On Twitter/X, Bob Elliott has been sharing his observations about American consumer behavior. Elliott, a seasoned investor and businessman, points out a paradoxical economic scenario: despite low mortgage rates and increased household wealth, both consumers and businesses are exhibiting remarkable restraint in borrowing and spending.

Elliott noted:

“So far the expansion hasn’t required increased household borrowing to continue, but with rates falling we are now likely to start to see some pent up demand (particularly for houses) start to emerge. Mortgage rates have fallen to their lowest levels in years.”

He further explained:

“Part of the reason why households can be confident about taking on new borrowing is that those with assets have much greater wealth than they did relative to nominal incomes back in the 2000 period. And up considerably from pre-COVID levels.”

Elliott’s analysis leads to an important question: Why aren’t American consumers and businesses taking advantage of these favorable conditions to borrow more?

His insights got me thinking about the Expansionary Fiscal Contraction (EFC) theory, a powerful economic concept that shaped policy thinking during my time as an economist at the Danish Ministry of Economic Affairs in the late 1990s. The theory suggests that fiscal austerity—cutting government spending and reducing deficits—can stimulate economic growth under certain conditions, and this idea was central to Danish policy-making for decades. It contributed significantly to Denmark’s fiscal discipline and economic resilience.

The Danish Model: Fiscal Discipline Drives Growth

Denmark’s experience in the early 1980s serves as a textbook example of EFC in action. Faced with economic challenges like high inflation, unemployment, and public debt, the Danish government implemented a stringent fiscal consolidation program. Under the Schlüter government, Denmark cut government spending, raised taxes, and pegged the krone to the Deutsche Mark to stabilize inflation.

The results were remarkable. Inflation fell, interest rates dropped significantly, and private sector investment surged. Denmark’s current account balance shifted from a deficit into a surplus. While unemployment took some time to respond, it began to steadily decline, leading to sustained economic growth. This period is a prime example of how fiscal responsibility can lead to economic expansion.

The US Scenario: Fiscal Uncertainty Hampering Growth

In contrast, Elliott’s observations about the US economy reveal a different picture. Despite mortgage rates falling significantly, housing demand remains relatively weak. Household wealth has increased substantially in recent years, but this hasn’t translated into higher consumer spending or borrowing, as might typically be expected.

The root cause of this cautious behavior appears to be public debt. With US public debt growing rapidly and continuous deficit spending, uncertainty has been cast over the economy. Rational economic actors, anticipating future tax increases or inflation to address this debt, are understandably holding back. This is a textbook case of Ricardian equivalence, where expansionary fiscal policy is offset by private sector restraint due to fears of future fiscal tightening.

The Case for Immediate Fiscal Consolidation

The solution to this dilemma is straightforward: the US must embark on a path of fiscal consolidation immediately. This isn’t just about balancing the books; it’s about restoring confidence in the economy’s long-term stability. The government must implement substantial spending cuts and reform entitlement programs to ensure their sustainability.

Additionally, the tax code should be simplified to encourage work, saving, and investment. A clear, legislated path for deficit reduction with enforceable targets should be established, ensuring that future fiscal policies maintain discipline.

These measures will undoubtedly face political resistance. However, as Denmark’s experience shows, short-term pain leads to long-term gain. By demonstrating a genuine commitment to fiscal responsibility, the government can restore confidence in the economy’s future.

The Benefits of Fiscal Consolidation

Critics may argue that fiscal tightening will lead to a recession, but the EFC theory, supported by evidence from countries like Denmark, suggests otherwise. When implemented decisively, fiscal consolidation can lead to lower interest rates, which in turn stimulates private investment and boosts confidence in the economic outlook.

In Denmark, fiscal consolidation not only stabilized the economy but also increased international competitiveness, as resources shifted from the public sector to the more efficient private sector. This created a stronger foundation for export growth and economic resilience.

