China and a Balance-Sheet Recession

This is a topic I’ve been thinking about a fair bit recently, and to the extent that it is possible to do so, I want to spend some time in thinking about this in greater detail throughout this week. What’s up with China, and how should we think about

  1. What got China where it is today?
  2. Where does China go from here?

There are other things to think about in this regard, particularly as an Indian, but that takes me into the realm of geopolitics, and I know very little about it. One day, maybe. But for now, the question of what got China where it is today.

And lots of things have gotten China where it is today. But one of the many strings that we need to pick up on and see where it takes us begins with a country and a person. The country is Japan, and the person is Richard Koo. Koo is most famous, of course, for having coined the phrase “balance-sheet recession”:

After its stockmarket bubble burst in 1989, share prices plunged by 60% in less than three years. Property prices in Tokyo fell for over a decade. Deflation, by some measures, persisted even longer. Even the price of golf memberships—tradeable on organised exchanges in Japan—tumbled by 94%. Many companies, which had borrowed to buy property or shares in other firms, found themselves technically insolvent, with assets worth less than liabilities. But they remained liquid, earning enough revenue to meet ongoing obligations. With survival at stake, they redirected their efforts from maximising profit to minimising debt, as Mr Koo put it.

How should you think about this? Well, here’s a very simplified example. Imagine that every single household in your locality decides to not spend more in the month of September 2023, but instead save more. That may be good news for each household, but can you imagine what the local grocer, the neighborhood restaurants and the local movie-theater might feel about such a move? Exactly the same thing happened in Japan, but at a national level:

In post-bubble Japan, things looked different. Instead of raising funds, the corporate sector began to repay debts and accumulate financial claims of its own. Its traditional financial deficit turned to a chronic financial surplus. Corporate inhibition robbed the economy of much-needed demand and entrepreneurial vigour, condemning it to a deflationary decade or two.

The question that The Economist article asks and answers is whether China is “going the Japan way”. Note that this has been covered on EFE before, by the way.

And the answer they come up with is yes, but only kinda-sorta. And there’s (of course) more to it than just that. Yes, Chinese firms have accumulated insane amounts of debt, yes China’s house prices are undergoing a massive correction, and yes credit growth has slowed sharply.

But most of the debt is held by SOE’s, and they will borrow more, if that is what the Chinese government desires. And Chinese households drawing down their debts has more to do with the peculiarities of the Chinese market for mortgages than with households having stressed balance sheets. In fact, if anything, Chinese household debts are reasonably low. On the whole, the article argues, Chinese businesses don’t seem to be behaving the way Japanese businesses were in the 1990’s.

So yes, debt reduction is a thing in China, but it’s not quite Japan all over again, not yet. There is a problem, in other words, and it kind of looks like the Japan problem, but only in some ways. Still, it is a problem, there is no getting around that fact.

So how should this problem be solved?

Richard Koo says that the government should spend its way out of this problem. If the private sector is running up fiscal surpluses (saving more), than those savings need to be deployed somewhere. Where? The government should borrow and spend that money, according to Richard Koo.

If you accept that this is a good solution, there is only one problem. It would seem the Chinese government is not willing to play ball:

The country’s budget deficit, broadly defined to include various kinds of local-government borrowing, has tightened this year, worsening the downturn. The central government has room to borrow more, but seems reluctant to do so, preferring to keep its powder dry. This is a mistake. If the government spends late, it will probably have to spend more. It is ironic that China risks slipping into a prolonged recession not because the private sector is intent on cleaning up its finances, but because the central government is unwilling to get its own balance-sheet dirty enough.

And if you’ve got a mild headache thinking through all of this, I’ve got worse news for you. There are other economists who would argue that it is a good thing that the Chinese government is not willing to play ball. Fiscal policy, they argue, is not the way to solve this problem.

So what is their solution?

We’ll find out tomorrow.

It’s Baaaack, But In China This Time

China’s Japanification, by Robin Wigglesworth in FT

What does Japanification mean? From the same article that came up with my favorite chart from 2023 (shown above):

“It can be described simply as a protracted period of deflation, economic sluggishness, property market declines and financial stress as households/companies/governments unsuccessfully try to deleverage after a debt binge.”

And is that where China finds itself today? Well, it is difficult to say, because data is hard to come by:

The statistics bureau stopped publishing a consumer-confidence index after April numbers fell to levels last seen during the depths of the pandemic. With youth unemployment climbing remorselessly, the same bureau stopped reporting that statistic this week, saying it is reviewing how to count jobless young.

Data has, of course, been hard to come by for a while:

Bu you can suppress bad news for only so long, and to even the most casual of China watchers, it has been clear for a while that China is slowing down. And China slowing down is, in a sense, both inevitable and predictable. If you know anything about China’s demographics, it’s emphasis on capital-led growth and the Great Decoupling, China had to slow down.

