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Thursday, January 30, 2025

The elusive promise of e-governance

There’s a widespread impression that persistent and deep-rooted problems and deficiencies in public systems can be addressed by digital solutions. It’s almost an article of faith that workflow automation of the kind that’s now pervasive in the private sector can be easily applied to public systems for similar uses.

Accordingly, over the last two decades, software applications with telemetry systems to manage and monitor transactions have been thought to have transformative potential in public policy and governance. Experts and tech optimists have extolled the benefits of these e-governance applications and projected them as low-hanging fruits with transformative potential. 

Doubtless, there have been some notable successes with e-governance. The biggest and most salient have been with the online processing of applications for statutory services like municipal services, permits and licenses, property registration, birth and death registration, and most iconically, the passport seva kendras. 

But there are numerous examples of evidently digitisable logistical activities where success has proven elusive. Consider the following tasks that involve services offered by or activities within governments and those which suffer from serious problems (leakages, inefficiencies, etc.).

1. Procurement of drugs, vaccines, and other consumables by state governments, indenting of requirements by field units from Primary Health Centres to tertiary care facilities, distribution of drugs to the field units, and inventory management at all levels (from central and district drug stores to field units).

2. Workflow automation of the procurement of paddy at procurement centres, conversion to custom milled rice (CMR) by rice millers, and distribution of CMR to state government stock points and then to Fair Price Shops, and the distribution by FPS as part of the Public Distribution System (PDS). And the settlement of the food subsidy accounts associated with each transaction from procurement to distribution. 

3. Allotment of rice and other items to mid-day meal programs and residential education schools and hostels, indenting by these institutions, and inventory management at all levels.

4. Monitoring of attendance of thousands of officials across schools/hostels, hospitals, anganwadis, and other widely dispersed facilities. 

5. Monitoring maternal and child health outcomes of pregnant and lactating women and infants through the four ante-natal checkups, delivery, seven post-natal checkups, and all the 10-12 immunisation doses. 

6. Recording of the bill of quantities of work executed in case of infrastructure works on the statutory Measurement books (M-Book). More generally, the processes of bid management, tender awarding, work agreement documentation, bill recording, check measurements, payment processing, other administrative actions (time extensions, quality control checks etc.), and work closure. 

7. Data acquisition systems that track and render on dashboards real-time information on the flow of water from the treatment facility along trunk lines to intermediate reservoirs (ground-level or overhead), and downstream to smaller distribution areas. Similarly, systems that track and render the flow of electricity from upstream 132/33 kV and 33/11 kV substations to distribution feeders and downstream to distribution transformers. 

8. Digitisation of post-harvest activities like assaying and grading, contracting between buyers and sellers, delivery of produce, and accounts settlement at notified agriculture marketplaces (implementation of e-NAM). 

In each case, there are serious implementation deficiencies or failures associated with the underlying transactions. There are either substantial subsidy leakages or monitoring challenges. And in theory, the digital solution has the potential to address them effectively. 

They are also areas where several rounds of e-governance applications have been experimented over the last two decades. Governments at central, state, and local government levels have responded with applications covering a vast range of sectors. They have been supplemented with similar efforts by aid agencies and philanthropies. 

Over nearly two decades, successive generations of officers at each level have experimented with applications spanning all these activities. At best, and that too very rarely, they have succeeded partially, but most have not survived the official or politician who implemented it. 

Despite these long-drawn series of efforts, I don’t think there’s even a single instance in any of these areas where the digital solution has solved the underlying problem at least in one state or city or agency. The underlying problems have remained just as persistent

Note that there will be several claims of successful IT applications involving the aforesaid problems and awards bestowed on them. We must go beyond the headlines and scrutinise the actual outcomes of those claims (whether the IT solution has actually resolved the underlying problems in any meaningful manner or not). 

There are several reasons for these failures. I have blogged hereherehere, and here, examining some of the reasons. The point of this post is less to examine the reasons and to draw attention to this headline reality of consistent failure to address the problems despite the numerous attempts. 

My big concern, therefore, and a reason for urging caution with digital solutions (as articulated here), is that the mere adoption of these solutions invariably instils a sense of complacency in the system that detracts from the good old-fashioned problem-solving of these issues that are essential for their meaningful resolution.

For all their apparent simplicity, each of these involves realigning a powerful set of entrenched vested interests that are impervious to technological innovations alone. Such applications face daunting technical and implementation challenges. On the technical side, they require very careful and thoughtful design and iterative adaptation once rolled out. On the implementation side, they require stakeholder ownership and change management. The technical aspect must be complemented with robust governance and strong stakeholder engagement for the solution to stand any chance of success. 

Monday, January 27, 2025

The economics and geo-politics of the emerging AI trends

This post will examine recent trends in artificial intelligence (AI) and its broader economic and geo-political implications.

The current interest in AI was triggered by the launch in November 2022 by Open AI of its ChatGPT, a generative AI solution trained with large language models (LLMs). Its versions and competitors have become fairly good at generating realistic writing, codes, videos, audio, and images. Successful applications have been in the generation of writing and codes. 

As the name suggests, this type of AI uses LLM to recognise patterns and generate its outputs. Its ability to think, reason, and imagine has been limited, much less replicate human neural networks. Accordingly, it struggled with simple verbal and visual reasoning tasks that humans do with ease. 

In recent months, there have been two important developments in AI’s journey. The first concerns the trends in the commercialisation pathways of AI, and the second involves the emergence of state-of-the-art AI models from China. 

On the first, in September 2024, there was the release of new generative AI models that can “think” (reason, plan, and solve) on more complex problems. Though they too use the same underlying pattern-recognising models-based generative approach, they “think” harder about the task assigned to them by talking to themselves and spending more time on the model inferences. OpenAI released o1 in September and then o3 just before Christmas while Google released its Gemini Flash Thinking. 

Such “thinking” AI models and solutions have been described as “digital versions” of Daniel Kahneman’s Type Two thinking (the LLM models that spit out its results quickly are the Type One models). These models indulge in structured, step-by-step thinking instead of blurting out their results. 

This was accompanied by OpenAI and Google releasing generative AI solutions in the form of products (as against generative AI models like ChatGPT and Gemini). OpenAI released Sora, a video generation product, and Canvas, a writing and coding product, while Google unveiled Astra and Mariner. This productisation of AI solutions is a response to the growing chorus that for all its hype, AI is yet to realise significant commercial success for its users. 

The two Google products belong to a new category of agentic AI tools that take actions on behalf of a user (as against chatbots that only talk), which raises the possibility of doing things in the virtual world like shopping on e-commerce sites or booking holidays and reservations. 