Restoring Confidence in the US Economy

Bob Elliott’s analysis suggests that there is untapped potential for growth in the US economy. Households have significant wealth, and businesses are financially strong. What’s missing is the confidence that the fiscal environment will remain stable in the long term. A credible commitment to fiscal consolidation would address this uncertainty and likely spur the private sector to borrow, invest, and spend more—unlocking the pent-up demand that Elliott identifies, especially in the housing market.

As I speculated in my own tweet, the US may be experiencing a “Ricardian” episode, where consumers and businesses are holding back due to fears of future tax hikes. This suggests that fiscal consolidation could be the key to restoring private sector confidence, just as it was in Denmark in the early 1980s.

Conclusion: Embrace Fiscal Responsibility

The path forward for the US is clear. To unlock the potential for growth that Bob Elliott’s analysis suggests is waiting in the wings, the government must embrace fiscal responsibility. This means making tough choices now to secure a prosperous future.

By learning from Denmark’s successful implementation of EFC principles, the US can create an environment of fiscal stability that encourages private sector activity. Only through a genuine commitment to sustainable public finances can we unleash the full productive potential of the American economy.

The time for half-measures and political compromises is over. The US needs a bold, unapologetic commitment to fiscal discipline. This is not just sound economics; it’s the only responsible course of action for a nation that aspires to long-term prosperity and economic leadership.

AI-Powered Synthesis of Austrian and Chicago ideas

Like many free-market economists, I began my early studies deeply influenced by the Austrian School, but over time I shifted toward the Chicago School, particularly when it comes to microeconomic theory.

These days, I consider myself primarily aligned with Chicago Price Theory on microeconomic matters, while my views on macroeconomics remain firmly in the Monetarist camp as regular followers of this blog should well-know.

That said, I haven’t completely abandoned the Austrian School. I still believe that Austrian economics offers essential insights into how real-world markets function.

Traditional neoclassical theory—especially Chicago Price Theory—excels at explaining equilibrium outcomes but does little to describe how markets actually work in the real world, where information is incomplete and dispersed. This is where the Austrian School, particularly through the works of F. A. Hayek and Israel Kirzner, provides valuable perspective by framing the market as a discovery process.

In this article, I introduce a new approach that I call Agent-Based Experimental Economics (ABEE).

ABEE blends the insights of both neoclassical economics and the Austrian School with modern artificial intelligence.

Using AI agents that learn and adapt over time, we can simulate market dynamics and observe how these agents interact in ways that are far more realistic than traditional economic models. ABEE captures not just the end state of markets, but the process of getting there—how agents discover information, adapt their strategies, and refine their behaviours.

The Experiment: AI Monopolist Learning in Action

In this particular experiment, we simulated a monopolist using reinforcement learning.

Reinforcement Learning (RL) is a type of machine learning where an agent learns by interacting with an environment. The agent takes actions, receives rewards or penalties, and aims to maximize its long-term reward by improving its decisions over time

The monopolist starts with no knowledge of the market and learns over time how to set prices through trial and error.

This simulation was coded in Python, with a little help from ChatGPT, which allowed us to create an adaptive AI that evolves its strategy based on profit feedback. See the code here.

Here’s how the experiment is structured:

Theoretical Expectations vs. Learning Process

Traditional economic theory provides clear predictions for a profit-maximising monopolist: set marginal revenue equal to marginal cost.

Given the demand curve and marginal cost MC=20 the monopolist should charge P*=60 and sell Q*=20 units.

In this experiment, however, the AI monopolist does not have access to this information.

Instead, it learns through exploration—setting different prices and observing the profit outcomes. Over time, the AI agent gradually “discovers” the optimal pricing strategy.

Results: Learning Toward Optimal Pricing

The results, visualised in the graph below, show how the AI monopolist learns to behave optimally (or very close to optimally) over time.

Initially, the agent’s pricing decisions are random and far from optimal, but as it gains experience, it converges toward the theoretical profit-maximising price and quantity.

