Macro is *Hard*, Edition #293483343643

I began teaching a course on introductory macro this past Saturday at a college here in Pune. I often tell my students that my job in a macro course is to leave them more confused at the end than they were at the start. That always evokes laugther by way of response, but as anyone who has learnt (and especially taught!) macro will tell you, I’m quite serious.

Macroeconomics is hard, it is confusing and as the person responsible for teaching it, you’re always on your toes, because you’re never sure if you’ve understood it yourself!

And I really do mean that, it is not a rhetorical statement. My own PhD is in macroeconomics (business cycles, more specifically), but I’ll happily admit to still not being sure about what exactly causes business cycles, what (if anything) to do about them and when to stop doing whatever it is that we’ve chosen to do about them. And I suspect that most macroeconomists will tell you the same thing.

This humility stems from a very good reason: macro is hard.

It is hard for lots of reasons, and not to get too meta, but quite a few debates within the field are also about which of these reasons are most relevant, and whether the relevance changes over time – and if so, due to which reasons!

But if I were to try and write a simple post for people who have no formal trianing in macro about why macro is so hard, here would be my reasons:

  1. Macro is really about trying to figure out everything that goes on in an economy, and if you try to think about all the things that go on in an economy, you very quickly realize that figuring them out is even more challenging.
  2. Time and uncertainty!
    • Macroeconomic decisions take time. It takes time to decide to start a new factory. It takes time to figure out the financing. Land acquisitions, regulatory approvals, construction delays will all add weeks to the planned schedule, if not months, and sometimes years.
    • These expensive decisions are made at the start, but there is no guarantee that macroeconomic conditions will be the same at the finish of the project as they were at the start. You want a relatable example? How sure are you that macroeconomic conditions will be the same when you graduate from college – as they were when you enrolled in it?
  3. The way macroeconomic variables interact with each other isn’t known for sure. We think we know how inflation and unemployment are related to each other, but we can’t really say for sure. We think we know how exchange rates impact the domestic economy, but we can’t really say for sure.We’re still figuring out how monetary policy and fiscal policy should interact in theoretical models, let alone in reality. The impact of monetary policy in America today on India’s economy tomorrow? Don’t get me started. I can go on, and folks with greater expertise than me will prbably not stop for years.
  4. Life has a way of throwing up surprises that macroeconomic models never thought about. You could (and probably should) blame macroeconomists for not getting enough finance into their models prior to 2008, but who, pray, could have foreseen 2020 and 2021? How do you come up with models and policies on the fly in such a scenario? And then, just for fun, throw in a jammed Suez canal. Life, I tell you.
    We call these things exogenous shocks in macroeconomics, but the name hardly matters. Reality will always be more complex and more unexpected than any model you can come up with, and that’s just a fact.
  5. Counterfactuals are impossible to test. How do we know that Ben Bernanke did the “right” thing in 2008? We don’t! What if he had done x instead of y? There’s no way to test this, since we can’t turn the clock back to 2008, and ask Mr. Bernanke to, well, do x instead of y. This is both a problem and when it comes to critiquing models, a great convenience.
  6. Attitudes towards risk, and the propensity to copy what others are doing change according to your outlook towards the macroeconomic environment. You can call this animal spirits, but what you’re really saying is that you don’t quite know how to think about it, even less model it cohesively.
  7. Building a model – any model – requires simplification. When you build a model, it will by definition be an approximation. Unfortunately (and I wish this weren’t so), this very real limitation isn’t always front and centre within the field while developing models.
  8. What are you optimizing for when you build a model? Is it fidelity to reality or is it a beautiful model that may or may not have anything to do with reality? Again, I wish this weren’t so, but the answer isn’t always clear cut.
  9. Any field that uses the pool player analogy is a field that is, by definition, unsure about how the world works.
  10. No matter how much data we have access to, there will always be data points that we cannot capture, and we don’t quite know how these data points, and their unavailability, will impact our understanding of the economy.
  11. Social structures, psychological make-up, cultural parameters will all have an impact upon the decision making capabilities of individuals, but quite how this works (and that too across space and time) isn’t well known. For example, how would your grandfather have reacted to the prospect of not being employed upon graduation? What about your dad? What about you? What does this say about the nature of India’s changing economy, and what does it say about cultural norms and expectations? Is your answer likely to be different depending upon how much your family earns, where in the country you are located and your family size? Can we model this? (Hint: no.)