Such models that “think” are a leap from those that quickly generate patterned answers like search engines, social networking, and chatbots. Once the fixed costs of model development and hardware investments are incurred, the marginal cost of the latter type of generative AI is very low. But models like o3 and agentic AI solutions use complex models whose processing requires more computing power, lots of memory, and low latency since they also interact with multiple tools like a web browser. They require more sophisticated hardware and consume more electricity. They can, therefore, be offered only as a subscription service.

In other words, while AI solutions have become more sophisticated, it’s also leading to a shift away from the world of low marginal cost services to the more traditional market of higher marginal cost products and services that come at a price. Accordingly, the “thinking” AI models and their productisation have the potential to transform the economics of the digital economy. Free or cheap services to large numbers at low marginal cost might become a thing of the past. The Economist has a good look ahead into the future of this market.

The power of such models relies on them bringing a version of the sector’s “scaling laws” closer to the end user. Until now, progress in AI had relied on bigger and better training runs, with more data and more computer power creating more intelligence. But once a model was trained, it was hard to use extra processing power well. As o3’s success on the ARC challenge (visual reasoning) shows, that is no longer the case. Scaling laws appear to have moved from training models to inference.

Such developments change the economics facing model-makers, such as OpenaiAI. The new models’ dependence on more processing power strengthens their suppliers, such as Nvidia... It also benefits the distributors of AI models, notably the cloud-service providers Amazon, Microsoft and Alphabet. And it may help justify the fortunes that these tech giants have invested in data centres because more inference will need more computing power. OpenAI will be squeezed from both sides…

High marginal costs mean the model-builders will have to generate meaningful value in order to charge premium prices. The hope, says Lan Guan of Accenture, a consultancy, is that models like o3 will support ai agents that individuals and companies will use to increase their productivity. Even a high price for use of a reasoning model may be worth it compared with the cost of hiring, say, a fully fledged maths phd. But that depends on how useful the models are. Different use cases may also lead to more fragmentation… providing AI services to corporate customers will require models that are specialised for the needs of each enterprise, rather than general-purpose ones such as Chatgpt. Instead of being dominated by one firm, some expect model-making to be more like a traditional oligopoly, with high barriers to entry but no stranglehold—or monopoly profits.

There’s a promise that the winner-takes-all dynamic of current technology markets could be upended. This is also amplified by the much reduced (or limited) network effects from such applications (compared to social media or search). 

This development in the commercial prospects of generative AI products should be seen along with the developments in areas like robotics. In the first half of this year, Nvidia is set to launch Jetson Thor, a specialised computer to power sophisticated humanoid robots that can tackle complex tasks with great speed and efficiency. 

Nvidia is positioning itself to be the leading platform for what the tech group believes is an imminent robotics revolution. The company sells a “full stack” solution, from the layers of software for training AI-powered robots to the chips that go into them…Robotics has so far remained an emerging niche that has yet to generate large returns… Data centre revenue, which includes its sought-after AI GPU chips, made up about 88 per cent of its overall sales of $35.1bn in the group’s third quarter… a shift in the robotics market is being driven by two technological breakthroughs: the explosion of generative AI models and the ability to train robots on these foundational models using simulated environments. The latter has been a particularly significant development as it helps solve what roboticists call the “Sim-to-Real gap”, ensuring robots trained in virtual environments can operate effectively in the real world… 

Nvidia offers tools at three stages of robotics development: software for training foundational models, which comes from Nvidia’s “DGX” system; simulations of real-world environments in its “Omniverse” platform; and the hardware to go inside the robots as its “brain”… Amazon has already deployed Nvidia’s robotics simulation technology for three of its warehouses in the US, and Toyota and Boston Dynamics are among other customers using Nvidia’s training software.

Trends like the productisation of AI solutions, “reasoning” AI offering specialised services, humanoid robots etc., are potential revenue streams to sustain the AI revolution. Given the promising pipeline of such streams, massive cash surpluses with the leading tech companies, and the large physical investments required in computing power (and are being made), it’s not a stretch to imagine the giant AI bubble to keep inflating for a few more years before it gets pricked. Each trend has the potential to inflate bubbles that can sustain capital market valuations. The fact that the so-called magnificent seven firms stand to corner most of the benefits from these trends and they have also contributed almost all the gains in the S&P 500 over the last two years may point to further runway for the US equity markets, before the inevitable crash. 

The second development in the AI space was news from China in December of its stunning progress in the new version of AI models. First Alibaba released its Qwen chatbot, QWQ, with similar “reasoning” capabilities, and originating from its own successive versions of generative AI LLM models. This was followed by news of progress made by others like ByteDance, Tencent, Moonshot, and o1.ai. The most remarkable news involved a small Chinese startup AI lab, DeepSeek, founded in 2023 by hedge fund manager Liang Wenfeng, which released its “reasoning” model, R1 (it had earlier released its regular generative AI model DeepSeek v3). This was developed with a small budget (model training was done in 2 months at a cost of $5.5 million), and apparently as a hobby by its eccentric founder who bought up several older versions of Nvidia GPU processors while running his successful quant trading hedge fund, High-Flyer. 

In terms of performance, DeepSeek’s R1 appears to fall short of only o1. Among LLM models, DeepSeek v3 has 685 billion parameters, the individual precepts that combine to form the model’s neural network (the biggest among all models released for free download), compared to just 405 bn parameters for Llama 3.1, Meta’s latest LLM. The Economist has a good set of graphics that compare the performance of DeepSeek.

The exceptional feature of DeepSeek’s success was that it could develop its “reasoning” models without access to the latest Nvidia H100 GPU chips that OpenAI and others have in plentiful numbers. 

DeepSeek claimed it used just 2,048 Nvidia H800s and $5.6mn to train a model with 671bn parameters, a fraction of what OpenAI and Google spent to train comparably sized models.

The v3 LLM’s billions of parameters took fewer than 3 m chip hours, a tenth of the computing power and expense that went into Llama 3.1, and its training requirement of just above 2000 chips compared with Llama 3.1’s 16,000 chips and that too of a higher version. Meta is now planning to build a server farm with over 350,000 chips and Elon Musk’s xAI is planning one with over a million chips.

More on this here

To reduce reliance on high-end chips from overseas, Chinese AI companies have experimented with novel approaches in algorithms, architecture and training strategies. Many have embraced a “mixture-of-experts” approach, focusing on smaller AI models trained on specific data. These can deliver powerful results while reducing computing resources… DeepSeek completed training in just two months at a cost of $5.5mn… DeepSeek has also drastically reduced inference costs, earning it the nickname the “Pinduoduo of AI”, a reference to the cost-slashing business model of the popular Chinese discount ecommerce giant. This breakthrough has profound implications. It challenges the widely held assumption that cutting-edge AI requires vast amounts of computational power and many billions of dollars. DeepSeek demonstrates how software ingenuity can offset hardware constraints… strict export controls have forced Chinese tech companies to become more self-reliant, spurring breakthroughs that might not have occurred otherwise.