• Price Evolution: The blue line in the graph represents the AI agent’s price decisions over time. Early on, the prices fluctuate as the monopolist explores different pricing strategies. However, as the episodes progress, the prices stabilise and converge toward the theoretical price of P=60 (shown by the red-dashed line).
• Quantity Evolution: The corresponding quantities sold also fluctuate early on as prices vary, but the AI agent gradually learns to settle around the optimal quantity of Q=20Q = 20Q=20.

It is also notable that once the AI agent reaches the optimal price and quantity, it doesn’t change the price significantly.

These results confirm that an AI agent, even starting with no knowledge of the market, can learn to mimic the behaviour predicted by traditional neoclassical economics.

Through trial and error, the AI monopolist discovers the price that maximises profit, demonstrating that markets can converge to the theoretically correct equilibrium even when participants begin with limited information.

Agent-Based Experimental Economics: A New Lens for Economic Research

The concept of Agent-Based Experimental Economics (ABEE) represents a new tool for exploring economic behaviour.

Traditional economic models often rely on simplifying assumptions, like the “representative agent,” to describe equilibrium outcomes. But ABEE allows us to simulate dynamic markets populated by AI agents that learn and adapt in real time, reflecting the diversity of decision-making we see in the real world.

In this experiment, we only simulated a single monopolist and fixed consumer behaviour. However, the potential of ABEE goes far beyond this simple model.

ABEE allows us to simulate large-scale economies with diverse agents, each learning and interacting in complex ways. This opens the door to a whole new range of experiments, from modelling market competition to testing different monetary policy rules.

The Market as a Process: Connecting Neoclassical and Austrian Insights

One of the things that to me makes ABEE particularly exciting is how it bridges the gap between neoclassical economics and Austrian insights.

While neoclassical theory is excellent at predicting equilibrium outcomes, it often overlooks how markets reach these outcomes. The Austrian School, particularly the works of Hayek and Kirzner, focuses on the market process—the idea that markets are constantly evolving as individuals learn and adapt.

In our simulation, the AI monopolist reflects this discovery process. The monopolist doesn’t start with perfect information or rational expectations but learns the optimal strategy through repeated interactions with the market.

This mirrors Hayek’s view of the market as a discovery mechanism, where decentralised information is gradually revealed through the actions of market participants.

Furthermore, the simulation demonstrates Israel Kirzner’s concept of entrepreneurial alertness—the idea that markets correct themselves through the discovery of previously unexploited opportunities. The AI monopolist gradually “discovers” the optimal price, much like how entrepreneurs discover profitable niches in real-world markets.

This dynamic approach also echoes Ludwig von Mises’ idea of the “Evenly Rotating Economy,” where equilibrium is a theoretical construct and real markets are always in flux. ABEE captures this dynamism by simulating markets as evolving systems rather than static, equilibrium-based models.

Applications of ABEE: From Policy to Strategy

The potential applications of ABEE extend well beyond academic research. Governments, businesses, and policymakers can use ABEE to simulate real-world scenarios, providing valuable insights into how markets react to policy changes, business strategies, and economic shocks.

1. Monetary Policy: Central banks could use ABEE to simulate the impact of different monetary policy rules on diverse agents.
2. Labour Market Studies: Policymakers can use ABEE to model the effects of minimum wage increases or changes in labour laws, capturing the varied responses of workers with different skills and backgrounds.
3. Business Strategy: Companies can use ABEE to test pricing strategies, product launches, or supply chain decisions in a simulated environment, observing how AI agents with diverse preferences react to these changes.
4. Income Distribution: Governments can use ABEE to simulate the effects of tax reforms or welfare programs, providing deeper insights into how different groups are affected by these policies.

Conclusion: ABEE as a Transformative Tool for Economics

Agent-Based Experimental Economics (ABEE) offers a powerful new framework for understanding market behaviour. By simulating dynamic markets where agents learn and adapt, ABEE allows us to explore the market process in ways that traditional static models cannot.

In this simulation, the AI monopolist learned to set prices that mirrored the predictions of neoclassical theory. But beyond simply confirming existing models, ABEE also provides new insights into how markets evolve over time, much in line with Austrian economics.

