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Understanding the depth of the 2020 global recession in 5 charts

Understanding the depth of the 2020 global recession in 5 charts

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Current forecasts suggest that the coronavirus (COVID-19) global recession will be the deepest since World War II, with the largest fraction of economies experiencing declines in per capita output since 1870.  Output of emerging market and developing economies (EMDEs) is expected to contract in 2020 for the first time in at least 60 years.

Chart 1. Deepest global recession since World War II

The global economy has experienced 14 global recessions since 1870: in 1876, 1885, 1893, 1908, 1914, 1917-21, 1930-32, 1938, 1945-46, 1975, 1982, 1991, 2009, and 2020. The COVID-19 recession will be the deepest since 1945-46, and more than twice as deep as the recession associated with the 2007-09 global financial crisis. 

Global per capita GDP growth

Global per capita GDP growth.png
Note: For multi-year episodes, the cumulative contraction is shown. Data for 2020 are forecasts.

Chrt 2. Highest synchronization of national recessions since 1870

In 2020, the highest share of economies will experience contractions in annual per capita gross domestic product (GDP) since 1870. The share will be more than 90% higher than the proportion at the height of the Great Depression of 1930-32.

Economies with contractions in per capita GDP

Economies with contractions in per capita GDP
Note: The proportion of economies with an annual contraction in per capita GDP. Shaded areas refer to global recessions. Data for 2020-21 are forecasts.

Chart 3. Sharpest contraction in multiple measures of activity

In 2020, many indicators of global activity are expected to register the sharpest contractions in six decades. A large swath of services has seen a near sudden stop, reflecting both regulated and voluntary reductions in human interactions that could threaten infection. Partly owing to an unprecedented weakening in services-related activities, global trade and oil consumption will see record drops this year, and the global rate of unemployment will likely climb to its highest level since 1965. 

Retail sales volume during global recessions

3 Retail sales volume during global recessions.png
Note: Year “t” denotes the year of global recessions (shaded in light gray). The darker shaded area refers to the range of the three global recessions with available data. For 2020, data are based on a year-on-year percent change in the first quarter.

Chart 4. Sharpest decline in oil demand

Oil consumption typically fell during global recessions. The previous largest decline in oil consumption occurred in 1980-82, when consumption fell by a cumulative 9% from its peak in 1979.  The outbreak of COVID-19 and the wide-ranging measures needed to slow its advance have precipitated an unprecedented collapse in oil demand. They also resulted in a surge in oil inventories, and, in March, the steepest one-month decline in oil prices on record.

Oil consumption during global recessions

4 Oil consumption during global recessions.png
Note: Year “t” denotes the year of global recessions (shaded in light gray). The darker shaded area refers to the range of the three global recessions with available data.

Chart 5. Declines in per capita output in all EMDE regions

Although the magnitude will vary across EMDE regions, current projections indicate that five out of six are projected to fall into outright recession.  The majority of EMDE regions will experience the lowest growth in at least 60 years, and all of them will see declines in regional per capita output for the first time during a global recession since 1960.

Per capita GDP growth in 2020, by region

5 Per capita GDP growth in 2020, by region.png
Note: Data are forecasts.

The COVID-19 global recession is unique in many respects. It will be the most severe since World War II and is expected to trigger per capita GDP contractions in the largest share of economies since 1870.  It is also associated with unprecedented weakening in multiple indicators of global activity, such as services and oil demand, as well as declines in per capita income in all EMDE regions.

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Ayhan Kose's picture

Ayhan Kose

Director of the World Bank Group’s Development Prospects Group

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Pay-to-use blockchains will never achieve mass adoption

Blockchain projects should learn from Google and Facebook by monetizing their users without directly asking for their money.
Pay-to-use…

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Blockchain projects should learn from Google and Facebook by monetizing their users without directly asking for their money.

Pay-to-use blockchains are done.

