, a holiday abroad or a health-insurance policy to.

The numbers are further validated by other sources. An income assessment by Rama Bijapurkar points to a more differentiated group, but a similar narrow base of the consumption class. She estimates that 93% of households have annual consumption of less than $5,700.

The data on penetration of discretionary goods and behaviours corroborates the Blume survey data. Almost every independent marker lands near the same Blume Rule of 30. Whether you measure cars, ACs, credit cards, foreign travel, or who actually pays income tax, the consuming class converges on the same narrow apex. If anything, the Blume data itself looks too optimistic.

Whichever marker you pick - cars (8%), ACs (13%), passports (~10%), income-tax payers (1.5–3%), credit cards (4.6%), foreign trips (2%) - the consuming class lands at ~10% of households or fewer. That is also exactly where the bottom-up Blume spending model, income surveys of Bijapurkar, and asset penetration estimates of NFHS/Bain lands.

India's "middle class" has been estimated at 29 million people one way and 600 million another, a multiple spread of 20 that reflects what one is choosing to count.

India has a consuming class of ~140M, an aspirant class of ~300M moving toward it, and a base of ~1bn for whom the "middle class" debate is moot. That picture is consistent with every methodology once you ask which question each one answers - how many can afford a car (~10%), how many are above poverty by global standards (~30%), how many participate in the digital economy (~65%). Conflating them is the single most common error in India market-sizing. This is a very nice summary of why nobody gets the elephant in full.

In this context, it is also useful to draw a nuance on India’s high consumption rate of 61% of GDP, which is far higher than China’s ~38% of GDP. India’s problem is that while it is already a consumption-led economy, the problem is the quality of that demand. It is too thin per person, skewed to the top decile, and increasingly financed by debt as households run down savings. In other words, the demand side constraint is not the consumption share of GDP, but that there are too few middle-income earners to generate broad, income-financed volume. That points policy at jobs, wages and investment, not at “stimulating consumption.”

Fundamentally, all the above is a reflection of two important factors - the low per-capita income and the widening inequality in sharing the benefits of aggregate growth.

India's per-capita output (PPP) trails Vietnam and Indonesia and is under half of China's. Its Mexico-like consuming tier is real, but at best, only ~10% of the country.

India imposes rich-country costs on poor-country incomes in capital, fuel and land - and because those costs are policy-made, they are the actionable half of the problem. The demand blade can only be widened the slow way: through jobs, wages and productivity.

Unfortunately, corporate surpluses are being captured, not recycled into the wages that would create the next tier of consumers. Profits race ahead while sales, jobs and pay lag far behind.

It also does not help that Indian businesses across sectors are averse to spending on R&D and innovation.

R&D is a fixed cost that only pays off over a large, contestable market. A thin domestic market depresses the return to it, which is why the economies that lead on R&D expenditures are either big-and-rich at home (the US) or export-driven (Korea, Germany, Taiwan). India is neither, and sits alone in the low-R&D corner.

This is an instructive comparison of India with China and South Korea.

The main takeaway is that India’s existing consumption class, confined to just 10% of the households, is too narrow to sustain high growth rates for long periods. It requires considerable broadening. But broad basing economic growth requires access to good jobs, a daunting task given that the gig economy is the biggest source of job creation and elsewhere it is mainly contractual jobs. Worsening matters is the reluctance of the private sector to invest or innovate.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 25 Jul 2026 06:23:32 +0000

1. SpaceX IPO is ample proof that the Chinese wall between equity research and investment banking in IB firms is a myth.

You might expect wildly divergent views on a company as speculative as SpaceX. Here the underwriters disagreed only over the scale of the upside. Sceptical voices came from outside the syndicate. Morningstar, for example, valued the shares at $63.

This is now dead

When the dotcom bubble burst, regulators uncovered emails showing that Wall Street analysts were privately disparaging stocks they were publicly touting. A 2003 global settlement between banks and regulators on analyst research imposed sweeping restrictions. It barred investment bankers from influencing analyst compensation, tightly controlled communications between research and banking, and banned analysts from IPO pitches and roadshows. New York Attorney General Elliot Spitzer’s premise was that shielding analysts from bankers would deliver truly independent — and better — research. On one level, the reforms succeeded. Banks have constructed a robust compliance apparatus to wall off research from investment banking. “Chaperones” now police interactions between analysts and corporate finance to prevent even the appearance of pressure. The Spitzer global settlement formally ended last December in favour of more flexible rules overseen by an industry association.

It is hard to see how SpaceX shares can avoid a cratering.

About 30 per cent of the roughly 640mn SpaceX shares available to trade have been borrowed to sell short, up 10 percentage points over the past 10 days, highlighting how traders are becoming increasingly sceptical of the company’s market prospects... About 900mn further SpaceX shares could become available to trade as soon as next month when certain lock-up provisions for pre-IPO investors expire. Traders who doubt there is sufficient demand for the deluge of extra equity are cashing out now as a result, market participants say.

2. After reducing their hiring last year, firms with jobs exposed to AI are planning to increase their entry-level hiring this year, but they come with a "seniorisation".

Candidates for starter roles in the most AI-exposed industries are now expected to show a mastery of the skills traditionally demanded of more seasoned staff, such as data-driven decision-making and people management... Eleanor Lightbody, CEO of Luminance, which develops AI for the legal profession, says these middle layers could be squeezed out altogether, “because we are going to hire more juniors, who are really going to understand how AI works, and more seniors [are] staying in the business because [by using AI], they can be more productive and have more capacity”. The challenge for job seekers is that it is hard to find entry-level jobs that allow them to develop the higher proficiency that seniorised roles now require... Reliance on AI is increasing the demand for distinctively human “soft skills” — or “power skills”, as some are now calling them — such as creativity, empathy, judgment and networking ability. PwC’s jobs report found that new tasks added to job adverts for AI-exposed roles were two and a half times as likely to call for such capabilities.

3. Very good description of how China became so dominant.

It enticed unsuspecting giants such as Apple, Tesla, Motorola, and Lucent with low-cost logic. When sufficient local manpower was trained, subcontractors developed, and stakes became important, China applied the squeeze. China would break contracts, cancel licences, coerce the transfer of technology, control pricing, withdraw incentives, force equity participation, conscript technology and evict them. Huawei, BYD, CATL, and SAIC are some examples of the resulting indigenous giants that emerged. In an act of silent invasion, conscripted technologies have been converted into military capability. It is dominant as a supplier of several raw materials, such as rare earth minerals, gallium, graphite, and lithium; a dominant buyer of soya from Brazil and iron ore and wines from Australia; a financier of BRI projects; and a provider of processing technologies for Chilean copper and lithium in select South American countries. With this web of dependencies, it can choke several factories.

4. Janan Ganesh feels that for Britain to start making real reforms, the incoming PM Andy Burnham must do more welfare and subsidies and discredit the whole .ideology.

What is the precedent for a rich democracy doing pre-emptive economic reform? Which nation ever made controversial structural changes — involving winners and losers — to prevent a crisis, rather than in response to one? Southern Europe needed the Eurozone panic of 2009 onwards to make spending cuts. Hawke, Keating, Margaret Thatcher and Ronald Reagan were reacting to 1970s stagflation. François Mitterrand in 1983 was reacting to a market shock that to some extent he’d created. Reform only happens when it absolutely has to happen. So try again, prime minister. Fail again. Fail worse.

5. Japan embraces a more proactive government-driven economic growth policy.

Sanae Takaichi's cabinet approved a policy blueprint that targets a combined $2.3tn of public and private sector investment between now and 2040 in 17 chosen sectors. Ministries will be able to make budget requests without upper limits; budget construction, according to the document, will be “fundamentally” changed. Much of the blueprint is about economic security, but a refreshingly large amount is about growth... possibly the most meaningful lines in the new strategy place Japan’s future efforts in the global context. “Among advanced countries, there is a big trend of the government and private sectors working together on large-scale, long-term industrial spending,” it read... The government would strive, the document further promised, to meet the challenges of “the era of great global competition between industrial policies”...

On the same day that the blueprint was agreed, the Ministry of Economy, Trade and Industry produced separate guidance for growth investment — an effort to encourage Japan’s 4,000-odd listed companies to shift more of their endeavours towards growth and a witheringly blunt critique of how matters are at the moment. Within Japan’s 350 largest companies (by sales), 65 per cent of invested capital remains locked in what it calls value-destructive segments, according to METI’s research. In the US, the equivalent ratio is 39 per cent. Both Takaichi’s blueprint and the new METI guidelines are attempting a new version of industrial policy that not only seeks to spur growth, but places a huge bet on the government’s ability to encourage companies in a way that market forces have not.

6. Kevin Warsh is trying to scale down forward guidance

“Financial market prices are probably the most important source of information to guide central bankers,” he said at his inaugural press conference last month. “But when all the financial markets are doing is reflecting back what we’ve said, then we’re taking the most important source of information and we’re being blind to it.” Many agree with the Fed chair’s view that central banks’ focus on predictability has led to a world in which markets obsess more over what officials say than what is actually happening in the economy. “Forward guidance has turned markets into a mirror,” says Ajay Rajadhyaksha, global chair of research at Barclays. “The Fed watches markets; markets watch the Fed. And no one’s actually watching the economy.”...

Recent research by the US central bank suggests its decisions also have an outsized impact on equity markets, leading to big changes in how investors price stocks. Advocates of forward guidance say it helps avoid the sort of surprises for the markets that feed overall volatility and eventually raise borrowing costs as investors demand greater compensation to stomach market swings. Warsh and his allies counter that the attempt to pacify markets simply encourages greater risk-taking, damping short-term volatility but storing up bigger shocks for later. The debate is all the more important because of the backdrop: the surge in borrowing over almost two decades. Government debt around the world has risen sharply following the global financial crisis, the Eurozone crisis, the pandemic and the wars of the 2020s...

Some analysts highlight the so-called taper tantrum of 2013 — when markets were unnerved by Fed statements about its plans to shrink its balance sheet as a result of officials’ false sense of certainty. Others link the 2023 collapse of the US’s Silicon Valley Bank to central bankers’ previous pledge to keep interest rates low.

This is a very important factoid about Fed communications.

Between 1990 and 2022, an era in which the status and prominence of central banks steadily grew, US 10-year government bond yields fell by more than 7 percentage points. All of the downward moves throughout that period took place during the three days around Fed meetings, rather than in response to political events or economic data. But the relationship broke down after the Fed appeared flat-footed on inflation in 2022. The Riksbank data shows that the central bank’s meetings had little to do with the subsequent rise in yields.

An important concern for the bond markets is the sharply increased volume of Treasuries held by hedge funds that use leverage to bet on tiny differences in interest rates.

The Fed estimates that large hedge funds’ holdings of US Treasuries doubled between 2023 and 2025, faster than the growth of the market as a whole. Such funds now own more than $2.5tn in Treasuries, according to the US central bank and US Treasury data. Such sums dwarf China’s official holdings — not an exhaustive account of the country’s exposure — which have fallen from $1tn in December 2021 to $659bn in May 2026, according to US Treasury statistics. Moreover, the Bank for International Settlements, the central bankers’ bank, has warned that the hedge funds might have to dial back their stakes in government bond markets even quicker than they arrived — a possibility it describes as one of the most troubling financial stability risks in the world today.

Then there are the other concerns for bond markets.

The Bank of England said this month that AI hyperscalers borrowed more in the first half of 2026 than in the whole of 2025. So far this year they have accumulated as much new debt as the UK government. An interest-rate surprise from the Warsh Fed might not only unsettle Treasury yields but set off a vicious circle of margin calls — when a sudden price movement in assets bought with borrowed money requires an injection of capital — and fire sales by hedge funds that could create a dash for cash.

7. A cautionary tale on the difficulty of private enterprise establishing and managing entire railway systems comes from the example of Brightline Express, which connects Miami and Orlando, and became operational in 2023.

Brightline traces its roots to 2007, when Edens’ Fortress spent $3.5bn to acquire Florida East Coast Railway, then a listed freight transport company. Its tracks spanned from north to south in the Sunshine State, and construction for what would become Brightline began in 2014, with the Orlando service beginning a decade later. In a 2024 bond prospectus, Brightline executives forecast that by 2026 they would have nearly 8mn annual riders, split roughly evenly between the Orlando-Miami route and a more local service in South Florida. That base of customers was expected to generate $700mn in revenue... Even with customer levels up 16 per cent year to date through May this year, ridership will struggle to hit 4mn this year. Operating income has at best reached break-even before debt service costs...

The promoter Wes Edens’ Fortress wrote off its investment long ago, and it is now hedge fund bondholders who are jockeying for control of Brightline. But alongside distressed debt specialists such as Nut Tree Capital Management, Aristeia Capital and Redwood Capital Management, there are a handful of more staid asset managers including Nuveen and First Eagle also at the table. As well as corporate bonds issued by subsidiaries, the group’s debt stack includes more than $2bn of traditional municipal bonds, half of which are guaranteed by a bond insurer.

The cost of doing business in India - land prices

noreply@blogger.com (Urbanomics) — Fri, 24 Jul 2026 04:34:26 +0000

I blogged here, highlighting the high cost structure that Indian businesses face. This post dives deeper and examines land cost.

A comparison with peers shows that urban land is priced like that of a rich country and ends up hoarding the nation's savings, with 77% of the households parking their wealth in real estate. Mumbai ranks among the world's 20 most expensive prime markets. Industrial land across the major Indian cities is prohibitively expensive compared to peers.

An Indian manufacturer pays roughly 5–15 times more for industrial land than a Chinese counterpart in a comparable tier-2 city and 1–4 times a Vietnamese peer.

I used Claude to generate the graphic below on the total land transaction cost across states, combining the stamp duty and registration fees and the change of land use fees for the conversion of agricultural land. While stamp duty has converged to 6-7% of land value, the total cost varies widely across states. Interestingly, the southern states have the highest transaction rates.

Four states can’t be reduced to a clean number - Maharashtra charges a premium of 25–100% of the ready-reckoner differential; Gujarat charges on the agri/non-agri differential; West Bengal rarely grants conversion at all; and Delhi’s urban land is already non-agricultural. For these, the combined figure shown is a floor, not a total (hence the ‘+’). Maharashtra, in particular, would likely top the chart if its premium were expressible as a share of land value.

Andhra Pradesh (deemed conversion since 2018, NALA Act now being scrapped) and Uttarakhand (permission eliminated for specified categories) are the only states that have removed conversion permission as a bureaucratic step. Assam, J&K, Odisha, Puducherry and Tripura have moved to negative lists.

I then asked Claude to generate the typical cost of industrial land in some representative locations across India. The wide variations in the construction levies as a share of land value arise from the variations in the underlying land valuations (the % is higher in places with lower land values). While the state and local taxes make up 10-17% of the land value, all the land and building-related taxes make up a near-uniform ~10% of the industrial project cost (land, construction, taxes).

I did the same for residential land cost at representative locations in a few cities. Ignoring the Gurgaon outlier, the state and local taxes make up 16-26% of the land value, and all the land and building-related taxes make up 18-25% of the price paid by the homebuyer. In other words, the tax burden is a fifth to a quarter of what a homebuyer pays.

In both cases, the lower land valuations in places like Lucknow or Indore or Vizag mean that the share of the nationwide levies (GST and labour cess) is much higher. In other words, these levies bite more in the second-tier and below cities and end up eroding their comparative advantage.

A like-for-like global comparison for industrial land is difficult given the lack of data. The graphic below is a broader international comparison, using the World Bank’s last Doing Business 2020 survey, for the construction of a typical (2 storeys, 1,300 sq m, on a 929 sq m plot, valued at 50× GNI per capita) warehouse (as a % of the warehouse value) in each country’s largest business city. It measures every statutory licence, permit, inspection, utility connection, non-recurring tax and property registration fee, but importantly, excludes VAT, capital gains and the price of the land itself.

India’s statutory fee stack is middling, sitting below Mexico, Indonesia, Bangladesh, and Delhi at 2.8% is not too far from the OECD high-income benchmark of 1.5%. This questions the reasoning that Indian construction-permit fees alone make land prices uncompetitive. However, India’s manufacturing competitors like Vietnam, Thailand, Brazil, and Poland levy less than a fifth of what India does on the same measures.

More importantly, the DB survey data excludes land price, stamp duty, conversion charges, and the GST input credit, precisely the costs that were the basis for the discussion earlier. The state’s tax share at 18-43% of industrial land cost, or 9-11% of total project cost is where the land-related competitiveness gap sits, and not in the counter fees that the WB’s DB Survey tracked.

Lessons from Spain for urban planning

noreply@blogger.com (Urbanomics) — Tue, 21 Jul 2026 04:34:47 +0000

Football is not the only area where we can learn from Spain. Energy transition, infrastructure construction and urban planning are some others. This one is about urban planning.

An essay in Works in Progress examined how traditional apartments have declined across Europe, except in Spain, and the role played by late development and public policy. It also underlines how Spain uniquely got all the basics of urban planning right - land readjustment, infrastructure development, mixed-use, densification, connected street network, walkability, mass transit, and low car usage.

This is a good description of mixed-use density in Spanish cities that promote walkability and mass transit commutes, and limit sprawl and carbon emissions.

Spain’s cities are unusual. They are much denser, tighter, and more deliberate than other European cities, let alone North American ones. They reject picket fence for apartment block and choose balcony over front lawn. Two thirds of Spaniards live in flats, against 41 percent of Poles, 36 percent of the French, and just 10 percent of the Irish. Of the remaining third, most live in terraced rowhouses. In Spain’s cities, over four fifths of people live in an apartment. At the edge of Madrid or Valencia, dense mid-rise blocks stand beside open countryside without sprawl in between, something that has almost never happened in an English-speaking country, and that has been rare in France or Germany for a century… Spain’s settlements have some of Europe’s lowest per capita transport emissions, in part because about 70 percent of trips in Madrid and Barcelona are made on foot, tram, or metro. Almost every neighborhood is mixed use; almost all urban Spaniards live in the ‘fifteen-minute cities’ that seem like remote ideals in most affluent societies.

This is a good comparison with other Southern European cities, and even here, Spain stands out.

Spain is not alone in Europe in having become wealthy only recently: most Southern European countries have a similar economic history. And Portugal, Italy, and Greece do share the distinctive features of Spanish urbanism to some extent, with relatively dense cities and relatively high shares of people living in apartments (46 percent in Portugal, 53 percent in Italy and 59 percent in Greece compared to 65 percent in Spain). In other ways, however, Spain is distinctive in Southern Europe. The cities of Portugal, Greece, and Southern or Central Italy are generally surrounded by ragged fringes of unplanned suburban development: their urban cores are dense in the same way as Spain’s, but their peripheries are a chaotic mixture. The transport situation is also dramatically different. About half of journeys in Lisbon and Athens are by car; in Rome, the figure is two thirds, with another substantial share on mopeds; in Nicosia, it is 85 percent, the highest of any European capital. In Madrid, the modal share of cars is below 30 percent. Cities tend to be dense all over Southern Europe, but they have not all achieved the transport outcomes that urbanists associate with density: in this respect, Spain is the outstanding model.

Public policy has played an important role in making this difference. In Britain and the US, the government develops the main arterial roads and allows development that follows the development control regulations, which results in fragmented urban forms (in terms of plot sizes and types of development). In Spain, the authorities undertake land re-adjustments like the Town Planning Schemes of Gujarat and thereby lay down clear boundaries and forms of development.

This also means a high level of infrastructure development with a high density of roads, based on plans that connect streets and localities to promote pedestrians and cyclists.

This has yielded cities with exceptionally good infrastructure. About 28 percent of Madrid’s surface area is taken up by roads, almost exactly the 30 percent recommended by UN specialists. This compares to 21 percent in Paris, 19 percent in London and 20 percent in New York. Even Los Angeles, a famously road-heavy city, uses only 25 percent for roads. As we have seen, these roads are also more skilfully interconnected, which is indispensable for pedestrians and cyclists.

A large road network has not detracted from policy focus on public transport.

Despite having far superior road infrastructure, Spanish cities also have high public transport use. Around 60 percent of trips in the Madrid metropolitan area and over 70 percent of trips in the Barcelona metropolitan area are made through public or active travel, similar to other major European cities and far higher than American, Canadian, or Australian cities, which typically fall below 30 percent. In other words, Spanish cities have Los Angeles-tier road infrastructure and Paris-tier public transport access. This is paired with some of the continent’s best intercity transport. Spain has significantly more motorways than any other European country: 17,228 kilometers, versus 13,183 in Germany and 11,671 in France. It has the second-longest high-speed rail network in the world, after China.

In fact, public policy played perhaps an even more important role by keeping infrastructure construction costs low and ensuring construction was done within budget and without delays. It was able to utilise something like €200 billion in cohesion funding received from the EU between the late 1980s and 2020.

More importantly, Spain kept costs low… Spain, combined with its non-EU injections, built 4,000 kilometers of high-speed rail, 10,000 kilometers of motorway, and numerous metros, trams, ring roads, and radial arterials, because it kept costs extremely low. It did this by maintaining good practice: flexible environmental rules (although these have since become more problematic), top-tier in-house capacity in engineering and contracting, a commitment to a steady pipeline of projects over decades, and, above all, rapid decision-making, avoiding the costly delays and redesigns common elsewhere. Another part of this was giving small areas the power to decide on and fund infrastructure, like the Madrid Metro, avoiding the ping pong between authorities seen in some high-cost countries.
Together, this has allowed it to build metros more than 20 times cheaper than in New York City. For the price of one mile of the New York’s Second Avenue Subway extension, Spanish builders covered the entire 35-mile 1995–1999 expansion of the Madrid Metro. As a result, Spanish transport infrastructure is both abundant and cheap. Madrid’s metro underwent one of the fastest growth spurts in the worldbetween 1995 and 2007, adding around 203 kilometers of new lines to its already impressive footprint. Barcelona has been continuously expanding its metro network since the Second World War: in a period of astonishing activity between 1990 and 2010 there were 18 separate extensions, and seven more since.
Cities including Valencia, Bilbao, Seville, and Málaga all built metro or light rail systems in the 1990s and 2000s. Madrid and Barcelona have both joined their old suburban rail lines up into Cercanías/Rodalies systems, similar to London’s Crossrail scheme but far more comprehensive (though not always well run). The upshot of this is that, despite having some of the world’s best road infrastructure, Spain still has relatively low levels of car use and ownership: the modal share of driving is low not because driving is a bad option, but because other options are so good.

Since the late 2000s, changes in laws placing a series of restrictions on buildings have adversely impacted Spanish urban planning. For example, before 2007, Spanish land was buildable by default; the same was inverted to allow housing construction only if specifically zoned by the local council. Today, in an emulation of the planning practices followed in the US and Europe, building in Spain has become extremely restrictive.

Planning a major Spanish urban extension now depends on agreement from the municipality, the community (the regional government), the landowners, and, individually, each of the environmental, water, roads, electricity, public transportation, and social housing authorities. The result is that creating new city plans now takes an enormously long time. Creating the plan takes between six to eight years, while designing the streets and plots takes another three to seven… This is leading to high prices. Madrid asking prices are now nearly €6,000 per square meter, and Barcelona over €5,000, above Hamburg, Berlin, Frankfurt, Brussels, Milan, and Rome… But Spanish wages are low… This has left leading Spanish cities with some of the worst house price to income ratios in Europe.

Spain offers important lessons for cities in developing countries like India. Spain’s late development means that its experience has even greater relevance for us.

Spain's cities are unusual not on any single dimension but on the combination - density, mixed use, mid-rise (not high-rise) apartments, walkability, cheap and abundant transit, high car ownership but low car use, infrastructure preceding development, and municipally planned street grids. Spain is unique in getting all the dimensions right.

Indian cities, from metros to the lower-tier ones, are characterised by a far lower share of multi-tenement units, rigid land-use restrictions, high setbacks, poorly maintained or absent footpaths, infrastructure coming well after habitations emerge, and sorely deficient mass transit facilities (especially bus networks).

In India, exclusive land-use zoning is the norm, with mixed-use permitted only beyond generally 15-18 m roads and above, which are present in a very small proportion of the localities. In the older areas, commercial facilities in residential areas have emerged informally over time, whereas in the planned colonies and new developments, mixed use is restricted.

Indian cities have among the lowest share of urban households living in multi-tenement units, with Spanish cities being at the other extreme. Only 31% of urban Indian households live in flats (NSS 2018) compared to about 80% in Spanish cities, with Mumbai being the exception. The India NSS “flat” includes chawls, single-storey shared tenements, and informal walk-ups - so Indian numbers overstate what a Spaniard would recognise as a flat.

Similarly, Indian cities have among the lowest share of road network, not even a third of the UN Habitat norm. Most Indian metros are 6–12%, with only Delhi, the only Indian city with municipally planned extension, getting close.

I can think of at least a few big takeaways. Foremost, there’s no alternative to mixed-use, densified development for both greenfield and brownfield areas.

Given its high share of detached housing, Indian cities have a great opportunity to reinvent themselves. This can be done by increasing FAR, and more importantly, doing it in a manner that allows upzoning adjacent to 9 m and 12 m roads. As I blogged here, the current upzoning deregulation, confined to plots with road width greater than 18 m, is largely superfluous.

This must be coupled with planning norms like allowing for relaxations of setbacks, even dispensing with them and encouraging row housing, multi-tenement units, and mixed-use in terms of encouraging commercial amenities. The latter is about ensuring that people living in a locality should be able to buy their regular groceries and other household items and services from within there. Further, the local government fee and property tax regimes must consider lowering the layout development charges, building permission fees, and property taxes to encourage the realisation of these objectives.

These policies and instruments must be deployed with a long-term perspective (as against expectations of immediate results). The objective should be that they would shape incentives and enable the gradual redevelopment of brownfield areas as densified communities.

Second, we must use urban planning to lay down street network configurations that enable connectivity and walkability. Master plans and their development plans should keep this in mind. Minimising or even eliminating setbacks would be one step to enable such street networks. The prioritisation of walkability requires the infrastructure of footpaths and street connectivity to be able to walk, and the supply in terms of mixed-use amenities to create the demand. And all this must be combined with local campaigns to promote the culture of keeping footpaths free of encroachments.