The results—visualised in our graphs—demonstrate that, even in the absence of perfect information, markets have the potential to spontaneously organize towards stable outcomes.

This aligns with Hayek’s and Kirzner’s views of markets as discovery processes, where entrepreneurs and firms learn by interacting with the market.

Looking forward, the potential of Agent-Based Economic Experiments (ABEE) to revolutionize economic research, policy-making, and business strategy is truly exciting.

The future of economic experimentation lies in these dynamic simulations, and with the help of AI, we are only scratching the surface of what’s possible.

These advanced tools promise to provide unprecedented insights into complex economic systems, allowing for more accurate predictions and better-informed decision-making across various sectors.

* Needless to say, my thinking these topics is greatly inspired by the great Vernon Smith – the father of traditional Experimental Economics.

———————

If you want to know more about my work on AI and data, then have a look at the website of PAICE — the AI and data consultancy I have co-founded.

I have asked NotebookLM to create a podcast on my blog post “AI Agents in Minecraft: Vernon Smith-Style Experimental Economics on Steroids“.

I think it’s pretty good – have a listen here:

If you want to know more about my work on AI and data, then have a look at the website of PAICE — the AI and data consultancy I have co-founded.

From Vernon Smith to AI Agents: A Quantum Leap in Experimental Economics

These days I spend most of my time talking and advising on the use of AI. I do this in my consultancy Paice that I have co-founded with my business partner Christian Heiner Schmidt. I therefore also spend a lot of time “playing” around with new AI tools and technologies to see how these technologies can help our clients – for example banks and pension funds.

Recently, I’ve been dedicating a significant amount of time to studying developments in the field of so-called AI agents. These AI agents represent an exciting advancement in artificial intelligence, where systems can perform more complex and autonomous tasks.

AI agents have the potential to revolutionize the way businesses, including banks and pension funds, handle various processes and interactions. They can, for instance, automate customer service at a more sophisticated level, perform complex analyses of financial data, or even assist in decision-making processes.

An example of this is a new truly groundbreaking project called Project Sid by Altera, a startup founded by former MIT researchers.

They’ve unleashed 1,000 autonomous AI agents in a Minecraft world, birthing intricate virtual societies complete with economic systems, governance structures, and even cultural norms. This is precisely the kind of innovation that could revolutionize our approach to economic research.

This brings to mind Vernon Smith’s pioneering work in experimental economics. Smith, who clinched the Nobel Prize in 2002, demonstrated how controlled laboratory experiments could offer priceless insights into economic theory and human behavior. But what Altera is achieving with Project Sid catapults Smith’s vision into an entirely new dimension.

While Smith’s experiments were constrained by human participant numbers and scenario complexity, AI-driven simulations like Project Sid break free from these limitations.

We’re on the cusp of creating entire worlds populated by billions of AI agents simulating the global economy. The potential to analyze impacts down to individual agent level, across myriad scenarios and timeframes, is mind-boggling.

Unleashing the Power of AI Simulations: Beyond the Representative Agent

Consider the implications. We’re moving from econometrics on data series with a few hundred observations at best, or small classroom experiments, to something far more powerful. In economic theory, we’ve long relied on the concept of a ‘representative agent’ to simplify our models. But with AI agents, we no longer need this abstraction. Our “agents” can now be as diverse and varied as real humans – and we can have billions of them.

This opens up possibilities for policy simulations that are truly astronomical. We could stress-test the long-term effects of various monetary policy regimes across multiple economic cycles, accounting for the full spectrum of human diversity.

We could simulate the real-world impact of minimum wage hikes or tariff impositions across incredibly diverse economic landscapes.

We could model the evolution of different market structures over time and analyze their effects on consumer welfare, capturing the nuanced reactions of countless unique individuals. We could explore how various tax structures and social programs shape income distribution across generations, with each AI agent having its own unique set of characteristics, preferences, and behaviors.