Clearly, Chinese companies are figuring out innovative ways to maximise the computing power of a limited number of older generation chips, the Chinese version of Indian jugaad innovation. Having said this, there are also disputing voices like this who claim that the actual number of chips is higher. 

As an aside, DeepSeek’s apparent parsimony and efficiency point to multiple paradoxes. It exposes the profligacy and inefficiency of Big Tech with its vast funding pool and sub-optimality of use cases. It also stands in stark contrast with China’s own industrial policy on semiconductor chip making, which appears riddled with waste and inefficiency. It highlights the benefits of scarcity and constraints in expanding the frontiers of innovation. When finance does not have any disciplining force, as is the case with Big Tech spending on AI and the private capital funding globally over the last fifteen years or more, the market may be no better than governments in allocating finance by safeguarding against excesses and waste. 

DeepSeek is not only remarkable in being at the cutting edge but also in being disruptive in other ways. It also revealed the technical recipe or the training model and methods (unlike OpenAI and Google, which have kept their methods confidential) in a detailed paper that outlines how to build a LLM that can learn and improve itself without human supervision. By releasing its technical recipe, it has opened up the possibilities for open-source AI and future business models for such endeavours in the US and elsewhere. In fact, here, DeepSeek has been following the route that appears to have been adopted by Chinese companies in general of making their systems available on an open-source license. 

If you want to download a Qwen AI and build your own programming on top of it, you can—no specific permission is necessary. This permissiveness is matched by a remarkable openness: the two companies publish papers whenever they release new models that provide a wealth of detail on the techniques used to improve their performance. When Alibaba released QWQ, standing for “Questions with Qwen”, it became the first firm in the world to publish such a model under an open licence, letting anyone download the full 20-gigabyte file and run it on their own systems or pull it apart to see how it works. That is a markedly different approach from OpenAI, which keeps o1’s internal workings hidden… Ask QWK to solve a tricky maths problem and it will merrily detail every step in its journey, sometimes talking to itself for thousands of words as it attempts various approaches to the task. “So I need to find the least odd prime factor of 20198 + 1. Hmm, that seems pretty big, but I think I can break it down step by step,” the model begins, generating 2,000 words of analysis before concluding, correctly, that the answer is 97.

This openness also points to a less discussed attractiveness to talented AI programmers and professionals.

Chinese labs are engaged in a battle for the same talent as the rest of the industry. Eiso Kant, the co-founder of Poolside, a firm based in Portugal that makes an AI tool for coders says, “If you’re a researcher considering moving abroad, what’s the one thing the Western labs can’t give you? We can’t open up our stuff any more. We’re keeping everything under lock and key, because of the nature of the race we’re in.” Even if engineers at Chinese firms are not the first to discover a technique, they are often the first to publish it, says Mr Kant. “If you want to see any of the secret techniques come out, follow the Chinese open-source researchers. They publish everything and they’re doing an amazing job at it.” The paper that accompanied the release of v3 listed 139 authors by name, Mr Lane notes. Such acclaim may be more appealing than toiling in obscurity at an American lab.

Chinese companies appear to be showing the world a different model for AI solutions compared to the restrictive approach adopted by US Big Tech firms. 

It has also avoided raising money from outsiders, with Liang paying top salaries to its AI developers using his income from his hedge fund

“DeepSeek is run like the early days of DeepMind,” said one AI investor in Beijing. “It is purely focused on research and engineering.”… “DeepSeek’s offices feel like a university campus for serious researchers,” said the business partner. “The team believes in Liang’s vision: to show the world that the Chinese can be creative and build something from zero.”.., Liang has styled DeepSeek as a uniquely “local” company, staffed with PhDs from top Chinese schools, Peking, Tsinghua and Beihang universities rather than experts from US institutions. In an interview with the domestic press last year, he said his core team “did not have people who returned from overseas. They are all local . . . We have to develop the top talent ourselves”. DeepSeek’s identity as a purely Chinese LLM company has won it plaudits at home.

Its singular focus on research (at least to date) and disinterest in monetising its models is a throwback to Silicon Valley in its early years. In fact, its focus on AI for the public good is reminiscent of OpenAI’s own founding ethos as a non-profit, one which it quickly sprinted away from once the commercial prospects became evident, in keeping with American capitalism’s reflexive pursuit of profits. 

Its small size and bootstrapped nature raise questions about entry barriers and the impregnability of AI leaders like Open AI, Google, Meta, Anthropic etc. In general, its approach threatens tech firms that have created a small oligopolistic club interested as much in keeping out emerging competitors (kill-zone) as in innovation. It’s also remarkable that such a market-upending innovation and business model has not emerged in the supposedly innovation-friendly US but in China. 

However, it remains to be seen as to how long can this Chinese catch-up continue without access to the rapidly growing sophistication and power of computing (Nvidia’s new generation Blackwell chips, use of more and more advanced chips to increase computing power etc.). There surely has to be a limit to how much ingenuity and innovation alone can do to make up for the sheer physical brutality of hard computing power. And especially when the US is doubling down on its own AI efforts, as evidenced by the new $500 bn investment announcement on the Stargate project involving Oracle, OpenAI, and Softbank. It’s hard to not feel that the gap will widen. 

Finally, the rise of DeepSeek and Chinese models raises uncomfortable questions for India’s software firms and startups. Despite having a head start of over half a century of competing in the global software industry, India’s iconic software firms have struggled to move beyond their low-value-capture business models. They have also failed to capitalise on successive technology waves like cloud computing, IoT, data analytics, and robotics, and now the same appears to be happening with AI, too. The startups, too, have been one step removed from the frontier, mostly focusing on copying and improving successful innovations and business models from the West. 

As the Chinese successes show, a combination of enterprise and reasonable funding can help leapfrog physical constraints to vault to the cutting edge of innovation in areas like AI and other cutting-edge software. India has AI talent, a well-developed startup ecosystem, and pockets of reasonable funding (tech and other billionaires) to be able to engage at the cutting edge. 

But it appears to lack the enterprise and ambition among its entrepreneurs and wealthy individuals from the technology and finance sectors to spawn a DeepSeek and Liang Wengfeng. The former appears happy enough copying successes from the West and adapting them for local markets, and the latter prefers to invest all their wealth in the public markets. Reluctance to defer gratification and risk aversion appear to be characteristic features of startups and investors. 

It’s one more example of how the private sector and its enterprise have lagged in supporting India’s high economic growth aspirations.

Saturday, January 25, 2025

Weekend reading links

1. For a country which imported 72% of its crude oil in 2022, has China reaches peak oil?

Its crude oil imports declined 2% in 2024, the first such decline. The implications are enormous.
If Chinese demand is reaching a plateau that would fulfil projections by the IEA of global oil demand peaking before 2030. The forecast sustains hope for the world to reach net zero carbon emissions by 2050. The milestone would also shake the global economy. Over the past three decades, China has accounted for half of all growth in the world’s oil demand — some 600,000 b/d. If that rate continues to level off, the $500bn that oil companies are spending every year on finding new sources of oil and gas may be far too high.