Third, the current dominant trend across Indian cities of the emergence of gated communities in the suburbs must be examined. These communities, while attractive for their residents and developers, go against all principles of sustainable urban development. These are largely monocultures of upper-middle and higher-income housing (the maids and drivers, and others, commute from distant places), with exclusive residential zoning, limited or no mass transit connectivity, exclusively car-based, and very poor external street connectivity (large gated enclaves with one or two entry/exit points). They impose massive negative externalities on the city and locality, while appropriating all the benefits.

Fourth, mass transit must be at the core of all developments. All greenfield areas, in particular, must be planned around mass transit, in the form of transit-oriented development (TOD). This post discusses some principles for TOD in Indian cities, and this post outlines some of the challenges. This would require going beyond the current norm of merely giving higher FAR and offering significant incentives on fees and taxes to make it more attractive for developers to build inside the TOD zones.

Like in Spain, the challenge is to get the combination more or less right. The good thing is that all of them lie with the states and mostly with the cities themselves. It is only required for 2-3 cities to take the lead and strike out on their own in following these principles and reinventing themselves. They can be the lighthouses that guide urban development in India.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 18 Jul 2026 12:35:13 +0000

1. Are people overreacting to small struggles?

When asked if they would consider someone experiencing typical fluctuations in mood (described as broad happiness but occasional moments of worry, frustration or loss of confidence) as having a mental illness, more than half of young Americans say yes, up from just a fifth 15 years ago.

2. A picture of state finances.

3. The promise of quantum computing.

Quantum computers can transcend the limitations of the traditional binary computer bit, which can exist in two states, denoted by zero and one. By contrast, quantum bits, or “qubits”, can exist in both those states at once. This allows quantum machines to survey multiple potential solutions simultaneously, rather than dealing with them one by one like a conventional computer. One analogy is a maze. Where a quantum computer can examine the whole map to find a way through, a traditional machine will keep exploring dead ends until it finds the route. Quantum computers’ superior processing power should make them better able to generalise from small amounts of data and sift through multiple complex patterns...

But many companies are already experimenting with the technology because of its promised leap in capability, predicting early uses for the machines in areas such as chemistry and materials science. The idea is that because of their own workings and structure the computers will be better able to analyse and predict chemical behaviour determined by atomic and subatomic interactions governed by quantum rules. In a sense, they will be speaking the same language rather than translating an analysis into a string of ones and zeros as a traditional computer does. As a result, a sufficiently powerful quantum machine should in theory be adept at predicting the interactions between drugs and living cells that determine whether a new pharmaceutical will work. Such possibilities have already led tech companies to pair up with industrial groups.

4. India's trade account in a nutshell.

In 2025-26, its exports of services, at $421.3 billion, was close to the export of goods worth $446.1 billion. On the other hand, imports of goods ($783.4 billion) were way above the imports of services ($204.7 billion). Thus, while India recorded a merchandise trade deficit of $337.3 billion, it had a surplus of $216.6 billion on the services account.

5. Brilliant article by Simon Kuper on how football came to be dominated by Western Europe.

Western Europeans didn’t start by asking, “How can we win the World Cup?” Instead, they pursued a different goal: making amateur football cheap and widely available. The intended outputs were happiness, community and public health. Winning World Cups was a byproduct... I began playing football aged six, in 1976, after moving from London to Leiden in the Netherlands. Most Dutch boys I met belonged to a football club... The little Leiden region had dozens of football clubs. Some fielded 20 senior teams, seven teams of under-eights and so on. Many people built their identity and social life on being the right-back or linesman of the 14th team. The Netherlands in the 1970s reached two World Cup finals. Everyone played and understood how to play. Football is geometry — about creating space when you have the ball, and shrinking it when you don’t. That knowledge is all around you in western Europe, unlike in Asia, Africa, the US or Brazil... In 2017, the average Dutch person lived 1.6km from a football field. Neighbouring Germany’s football federation is the world’s largest sports association, with more than 7.7mn members...

As a father, I raised three footballers in Paris, now the game’s deepest talent pool. Almost all Parisian suburbs, or banlieues, have well-kept sports complexes, with artificial fields, used nonstop: at half-time of any amateur game, children storm on to the field for a kickaround. So structured is the system that my son had to earn a coaching diploma to train his little club’s under-eights. His own beloved coach, Mustapha Sangaré, who only joined a football club aged 15, now plays for Bulgaria’s Levski Sofia and Mali. He is far from an anomaly: almost 100 players across all squads in the current World Cup were born in France and just under 70 in the Netherlands.

In another article Tej Parikh looks at why China and India does so badly in football.

This is a striking statistic, pointing how globalised football has become and how the leading European clubs have become the feeding grounds for national teams.

At this World Cup, more than 72 per cent of players appear for a club outside the country of their national team, and almost one in four are foreign born. (More than half of Cape Verde’s squad was born outside the nation and ply their trade in various European leagues.)

6. In what will prove to be a dramatic decision, DP World, which operates the Jebel Ali port that has been paralysed by the closure of the Strait of Hormuz, is reportedly planning to build a new port and a container terminal on the UAE's eastern coastal area of Fujairah.

Shifting some of the port’s capacity outside Dubai marks a seismic change for the emirate, which has established itself as a global trade and finance hub partly off the back of Jebel Ali’s growth... But DP World’s plans align with a broader UAE government initiative to attempt to bulletproof its economy against future hostilities with Iran by reducing its dependence on the strait, where shipping has been disrupted by Iranian drones and missile strikes since the US-Israeli attack. The new project would deepen DP World’s presence on the Gulf of Oman, allowing containers to enter and leave the country without having to pass through the strait, before moving them on trucks overland to Dubai, Abu Dhabi and neighbouring Gulf countries. Since the war began at the end of February, Iran has fired nearly 3,000 drones or missiles at the UAE — more than any other country... DP World’s plans underline how the Iran war has forced governments and companies in the region to reconsider infrastructure and economic corridors developed on the premise that there would be uninterrupted passage through the strait.

7. Rote memorisation in schools is celebrated in China.

The guidelines to the gaokao, an exam for 18-year-olds and the world’s largest standardised test, describe memorisation as “the most basic level of ability”, placing it first among six traits that include comprehension, analysis and synthesis, appreciation and evaluation, expression and application, and inquiry. At the simplest level, the Chinese script itself, which operates at the level of the syllable and involves thousands of individually meaningful characters, requires years of memorisation... It is hard not to draw a contrast with the English-language west, where rote memorisation has taken on a faintly pejorative meaning. More than a century and a half ago, at the height of Britain’s industrial age, Charles Dickens was skewering the “facts alone are wanted in life” approach of fictional educator Gradgrind in the novel Hard Times.

8. Trump's makeover of the US State Department.

Abandoning the precedent of the past 60 years, Trump has brushed aside the foreign service officers who have typically run at least two-thirds of embassies. Of the 101 nominations for ambassadorships in his second term, just nine were career diplomats. All this is against the backdrop of swingeing cuts to the department, whose workforce has shrunk by more than 3,000, over 20 per cent, since Trump resumed office.

9. China's remarkable success in reducing air pollution by 60% since 2013.

10. Interesting that the IT sector explains half the difference in productivity growth between the US and EU.

Strikingly, even though the tech sector was only 9.2 per cent of US GDP, against 5.4 per cent of the EU’s, almost half of the difference in productivity growth between the two economies was explained by differences in the relative size of this one sector. Moreover, productivity growth in the EU’s (relatively small) tech sector was also measured as being lower than in the US one. So, overall, the tech sector alone accounts for well over half of the overall difference in growth of GDP per head.

This is important

Life expectancy for US men was 76.5 in 2024, against an average of 80.5 in comparable high-income countries. For women, it was 81.4 against 84.8. That is despite spending a far higher proportion of its GDP on health. The US homicide rate was 5.9 per 100,000 in 2023, against 1.3 in France and 0.9 in Germany. Its prison population was 542 per 100,000 in 2023, against 130 in France and 69 in Germany. Thus, if one takes a wider view of human welfare, the US is very far from superior.

11. China reports the lowest quarterly growth rate in decades at 4.3% for the second quarter of 2026. Industrial production and exports are propping up growth, even as consumption declines.

Retail sales added just 1 per cent in June from a year earlier, while fixed-asset investment was down 5.7 per cent year on year for the first half of the year, compared to 4.1 per cent in the first five months. Industrial production, one sign of strength, grew 5.3 per cent last month on a year earlier... Separate data on Tuesday showed exports soared 27 per cent year on year in June, adding to signs of reliance on trade to support economic activity... Julian Evans-Pritchard, head of China economics at Capital Economics, noted that the GDP data brought it “closer in line” with the consultancy’s alternative measure, which has been around 3 per cent.

Underlining the importance of exports, there was a surge in China's EV exports in June.

China’s monthly car exports rose to a record 1mn cars in June as part of an overall surge in trade that will heighten tensions with partners such as the EU. Shipments of cars rose 71.2 per cent from a year earlier to 1.06mn, putting the country on track to export more than 10mn cars this year, up from 7.1mn last year and more than double the 4.9mn in 2023. The surge in exports comes as domestic sales slow sharply following the phaseout of EV subsidies and a decline in demand for fuel-powered cars... The wave of Chinese exports has been driven by lower-cost cars boasting superior software, further threatening carmakers from Japan, South Korea, Europe and the US... China’s exports of rare earths in June fell 34 per cent year on year and 6.4 per cent in the first half, following tight export controls on the minerals, which are essential for high-technology products... The NBS’s Wang said China’s exports of green energy-related products such as lithium batteries and wind turbines increased 37.6 per cent and 35.6 per cent, respectively, during the first half.

And this highlights how production and exports have sustained growth.

Industrial production rose by 5.4 percent in the first six months of the year, versus the same period last year. High-tech manufacturing rose by more than 13 percent over that period. But fixed asset investment — which includes infrastructure, property construction and manufacturing — fell by 5.7 percent. Real estate development dropped 18 percent. The value of China’s exports surged by more than 20 percent in the first half. But consumer spending, which a Moody’s Analytics report said “remains the economy’s weakest link,” faltered. Retail sales of consumer goods increased by 1.3 percent over the first half of the year.

12. Ruchir Sharma holds that peak China was reached in 2021, and since then it has been a story of decline, papered over by exports and AI.

Since then, China’s share of global GDP has fallen in nominal terms from 18 to 16.5 per cent, while the US share has risen to 26 per cent. China’s growth rate has dropped below the rest of the world, including the US. In real terms, independent estimates now put China’s growth in real terms closer to zero than to the official target of 4.5 to 5 per cent... China’s population also peaked in 2021. Last year, births hit a record low, and deaths hit a record high. The working-age population is on pace to shrink by 75mn every decade this century... After adding little in the 2010s, net exports now account for about a third of the country’s growth, driven mainly by AI-related goods... And though every country now hopes for an AI-driven productivity miracle, the expected boost in China is about a third of a percentage point by 2030, hardly enough to halt its decline.

13. India agriculture statistics

If one looks at the growth areas in agriculture in the 12 years between 2011-12 and 2023-24, production of paddy and wheat rose by just 27 per cent. Fruit and vegetable production rose by 52 per cent, and milk production by 85 per cent, even though they did not receive any substantial support by way of subsidies or minimum support price procurement by the government. The true growth areas in agriculture are out of direct central government support and depend largely on producer enterprise... In 2023-24, the value of output of cereals (mainly paddy and wheat) was ₹8.5 trillion, while the value of the largely cooperative-controlled milk sector was ₹12.2 trillion.

14. Outbound corporate investments from India on the rise.

Indian companies have announced overseas equity investments worth more than $14bn in the first four months of the fiscal year that began on April 1, compared with $18.7bn in the previous 12 months. The outflows come as foreign investors flee India’s markets at the fastest pace ever this year, pulling out more than $23bn as of the end of June over a lack of AI champions.

15. Simon Kuper on Lionel Messi.

On the field, Messi sees everything. All his career, he ignored the ball for the first five minutes and instead walked around, memorising the position of each opponent and the spaces between them. But now he spends almost the entire game walking and scanning. When he breaks into a run, his teammates know he has seen an opening. They play to serve him. When he moved to the right wing against Egypt, seeing space there, the team remade itself around him. Argentina, two goals down after 78 minutes, won 3-2. Scaloni said afterwards: “We were not the ones who told him to go out to the right.” Messi moved right again against England and again Argentina came back to win.

16. The AI-boom is spilling over to energy sector.

Initial public offerings for energy firms raised $12.6bn in the first half of this year, according to data firm Dealogic. That marks the highest half-year level since the peak of the dotcom bubble in late 1999 and the highest first-half figure on record. It is well above 2025’s full-year total of $4.3bn. The surge in fundraising comes as access to the vast amounts of energy needed to run data centres emerges as a bottleneck in a multi-trillion-dollar AI investment boom... US electricity demand is projected to increase 39 per cent between 2026 and 2035, according to consultancy ICF, in large part due to ballooning demand from data centres...

Companies that have been able to raise money on public markets include those involved in complex, capital-heavy projects such as nuclear and geothermal power plants, while investors have also been willing to back businesses trying to develop new technologies... “This is a moment in which speculative projects are being funded and underwritten,” said Julien Dumoulin-Smith, a Jefferies research analyst covering power, utilities and clean energy. “They’re not just limited to venture capital or private equity.”... Nearly two-thirds of the energy companies that floated this year and last are now trading below their offer price, according to Dealogic. That compares with less than 40 per cent of IPOs across all sectors that are underwater.

And Wall Street Banks are already AI trades.

Four of the five big Wall Street banks reported yesterday: JPMorgan Chase, Bank of America, Citigroup and Goldman Sachs (Morgan Stanley chimes in today). The numbers were outstanding, as one would expect in a quarter when markets whipped around and big deals were done. In aggregate, equity and debt trading revenue at the four hit $38bn, up more than a third from a year ago and 60 per cent higher than two years ago. Investment banking fees, at $10bn on the quarter, have grown almost as much... It is AI that has markets churning and drives capital-raising. The banks are another example of the false “broadening” of the stock market that has also driven up industrial and utility stocks in the past few years. All these sectors have lived, and could die, with AI.

And their profits are not confined to the US, with even Asia becoming a major source.

Equities trading in Asia is helping power a record-breaking run from Wall Street’s banks, with the region on course to surpass Europe as the industry’s second-largest source of revenue behind the US. In the past 12 months, clients of large investment banks have ploughed into companies in Asia that provide critical infrastructure to the AI semiconductor industry, including South Korean SK Hynix, Taiwan’s TSMC and China’s Cambricon Technologies... In the most recent quarter, the largest investment banks collectively reported an unprecedented $25.7bn in earnings from equities trading and called out Asia as a crucial factor in the growth.

17. Even by the low standards of Trump 2.0, this is surely an outrageous example of private profiteering from public office.

Donald Trump’s social media company has discussed charging traders and investors as much as $100,000 a month for faster access to the US president’s posts on his Truth Social platform. Trump Media & Technology Group (TMTG) has quoted the six-figure monthly sum in talks with prospective buyers of the “Truth API” data service, according to people familiar with the matter. Proprietary trading firms and hedge funds pay huge sums for ultrafast data feeds because every millisecond counts when reacting to market-moving news. Trump often makes major announcements on Truth Social that trigger huge fluctuations across global markets... TMTG, which is majority owned by the Trump family, controls Truth Social...

A pitch sheet circulated by TMTG to promote Truth API, seen by the FT, lists 10 “documented market-moving posts” from the president’s Truth Social account. On April 9 2025, for example, the document says Trump’s “THIS IS A GREAT TIME TO BUY!!!” post “restored” $4tn to the market capitalisation of the S&P 500. Trump’s post in early June that the US would hit Iran “very hard tonight” caused a 6 per cent intraday jump in oil prices, the document says. “When @realDonaldTrump Truths, the world reacts,” the document continues. “No comparable signal exists. No official API has ever been offered. Until now.” Trump has also touted specific stocks, complimenting companies such as Nvidia and Apple and fuelling rallies in their share prices. More recently after the outbreak of the war with Iran, Trump posted on March 23 that there had been “very good and productive conversations with Iran”, sending oil prices falling sharply.

The probation problem in India's public sector

noreply@blogger.com (Urbanomics) — Thu, 16 Jul 2026 02:37:14 +0000

Public recruitments come with a one- or (mostly) two-year probation period. The idea is to correct a Type I error (a false positive recruit) in the recruitment process.

Underlining its importance, the DoPT Master Circular on Probation/Confirmation of 2019 has this to say about probation:

‘Probation should not be treated as a mere formality. The existing powers to discharge probationers should be systematically and vigorously used so that the necessity of dispensing with the services of employees at later stages may arise only rarely.’

Unfortunately, while it is part of all recruitment rules, it is widely believed that there are few instances of discharge during probation in any local, state, or central government recruitments.

Before we analyse this, it must be clearly stated that data on this is very patchy in the absence of disclosures by either the DoPT, UPSC, state PSCs, or the central or state government cadre controlling agencies. Empirical evidence, or whatever can be gathered, shows that across every service - All-India Services, central Group A civil, central Group A technical, state civil services, state police officers, state medical services, the great mass of teachers, constables and healthcare workers - the discharge rate on performance grounds is effectively zero.

This is what Claude gathered about central government cadres.

And this about state government cadres.

It must be disclosed that both the data are not validated, but conforms to the widely known anecdotal knowledge.

The rare discharges that do occur are almost invariably for fraud (fake caste, disability, TET or degree certificates) discovered post-selection, or for medical/physical failure during pre-probation training. Fraud detection and physical fitness testing are legitimate filters, but they are not the probation function. This has been confirmed by every major civil-service review (Hota 2004, Yugandhar 2003, Second ARC, Baswan 2016).

There are three examples of probation discharges outside of fraud and medical or physical ineligibility. An estimated 2-5% of the scientists and engineers recruited by the Department of Atomic Energy are discharged for non-completion of the academic curriculum. An estimated 3-15% of the police recruits by state and central government paramilitary forces are discharged for physical and disciplinary reasons (not deficiencies in the acquisition of policing capabilities). Thanks to supervision by the High Courts, an estimated 0.5-1% of every batch of state civil judges and munsiffs are discharged for failure to meet defined output and quality metrics during probation.

The absence of any discharge deterrent increases the stakes associated with the recruitment process itself. This most likely contributes to the fraudulent practices that are pervasive across recruitments at all levels.

This is a comparative assessment of probation discharges globally and from India’s own private sector.

The deterrent effect of even a few discharges can be significant.

So what can be done about this?

The probation instrument has not served its purpose because the framework it operates within provides neither the assessment infrastructure (objective performance criteria linked to a role profile), disclosure requirement (not even DoPT publishes data on probations), nor the political-economy incentive (senior officers who discharge a subordinate invite litigation and administrative-tribunal action). In the circumstances, reforming the probation clause without reforming both the criteria and the incentive structure will change nothing.

A low hanging fruit is to shine light on the problem and make it mandatory for all departments to disclose the status of probation confirmation, extensions of probation, and affirm that all the probationers met the requisite benchmarks for the same.

The first step in any systematic effort would be to define a few objective and easily captured metrics of probation performance that are proximate to their roles. This would usher in transparency and shape expectations among probationers about their roles.

Second, the probation performance evaluation should be made a mandatory exercise, by a committee consisting of the Departmental head (or representative), officer responsible for training within the Department, and the Director of the Training Academy. This would mitigate the political economy deterrent to discharges.

Third, there could be a mid-way evaluation of the probationers by the same committee, which discusses any laggards and inform them about where they are falling behind. The same should be documented. This could shape expectations and ensure that the probationers are forewarned before any discharge.

Fourth, the evaluation reports should be submitted every year by the Departments to DoPT (and its state government equivalents) and the UPSC/SPSC, failing which no recruitments by the Department should be allowed. This would bring departmental accountability to the process of probation confirmation.

The aforesaid measures would constitute a simple and realistic start to addressing one of the most farcical features of the recruitment process.

Higher FARs, but very few plots can avail them

noreply@blogger.com (Urbanomics) — Wed, 15 Jul 2026 02:31:00 +0000

Many state governments in India have issued executive directions increasing the permissible Floor Area Ratios (FARs) in their cities. However, these upzoning reforms are likely to struggle to meet the objective of densification due to restrictive conditions to avail the increased FAR. Specifically, three gate-keeping elements - minimums on road width and plot size, and a maximum on height - leave the upzoning reforms largely stillborn.

To understand why, we need to keep in mind the street layout of the typical Indian city. The colony street widths are typically 9 m (30 ft) or less, and at best 12 m (40 ft). Even the connecting roads are no more than 12 m. In any city, a very small proportion of properties, and an even smaller proportion of residential land use, will have road widths greater than 9 m. Only the arterial roads, which are in any case mostly commercial and higher-valued, are above 12 m.

A comparison of upzoning reforms across the ten biggest states reveals some interesting insights. For a start, the upzoning itself is generally marginal, and even where significant, the higher FARs can be realised only on wider roads. In simple terms, the upzoned FAR apply to a tiny minority of parcels - greenfield layouts and edge plots on arterial roads. Every state except Gujarat, UP (individual) and Haryana (small plot) sets the FAR uplift threshold above 12 m - typically 18 m, 24 m or 30 m. But even for the three, the uplift is marginal and only for a few categories of properties. Also, none of the three touches group housing or vertical redevelopment on narrow-road plots.

Further, plot size and setbacks compound the problem of a minimum road-width gate. Even where a 12 m road technically qualifies, high-rise / group-housing rules require minimum plot sizes of 750–2000 sqm and setbacks of 6–12 m. In existing settlements, individual plots average 60–150 sqm, and assembling five to twenty of them is legally and commercially nearly impossible without a TDR/land-pooling instrument. Even then, practical challenges are daunting.

This also means that even the TOD zones cannot benefit from the upzoning. In existing town cores, which are where TOD catchments actually sit, FAR reform delivers almost nothing until the road-width and plot-size gates are lifted or bypassed.

In other words, the upzoning reforms largely bypass the built-up city and are relevant only to the greenfield areas. In these areas, the uplifts linked to higher road widths end up benefiting only the large developers. They, in turn, build high-rise gated communities of higher-end housing, mostly unconnected to mass transit and with multiple car-users in each household. Ironically, this also ends up expanding the sprawl, flooding the roads with cars, thereby worsening traffic and increasing pollution.

On the other hand, it does nothing for the smaller developers who are likely to develop affordable mid-rises (say, 6-12 floors) inside the existing colonies. Instead, they end up constructing low-rises (up to 4-5 floors). Further, the unit economics given high land prices mean that even these low-rises gravitate towards the suburbs.

It is these mid-rises that are likely to contribute meaningfully to expanding supply and addressing the affordable housing problem. Unless the upzoning covers the 9 m road width and smaller plots (which make up the vast majority of the potential developable properties in any city), there cannot be any significant impact on housing supply and the affordable housing problem.

Further, to realise the full potential of such upzoning, it must be complemented with sharply increased mass transit services, especially buses, that cover these colonies. The quality, frequency, and connectivity of the bus network must be high enough to induce people to shift from car usage.

Workers and startups are helping train AI to replace them

noreply@blogger.com (Urbanomics) — Mon, 13 Jul 2026 02:01:57 +0000

Data annotation work is increasingly moving up the value chain, from tagging and labelling data to replicating the work of semi-skilled (on the factory floor) and skilled (consultants, analysts, lawyers, engineers, and doctors) workers.

Startups sell data to AI labs, which use it to train and refine their AI algorithms and develop software products/solutions that replicate the work of these workers. In other words, the semi-skilled and skilled workers, or at least some among them, are feeding their time and skills into the AI algorithms that seek to replace them and their kind. Both the training startups and those workers offering their services to them are basically helping make themselves redundant.

On this, the FT has a very good film about how Indian startups are paying factory floor workers and gig workers (and even people in their homes doing regular household chores) to use cameras and record their work. Data annotation is becoming the new BPO for India’s IT industry.

The Ken has an article that raises the possibility that for all the attention and hype around robotics, Indian startups might remain stuck at the lowest end of the robotics value chain - data collection.

India was the back office for the IT boom. It became the annotation and reinforcement-learning labour pool for the generative AI boom. It is now emerging as the behavioural data factory for the physical AI boom... Building the robot is only half the problem. Building the intelligence behind it is much harder. That requires data. Vast amounts of it. Unlike large language models, which were trained on the equivalent of hundreds of years of human reading scraped from the internet, robotics companies are working with barely a fraction of that in video... What they need is meticulous, first-person recordings of humans interacting with the physical world, carefully collected, annotated, and painstakingly structured.

So the industry turned to India. Across the country, workers are recording themselves doing everyday chores for data-collection firms, which then sell that footage to companies such as Tesla, Figure AI, and Agility Robotics to train their humanoids. Indian startups see this as a moment to claim a seat in the global AI value chain. The country has over 260 robotics startups, and investors are beginning to pay attention... The footage being recorded by Indian workers becomes proprietary once it leaves the country. The datasets assembled from it are accumulating on foreign servers. The foundation models trained on them are owned by foreign companies.

The NYT has an article about how startups like Handshake, Mercor, and Surge in the US are paying skilled workers to collect data on their work.

Mercor and a handful of similar start-ups are the primary middlemen in a supply chain of “human data” that may power the next generation of A.I. As OpenAI, Anthropic and other major ventures compete to become the industry’s dominant platform, the market for premium data that has been vetted by experts is exploding…They need mathematicians to annotate proofs, lawyers to mark up briefs and professors to grade essays… To use the parlance of the industry, data labeling has moved up the “value chain,” and the start-ups that offer this service have become some of the fastest growing in Silicon Valley… The data-training start-ups see a lucrative opportunity in recreating workplaces in miniature: controlled environments in which their gig workers can evaluate and reproduce emails, memos and slide presentations in context. The information emerging from such a setup, the companies boast, will help shrink the gap between what A.I. models can accomplish and what office workers actually do from one minute to the next, as ideas and instructions flow between meetings, documents and applications…
To keep improving their models — to make them more useful, more sophisticated, less prone to hallucination and mistakes — A.I. companies heavily refine what goes into them. That’s post-training, and it includes buying data from vendors like Handshake and its competitors… Deeptune, a start-up that makes “training environments” with simulations of the software programs, like Slack and Salesforce, that many workers toggle between all day long to get their work done. The idea is to painstakingly create a mirror image of, say, an investment bank so that A.I. can observe every interaction…
It may turn out that once OpenAI, Anthropic and others have taught their models to perform a certain job, their need for more training data in that area could sharply decline. In this way, Mercor, Scale, Handshake and their peers are much like the elite freelancers they employ: making money today, but in danger of being dropped tomorrow… People sign up for data-training gigs for a variety of reasons. The main one is, of course, money… Though the labor is unpredictable and rates vary… the workers who cobble together enough shifts can generate meaningful income. People might sign up because they have been laid off, or because they can’t find enough work in their field. They might do it because they’re eager to get “A.I.” on their résumé, or because they need extra cash in retirement… Many people who contract for these companies understand that this is a short-term opportunity, a brief chance to train the models to automate jobs before they themselves are automated out of the job of training models.

