The Stock Market numbers are no reflection of the performance of an economy: During the Covid Lockdowns the Stock Markets Boomed.

 

'Our Economic Delusions on Predictions, Money': Mervyn King former head of the Bank of England

 

In Broken Britain, even the statistics don’t work

 Tim Harford in The FT 

From the bone-jarring potholes to the human excrement regularly released into British rivers, the country’s creaking infrastructure is one of the most visceral manifestations of the past 15 years of stagnation. To these examples of the shabby neglect of the essential underpinnings of modern life, let me add another: our statistical infrastructure. 

In her new book, How Infrastructure Works, engineering professor Deb Chachra argues that infrastructure is an extraordinary collective achievement and a triumph of long-term thinking. She adds that a helpful starting point for defining infrastructure is “all of the stuff that you don’t think about”. 

Statistical infrastructure certainly matches those descriptions. The idea that someone needs to decide what information to gather, and how to gather it, rarely crosses our mind — any more than we give much thought to what we flush down the toilet, or the fact that clean water comes from taps and electricity from the flick of a switch. 

As a result the UK’s statistical system, administrative databases, and evidence base for policy are suffering the same depredations as the nation’s roads, prisons and sewers. Easiest to measure are the inputs: the Office for National Statistics faces a 5 per cent cut in real terms to its budget this year, has been losing large numbers of experienced staff, and is hiring dramatically fewer than five years ago. 

But it is more instructive to consider some of the problems. The ONS has struggled to produce accurate estimates of something as fundamental as the unemployment rate, as it tries to divide resources between the traditional-but-foundering Labour Force Survey, and a streamlined-but-delayed new version which has been in the pipeline since 2017. 

That is an embarrassment, but the ONS can’t be held responsible for other gaps in our statistical system. A favourite example of Will Moy, chief executive of the Campbell Collaboration, a non-profit producer of systematic reviews of evidence in social science, is that we know more about the nation’s golfing habits than about trends in robbery or rape. This is because the UK’s survey of sporting participation is larger than the troubled Crime Survey of England and Wales, recently stripped of its status as an official National Statistic because of concerns over data quality. Surely nobody made a deliberate decision to establish those curious statistical priorities, but they are the priorities nonetheless. They exemplify the British state’s haphazard approach in deciding what to measure and what to neglect. 

This is foolishness. The government spends more than £1,200bn a year — nearly £18,000 for each person in the country — and without solid statistics, that money is being spent with eyes shut. 

For an example of the highs and lows of statistical infrastructure, consider the National Tutoring Programme, which was launched in 2020 in an effort to offset the obvious harms caused by the pandemic’s disruption to the school system. When the Department for Education designed the programme, it was able to turn to the Education Endowment Foundation for a solid, practical evidence base for what type of intervention was likely to work well. The answer: high-quality tutoring in small groups. 

This was the statistical system, in its broadest sense, working as it should: the EEF is a charity backed by the Department for Education, and when the crisis hit it had already gathered the evidence base to provide solutions. Yet — as the Centre for Public Data recently lamented — the DfE lacked the most basic data needed to evaluate its own programme: how many disadvantaged pupils were receiving tutoring, the quality of the tutoring, and what difference it made. The National Tutoring Programme could have gathered this information from the start, collecting evidence by design. But it did not. And as a result, we are left guessing about whether or not this was money well spent. 

Good data is not cheap to collect — but it is good value, especially when thoughtfully commissioned or built into policymaking by default. One promising avenue is support for systematic research summaries such as those produced by the Cochrane Collaboration for medicine and the Campbell Collaboration for social science and policy. If you want to understand how to promote literacy in primary schools, or whether neighbourhood policing is effective, a good research synthesis will tell you what the evidence says. Just as important, by revealing the gaps in our knowledge it provides a basis for funding new research. 

Another exciting opportunity is for the government to gather and link the administrative data we all produce as a byproduct of our interactions with officialdom. A well-designed system can safeguard personal privacy while unlocking all manner of insights. 

But fundamentally, policymakers need to take statistics seriously. These numbers are the eyes and ears of the state. If we neglect them, waste and mismanagement are all but inevitable. 