2. Europe's stunning reversal of economic fortunes between its core (northern) and peripheral (southern) economies, the so-called PIIGS. 

In a stark reversal of fortune, the once-ailing “periphery” countries have stolen the lustre of its previously dominant “core”, including Belgium, the Netherlands, Austria and, at the centre, Germany. In the 15 years to the pandemic, German GDP on average grew by 1.5 per cent a year while the four southern states eked out just 0.3 per cent on average. Since 2020, Spain, Italy, Portugal and Greece have on average expanded by 1.3 per cent a year... on average, the four economies are nearly 6 per cent larger than they were at the start of the pandemic. Meanwhile, Europe’s largest economy Germany had no increase in economic activity at all over the past four years, and the Bundesbank has warned that this stagnation may drag on well into 2025. By contrast, the EU commission expects that Spain and Greece will grow by 2.3 per cent this year, Portugal by 1.9 per cent and Italy by 1 per cent.
Impressively, the much derided bureaucracy in Brussels may have contributed to this reversal for the PIIGS!
The newfound economic fortunes of Europe’s debt crisis countries can in part be traced right back to Brussels itself: A €800bn debt-funded investment programme that the EU launched during the pandemic. Through the so-called NextGenerationEU, member states are being provided with funds to invest in transportation and digital infrastructure, green energy generation, research and development among other areas, in exchange for undertaking productivity-enhancing structural reforms. Portugal, Italy, Spain and Greece are the main recipients. Though the four countries account for just 28 per cent of the Euro area’s GDP, they are expected to receive 78 per cent of all funds through the programme, according to ECB data. The scheme is currently set to run until mid-2026. In Italy, around €25bn of NextGenEU funds is being used for a major upgrade of the railway network, including new high-speed train lines into the country’s south, where travel is far slower than in the prosperous north. Billions of euros in infrastructure investment are generating much-needed employment in a region that has historically been short of jobs.
The money has come attached with reform conditionalities.
To unlock the funds, Italy has had to undertake major reforms of its public administration and judicial systems, with the aim of streamlining, simplifying and accelerating procedures and decision-making to boost efficiency and the country’s long-term competitiveness. The structural reforms demanded by Brussels are more important than the money itself, argues Yannis Stournaras, the governor of the Bank of Greece... Stournaras points to research by the Greek central bank suggesting that those measures alone could lift GDP up to 10 per cent by 2040.

3. German economy graphic of the day. Industrial output is falling and a quarter of manufacturing capacity is going unused. 

There are several other graphics in the article that point to the decline in the country's economic fortunes under Olaf Scholz. 

This graphic is both glass half full and half empty.

On the one hand, it's an impressive achievement that Germany was able to completely phase out a nearly 50% dependency on Russian gas in just over a year. On the other hand, Russian gas has not been substituted or offset by gas from LNG terminals. 

This means that Germany has either managed to improve its energy efficiency or figure out alternative energy sources or foregone output. More likely a combination of all, and subsequent economic contraction appears to indicate that it has been more of the last. 

Public investment is an area that Germany should focus.
Germany’s transport, energy and communications infrastructure suffers from years of under-investment, in part because of the country’s strict public deficit rules. The debt brake, a constitutional provision introduced under former chancellor Angela Merkel in 2009, prevents regional governments from taking on any new debt and the federal state from borrowing no more than 0.35 per cent of GDP in any given year. The result is an ageing railway network, crumbling highways and collapsing bridges. Deutsche Bahn, whose trains increasingly run late — if at all — has estimated it needs €45bn to modernise. The country’s adoption of new technologies has been slow. In a 2023 survey, 82 per cent of companies in Germany said they were still using fax machines. Germany also has one of the lowest penetration rates of fibre broadband in the OECD.

4. A peek at Donald Trump's commercial interests

5. China routinely overstates its GDP growth rates by 2-4 percentage points, says Rhodium Group. 

6. Good article that explains why it's not easy for ICE manufacturers to shift to EVs. This snippet illustrates the challenge,
In comparing their parts, the most important metric is weight reduction. For the electric business to keep growing, the cars need to better compete with gas-guzzlers on range. Therefore most every design decision must take into account whether it makes the car lighter. As a basic example, consider one component: Toyota part #55330-42410, a 20-pound steel bar, known by engineers as a cross-car beam. The beam holds the steering wheel and dashboard instruments in place and helps protect the cabin during a collision. This part is inside the bZ4X, the Toyota brand’s only global, mass-market fully electric car, because it’s of a tried-and-true design used in countless other models. Today’s standard cross-car beam is the product of incremental improvements made across decades, and most versions of it have wound up under the hoods of internal combustion cars. This is a testament to the Toyota Production System (TPS), which continuously refines even the tiniest details of individual auto parts.

Over untold iterations, the beam has been designed to keep the vibrations of an internal combustion engine from making their way to the passengers. But electric motors don’t vibrate, and steel is heavy. These are among the reasons why Tesla Inc. and BYD Co., the top makers of battery-electric vehicles, manufacture similar beams out of plastic. Theirs weigh only about 14 pounds, according to Caresoft, and they’re cheaper and easier to install, too... researcher Yole Group, which notes that BYD makes 40% of its parts. “It’s like, holy cow, these guys make everything,” says Woychowski, the Caresoft president. “They make their own batteries. They make their motors. They make their own body. They make their front and rear fascia, their headlights, their door trim panels, their console. It’s a quantum jump. That’s not conducive to kaizen.”
This is an important snippet

A typical electric car has about 11,000 parts, Goldman Sachs Group Inc. has estimated, about two-thirds fewer than its gas equivalent.

Many components are completely new.  

7. Andy Mukherjee compares the real cost of doing business in India and Thailand.

All told, 19% of a $2.3 million factory in India is an extra burden of governance — or lack of it — that doesn’t exist in Thailand. This may not be an showstopper for a high-margin business that relies on skilled, productive labor and cutting-edge technology. But for a labor-intensive startup operating with slim profits in an industry like readymade garments, going into production from a weak financial position means fewer resources left to scale up. And therein, the entrepreneur tells me, lies the basic difference between India and its East Asian neighbors. No ordinary Thai businessmen fears bankruptcy because of something his government may do; in India, such a prospect is very real.
8. Good description of the fissures building up in the Trump coalition between the Tech Right and the Nationalist Right. 
The core of the aspiring Trumpian aristocracy are still reactionaries and nationalists aching to restore an American way of life thought to be lost after decades of “globalist” technocracy. They are often deeply skeptical of the idea that the innovations promised by tech companies represent progress, and they describe America as “not just a country, not just an economy, but a people with a common history,” as Jeremy Carl, a deputy assistant secretary of the interior in the first Trump administration and a senior fellow at the Claremont Institute, told me. The tech figures who came to the movement in 2024 were often sympathetic to Trumpian nationalism. But they tended to be more interested in making money and launching a new era of “American dynamism.”... The coalition is achingly close to achieving a long-held conservative dream — of fashioning a high-low alliance powerful enough to supplant the liberal establishment and remake America. It is a project that might well collapse if one side or the other gets too much of what it wants, and ends up driving the other away... Mr. Bannon accused the tech barons of promoting “technofeudalism” and “transhumanism”— bending human life into technologized and unnatural new forms.