I asked Claude to generate a visualisation of this market landscape, including an assessment of the Indian landscape. The numbers are clearly estimates and must be validated (though at a ballpark they appear alright).

The unit economics of the data chain shown below for a garment worker in India is instructive. She gets roughly ₹400 a day to wear the camera (or $0.60 per hour); the startup pays the factory ₹450–500 per hour; US-based startups like Human Archive price data at $1–10 per hour; and once annotated and packaged, it sells to global robotics labs at $15–50 per hour. That is a 25–85 times markup, and every rung above the worker is owned outside India.

The graphic also shows that the vast majority of AI workers are doing the BPO equivalent, whereas the vast majority of funding is going to those building the data centres. India has 170-odd AI startups that have raised $2.6 billion in total and over 260 robotics startups, but the genuine model/product builders are a tiny set, and the majority have rebranded annotation as an AI line of business (iMerit, Objectways, Awign, Karya, Deccan AI, Human Archive, Egolab, Neo Cambrian, Humyn Labs, RoBoEra, etc.).

It must also be highlighted that in the majority of cases in India, the data goes from the garment worker to an Indian data aggregator to a robot-brain lab in San Francisco, and comes back as a robot/humanoid. The frontier LLM labs are not in that loop. This also means that none of the emerging governance conversations about frontier models - safety frameworks, export controls, model-access negotiations - touches the mainstream data collection work being done in India. India is negotiating hard for access to frontier language models while simultaneously handing over, for ₹400 a day, the training substrate for the physical models that will actually displace its manufacturing workforce. Those are two different conversations, and only one of them is being had.

Further, as the Times article highlights, while these annotation startups are flourishing now, they may not be sustainable ventures. Once experts teach the models to do something, their services are no longer needed in the same way, and the vendors themselves need the models to keep improving to show they add value, while needing them to remain imperfect so clients keep coming back.

I asked Claude for historical precedents and got this:

Frederick Winslow Taylor’s explicit programme, from the 1890s, was for management to “gather in all of the great mass of traditional knowledge which in the past has been in the heads of the workmen.” Skilled machinists were stopwatched; the Gilbreths filmed them with chronocyclegraphs — a literal 1910s head-camera. Workers cooperated because they were paid piece-rate bonuses to do so. The tacit craft was decomposed into instruction cards and handed to cheaper, unskilled labour. Outcome: enormous productivity gains, the collapse of the craft wage premium, a machinists’ revolt, congressional hearings in 1911–12, and Taylorism banned in US government arsenals by 1915. It took roughly fifty years and the postwar labour accord before the gains were broadly shared…
In the 1990s American hospitals routed physician dictations to transcriptionists in Bengaluru and Chennai; it was unglamorous work, but India was good at it. That corpus is precisely what trained speech recognition. The industry peaked and then largely evaporated. Compensation to the transcriptionists: zero… most startups in this space risk meeting the same fate as the transcription companies of the 1990s.

In this context, I am reminded of the claim made by Daron Acemoglu and Simon Johnson in their book Power and Progress that the trajectory of technological progress is a political choice made by society and should not be left to corporations and technocrats. Their central claim is that the direction of technology is a social choice, not a technical destiny, and that redirecting it requires countervailing power rather than better-intentioned technocrats.

The problem, though, is that globally, and especially due to the Trump 2.0 regime, the rule makers have surrendered agenda-setting to Big Tech and AI Labs. Closer home, India has almost no leverage over the direction of frontier AI. Instead, its leverage is confined to the terms on which its labour and data enter the supply chain, and not to bending the technology’s arc.

In the circumstances, what can a country like India do?

Here are some thoughts for consideration. One, a statutory floor rate for training-data contribution and an industry-led collective licensing body for data work are both administratively feasible and could increase value capture (from the worker’s current share of 1-2% of the value created) without banning anything. A comparator is the model of SoundExchange (US) or PRS (UK) in the music industry, which acts as a government-designated clearinghouse that collectively licenses music, collects usage fees, and distributes royalties to creators, effectively removing the burden of individual licensing. This model would also subtly frame the market in terms of treating data as labour, and not as mere raw material.

Second, on the regulatory side, it may be useful to revisit the DPDP Act provision that permits employers to process worker data without explicit consent under “employment purposes”. Instead, there should be purpose limitation, or restrictions on repurposing training data for other activities, and consent requirements of all involved.

Third, public spending on AI innovation and procurement preference could be made conditional on the recipient retaining licensing rights to datasets collected from Indian workers rather than doing work-for-hire. This would frame the collection of data as an input and not a product, and industrial policy could price it appropriately. Fourth, there is the argument about extending statutory instruments like the gig worker welfare boards or the Code on Social Security present in some states (Rajasthan, Karnataka, etc.) to cover data work. It could help build countervailing power.

But pursuing these agendas can be costly. This being a global market, prohibiting or putting too onerous terms on value capture and the entry of data into the supply chain will backfire by moving the work to Vietnam, Ethiopia, or the Philippines. Besides, for the Indian workers, already facing an acute scarcity of jobs, the choice isn’t really on offer, and ₹400 a day is ₹400 a day. There is a collective action problem here which calls for multilateral engagement through a forum like the ILO.

But this should not mean that we sit back helplessly and allow the market dynamics to play out. Instead, before enacting any of them, there must be a public debate on the merits or otherwise of these proposed measures. What are their respective costs, and what can be done to mitigate them? What versions, if any, of these measures should be enacted? Such debates are essential to make informed and collective social and political choices.

The public debate is important since the agenda-setting process here, like with any technology change, pushes certain considerations to the forefront while also marginalising certain others. Almost always, the former represents the interests of the corporations and elite beneficiaries of the change, and the latter represents those of the vulnerable and voiceless. Therefore, such agenda-setting debates are a purely political activity, with profound social implications.

It is also important since there is the distinct likelihood that India could spend the next five years as the world’s back office for the third time, and when the juice has been sucked out and value captured, there could be nothing left standing that India owns.

PS: In this context of collective action problems, it is worth taking inspiration from one very impressive and encouraging breakout (which has not received the level of attention it deserves) from South Korea. It is a tribute to the maturity and wisdom of the country’s corporate and political system and the robustness of its democracy that Samsung and SK Hynix agreed to share 10% of their windfall profits from memory chip sales, with no ceiling on payouts, with their employees for the next ten years. Sample this.

Samsung Electronics... agreed last month for employees to share the chipmaker’s blockbuster profits from an AI-led boom... SK Hynix... handed employees a similar profit-sharing deal last year... Samsung is also going to give Won500mn loans at low rates to employees... Samsung and SK Hynix together control much of the market for the advanced memory chips used in AI servers. Employees at both companies are in line for average annual bonus payouts of Won600mn, which compares with a national average salary of about Won50mn... district of Hwaseong... expected to gain corporate income tax receipts of Won1tn to Won1.3tn from Samsung alone this year, an extraordinary sum for a city authority whose annual budget is about Won3.5tn.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 11 Jul 2026 04:05:56 +0000

1. FT long read on how senator Deborah O'Neill, as chair of the Parliamentary joint committee on corporations and financial services, has single-mindedly exposed and brought the knees the Big Four auditing and consulting firms in Australia.

Deborah O’Neill has led the charge against KPMG in Australia over a client confidentiality scandal that prompted the departure of the firm’s chair, chief executive, chief operating officer, audit leader and a senior partner over the past month... comes on the heels of a similar implosion at PwC. The rival Big Four firm came unstuck when a data leak led to the exit of senior management... An EY employee was charged with accessing the bank details of Prime Minister Anthony Albanese while working on contract at Australia’s biggest bank. Meanwhile, Deloitte partially refunded the Australian government after admitting that it used AI to compile a report...

In 2023, the Labor senator forced the publication of emails that implicated PwC partners in the tax leaks scandal. PwC partners were caught sharing secret government tax plans that one of them had obtained from his work on an advisory board in Canberra, in the hopes of winning business in the US. This year O’Neill used parliamentary privilege to air allegations made by a KPMG whistleblower that had been inadequately investigated by the firm. KPMG has now been exposed as having used confidential information from existing audit clients to try to win new business from rivals — some from PwC as its audit customers looked to switch in the wake of that firm’s woes...

She entered parliament in 2010 having spent her career in education. O’Neill soon discovered that some Big Four consultants acted like some of her former pupils — copying the answers from the back of the book and then marking their own work, as she puts it — and used her role to put the leaders of the firms under pressure.

2. Soumaya Keynes points to a fascinating study by Rebecca Diamond of Harvard University of the use of GLP drugs that appears to show increased confidence and employment rates among women in the US. The study finds, using data gathered between 2021 and 2023, that the poorest third of women in the US suffered an obesity rate 14 percentage points higher than the richest third, whereas the gap was negligible for men.

The study's main findings:

After 18 months, women using GLP-1 drugs who start off without a job enjoy employment rates 27 percentage points higher than otherwise similar non-users. Women who start off with a job see their employment rate fall slightly, and although the data is too noisy to pick out effects on their earnings, it looks like their household income rises by 10 per cent. That second effect is a bit surprising, and possibly explained by parallel developments in these women’s love lives. Diamond estimates that GLP-1 drugs give single women a dramatic 29 percentage point increase in their chances of coupling up. On average, their new partners are richer than them, giving their household income a bump. Which could explain why a few of the women losing weight then feel able to drop out of work.

The study also points to a more disturbing consequence.

So far at least, GLP-1 drugs are disproportionately used by the rich. In Diamond’s study two-fifths of the women paid for the drugs out of pocket, at a median cost of $275 a month. Research based on Voy prescriptions shows how, adjusting for relative obesity rates, uptake is skewed towards more affluent areas. If obesity becomes an even stronger signal of economic disadvantage, the stigma attached could grow.

3. The rise of London's King's Cross area as perhaps Europe's AI capital.

Two decades ago, King’s Cross was central London’s most neglected district. Today, it is home to the main foreign outposts for several of the world’s wealthiest companies, from Big Tech giants Google and Meta to their richly funded AI challengers including OpenAI, Anthropic and Jeff Bezos’ Prometheus. AI researchers and entrepreneurs are packing out the area’s canal-side cafés so densely that venture capitalists prowling for their next deal are struggling to prevent their coffee meetings from being overheard by rivals.

For many, this resurgence can be traced back to one individual: Sir Demis Hassabis, the DeepMind co-founder and Nobel laureate who stayed in London to build his AI lab following its sale to Google in 2014 for £400mn. “Demis keeping DeepMind in London and resisting the gravitational pull of [America’s] West Coast is the most important thing that has ever happened to the London tech ecosystem,” says Tom Hulme, a tech investor at Alphabet’s GV venture capital unit... Just as PayPal helped launch the careers of a generation of Silicon Valley founders and investors including Elon Musk and Peter Thiel, a “DeepMind mafia” in London is pulling in billions of dollars to AI start-ups founded by Hassabis’s former lieutenants, including David Silver’s Ineffable Intelligence and Tim Rocktäschel at Recursive Superintelligence...

It was Hassabis’s pursuit of an “artificial general intelligence” capable of scientific research and a wide range of human tasks that in many ways kick-started the current AI boom. DeepMind’s sale to Google prompted Elon Musk to set up a research lab to counterbalance the internet group’s dominance of AI; that lab was OpenAI. DeepMind went on to make a series of AI breakthroughs including AlphaGo, which in 2016 beat the board game Go’s world champion Lee Sedol, and AlphaFold, which used deep learning to predict protein structures with superhuman speed.

King's Cross has emerged as the Canary Wharf of tech in UK, and this description is apt and underlines the continuing importance of personal interactions and connections.

The density of AI talent in King’s Cross was why the government’s scientific research agency Aria took a “very conscious decision” to base itself there rather than Whitehall, says Pippy James, its deputy chief executive. “We were definitely inspired by Kendall Square in Boston,” she says, referring to the area surrounding MIT where Google, Microsoft and Amazon, as well as biotech companies Moderna and Novartis, have offices. “Value creation comes from those serendipitous collisions.” In recent weeks there has been a steady stream of American AI companies announcing moves into the area. OpenAI and Anthropic have signed leases for tens of thousands of square feet in King’s Cross. Others moving in include Bezos’s “physical AI” company Prometheus, Cursor, the AI coding company that recently agreed a $60bn sale to SpaceX, AI agent group Perplexity and open model developer Reflection. Google, which already has many researchers and engineers in the area, plans to start moving staff into its vast new “Platform 37” office this summer after almost a decade in development.

4. This is a striking factoid about the importance of chips now.

With SpaceX going public, the list of the 10 most valuable US public companies is entirely made up of tech companies for the first time. Of those, three are semiconductor specialists. But with chips a key ingredient in AI, the other seven are also now all designing their own chips.

Some stats about the global chip squeeze.

... Elon Musk’s xAI to rent out spare capacity in its data centres. In recent weeks, Anthropic, Google and start-up Reflection AI have agreed to pay a total of around $2.3bn a month — or $28bn a year. This looks like a big return on Musk’s data centre investments. As of March this year, xAI’s total capital spending over its lifetime totalled $26.5bn... this surge in demand has already prompted a huge increase in supply, both of planned chipmaking capacity and newly minted chip stocks. One sign is the $600bn that memory chipmakers Samsung and SK Hynix said this week they plan to invest in Korea. Another is the $29bn that SK Hynix hopes to raise when its American depositary receipts begin trading in the US next week... TSMC has said it will boost its capital spending by as much as 37 per cent this year, as it did in 2025. But those increases follow two years of retrenchment and would leave 2026 capex only around 50 per cent higher than 2022. Contrast that with the biggest buyers of AI chips. Seven of the largest data centre operators are planning to spend an astounding $848bn this year, at least five times what they spent in 2022, according to a calculation by the newsletter Exponential View.

5. On the new bonds issued by SpaceX.

The bonds enjoyed very robust demand at the point of issuance, but some see that as a problem in itself. Allianz’s chief investment officer has described the market’s willingness to hand money over to Musk as a clear sign that we have moved from “a healthy boom, a stretched boom . . . into bubble territory”. Ominously, the bonds have weakened since they launched.

6. The AI LLMs scorecard.

Where will Sarvam stand?

Also national scorecard.

7. The low-margin business of mobile phone assembly. Amber Industries which makes air conditioners for eight of the top 10 brands and more than a quarter of all ACs made in the country, now proposes to assemble smartphones for Oppo.

Now, the company plans to sub-lease a part of Oppo India’s factory in Noida, set up SMT lines, and start assembling phones there... But smartphone assembly is one of the toughest businesses in electronics manufacturing... The target is to eventually assemble about a fifth of Oppo India’s volumes, scaling from roughly 8 million handsets in the first year to nearly 15 million in the second... Amber expects Ebitda margins of 1.5–2%, excluding benefits from the government’s production-linked incentive (PLI) scheme. That’s well below the 8.8% Ebitda margin generated by its broader electronics business in FY26, and the 7.1% operating margin recorded by its AC-heavy consumer-durables segment... Take Dixon, for instance. The company already accounts for nearly one-fifth of India’s smartphone output. Even at that scale, smartphone assembly, aided by PLI incentives, generates Ebitda margins of only about 3%.

8. AI boom compared with historical episodes.

9. On the rise of non-compete clauses in OECD countries and their adverse impact on productivity.

About 30 per cent of employers surveyed by the OECD said they had increased their use of the clauses in the past five years... It estimates that a 10 percentage-point increase in the prevalence of non-compete clauses in an industry was associated with a 1.9 per cent decline in the level of labour productivity, with workers stuck in sub-optimal jobs and firms less able to gain new skills. In many countries, non-competes have spread into parts of the labour market where the original justification of protecting sensitive information and firm-specific information is “weak or absent”, the research found, pointing to their use among entry-level fast-food staff in the US, manual workers in Italy and childcare workers or yoga instructors in Australia.

10. The market concentration in DRAM chips.

A market in which a monopolist owns 100 per cent gets an HHI of 10,000, a duopoly scores above 5,000, and a perfectly competitive market approaches zero... The US DoJ considers anything between 1,000 and 1,800 points to be “moderately concentrated”. Per Counterpoint Research, as of the first quarter of 2026 the memory market is 38 per cent Samsung (South Korea), 29 per cent SK Hynix (South Korea), 22 per cent Micron (US), 8 per cent CXMT (China), 2 per cent Nanya (Taiwan), and 1 per cent everyone else. This gives us an HHI of 2,838... Both Samsung and SK are two of Korea’s largest conglomerates (the so-called chaebols) which benefit from cosy relations with the state, so viewing them as fierce competitors in the same memory market might be wrong-minded in this case... And if the two companies function as a single economic entity in the global DRAM market, we should probably count them together for HHI purposes. And doing this we get a much higher HHI reading of 5,042. That’s above 5,000 —the HHI of a perfect duopoly.

11. China’s excess capacity in manufacturing requires something similar to what was done by the former Prime Minister Zhu Rongji.

In the late 1990s and early 2000s… under Zhu’s slogan of “zhua da, fang xiao” or “grasp the large, let go of the small”, Beijing retained its grip on key strategic industries while relinquishing control of a vast sea of smaller companies and factories…Thousands of mines, steel mills and other industrial sites were shut for good. An estimated 30mn to 40mn workers lost their jobs. The process was deemed painful but necessary: not only in setting up China’s accession to the World Trade Organization in 2001, but in freeing Beijing from supporting uneconomic industries…
Over the past 15 years, as China’s share of global manufacturing surged to around one-third, the share of lossmaking industrial businesses jumped from about 10 per cent in 2010 to nearly 25 per cent last year, according to the MERICS China Overcapacities Monitor. This dynamic exists across everything from steel and cement to cars, computer chips and robots. Take the automotive sector for example. Domestic car sales last year totalled 23.9mn against estimated production capacity of 45mn to 50mn. Sales are highly concentrated among a clutch of leading companies. According to HSBC, more than 70 per cent of EV sales — including plug-in hybrids — are being soaked up by 10 brands, leaving 47 others jostling for the remainder. In the shrinking market for petrol and diesel cars, 10 brands have about 70 per cent of sales and 73 others compete for the rest.

12. South Korean capitalism and windfall profits sharing - SK Hynix and Samsung edition.

Soaring global demand for high-bandwidth memory chips used in AI systems has propelled SK Hynix and Samsung Electronics to record earnings. This week Samsung announced quarterly operating profit of Won89.4tn ($59.7bn). The windfall is being shared with employees. Last September, SK Hynix agreed to pay workers 10 per cent of annual operating profits for a period of 10 years. Samsung followed with a similar arrangement in May after its union threatened strike action. With both firms expected to earn hundreds of billions of dollars this year, average bonus payouts per memory chip worker could reach about Won600mn ($400,000) at Samsung and even more at SK Hynix. Such amounts are staggering in a country where the average worker earns Won50.6mn per year, according to Korea Enterprises Federation data.

The Bank of Korea has warned of potential inflationary pressure as a result, and towns where many semiconductor workers live are undergoing property price jumps. Competition is intensifying for places at universities offering semiconductor “contract” programmes that guarantee jobs at Samsung Electronics or SK Hynix upon graduation. Admission scores required for some such courses now exceed the average for natural sciences at Seoul National University, the country’s top-ranked university, and are just below those needed for medicine... The boom is also reshaping Korea’s marriage market. Matchmaking agencies, which are known for using meticulously harsh metrics to rank clients, are now giving higher points to chip workers.

13. Data centres are consuming massive amounts of power and water.

Data hubs already devour more electricity globally than all but 10 countries. About 448 terawatt hours last year if you’re interested. The AI boom means that amount is on track to roughly double within four years... By 2030, they could be using enough water to meet the basic needs of all 1.3bn sub-Saharan Africans for a year, UN researchers estimate.

And it is provoking backlashes. Sample this from the US.

An unprecedented 75 US data centre projects worth around $130bn were blocked or delayed in the first three months of this year, nearly as many as in the whole of 2025, says the Data Center Watch research group. It reckons active opposition group numbers have grown from 396 at the end of 2025 to 833 by the end of March.

14. The Strait of Hormuz squeeze was not as bad as earlier episodes.

15. Transformers are at the heart of power transmission, distribution, and use. Thanks to the AI and data centre boom, transformer prices have gone over the roof.

Specialist electrical steel — essential for transformer cores — is produced by only a handful of global suppliers, many of whom are struggling to keep up with the surge in demand. Market growth, price volatility and limited mining capacity have also strained supplies of copper, crucial to the conductivity of windingsaround a device’s core. But one of the most acute bottlenecks is the shortage of skilled workers needed to carry out complex, labour-intensive manufacturing tasks...

With up to 80,000 different designs, most transformers still have to be built largely to order, taking three to six months to make. The most demanding stage is the windings, when copper wire is applied around a transformer’s core — a “beautiful” process, according to Bruno Melles, an engineer now leading Hitachi’s global transformer business. Each winding is unique and is “still a human manual activity that we’re very proud of”, he says. The number and pattern of these windings dictate voltage — fewer turns lead to lower voltage while higher-voltage devices can have multiple windings stretching hundreds of kilometres. Once complete, the assembly is placed in a protective outer metal shell, where oil acts to insulate and cool the device...

The world’s biggest transformer manufacturers have reported tens of billions of dollars in backlogs in the first quarter of 2026... US developers are turning to imports. The EU, Mexico, South Korea and Brazil are the biggest suppliers of power transformers to the US, together accounting for more than three-quarters of imports by value last year.

Into this mix come innovations in the form of modular solid-state transformers that use modern power electronics and respond dynamically to changing power needs, enabling real-time monitoring and control (which legacy transformers with their steel and copper cannot do).

The loss of financial market discipline

noreply@blogger.com (Urbanomics) — Fri, 10 Jul 2026 02:09:09 +0000

This post will provide a framework for thinking about the erosion of financial market discipline.

The FT has an article on an inflating earnings bubble in the financial markets.

Analysts are now forecasting a 25 per cent increase in S&P 500 company earnings for the coming year, according to Bloomberg data… Ben Inker, co-head of asset allocation at GMO, said forecasts for the next two years were “rising at an exceedingly high rate, nothing we have seen outside of a crisis recovery”. Consensus estimates for coming-year profits have risen by almost 20 per cent in six months, the biggest such jump since 2021…
Capital Economics analysts warned this week that “AI-related equity markets may be approaching a point where earnings expectations and capital expenditure assumptions become difficult to sustain” and a correction in these could “trigger a broad equity market pullback”. Michel Lerner, head of UBS’s investment analytics platform HOLT, said “shares in the AI food chain are priced to maintain supernormal profits” and warned of an “earnings bubble” forming in the market. While exceptional profits appear likely to keep being delivered in the immediate future, he said, “the likelihood of sustaining these levels of profitability and growth is incredibly low”.

This earnings bubble supplements an already inflated valuations bubble.

The BIS Annual Report has an excellent graphic that shows that stocks are pricing an earnings bubble, underpricing risk, and household exposure has increased sharply.

The implied long-term earnings growth for the largest corporations sits well above recent historical benchmarks, with US stocks often trading at large premia to peers in other major markets. These implied rates often exceed even the elevated growth that some of the technology firms have delivered in their relatively short lifetimes. As these firms mature and command a larger share of the market, sustaining such high growth could become increasingly challenging… Risk premia on the largest US stocks have compressed markedly since the COVID-19 pandemic, with the distribution shifting clearly to the left. This points to growing investor complacency and reduced compensation for risk-bearing… A major equity market correction could have larger macroeconomic consequences today than in the past. Household equity exposures have grown over the past few decades, both relative to total wealth and income. A large correction in valuations could have more pronounced wealth effects and sharper consumption pullback than in the past. And with US stocks accounting for an outsized share of global equity markets – about 64% of the MSCI Global index – the wealth impact from a US-led repricing could propagate globally.

And, the report says, all this is fuelled by a complex web of private circular transactions involving hyperscalers, chip makers and AI labs.

Chip makers and hyperscalers take equity stakes in AI labs or neocloud providers, who in turn commit to multi-year purchases of chips or computing power. Data centre construction is increasingly outsourced to third parties that lease facilities back to hyperscalers on long-dated contracts with embedded exit clauses. The terms of such deals are typically poorly disclosed, with risks of the same asset being pledged multiple times. Together, such arrangements account for a sizeable share of sector-wide financing and forward revenue. A sharp repricing of equity risk could prompt a reassessment of corporate credit risk and lead to tighter credit conditions more broadly…
Any tightening in credit conditions could expose existing vulnerabilities in the less transparent private credit space, whose reach has expanded among middle market and small firms… A larger shock, whether from a renewed inflation surge or a sharp AI-led repricing, could trigger a more widespread credit crunch… The growing role of private credit also raises concentration risks. Direct lending funds, dominant players in the private credit ecosystem, have quadrupled their lending to the AI and information technology (IT) sectors in the past five years, to about 15% of their portfolios. These loans tend to be larger than those in other sectors, while their terms such as tenor and pricing remain broadly similar, raising questions about lending standards and risk pricing.

In this backdrop, it is useful to ask the question whether financial markets have lost their disciplining powers. What is it about modern financial markets that makes them underprice risk?

For sure, greater access to information has bridged information asymmetries, increased transparency, and reduced uncertainties and risks. And this has, in turn, lowered price discovery frictions and increased market efficiency. However, this has gone alongside trends in the opposite direction. Already, the complexity of financial instruments obscures risks and distorts price discovery. But other market practices have emerged in the last two decades that distort incentives and erode the market discipline. Some of them are consequences of the aggressive expansion of central bank monetary policy toolkits in the aftermath of the Global Financial Crisis.