So sure, I’ll teach them about the variables, the models and the case studies.

But I’ll let you in on a dirty little secret, so long as you promise not to tell anybody: I’m just not sure if I really and truly understand what I’m teaching in macro.

No seriously, Macro *IS* Hard

It’s one of my favorite phrases while writing on EFE. And it is a favorite for a reason: it is true.

And today’s post is about an excellent essay by David Glasner, author of the excellent blog Uneasy Money.

I usually excerpt bits and blobs of whichever essay I am recommending to you, and I will get to that part eventually, but today, I want to spend some time in explaining why it is that I find macro so hard.

One, modeling a firm is hard enough. Trying to model an entire economy entails massive abstraction, and so whatever conclusions you reach are likely to be little more than informed guesses.

Two, time. A simple word, but with massive implications. You can call it what you like, but the basic simple point is that any project that lasts for more than a day is taking a bet on what the future is going to look like. And the uncertainty that is necessarily associated with the future means that your estimates are guaranteed to be wrong. Mostly correct if you’re lucky, somewhat off the mark if it is business as usual, and hopelessly off target if you’re unlucky.

Consider this from The Economist:

The dearth of chips is a consequence of the pandemic, which boosted demand from makers of electronic devices for those stuck at home during lockdowns. Car firms also underestimated the rapid pace of recovery this year. Expecting weak sales, in 2020 they pared back orders. Although carmakers spent $40bn or so on chips in 2019, that accounted for only a tenth of global demand, which puts them low in the semiconductor pecking order. This makes orders hard to reinstate.

Three, in my mind, used to be kind of related to two. Time also meant that much like your assumptions would eventually be wrong, so also would the assumptions of your suppliers and customers be wrong. And those assumptions being wrong would mean that all plans would need constant modification on an ongoing basis. Which makes the study of macroeconomics hard, but also endlessly interesting.

But David Glasner’s post raised a point that is well worth thinking about:

When contesting the presumed necessity for macroeconomics to be microeconomically founded, I’ve often used Marshall’s partial-equilibrium method as a point of reference. Though derived from underlying preference functions that are independent of prices, the demand curves of partial-equilibrium analysis presume that all product prices, except the price of the product under analysis, are held constant. Similarly, the supply curves are derived from individual firm marginal-cost curves whose geometric position or algebraic description depends critically on the prices of raw materials and factors of production used in the production process. But neither the prices of alternative products to be purchased by consumers nor the prices of raw materials and factors of production are given independently of the general-equilibrium solution of the whole system.
Thus, partial-equilibrium analysis, to be analytically defensible, requires a ceteris-paribus proviso. But to be analytically tenable, that proviso must posit an initial position of general equilibrium. Unless the analysis starts from a state of general equilibrium, the assumption that all prices but one remain constant can’t be maintained, the constancy of disequilibrium prices being a nonsensical assumption. (Emphasis added)

That’s…a convenient assumption, at the very least, even for an economist.

It gets worse (or if you enjoy thinking about this sort of thing, better):

Unless general equilibrium obtains, prices need not equal costs, as measured by the quantities and prices of inputs used by firms to produce any product. Partial equilibrium analysis is possible only if carried out in the context of general equilibrium. Cost cannot be an independent determinant of prices, because cost is itself determined simultaneously along with all other prices.

Towards the end of the post, David Glasner helps us understand why comparative-statics are extremely limited tools.

Very briefly (please do read the entire post),

1. the fact that you must begin in a state of disequilibrium,

2. plus the fact that the movement towards some (potential) equilibrium will take time,

3. and finally, the lack of a guarantee that changes in this dynamic system will move us towards equilibrium…

… imply that one should use partial-equilibrium analysis only when fully aware of its limitations.

As David Glasner reminds us, none of this is new or path-breaking, but as students of economics, it is helpful to remind ourselves that, well, macro is hard.

So You Think You’ve Understood Macro…

Warning: this post actually isn’t “for everybody”.