Another description of all the Republican factions that animate the Trump coalition.  

9. A study by LCH Investments, an investor in hedge funds, shows that hedge funds have pocketed nearly half their returns since their inception in 1969!
Managers generated $3.7tn of total gains before fees, but fees charged to investors were $1.8tn, or about 49 per cent of gross gains... The figures... date back to 1969... “Up to the year 2000, the hedge fund fee take had been running at around a third of overall gains, but since then it has increased to a half,” said Rick Sopher, chief executive of Edmond de Rothschild Capital Holdings and chair of LCH Investments. “As returns came down, fees went up.” New research comes after the world’s 20 most successful hedge funds made their biggest profits on record in 2024... The top 20 managers in the $4.5tn hedge fund industry made total profits for investors of $93.9bn in 2024... up from the previous record of $67bn in 2023. Together the top 20 generated asset-weighted returns of 13.1 per cent, significantly outperforming the average hedge fund, which made 8.3 per cent...

Hedge funds have historically been known for a “two and 20” fee model, where investors pay 2 per cent in management fees every year and a 20 per cent performance fee on investment gains... The increase in the overall fee take from 30 per cent to about 50 per cent of gross gains is largely due to higher management fees... Whereas management fees used to eat up less than 10 per cent of gross gains in the late 1960s and 1970s, they have represented almost 30 per cent in the past two decades... firms have a “pass-through” expenses model, where the manager passes on all costs to their end investors instead of taking an annual management fee. That can cover office rents, technology and data, salaries, bonuses and even client entertainment. It typically varies from 3 to 10 per cent of assets annually. A performance fee of 20-30 per cent of profits is usually charged on top.

This is the list of the biggest hedge funds and their life-term gains

10. Important point to be considered in the context of US government restrictions on the sales of high-end semiconductor chips by its chip designers.  
Indian firms that want more than 1,700 chips a year will require “National Verified End User” (“NVEU”) authorisation. The American side has been alarmed by events in India, eg reports of a company in India which imported 1,100 Nvidia chips from Malaysia and re-exported them to Russia for $300 million. Action by the Indian state blocking leakages of high technology to Russia, China, and Iran will help more Indian firms get to this NVEU status.

11. Important point about the trends in central government's assets.

The Central government’s assets as a proportion of its liabilities... rose from 67 per cent in 1950-51 to 100 per cent by the early 1960s and stayed at that level till the early 1980s because of the emphasis on investment in public sector industrial and infrastructure corporations. Since then, it has declined steadily to 75 per cent in 1990-91 and 42 per cent in 2023-24, with the shift towards subsidies and handouts. The growing role of freebies in electoral contests may well reduce the ratio of government assets to liabilities even further.

12. This captures the nature of the Indian market more than anything else

“India is a L1 (in the world of business contracts, L1 stands for the lowest bid in a tender)country. It is price that matters, not quality or source of imports," said N. Krishnamoorthy, deputy managing director, commercial, Chemplast Sanmar, a large PVC producer.

This is Uber CEO Dara Khosrowshahi

“Indian customers are so demanding and don’t want to pay for anything. I am so proud of the team. India is the gateway to the world for us. It has been the toughest market to succeed in. But if we succeed here, it sets the standards for us to succeed in other markets in the world.”

13. India state capacity fact of the day, Directorate General of Foreign Trade (DGTR) edition,

The time taken by DGTR to levy anti-dumping tariff measures is much longer than its peers. “It takes anywhere between 18 to 30 months from the start of dumping to imposition of duties," said A.K. Gupta, founder and director, TPM Consultants, a consultancy into trade remedies. Other countries do it in 9 to 12 months... That apart, the case should ideally be initiated within 15 days of the filing of the application. “In other countries, cases are initiated once there is prima facie evidence and then the investigation begins. But DGTR generally takes a couple of months or more to initiate a case as its officers first start a preliminary investigation, which takes weeks before they even accept the case. This is a typical Indian bureaucratic mindset," said Arora... DGTR has about 25 officers while its equivalent organization, in the US, has over 250. Each officer, at any given time, handles 10 to 20 cases. “In the US and EU, it is not more than two or three cases," said Arora.
14. Parking minimums come full circle.
When cars became the dominant mode of transportation after World War II, cities began adding parking requirements to ease road congestion. By 1969, nearly all municipalities with populations of at least 25,000 had minimum parking requirements for many buildings, including beauty salons and bowling alleys... Hundreds of cities and municipalities have rolled back or completely thrown out requirements on real estate projects since the nonprofit organization Strong Towns began keeping track a decade ago. In 2022 alone, 15 of them, including San Jose, Calif., Raleigh, N.C., and Lexington, Ky., repealed their parking rules. In late 2023, Austin became the largest U.S. city to eliminate parking minimums. And in December, New York City lawmakers put policies in place that reduced or eliminated parking requirements for new housing in some parts of the city... In November 2023, Austin, Texas, became the largest U.S. city to end parking mandates.

Its impact,

A 2022 study by the Regional Plan Association, a nonprofit group focused on the New York City area, found that more low-income housing was built in city neighborhoods where parking requirements were reduced... Seattle, considered a pioneer in parking policy, took an incremental approach. In 2012, the city relaxed minimums in central neighborhoods and areas served by public transit. Then in 2018, it expanded the approach to more locations and types of development. Roughly 60 percent of the housing developed in Seattle since the changes were put in place would not have been possible under the old rules, according to a 2023 study by Sightline Institute.

Wednesday, January 22, 2025

What can state governments do in the short-run to boost economic growth?

A friend recently asked me what can a state government in India do to boost GDP growth over a government’s five-year tenure. 

So I thought of a few areas where public policy can be useful for Chief Ministers and their governments who are seriously committed to this objective. 

These areas cover the typical economic growth drivers of government spending, private spending, and private investment. It excludes reforms on human resource development and capital formation that are more systemic and will play out over longer time frames. 

1. Completion of all ongoing infrastructure works will have the greatest impact. Since they are already ongoing, they involve immediate expenditure and, once completed, will generate at least some of the expected benefits. The government can appropriate political capital from the inauguration of these projects. This could be complemented with a few carefully identified highest value/impact infrastructure projects (critical connectivity roads, small and medium irrigation projects, water supply etc.). 