But ah, the debt. That is where the problems become truly worrisome:

According to the BIS, China’s total non-financial credit/GDP ratio approached 297% of GDP by end-2022, similar to Japan in the 1990’s. Also similarly, debt is mainly domestic, and the domestic saving rate is high in both countries.

China’s Japanification, by Robin Wigglesworth in FT


  1. China is ageing more rapidly than Japan was in the 1990’s
  2. China’s debt, when properly accounted for, is even more than Japan’s was.
  3. China has less wriggle room when it comes to monetary policy.
  4. China also seems to be less willing to use monetary policy as a tool.

And above all, culture. I wish I knew more about China, and I wish I could travel to China. Lots of reasons for me to say this (food included, and it is one of the top three reasons) – but in this blog post, I say I wish I could travel to China to make sense of this from The Economist.

It is difficult to excerpt from it, but here are the concluding paragraphs:

Does Mr Xi understand this? His thoughts on how to achieve national greatness have evolved, along with his message to young people. A few months after coming to power in 2012 he met a group of young entrepreneurs, volunteers and students, telling them to “dare to dream, to bravely chase their dreams and to strive to fulfil them”. Their ambitions will make China great, he said. One beaming participant, who had recently climbed Mount Everest, said it was a good time to be young.
Now, though, Mr Xi says the “Chinese Dream” of national rejuvenation is to be achieved by focusing on collective goals, rather than by encouraging individual aspirations. He admonishes the young to obey the party and toughen up—to “engrave the blood of their youth on the monuments of history, just as our fathers did.” That is a message that relatively few young people are taking to heart. Told to eat bitterness, they prefer to let it rot.

Keep a close eye on China in the coming months, it is a story that is likely to be interesting and of immense relevance to everybody who lives on this planet.

By the way, did you get why today’s blogpost is titled the way it is? If you are a student of macro, you should have. You might think there’s a typo on my part, but hey, China deserves an extra “a”.

At least.

Nothing is so permanent as a temporary government program

The title of today’s blogpost is a line by Milton Friedman, of course.

This chart, from Andrew Batson’s blogpost, caught my eye:

Quite a few things to note over here:

  1. The difference between the three lines, which is in and of itself interesting
  2. The persistence of augmented net borrowing (including unofficial local government borrowing)
  3. China is, in a sense, still recovering from the fiscal excesses of 2008

Here’s Andrew on the third point:

China’s political system therefore does not have a good track record of being able to take away the punch bowl in good economic times in order to be able to share out more punch in the bad times. The dubious legacy of the 2008 stimulus means that China now needs fiscal consolidation to get long-term debt dynamics under control–at exactly the moment that it once again faces a shortage of aggregate demand.

The context, of course, is that China is struggling to recover from the pandemic. There’s lots of reasons this is happening:

There are several reasons to be gloomy about China’s economic prospects, from America’s export controls on advanced semiconductors and skittish foreign investors, to President Xi Jinping’s crackdown on big tech firms. But the main culprit for the recent weakness is property, which before the pandemic was a crucial source of growth across the economy. Activity slowed, first as the government sought to rein in heavily indebted developers, and then more recently as sales have stayed weak. Between January and May, for instance, real-estate investment fell by 7.2%, compared with the same period a year ago. The danger is that the property bust now becomes an enduring malaise.

…but given what happened in 2008 (and since), a fiscal stimulus seems to be off the table where China is concerned.

Fiscal profligacy is not, in other words, a good idea. Don’t get me wrong: I am not saying fiscal expenditure is a bad idea. In fact, in a country like India, it is unavoidable, and for a variety of reasons. But the answers to the questions:

  1. How much to spend?
  2. Where to spend?
  3. When to spend?

…are always important in the context of fiscal policy.

Note that this is not a comment about India and her fiscal policy both during and after the pandemic. It is simply a reminder that fiscal policy is extremely difficult to get right in terms of when to start, what the magnitude should be, and when to stop.

For example:

When Japan’s asset-price bubble burst at the end of 1989, growth slowed dramatically. Firms and households, burdened by debt, paid off their liabilities rather than spending on goods and services. Together with a shrinking workforce, this meant that Japan’s gdp growth lagged behind the rest of the rich world.
Part of the problem was that policymakers were too slow to respond. It was not until 1999 that the Bank of Japan cut its benchmark rate to zero; the government directed stimulus towards investment, rather than consumption.
The bust turned into decades of stagnation. Unfortunately, China looks as if it may repeat the same mistake. The government remains fond of directing stimulus to investment, rather than towards handouts.

To spend the right amount, to start at the right time, to end at the right time, and to make sure one spends it right where it’s needed – it’s easy for a blogger to say that this matters. It is easy to point out where a country has gone wrong with the benefit of hindsight. But to actually do it when it is needed the most, that’s a whole other challenge.