Teaching macro is hard enough. Teaching macro to non-economists is all but impossible, because things get really messy really quickly – and I cannot emphasize how messy, and how quickly. The simplest way to teach macro to non-economists is to say that macroeconomics attempts the impossible – it tries to analyze too many variables at the same time in a gloriously inadequate framework, with not enough attention being given to how to understand, measure and forecast risk uncertainty.

And that’s before we’ve even touched the concept of time and inherent unknowability!1

Shackle went on to write that what the market equilibrium conception showed was a world of perfect knowledge frozen in time. It thereby negated itself as being of any use in a world where knowledge of the future is impossible and time moves in one direction. In such a world the action of human beings must be in part based on reason and in part on imagination—specifically, imagination with respect to what various individuals imagine the future might be or even should be. Shackle wrote that neoclassical economics rested on a teleological or pre-determined future and thus left no space for human choice which was inherently tied up with a human being’s capacity to freely imagine what might be in store in the future.

I’m going to sound very woo-woo when I say this, but if at the end of your macro semester you think you’ve understood the subject, then both you and your prof haven’t done a very good job. Macro is hard, and the macroeconomy is inherently unknowable, and yes, I’m willing to die on this hill.

But that does not mean it is not worth studying! Quite the contrary, in fact: it is precisely this reason – the inherent unknowable nature of macro – that makes it so fascinating to study.2

And if you are somewhat familiar with macro – say you’ve spent a semester or so studying it, maybe a bit more – then a good way to check if you have “understood” the subject is to read this lovely little essay by Trevor Chow. (Please, be warned, if you have not had a course in theoretical macro, this essay will make very little sense, and you absolutely should not read it. )

Description: The goal is to bring you up to speed from knowing nothing about business cycle macroeconomics till you know everything you want to know about it at an intermediate macro level within a single post. We’ll mess around with the notion of goods and money market equilibrium to see where it takes us, though if you want to get to the interesting stuff and already know enough about IS-LM etc, feel free to skip to Part 4 and onwards. This is probably, even more than my growth series, the hardest I’ve tried at making things accessible and clear, so please do get in touch if you think there are things which are underexplained or could be rewritten. And check out Miles Kimball and Nick Rowe, whose ideas I borrow very generously from in this post.

It’s very simply written, and is easily understandable – and trust me, that is hard to do when it comes to macro. It covers a lot of useful concepts, and there is a lot of back and forth between various schools of thought in macroeconomics.

My favorite excerpt was this one:

Macroeconomics is itself quite difficult, because even in the simplest business cycle models we are interested in all sorts of things: output, consumption, investment, the real interest rate, the nominal interest rate, prices, the money supply and inflation. Squeezing all of this into a static model is nigh impossible. Although I do think the canonical IS-LM model can be a bit deceptive with respect to interest rates, the idea of reconciling the goods and money markets is a useful approach. And by putting the IS-LM model through its paces, we’ve already illustrated some important ideas:

That the short run is a monetary question and not one of price adjustment
That there can be indeterminacy or unstable equilibria with bad monetary regimes
That liquidity traps and debt deflation can cause problems, but liquidity traps are really expectations traps
That there are good reasons for the Taylor rule and the Taylor principle

Again, let me reiterate my basic point: if you are left with the feeling that you “get” macro, beware. Read more, and keep asking how you might be wrong in your understanding of the subject. And excellent places to begin would be Frank Knight and GLS Shackle – even the Wikipedia articles are more than enough to get started!

Bonus reading material: Snowdown and Vane.3

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

  1. I’m genuinely curious: if you’ve been taught a course in theoretical macro, did G.L.S. Shackle ever come up for discussion?[]
  2. Quite like studying theology, no?[]
  3. These prices make no sense whatsoever. Pah.[]

A Summer Spent Doing Macroeconomics

Say you’re a student, and you’ve just finished learning a fair bit about macroeconomics. You’ve read and not understood Keynes, you’ve read and think you’ve understood Friedman, and you don’t have the faintest idea what folks in macro have been up to since Robert Lucas.

OK, all that is fine, but how should a budding macroeconomist spend her summer this year?

You could do a lot worse than reading this article, and asking yourself some simple questions.

Such as, do I hear you say? Read on!

Google mobility, for instance, is down more than 40 per cent since the start of April and currently at levels seen a year ago, when the national lockdown was in effect. This dynamic is also visible in the cross-section: states that forced down mobility more strongly have, in general, also seen a larger drop in positivity rates.