2. Improve the Ease of Doing Business (EoDB) and ensure their implementation in letter and spirit. It pays to build the state’s value proposition among investors in terms of its real EoDB. Real EoDB is about changing a culture and not merely tweaking rules, and it must be infused across the public system. This would entail going beyond EoDB for large industries (which is what EoDB has come to signify) to include micro and small businesses, including in the informal sector, and Ease of Living for citizens in accessing statutory and other government services. While changing culture in five years is hard, there are several ways in which leadership can signal its intent and reap transformational effects. 

3. Complement EoDB improvements with initiatives on the supply side of employment (labour market). This would include improving the quality of the ongoing skill development initiatives, focusing on effectively implementing the PM Internship Scheme, and reducing labour market frictions (facilitating labour market matching for both employees and employers). 

4. Focus efforts on the growth of existing industrial clusters by encouraging its current units to expand and also attract new manufacturing firms. This would involve addressing critical gaps in infrastructure and shared services, single-window facilitation for permissions and clearances, coordinating on labour supply and credit access etc. Given the prohibitive cost of land, facilitating access to land at reasonable rates alone can provide a big competitiveness boost for businesses. The aforesaid EoDB and labour market efforts should be implemented with greater intensity in these clusters. 

5. Prioritise the development of the major cities, which are the engines of economic growth. Complete all ongoing infrastructure works, take up some high-impact projects (road widenings, new infrastructure etc.) that are in long-standing demand and complete them in no more than 3 years, focus on affordable housing and generally lowering the cost of housing construction (see this proposal), and increase property tax collections (by improving collection efficiency and expanding the tax base) and other revenues (see this on adoption of land value capture methods).

Each of these must be developed as a package of a few interventions, and enabling government orders and guidelines must be issued. 

Their effective implementation would require focused four-level engagement - Chief Minister, Chief Secretary, Secretary/Head of Department, and District Collector. 

The initiatives on the five pillars should be identified and clear guidelines issued. The monitoring mechanism (parameters, periodicity, and follow-up) should be minimal and different for each level but decrease in granularity upwards. At the Chief Minister’s level, the monitoring should be monthly (to start with) and be such that it covers just 1-2 parameters proximate to outcomes for each initiative and ensures clear downward accountability. 

The Chief Secretary’s role would be to coordinate on emerging issues brought forth by the Departments and Collectors, as well as to follow up on issues emerging from the Chief Minister’s reviews. This monitoring system should establish clear performance accountability of the Secretary/HoD and Collector.

Monday, January 20, 2025

Responding to the world economy's China problem

I have blogged on several occasions on the world economy’s China problem. This post will examine the more influential views on how countries should respond to the surge in Chinese exports since 2021, described as the Second China Shock (the first being in 2001 after its WTO entry).

As President Trump assumes charge, it has emerged that China’s trade surplus nearly touched $1 trillion in 2024, a tripling since 2018 when he dramatically surfaced the China problem and made it a bipartisan consensus in the US. His strategy of tariffs and restrictions, while not effectively applied, has since gradually tightened, and the China problem is now acknowledged across countries developed and developing.

Amidst all the growing restrictions and sanctions, China's export engine appears to be motoring ahead at increased speed. After the bursting of the property bubble, since 2021 Beijing has supported a massive expansion of manufacturing capacity. Across sectors, Chinese factory production has been in multiples of what domestic demand would warrant, and the excess capacity is being exported at discounted prices underwritten by heavy subsidies

This turbocharging of the Chinese trade surplus has been driven by surging exports to countries other than the EU and the US, a matter that should be of great interest and concern to countries like India. 

The NYT has a very good article that puts this in perspective and examines the reasons and consequences.

On Monday, China’s General Administration of Customs said that the country exported $3.58 trillion worth of goods and services last year, while importing $2.59 trillion. The resulting surplus of $990 billion broke China’s previous record, which was $838 billion in 2022. Strong exports in December, including some that may have been rushed to the United States before Mr. Trump can take office and start raising tariffs, propelled China to a new single-month record surplus of $104.8 billion. While China ran a deficit in oil and other natural resources, its trade surplus in manufactured goods represented 10 percent of China’s economy. By comparison, U.S. reliance on trade surpluses in manufactured goods peaked at 6 percent of American output early in World War I, when factories in Europe had mostly stopped exporting and shifted to wartime production...

China has not run a trade deficit since 1993. Its 2024 trade surplus dwarfs earlier records when adjusted for inflation. Japan’s surplus, for example, peaked in 1993 at $96 billion. That works out to $185 billion in today’s dollars, or less than a fifth of China’s surplus last year. Germany ran enormous trade surpluses in the years following Europe’s financial crisis a decade ago. But its surplus peaked in 2017 at a sum equal to $326 billion in today’s money. Japan’s and Germany’s trade surpluses each topped out at about 1 percent of the rest of the world’s economic output. China’s trade surpluses are twice as big by that measure, said Brad Setser, a senior fellow at the Council on Foreign Relations… China now produces about a third of the world’s manufactured goods, according to the United Nations Industrial Development Organization. That is more than the United States, Japan, Germany, South Korea and Britain combined…

China’s exports are booming as its domestic economy is suffering. The trade surplus has offset some of the harm from a housing market crash that has scarred businesses and consumers. Millions of construction workers have lost their jobs, while China’s middle class has lost much of its savings. This has left many families reluctant to spend on either imports or domestic goods and services. Overbuilding of China’s factories has started hurting many Chinese companies, which face falling prices, heavy losses and even loan defaults... Beijing is using its control of China’s state-owned banks to invest excessively in factory capacity. The banks’ net lending to industry was $83 billion in 2019, before the pandemic. That increased to $670 billion by 2023, although the pace slowed somewhat in the first nine months of last year... China has built up its exports through huge investments in education, factories and infrastructure, while maintaining fairly high tariffs and other barriers to imports. Universities churn out more graduates in engineering and related subjects each year than the combined total of graduates in all majors from American colleges and universities…

The backlash to China’s trade imbalance has come from industrialized and developing countries alike. Governments are worried about factory closings and job losses in manufacturing sectors that cannot compete with low prices from China. The European Union and the United States raised tariffs last year on cars from China. But some of the broadest barriers to China’s exports have been put up by less affluent countries with middle-income manufacturing sectors, like Brazil, Turkey, India and Indonesia. They have been on the cusp of industrialization but fear that could slip away. The volume of China’s exports has been rising more than 12 percent a year. The dollar value of its exports has been growing at half that pace, as prices plunged because Chinese companies were producing even more goods than foreign buyers were ready to purchase.