Some might say it is all but impossible, but if you are a serious student of the Indian economy, you’re going to have to admit it is unavoidable.

Macro is hard.

To work from home or not to work from home?

The Economist says not to work from home because “it is not more productive than being in an office, after all”.

A gradual reverse migration is under way, from Zoom to the conference room. Wall Street firms have been among the most forceful in summoning workers to their offices, but in recent months even many tech titans—Apple, Google, Meta and more—have demanded staff show up to the office at least three days a week. For work-from-home believers, it looks like the revenge of corporate curmudgeons.

They cite the case of the paper that went from showing an eight percent increase in productivity due to working from home when it was a working paper, to showing that there was a four percent reduction instead. There was no problem with the paper or its methodology, to be clear. The difference was simply because of better quality of data. There is a world of other research worth going through in The Economist article, and I would urge you to read it.

What reasons come through for the decline in productivity? Well, it’s just hard to work from a dining table! The ability to go to a co-worker’s desk to chat about work, to get some help, to resolve an issue – that is harder to do online. Why is it harder? Because “teleconferencing is a pale imitation of in-the-flesh meetings”. That’s a fancy way of saying online sucks. To use Coasean terminology, as the article in The Economist does, coordination costs matter.

Most important of all, networking becomes harder. We are, at heart, a social species, and we need proximity to other people. Not only for the psychological benefits, but also because we learn best in person. That might seem like a contradiction given yesterday’s post, but it is not. Learning in person doesn’t necessarily mean listening to someone like me drone on in a classroom!

But to me, the article came alive towards the end. As with all well written articles, this one too segues into an implicit “on-the-other-hand” section.

Perhaps the greatest virtue of remote work is that it leads to happier employees. People spend less time commuting, which from their vantage-point might feel like an increase in productivity, even if conventional measures fail to detect it. They can more easily fit in school pickups and doctor appointments, not to mention the occasional lie-in or midmorning jog. And some tasks—notably, those requiring unbroken concentration for long periods—can often be done more smoothly from home than in open-plan offices. All this explains why so many workers have become so office-shy.

“What are you optimizing for?” remains an underrated question! If you’ll allow me to cite an example from my own life: I’ve been working from home for the last year and a half, approximately. I stay on Baner Road in Pune, and not having to battle University signal everyday is something that saves me time, gives me more energy and enthusiasm to work, and frees up time to do other stuff (work/exercise/napping/whatever). I have the pleasure of picking up my daughter from her bus-stop every day. I get to go for morning and evening walks with my dog. Sure I miss the conversations with some of my colleagues, and god knows I miss being able to interact with my students on campus. But hey, opportunity costs are everywhere, no?

As the article goes on to say in its concluding paragraph, hybrid weeks are here to stay. Sure it is not as productive as working in an office, but woking from home is more soul satisfying. And so the answer to the question “work from home or not” is, well, both.

The truth, and stop me if you’ve heard this one before, lies somewhere in the middle.

Ellsberg, Knight and Climate Change

Placed before you are two urns. Each contains 100 balls. You are given a clear description of the first urn’s contents, in which there are 50 red balls and 50 black balls. The economist running the experiment is tight-lipped about the second, saying only that there are 100 balls divided between red and black in some ratio. Then you are offered a choice. Pick a red ball from an urn and you will get a million dollars. Which urn would you like to pull from? Now try again, but select a black ball. Which urn this time?

Most people plump for the first urn both times, despite such a choice implying that there are both more and fewer red balls than in the second urn.

Why do most people plump for the first urn both times? Because, as the saying goes, “Better the known devil…”.

This is the well known Ellsberg paradox, of course. If you’ve studied micro, decision theory, probability or behavioral economics, you’ve probably come across it.

But climate change? What does the Ellsberg paradox have to do with climate change?

The experiment may seem like just another of the cutesy puzzles beloved by economists. In fact, it reveals a deeper problem facing the world as it struggles with climate change. Not only are the probabilities of outcomes not known—the likelihood, say, of hurricanes in the Caribbean ten years from now—nor is the damage they might do. Ignorance of the future carries a cost today: ambiguity makes risks uninsurable, or at the very least prohibitively expensive. The less insurers know about risks, the more capital they need to protect their balance-sheets against possible losses.
In May State Farm, California’s largest home-insurance provider, retreated from the market altogether, citing the cost of “rapidly growing catastrophe exposure”. Gallagher Re, a broker, estimates that the price of reinsurance in America has increased 50% this year after disasters in California and Florida. Few firms mention climate change specifically—perhaps a legacy of Republican attacks on “woke capitalism”—but it lurks behind the rising cost of insuring homeowners against fires, floods and hurricanes.