This is essentially Vernon’s experimental economics on steroids – a new frontier of AI-driven economic experimentation that finally allows us to move beyond the limitations of the representative agent model. We’ll be able to amass and analyze colossal datasets, unearthing economic insights that were previously beyond our reach.

Real-World Applications: Taking Vernon’s Ideas Beyond the Lab

And it’s not just for academic ivory towers.

The business applications are equally thrilling. Imagine a company wanting to test a new ad campaign. They could run it past billions of unique AI consumer agents, each with its own preferences and behaviors, fine-tuning in real-time based on the results.

Businesses could simulate complex global supply chains under various scenarios, from natural disasters to geopolitical shifts, with each node in the chain represented by a unique AI agent.

Companies could test new products in virtual markets teeming with diverse AI consumers, gauging responses across different demographics and psychographics. Firms could model different organizational hierarchies and decision-making processes to optimize efficiency, accounting for the full spectrum of employee personalities and skills.

It’s like having Vernon’s experimental lab at your fingertips, but infinitely more powerful, versatile, and representative of real-world diversity.

Project Sid: A Glimpse into the Future of Experimental Economics

Project Sid is just the tip of the iceberg, but it’s already showing immense promise. The AI agents have developed sophisticated economic behaviors – creating currency systems, specializing in different roles, even voting on a shared constitution. It’s a microcosm of real-world economic and social systems, taking Vernon’s experimental approach to unprecedented heights.

As we scale up these simulations, the potential for economic research and policy-making is staggering. We’ll be able to test theories and strategies with a level of detail and accuracy that Vernon could only dream of. This technology isn’t just going to change economic research – it’s going to transform our understanding of complex social and economic systems as a whole.

The Road Ahead: Challenges and Opportunities in AI-Driven Experimental Economics

Of course, we must approach this new frontier with the same rigorous skepticism that characterized Vernon’s work. We’ll need to ensure that our AI agents accurately reflect human behavior and decision-making processes. There’s also the risk of over-reliance on simulations at the expense of real-world observations.

But if we can navigate these challenges, the rewards could be immense. We might finally be able to tackle some of the most persistent economic puzzles – from the microfoundations of macroeconomics to the dynamics of technological change and economic growth.

Lately, my focus has been on advising and discussing the use of AI, a core activity at Paice, the consultancy I co-founded with my business partner Christian Heiner Schmidt. As part of this, I experiment with new AI technologies to explore how they can benefit our clients, especially banks and pension funds.

Recently, I’ve been captivated by the progress in AI agents, a field where systems execute more complex, autonomous tasks. These agents have the power to redefine how businesses, including financial institutions, manage operations and client interactions. They can automate customer service with greater sophistication, analyze financial data at a deep level, and even aid in strategic decision-making.

A prime example is Altera’s revolutionary Project Sid, launched by former MIT researchers. This project introduces 1,000 autonomous AI agents into a Minecraft world, forming intricate virtual societies that develop their own economies, governments, and cultures. The implications for economic research are extraordinary.

This reminds me of Vernon Smith’s groundbreaking work in experimental economics, which earned him the 2002 Nobel Prize. Smith’s controlled lab experiments continue to offer invaluable insights into economic behavior and theory. However, what Altera is doing with Project Sid takes Smith’s vision into a whole new realm.

While Smith’s work has naturally been limited by the number of human participants and the simplicity of scenarios, AI-driven simulations like Project Sid transcend these boundaries.

Imagine building entire worlds populated by billions of AI agents simulating a global economy. The potential for analyzing individual agents across countless scenarios and timeframes is staggering.

The Power of AI Simulations: Breaking Free from the Representative Agent

Think about the possibilities this opens up. We’re moving beyond small datasets and classroom-sized experiments into something much more powerful. In economic theory, we’ve often relied on the concept of a “representative agent” to simplify complex models. With AI agents, we no longer need such abstractions. These agents can be as diverse and varied as actual humans—and there can be billions of them.