More on this extraordinary manufacturing dominance

Last year, it produced 12.6 times as much steel as the US, 22 times as much cement, and three times as many cars—with whose electric models it now threatens to overwhelm the Japanese and German car markets. China’s shipyards also accounted for over half the ship output. By 2030, China’s manufacturing sector is projected to be bigger than that of the entire Western world. It is already far and away the global leader in every sunrise industry.

Interestingly, the surge in Chinese exports since 2021 can perhaps be partly explained by the depreciation of the renminbi, which on real trade-weighted terms has fallen 15% in the last three years. See also Krugman here

In fact, even apart from the serious problems engendered by distortions to the economic structure, there are other risks for the Chinese economy. For one, this outsized dominance of the global markets places a prohibitive level of external economic dependence

The widening of China’s trade surplus accounted for up to half the entire country’s economic growth last year. Investment in new factories for exports represented much of the rest of the growth. In a report scheduled for Friday, China’s government is expected to say that the country’s economy expanded about 5 percent last year.

Or sample this

Citi economists estimated in a research note that a 15 percentage point increase in US tariffs would reduce China’s exports by 6 per cent, knocking a percentage point off GDP growth.

Deflationary pressures are already afoot, and the risks of Japanification loom. Producer prices have declined for 28 months in a row. China’s 10-year bond yields have fallen sharply in recent months to a record low of 1.6%, and the yield curve has shifted downwards at all maturities.

Chinese corporate profits have declined for the third consecutive year. 

In addition, 25 per cent of companies in China with revenue of more than Rmb20mn made outright losses between January and November 2024, compared with 16 per cent in the full year of 2019 before the pandemic, NBS data showed. The agency’s data covers 500,000 companies.

So what’s the way out?

In a new study, Sander Tordoir and Brad Setser (also this X thread) connect Germany’s five-year decline in industrial production and the second consecutive year of economic contraction and China’s trillion-dollar surplus. 

Germany is starting to realise that China’s new automotive, clean technology and civil aviation industrial base directly competes with Germany’s manufacturing foundation. China’s macroeconomic imbalances now directly infringe on German industrial interests. Since the property bubble burst in 2021, China has doubled down on directed investment in priority manufacturing sectors, despite a lack of internal demand for much of its output. The result has been a turn back toward export-led growth, with Chinese exports (in volume terms) wildly outperforming global trade in 2024, while German exports in capital and durable goods shrank.

Germany was relatively sheltered from the initial China shock immediately before and after the country’s accession to the World Trade Organisation (WTO) in 2001. Then, China’s exports were in consumer electronics, furniture, apparel and household appliances – not the automotive and engineering sectors at the heart of the German economy. Wage restraint and the cost-savings from expanding supply chains to Central and Eastern Europe created a German competitive export sector able to benefit from Chinese and American demand for machinery. That is no longer the case: China’s economy is much larger; its industry now produces the same goods as Germany and its export-biased growth is cutting into Germany’s European and global export markets.

They propose some policy measures for the German government.

First, Germany should abandon its past opposition to scrutiny of large trade surpluses. It should join the US and the other large G7 economies in encouraging the International Monetary Fund (IMF) to prioritise policies to reduce China’s surplus… Second, Germany should support EU protection of viable European industrial sectors facing an onslaught as a result of China’s active industrial policies. At the same time, it should allow cheap imports in areas where Europe has little to no manufacturing. China’s widespread use of subsidies creates ample scope for WTO-consistent duties, such as the ones the EU pursued for electric vehicles (EVs). Third, Germany and other EU countries should equip existing and new subsidy schemes with de facto buy-European requirements to offset China’s own local content requirements. The EU… can tighten single market regulation (like the Net Zero Industry Act) to ensure EU countries align their national subsidies, for example by linking them to environmental and labour standards which China cannot meet. Fourth, Germany should lead on designing a unified EU industrial policy. Customs income already belongs to the EU and the growing tariff revenue from Europe’s trade defence instruments could be earmarked to fund a common policy.

The study has a striking graphic which shows how China has taken the lead in clean technologies with its exports and how the rest of the West (excluding Germany and Japan) are struggling.

In an excellent blog post, Noah Smith channels Michael Pettis who advocates a combination of tariffs by trade partners and structural rebalancing towards domestic consumption by China. 

The point Pettis makes is that the US is the global purchaser of last resort, and the low import prices of Chinese goods encourage American consumers to buy more of those products, which, in turn, comes at the cost of American manufacturers and also poses serious national security risks. Given the current situation of Chinese dominance, a reversal of this trend can happen only through tariffs that must also be complemented with measures to revive manufacturing and improve its competitiveness. On the other hand, the only way China can absorb this shrinkage of production (associated with exports) is by boosting its current low level of domestic consumption to channel the domestic manufacturing capacity that’s currently used for exports. 

As Noah Smith points out, the export of excess capacity is critical to sustaining economic growth and ensuring the giant investments and production-based Chinese growth model. 

Export profits are keeping many Chinese manufacturing companies — and, increasingly, the Chinese economy itself — afloat… China’s export boom is heavily subsidized, both with explicit government subsidies, and — more importantly — with ultra-cheap abundant bank loans. Subsidies are distortionary — they mean that China is making the cars that Germany and Thailand and Indonesia and other countries would be making for themselves if markets were allowed to operate freely. By subsidizing exports on such a massive scale, China is distorting the whole global economy. 

And the problems of this strategy are obvious 

First of all, if a wave of underpriced Chinese exports forcibly deindustrializes the rest of the world — a possibility I’m sure Xi Jinping has considered — then it could weaken the world’s ability to resist the military power of China and of Chinese proxies like Russia and North Korea… Second of all, even if a bunch of cheap Chinese stuff looks like a gift in the short term, it can create financial imbalances that cause bubbles and crashes in other countries. This is the “savings glut” hypothesis for why the global economy crashed after the First China Shock in the 2000s. And third, a flood of cheap Chinese stuff can cause disruptions and chaos in other economies, hurting lots of workers a lot even as it helps most consumers a little.

It’s in this context that the likes of Pettis argue in favour of tariffs. Noah lays down the theory of change associated with tariffs. 

If the world raises tariffs on China high enough, exchange rates will have difficulty adjusting, and Chinese products will have difficulty penetrating foreign markets. Chinese companies will then have to fall back on their domestic market. This will intensify the effect of competition, and reduce their profits much more quickly. The sooner Chinese companies’ profits collapse, they will cut back on production. They’ll also probably pressure the government to stop subsidizing overproduction, in order to lessen the competitive effect and keep themselves in the black. This political pressure could be what finally pushes Xi Jinping and the CCP to change China’s economic model, reducing incentives for overproduction.

This would be good for Chinese consumers. They get a temporary flood of cheap goods when Chinese companies flood the domestic market. If and when China’s government reduced the fiscal and financial incentives for overproduction, China’s taxpayers and savers would get a much-needed reprieve. And in the long run, a less distorted Chinese economy would be good for productivity, since resources would be diverted to sectors that have more room for improvement, like health care and other services.