There is a moral hazard created by the market participants’ internalising the belief that too-big-to-fail institutions or industries will always be bailed out. The institutions themselves come to view this as an insurance against their risk-taking. Well-intentioned actions of central bankers like forward guidance and commitments to backstop against shocks (Greenspan Put and Draghi’s “whatever it takes”) create similar moral hazard across markets. The gradual erosion of gatekeeping standards (ratings inflation, audit failings, index inclusion - e.g., for SpaceX) distorts incentives and leads to underpricing of risks. Excessively bullish market guidance, like with AI stocks now, fuels a cycle of irrational exuberance that amplifies bubbles. Finally, the inherent dynamics of financial markets bake in the fear of missing out among the institutional investors.

The fundamental basis for any efficient market is that information flows facilitate price discovery, and the costs of the actions of market participants are internalised. Based on this, an analytical framework on financial market discipline can be built on three pillars - information quality (participants must get accurate signals); price discovery (prices reflecting the underlying value); and skin in the game (losses must fall on risk takers). All three must hold simultaneously for market discipline.

The three erosions are not independent. Mechanical demand distorts prices; distorted prices reward gatekeepers who validate them; validated prices attract more backstops when they wobble. Each pillar’s erosion masks the erosion of the others. By the time discipline is missed, all three have been hollowed out simultaneously. This is why the current market environment can display record-high valuations (Price Discovery pillar broken), narrow risk premia (Skin in the Game pillar broken), and record-optimistic analyst forecasts (Information Quality pillar broken) - all at the same time, without triggering any traditional warning system. The system that would normally catch such a configuration has been progressively disassembled.

Interestingly, each erosion channel began as a reasonable response to a specific problem. Deposit insurance solved bank runs. Forward guidance solved communication ambiguity. Passive investing solved active-fund underperformance. Rating agencies solved information asymmetry. None of them is wrong in isolation. The problem is what happens when all the individual protections are stacked simultaneously, and all of them are pursued to their extremities. The system loses its balance.

The market’s self-correcting machinery is gradually replaced by a suite of external supports, and the participants who set prices become smaller and smaller relative to the participants who follow rules. At some point, arguably reached, the market is no longer a disciplinary mechanism at all. It is a policy instrument with an ambiguous owner. That is why the current earnings-bubble concern is qualitatively different from previous bubbles in that there is no obvious mechanism left through which the market disciplines its own excess.

So what can be done to restore the disciplining powers of the market?

Quite simply, it should be about restoring accurate price signals, consequences and costs of risk taking, and the trustworthiness of information, and all by going back to first principles.

The restoration of price discovery requires both thickening the market (a high concentration of buyers and sellers) and increasing liquidity (allowing assets to be transacted without significantly impacting the price). The internalisation of the costs of risk-taking demands rolling back moral hazard-inducing backstops and bailouts, or at the least ensuring these interventions are priced appropriately. Finally, restoring the quality and credibility of information requires separating information provision from the transactions side of financial intermediaries, and letting the market guide with information. An agenda is outlined below.

This must be complemented with cross-cutting reforms such as restrictions and greater oversight on revolving-door personnel movements, enhancing the white-collar prosecutorial capabilities of regulators, enhancing the supervision of non-bank financial institutions (NBFI), macroprudential capital surcharges for risk concentration, etc.

The problem, though, is that these are all very difficult reforms even at the best of times, but particularly difficult now. The silver lining is that a big crash can create the conditions for such reforms.

AI for organisational and bureaucratic reforms

noreply@blogger.com (Urbanomics) — Wed, 08 Jul 2026 03:49:00 +0000

The debate rages about the extent of AI’s likely impact on the economy and human lives. So far, there has been an apparent lack of commercial value creation to justify the gigantic and exponentially increasing volume of AI investments.

I blogged here on the distinction between horizontal and vertical use cases of AI, with success on the latter being limited, here cautioning about the likely impact of AI on development and in developing countries, and here on some possible high-impact use cases for AI in lower-income countries.

The most common horizontal use of AI is in personal productivity improvements. Claude, ChatGPT, etc., are already having large effects on personal productivity. But its translation into vertical use products is muted.

On this, John Burn-Murdoch points to the work of Mert Demirer, Leon Musolff and Liyuan Yang to make two important points. The first point is the “disconnect between reported increases in coders’ output and the apparent lack of a corresponding boom in product or value creation,” which creates a very steep funnel between inputs and outputs.

The study by MIT’s Mert Demirer and co-authors tracked software developers’ work before and after they adopted AI tools. Importantly, they measured this at several different levels, from the amount of code written, to the number of discrete files edited, to the number of projects or features worked on, to actual releases of new software. They found an explosive impact at the top of this funnel — coders created or edited almost 300 per cent more files — but that boost was halved to 150 per cent by the time they got to the number of discrete pieces of work submitted for review, and that in turn shrunk fivefold to a roughly 30 per cent uplift in the number of full software releases.

The authors also found little evidence of AI-assisted increases in software development, leading to increased consumption of Apps.

This brings us to the second point made by the authors about AI’s impact - it is likely to be fully realised only when new organisational structures, markets, and business models emerge.

But Demirer and his co-authors feel the more likely explanation is that current organisational structures and marketplaces are not set up to take advantage of real underlying gains. That view is supported by the evidence from past technological revolutions, where the real jumps in productivity and job displacement came from new companies and processes rather than incumbents grafting new technology on to existing workflows. In the case of electricity in the late 19th and early 20th century, productivity gains were modest where factories simply replaced giant steam engines with giant electric motors but left the rest of the machinery and layout unchanged. The boom arrived decades later when engineers fitted individual workstations with their own small motors.

In this backdrop, Jack Dorsey and Roelof Botha have an insightful article on organisational impact. Specifically, they claim that AI’s productivity-enhancing value can address the fundamental coordination problem in large organisations that manifests in the form of a trade-off between span-of-control limitations (which add organisational layers) and speed of information flows. They argue that AI sharply increase people’s span of control, thereby reducing organisational layers and hastening decision-making.

The first organisational models emerged in the military to organise large numbers of soldiers into a coherent and effective fighting unit. It involves a hierarchical chain of command that allows for a span of control and a seamless flow of information and instructions. The model then entered the corporate world through the US railroads in the 1840s and 1850s, which borrowed West Point-trained engineers from the US Army. They trace the evolution of the modern organisational form,

In the mid-1850s, Daniel McCallum of the New York and Erie Railroad created the world’s first organizational chart to manage a system stretching over 500 miles with thousands of workers… McCallum’s chart formalized the same hierarchical logic the Romans had used: layers of authority, defined reporting lines, structured information flow. It became the blueprint for the modern corporation… Frederick Taylor (1856-1915), often called the “Father of Scientific Management,” optimized what happened within that hierarchy. Taylor broke work into specialized tasks, assigned them to trained experts, and managed through measurement rather than intuition. This produced the functional pyramid organization - a structure optimized for efficiency within the information routing system that the military had pioneered and the railroads had commercialized…
In 1959, McKinsey’s Gilbert Clee and Alfred di Scipio published “Creating a World Enterprise” in the Harvard Business Review, providing an intellectual framework for a matrix organization that combined functional specialties with divisional units. Under the leadership of Marvin Bower, McKinsey helped companies like Shell and GE implement these principles, balancing central standards with local agility. This became the “professional” or “modern” corporation that propelled the postwar global economy… The McKinsey 7-S framework, developed in the late 1970s by Tom Peters and Robert Waterman, distinguished the “hard Ss” (Strategy, Structure, Systems) from the “soft Ss” (Shared Values, Skills, Staff, Style). The core idea was that structural elements alone were insufficient. Organizational effectiveness required alignment across cultural traits and the human factors that determine whether a strategy actually succeeds.

They suggest that AI makes it possible to solve the fundamental coordination problem within large organisations that necessitate hierarchical formations.

For the first time, a system can maintain a continuously updated model of an entire business and use it to coordinate work in ways that previously required humans relaying information through layers of management… In a traditional company, a manager’s job is to know what’s happening across their team and relay that context up and down the chain. In a remote-first company where work is already machine-readable, AI can build and maintain that picture continuously. What’s being built, what’s blocked, where resources are allocated, what’s working and what isn’t. That’s the information the hierarchy used to carry. The company world model carries it instead…
In a conventional company, the intelligence is spread throughout the people and the hierarchy routes it. In this model, the intelligence lives in the system. The people are on the edge… The edge is where the intelligence makes contact with reality… the edge doesn’t need layers of management to coordinate it. The world model gives every person at the edge the context they need to act without waiting for information to travel up and down a chain of command… Everything else the old hierarchy did, the system coordinates, and everyone is empowered, with a role that’s much closer to the work and the customer.

They identify three roles - Individual contributors (ICs) who are deep specialists and experts who build and operate system capabilities; Directly Responsible Individuals (DRI) who own specific cross-cutting problems or opportunities and customer outcomes; and player-coaches who replace the traditional manager whose primary job was information routing, who do both building and handling people.

All this makes great sense and points to how corporate organisational models are likely to emerge as the application of AI progresses. There will be frontier firms in a few sectors that will lead the way for others to follow.

AI applications are a promising opportunity to address inefficiencies and coordination failures in public bureaucracies, too, and improve the quality of public administration.

For a start, it has the potential to restore internal capabilities, which have eroded steeply. Over the years, thanks to practices like outsourcing all analytical and documentation work to consulting firms and the hiring of individual consultants (most notably now, the system of Young Professionals, YPs, in governments), there has been a complementary erosion of in-house expertise. The capabilities to articulate proposals for internal deliberations and file circulation have atrophied. Given that bureaucracies run on deliberations and files, this trend is an underappreciated aspect of state capability weakness.

AI provides an opportunity to reverse these trends and develop internal capabilities. The primary reason for the reliance on external expertise is the extent of analytical work and documentation required during the deliberative process (everything from a concept note on the proposal to reports for appraisals, and Cabinet Notes). The bureaucratic leaders who are burdened with a multiplicity of tasks, work under tight timelines, face increased fetters from oversight agencies and courts, and must rely on an increasingly enfeebled internal bureaucracy. In the circumstances, they prefer to outsource the thinking and documentation to outsiders. I have blogged earlier on the perils of this approach.

AI tools like Claude are excellent at analytical work and the generation of these documents in response to clearly articulated prompts. It becomes a simpler proposition if bureaucrats can quickly and easily obtain a draft concept and supporting documents, and then scrutinise, validate, and refine it before circulation for approval. AI tools can then become a force multiplier for bureaucratic leaders, who are now constrained by their limited bandwidth and acute dependencies.

This would also empower bureaucratic leaders, or at least some among them, and could enhance the quality of their engagement with the decision-making process. Besides, by minimising the drudgery of the bureaucratic process, it would also allow bureaucratic leaders to apply their minds and exercise judgment more effectively, thereby improving the quality of decisions and policy design and implementation. It would also lower decision-making delays.

It should therefore become a priority of the National Informatics Centre (NIC) (or an AI division within it) to develop or license AI application that is embedded in the e-office software and enables officials to sift through large documents and generate proposals/presentations, circulation notes and reports using prompts. This has transformative potential for productivity improvements, not only stopping the erosion of internal capabilities but also helping rebuild them.

If this can be done, it opens up opportunities for far-reaching administrative reforms. The current bottom-heavy pyramid can be rationalised to make it fit-for-purpose.

A major inefficiency is the presence of multiple layers within the administrative system. It is a widespread practice across governments to have YPs, and those recruited as data entry operators originate the note file (a task earlier performed by the clerical staff). The note then gets circulated across several layers, often seven or eight till the approver. This can be radically pruned down to no more than three or four, including the approving authority.

Such de-layering is especially relevant for technical ministries and departments whose activities are more amenable to AI-based support. Such ministries should have a separate administrative staffing plan, one that takes into account the role that AI can play in generating documentation and considerably reducing any drudgery associated with analytical work.

As a general illustration, there are perhaps three kinds of activities in any department - shared services (HR, procurement, establishment issues, statutory matters, etc.), administration of departmental programs, and analytical and technical work. There are significant low-hanging likely process-efficiency improvements in all three, and substantive value-addition potential in the third activity.

This would also necessitate a reassessment of public recruitments. The advent of AI applications means that, unlike in earlier times, apart from merely documenting the issues in a note file, the case worker (the ASO or SO) can now be expected to do some analysis and provide comments. This also means that a smaller base can serve the clerical roles (the entire paraphernalia of clerical cadres can be collapsed into just two functional levels - maker and checker), and their educational qualifications and skills must reflect the requirements for the revised scope of work. I’ll blog separately on this.

The increased use of AI applications to analyse and document, and a compact and delayered deliberative process captured in the file circulation can also increase the quality of collective engagement and ownership of the bureaucracy in decision-making. It lets (and nudges or forces) everyone contribute meaningfully to the process instead of being passive pass-throughs of instructions and note files. It presents the opportunity to shed reliance on outsourced expertise and build back state capabilities.

This is deep work and, even in the best case, is likely to be adopted only by a few units in the first phase. The objective should be to create the conditions that encourage the emergence of these lighthouses and channel them to diffuse change more widely.

Some thoughts on the AI trade

noreply@blogger.com (Urbanomics) — Sun, 05 Jul 2026 23:30:00 +0000

The AI trade is clearly blowing into a bubble of utterly spectacular proportions.

FT Alphaville points to the interesting reality that both equity valuations and earnings of the underlying companies are in bubble territory. They quote from the monthly market update by Joachim Klement and Francisca Reis of Panmure Liberum.

In 1929, the cyclically-adjusted P/E-ratio (CAPE) of the S&P 500 reached 32.6x according to Prof. Robert Shiller’s data. This was 1.8 standard deviations above trend at the time. In 2000, the CAPE reached 44.2x, or 3.3 standard deviations above trend – a clear sign of a bubble. However, as our chart below shows, earnings in both instances were within normal range, less than one standard deviation above trend. Today, the CAPE is at 41.0x, or 2.9 standard deviations above trend. Once again, we are clearly in bubble territory for stock market valuations.
However, unlike in previous bubbles, we are having extremely high CAPE at a time when earnings themselves are 1.8 standard deviations above trend. In other words, we are in a valuation bubble at a time when earnings are in a bubble themselves. If we correct for the earnings bubble, the current CAPE would be 67.6x or 4.6 standard deviations above trend, a bubble that surpasses anything ever seen in US history by an extreme margin. If valuations followed a normal distribution (which they don’t, so don’t take this literally), this would happen in 0.00019% of months or once every 43,432 years.

See just the Shiller CAPE ratio.

That being the case, what makes any assessment of the AI trade problematic is its unique nature.

It is the creation of a general-purpose technology that is most certain to transform several aspects of business models, work practices and trends, and life itself (and maybe even more). There are five stakeholders on the supply side of the AI story - semiconductor chips, cloud and data centre infrastructure, LLMs, financing, and support services. They encompass a vast spectrum of businesses - chip designers and makers, equipment makers, cloud infrastructure services, AI LLM developers, data centre developers, utilities, financial institutions, etc. On the demand side are all the different kinds of AI applications users, spanning every imaginable industry.

While the LLMs and the cloud services are software, the rest are all hardware. At the vanguard are the top public limited companies of our times, all with formidable moats and strong balance sheets. Till the recent surge in private credit, the AI investments were being financed from their large cash surpluses. Further, all of them make massive and growing profits, even creating an earnings bubble. Finally, given the network effects generally associated with digital technologies, the AI landscape is more likely to create winner-takes-all markets.

Also, so far, the AI trade has been running on the massive investments pouring into the development of AI LLMs and their applications. This investment binge is being driven by an intensely competitive race to outdo each other in improving the LLM algorithms and building up scale capabilities. The achievements to date on this have been truly spectacular. The applications development, though gathering pace, is lagging. No killer Apps or hardware have emerged on the landscape.

The true reckoning for the AI trade will depend on whether the AI algorithms developed will find commercially valuable use cases. More specifically, whether these use cases, which will emerge in the years ahead, will justify the massive investments already made and being committed.

Even more specifically, if the market smell tests of the AI promise endure, the bubble will continue to inflate. Such persistence of bubble inflation is, to a great extent, the perpetuation of stories. Elon Musk and SpaceX are the totemic illustrations. The two critical metrics of aggregate profits possible from the AI industry are the addressable market and margins. However, both are nearly maxed out in the valuation pricing on the upstream (supply) side. The best that can therefore be expected is that the downstream (demand) AI applications industry starts to create successful products. This would let the AI trade inflate more, at least for the immediate future.

On the contrary, if the market smell test points to the other way, at least in a few vanguard areas like autonomous driving or software development or healthcare and life sciences, then all bets are off and the wheels will start to come off. Another downward pathway is if the upstream supply-side margins start to get squeezed due to competition or disruption or rising costs. Given the asymmetric nature of such turns (bubble inflated gradually upwards, but the pop could be rapid), we could then see a rapid unravelling of the AI trade, with all its disruptive implications. The disruptions could be economy-wide, far deeper and broader than with the dotcom and other bubbles, given the wide sweep of markets that the AI industry has enveloped.

Interestingly, even as the seven largest data centre operators are planning to spend $848 bn this year, five times what they spent in 2022, the stock prices of the applications-side hyperscalers are struggling. Microsoft is down nearly 20% this year, and Meta is down 11%. The AI trade is now running purely on the chipmakers.

The critical place in the market to keep a close eye on may be the downstream side of AI applications and product development.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 04 Jul 2026 07:54:50 +0000

1. Indian universities have come to dominate the global rankings for research paper retractions, following complaints of plagiarism, fake peer reviews, and other misdemeanours. According to the Retraction Watch database, India recorded 887 research paper retractions in 2025, form 21% of all retractions (second to China with 41%) despite contributing just 5% of all publications, and occupying six of the world's top 10 universities for retractions.

2. Stunning statistic about the Algerian demographic dominance of the Les Blues, the French national soccer team.

Only two of France’s 26 players are of non-immigrant ancestry — 22 of them have African roots. Some, such as Mbappé, are deeply connected to their country of origin.

And this dynamic of reverse migration and migrant domination elsewhere.

Of England’s 26 players, eight have Caribbean forebears, 10 African, and 20 who were eligible to represent at least one other country because of their family heritage or birthplace. Belgium has players who trace their lineage to its former colonies — Congo, Burundi and Mali. The Portugal team has those of Cape Verdean, Angolan, and Guinean lineage. Those surplus to their requirements — players of African descent born and raised in Europe — in turn, populate most of the African teams. Ten of the 11 Senegalese players in the starting lineup against France in the opening game were born in France... Six of the players in the US national team are of Afro-Caribbean heritage... There are those from Nigeria, Ghana, and Jamaica. Three are from Hispanic backgrounds — Christian Roldan (Guatemala), Ricardo Pepi (Mexican-American) and Jesús Ferreira (Colombian-American). As many are from Europe, and three others have dual nationalities... For Australia's Socceroos, four players were born in refugee camps. The players represent 15 ethnic backgrounds and include a Malaysian with Sri Lankan roots. Eighteen other players have direct immigrant or refugee heritage. To contextualise, only one Black player (Sam Morris) and another of Caribbean descent (Andrew Symonds) have represented Australia in cricket.

This is a brilliant article by Sandip G about the magnificent quartet in the French team - Kylian Mbappe, Ousmane Dembele, Michael Olise and Desire Doue.

3. The US military learns from Iran.

The US has, similarly, begun to build its production of drones. It used the Lucas, a one-way attack drone reverse-engineered by start-up SpektreWorks from an Iranian Shahed-136, for the first time in combat in February. The Pentagon is looking to begin mass-producing them, and has requested to triple its spending on drones and related technologies to over $74bn next year.

4. Shenzhen, with 400,000 taxi drivers licensed to provide services in 26 platforms, will permit robotaxis from July 1. The new rules will allow the Shenzhen government to promote the “orderly development” of robotaxi tests, demonstrations and commercial pilots either in select zones or citywide.

5. Friedrich Merz's landmark pension reforms

Under proposals agreed by a bipartisan commission, a compulsory initial contribution of 0.5 per cent of employees’ pre-tax income, rising to 2 per cent by 2031, will go into a Swedish-style public pension fund managed centrally and invested in capital markets. Contributions are split 50/50 between employees and employers. The statutory minimum retirement age of 67 is set to rise in line with life expectancy; rights to early retirement for people with 45 years of contributions will be restricted.

Such measures have become vital to reduce the deficits of Germany’s unsustainable pay-as-you-go system. Some 16.5mn baby boomers will retire by 2036 with only 12.5mn new workers joining the workforce, according to some estimates. The government spent about a quarter of the total federal budget on plugging gaps in the system in 2024; economists say that could double to 50 per cent in two decades.

Linking the retirement age to life expectancy is projected to mean only a gradual increase — to 67.5 by 2041 and 70 by 2091. But economists say this is the only sound way to stabilise the system without spiralling payroll taxes or huge federal subsidies. Narrowing early retirement rights will address a drain of experienced workers amid acute skills shortages.

6. Important graphic that highlights the extent of renminbi depreciation since the beginning of 2022 and the surge in surplus.

7. China expands export restrictions on dual-use items against Japanese companies in the latest instance of weaponisation of its manufacturing dominance.

The companies added to the export control list include subsidiaries of Mitsubishi Electric and Mitsubishi Heavy Industries. The restrictions will also apply to several Japanese government research organisations including the National Institute for Defense Studies. Chinese exporters are banned from selling to the entities on the restricted list, and foreign organisations or individuals are also prohibited from selling dual-use items that were built or originated in China. China last expanded the list to 40 companies in February. In parallel with the expanded export control list, China’s commerce ministry on Monday put 20 Japanese companies and organisations on its watchlist, meaning they will get closer scrutiny in any matters relating to potential dual-use technologies. The list includes subsidiaries of Fujitsu, Mitsui E&S, Hitachi, Komatsu and Terra Drone. Beijing’s targeting of Japanese companies is the latest example of China’s weaponisation of trade in recent years. The EU Chamber of Commerce in China in April has found that Beijing has nearly tripled its use of export controls in the past five years. While some instances have been in response to western measures, the researchers noted that Beijing’s controls have also frequently targeted trade chokepoints.

8. GST balance sheet.

Though average GST collections in absolute terms are almost 90 per cent higher than those in the pre-GST period, other parameters tell the real story. The average growth rate in tax collections and the tax-GDP ratio are lower under the GST regime compared to the pre-GST era. Collections were further marred by GST cuts starting September 22, 2025.

9. India's labour market challenge.

With high-skilled IT services under pressure, a financial sector with limited capacity to create low and medium skilled jobs and manufacturing struggling to gain traction, the one area where jobs are being created is low-end services. Delivery riders for Zepto, which has just filed its papers for an IPO, have gone up from 49,278 in 2024 to 2.21 lakh in 2026 — more than a four-fold increase. Zomato and Blinkit have almost doubled to 10 lakh riders in two years. Swiggy now has 6.1 lakh riders, while Uber, at 14 lakh active drivers, outstrips Indian Railways. The gig economy is emerging as an urban employment sink.

10. The Uniqlo-Toray partnership that underpins Fast Retailing's spectacular growth.

In April 2000, Uniqlo founder Yanai paid a visit to the offices of Toray, a Tokyo-based chemicals and materials conglomerate he had read about in a magazine. It marked the start of a strategic collaboration that provided Uniqlo with arguably its most important advantage over rivals: access to high-tech, specialised fabrics. After testing 10,000 prototypes, Toray invented a material combining four types of synthetic yarn that absorbs moisture from the body and converts it to heat. Uniqlo branded it Heattech, and since 2003 has sold 1.5bn garments made from or containing it. Toray also helped develop the fabrics behind AIRism, used as a breathable base layer, and the Ultra Light Down ranges of packable jackets insulated with bird plumage... control over fibre shape and fineness, dubbed nanodesign, has helped to make highly water-repellent and durable $50 lightweight parkas, creating a far cheaper alternative to specialist outdoor brands such as Patagonia. The two sides are entering a fifth phase of collaboration that aims to combine synthetic fabrics with natural ones, such as introducing cashmere into Heattech products to make them softer. Okawa believes few other retailers have such deep relationships with their key suppliers.

11. Good FT long read about the Jamie Dimon succession struggle at JPMorgan. The things that stand out are the following:

One, the appointment of someone to the top position in any organisation, public or private, is bound to be opaque and involve considerable discretion. The only disqualification would be the egregiously ineligible. For any others, there will always be ways to spin it as a fair process. This holds with greater effect as the stakes go up.

Two, in the succession struggle, dominated as they are by a multiplicity of considerations, among those eligible or qualified, it is rare that the most professionally competent will emerge as the successor.

Three strong leaders will always delay succession, and even when they choose to retire, will seek to retain enough levers to influence the decisions of their successors. Most often, it is about decisions involving the promotion of the interests of those in the organisation closest to them, their pet projects or initiatives, broader organisational strategic shifts, etc.

More on the limits to China's growth trajectory

noreply@blogger.com (Urbanomics) — Fri, 03 Jul 2026 02:20:56 +0000

The valuations of the US AI stocks and its public debt binge are not the only bubbles waiting to pop. An equally big bubble that could pop is China’s investment-driven and debt-fuelled growth model. Of the three bubbles, the last could well be the most consequential.

I had blogged here explaining the hard limits to China’s current growth trajectory. This post highlights the unsustainability of the country’s credit-driven growth.

The Economist has a very good read on China’s economic growth strategy, with its latest focus on high-technology sectors.

This puts China’s infrastructure investments in perspective.

The old model of growth took shape on China’s coasts before spreading to the interior. Factories in the wealthy east employed poor migrant labourers from the hinterland. Those migrants in turn, unable to obtain residency in metropolises, often used their earnings to invest in property back home. Towering apartment blocks erected during the two-decade property boom have sprung up in the smallest towns, employing tens of millions of construction workers each year and hoovering up low-end manufactures. High-speed rail has penetrated the poorest counties.