This could lead to policy simulations of incredible complexity. We could examine the long-term effects of different monetary policies, stress-testing them across multiple economic cycles and accounting for every aspect of human diversity.

We could simulate the effects of minimum wage increases or tariffs on a vast, varied economic landscape. We could model how market structures evolve and their impact on consumer welfare, capturing the nuanced reactions of countless unique agents. Furthermore, we could study how different tax systems and social programs affect income distribution over generations, with each AI agent exhibiting its own distinct characteristics, preferences, and behavior.

This is Vernon Smith’s experimental economics supercharged—a new frontier in AI-driven experimentation that moves beyond the limitations of representative agents. We’ll have the ability to generate and analyze enormous datasets, unlocking insights previously inaccessible to us.

Real-World Applications: Expanding Vernon’s Legacy

And this isn’t just for academia.

The business world can also tap into these innovations. Imagine a company testing a new advertising campaign. They could run it by billions of unique AI consumer agents, each with distinct preferences and behaviors, fine-tuning the campaign based on real-time results.

Companies could simulate complex global supply chains under different conditions—from natural disasters to geopolitical changes—with each point in the chain represented by an individual AI agent.

Businesses could launch new products in virtual markets, observing how diverse AI consumers respond across various demographics. They could also model different organizational structures and decision-making processes to maximize efficiency, factoring in employee skills and personality traits.

It’s like having Vernon Smith’s experimental economics lab at your disposal, only vastly more powerful and reflective of real-world diversity.

Project Sid: A Look into the Future of Economics

Project Sid is just the beginning, but its potential is already remarkable. The AI agents have developed advanced economic behaviors—creating currency systems, specializing in roles, and even voting on a constitution. It’s a microcosm of real-world economic and social systems, taking Vernon’s experimental framework to unparalleled heights.

As these simulations grow in scale, the potential for economic research and policy design becomes limitless. We’ll be able to test theories and strategies with a precision and depth that would have seemed impossible to Vernon.

This technology isn’t just going to revolutionize economics research; it will transform our entire understanding of complex social and economic systems.

Challenges and Opportunities in AI-Driven Experimental Economics

Naturally, we must proceed with caution, maintaining the rigorous skepticism that has characterized Vernon Smith’s work throughout his career. We need to ensure these AI agents accurately replicate human decision-making and behavior. And there’s always the risk of becoming overly reliant on simulations without real-world validation.

But if we can navigate these challenges, the rewards are boundless. We might finally unravel long-standing economic mysteries—from the microfoundations of macroeconomics to the forces driving technological change and economic growth.

I’m thrilled about what lies ahead. Vernon Smith has shown us the power of experimental economics. Now, with AI, we stand poised to take his vision to new heights—beyond even what was previously imagined. The future of economic research has never been more exciting, with AI agents in virtual worlds like Minecraft serving as our next-generation laboratories!

If you’re interested in discussing these developments or other AI and data topics, feel free to reach out to me and my colleagues at Paice.

* Picture created with fal.ai/FLUX.

If you want to know more about my work on AI and data, then have a look at the website of PAICE — the AI and data consultancy I have co-founded.

China and Russia Face Mounting Pressures as US Economy Remains Resilient

Historical Parallels and Potential Consequences

Yesterday we got the US ISM numbers, which surprised somewhat on the downside, which has caused recession fears in the US to rise again. The market is now again increasingly seeing a chance that the Federal Reserve will cut its key policy rate by 50bp in two weeks. Frankly speaking, I am also increasingly leaning in that direction, and if I had been a FOMC member, I would likely also have voted for a 50bp cut to avoid pushing the US economy into an actual recession.

Source: Institute of Supply Management

That being said, if I look at my favourite high-frequency indicator for the US economy – the Weekly Economic Indicator from the New York Fed that historically has tracked actual US real GDP growth very well – there are no signs of a slowdown in US growth.

So if US real GDP is actually growing around 2.25-2.50% right now, what is the problem then?