The key phrase in that excerpt is this one: ambiguity makes risks uninsurable. Another way to put it is: ambiguity (uncertainty) means a risk can no longer be called a risk.

Why? Because risk… and have a sip of coffee before reading this next bit… risk is about certainty. Nope, not a typo! Risk is about certainty. Well, ok, I’ll kind of put you out of your misery. Risk is about the absence of uncertainty.

Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated…. The essential fact is that ‘risk’ means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating…. It will appear that a measurable uncertainty, or ‘risk’ proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all.

Knight, F. H. (1921) Risk, Uncertainty, and Profit

How bad will the impacts of climate change be? How bad will hurricanes get? When will parts of Mumbai go under water? How long before Maldives ceases to exist? There is only one answer to this question: we just don’t know.

Then how do we price the risk associated with these events? We can’t, which is why home insurance firms in California are exiting form the market. This is where I and the article disagree a little bit, for it goes on to say that when it comes to climate change, reality isn’t quite as bad. We can resolve the uncertainty, in other words, by guessing how bad things may get.

How do we guess? By taking a look at how climate change affected the planet in the past. These changes can be deciphered by studying things such as the Arctic ice cores, for example. Or oceans. For a given change in x (say carbon dioxide emissions), this is how y changed (say patterns seen in the Arctic ice-cores).

But this will, at the end of the day, still be a guess. What we are saying is that because this is how things played out in the past, this is how things will play out in the future. Well, maybe. And maybe not!

As I’m fond of telling my students when I explain the concept of value-at-risk, predicting I will not die tomorrow because I haven’t died so far isn’t a great idea. Or predicting the height of the waves on some beach in South East Asia (given the data of the past hundred years) wouldn’t have worked out so well on the day the tsunami struck in 2004. Both those examples have their own problems, I will happily admit – but the point I wish to make is a simple one.

The future doesn’t always look exactly like the past. We can, at best, reduce some of the uncertainty. Not resolve it. And when you can’t resolve uncertainty, you can’t price it, and when you can’t price it, you are going to struggle with a viable insurance market.

The article goes on to say as much, but along a different dimension: political uncertainty. As they put it “there is no model that can predict whether policymakers will pull the levers that are available to them to prevent such fires from happening”. Indeed.

And worse: policymakers can sometimes not only not pull all the available levers, but they can go out of their way to prevent others from using them.

Policy can also prevent a proper accounting of risk. Californian regulations forbid insurers from using the latest climate models to set prices, since protection would become more costly. Premiums must be based on the average payout over the past 20 years, rather than the latest science. Shying away from ambiguity is understandable. Sticking your head in the sand is plain foolish.

If price is a signal wrapped up in an incentive (and I think it is), then a good way to figure out how seriously markets are taking climate change is to look at the price. But for that, price discovery mechanisms must be allowed to flourish!

Steam Engines, AI and Diffusion

Steam-powered manufacturing had linked an entire production line to a single huge steam engine. As a result, factories were stacked on many floors around the central engine, with drive belts all running at the same speed. The flow of work around the factory was governed by the need to put certain machines close to the steam engine, rather than the logic of moving the product from one machine to the next. When electric dynamos were first introduced, the steam engine would be ripped out and the dynamo would replace it. Productivity barely improved.
Eventually, businesses figured out that factories could be completely redesigned on a single floor. Production lines were arranged to enable the smooth flow of materials around the factory. Most importantly, each worker could have his or her own little electric motor, starting it or stopping it at will. The improvements weren’t just architectural but social: Once the technology allowed workers to make more decisions, they needed more training and different contracts to encourage them to take responsibility.

This is the second time this quote is appearing in a post on EFE. By the way, do read that earlier post, especially if you are in academia, and please let me know how your university has adjusted to the post pandemic world – have we just gone back to a fully offline world, or not?

But to come back to why I wanted to talk about this excerpt again – it is because The Economist asks an inevitable and obvious question regarding the deployment of AI in offices the world over:

Speculation about the consequences of ai—for jobs, productivity and quality of life—is at fever pitch. The tech is awe-inspiring. And yet ai’s economic impact will be muted unless millions of firms beyond Silicon Valley adopt it. That would mean far more than using the odd chatbot. Instead, it would involve the full-scale reorganisation of businesses and their in-house data. “The diffusion of technological improvements”, argues Nancy Stokey of the University of Chicago, “is arguably as critical as innovation for long-run growth.”

Having technology is not the same as using it. And in fact people will take a long time to adopt to a new technology, and that for a variety of reasons. Some may be cultural, some may be about being comfortable with the “old” workflow, and some may be, well, irrational, plain and simple.