Not for us, of course — the nerdy crypto crowd. We’re perfectly happy to open wallets, engrave seed phrases on steel cards we bury in the ground, find exchanges we haven’t been blocked from yet, wrap some assets to leverage yield, and become OpSec professionals while we pray to the blockchain gods that the North Koreans aren’t online right now.

We’re fine with this. Years of experience have dulled the pain.

But the mass adoption we all hoped for? It relies on the 99% of people who have zero appetite for such trauma.

Related: An ETF will bring a revolution for Bitcoin and other cryptocurrencies

If permissionless blockchains are to become the backbone of our online experiences, three major changes need to happen:

  1. They need to become free.
  2. They need to become frictionless.
  3. They need to become familiar.

“Free” means free for the user, “frictionless” means as easy as opening an app or playing a video game, and “familiar” means we need to stop asking regular people to change their behavior to meet the limitations of our tech. We need to meet them where they already are.

Right now, we are zero for three. In fact, we’re so far away from where we need to be that we’re not even trying to address these problems seriously — we’re busy making small, incremental improvements to dysfunctional tech rather than addressing the root of the dysfunction itself.

Free to use

Layer-1 blockchains have been designed, built and funded by people who figure that their value is in directly monetizing the user.

This is a fallacy.

Google serves you ads. It monetizes you indirectly. Facebook monetizes your data, but it doesn’t charge you to use its platform. Apple’s store takes a 30% cut from developers and publishers, not from you.

In all cases, you’re paying — but not with cash.

Google is visited 85 billion times a month. If it monetized directly, charging just one-tenth of one cent to visit its homepage, it could theoretically pull in $85 million every single month.

It doesn’t, as the pool of people who want to pay for that experience with cash is infinitesimally small compared with those who are fine with Google serving them ads and keeping it free.

We are used to being monetized indirectly. But current blockchain protocols monetize us directly, asking us to pay gas fees for each transaction.

One of the most exciting premises of Web3 is that it creates the possibility for aligned incentives between creators and consumers. Countless nonfungible token (NFT) creators have found ways to grow communities around such incentives — but layer-1 blockchain builders just keep doing the same thing, over and over again.

And no matter how small their fees get, thanks to incremental reductions from the likes of Solana or the myriad layer 2s out there, it’s still a fee that most people won’t pay.

Frictionless and simple

We are not very loyal to our apps. Around 77% of daily active users abandon Android apps within three days. Estimates suggest that 25% of all downloaded apps are abandoned within minutes due to poor onboarding.

Andrew Chen, a partner at Andreessen Horowitz investing in games, metaverse and consumer tech, shared the following graph. He suggested that “the best way to bend the retention curve is to target the first few days of usage, and in particular the first visit.”

Average retention curve for Android apps. Source: Andrew Chen/Quettra

Compare the onboarding process of a poorly designed app to onboarding to crypto. It may be bad, but it’s not even the same sport. Crypto is the most user-unfriendly technology ever hawked to the public. To those who struggle with tech, it’s the digital equivalent of being punched repeatedly in the face.

By Mike Tyson.

In his heyday.

And over time, crypto has not become much friendlier. You, dear reader, are enjoying a specialist publication. You’re probably a degen with a liquidity position on Uniswap and a Milady in cold storage. But even the words in that sentence make no sense to a normal person.

So, blockchain has to change. It has to become a frictionless experience, a background technology, like everything else we use — from the internet to our phones to our TVs.

We don’t care how they work. We just care that they work.

Familiar and fun

Lastly, and perhaps my single biggest critique of the crypto industry, is how utterly nonchalant we have come about asking billions of people to do things they don’t really want to do.

Crypto has not been good at creating decentralized social media alternatives to Facebook. It has not been good at creating unique gaming experiences. It has not been good at replacing traditional supplier-user Web2 models with aligned-incentive Web3 models.

Related: Ethereum is about to get crushed by liquid staking tokens

It has been good at monkey pictures, scams, arguing on Twitter and speculative trading.

This is not to say that crypto is of no use. It absolutely is. The economic models that crypto enables will eventually be seen as a defining shift in power structures and personal autonomy, if we stop replicating the financial system and inequality that made crypto necessary in the first place.