All this investment was fuelled by local-government borrowing. One tally puts these debts at around 60trn yuan ($9trn), or 43% of GDP. The comparable figure in America is 12%. The poorest regions often relied the most on debt-fuelled construction of houses, roads and bridges. This has left some places, such as Guizhou province in the south-west, with dazzling infrastructure (including a bridge 626 metres high, the world’s tallest) along with insurmountable debts. Few of these costly public works have so far come close to generating the revenues needed to pay back creditors.

As the Cold War with the US and the West intensifies, President Xi has set the goal of global leadership in advanced technologies - EV, batteries, semiconductors, AI, robotics, fusion, etc. This has set the stage for a new round of competitive investments by governments across the country, with enabling credit supply measures.

A national semiconductor fund has raised roughly 687bn yuan over the past 12 years. Government-backed fund managers watched their coffers swell to nearly 400bn yuan last year, an increase of 75% from 2024. In December the state launched a 100bn-yuan national venture fund with a mandate to invest in aerospace, semiconductors, brain-linked machines and quantum technology. Many local governments, including in small cities, are creating similar vehicles using tax revenues and capital from local state companies. They are setting up “high-tech zones” and “AI parks” to lure innovative companies with tax breaks and other perks. These new tech businesses are meant to generate tax revenue and help local governments grow out of their debts, says Jean Oi of Stanford University. While officials wait for their homespun DeepSeek, the AI lab that stunned the world last year with its powerful model, the central government relaxes the rules to give them more time to repay their debts.

The massive expansion of investments and intense competition have generated spectacular failures.

In 2021 the city government of Yichun invested 2.3bn yuan to help build an EV factory in a sprawling National High-tech Development Zone. But in contrast to successful EV clusters like those in Shenzhen and Hefei, the facility was isolated from suppliers and expertise required to build cars efficiently. It has since halted production. The rest of the industrial zone looks just as lifeless… A decade ago a fund with local- and central-government money poured around 150bn yuan into Guizhou, a mountainous province in central China, mostly into data storage and cloud-computing. But these ventures could not be integrated with local industry. The companies building the data centres are based on the coasts, the server parts are made elsewhere and local demand for the data capacity is scarce… The north-western industrial city of Lanzhou has invested in commercial space flight and a “drone economy” project even as it struggled to pay its bus drivers for several years (asking them to take out personal bank loans to tide them over)…
Mr Xi’s industrial policy promotes fierce competition in which companies and their host places, sometimes down to city districts, duke it out. This competitive pressure pushes down prices and elevates quality. The best businesses which emerge from this free-for-all, like BYD in carmaking, Huawei in electronics or Xiaomi in both, are formidable and ready to take on the world. They are also rare—and concentrated in established commercial centres, with deeper talent pools and pockets. Profits are even rarer. Investment returns accrue less to individual companies and more to integrated supply chains, which lower costs and speed up product cycles and innovation, says Chi Lo of BNP Paribas, a bank.

The share of industrial firms generating losses has shot to a record high of around 32% in April, up from 10% in 2011 and above the previous peak during the Asian financial crisis in 1998. Corporate debt is also high and rising. Mark Williams of Capital Economics, a consultancy, notes that Chinese firms owe twice as much to domestic banks and bond investors today as they did in 2019. In that period, GDP has expanded by a third. Companies may move away from productive activities and instead chase subsidies that are available for centrally supported sectors, he says… a trio of IMF economists calculated last year, China’s “total factor productivity” (which captures how efficiently both capital and workers are used) was 1.2% lower than it would have been in the absence of industrial policy over the past decade or so. GDP was 2% lower, equivalent to forgoing around $400bn in value added each year. The more companies get caught up in the chase for subsidies and, by slashing their prices, for customers, the harder it will be for them to wring out profits.

China faces a confluence of headwinds - slowing economy; weakening aggregate demand and consumer sentiments (with persistent low domestic consumption); ten quarters of factory deflation (PPI −2.6%) (involution or neijuan); the property market still in crisis and recovery perhaps still a few years away; local governments deeply indebted; large excess capacity across industries; growing backlash among trade partners against surging Chinese exports; intense competition among domestic companies squeeze margins and are leaving companies running losses; and zombie firms kept alive by local and central government subsidies, cheap credit, and debt repayment rescheduling.

Each panel below is a single number with its recent track. Red is contracting or dangerous; amber is stalling; green is the part still running hot, which is exactly where the next overcapacity is building.

In an environment where consumption is weak, any squeeze on investment and reduction of exports (due to rising backlash among trade partners) will only lead to job losses and social discontent. This has increased the reliance on investments to achieve the 4.5-5% GDP growth rates.

And exports are critical to absorb the excess capacity that has been built up.

After infrastructure, real estate, and manufacturing in the first two decades, the current focus of the investment-driven growth model has been advanced technology sectors like EVs, batteries, semiconductors, robotics, AI, etc., where there is also a geopolitical imperative arising from the Cold War with the US. The macroeconomic policy mantra has been to “hold growth at ~5%, protect jobs, and roll the borrowing forward each time.”

All this points to the model of a giant economy-scale Ponzi scheme where investment is shifting from one sector to another in order to sustain a target GDP growth rate and prevent job losses, while also accumulating a growing pile of massive debts.

A debt-funded model works only while each new yuan of credit produces enough output to service it. In China, that link has snapped: credit keeps compounding while nominal growth fades - the classic signature of a system paying old debts with new ones.

The cleanest unsustainability signal isn't the debt level per se but its productivity: total social financing has passed 309% of GDP, and in 2025 credit grew +8.9% while nominal GDP grew just +4.1% — more than twice as much debt as output. When each extra point of growth costs ever more borrowing, new credit is increasingly servicing yesterday's liabilities rather than funding tomorrow's, which is the arithmetic that ends the loop. And this is also reflected in the continuously rising fiscal deficit, especially in the off-balance sheet side.

Worsening matters, these trends coincide with a period in which fiscal revenues have flatlined in nominal terms and declined relative to the economy since 2020.

As the RAND report writes, with the fiscal space disappearing, the government has relied on credit and mandates to keep the wheel spinning.

Beijing can still mobilize large-scale borrowing for industrial policy. The central government and policy banks retain substantial capacity because Beijing can expand bond issuance, and national commercial or policy banks raise trillions of yuan annually in quasi-sovereign debt to finance strategic sectors. By contrast, local governments are squeezed; bond quotas are capped, land sale revenue has fallen, and off–balance sheet LGFV borrowing is under regulatory pressure. The most important immediate trigger was Beijing’s “three red lines” policy introduced in 2020, which sharply curtailed developer borrowing and land sales. Because land sales to developers had long been a major source of revenue for local governments, the resulting property downturn led directly to a collapse in land sale proceeds and fiscal capacity at the local level. A correction was likely inevitable because of the structural exhaustion of a land finance model that relied on perpetually rising property values to fund local growth. Therefore, localities are less able to cofinance subsidies or guidance funds.
Policy is shifting as a result. Centrally directed (or funded) credit and investments remain important. Meanwhile, traditional cash subsidies and local incentives play a reduced role. Government procurement has fallen relative to GDP. Instead, Beijing is leaning on lower-cost directives and mandates (including procurement requirements, regulatory obligations, and selective credit guidance) that require less fiscal outlay but effectively spread the costs of industrial policy across firms and institutions required to comply.

There are hard limits to how far this can go.

Since around 1980, the political bargain between the Communist Party and the Chinese people was simple - rising prosperity in exchange for political acceptance. With consumption weak, any squeeze on investment, or a real loss of export markets to a rising trade backlash, flows straight into jobs. Youth unemployment is already near 18%. The flywheel is kept spinning not only for growth, but to keep that bargain intact. How long can the flywheel keep spinning?

The problems of additionality and technology sector skew in the public funding of startups and innovation

noreply@blogger.com (Urbanomics) — Wed, 01 Jul 2026 01:36:25 +0000

Public risk capital funding of innovation and startups in India is done almost entirely through the VC-driven Fund of Funds (FoF). The RDIF is only the latest effort.

However, as I have blogged earlier, there are two important concerns with public funding of innovation through the FoF approach.

One, would it primarily expand the investable universe of startups (genuine additionality) or primarily subsidise returns on investments that would have happened anyway (returns-amplification for private investors)? Second, would it prioritise scalable digital technology innovations at the cost of manufacturing and industrial innovations?

The evidence on both suggests returns amplification for private investors and the dominance of technology innovators. This should raise concerns about whether scarce public funds are being deployed most effectively. This post points to yet more evidence in this regard.

The Ken has analysed the performance of the Self-Reliant India (SRI) Fund, a Rs 50,000 Cr fund with 20% government contribution (the rest coming from VC and PE) to finance MSMEs, and found both concerns being validated.

The SRI fund model is described here.

SRI Fund is being implemented by NSIC Venture Capital Fund Limited (NVCFL), which is an Alternative Investment Fund (AIF) of Category II registered with SEBI. SRI fund is oriented to provide the funding support through NVCFL to the Daughter Funds for onward provision to MSMEs as growth capital, in the form of equity or quasi- equity, for the following:

Since the start of the fund in 2020, it has backed around 750 companies and catalysed more than Rs 16,000 crore of investment.

Nearly seven out of every 10 companies it has backed are tech-heavy businesses. Only three are in traditional manufacturing… In fact, several of these companies had already been backed by VC firms before the SRI Fund got anywhere near them. Truemeds, for instance, is backed by Accel, Peak XV, and Westbridge, while Chai Point is backed by Eight Roads Ventures and Saama Capital. Understandably, this dims, if not outright flouts, the proposition of the SRI Fund, which was supposed to scope out companies “ignored” by venture capitalists…

“Fund managers get empanelled under SRI with the intention of backing underserved MSMEs,” said Ishaan Ajay, a development-and-sustainable-finance consultant. “But when faced with the choice between a profitable but slow-growing industrial supplier and a software platform capable of scaling rapidly, they tend to gravitate towards the latter. It’s what they understand the best.” Just consider the numbers. Suppose there’s a precision components manufacturer growing 15% annually with, say, 18% Ebitda margins. That may be a great business, but if you invest Rs 10 crore today, you’ll get only 2–3X your money over seven years. Contrast this with a spacetech company, which, if it succeeds, could be worth hundreds of crores.
“Venture funds are built around the second outcome,” said an analyst at a VC firm. This explains why SRI-funded companies tend to be tech-heavy. For every Bellatrix Aerospace in the portfolio, there’s an invisible machine-parts manufacturer outside it that was passed up… startups are generally more innovation- and tech-heavy, whereas MSMEs tend to be traditional, manufacturing-heavy businesses…
The government, for its part, tries its best to redirect funds towards their intended purpose. If it doesn’t agree with a fund’s investment choices, it expresses its objections, according to a VC with knowledge of the matter. But at the end of the day, it’s one LP among many. And when it can’t sway others, it ends up “recusing” itself from investing in that particular portfolio company, added the VC… “There is no prescription that a certain percentage of the fund must necessarily go towards traditional manufacturing or non-tech MSMEs,” a VC said. Which is the one thing that might have prevented all the confusion.

The point about returns amplification for private investors has also been highlighted in a study of IFC’s blended finance deals in the 2000-20 period, which found comparable financial returns to non-blended projects but “no statistically significant excess private mobilisation beyond what IFC’s standard lending would attract.” In other words, blending did not increase the quantum of private investment — it redistributed risk between IFC and private co-investors.

This finding is echoed in a 2023 study of SIDBI’s Fund of Funds for Startups (FFS) 1.0 by the Impact and Policy Research Institute (IMPRI), India’s Startup Engine: A Policy Review of the Fund of Funds Initiative. It finds that most FFS 1.0 capital went to established VC funds that would have raised capital independently. It also found that “crowding-in effect was primarily reputation/signal, not financial additionality”; the government’s involvement functioned more as a validation of fund managers to private investors than as a necessary injection of capital. While FFS 1.0 delivered a 2x mobilisation ratio, it was mostly in already-functioning VC markets.

As I have blogged several times, the startup and innovation sector is going through the same journey that the infrastructure sector has undergone over the last three decades. Multiple policy experiments to catalyse DFIs - IDFC, IIFCL, NIIF, and now NaBFID - have struggled to crowd in private capital into the riskier infrastructure segments like water supply and sewerage, mass transit, solid waste management, street lighting, energy-saving companies, electricity distribution, etc. Instead, these institutions and instruments may have ended up competing and crowding out private capital in the entirely derisked segments of the infrastructure sector.

In this context, it is also useful to ask the question whether the VC model is the right instrument for the funding of non-technology startups and innovations.

Livemint has two long reads that describe the emergence of consumer food brands catering to the niche category of health-conscious people. Sample this

The past few years have seen a spurt in new food brands specializing in regional staples and cooking ingredients. From rural Bengal winter specialty date palm jaggery (nolen gur) to ancient emmer wheat flour (Khapli atta) from Maharashtra, regional staples are finding customers beyond their states of origin. Some brands like Gurugram-based Anveshan and Two Brothers Organic Farms from Pune have crossed a critical mass, with annual sales close to ₹200 crore in 2025-26… Anveshan sources ingredients from key growing regions—like Pollachi in Tamil Nadu known for its high-quality coconuts and aromatic varieties of groundnut from elsewhere in Tamil Nadu and Karnataka—suited to make cold-pressed oils. The ghee, made from the milk of native breeds like the Gir from Gujarat is made using the traditional bilona process where milk is first set to curd and later churned to separate the butter (this has resulted in a new category called ‘cultured ghee’)…
Venture capital funds are betting on these brands: in September last year, Two Brothers raised a $15 million round taking its total fundraise to $25 million (and its post-money valuation to $85 million or ₹781 crore as per data from market intelligence platform Tracxn)… In April this year, KisaanSay, which markets single-origin grocery items like cardamom from Idukki and black raisins from Nashik, raised ₹34 crore ($3.6 million), taking the total funding at the Gurugram-based startup to $5.6 million since it was set up in mid-2022.
In a way, these brands have also helped promote traditional farming practices with more farmers returning to heirloom grain varieties such as the fragrant, short-grain Kala Namak rice grown in eastern Uttar Pradesh and emmer wheat in Maharashtra and Karnataka. These grains have a low glycemic index (a measure of the spike in blood sugar levels from carbohydrate intake) making them suitable for diabetics and often have higher protein and fiber content compared to conventional hybrid varieties… Three distinct factors—growing consumer willingness to pay for clean food driven by a surge in lifestyle diseases, the rise of quick commerce (allowing brands to quickly test consumer response), and influence of social media platforms are reshaping the premium staples market.

All these businesses are distinct from the rapidly scalable technology startups. Take the story of the Two Brothers Organic Farms.

They use organic farming and traditional methods of primary processing to make ghee, jaggery, Khapli flour, and cold-pressed oils. The jaggery is made using native sugarcane with lady finger extract used as a coagulant. “In Khapli, we have created a revolution. We own a seed bank and pay farmers 2.5-times the price of regular wheat. More than 800 farmers grow this wheat for us in 3,000+ acres,” Satyajit said. “It took us ten years to build this brand. At the back-end, our farms are open to everyone (to visit). Consumers trust us and we have a 70% retention rate. But a proliferation of brands (offering traditional, hand-processed staples) also carries the risk of a dilution in quality standards,” he added with a note of caution.

Much the same can be said about most businesses outside of technology - food, textiles, footwear, manufacturing, recycling, etc. Building these businesses requires painstaking efforts for several years, and there are natural limits to their scalability. They are unsuitable for the VC model of funding, as the second Livemint story about the plant-based nutrition startup Oziva shows.

Venture funding, Aarti Gill (co-founder of Oziva) points out, comes with built-in expectations around exits within five to eight years. Unlike venture-backed software companies, consumer health brands compound slowly through trust, habit and repeat behaviour. “If you have investors willing to stay for 10 or 15 years, that changes the equation completely,” she adds. Gill broadly sees three paths for consumer brands: staying profitable and scaling independently over a long period, going public after reaching meaningful scale, or partnering strategically with a larger company. For Oziva, the third route eventually felt like the most practical one. And, that’s where HUL came in… For Oziva, the partnership offered distribution scale, capital and the ability to build beyond a digitally native audience.

Therefore, at a conceptual level and as a framework, it may be a prudent choice for public policy on the funding of startups to distinguish between technology and non-technology sectors. While VCs are appropriate for technology, with their smaller and rapid growth phase, the same may not be appropriate for non-technology startups, which require longer incubation and growth periods. However, in India, most public risk capital funding happens through the VC-driven FoF strategy, which raises concerns of returns amplification and a preference towards technology startups and innovations.

This is also a concern since technology innovations, far from creating jobs, often also tend to destroy jobs, whereas the non-technology innovations, especially in manufacturing, create good jobs. The graphic below captures the problem.

Note: The percentage breakup is probably even more skewed in favour of technology startups.

It also raises the question of effective strategies - instruments and institutions - for funding such non-technology innovations. How are such innovations funded globally? Are there institutional structures in India (state or central governments) that offer promise and can be adopted with tweaks? What are the lessons from the likes of the Maharashtra Aerospace and Defence Fund? What are actionable recommendations on funding of non-technology startups? If it also requires the government to directly fund them, what institutional structures are most practical and realistic? I’ll explore these in another post.

Indian economy's cost competitiveness constraints - a graphical summary

noreply@blogger.com (Urbanomics) — Mon, 29 Jun 2026 01:43:01 +0000

I had blogged here, arguing that the Indian economy faces a cost-competitiveness constraint. I had written that on many inputs, domestic firms face the cost structure of a developed country. This post will examine the empirical evidence in this regard.

This cost structure reflects in the global competitiveness of the country’s manufacturers. India suffers from a persistent and high price disability compared to peers across manufacturing sectors.

What are the contributors to this competitiveness wedge?

Indian factory wages (~$2.1/hour) are the lowest in Asia, about two-thirds of Vietnam's and a third of China's. Its effective corporate tax rate at 17.2% is among the lowest, its GST rates are comparable, and its logistics costs (while contested) are at least not much higher.

So if India loses on net cost, the disability is entirely in non-wage factors: land, capital, power, fuel, scale, tariffs on inputs, and labour productivity.

Start with land, the largest cost wedge. Urban land is priced like that of a rich country, and, most problematically, hoards the nation's savings. Mumbai ranks among the world's 20 most expensive prime markets, and Indians park ~77% of household wealth in real estate. The high land valuations result in capital misallocation and squeeze out the capital that would otherwise have resulted in investment.

In fact, high land valuations, even in the smaller cities, appear to be a big entry barrier for businesses. An Indian manufacturer pays roughly 5–15 times more for industrial land than a Chinese counterpart in a comparable tier-2 city, and 1–4 times a Vietnamese peer. That gap is policy-made. China's local governments subsidise industrial land to attract production, whereas India's restrict supply (FSI, fragmented titles, slow acquisition) and treat land as a fiscal asset to maximise revenues.

Now, come to power tariffs. Indian industrial users pay ~₹7.5–9/kWh ($0.09–0.11), materially above what comparable Asian peers' large industrial users effectively pay. Indian electricity is among the world's most expensive on a PPP basis, with one of the world's widest spreads across consumer types.

The comparisons of electricity prices based on averages are misleading. For example, India and China have similar average electricity tariffs at $0.08/kWh. It conceals that India has one of the world's widest cross-subsidy spreads (farms near zero, industry penalised). The rate that matters for competitiveness is the industrial slab, which is materially higher. Industry overpays to cross-subsidise farms and households, again, another policy choice. To this, we must add another 15–25% for diesel backup against unreliable supply.

The same applies to gasoline prices. Kept outside GST as a revenue mainstay for state governments, petrol and diesel carry stacked central excise plus state VAT. Roughly half the pump price is tax. In raw dollars, India's fuel is dearer than China's, Vietnam's or America's, despite far lower incomes.

The cost of capital adds to the wedge. The credit-to-GDP ratio measures the availability of capital. The other half of the problem is the price. Indian firms pay materially more than peers - across banks, bonds and the SME segment. In fact, after stripping out inflation, the gap stays large, which is the more realistic measure of whether real investment can clear its hurdle rate.

The capital markets are no different. An Indian mid-cap pays roughly 2–3 times the bond yield a Chinese mid-cap pays and ~50% more than a US one, in nominal terms. After inflation, the gap narrows but doesn't vanish. India's real lending rate (~5%) is ~2 percentage points above China's, and the real MSME rate is among the world's highest. Combined with credit availability stuck at 55% of GDP, this means Indian firms, and especially the missing middle, face the worst combination of dear and scarce capital among large economies.

Let’s dig a bit deeper into the capital cost wedge.

Econ 101 informed that the high cost of capital is a reflection of the demand-supply mismatch. On the demand side, as we have seen, the high land valuations are encouraging resource misallocation. What about the supply side?

The country’s reasonable gross domestic savings rate (~30%) conceals a disproportionately high share of illiquid assets. When 77% of wealth sits in physical assets (land and gold), and only ₹40 for every ₹100 of household financial savings reaches the financial system, the banks have less to lend, the bond market stays thin, and the price of credit rises.

In other words, there’s a link between the land and capital problems facing the economy. The causal chain goes something like this - physical-asset preference (land + gold) → low household financial savings (5.3% of GDP) → shallow bank deposits & bond market → low credit-to-GDP (55%) → dear capital (~9% vs ~3.5% in China). The same wealth-allocation pattern that raises land prices also raises the cost of capital. It is one mechanism, not two.

Worsening matters, the net household financial savings fell from ~11.5% of GDP in FY21 to 5.3% in FY24, a 50-year low. The table below breaks down how the cost wedge feeds itself. Households are borrowing more (against assets), and routing more new savings back into physical assets.

The cost constraints impact the economy in two ways. One, it restrains private investment. Second, it weakens global competitiveness and hurts exports.

Take the first. Overall investment slid from a 35.8% peak in 2007-08. Public capex has since surged, but private corporate investment never recovered, flat near 11-12% of GDP. Flush with cash, firms deleveraged and bought financial assets instead of building capacity. I blogged here on this.

The East Asian economies found exports to rich countries as the outlet to overcome thin domestic demand and drive economic growth. Unfortunately, in India’s case, this is exactly the outlet that the cost wedge closes: dear capital, costly logistics, unreliable power and the world's highest tariff walls all erode competitiveness. The result is that India is losing its share of labour-intensive exports to Vietnam and Bangladesh , even as its overall merchandise share stalls near 1.8%.

In fact, the Economic Survey 2016-17 chapter Clothes & Shoes: Can India Reclaim Low-Skill Manufacturing? warned that the space China vacated was being taken by Bangladesh and Vietnam in apparel, and Vietnam and Indonesia in leather and footwear. A decade later, the warning could not have been more prophetic.

In the net, India wins on wages (~$2.1/hour) but loses on net cost. So the disability is entirely from non-wage factors: capital, power, logistics, scale, tariffs on inputs, and productivity. India has a cost structure that is comparable to that of a developed country. That's the central analytical point.

India enters every labour-intensive sector with the world's cheapest workers and exits with a 10–20% cost disability. This is proof that wages aren't the binding constraint. The wedge comes from the rest - scale, input tariffs, capital, power, logistics and productivity, in a market structure that punishes the firms that should be growing. This is why India hasn't replaced China, where Vietnam and Bangladesh have, despite the structural opportunity being identical for all three.

Indian businesses (and especially manufacturers) face a structural cost constraint. It does not have to do with taxation or wages, but with factors like land, power, fuel, cost of capital, input tariffs, labour productivity, and business size/scale. A disproportionate attention and effort go into taxation reforms instead of these more important structural limiting factors.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 27 Jun 2026 02:52:58 +0000

1. New research by Emma Harrington, Natalia Emanuel, and Amanda Pallais shows that remote work is adversely impacting mental health. They paraphrase Robert Putnam to argue that Americans "typing alone" brings serious social consequences.

In 2024, nearly 80 percent of workers said they would be happiest if they could work remotely... Surveys of over half a million Americans from the last decade and a half revealed an uncomfortable truth: Despite its advantages, remote work has significantly deepened Americans’ isolation and distress. Our estimates indicate that remote work explains a third of the deterioration in mental health between 2011 and 2024... Our study compares workers in jobs that could be done remotely, such as finance and software engineering, with workers in jobs that must be done in person. People in remote-capable jobs worked from home three times as often in 2024 as in 2019. As they did, their days became far more solitary. Eighty-four percent of remote workers spend their workday entirely alone. Over half report feeling less connected to their colleagues. Even when communicating online, people working from home receive less feedback from their co-workers and contact fewer people outside their immediate teams.

These workers did not compensate by socializing more outside work. More days passed with no social contact of any kind... In one study, when commuters were instructed to connect with a stranger near them, they reported being happier than those who continued in silence as usual, much to their own surprise. With fewer social encounters, workers in jobs that can be remote saw steeper increases in distress, mental health visits and prescriptions for antidepressants than other workers did... The pain was not evenly shared. People who lived with their spouse and kids saw their mental health hold fairly steady, while those who lived alone experienced a 20 percent decrease in mental well-being. Overall, we found that the rise of remote work increased distress by 7 percent, which accounts for a third of the total increase over the 13-year period we measured.

They argue that face-to-face time with colleagues has no substitute.

2. Katie Martin points to the different ways in which bonds and equities are reacting to Trump policies.

US government bonds, or Treasuries, have never recovered from the drop in price they suffered around the start of the war. Investors in this market, who broadly consider themselves a more cerebral bunch than those in stocks, never bought the hints of a ceasefire with Iran. Bond prices have still not returned to square one, leaving borrowing costs markedly higher. With the prospect of interest rate rises ahead to douse inflation pressures exacerbated by the Iran war, and relentless more borrowing, this is likely to remain the case for some time.

3. As AI threatens to bring down India's tech sector, this is a good article.

On the whole, Indian IT companies spent around 3.7 per cent of their total revenue on R&D in the year that the report covered. This is minuscule compared to around 15 to 25 per cent that Silicon Valley companies spend on R&D. The top IT companies are laggards of first order. For example, in 2022-23, Infosys spent just 0.9 per cent of its total revenue on R&D. The figure for TCS was 1.30 per cent. For Wipro it was 0.5 per cent while for HCL it was 1.60 per cent. The other big companies don’t fare all too well. Reliance, a giant in every way, spent only 0.53 per cent of its total turnover on R&D in 2022-23. Tata Steel is at 0.67 per cent. Maruti Suzuki spent 0.65 per cent on R&D.