In this backdrop, it’s worth examining India’s trade challenges. Shankar Acharya helpfully summarises the country’s exports balance sheet in recent times.

In the first dozen years of this century, India’s exports of both goods and services grew strongly. As a share of GDP total exports almost doubled from 13 per cent in 2000-02 to an average of 25 per cent in the three years 2011-14, with the goods share accounting for 17 per cent and services share for 8 per cent of GDP, much of it due to IT-enabled services exports. Since then, the services exports share of GDP has held its ground and then increased to over 9.5 per cent in the last two years. However, the performance of goods exports has been seriously disappointing, with its ratio to GDP falling markedly to around 12 per cent by 2016-17 and languishing at that low level except for a temporary uptick in 2021-23. As a consequence, the share of total exports in GDP was below 22 per cent in 2023-24, as compared to the 25 per cent peak a decade earlier.

India faces several external headwinds that it cannot control - trade distortions (China), trade protectionism (non-tariff barriers), restrictions on security and strategic considerations (telecommunications and frontier technologies), climate adaptation (EU’s CABM). All these trends are most certain to worsen in the time ahead. 

It’s therefore important for India to carve out the space within the existing international regimes. Apart from creativity, this also requires a sophisticated understanding of the country’s willingness to tinker with the prevailing trade-related rules and regulations. It must follow the emerging trends globally. 

As mentioned earlier, Tordoir and Setser openly advocate not only tariffs and industrial policy measures but also restrictions against Chinese imports. They warn against delaying action. 

If it does not act to counter Chinese policies, its current advantages in other sectors will go the same way as those in EV batteries and solar panels… the EU should target its scarce industrial policy funds to maximise European competitiveness. This means supporting value chains (and regions) with strong potential, many of which are centred in Germany. Germany would be a primary beneficiary of a significant European funding programme for decarbonising industry or expanding chip production, for example.

They make the important and clear distinction and divergence between corporate interests and national interest, and urge Germany to act in its national interest. 

Berlin frets that a more muscular EU industrial and trade policy would lead to Chinese retaliation against German multinationals operating in China. But a new German government should not equate the interests of German businesses operating in China with the interests of the German economy. What is good for Volkswagen is no longer always what is good for Germany. It would not be in Germany’s long-term interest, for example, if German firms turned China into their hub for the design and production of all luxury EVs, eroding the technical skill set and advanced quality production of cars that has long been the mark of the German economy. For German workers the trade-offs are stark and real; German firms that succeed by moving production of cutting-edge technologies out of Germany ultimately weaken, rather than strengthen, the German economy.

In short, across the developed world, textbook theories on free trade are being discarded in favour of policies that not only directly promote domestic industry but also penalise Chinese and foreign manufacturers. Academic experts and others who peddle orthodoxy are being marginalised everywhere. Pettis captures this mood nicely

If you want to understand the effects of trade intervention, its ok to ask economic historians, but never ask economists. That's because their answer will almost certainly reflect little more than their ideological position. Depending on their ideology, they will either only explain how trade intervention affects consumers (it lowers the consumption share of GDP) or how it affects producers (it raises the production share of GDP). They will never explain that it matters to both. 

This is a great X-thread from Pettis on economists and free-trade.

This is a reminder for India to be cautious about embracing the opinions and comments of economists and experts on tariffs, joining free trade agreements and so on. In fact, in a 2016 survey of academic economists, not a single respondent agreed with the idea of putting tariffs to encourage domestic production. 

Given the circumstances and the policies pursued by others in their national interests, there’s a need for India to take a strategic view on these issues. The two largest economies flagrantly violate the provisions of WTO and the third, EU, does so with finesse. 

China has been using industrial policy to build up capacity across industries that are multiples of domestic demand and is discounting and exporting their production. This Kiel Institute paper documents its subsidies for green technologies and this CSIS study examines its general industrial policy subsidies. Its economy-wide practices of providing land, utilities, and credit at concessional rates tilt the playing field in favour of its exports and have distorted global trade. 

The US has shown little interest in even the existence of WTO, and this is certain to be accentuated in the next four years. The IRA Act and CHIPS Act, for example, both enacted by the supposedly multilateralist Biden Administration, have several provisions that fly against the WTO Agreements. The EU (and also the US) resort to non-tariff barriers (NTBs) that tend to have the effect of making Indian exporters ineligible or uncompetitive in public procurements and from general markets. 

The case for India joining FTAs, as advocated by several economists and experts, while good in theory, does not pass the test of evidence. Ajay Srivastava of GTRI has explained with evidence that India’s FTAs with ASEAN, Japan, and South Korea, all signed in 2010-11, have not proved beneficial to India. India’s high existing tariffs, compared to the very low tariffs of its trade partners, mean that any FTA would inherently benefit its partners much more than India. Besides, Indian exporters face non-tariff barriers - environment, sustainability, labour, intellectual property rights, digital trade, governance, and gender - whose compliance is prohibitive. 

In light of such practices that are now becoming pervasive, the problem statement for India should be about creating the external space to shape its industrial policy to promote its national interests without being perceived as an egregious violator of its multilateral commitments (like the US or China). How can India subsidise its domestic manufacturers and incentivise exports, or differentiate incentives between domestic consumption and export while also maintaining a fig leaf of compliance with WTO Agreements? This ought to be our strategy on trade and WTO.

There’s a practical problem in the pursuit of this strategy. From the perspective of bureaucrats in the different line Ministries, it’s perfectly reasonable to examine every policy from the narrow view of its compliance with the WTO regulations that concern their Ministry. There are several examples of narrow perspectives coming in the way of India pursuing policies that are in the national interest. 

It’s not as if India has not pursued such policies earlier. An example is the use of domestic content requirements to access subsidies and other incentives. For example, the Jawaharlal Nehru National Solar Mission, launched in January 2010, contained domestic content requirements to avail of its incentives. These measures violate Article III:4 of GATT 1994, Article 2.1 of the TRIMS Agreement and several provisions of the WTO’s Subsidies and Countervailing Measures Agreement. Notwithstanding this, there are numerous examples of its violations by India itself and others. Incidentally, in July 2023, India and the US withdrew their respective complaints against each other pending before the WTO’s DSB on subsidies and domestic content requirements. In fact, domestic content requirements have now almost become a norm in the industrial policies of developed countries. 

Notwithstanding the WTO, it’s, therefore, time for India to take a leaf out of the playbooks of the Americans, Chinese and Europeans and design policies that promote its national interest through the likes of incorporation of domestic content requirements and supporting exporters with concessional credit and other incentives. It’s all the more relevant now that President Trump, who prefers the transactional approach (Art of the Deal), takes office for the second time. India could embrace the strategy of ‘appeal into the void’ and strike bilateral deals with its contesting trade partners.