Chachra writes, “We should be seeing [infrastructure systems], celebrating them, and protecting them. Instead, these systems have been invisible and taken for granted.” 

We have taken a lot of invisible systems for granted over the past 20 years. The Resolution Foundation has estimated that in this period, UK public investment has lagged the OECD average by a cumulative half a trillion pounds. That is a lot of catching up to do. The next government will need some quick wins. Investing in better statistical infrastructure might be one of them.

How Disinformation Works

From The Economist

Did you know that the wildfires which ravaged Hawaii last summer were started by a secret “weather weapon” being tested by America’s armed forces, and that American ngos were spreading dengue fever in Africa? That Olena Zelenska, Ukraine’s first lady, went on a $1.1m shopping spree on Manhattan’s Fifth Avenue? Or that Narendra Modi, India’s prime minister, has been endorsed in a new song by Mahendra Kapoor, an Indian singer who died in 2008?

These stories are, of course, all bogus. They are examples of disinformation: falsehoods that are intended to deceive. Such tall tales are being spread around the world by increasingly sophisticated campaigns. Whizzy artificial-intelligence (ai) tools and intricate networks of social-media accounts are being used to make and share eerily convincing photos, video and audio, confusing fact with fiction. In a year when half the world is holding elections, this is fuelling fears that technology will make disinformation impossible to fight, fatally undermining democracy. How worried should you be?

Disinformation has existed for as long as there have been two sides to an argument. Rameses II did not win the battle of Kadesh in 1274bc. It was, at best, a draw; but you would never guess that from the monuments the pharaoh built in honour of his triumph. Julius Caesar’s account of the Gallic wars is as much political propaganda as historical narrative. The age of print was no better. During the English civil war of the 1640s, press controls collapsed, prompting much concern about “scurrilous and fictitious pamphlets”.

The internet has made the problem much worse. False information can be distributed at low cost on social media; ai also makes it cheap to produce. Much about disinformation is murky. But in a special Science & technology section, we trace the complex ways in which it is seeded and spread via networks of social-media accounts and websites. Russia’s campaign against Ms Zelenska, for instance, began as a video on YouTube, before passing through African fake-news websites and being boosted by other sites and social-media accounts. The result is a deceptive veneer of plausibility.

Spreader accounts build a following by posting about football or the British royal family, gaining trust before mixing in disinformation. Much of the research on disinformation tends to focus on a specific topic on a particular platform in a single language. But it turns out that most campaigns work in similar ways. The techniques used by Chinese disinformation operations to bad-mouth South Korean firms in the Middle East, for instance, look remarkably like those used in Russian-led efforts to spread untruths around Europe.

The goal of many operations is not necessarily to make you support one political party over another. Sometimes the aim is simply to pollute the public sphere, or sow distrust in media, governments, and the very idea that truth is knowable. Hence the Chinese fables about weather weapons in Hawaii, or Russia’s bid to conceal its role in shooting down a Malaysian airliner by promoting several competing narratives.

All this prompts concerns that technology, by making disinformation unbeatable, will threaten democracy itself. But there are ways to minimise and manage the problem.

Encouragingly, technology is as much a force for good as it is for evil. Although ai makes the production of disinformation much cheaper, it can also help with tracking and detection. Even as campaigns become more sophisticated, with each spreader account varying its language just enough to be plausible, ai models can detect narratives that seem similar. Other tools can spot dodgy videos by identifying faked audio, or by looking for signs of real heartbeats, as revealed by subtle variations in the skin colour of people’s foreheads.

Better co-ordination can help, too. In some ways the situation is analogous to climate science in the 1980s, when meteorologists, oceanographers and earth scientists could tell something was happening, but could each see only part of the picture. Only when they were brought together did the full extent of climate change become clear. Similarly, academic researchers, ngos, tech firms, media outlets and government agencies cannot tackle the problem of disinformation on their own. With co-ordination, they can share information and spot patterns, enabling tech firms to label, muzzle or remove deceptive content. For instance, Facebook’s parent, Meta, shut down a disinformation operation in Ukraine in late 2023 after receiving a tip-off from Google.