The Real Problem: China’s Deepening Crisis

I think the core issue really isn’t the US, but rather China. The crisis is clearly deepening in China, and that is what, for example, is visible in the recent decline in global oil prices.

So what is happening? In my view, this is mostly about China – and therefore about the global manufacturing sector.

In my view, the Chinese economy is in serious trouble and is facing very serious structural economic headwinds. The People’s Bank of China (or rather the Chinese Communist Party) should allow the Chinese Yuan to weaken – likely substantially – to avoid a debt-deflation crisis as I stressed in my recent post.

The CCP’s Reluctance and Its Consequences

However, as I also stressed in the post, the CCP is not willing to accept a major weakening of the yuan as this would make it blatantly clear to the Chinese public that China is in a much bigger and much more fundamental crisis than the Communist Party is willing to admit. Consequently, the yuan is being kept artificially strong, and this is very visible in money supply growth that is turning negative and is indicating that the Chinese economy is already in a recession and is likely to deepen.

Historical Parallels: The 1990s Asian Financial Crisis

Therefore, what is happening now in many ways is similar to the global economic situation in the second half of the 1990s.

At that time, the US economy underwent a major positive supply shock in the form of technological innovation (the internet) and significant immigration. Exactly as now.

This, however, was not to the same extent the case for the Asian economy that had seen an unsustainable boom starting to come to an end, and countries like South Korea could not ease monetary policy – even though it was needed due to the won’s peg to the dollar.

The Domino Effect of Currency Crises

And as the dollar kept strengthening, the Asian pegs increasingly came under pressure, and we all know how that ended – currency and banking crises spread across South East Asia as one country after another was forced to give up their pegs.

The problem, however, was not that countries floated their currencies – the problem was that they waited so long to do it and in the process caused a deflation-debt crisis. And as the crisis spread throughout Asia and to global Emerging Markets, oil prices plummeted.

During 1997, oil prices were cut in half, and the decline continued during 1998. This was a major contributing factor igniting the Russian debt and currency crisis that led to the collapse of the ruble and the Russian default in August 1998.

Present Day: China as the New Epicenter

This in many ways is exactly what we are seeing now. The dollar is strengthening – the Fed has been hiking rates and the “peggers” – at that time, for example, South Korea and Taiwan – got “squeezed”. This time around, it is China that is heading for a squeeze – and potentially a crash.

This obviously is negative for the global manufacturing sector, but as in the 1990s, the US economy keeps pushing ahead driven by technological progress and immigration.

Russia’s Vulnerable Position

And this time around, Russia is likely to get into very serious trouble if oil prices continue to drop as the China crisis deepens further.

Geopolitical Differences: Allies vs. Foes

The difference this time around is that in 1997, it was allies of the US – South Korea, the Philippines, Taiwan, and Thailand that got into trouble, and consequently, the US and the IMF flew in and saved the day (so to speak). This time around, it is a foe of the US that is in trouble, and there is no political will in the US to try to bail out China in any way. Other than, of course, China owns a lot of US government bonds.

And for Russia, it looks even worse. In 1998, the US and the West were eager to help Russia despite massive corruption problems and economic mismanagement. At that time, Russia was seen as a young democracy and a partner of the West. Now Russia is a foe and a totalitarian regime.

Implications for Global Economic Policy

So what does this mean? In my view, the Fed surely will react to any deepening of the Chinese crisis that has global ramifications, but policy will be focused on the impact on the US economy, and there will not be any US Treasury Secretary or Fed chairman jump in and help China – as Larry Summers and Alan Greenspan did in 1997 in South East Asia.

So yes, we will surely get US rate cuts, and 50bp is probably warranted already in two weeks, but there will not be any “global bailout”. And markets surely know this.

The Inevitable Outcome

Therefore, the exodus of capital from China in my view is likely to intensify going forward, and yes, eventually China will have to do what the South Koreans did in 1997 – accept a significant devaluation of its currency.

If you want to know more about my work on AI and data, then have a look at the website of PAICE — the AI and data consultancy I have co-founded.