But only if we make it as easy to use as opening an app or clearing a level in a game. Because that’s what people actually do, in real life.

This is all silly, impossible and just wishful thinking — right?

None of this is impossible.

We’ve just been conditioned to believe it is, as a few people have become very, very (very) rich by promoting pay-to-use foundational blockchains that have niche appeal, at best.

Ethereum is a wonderful innovation that will continue to serve as the foundation for decentralized finance precisely because it is secure, decentralized and slow-moving. But it’s not going to revolutionize gaming, as gamers will not pay gas fees. Period.

Solana is great for NFTs, maybe even for stablecoins. It won’t work for smart cities or the Internet of Things.

It’s time for the blockchain industry to acknowledge that our path toward becoming a foundation for consumer tech is blocked by these fundamental truths:

  • People don’t want to pay for what should be free.
  • They don’t want to do difficult things that should be easy.
  • And they don’t want to change their behavior to fit our vision of the world.

The sooner we build protocols and applications that accept these realities, the sooner we silence the critics and change the world.

Jon Rice is the founder of the Koinos Federation, an alliance of projects building on the free-to-use Koinos blockchain. He was previously editor-in-chief at Cointelegraph, Blockworks and Crypto Briefing.

This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

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Why so sad?

Over the past few years, consumer sentiment has increasingly run far below the level predicted by models based on economic data. The Economist illustrates…

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Over the past few years, consumer sentiment has increasingly run far below the level predicted by models based on economic data. The Economist illustrates the issue with a graph:

The Economist attributes the gloomy outlook to the lingering effects of Covid.  I suspect the actual explanation is growing political polarization.  Consider the growing partisan gap in how voters evaluate the economy:

Back in the 1990s, there wasn’t much partisan difference in how voters evaluated the condition of the economy.  This was before the public had come to view people with different points of view as the enemy.  I suspect that the responses to polls were more honest back then.  After 9/11, opinion became more polarized.  After Trump was elected, polarization increased even further.  Today, voters in the two major parties live in completely separate worlds, consuming media that is tailored to fit their prejudices.  Thus it’s not surprising that they have radically divergent views of the world.

Voters seem to rate the economy much more highly when their preferred candidate is in power, perhaps partly due to the mistaken assumption that presidents somehow control inflation and the business cycle.  (A myth that is encouraged by our media.)

Until 2021, the biases of the two parties roughly offset, leaving the overall rating roughly equal to the rating one would expect based solely on the economic data.  This changed after Joe Biden became president.  Unlike with President Obama (who inherited a weak economy), Democratic voters are only lukewarm on the current president. 

In contrast, Republican voters have an extremely negative view of President Biden.  With only lukewarm sentiment from Democrats, there is nothing to offset the extremely low economic rating of Republicans.  This leaves the overall rating for the economy far below the level you’d expect with rising real wages, 3.8% unemployment, and 3.7% inflation.  At one point in 2022, consumer sentiment fell below the lowest reading of the early 1980s, when the economy was in far worse shape.

I don’t believe these consumer sentiment figures represent the actual views of the public.  Consumer spending is still very strong, an indication that people feel pretty good about the economy.  Actions speak louder than words.  I suspect the low reported sentiment is mostly a reflection of GOP voters expressing anger at the current political situation.

My own view is that recent economic policy (since 2017) is quite bad, but the negative effects will show up in future years, at a point where we will need to confront the effects of an out of control federal budget.  If people think the current economy is bad, wait until they see what’s coming down the road in a few years!

PS.  Note to commenters:  If you think the economic model is wrong, you need to explain why it fit the data for the 40-year period from 1980 to 2020.

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Generative AI’s growing impact on businesses

Over recent years, artificial intelligence (AI) has gained considerable traction. And on the back of the resultant excitement, price-earnings (P/E) ratios…

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Over recent years, artificial intelligence (AI) has gained considerable traction. And on the back of the resultant excitement, price-earnings (P/E) ratios for stocks even remotely related have soared. Is the excitement premature?