But deeper understanding also requires better access to data. In today’s world of algorithmic feeds, only tech companies can tell who is reading what. Under American law these firms are not obliged to share data with researchers. But Europe’s new Digital Services Act mandates data-sharing, and could be a template for other countries. Companies worried about sharing secret information could let researchers send in programs to be run, rather than sending out data for analysis.

Such co-ordination will be easier to pull off in some places than others. Taiwan, for instance, is considered the gold standard for dealing with disinformation campaigns. It helps that the country is small, trust in the government is high and the threat from a hostile foreign power is clear. Other countries have fewer resources and weaker trust in institutions. In America, alas, polarised politics means that co-ordinated attempts to combat disinformation have been depicted as evidence of a vast left-wing conspiracy to silence right-wing voices online.
One person’s fact...

The dangers of disinformation need to be taken seriously and studied closely. But bear in mind that they are still uncertain. So far there is little evidence that disinformation alone can sway the outcome of an election. For centuries there have been people who have peddled false information, and people who have wanted to believe them. Yet societies have usually found ways to cope. Disinformation may be taking on a new, more sophisticated shape today. But it has not yet revealed itself as an unprecedented and unassailable threat.

'Today, there are doubts about Indian data': Parakala Prabhakar

 

Acchhe Din: Real wages in the formal sector fell by 10% between 2012-2022

 

The Economist examines India's Economic Performance

 From The Economist

In the second week of 2024 business leaders descended on Gujarat, the home state of Narendra Modi, India’s prime minister. The occasion was the Vibrant Gujarat Global Summit, one of many gabfests at which India has courted global investors. “At a time when the world is surrounded by many uncertainties, India has emerged as a new ray of hope,” boasted Mr Modi at the event.

He is right. Although global growth is expected to slow from 2.6% last year to 2.4% in 2024, India appears to be booming. Its economy grew by 7.6% in the 12 months to the third quarter of 2023, beating nearly every forecast. Most economists expect an annual growth rate of 6% or more for the rest of this decade. Investors are seized by optimism.

The timing is good for Mr Modi. In April some 900m Indians will be eligible to vote in the largest election in world history. A big reason Mr Modi, who has been in office since 2014, is likely to win a third term is that many Indians think him a more competent manager of the world’s fifth-largest economy than they do any other candidate. Are they right?

To assess Mr Modi’s record The Economist has analysed India’s economic performance and the success of his biggest reforms. In many respects the picture is muddy—and not helped by sparse and poorly kept official data. Growth has outpaced that of most emerging economies, but India’s labour market remains weak and private-sector investment has disappointed. But that may be changing. Aided by Mr Modi’s reforms, India may be on the cusp of an investment boom that would pay off for years.

The headline growth figures reveal surprisingly little. India’s gdp per person, after adjusting for purchasing power, has grown at an average pace of 4.3% per year during Mr Modi’s decade in power. That is lower than the 6.2% achieved under Manmohan Singh, his predecessor, who also served for ten years.

image: the economist

But this slowdown was not Mr Modi’s doing: much of it is down to the bad hand he inherited. In the 2010s an infrastructure boom started to go sour. India faced what Arvind Subramanian, later a government adviser, has called a twin balance-sheet crisis, one that struck both banks and infrastructure firms. They were left loaded with bad debt, crimping investment for years afterwards. Mr Modi also took office at a time when global growth had slowed, scarred by the financial crisis of 2007-09. Then came the covid-19 pandemic. The difficult conditions meant average growth among 20 other large lower- and middle-income economies fell from 3.2% during Mr Singh’s time in office to 1.6% during Mr Modi’s. Compared with this group, India has continued to outperform (see chart 1).

Against such a turbulent backdrop, it is better to assess Mr Modi’s record by considering his stated economic objectives: to formalise the economy, improve the ease of doing business and boost manufacturing. On the first two, he has made progress. On the third, his results have so far been poor.