The article in The Economist gives the examples of Japan and France, and that section is well worth a read, but what is true for countries is true, of course, at the level of organizations and institutions too. Resistance to change is hard to overcome, and the diffusion of technology simply doesn’t happen as fast as some might hope. For example:

In 2017 a third of Japanese regional banks still used cobol, a programming language invented a decade before man landed on the moon. Last year Britain imported more than £20m-($24m-) worth of floppy disks, MiniDiscs and cassettes. A fifth of rich-world firms do not even have a website. Governments are often the worst offenders—insisting, for instance, on paper forms. We estimate that bureaucracies across the world spend $6bn a year on paper and printing, about as much in real terms as in the mid-1990s.

But other factors are at play, beyond my simple list of factors from above (cultural reasons, inertia and irrationality). There may simply be no incentive to move to a better technology, if you are a business that is doing well in a sector with no young upstarts for competition. Particularly in the western world, it may simply be a case of an aging population that prefers to not learn new tricks. Governments may be ham-handed in terms of regulating the deployment of new technologies, and society may wish not to adopt technologies that save on labor. Costs, data privacy concerns, legal compliance issues, inevitable mistakes that AI will make – all are hurdles to be overcome.

The study of how this will change in the years to come will fascinate economists, sociologists, psychologists and many other -ists.

It is impossible to say how this will play out, but it will be a fascinating topic of study, that is for certain.

Buckle up!

Further reading, if you are interested in an economic analysis of some of these issues.

Reflections on RE and China

The love-hate relationship goes on. For almost two years China’s leaders cracked down on borrowing to build and bet on property, plunging the market into a crisis. Now that the economy has been weakened by the failures of the “zero-covid” policy, the government is racing to rescue real estate. Ni Hong, China’s housing minister, has said his ambition this year is to restore confidence; a series of measures announced in the past few months seek to make it easier for developers to raise capital. These efforts are reviving the property market. Unfortunately, they leave it just as vulnerable to boom and bust as ever.

That’s The Economist, in January of this year. China’s RE problems are well known, of course. But the economics of how the Chinese government is “managing” this market is a fascinating story. As most economists (but not all!) will tell you, managing markets in general isn’t that easy. And “managing” the Chinese RE market is about as challenging a task as you can imagine.

As with diseases and doctors, so with problems in a market and its management. Getting the dose of the medicine right is tricky, and figuring out how long to keep the dose going is trickier still. As the same Economist article points out, “technocrats tend to respond to crises with lots of liquidity”. In other words, they apply too strong a dose, and keep it going for too long. This has happened in the past with many markets, but especially with the Chinese real estate market back in 2014. That led to a predictable boom, with predictable consequences.

The current crisis isn’t just bad for real estate buyers and sellers, however. It is also bad for government, because a slump in real estate markets takes away the chief source of revenue for local governments (land auctions). And so the real estate market needs to grow, but well, isn’t.

And that comes through not just in the case of the market for RE, but also in the labor market associated with RE:

In 2013, architecture was named the top career choice for Chinese graduates in education consulting firm MyCOS’s annual report. Architects not only enjoyed the highest employment rate and job conditions, they also reported the greatest career satisfaction scores. But by 2015 it had disappeared from the league table completely, and it has never featured in the list since.
When technology media outlet 36Kr asked over 1,200 Chinese graduates whether they regretted their choice of major this month, architecture students were among the most likely to say yes. “Architecture may have fallen from the throne faster than any other major,” the newspaper Southern Weekly commented in a recent report.

And there are two factors to keep in mind from a long term perspective. First, of course, is the fact that China’s population has almost certainly peaked, and that will of course have a knock-on effect on the RE market in the years to come. Second:

Another parameter also showed signs of peaking. The average urban residential area per capita in China was only 18.7 square meters in 1998, but reached 41.76 square meters by 2020. As a reference, the average residential area per capita in major developed European countries falls within the range of 35-45 square meters. Based on this information, the future space for growth under this metric is limited in China.

You could fire three arrows, or throw in a fourth. The market is likely to be a challenge in 2023, and will continue to remain so for some time. What the Chinese would like, in effect, is a moderation of prices with not much impact on demand. A soft landing, in other words. Easier said than done, as Michael Pettis says:

And the reason this matters is because the real estate sector was a major source of employment, investment, tax revenue, and a way to save for millions of Chinese folks. That’s a dangerous mix!

Is there no way out? Are there no bright spots? Well, consider Chengdu:

What explains this success? Since 2016 officials in every Chinese city have been able to devise their own measures for cooling or heating local property markets. Most of the rules employed are restrictions on who can buy a flat, how many they may purchase and the size of the downpayment required. In most large cities, only people with local hukou, or residence permits, are allowed to buy homes. In Chengdu, high-level purchase controls remain in place. But officials have sought to attract families as a way of expanding the city and increasing demand for homes. Residents with two or more children are, for instance, allowed to buy additional homes, and local hukou-holders may buy up to three. Even those without a hukou may buy two. Since the start of the year, elderly parents who move to Chengdu to join their adult children may also purchase a flat.