McKinsey recently  published an article titled The State of AI in 2023: Generative AI’s Breakout year, draws on the results of six years of consistent surveying and reveals some compelling findings. My takeaway is that service providers are buying the chips and working furiously to offer AI-enhanced solutions, but corporate customers are still some way off embedding those solutions in their own workflows. There exists a lack of understanding, necessitating more education.

The highest-performing organisations however, as showcased in the research, are already adopting a comprehensive approach to AI, emphasising not just its potential but also the requisite strategies to harness its full value.

Irrespective of the industry, and of whether they are service organisations or manufacturers, the most successful industry leaders strategically chart significant AI opportunities across their operational domains. McKinsey’s findings suggest that despite the buzz surrounding the innovations in generative AI (gen AI), a substantial portion of potential business value originates from AI solutions that don’t even involve gen AI. This reflects a disciplined and value-focused (cost) perspective adopted by even top-tier companies.

One of the critical takeaways from McKinsey’s research is the integration of AI in strategic planning and capability building. For instance, in areas like technology and data management, leading firms emphasise the functionalities essential for capturing the value AI promises. They are capitalising on large language models’ (LLM) prowess to analyse company and industry-specific data. Moreover, these companies are diligently assessing the merits of using prevailing AI services, termed by McKinsey as the “taker” approach. In parallel, many are working on refining their AI models, a strategy McKinsey labels the “shaper” approach, where firms train these models using proprietary data to build a competitive edge.

But the number of organisations doing so are relatively few (Figure 1.)

Figure 1. Gen AI is mostly used in marketing, sales, product and service development

Figure 1 Gen AI is mostly used in marketing, sales, product and service development

Nevertheless, the latest McKinsey global survey reveals the burgeoning influence of gen AI tools is unmistakably evident. A mere year after their debut, a striking one-third of respondents disclosed that their companies consistently integrate gen AI in specific business functions. The implications of AI stretch far beyond its technological aspects, capturing the strategic focus of top-tier leadership. McKinsey quotes, “Nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work,” signalling the mainstreaming of AI in executive deliberations.

In other words, however, a common finding is individuals are using gen AI personally, but their organisation have yet to formally incorporate it into daily processes and workflows. This, despite the “three-quarters of all respondents expect[ing] gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years.”

As an aside, AI’s disruptive impact is expected to vary by industry.

McKinsey notes, “Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 per cent of global industry revenue—knowledge-based industries such as banking (up to 5 per cent), pharmaceuticals and medical products (also up to 5 per cent), and education (up to 4 per cent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotive, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labour.”

Moreover, the journey with AI isn’t devoid of challenges. McKinsey’s findings highlight a significant area of concern: risk management related to gen AI. Many organisations appear unprepared to address gen AI-associated risks, with under half of the respondents indicating measures to mitigate what they perceive as the most pressing risk – inaccuracy.

Drawing from McKinsey’s comprehensive survey, it’s evident that while the realm of AI, particularly gen AI, presents immense potential, it’s a domain still in its very early stages. Many organisations are on the brink of leveraging its power, but there’s still a considerable journey ahead in terms of risk management, strategic adoption, and capability building. As the landscape continues to evolve, McKinsey’s research offers a crucial ‘Give Way’ sign in the roadmap for businesses to navigate the AI frontier.

And that means there is every possibility the boom in AI-related stocks is a bubble. Stock market investors are notoriously impatient and if the benefits (measured in dollars) aren’t coming through investors will recalibrate their expectations. There is every possibility AI is as transformative for the world as promised, but the stock market’s journey is likely to be rocky, inevitably rewinding premature expectations ahead of more sober assessments.  Think, ‘fits and starts’.

As a result, investors should have ample opportunity to invest in the transformative impact of AI at reasonable prices again and shouldn’t feel compelled to pay bubble-like prices amid a fear of missing out.

The full McKinsey article can be read here

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