India’s economy has certainly become more formal under Mr Modi, albeit at a high cost. The idea has been to draw activity out of the shadow economy, which is dominated by small and inefficient firms that do not pay tax, and into the formal sphere of large, productive companies.

Mr Modi’s most controversial policy on this front has been demonetisation. In 2016 he banned the use of two large-value banknotes, accounting for 86% of rupees in circulation—surprising many even within his government. The stated aim was to render worthless the ill-gotten gains of the corrupt. But almost all the cash made its way into the banking system, suggesting that crooks had already gone cashless or laundered their money. Instead, the informal economy was crushed. Household investment and credit plunged, and growth was probably hurt. In private, even Mr Modi’s supporters in business do not mince words. “It was a disaster,” says one boss.

Demonetisation may have accelerated India’s digitisation nonetheless. The country’s digital public infrastructure now includes a universal identity scheme, a national payments system and a personal-data management system for things like tax documents. It was conceived by Mr Singh’s government, but much of it has been built under Mr Modi, who has shown the capacity of the Indian state to get big projects done. Most retail payments in cities are now digital, and most welfare transfers seamless, because Mr Modi gave almost all households bank accounts.

Those reforms made it easier for Mr Modi to ameliorate the poverty resulting from India’s disappointing job-creation record. Fearing that stubbornly low employment would stop living standards for the poorest from improving, the government now doles out welfare payments worth some 3% of gdp per year. Hundreds of government programmes send money directly to the bank accounts of the poor.

It is a big improvement on the old system, in which most welfare was distributed physically and, owing to corruption, often failed to reach its intended recipients. The poverty rate (the proportion of people living on less than $2.15 a day), has fallen from 19% in 2015 to 12% in 2021, according to the World Bank.

Digitisation has probably also drawn more economic activity into the formal sector. So has Mr Modi’s other signature economic policy: a national goods and services tax (gst), passed in 2017, which knitted together a patchwork of state levies across the country. The combination of homogenous payments and tax systems has brought India closer to a national single market than ever.

That has made doing business easier—Mr Modi’s second objective. gst has been a “game-changer”, says B. Santhanam, the regional boss of Saint-Gobain, a large French manufacturer with big investments in the southern state of Tamil Nadu. “The prime minister gets it,” adds another seasoned manufacturing executive, referring to the need to cut red tape. The government has also put serious money into physical infrastructure, such as roads and bridges. Public investment surged from around 3.5% of gdp in 2019 to nearly 4.5% in 2022 and 2023.

The results are now materialising. Mr Subramanian recently wrote that, as a share of gdp, in 2023 net revenues from the new tax regime exceeded those of the old system. This happened even as tax rates on many items fell. That more money is coming in despite lower rates suggests that the economy really is formalising.

Yet Mr Modi is not satisfied with merely formalising the economy. His third objective has been to industrialise it. In 2020 the government launched a subsidy scheme worth $26bn (1% of gdp) for products made in India. In 2021 it pledged $10bn for semiconductor companies to build plants domestically. One boss notes that Mr Modi personally takes the trouble to convince executives to invest, often in industries where they face little competition and so otherwise might not.

image: the economist

Some incentives could help new industries find their feet and show foreign bosses that India is open for business. In September Foxconn, Apple’s main supplier, said it would double its investments in India over the coming year. It currently makes some 10% of its iPhones there. Also in 2023 Micron, a chipmaker, began work on a $2.75bn plant in Gujarat that is expected to create some 5,000 jobs directly and 15,000 indirectly.

So far, however, these projects are too small to be economically significant. The value of manufactured exports as a share of gdp has stagnated at 5% over the past decade, and manufacturing’s share of the economy has fallen from about 18% under the previous government to 16%. And industrial policy is expensive. The government will bear 70% of the cost of the Micron plant—meaning it will pay nearly $100,000 per job. Tariffs are ticking up, on average, raising the cost of foreign inputs.

image: the economist

So what matters more: Mr Modi’s failures or his successes? As well as economic growth, it is worth looking at private-sector investment. It has been sluggish during Mr Modi’s time in office (see chart 2). But a boom may be coming. A recent report by Axis Bank, one of India’s largest lenders, argues that the private-investment cycle is likely to turn, thanks to healthy bank and corporate balance-sheets. Announcements of new investment projects by private corporations soared past $200bn in 2023, according to the Centre for Monitoring Indian Economy, a think-tank. That is the highest in a decade, and up 150% in nominal terms since 2019.