4. Indian markets have more to fall before they become competitive.

The FPI outflows have tracked the decline of rupee, feeding a self-fulfilling cycle.

5. The costs of RBI's FCNR (B) deposits and foreign currency borrowing schemes.

If the scheme were to attract $50 billion of FCNR (B) deposits and $20 billion of foreign borrowing by banks and public-sector enterprises, the mark-to-market loss on the RBI’s swap position could approach ₹64,000 crore at current market prices, besides increasing the RBI’s balance-sheet risk. This is not merely an accounting cost. The subsidy is real and will be monetised by participating non-resident Indians (NRIs), banks and borrowers.

Large Indian banks are raising five-year FCNR (B) deposits in dollars at 6 per cent. Their attractiveness is evident from the willingness of overseas banks to lend against the same deposits at around 5 per cent. This, in turn, will allow wealthy NRIs to achieve double-digit leveraged dollar returns against India cross-border risk. Indian banks can further transform the FCNR (B) deposits into clean five-year rupee funding at around 6.4 per cent, below comparable government bond yields.

Will the RBI regret having offered the FCNR (B) deposits to NRIs?

Banks are being permitted to offer leverage to NRIs. The currency risk on such deposits will be borne by the RBI. As reported by this newspaper, State Bank of India is offering leverage of up to nine times on deposits of more than $1 million. Calculations indicate that this could translate into returns of over 14 per cent. Other banks are likely to come up with similar schemes for NRIs.

Banks are competing aggressively for FCNR (B), and are also offering leverage to increase returns.

6. A new large-scale survey experiment of EU companies shows that firms substantially underestimate competitors' current AI investment, and when updated about their competitors' future AI investment plans they increase their own AI investment plans in a statistically significant manner. But this effect, while strong for domestic peers, is weak for information on foreign peers.

We documented large underestimation of competitor AI investment, substantial belief updating in response to information, and a clear asymmetry in how firms react to domestic versus foreign competition... A 1 pp increase in the expected share of domestic peers investing in AI raises a firm's own expected AI investment rate by 0.570 pp. These complementarities are absent across borders: the effect of an increase in the expected share of foreign peers investing in AI on a firm's own expected AI investment rate is statistically insignificant... Firms update both domestic and foreign beliefs when informed, but their own expected AI investment rate responds primarily to domestic posterior beliefs. These findings suggest that strategic complementarities in innovation weaken with distance, broadly understood to include not only geography but also informational, cultural, and market frictions... This asymmetry helps explain why AI diffusion may remain geographically uneven, even within an integrated economic area like Europe. While firms may observe and learn from foreign competitors, their behavioral response to such foreign signals is much weaker compared to domestic competitors.

7. Aswath Damodaran makes a great point about hedge funds, private equity, and private credit - all niche businesses which had a role, but have vastly overextended themselves and set themselves up for failure.

Each one began as a genuinely good niche business solving a real problem. Hedge funds 30 years ago produced positive alpha, beating passive investing by 3 to 5 percent annually. Today they look like expensive mutual funds, underperforming passive by roughly 1.5 percent. Private equity started as a focused, disciplined strategy for a small set of operators and has grown into a sprawling category that now struggles to deliver the returns that justified its emergence. Private credit had a legitimate original purpose, which was lending to borrowers that banks structurally could not serve. What killed each of these businesses was the same disease. Overreach. A $200 billion niche business gets sold as a $20 trillion opportunity. When that scaling happens, sloppiness follows, bad actors enter the space, and the average quality of every participant deteriorates. The original alpha disappears not because the strategy stopped working, but because too much money chased too few good deals. The danger with private credit is far more severe than the parallel problems in private equity and hedge funds. Equity investors take their losses and move on. Lending businesses, when they overreach, take others down with them. Banks. Pensions. Insurance companies. Sovereign wealth funds. The systemic linkages run far deeper than most participants understand, and the social costs of a real default cycle in private credit would extend well beyond the funds themselves.

8. Friedrich Merz initiates measures to address Germany's rising pension burden, which took up 41% of all federal government welfare spending in 2024. The proposals came from a bipartisan committee of MPs who were appointed to examine and make suggestions.

Germany’s pay-as-you-go system is facing widening deficits, with 16.5mn baby boomers retiring by 2036 and only 12.5mn new workers joining the workforce, according to the Cologne Institute for Economic Research. The government in 2024 paid €118bn to plug holes in the system, or about a quarter of the total federal budget. That share could double to 50 per cent within the next two decades, according to economists... Under the proposal, a compulsory individual contribution of 2 per cent of salaries would “be managed centrally and invested in capital markets”... The move would be a novelty for risk-averse and cash-loving Germans, who have been more reluctant than European peers to embrace capital markets to invest their large savings... Other recommendations include linking the statutory retirement age — currently 67 — to the country’s life expectancy and withdrawing early-retirement incentives. For every year gained, people should work eight months longer, the commission proposed. The experts also suggested raising the age — currently 64 — at which people who have made contributions for 45 years are able to go into retirement with their full pensions. Unions are likely to oppose the measure.

9. India has been a laggard in attracting FDI.

10. Ten years on, Brexit has turned to 'Bregret'!

The Brexiteers persuaded a small majority — the vote was 52 percent to 48 percent — that Britain could throw out the austerity that had followed the 2008 global financial crash, reverse the hollowing out of well-paid manufacturing jobs and trade freely and profitably on international markets. Immigrants who had flocked to Britain from Eastern and Central Europe would be sent home. Europe merely held Britain back, and to choose to leave was to believe, as Britons had before, that the nation was meant for more...

It was, of course, a fantasy... The economy has stalled and trade has shrunk. Britain is poorer than it might have been. Its gross domestic product is at least 4 percent — but could be as much as 8 percent — lower, according to independent calculations, while business investment is more than 10 percent lower. It added new frictions to the lives of Britons: new border checks when traveling to E.U. countries, stricter residency rules for living there, fewer opportunities for students to study abroad. Even just using a cellphone while “roaming” often costs more than it used to. There have been other costs, one of them a weakening of the glue between the nations of the United Kingdom itself. The referendum result was more a statement of English than of British nationalism — majorities in Scotland and Northern Ireland voted to remain. Forced to leave, Scottish nationalists claimed stronger cause to promote their case for full independence from England, and the complex political arrangements for Northern Ireland needed to protect the Good Friday peace agreement between Irish nationalists and British unionists in the province have weakened the cause of the unionists.

Rather than a newly independent Britain cutting a swath on the international stage, economic realities forced cuts in spending on foreign aid and diplomacy. The hopes among Brexiteers for a new Anglosphere, adding the English-speaking Commonwealth nations of Canada, Australia and New Zealand to Britain’s “special relationship” with the United States, turned to dust, and Britain’s privileged place in Washington was lost to Mr. Trump’s disdain for traditional alliances.

11. Fascinating graphic that maps the values of AI models.

The models’ answers, in English, on topics ranging from political petitions to God, suggest values that are different from those of most people. In fact, the models are often more extreme than the average respondent in every country included in the polling. On the survey’s “cultural map”, " AI models fall overwhelmingly into the quadrant populated by rich countries. The worldview of GPT models, created by OpenAI, is more secular than any country on earth (see chart 1). Gemini models, made by Google, place more weight on individual freedom (for example, “homosexuality is justifiable”) than people do anywhere. No model reflects the worldviews of most African or Muslim countries.

12. The Economist looks at the issue of popular backlash against AI. This scenario in particular is important.

Scenarios in which some countries give in to popular rage but others forge ahead are also worrying. If America succumbs, it could cede the global ai frontier, and the attendant cyber and military capabilities, to authoritarian China. Europe and Canada are more risk-averse than America. If they choked off ai while the rest of the world kept pushing forward, their losses could be unrecoverable. More than two centuries after the Industrial Revolution, few countries have managed to catch up with the first movers.

13. Rolex SA is a profit-making company with $12-13 bn in revenues and $3-4 bn in profits whose ultimate owner is a spiritual holding company (SHC), a charitable trust called the Hans Wildorf Foundation. Rolex SA has no public shareholders, investors, or owning family, and has been so since 1960. A similar example is Robert Bosch GmbH, the German engineering giant, which has 94% ownership by the Robert Bosch Foundation (SHC) holds 94 per cent and the Bosch family the rest. In both cases, the management and ownership have been clearly separated.

Why it's hard to see beyond the dollar?

noreply@blogger.com (Urbanomics) — Thu, 25 Jun 2026 02:07:52 +0000

A recurrent theme in international economics is the debate on the role of the dollar. As the global reserve currency, the dollar confers on the US an exorbitant privilege to borrow in its own currency, thereby allowing it not only to run large current account deficits but also to finance its fiscal deficit. The exorbitant privilege means that the normal rules of fiscal and monetary policies and capital flows management do not apply to the US in running its twin deficits.

As the US-China tensions intensify, there have been debates on whether the renminbi can emerge as an alternative to the US dollar. China has taken several measures to internationalise its currency. Today, some estimates suggest that 40% of its trade is settled in renminbi.

But there's a fundamental problem with China's efforts to prop up the renminbi as a global reserve currency. A currency can be a global reserve not only if it becomes used in cross-border trade transactions, but also, perhaps more importantly, if it has a deep, liquid, and stable market for investing the surpluses generated. In other words, countries should have confidence not only in transacting in this currency but also in retaining and investing their surpluses in it. What use is a foreign currency surplus when there are limited options to deploy it?

Such investments can be in the debt and equity markets in the host country of the reserve currency. This brings into play a third and fourth dimension, which are an accounting reality: the host country must be a net borrower (or run a current account deficit) and must be able to finance it with a financial account surplus. Both in turn require an open capital market (into which capital can be brought in and taken out with ease), the fifth dimension. In the absence of the last three dimensions, the host country will be unable to absorb and deploy the surpluses, and foreigners will not have the confidence to plough their surpluses into this currency.

China is very far from being in a position to offer the renminbi as a credible reserve currency. In fact, the Euro stands a far better chance of being an alternative.

This graphic captures what can be called a five-gate test of a reserve currency.

While the exorbitant privilege may have allowed the US to overcome the Triffin paradox (the only way to be a reserve currency is to run a trade deficit, which will, in turn, erode its status as a haven currency), the burgeoning US public debt and persistently high budget deficits (both of which debase the dollar) threaten this ability.

However, this must be seen against the reality of a world with surpluses that must primarily be parked in some credible currency asset and the absence of any alternative, there are three possibilities: the dollar continuing to be the reserve currency, the euro emerging as an alternative, and the global imbalances getting corrected.

The second option is more feasible than renminbi because Europe already has deep, liquid, and stable financial markets and an open capital account. However, it would also require Europe to run deficits. While Europe has traditionally run surpluses, there are signs that it might be declining. Besides, the European governments, especially Germany, have shown greater willingness in recent times to borrow and undertake fiscal expansion to invest in infrastructure and defence.

The third option is perhaps the most desirable and sustainable insofar as it addresses the distortions in international economic relations. But it would require the US to rebalance towards fiscal consolidation and reduce consumption, China to abandon its beggar-thy-neighbour trade policy and embrace domestic consumption, and Europe to shift towards public investment. The first two are deeply problematic political economy challenges and are unlikely without crises and convulsions.

This leaves us with the conclusion about the dollar’s continued dominance, not because Americans are clever or the Federal Reserve is wise, but because of an unusual coincidence of architecture: a large enough economy, a deep enough financial system, a current account deficit large enough to absorb the world’s surpluses, an open capital account that lets capital exit, and political institutions stable enough to make the open vault credible. No other country currently has all five, and China is structurally prevented from acquiring three of them without abandoning its development model.

Whether you like it or not, for the foreseeable future, the dollar is likely to remain the Hobson’s choice.

Comparing the R&D expenditures by Indian firms and their global peers

noreply@blogger.com (Urbanomics) — Wed, 24 Jun 2026 02:49:21 +0000

This blog has been a consistent critic of corporate India’s reluctance to invest in R&D. With the emergence of AI applications that are disrupting software development, the big Indian IT companies have been criticised for their low R&D expenditures. There has been a slew of commentary in recent days bemoaning India’s deficient private sector R&D spending and urging corporate India to embrace innovation. See this, this, this, and this.

This blog has argued that the nature of India’s market, with its price-sensitive customers and small premium market segments, may not allow Indian firms the cash flow cushion required to invest in R&D. Others have pointed to cultural and other factors as being responsible. There are problems with each of these lines of reasoning.

I used Claude and analysed the annual accounts and statements for the last five years of the 3-4 top Indian companies and their like-to-like peers in Europe, Northeast Asia, and the US on revenues, profits, margins, and R&D expenditures across eight industries. Specifically, how do the sizes (by turnover) of the median Indian companies and their peers compare? How do they compare on PAT margins and R&D as a share of revenues?

The headline takeaway is that Indian companies tend to be more profitable than mature Western peers (services, autos, pharma, telecom) but smaller in scale and far less R&D-intensive than the global leaders in product/innovation-driven sectors (consumer electronics, software products, EMS modules, speciality chemicals).

While it confirms the low R&D spending of Indian companies, it also points to a more nuanced narrative. While Indian companies are much smaller than their global peers, they are either the leaders or are at the top in profitability.

Let’s examine the headline findings.

The big IT consulting/services firms are the main targets of the growing chorus of criticism in the mainstream media on the lack of dynamism and low R&D spending. It is worth noting that while Indian software service firms have margins that are significantly higher than their peers (arising not from any innovation or efficiencies but from the labour-cost arbitrage), their R&D spending lags. The R&D as a percentage of revenue runs 0.3-0.5% at Indian firms vs 0.8-1.0% at say, Accenture, a 2-3 multiple gap. Further, while the R&D spending shares of the likes of Accenture have been rising, those of the Indian firms have been stagnant or even declining (Infosys) in recent years.

Also, the R&D expenditures of the big software service firms in the US do not include the significant amounts spent on acquisitions each year. For example, Accenture deploys $2-5bn annually in 30-40 acquisitions per year, many in AI specialities (data engineering, vertical-domain AI, ML platforms), whereas the largest Indian firms do 2-5 acquisitions per year, usually smaller and more conservatively-priced. Accenture treats acquisition as a substitute for internal R&D, while Indian firms treat it as a supplement to organic build-out. The cumulative effect is that Accenture has accumulated dozens of niche AI consulting practices acquired pre-2024, whereas Indian firms have built mostly organically and more slowly.

However, it must also be said that some of the criticism also reflects a tendency to conflate what is an inherently low R&D industry with the R&D-intensive product-focused Big Tech and AI firms. IT services have never been an innovation-focused industry. Further, compared to several other industries (as we shall see), the R&D spending of Indian IT services firms is not that far behind their Western peers.

While Indian software product firms hold up on margins, they too lag on R&D. The 5-6 percentage-point gap between US and Indian product company R&D spending is the closest real number to “the innovation gap” people often talk about. On size, if you take out OFSS, which is a subsidiary of Oracle, there is no Indian company with even $300 million in revenues. The four Indian product companies combined generate ~$1.5 bn in revenue. Salesforce alone does $38 bn. Adobe does $21 bn. They are dwarfs to their global peers. This is the real software industry gap.

In the automobile industry, Indian OEMs outperform their global peers on profitability. The R&D intensity at 3.6% is half of Europe's but comparable to Japan and the US. This conceals the fact that, despite being in the business for decades, the big Indian OEMs continue to depend on foreign designers and engines. They have been comfortable doing business by licensing technology and importing engines. Further, where India really lags is in the frontier technologies like batteries and electric vehicles. All these point to an ambition or aspiration gap.

Indian EMS profitability is again slightly better than that of Chinese and Taiwanese, but the R&D gap is the starkest in the entire analysis. Indian EMS spends 0.5% of revenue on R&D vs Chinese 4.1% vs Taiwanese 2.2%. Indian players are doing pure box-build assembly, whereas the Chinese players are designing modules. This is the value-capture gap. This is an area where the market is at the cusp of a massive expansion, and it is disappointing that Indian EMS’s have not sought to move up the value chain despite the promising opportunities that they face.

Indian pharma companies fare better than their software counterparts in both margins and R&D spending compared to their Western peers. Here, however, the Chinese are far ahead. Chinese pharma R&D intensity (17.5%) is more than double Indian (7.5%), with Hengrui, Sino Biopharm aggressively pivoting to innovator drugs. India's generics-and-biosimilars model is very profitable today but less R&D-intensive, raising the question of where margins go in 5 years.

The consumer electronics industry must count as one of the biggest disappointments. There is essentially no Indian consumer electronics industry comparable to its global peers. Even the biggest Indian firms are tiny when compared to their global peers. Indian companies (Havells, Voltas, Whirlpool India, Crompton) are appliance brands relying on outsourced electronics, explaining the 0.6% R&D figure compared to Korea's 7.8% or Japan's 5.4%. No Indian brand has any comparable R&D capability. Most of what's made in India is for foreign brands (Apple via Foxconn, Samsung via Dixon).

Chemicals is one of India's better stories. While the top Indian firms are large and their margins are second only to Chinese firms, their R&D expenditures again lag.

On the telecom side, Indian service providers are now the most profitable in the world, driven entirely by Jio and Airtel post-Indus-Towers consolidation. Verizon and AT&T look mediocre by comparison. The R&D number for telecoms is essentially zero everywhere except China Mobile and NTT since telecom is considered a capex/spectrum business, not an R&D business.

So what do all these mean?

The failure to produce even a mid-sized IT product firm, even after five decades of being a leader in the software services industry, is more an indictment of India’s entrepreneurship than of the IT services firms themselves. The IT product industry has had several favourable factors confluencing - the IT services industry produced enough talent and experienced professionals to supply both entrepreneurs and team leaders, there is an abundant low-wage workforce, it does not suffer regulatory failures like an inverted duty structure, high input costs or taxes, and there’s a large global market to serve. But even this combination was not enough to make even a one-billion-dollar IT product firm.

More than the IT services industry, it is perhaps the Indian pharma industry that is emblematic of the lack of business dynamism and entrepreneurship. The country has had a serious pharma industry, with several large generics manufacturers, for over six decades. Many of the leading firms of today were established by entrepreneurs who worked in the public sector entities. They had the opportunity to move up the value chain by building massive integrated industrial facilities. Even in contract manufacturing, they have remained stuck at the small-molecule synthesis and have struggled to move up the value chain to complex therapeutics and contract research.

Alongside the software product industry, consumer electronics should perhaps count as corporate India’s biggest failure. India has had a consumer electronics industry for several decades. With a very large market, Indian firms had the opportunity to ride the economic liberalisation, expansion of the middle class, and the global export market.

Interestingly, the Korean and Japanese OEMs (LG, Samsung, Daikin, Hitachi) have deeper Indian manufacturing (in terms of value addition in products like refrigerators, air conditioners, and washing machines) than most Indian brands because they invested in component plants in the 2000s-2010s when Indian brands were happy to rebrand imports.

One structural reason for this difference also points to the lack of ambition and reluctance to pursue the export markets. The Korean and Japanese OEMs treat India as a manufacturing base for both the domestic market and exports (LG exports refrigerators to the Middle East from India; Daikin to Southeast Asia). That export-anchored manufacturing economics justifies deeper component investment. Indian brands have historically been domestic-market-only and saw little case for backward integration when components were cheap to import from China.

It is therefore an unfortunate reality that the vast majority of AC and refrigerator compressors, and higher-end motors (for front-load washing machines) are imported.

The above analysis points to a corporate world that is stuck in a comfort zone, reluctant to assume risks by trying to move up the value chain or push aggressively into the next generation of products or technologies. There is a strong preference to stay on the sidelines and wait for technologies and products to emerge elsewhere. I’m not sure whether there is even one industry where Indian firms have been pioneers in showing the way with the next generation of products. Across industries, they always follow the trends in developed markets by copying and imitating.

The large captive market (domestic and foreign) is considered a safe enough moat (thanks to a combination of price-sensitive customers and import protections) that would allow these firms to grow for a long time to come. There is little incentive (apart from inclination) to explore and expand beyond this comfort zone.

In a globalised market, across industries, competitiveness is critically dependent on continuously moving up the value chain. It is a treadmill where, like the Red Queen, firms must run hard to retain their global competitiveness. And Indian firms, across industries, have shown consistent reluctance on this. It points to a problem of what I have described earlier as an entrepreneurship deficit.

With this entrepreneurship deficit comes a low risk appetite. This reflects in the reluctance to deploy capital. Moving up the value chain and expanding to foreign markets, essential requirements to becoming globally competitive, demand assuming significant risks by making large capital investments with long-term bets. These investments would also include cultivating supplier ecosystems, funding and nurturing startups, and long-term partnerships in general. Indian firms, especially the largest ones, have shown great reluctance to assume the risks and make these investments.

The reluctance to invest despite the consistently high margins across industries may be a symptom of the entrepreneurship deficit. As we have observed, firms are satisfied with their domestic markets and have limited or no appetite to expand into export markets. Further, the tepid growth of the domestic market (a reflection of the low aggregate demand growth, in turn a reflection of the narrow base of the consumption class) also discourages significant investments. All this manifests in a preference for short-term gains and avoidance of long-term competitiveness.

This is a nice summary of the motivations driving Indian firms.

India has a large domestic market, and the economy is growing at 6–7%. So, you can bring what has worked elsewhere, deploy it in the market, and make a lot of money. It’s less risky. That’s what corporates have been doing. It’s the cycle of development. But when you want to compete internationally, you need to think about your own ideas.

Another reason to invest more and pursue export markets is to increase size. As the analysis shows, even the largest Indian firms across the eight sectors are small compared to their global peers. Despite being more profitable than their global peers, their much smaller size is an important obstacle to global competitiveness.

The argument that government policies have been a binding constraint is not convincing. For one, the software industry, despite largely serving the global market and not being significantly constrained by public policy, did not produce any product firm or product of note despite several technological trends sweeping the industry in the last three decades. Second, even among the leaders in different industries, there has been little appetite to move up the value chain, expand into global markets or pursue new generation technologies and products. Third, even in localising manufacturing by nurturing local supply chains and partners, storied Indian OEMs have been behind foreign OEMs who have entered the market much later.

Fourth, the argument that import restrictions have prevented Indian firms from becoming competitive flies against the reality that the Northeast Asian economies built their manufacturing successes in highly restricted markets. Instead, as Joe Studwell has written, they gained competitiveness by competing in the export markets.

Now the government has thrown caution to the wind and, through the Rs 1 lakh Cr Research Development and Innovation Fund (RDIF), is funding even large corporates on their R&D endeavours. This may be the most that governments can do to push their industries towards innovating. It remains to be seen whether even this is sufficient.

In conclusion, it appears that Indian firms suffer from an entrepreneurship deficit, risk aversion, and a lack of intrinsic desire to think big (by moving up the value chain, pursuing next-generation technologies, and expanding into export markets). They seem satisfied with serving their captive local markets, continuing their existing product lines and business models, and following global leaders in technology and product trends. This is a reality, borne out strikingly by evidence. Whether it is culture or something else can be a matter of debate.

PS: On the issue of entrepreneurship, Claude had this comment on the prospects for India’s software services industry.

Accenture sells outcomes and prices its services on the value of the transformation. Indian firms sell capacity and price on the cost of the underlying labor. Generative AI threatens the capacity-pricing model directly because it compresses the labor hours needed. It enhances the outcome-pricing model because AI-enabled transformations are higher-stakes and command premium fees.
This is why the next 2-3 years will be the real test: not whether Indian firms have AI capabilities (they clearly do), but whether they can shift their pricing and packaging model fast enough before Generative AI deflation hits their core managed-services contracts. Accenture has already crossed that bridge; TCS, Infosys and Wipro are mid-bridge with the macro tailwind weakening.

This is a test of entrepreneurship and reinvention of business models for the Indian IT services firms.

A framework for the application of AI in public systems

noreply@blogger.com (Urbanomics) — Mon, 22 Jun 2026 02:14:52 +0000

I have not blogged about AI in development. One reason is that, notwithstanding several claims, I have not come across promising examples that have been successfully applied at a reasonable scale within public systems in the Indian context.

I have blogged here, urging caution on the expectations of the impact of AI on development in countries like India. The central argument is that the binding constraint on AI impact in the public sector is rarely technical capability. It is the gap between what AI can do and what institutions are prepared to validate, adopt, and integrate, a gap shaped by the validation cost, regulatory frameworks, incumbent system stickiness, and the political economy of transition.

As an analytical framework for the application of AI in development, I can think of three lenses: the layer where the AI intervention is proposed, the nature of the problem to be addressed, and the constraints on deployment and adoption.

In the first lens, AI can provide structured information to feed into decision-making; or directly enable decision-making by synthesising information into ranked options or risk scores, thereby augmenting human judgment; or deliver specified outputs in the form of drafts, transaction alerts, assessments, etc.

The second lens can distinguish between complicated/complex and wicked problems. The former has many variables but is tractable with better data and modelling (crop yield prediction, supply chain optimisation, credit risk scoring, traffic signal timing, etc.), whereas the latter involves contested goals, adaptive actors, and systemic feedback (improve learning outcomes or skills, institutional reform, manage urban growth, etc.). While there are limits to AI, especially in the latter, complicated sub-problems within the former are amenable to AI.

The third lens covers the gap between technical feasibility and deployed impact. This gap is shaped by validation cost (regulatory standards must be met), the incumbent system stickiness (when the AI solution demonstrates value that helps overcome the systemic inertia), trust and legitimacy (system or society must accept the efficacy and reliability), and political economy (overcome the entrenched vested interests).

The three lenses are analytical dimensions, or orthogonal axes on which any individual intervention can be located. Then we have the horizontal or vertical use cases, which are a typological cut across the universe of interventions. It sorts use cases by their organisational footprint (does this sit in every department or only one) rather than analysing the nature of any single use case.

The graphic below applies the three lenses to each item in an illustrative catalogue of horizontal/vertical use cases.

As a prudent strategy, it may be useful to move first in low-friction domains - where there are no regulatory incumbents, where information asymmetry is high, and where the beneficiary is a motivated adopter - and use the evidence and trust built there to lower the political cost of adoption in high-friction domains. In this reading, as is the trend in the private sector, it will be some time, if ever, before vertical sectoral use cases of AI become efficacious and reliable enough to be adopted at scale.