There are other factors at play too in Chengdu, but my biggest takeaway is that Chengdu is attempting to raise its population, and therefore the demand for RE. But that will only work by pulling people away from other parts of China – and so it’s a band-aid solution at best. As the Economist article itself points out “there simply aren’t enough people in China for another population boom”.

I’ve said it before, and I’ll say it again: the Solow model remains underrated!

Which begs the question: how should we use the Chinese experience today to think about the Indian RE market of, say, 2040?

That would need us to be familiar with the trajectory and current status of the Indian RE market – and if any of you have any information or stuff worth reading about this, please do share. Thank you!

The Solow Model in Action, Again

I’ve written many blogposts about the Solow Model in the past, and it remains one of my favorite workhorse models to teach at an introductory level. There are problems with the model, to be sure, and much better, updated versions are available. But if you want a very simple, but very powerful way of looking at the world, you could do a lot worse than the Solow Model.

Read my old posts about the Solow Model, if you like, or watch this video, or consider this description of the Solow Model that ChatGPT4 came up with:

The Solow Model is the lively symphony of an economy, showcasing how savings, population growth, technology, and the tune of institutions together shape its rhythm, while the constant drumbeat of depreciation subtly dictates the tempo of this economic dance.

By the way, if you’re wondering, this is the prompt that I used:

“Give me a one sentence description of the Solow model. The sentence must be short, in simple english, and written in a catchy style, but it must also be correct, thorough and succinct”

I wasn’t happy with it’s first pass answer, so I gave it an additional prompt: “Make it slightly longer if you need to, but the fact that institutions matter must also come through”, and the professor in me couldn’t resist one final prod: “And shouldn’t you be mentioning depreciation?”

And if you’ve seen the video, you’ll probably agree that ChatGPT4 has done a reasonably good job. But what is the point? The point is that being familiar with the Solow Model helps you understand why this chart is such a big problem:

What is the rhythm of the Japanese economy? One should take a metaphor only so far, but it does seem as if the rhythm is a little slow, no? And why is it slow? Well, one of the reasons is that population growth has been slowing down a fair bit in Japan:

In fact, as you probably can tell, it’s not just been slowing down – it’s actually declining!

And it’s not just Japan. Take a look at Italy, South Korea and Singapore:

And ask yourself what the future holds for these countries:

Italy and Japan, in particular, are the poster pensioners for demographic decline and its economic consequences. In both countries the fertility rate (the number of children a typical woman will have over her lifetime) fell below 2.1 in the 1970s. That level is known as the replacement rate, since it keeps a population stable over time. Anything lower will eventually lead to a declining population, something both Italy and Japan have suffered for about a decade. The median Italian is now 47; the median Japanese 49. Earlier this year, Kishida Fumio, Japan’s prime minister, warned that the country is “on the brink of being unable to maintain social functions” because of its baby bust.
But Italy and Japan are no longer the most extreme examples of demographic decline. In 2022 South Korea had a fertility rate of just 0.8. A rate below one means that the next generation will be less than half the size of its parents’. As recently as 2012 the un projected that South Korea’s population would shrink by only a fifth or so by the end of the century, from 52m today to 41m by 2100. More recent forecasts, however, suggest that the population will fall by more than half over the same period, to just 24m

So what?

Well, so the following:

  • Older people in your country will mean rising health expenditures
  • It will also mean lesser people of working age
  • More people drawing out of your pension schemes, and not enough young folks putting money in to them
  • Smaller workforce, which means lesser economic output, but also lesser patents – among other things

Are you tempted to laugh this off and say “Hah! There’s 1.4 billion of us, we don’t need to worry about this”? Consider China:

…the number of Chinese aged between 21 and 30 has already fallen from 232m at its peak in 2012 to 181m in 2021. The decline will accelerate rapidly in the 2040s, leaving China with fewer than 100m people in the same pool in the mid-2050s.

Take a look at the same chart as earlier, but now with only China and India added in:

Ask yourself: how much longer before we find ourselves where China is likely to be ten years from now? Before we start to see a rapid decline in young people of working age? I’d say we have about thirty years to go – around 2050 would be a safe guess. Note that demographics is hard, and forecasting what a country’s demographics are going to look like is even harder – all of which is to say that this is at best an educated guess on my part.

But if you are, say, in your twenties right now, you’re looking at retiring at around that mark, or a little later.

What do you think India is going to look like then? And what are we doing about it?

The Solow Model remains underrated!

Argue More!

Argue More!

The point of arguing with an author is not to “win” the argument. 

Quite the opposite. The point of arguing with the author is to work with the author.