Although higher interest rates have sapped foreign direct investment in the past year, firms’ reported intentions to invest in India remain strong, as they seek to “de-risk” their exposure to China. There is some chance, then, that Mr Modi’s reforms will kick growth up a gear. If so, he will have earned his reputation as a successful economic manager.

The consequences of Mr Modi’s policies will take years to be felt in full. Just as an investment boom could vindicate his approach, his strategy of using welfare payments as a substitute for job creation could prove unsustainable. A failure to build local governments’ capacity to provide basic public services, such as education, may hinder growth. Subhash Chandra Garg, a former finance secretary under Mr Modi, worries that the government is too keen on “subsidies” and “freebies”, and that its “commitment to real reforms is no longer that strong.” And yet for all that, many Indians will go to the polls feeling cautiously optimistic about the economic changes that their prime minister has wrought.

Generative AI will go mainstream in 2024

 

Data-savvy firms will benefit first predicts The Economist

image: mariano pascual

Nov 13th 2023

By Guy Scriven

Listen to this story.

 Enjoy more audio and podcasts on iOS or Android.

When new technologies emerge they benefit different groups at different times. Generative artificial intelligence (ai) first helped software developers, who could use GitHub Copilot, a code-writing ai assistant, from 2021. The next year came other tools, such as Chatgpt and dall-e 2, which let all manner of consumers instantly produce words and pictures.

In 2023 tech giants gained, as investors grew more excited about the prospects of generative ai. An equally weighted share-price index of Alphabet, Amazon, Apple, Meta, Microsoft and Nvidia grew by nearly 80% (see chart). Tech firms benefited because they supply either the ai models themselves, or the infrastructure that powers and delivers them.

image: the economist

In 2024 the big beneficiaries will be companies outside the technology sector, as they adopt ai in earnest with the aim of cutting costs and boosting productivity. There are three reasons to expect enterprise adoption to take off.

First, large companies spent much of 2023 experimenting with generative ai. Plenty of firms are using it to write the first drafts of documents, from legal contracts to marketing material. JPMorgan Chase, a bank, used the technology to analyse Federal Reserve meetings to try to glean insights for its trading desk.

As the experimental phase winds down, firms are planning to deploy generative ai on a larger scale. That could mean using it to summarise recordings of meetings or supercharging research and development. A survey by kpmg, an audit firm, found that four-fifths of firms said they planned to increase their investment in it by over 50% by the middle of 2024.

Second, more ai products will hit the market. In late 2023 Microsoft rolled out an ai chatbot to assist users of its productivity software, such as Word and Excel. It launched the same thing for its Windows operating system. Google will follow suit, injecting ai into Google Docs and Sheets. Startups will pile in, too. In 2023 venture-capital investors poured over $36bn into generative ai, more than twice as much as in 2022.

The third reason is talent. ai gurus are still in high demand. PredictLeads, a research firm, says about two-thirds of s&p 500 firms have posted job adverts mentioning ai. For those companies, 5% of adverts now mention the technology, up from an average of 2.5% over the past three years. But the market is easing. A survey by McKinsey, a consultancy, found that in 2023 firms said it was getting easier to hire for ai-related roles.

Which firms will be the early adopters? Smaller ones will probably take the lead. That is what happened in previous waves of technology such as smartphones and the cloud. Tiddlers are usually more nimble and see technology as a way to gain an edge over bigger fish.

Among larger companies, data-centric firms, like those in health care and financial services, will be able to move fastest. That is because poor data management is a big risk for deploying ai. Managers worry about valuable data leaking out through ai tools. Firms without solid data management may have to reorganise their systems before it is feasible to deploy generative ai. Using the technology can feel like science fiction, but getting it to work safely is a much more humdrum affair.