Based on the above, what are the most valuable and widest spanning productivity and efficiency increasing uses of LLMs by governments in developing countries (with low state capabilities)?

I can think of two in particular - application in the generation of quasi-judicial and adjudication orders of all kinds, and in the recording of M-Book in engineering works. The breadth of their use, the stakes involved, the low current baseline, and the manageability of their deployment gaps make them areas with potentially transformative impacts.

The first one ranges from charge sheets to orders in service matters and disciplinary cases, to regulatory and licensing decisions, assessment and adjudication by tax, land, and other authorities exercising statutory powers, including court and tribunals.

Each of these has a standard structure - factual matrix, applicable legal provisions, consideration of arguments, findings, and operative order - that AI can populate with high-quality given structured inputs. The official provides the facts and the decision, and AI generates the legally coherent reasoning and formal language.

The value of this comes from the low baseline of generally poor quality of orders issued that suffer both from basic procedural/hygiene deficiencies and more substantive application of judgment. These deficiencies and lapses, especially of the former kind, immediately invite disputes. In fact, such poorly crafted orders become the starting point for a long series of administrative processes that manifest in disputes and harassment, appeals and litigations, that clog administrative bandwidth, lock up scarce capital, impair balance sheets, and generally waste effort and resources.

These kinds of orders also contribute a very large share of the litigation against the government that clogs the judiciary. The major reason why governments lose such cases is the quality of orders in terms of poor drafting and adherence to basic administrative compliance and procedures. Poorly drafted orders are often set aside, not because the underlying decision was wrong, but because the reasoning was inadequately articulated. AI can have a significant impact in this area in both improving the quality of orders and even forcing systems to comply with procedural requirements.

The volume of such orders is enormous. A mid-level income tax officer may need to issue hundreds of assessment orders annually; a district collector may handle thousands of revenue proceedings. The drafting burden is a critical bottleneck in disposal rates. Further, AI-generated drafts can be trained to flag inconsistencies, missing procedural steps (e.g., failure to give opportunity to be heard), and citation of superseded legal provisions, thereby acting as a compliance check before the order is issued.

An AI application that generates the first draft of the order, for the adjudicating officer to revise and issue, can have a transformative cascading impact down the chain. The first draft can be hard-coded to serve as a forcing function to ensure compliance with a checklist of processes; the order itself could be validated, and its logic could also help with tightening the substantive exercise of judgment itself.

On the face of it, this is a low-hanging fruit. All it requires is a library of orders of all kinds bundled together, and a template for different kinds of orders. An enterprise or team subscription to Claude or OpenAI, that has a Zero Data Retention (ZDR) configuration for their API (thereby ensuring customer data is not retained and used for training the algorithm), and which allows access to Claude through Amazon Bedrock or Azure OpenAI within a private Virtual Private Cloud, can address any privacy and security concerns on sharing internal data.

This would be a quick deployment and superior to any indigenous or sovereign LLM model development (which should continue and could perhaps be deployed in parallel to learn and get refined).

The latter work can be taken up in a mission mode by the National Informatics Centre or a public think tank (say, the National Centre for Good Governance, NCGG) by deploying a team of competent experts. It can consolidate the library of orders and develop standardised LLMs for a few high-volume and value use cases - disciplinary cases, GST and income tax adjudication orders, and orders on a few categories of land claims settlements. They could work with a few state governments, the CBIC (state indirect tax departments), and the CBDT.

The other high-value and promising application can be in the recording of the Measurement Book (or M-Book) for engineering works - roads, buildings, canals, drains, electricity lines, etc. It records the quantity of work done, certified by a junior engineer, against which payment is authorised. It is also one of the most fraud-prone documents in public works administration, susceptible to over-measurement, fictitious entries, and post-facto alteration. Besides, it is also a procedurally burdensome activity. Its importance arises from the fact that it is the basis on which contract payments are made.

For certain categories of works, AI-assisted quantity and quality measurement from photographs and drone-based progress tracking and volume estimation can be very reliable. Smartphones equipped with computer vision applications can estimate dimensions and quantities from site photographs (lengths of pipes laid, areas of surface plastered, volumes of earthwork excavated). Computer vision models can be trained to assess the quality of construction work from photographs by detecting visible defects in concrete, checking alignment of masonry, and identifying sub-standard finishing. For large civil works (earthwork, embankments, reservoirs, large buildings), drone imagery processed through photogrammetry software can generate accurate volumetric estimates and three-dimensional progress models.

At the very least, AI-assisted photographs and drone outputs can be used to validate M-Book entries, surface quality concerns and other discrepancies. Once M-Book entries are digitised, AI can generate contractor bills from certified measurements, cross-check against contract rates, identify arithmetic errors, flag unusual patterns (e.g., a sharp increase in claimed quantities near bill submission deadlines), and route completed bills for authorisation. Apart from improving the quality and increasing accuracy of payments, the time savings and reductions in delays in payments will be significant.

The examples of order-drafting and M-book opportunities are likely to be particularly appealing for officials because they also significantly reduce their workload and drudgery. They are deployable at medium friction, directly legible to senior officials, and capable of generating rapid, measurable impact that builds institutional confidence in AI tools.

Another area of promise is the categorisation or triaging of cases in several public domains.

The Ken has an article on the application of AI by courts in India. A promising application of AI is in the categorisation and triaging of cases.

Half of India’s backlog—25 million cases—could disappear quickly if AI were used not to decide cases, but to triage them. The opportunity lies in the huge volume of matters that are effectively already dead on arrival: cases filed beyond limitation, petitions missing mandatory disclosures, matters rendered moot by changes in law, or disputes where precedent has already settled the question, leaving only a narrow technical point to close. Courts identify these only in occasional manual clean-ups—slow, inconsistent, and impossible to scale.
An AI system, by contrast, could scan filings in bulk, flag statutory defects, detect outdated or defective pleadings, classify time-barred matters, and surface cases appropriate for summary disposal. These could then be bundled and placed before judges for quick orders, clearing the undergrowth so courts can focus on disputes that actually require judicial time.

Like with court cases, triaging of outpatient (OP) cases in primary health centres (PHC), community health centres (CHC), district hospitals, and medical colleges is an area where AI can play a potentially significant role. The daily OP load in these hospitals (at least the better ones among them) is multiples of what a doctor can manage, leaving them overburdened and stressed. The result is inefficient use of the doctor’s time, inadequate diagnosis time, incorrect diagnosis, wrong OP referrals, and so on.

In all these hospitals, OP cases come to doctors with limited or no triaging. An AI-based triaging application where the symptoms are entered at the OP-registration, nurse and doctor-level, can significantly improve work conditions, increase hospital productivity, and enhance the quality of treatment.

Similarly, citizen grievances or consumer complaints received in any office, especially public-facing ones, can be triaged for routing them to the right desks/officials, escalating to senior officials, analysing repeat complaints, and so on.

Triaging is already one of the early emerging successes of AI, with examples like Bank of America’s digital assistant “Erica”, which handles billions of client interactions and has reduced call centre volumes by 40 per cent.

In general, the judiciary’s case load management is an area where, in theory, AI applications can have a transformative impact, especially in categorising cases, transcription of witness statements, and the preparation of orders. The Ken article has a good description of an AI application in a courtroom in Kerala.

A witness spoke; an AI-powered tool listened. A clean, searchable transcript appeared in real time—punctuated, structured, permanent. Stenographers were not clambering to catch up, litigants were not begging for readable copies. This split-screen view—one courtroom running on memory, the other on machine comprehension—is not a metaphor. It is India’s judiciary in 2025: a system where 19th-century workflows and modern AI systems operate side by side, neither quite replacing the other. Earlier in 2025, the Kerala High Court issued an office memorandum, making AI-assisted live transcription mandatory across all district courts…
“Today, the AI-transcribed witness testimony is uploaded as soon as the proceedings are completed. Earlier, the witness testimony would be handwritten by the judge, and a party’s lawyer would apply for a readable copy and seek adjournment on that basis,” said Joseph Rajesh, the IT Registrar of the Kerala High Court. “All this time delay is now cut down to nil.”… A court order that once took four or five hours to type can now be generated in under an hour. A witness deposition that once required handwritten dictation, shorthand transcription, and final review can be captured in a single digital stream.

The article also has this graphical exploration of the various possible use cases for AI within the judiciary.

There can also be daunting practical challenges to its adoption

Consider defect detection—the clerical step where filings are checked for completeness. In Delhi’s Tis Hazari courts, the rules are so complex that Nyaay’s engineers joked: If we can crack defect detection here, we can crack it anywhere. The joke holds true. AI had to be customised court by court. And transcription? In many urban courts, there are stenographers, but in district courts, they are scarce. AI fills a vacuum, but only if the court has electricity, microphones, and a judge willing to trust the output.

So how can these AI applications be deployed?

For all these use cases, the development of robust AI applications that can be deployed across institutional levels nationwide is an industrial engineering endeavour. It requires diligent, long-drawn, and high-quality problem-solving, including pilots (or beta testing), iterative adaptation and refinement. It cannot be achieved by an experiment undertaken by a district or state-level entity (or even as ad-hoc officer-driven initiatives within central government departments), as is often the case today.

The Ken article cited above points out the problems of not having one entity in charge of the creation of these national public goods.

The downside is that every state is effectively running its own lab experiment: non-standard, improvised, and dependent on the priorities (or disinterest) of whoever happens to be leading the High Court. Some innovate aggressively. Some slow-walk. Some judges independently approach AI companies, but without formal approval, nothing can enter the courtroom workflow… High Courts and district courts continue testing tools piecemeal. Language models multiply while vendors proliferate and workflows diverge… India could end up with 25 distinct judicial AI ecosystems, none interoperable, standardised, or scalable nationally…
India’s AI push is happening largely outside the Supreme Court’s flagship e-Courts project—the initiative meant to modernise the judiciary. Phase III of the programme still focuses on the basics: creating digitally readable records, enabling e-filing, and automating service of summons. A February Press Information Bureau note pegs the Phase III budget at Rs 7,200 crore, but only a little over Rs 50 crore is reserved for “integration of AI and blockchain technologies across High Courts”. In effect, the national blueprint is still laying the plumbing while the states are already experimenting with smart faucets…
If India manages to fuse Kerala’s mandate with Karnataka’s case clustering, Delhi’s defect detection, the Supreme Court’s governance frameworks, and the entrepreneurial urgency of tools like Adalat and Nyaay, the judiciary could undergo a systemic leap.

It would require a dedicated team at the national level (government department, Supreme Court, CBIC/CBDT, etc.) engaging single-mindedly on the endeavour - formulating the problem, collecting and cleaning data, developing models for different levels of government, undertaking pilots with close oversight, iterating with tight feedback loops, documenting processes, and scaling solutions gradually.

The models required for lower courts, High Courts, and the Supreme Court would vary and must therefore be customised, just as those required for hospitals of different kinds, tax and land adjudicating and appellate officials at different levels, and disciplinary authorities for different kinds of functional entities. In each case, there would be a need to do rigorous pilots to be able to refine and finalise robust enough solutions that can be deployed at scale.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 20 Jun 2026 02:33:29 +0000

1. PE firms sitting on $4 trillion of unsold assets, on investments largely made between 2020 and 2022 when rates were slashed to zero, are finding creative ways to offload them. Sample this

Blackstone is marketing a so-called collateralised fund obligation that will bundle more than $2bn of stakes in leveraged buyout funds into bonds to sell to investors and insurers, according to people familiar with the matter. The deal would provide an infusion of cash to investors in a Blackstone Strategic Partners fund, the firm’s unit that invests in other private equity groups’ funds. It is unclear if Blackstone will ultimately go ahead with the securitisation or seek to sell the stakes in a secondary transaction, one person briefed on the matter said... The vehicles, which are sliced and diced to give investors exposure to different levels of risk and return, have boomed. Issuance of CFOs soared to a record of $25.9bn last year from a modest $4.8bn in 2021, according to credit rating agency KBRA.

2. This is a brilliant articulation of the problem with articulating something purely in terms of absolute numbers and aggregates.

In his novel Hard Times, Charles Dickens described a girl called Sissy, who was having a terrible time in her lessons. Her schoolmaster told her to imagine that her schoolroom was a nation in possession of “fifty millions of money”. Wouldn’t that mean it was a prosperous and thriving state? “I said I didn’t know,” she relayed afterwards to a friend. “I thought I couldn’t know whether it was a prosperous nation or not, and whether I was in a thriving state or not, unless I knew who had got the money, and whether any of it was mine. But that had nothing to do with it. It was not in the figures at all.”

3. Interesting story about how old companies are reinventing themselves to profit from the AI-boom.

AI servers must be more tightly linked together, increasing the need for advanced cabling and optics. Shares in Corning, the 175-year-old inventor of Pyrex glass that also supplies screens for Apple’s iPhones, have increased by more than 270 per cent in the past year after it signed deals with Meta and Nvidia to supply optical fibre cabling to AI data centres. The vast amounts of electricity needed for AI training are also fuelling demand for specialised power management, high-voltage electronics and cooling technologies. This has led to big interest in traditional suppliers of electrical equipment, typically deployed in residential and industrial projects. Eaton, an Ohio-based power management company, received 240 per cent more data centre orders in Q1 this year...

French electrical equipment maker Legrand has doubled its revenues this decade with half of the growth coming from data centres, which now make up more than a quarter of its turnover. Air conditioning and liquid cooling — using water to stop chips from overheating — are in demand too. Shares in AC maker Comfort Systems USA have shot up 260 per cent over the past year, while Schneider Electric bought a stake in data centre liquid cooling specialist Motivair for $850mn last year. Utilities are rushing to supply AI companies with power — including Spain’s Iberdrola, a leading supplier of power contracts to tech groups in Europe, according to Pexapark data, and Entergy in the US, whose share price hit a record high after a $10bn deal with Meta... Several generator and engine companies have also pivoted to supplying data centres, including Caterpillar, Boeing supplier Howmet Aerospace, Finnish ship engine maker Wärtsilä and Baker Hughes, which formerly focused on oilfield services.

4. China demographics facts of the week.

This year’s cohort of gaokao-takers were mostly born in 2008, a year of 16.1m births. By 2025 births had more than halved, to just 7.9m. The demographic cliff is already visible in nurseries, which saw pupil numbers plummet from 46m to 32m between 2022 and 2025. Numbers in primary schools have also started to thin. Inevitably, over time, secondary schools and then colleges will follow.

And technology adoption

A survey of 322,000 students last year by the China National Academy of Educational Sciences, a state-affiliated think-tank, found that 85.6% of them had already tried using AI to complete their homework. On popular apps such as Zuoyebang (“Homework Help”) and Yuanfudao (“Ape Tutoring”), pupils snap photos of questions and ai walks them through the solutions. (Teachers are using similar technologies to help mark homework.)

5. Public moods on the role of government in the UK - 70% support nationalising energy and 82% water.

Rail subsidies have been rising, £12bn in operational support in 2024-25, up in real terms from £2bn in 2000-01.

6. Indian economy facts of the week.

The World Trade Organization data show that for non-agricultural goods, the share of tariff lines in the category 10-15 per cent increased sharply from 1.4 per cent to 33.5 per cent between 2014 and 2024 and for the tariff category 15-25 per cent from 1.7 per cent to 14.9 per cent, while the share of tariff lines in the category 0-10 per cent fell steeply from 90.5 per cent to 42.5 per cent over the same period.

7. From Thomas Astbridge's book on the Black Death

In its most intense phase, from 1347 to 1353, the Black Death killed more than 100mn people, or about half the population in the areas infected, Asbridge estimates. This makes it more lethal than two other great pandemics — the 6th-century Plague of Justinian in the Mediterranean and the pestilence that swept across Asia from the mid-19th century until the aftermath of the second world war — and far worse than Covid-19 in our times... Asbridge demonstrates that the Black Death was probably more devastating in cities such as Cairo and Damascus than in, say, Constantinople or Florence. In Cairo, a metropolis of 500,000 people, almost 10 times larger than London’s population, perhaps 250,000 died, Asbridge suggests.

8. On the role of luck in football tournaments.

According to one study of historical matches, the chances of the team with the worse record winning was 45 per cent in football, compared with just 36 per cent in America’s National Football League. (Yes, this pep talk has statistics. Bite me.) The knockout structure raises the role of chance, as just one dodgy penalty can crash a team out of the competition. According to numbers crunched by James Tozer of Prospect, a sports analytics company, betting odds gave the top four teams in the most recent Premier League a combined 89 per cent chance of winning (after adjusting for bookies’ ability to take advantage of fans’ optimism that their team would win). In the World Cup an upset is more likely, as that figure is only 48 per cent.

9. Aldi effect, as the discount grocery retailer seeks to expand aggressively, as it envisages 4000 stores at an investment of $9 billion in a US market where consumers are facing higher prices due to persistent inflation.

Credit card data analysed by the bank found that when an Aldi store opened, it shaved an average of one percentage point off annual sales from competitors within a 10-mile radius... Aldi prospered in postwar Germany under brothers Karl and Theo Albrecht before a disagreement led to a split in the 1960s. One offshoot, Aldi Süd, oversees Aldi’s US business after opening the first store in Iowa in 1976. The other, Aldi Nord, owns the quirky US grocer Trader Joe’s. The discounter’s stores are austere places with only about six staff on duty. They are designed for maximum efficiency: groceries are shelved without leaving their cardboard shipping trays and oversized bar codes are printed on packing so checkout operators can scan at pace. Customers must deposit a coin to obtain a shopping trolley, which is refunded if they return it. Operating cost savings fund the chain’s low prices... Aldi’s compact stores, which stock only about 2,000 product lines, are often located near competitors such as Walmart, whose large-format stores carry about 120,000 products, including low-priced groceries.

10. Andhra Pradesh shrimp production facts.

India exports approximately 8 lakh tonnes of shrimp a year, with Andhra Pradesh accounting for over 60 percent of production. The state accounts for 80 percent of the country’s shrimp exports and 34 percent of marine exports, valued at around Rs 21,246 crore annually. The state has 2.5 lakh aqua farmer families, of which 2 lakh are small and medium farmers. Another 30 lakh people depend on allied sectors. According to the Union Ministry of Commerce and Industry, India exported a record 17,81,602 MT of seafood worth US$ 7.38 billion (Rs 60,523.89 crore) in 2023-24, of which frozen shrimp alone accounted for 92 percent — a significant share from Andhra Pradesh.

11. India's PPP pioneers

GVK’s 216-megawatt (Mw) Jegurupadu plant became an early proof-of-concept under a power purchase agreement. IL&FS built a 12-km toll road between Rau and Pithampur in Madhya Pradesh, marking India’s first private toll concession.

12. This is a true success story for the Indian economy.

Between 2020 and 2026 the number of Indian retail investors rose from around 40m to 130m.

The quartet of global imbalances

noreply@blogger.com (Urbanomics) — Fri, 19 Jun 2026 02:05:13 +0000

President Donald Trump’s single-minded pursuit of rebalancing the US economy using tariffs is unlikely to yield much without addressing fundamental distortions that have crept into the US economy and financial markets. Trade surpluses are a mere symptom of deeper malaises. I had blogged about the global twin structural imbalances, arguing that an American economy skewed towards consumption and a Chinese one skewed away from consumption are two sides of the same coin.

Helene Rey (see also this report to G7) has a very nice summary of global imbalances, where she locates it within the dynamics of saving and investment, and questions the focus on tariffs.

A country that saves more than it invests lends abroad and runs a current account surplus; one that invests more than it saves borrows and runs a current account deficit. It is the collective saving and investment decisions of a country’s households, companies and government that drive imbalances. There is now some agreement — crystallising in the G7 discussions — that the sources of imbalances are linked to unbalanced growth models and mostly made at home: chronically weak consumption in China, feeble productive investment in Europe and outsized fiscal deficits in the US. Tariffs are not an effective mechanism to change any of those and issuing an international currency is no justification to run current account deficits.

She also proposes the solutions in terms of complementary actions.

China rebalancing towards consumption, Europe lifting productive investment and America repairing its public finances are not three grudging favours. They are parts of a single, mutually reinforcing policy. One country’s exports are another’s imports; one country’s capital outflow is another’s inflow. When all three move at once, each adjustment cushions the others: the deficit country that consolidates finds external demand waiting as surplus countries spend more at home, and the surplus country that stimulates demand finds a market at home rather than a protectionist wall… The IMF’s scenario of simultaneous rebalancing raises global output by around 0.8 per cent and narrows medium-term imbalances by half a percentage point of world GDP.

I think this analysis misses a critical fourth leg of the imbalance, financialisation. It cannot be seen as merely a symptom of US borrowing. As evidence, there are two natural experiments. The last two episodes of US fiscal deficit reduction (in the late nineties and mid-2000s) were accompanied by financial market bubbles (the dot-com bubble and housing and mortgage market bubble).

Underlining this, Ricardo Caballero, Emmanuel Farhi, and Pierre Olivier Gourinchas have shown that if the financial markets do not work well, the economy might accommodate investments that deliver a rate of return that is below the growth rate of the economy. In this situation, both stock market bubbles and government debt can play the useful role of displacing inefficient investments.

Foreign savings flowing into the US must find a US asset to absorb them - either Treasury debt (the fiscal deficit channel) or private financial assets (the financialisation channel). These two are substitutes, not complements. Squeezing fiscal deficits without restraining private credit pushes the same global savings glut into asset bubbles. So US fiscal consolidation alone is not sufficient, and must be paired with financial-sector reform that prevents the likes of private credit from filling the void.

I asked Claude to generate a graphic that describes the global imbalances quartet.

So, how to address these imbalances?

The rebalancing would require global diplomacy and coordination to mobilise support from all key stakeholders. This looks onerous in a deeply polarised world, of rising tensions between the West and China and the unpredictable and whimsical nature of the Trump Presidency.

In fact, among the four adjustments required, interventions pertaining to the much-derided Euro area appear to be the most promising and likely to materialise. In fact, on investments, the train has already started.

It is possible that a deep crisis on the economic front, a very likely near to medium-term possibility in either case, may force both China and the US to rebalance towards consumption and consolidation, respectively. However, there are daunting political economy challenges to be overcome in both countries, especially the US.

It is the fourth leg that might prove the most challenging. The financial markets are where the power of entrenched interests is so strong that it might require a counter-revolution to upend the order and regulate financial markets more tightly. This will also require global coordination and collective action, and not mere reforms at the US front.

Countering China's weaponisation of its manufacturing dominance

noreply@blogger.com (Urbanomics) — Thu, 18 Jun 2026 04:04:01 +0000

China’s weaponisation of its manufacturing dominance, most famously through its control of rare earth magnets production, is generally accompanied by a narrative of resignation that its trading partners must live with this reality till they develop alternative supply chains. It is widely perceived that China has a definitive upper hand, and all other countries, including the US, must play catch-up.

This begs a few questions. Isn’t China susceptible to imported products and services that are essential to its economy? Aren’t there rare earth equivalents that the US and Europe can restrict access to China, thereby bringing a bargaining equivalence between the two sides? What are those rare-earth equivalent dependencies for China today? What is the economic leverage that the West has over China that can be exercised in response to the rare earths restrictions?

This post will examine this question in greater detail.

In a 2025 G-7 summit, the European Commission President Ursula von der Leyen aptly described China’s industrial policies as creating a pattern of “dominance, dependency, and blackmail”.

Having built up dominance and dependency, the blackmail is now intensifying. The hide your strength and bide your timephase is past. In the recent past, China has come up with several measures to leverage its dominance.

China’s widely known weaponisation of its manufacturing dominance has been underpinned by a series of regulations that impose restrictions and penalties. Pre-empting efforts by Western multinationals to diversify away from China, in April this year, to “prevent security risks in industrial and supply chains”, the State Council issued Regulations on Industrial and Supply Chain Security to investigate and punish foreign firms that stop using Chinese suppliers in response to political pressure from their governments. This is a summary.

Under the new rules, regulators can question employees and examine corporate records during investigations. The regulations also allow the authorities to bar companies and individuals from leaving China if they are suspected of moving supply chains elsewhere under foreign pressure… The State Council, China’s cabinet, justified the measures as necessary to protect the country’s economic stability and national security… China’s global network of ports and port-management software gave Chinese officials detailed insight into multinationals’ supply chains, allowing them to detect when companies shift to suppliers elsewhere.

In February, it amended the state secrets law by broadening the scope of the type of information that would be considered a national security risk. It includes a new legal concept called “work secrets”, defined as information that is not an official state secret, but that “will cause certain adverse effects if leaked”. This broad sweep allows for interpretation as convenient for the government, and makes foreign companies and their employees further vulnerable.

The restrictions are not confined to foreign companies. In early June, the State Council announced rules requiring national security screening for Chinese companies seeking to invest overseas.

The rules also give the authorities new powers to scrutinize Chinese companies seeking opportunities abroad, subjecting them to national security reviews that place investments into one of three categories: encouraged, restricted or prohibited. Part of the motivation for this, lawyers say, is to keep money, talent and intellectual property in fields where China has a competitive edge from leaving the country… The measures restrict the movement of certain talent in sectors deemed sensitive, though Beijing has not defined which sectors qualify. They also give officials broader authority to review the movement of capital, including the power to force investors to sell shares or halt investments if national security concerns arise. The rules also lay the legal groundwork for regulators to bar foreign entities from investing or operating in China, including expelling them from the country, in retaliation for actions taken by their governments against Chinese investments.

In this context, it is surprising that even as China increases its bellicosity both in trade and in its foreign policy, especially the breadth and frequency of military actions in the Taiwan Straits, the response from the West has been remarkably muted. Doubtless, the dysfunctional nature of the Trump Presidency has been a major contributor. But the lack of proportionality of response from the US holds, even including the Biden Presidency.

So what are the chokepoints and vulnerabilities that China faces from the West?

Ironically, China’s biggest vulnerability is in the very industry that it utterly dominates. China dominates the downstreamof electronics (assembly, packaging, volume manufacturing) and the upstream of raw materials (rare earths, gallium, refining), but it is deeply dependent on imports for the midstream - the tools and ultra-pure materials that actually make advanced chips. Underlining this reality, China imported $385 billion of integrated circuits in 2024, more than the $325 billion it spent on crude oil, making chips its biggest single import.