Mihir Mahajan, regular reader of EFE, raised some questions about my post the other day on middle income traps.

It might help to take a look at the chart from the earlier post before you go through his questions.

Here are his questions:

  1. “The 1960 vs. 2022 nature of graph and the 1-6 ratings of income are quite confusing”
  2. “The “middle income trap” is too dense and you pointing to Nicaragua shows that the journeys of different countries could be going in different directions within that group”
  3. “The range of 1.75-3.75 on both axes is deceptive though. While the higher scales 4+ is rich in general, the relative gap between India/Nigeria and China is very high — not sufficient distinction there.”
  4. “Putting China in “middle income trap” is odd because it has gone from below 2 in 1960 to above 3 in 2022 (based on the axes).”

Before I get around to answering his questions, I have a question for you. 

Do you have any questions of your own, for having read his questions? Go read my post again, stare at the chart, go over Mihir’s questions, and then think about whether you have any questions of your own.

I’ll answer each of Mihir’s questions below, but the point of this post is really what follows after, so please do stick around until the end!

  1. “The 1960 vs. 2022 nature of graph and the 1-6 ratings of income are quite confusing”

    Yup, absolutely. It takes a while to figure out what is going on in an Economist chart, and while that is a problem, I’d argue that the rewards are usually worth it. By the way, if you are an Economist subscriber, you absolutely should read their newsletter on visualization and charts.

    A useful principle to keep in mind is that when you look at a chart, train yourself to not look at the data first. First be clear about what is on the axes (all of them). Then be clear about the title of the chart. It helps to take a look at the source of the data. Then start taking a look at the chart itself.

    Homework: what does “income per person, relative to the United States, log of %” mean? Can you explain this phrase to somebody else? If you can’t, you haven’t understood it well enough!
  2. “The “middle income trap” is too dense and you pointing to Nicaragua shows that the journeys of different countries could be going in different directions within that group”

    The central square in the chart is too dense, but that’s just fine by me. Why? Because the outliers are then even more worthy of analysis. If you cannot “make it” into the central square, then you’re even more special relative to that crowded space.
    Botswana is special because it was poor in 1960, and is not just middle-income today, but on the verge of breaking into the high-income space. That’s a special story!

    Argentina, on the other hand, is special for the wrong reason. It was a high-income country back in 1960, but has since slid down into a middle-income country grouping.
    Both of these countries, within the context of this chart, also help you understand Mihir’s second comment here. Because this is a static image, and because we’re comparing two different points in time, we don’t get a sense of the trajectory of a country. Botswana is on the way up, and Argentina has slid down – but you need to know this separately. This isn’t clear from looking at the chart.

    To be clear, this isn’t a criticism of the chart, but rather a way of recognizing that your work as a student doesn’t stop for having studied the chart. Au contraire, this chart should spur you to read more about whichever country seems interesting to you.

    “Tell me more about Botswana’s growth story over the last sixty years or so. Assume I know very little about Africa in general, and Botswana in particular. Your answer should include Botswana’s internal politics, key leaders, relationship with her neighbors and with the superpowers during the cold war, her natural resources and some background on major ethnic and religious groups in Botswana”

  3. “The range of 1.75-3.75 on both axes is deceptive though. While the higher scales 4+ is rich in general, the relative gap between India/Nigeria and China is very high — not sufficient distinction there.”

    Log scales can be tricky, and the best way to understand this is by thinking about how earthquakes are measured. And yes, Mihir is spot on about how you need to keep this in mind. The lower ends of the middle income square (left to right and bottom to top) actually cover very large ground, and countries in the left-bottom corner are very different from countries in the right-top corner of the middle square. Dividing the middle square into a 3×3 grid would be a great idea. (Hi, The Economist. Hint, hint)
  4. “Putting China in “middle income trap” is odd because it has gone from below 2 in 1960 to above 3 in 2022 (based on the axes).”

    It’s their chart to make, and ours to interpret as we see fit, so while I get where Mihir is coming from, I’m fine with both the boundaries of the middle square, and with the framing that The Economist has used. China’s growth trajectory over these past sixty years or so has been fantastic, but the question is about whether it can keep that break-neck growth rate up going ahead. A very wise economist won a Nobel Prize for coming up with a simple model that says “Nah, probably not”. So while I understand Mihir’s point, I can see the logic used by The Economist as well. Stop me if you’ve heard this before, but macro is hard.

But now that I’ve replied to his comments, let me come to the main point of today’s post.

What stops us from asking questions as we read? Why, that is, do we read unquestioningly?

Maybe that’s too specific a question, so let me step back and frame it more generally. 

Why don’t you argue more often with whatever you’re reading?

Make sure you understand where they’re coming from, and that you understand their line of reasoning, to the extent possible. And also that you understand how and why they reached the conclusions they did. You don’t have to agree with either the line of reasoning or the conclusion, to be clear.