China's vulnerabilities cluster at the highest-precision, most knowledge-intensive nodes, the "tools that make the tools." Photoresist is the cleanest analogue to rare earths: a narrow, chemically exotic input where Japan holds a near-monopoly and a cut-off would, in one analyst's framing, leave Chinese manufacturing with "no rice to cook with". China could meet only about 5% of its own demand for the KrF resists used in 110–180nm chips, and high-end localization was under 5% in 2022. In simple terms, semiconductor equipment is the broadest lever by value, and machine tools are the most pervasive across general manufacturing. More than 60% of the indigenous passenger jet C919’s components, including engines and flight controls, are imported.

The graphic below plots the levers (or chokepoints) that the West have over China in terms of how hard it bites and how long it would take China to replace them. The colour represents the usability of the lever, without unacceptable self-harm; the dashed arrows show the levers China is actively closing.

The red bubbles (EUV, photoresist, EDA) are near-monopolies a single country can switch off. The amber ones (equipment, foundry, CNC tools, bearings, jet engines, instruments) are oligopolies that only work as leverage if allies coordinate, which is exactly why the US has spent two years building the Netherlands-Japan-Germany-Taiwan coalition rather than acting alone.

The West’s strongest cards (AI chips, EDA software, jet engines) are precisely the ones with a shelf life, because Beijing is pouring state money into domestic substitutes. The genuine bargaining equivalence lives in the top-right quadrant - frontier technology that bites hard and takes a decade-plus to replace (EUV lithography, leading-edge foundry).

It is to be noted that the arrows point left because it points to China travelling in the direction of self-sufficiency. It highlights that while real and binding today, there is a declining shelf-life or option value with these chokepoints.

However, having said this, any escalation risks retaliation. In 2025, the US suspended engine and EDA exports, China tightened rare earths, and within weeks, both sides walked it back into a one-year truce because each could hurt the other badly. It also points to the value of a bargaining strategy where the West gradually introduce restrictions on multiple such products where China depends on imports. The restrictions should be phased in carefully and subtly by plugging the procedural and process links that China exploits to its advantage.

In this backdrop, Ely Ratner and Nick Danby have a very good essay in Foreign Affairs that outlines the broad contours of a plan to identify and squeeze China’s vulnerabilities and do unto it what it is clinically doing by weaponising its strengths. The most obvious one is, as discussed above, to tighten the restrictions on access to the semiconductor chip supply chain.

China continues to leverage chip-smuggling networks, overseas data centers, and model distillation, a technique that exploits access to frontier AI models to replicate their capabilities. New policy measures should target the channels China uses to acquire restricted chips and supporting architecture, including shell companies and unlisted subsidiaries, as well as cloud-based access to U.S. computing power and servicing arrangements that keep older semiconductor manufacturing equipment operational. Equally urgent is synchronizing U.S. export restrictions with those of the Netherlands and Japan, whose companies—ASML and Tokyo Electron—control critical chokepoints in the advanced semiconductor supply chain. Although both governments began strengthening their own policies in 2023, their controls on equipment sales, servicing, and subcomponent exports to Chinese fabrication plants and toolmakers fall short of U.S. restrictions. Washington should press The Hague and Tokyo to close these gaps. If diplomacy fails, it should consider invoking the Foreign Direct Product Rule, which extends the extraterritorial reach of U.S. export controls to restrict products made with U.S. software or technology.

The authors also write that China’s huge export volume and $1.2 trillion trade surplus can be as much a liability as it is a strength, especially at a time when the economy is weakening and struggling for anchors of growth.

It should push back against China’s export surge by bringing advanced economies facing deindustrialization together with developing countries whose own manufacturing aspirations are being displaced. This coalition could then coordinate trade measures to protect their industries, including steel, shipbuilding, batteries, and drones. Alongside tariffs, the United States could pursue high-standard trade agreements that institutionalize requirements for subsidies, state-owned enterprises, and forced technology transfers that China cannot meet. Like-minded partners could also create an anticircumvention regime by strengthening rules of origin, sharing customs data, and imposing penalties on goods routed through third countries to avoid trade restrictions. They could further impose outbound investment screening to prevent companies or individuals in the United States and allied countries from financing Chinese capabilities that the controls seek to limit.

Notwithstanding its large reserves, for a country which imports three-fourths of its crude oil with 90% delivered through vulnerable sea routes, China is extremely vulnerable to energy security.

Below the threshold of a full blockade, the U.S. Treasury Department, through the Office of Foreign Assets Control, can use maritime sanctions to dissuade shipping companies, insurers, brokers, and banks from supporting prohibited shipments. Pressure on insurance, port access, and flag registration would raise costs and create uncertainty for China-bound tankers without requiring direct military action. The Pentagon should nevertheless demonstrate its ability to disrupt or interdict China’s seaborne energy imports by exercising U.S. naval control over key chokepoints along energy trade routes.

Commodity imports are another chokepoint

China imports roughly 80 percent of its iron ore, a foundation of its steel industry, predominantly from Australia. And most of its copper and lithium inputs, which are critical to battery and defense manufacturing, come from Australia, Chile, the Democratic Republic of the Congo, and Peru. As with oil, these dependencies offer additional pressure points that can be leveraged to strengthen deterrence and compound China’s challenges across multiple sectors simultaneously. If Australia were prepared to restrict exports of iron and lithium ore, and the United States and its partners had a plan to tighten access to copper and cobalt, they would send a message to China that its industrial base could be easily disrupted and its defense production capacity degraded if circumstances warranted.

Finally, the US dollar’s dominance is the nuclear option available.

Were Washington to restrict China’s dollar access—moving from sanctions on banks supporting PLA activities to broad limits on dollar transactions in advanced technology and military manufacturing—it could impose severe costs on Beijing, disrupting Chinese financial markets and potentially triggering wider economic instability… Washington must prepare for this scenario by first communicating unambiguously that only severely destabilizing acts would trigger consequences of this magnitude: for example, large-scale cyberattacks on critical U.S. infrastructure, Chinese export restrictions that seriously imperil the U.S. economy, or an armed attack against U.S. allies and partners.

The Cold War and trade tensions between the West and China are here to stay for the foreseeable future and are most likely to be ratcheted up over time. It is therefore important that others can mobilise sufficient bargaining levers with China. All the aforesaid are likely to be very effective in restricting the Chinese economy if deployed in a coordinated manner. This would require the mobilisation of a global alliance, something the US-led West did with great effectiveness during the Cold War with the Soviet Union.

Now, with the hostility and dysfunctionality of the Trump administration, any such cohesive and credible global alliance looks very unlikely. In its absence, whatever restrictions are imposed by the US and EU independently are merely band-aid solutions, and likely to get circumvented in various ways against an antagonist who is disciplined, plays the long game, and does painstaking groundwork to accumulate its strengths and overcome restrictions. Trump 2.0 is, therefore, perhaps the best thing that an embattled Chinese government could have been gifted by its opponents.

Examining the gravitational pull of index investing

noreply@blogger.com (Urbanomics) — Sun, 14 Jun 2026 23:30:00 +0000

The blockbuster IPO of SpaceX has drawn attention to the role of the index investing market segment, which originated fifty years ago. The scrutiny will only intensify in the days ahead as both Anthropic and OpenAI are listed.

Despite only about 4 per cent of SpaceX’s shares being listed, a high level of demand was built in, also because some index investors will soon be required to add the company to their portfolios. Nasdaq amended its rules (as also CRSP and FTSE Russell) to allow SpaceX to enter the Nasdaq-100 index via fast-track, thereby providing it free liquidity through a captive market. Morningstar follows 6,006 US-registered mutual funds and 5,100 ETFs, covered by 3,203 separate benchmark indices against which $41.1tn of assets are managed.

The current entry requirements for indices are very liberal for large companies.

However, what distinguishes SpaceX is its very low float (just 4.25% compared to above 90% for the other big companies) and the fact that it does not yet make any profit. Toby Nangle and Co at FT Alphaville tabulated that active fund managers (who want to ignore Musk) and passive portfolio managers (who have no choice) must purchase $14.2bn of SpaceX stock in the first three weeks of trading to avoid going short.

We think that managers who have absolutely no interest in going either long or short Elon will need to collectively buy $8.5bn of SpaceX stock on the 19th June, a further $1bn on 26th June and a final $4.7bn on July 3. And so we’re talking a cumulative, de facto mandated $14.2bn of mutual fund and ETF orders by July 4 to avoid having to take a view on Elon. That number would’ve been around $11bn higher if the S&P 500 index committee had leaned a different way. But it’s $13.2bn bigger than the $1bn it would’ve been if index committees had sat tight on their existing fast-track methodologies. And this is before we count institutional assets like pension funds, insurers and foundations, as well as every single foreign owner whose benchmarking habits we currently lack information.

So this is a big deal. Clearly, the market has been brazenly manipulated by changing the rules to create the stage for SpaceX (and the other two mega IPOs).

I took the help of Claude to generate a few graphics to understand the scale of index investing, its dynamics and distortions, and possible reforms.

The conventional measures dramatically understate index investing because they count only labelled index mutual funds and ETFs. However, using the indexation definition, passive ownership of global equity mutual funds and ETFs is 50% and 60% for US equities. In fact, a stunning three-fourths of the equity market exposure of the big three US asset managers is through ETFs.

SpaceX’s shares could have an index exposure of 43% in a year when it joins the S&P 500, and that too on a volume which would be a fraction of that for the Mag7 firms. It is estimated that S&P 500 funds would need to absorb 19% of SpaceX's public float upon inclusion, with the Russell 1000 and Nasdaq-100 funds absorbing another 24%. A tiny supply of $22-27 bn would meet a mandatory demand of 43%.

The research on the impact of index inclusion on stock price offers striking findings. Gabaix and Koijen find investing $1 in the stock market increases the market’s aggregate value by about $5. Since marginal holders of equity (index funds, pension funds, insurance companies) operate under mandates that fix their equity allocations within narrow bands, when aggregate demand shifts, few participants can absorb the change, and prices must move substantially to clear the market.

Haddad-Huebner-Loualiche point to a Mathew Effect in indiex investing. They find that a $3.6trn market cap company is only about five times as liquid as a $100bn company, despite being 36 times the size. The largest stocks are disproportionately impacted by each dollar flowing into passive funds, causing the largest stocks to outperform and stock market concentration to rise. Passive investing has reduced market efficiency by over one-third.

Index investing brings the benefits of access to a low fee, diversified, and tax efficient asset pool to the retail investors. In the words of the legendary investor Jack Bogle, index investing is the equivalent of not looking for the needle but instead buying the haystack.

However, it distorts price discovery, creates inelastic demand, amplifies concentration in stocks and asset managers, and erodes governance. All of these distortions are greater for the mega-cap stocks.

Further, index-investing comes from asymmetric flow elasticity. While passive funds are mechanically symmetric (the 1:5 multiplication), the rest of the system (margin lenders, derivatives dealers, momentum funds, retail behavioural agents) is not. When flows reverse, the multiplier still applies, but additional positive feedback channels switch on, creating an overshoot below fundamental value before mean-reversion can bring prices back. The resultant instability can tip markets into prolonged and deeper downturns. This is an illustrative example

Suppose 2 years post-IPO, growth disappointments and rate-policy reversal trigger a ~20% net outflow from Nasdaq-100 trackers ($280 bn × 20% ≈ $56 bn). Applying the firm-level multiplier (~2 for stock-specific flows, higher for concentrated names): mechanical impact on SpaceX share price could be ~30–45%, on top of any fundamental revaluation. The same flow proportionally affects all Mag 7. Index investing does not just amplify upside — it removes the price-discoverers who normally cushion downside.

Below is a listing of some channels that amplify the downside.

All things taken together, index investing must reconcile the conflicting requirements of democratising investing (through passive funds) and limiting market instability. The answer lies in the empirical fact - the more capital becomes index-tracking, the less price discovery the market performs, and the more the system is exposed to flow-driven valuation and procyclical unwinding. Clearly what is optimal for individual investor is not optimal for market function. In the circumstances, this can be an illustrative framework to reconcile the conflicting requirements.

The problem with index investing is that of internalisation of its negative externalities. Passive investors capture the upside of cap-weighted indexation (low fees, diversification, momentum) without bearing the price-discovery cost, whereas active investors bear the cost of analysis but cannot compete on fees.

This free-riding can be resolved only if passive investors internalise the marginal cost they impose on the system without eliminating the substantial benefits they deliver to retail investors. Like with carbon emissions, this can be done through fees, governance obligations, or structural caps.

To dive a bit deeper, there are perhaps four levers for reforming index investing - index methodology (rules of index construction), voting and governance obligations, fund-level liquidity thresholds, and limiting structural concentration in the passive investment ecosystem. A combination of all of them is a formidable regulatory toolkit.

Each lever will have its set of reforms. It may be required to prioritise a limited set reforms that have the highest-impact-per-cost. The simplest and highest value reform could be mandatory free float minimums (say 10-15%), a twelve-month public trading history before index inclusion, and a 4-5% cap on any single stock in an index. Second, bring index providers under SEC regulation as “investment advisers” subject to fiduciary duty and disclosure requirements.

Third, for passive funds with more than 1% holding, return governance to the ETF or index fund holders by allowing them to vote their proportionate share directly via the fund, thereby reducing the influence of the Big Three asset managers. Fourth, cap the passive fund ownership of US banks at 10% (the regulatory "controlling interest" threshold) and impose mandatory liquidity stress tests for funds with more than $5 bn AUM in cases of extreme outflows and market dislocation.

Fifth, the Department of Labour should tighten defined-contribution retirement-plan portfolio diversity rules, thereby providing a structural counterweight to cap-weighted default portfolios. Such plans must offer either equal-weight or capped-weight default or cap cap-weighted defaults at e.g. 80% of the plan portfolio. This would reduce forced concentration in 401(k) default portfolios. Finally, there could be regulatory roadmap for orderly winding-down of overweight positions during the life-cycle or retirement transition. A coordinated guidance, including some rule-based gradual decumulation, reduces fire-sale risk.

The proposals above seek to reconcile the diversification and fee benefits of indexation while also internalising its costs. Once index providers face fiduciary duty, once Big Three votes are partially passed through, once retirement defaults include non-cap-weighted options, once liquidity is stress-tested, and there is some transition guidance, the same products can continue to serve retail savers while delivering far less of the distortion that has been documented.

Weekend reading links

noreply@blogger.com (Urbanomics) — Sat, 13 Jun 2026 05:32:29 +0000

1. Ruchir Sharma goes behind the record corporate profits share of GDP in the US (11% of GDP, up from 7% in the 1990s) and finds two problems. One, the share is boosted by the lower corporate tax rates which translate into higher fiscal deficits.

Lately the US deficit has risen to more than 6 per cent of GDP and a deficit that high reflects a large transfer of income to households and corporations. Under a well-established accounting formula, the Kalecki-Levy Equation, corporate profits are in part a mirror image of the government’s deficit. Based on this framework, deficits were the single largest contributor to the increase in earnings as a share of GDP since the late 1990s. And in this decade, deficits have accounted for more than half of corporate profits, twice the level of the dotcom era. Strip away government support, and US profits look less extraordinary.

Second, the share falls sharply when we include the universe of private companies.

Since the dotcom bust in 2000, the number of publicly listed businesses has fallen by half, with many new companies remaining private for longer, funded by private equity and venture capital. This is the new home of excess and weak earnings. As a result, the profit growth of the average business listed in standard indices provides a misleading picture of the overall economy. Profit growth has been less impressive once the private companies are included in the data. Further, private firms planning to go public are much larger and less profitable today than in the 1990s. The biggest names in the IPO pipeline, including SpaceX, Anthropic and OpenAI, have little to no profits.

2. Britain's much-delayed HS2 railway project's cost has increased by another £20bn to £102.7bn (a range of estimates ( £87.7bn to £102.7bn in 2025 prices) and will be completed only by the 2040s, with the first trains not expected till 2036.

3. Why has oil not hit $150 or $200 despite about 12-15 mbpd being taken out?

One of the biggest surprises for the oil market has been China, the world’s largest importer. It slashed inbound shipments by almost 40 per cent in May compared to last year’s average, according to Vortexa Ltd. The reduction is enough to offset anywhere between a third and a fifth of the barrels lost to the war, depending on the estimates used. At the same time, the US has emerged as the world’s most important swing supplier since launching strikes on Iran in late February. American crude and fuel exports in May were more than 2 million barrels a day higher than the average for all of last year. Other emergency measures have also eased the strain. Governments around the globe coordinated a historic release of strategic reserves, while Gulf producers rerouted shipments through alternative export routes. Some tankers continued moving cargoes via the strait despite the risks, using increasingly opaque methods to avoid military threats... Saudi Arabia’s East-West pipeline shipped millions of barrels a day to the Red Sea, while the United Arab Emirates has been piping barrels to the port of Fujairah outside the gulf.

And this is important.

Another factor keeping a lid on prices has been Trump’s relentless jawboning, making it hard for even the most bullish traders to hold long positions for prolonged periods of time. Open interest in Brent crude futures is the lowest since August as elevated market volatility forces traders to roll back risk exposure. Steep price drops on the prospect of peace have pushed many oil bulls to the sidelines, leaving them to hold small positions for very limited periods of time, several traders said. The lack of risk-taking has helped keep a lid on financial flows, while supply levers have averted the worst hit to the market. The question now, is whether that can last without a peace deal.

4. The evidence to date points to widespread AI use not translating into anything proportionate in terms of business value creation. Sample this from the software industry pointed out by John Burn-Murdoch.

5. EU energy subsidies during the Ukraine invasion and spike in gas prices.

Between late 2021 and mid 2023, EU countries spent about €540bn on subsidising energy prices to protect consumers from price shocks in the aftermath of Russia’s full-scale invasion of Ukraine and to cushion energy-intensive industries from soaring costs. Of that total, €158bn was accounted for by Germany, whose industry heavily depended on Russian gas supplies. In the wake of the Ukraine energy shock, Brussels also relaxed state aid rules for the rest of the decade to allow governments to subsidise clean technologies and industrial decarbonisation.

6. Emerging AI-related roles in the software industry.

Agent engineers who build and fine-tune agents; architects who determine how humans and agents divide tasks; AI governance specialists who keep agents compliant and accountable; AI transformation advisors who help enterprises navigate workflow reimagination; solution designers who translate business problems into agentic solution architectures; and AI assurance partners who help clients govern their own AI deployments.

7. Rana Faroohar makes the point that we may be at the cusp of a new investment super-cycle driven by the combination of AI, clean energy, defence, and manufacturing.

In a recent issue of his TPW Advisory Monthly report, investor Jay Pelosky did just that, collating data on AI, clean energy and defence spending around the world from sources including Gartner, BloombergNEF (on energy), the Stockholm International Peace Research Institute and the International Institute for Strategic Studies (on defence) and others. So far, $6.9tn was spent globally in 2025 in the three areas, and the number will probably reach $10tn by the end of this year and $16tn by 2030.

What’s more, says Pelosky, these three areas reinforce one another, amplifying potential investment. AI requires more energy. The move towards tech sovereignty in the US, China and even Europe (in a nascent way) adds to the need for investment in AI and energy, while the move towards a more 19th-century “spheres of influence” geopolitics calls for greater defence spending globally. Add to this the desire of policymakers in all three regions to increase resilience in critical sectors affected by concentration or globally dispersed supply chains: products such as advanced semiconductors, active pharmaceuticals and lithium batteries, for example.

8. Rupee trajectory over the last two years

The rupee depreciating by nearly 6 per cent against the dollar in the first five months of 2026 alone, exceeding the combined full-year declines recorded in 2025 (5 per cent) and 2024 (2.8 per cent). The rupee touched a record intraday low of 96.57 per dollar on May 19 and has lost roughly 14 per cent of its value against the greenback over the past two years. Against the euro, the erosion has been equally stark, with the Indian currency declining by more than 7 per cent over the past 12 months and nearly 16 per cent in the past two years, he added.

The problem with a depreciating rupee is that foreign investors now must earn an extra 14% over the last two years to offset the depreciation.

9. India's FTAs may be creating a perverse incentive for even domestic manufacturers due to the inversion of duty structures.

Many finished goods now enter India at low or zero duty from partners such as ASEAN, Japan, South Korea, the UAE and Australia. As a result, Indian manufacturers often pay high duties on imported inputs, especially those sourced from non-FTA countries, while competing against finished products imported duty-free under FTAs. For example, steel and aluminium attract MFN duties of 7.5-10 per cent, but machinery, industrial equipment and engineering products made from these materials can enter India duty-free under several FTAs. Indian manufacturers, therefore, face higher input costs when competing with tariff-free imported machinery produced with globally priced inputs.

Similar distortions exist in chemicals, plastics, rubber and textiles. Duties on inputs such as caustic soda, soda ash, polypropylene, PVC and SBR raise production costs. At the same time, many finished products in these sectors can be imported at low or zero duty... the growing incentive for firms to manufacture outside India rather than within it. When raw materials and components attract duties in India, but finished products can be imported duty-free from FTA partners, companies may find it more profitable to locate production abroad and export back to the Indian market. In such cases, FTAs effectively encourage offshore manufacturing at the expense of domestic value addition. ASEAN countries are increasingly becoming manufacturing hubs for supplying the Indian market.

10. The Gulf War has resulted in a surge in Chinese exports of solar panels, especially to South East Asian and African countries. This came at a time when many Chinese firms laden with excess capacity, low margins, and large indebtedness were facing an existential crisis.

11. The revival of manufacturing in the US hits the wall of labour scarcity.

Perhaps the largest problem for would-be reshorers is a lack of labour. Despite widespread nostalgia for manufacturing jobs, new US factories often struggle to find reliable workers. When Japanese group Panasonic started producing electric vehicle batteries near Reno, Nevada in 2017, the company suffered more than 100 per cent annual turnover in its early years. Not only were employees reluctant to take jobs in an access-controlled, sterile environment, but every November, the group would lose workers to seasonal logistics jobs at nearby Amazon facilities that paid a similar wage... Despite political enthusiasm for reviving manufacturing, jobs posted on LinkedIn receive fewer applications than other sectors it competes with, such as technology.

12. More on the SpaceX bubble.

Goldman Sachs, the investment bank leading the IPO, projects that SpaceX’s AI revenues need to increase 100-fold by 2030, reaching $322bn from $3.2bn today. But its AI lab remains far behind Anthropic, Google and OpenAI at the frontier, and shows little sign of catching up. Morgan Stanley also estimates that overall revenue needs to increase 180-fold to $3.4tn by 2040, up from $18.7bn last year. Earnings must flip from a $4.9bn loss in 2025 to $2.7tn.

SpaceX ends the first day of trading at 20% premium on its $135 listing price, which raised $75 bn and left it with a valuation of $2.1 trillion. The listing prospectus claimed at $28.5 trillion addressable market.

SpaceX plans to use the IPO proceeds for a range of ambitious projects, from its skyscraper-sized reusable Starship rocket, founding a 1mn-strong colony on Mars, starting a lunar economy to building a network of orbital AI data centres capable of delivering vast amounts of computing capacity... A portion must also go towards repaying a $20bn bridge loan the group took out in March to back its merger with Musk’s lossmaking AI start-up, xAI, and social media platform X.

This is staggering.

It also hands Musk a vast financial windfall, with his 42 per cent stake in SpaceX valued at more than $800bn. Combined with his $280bn holding in electric vehicle maker Tesla, his wealth has now surpassed $1tn, equivalent to about a third of the market value of the UK’s FTSE 100 index.

Richard Waters explains the rationale driving the astronomical valuation.

It is hard to find changes in SpaceX’s business prospects that account for a fourfold valuation jump within the space of a year. Rather, it is testament to Musk’s unrivalled grip on the public imagination, transmuted into Wall Street gold. When the history of SpaceX’s record-breaking IPO is written, it will go down as a textbook case of Musk’s ability to conjure a compelling vision of the tech future for both Silicon Valley and Wall Street, ably backed by an army of promoters in the financial world. (With fees estimated at about $500mn, it’s probably not surprising that there were few warnings from the Wall Street establishment that the shares might be overvalued.)...

Musk has been busy in recent months spinning up new visions capable of embellishing his company’s prospects... What starts out sounding like science fiction fantasy doesn’t take long to seem not only plausible but even downright likely. Wall Street has been more than willing to translate Musk’s tech dreams into financial projections capable of supporting the sky-high valuation, implausible as those numbers may seem. Goldman Sachs, the lead bank on the IPO, predicted that revenue would soar to $474bn by 2030, most of it from AI...

The IPO involved only about 4 per cent of SpaceX’s shares, but a high level of demand was built in, partly because some index investors will soon be required to add the company to their portfolios. Even without the index funds, SpaceX’s sheer size — at about 2.5 per cent of US stock market capitalisation — has made it too big for many investment managers to avoid.

13. South Korean capitalism is spreading wealth around

Samsung Electronics... agreed last month for employees to share the chipmaker’s blockbuster profits from an AI-led boom... SK Hynix... handed employees a similar profit-sharing deal last year... Samsung is also going to give Won500mn loans at low rates to employees... Samsung and SK Hynix together control much of the market for the advanced memory chips used in AI servers. Employees at both companies are in line for average annual bonus payouts of Won600mn, which compares with a national average salary of about Won50mn... district of Hwaseong... expected to gain corporate income tax receipts of Won1tn to Won1.3tn from Samsung alone this year, an extraordinary sum for a city authority whose annual budget is about Won3.5tn.

14. Germany amps up spending on its ailing railways sector, where on-time arrivals of long-distance trains have fallen from 84% two decades back to 60%.

By the end of 2026, Deutsche Bahn will have rebuilt 900 kilometres of train lines since 2024, close to a quarter of its 2036 target. That is equivalent to half of the roughly 1,900 kilometres of new lines built after 1945. Flix, which started as a coach operator but also runs long-distance trains, has earmarked €2.4bn for up to 65 new high-speed trains that it plans to roll out from 2028. Italian high-speed train operator Italo also has ambitions for Germany, promising up to €3.6bn of investment in new trains if it gets multiyear access to the network.