But asking smart, probing questions about both the premises and the conclusions can help you become a much  more engaged reader. This, in turn, can help you to both understand what you’re reading, and to decide whether you agree with the author.

It’s a rare old skill, and I’d encourage you to apply it, always, while you’re reading.

So please, disagree more with what you read on my blog, and let me know of your disagreements.

Help me learn better!

The Economist on The Middle Income Trap

But first things first: what is a middle income trap?

Here’s my assistant’s answer:

The middle-income trap is a concept that describes a situation where a country’s economy gets stuck in a middle-income range, and it struggles to advance to higher levels of development.
To understand this, we need to first know about development economics and growth theory. Development economics is the study of how countries become richer and more developed over time. Growth theory is a part of development economics that studies how economies grow and what factors help or hinder growth.
Now, let’s go back to the middle-income trap. When a country starts to develop, it usually grows quickly and becomes a middle-income country. However, some countries find it hard to keep growing and end up getting stuck in this middle-income range.
The problem is that these countries face new challenges when they reach this stage. For example, they may have to compete with cheaper goods from other countries, or they may need more advanced technology to keep growing. These challenges can be difficult to overcome, and the country’s growth slows down or even stops.
This can be a problem because being stuck in the middle-income trap can mean that a country’s people do not get richer over time, and the country may not be able to provide the same opportunities as wealthier countries.
To avoid the middle-income trap, countries need to keep innovating and adapting to new challenges. This can involve investing in education and training, creating new technologies, and finding new ways to do things. By doing this, a country can keep growing and move towards becoming a high-income country.

So which countries find themselves in the middle income trap? The Economist has a lovely chart within an article that helps us find out the answer to this question

As a country, you want to be in the top row, since that implies you are doing well today. You do not want to be in the bottom row, since that implies that you are not doing well today. Things get interesting in the row in the middle.

The first column (read left to right) in the middle row is a great story. You were “low” in 1960, and are “middle” today, so you’ve covered impressive ground, and are hopefully on your way up from here on in. The third column (read left to right) in the middle row, is a problematic story (to put it mildly). You were “high” in 1960, but have reached “middle” today.

The square bang in the middle? That’s the middle income trap.

These are countries that were doing relatively well back then, in 1960, and are doing relatively well today – but in the sense that there hasn’t been a relative improvement, they find themselves in a middle income trap.

What does “relatively well” mean, and what does “relative improvement” mean? Take China as an example – China was a middle income country in 1960 (it falls in the middle, read left to right), and it is a middle income country today (it falls in the middle, read bottom to top). That’s not to say that there has been no improvement for the Chinese since 1960, of course! There has been remarkable improvement.

But relative to the USA, China was in the middle of the pack in 1960, and finds itself to be middle of the pack in 2022. Therefore the middle income trap.

Even within that square itself, by the way, there are stories to be discovered. If you are in the right top of that square (Mexico, for example), you were on the verge of becoming a high-income nation in 1960, and you are on the verge of becoming a high income nation today, but you haven’t actually achieved that status as of yet.

If you are in the top left of that square in the middle, you were barely better than low income in 1960, and are about to break through the metaphorical ceiling today (China, for example). India hasn’t moved much within that middle square, and that is therefore a frustrating story for us in India.

Homework: how would you describe Nicaragua’s position in this graph? Is it better off or worse off over these past sixty years or so?

Finally, you might also want to think about whether the middle income trap is such a bad thing in the first place!

Poland and Malaysia may now be running into this [he’s referring to the middle income trap here – Ashish] problem. McKinsey cites Poland’s need to develop or acquire strong brands in order to catch up with West Europe. The failure of Malaysia’s attempt to build domestic champions is worrying.
And yet I see two responses to this. The first is: Do we really care? Poland and Malaysia may not be as rich as Germany or Korea, but they’ve definitely escaped poverty. Countries like Bangladesh or Vietnam or Ghana or even Mexico would kill to have a per capita GDP of $30,000. That’s about the GDP of the U.S. in the early 1980s. Is it really fair to call that level of development a “middle income trap”? If you’re a poor country, and you have a reliable, dependable way of getting as rich as the U.S. was in the early 1980s, dammit, you take it. You don’t worry about whether that strategy will eventually make it harder to get as rich as the U.S. of 2023.

Let me be clear – I am not saying (and I don’t think Noah is either) that more growth is a bad thing. But a targeting of rapid growth at all costs, and above all else, isn’t necessarily a great idea.

Why not? Because opportunity costs matter! At what costs (to the climate, to the distribution of income, to the development of social capital, to give you just three examples) do we achieve this growth? Remember, the answer to the third question depends on how you define the word “better”.

I’ve said it before, and I’ll say it again: macro is hard.