What is Google Mobility? What does the data for India look like? How does this data correlate with statewise Covid-19 numbers? Can I create simple tables and charts in, say, Google Sheets that show a link between the two? And write up a blog about how I did it? Or maybe create YouTube tutorials that show how I did it?

That said, there’s growing evidence the impact will not be trivial even if not of the same scale as the first wave. By the middle of May, power demand was down 13 per cent and vehicle registrations were down 70 per cent compared to the start of the quarter, while e-way bills in the first half of the month were at 40 per cent of where they should be. A broader composite index would suggest activity is tracking a 6-7 per cent sequential decline this quarter and, while this is much shallower than the 25 per cent sequential contraction witnessed last year this time, the fact that it comes on the heels of the first shock, and can potentially trigger more hysteresis, remains a source of concern.

Where does the data for power demand come from? Where does the data for vehicle registration come from? Where does GST data come from? What does the phrase “tracking a 6-7 percent sequential decline” mean? What is hysteresis?

Household income uncertainty and precautionary savings can be expected to rise. Even before the second wave, households had signalled caution about future spending (manifested in the RBI Consumer Confidence Survey) likely reflecting both an income hit and a precautionary savings motive. This behaviour is consistent with labour market dynamics wherein the unemployment rate, once adjusted for reduced labour force participation, had increased meaningfully even before the second wave.

What is the RBI Consumer Confidence Survey? How is it calculated (see Annexure A in this document)? Where do we get unemployment data from?

Private investment could also take time to pick up. Even before the second wave, utilisation rates were in the mid-60 per cent range, much lower than needed to jumpstart investment.

What is OBICUS? It stands for Order Book, Inventory and Capacity Utilization Survey. How else do we track capacity utilization?

We have previously found a strong elasticity of India’s exports to global growth and, if that holds, this should drive a strong export rebound in India. Some of this is already visible in the data with manufacturing exports surging in recent months, and currently 18 per cent (in nominal dollar terms) above pre-pandemic levels.

Where might that paper/research be, the one that talks about the strong elasticity of India’s exports to global growth? What does it tell us? What is different between the time that paper was written and today? Is that to India’s advantage or not? How do we tell?

If crude prices average close to $70 this fiscal year, as is expected, that would constitute a 50 per cent increase over last year and serve as a negative terms of trade shock that impinges on household purchasing power and firm margins — a process already underway.

EIA? Or something else? Should we take lagged data? If yes, with what lag? If no, why not? Where do we get information on firm margins? Bloomberg/Reuters? If yes, do we have access to a terminal? If no, whom do we ask for a favor?

When all is said and done, the completeness of an economy’s recovery from Covid-19 — and therefore the level of scarring — is assessed by comparing its post-Covid-19 path of the level of GDP with the path forecasted pre-Covid-19. If the aforementioned forecasts fructify, the level of quarterly GDP at the end of this year would be about almost 8 per cent below the level forecasted pre-pandemic. To be sure, India will not be the only emerging market to be below its pre-pandemic path. In fact, among the large economies, only the US and China will surpass it. But that said, an 8 per cent shortfall is meaningful.

What is the level of GDP, and how is it different from the growth rate of GDP? Which should one use, and how does the answer change depending on the context? Where do we get data on GDP of all countries at one time? Which one of these measures should we use for comparison, and why?

Macro is hard, and in many different ways. Understanding the theory is hard, but piecing together parts of the puzzle from disparate (and at lest in India, gloriously unfriendly) data sources is perhaps harder still. But if you want to “do” macro for a living, being familiar with the answers to these questions is table stakes.

That is, getting familiar with the answers to the questions I have asked here gets you the right to sit at the table. Playing the game better than the others once you’re in is a whole different story. And playing the game means using this data with your knowledge of theory to try and take a stab at the really important questions:

The question, therefore, is how should economic policy respond to this second shock? With fiscal and monetary policy already quite expansive, is there space to respond further? We assess policy options and tradeoffs in a companion piece tomorrow.

Trust me, macro is hard.

Imports, Exports and GDP

“The key is to understand that imports are also included in consumption, investment, and government spending. The real GDP breakdown looks like this:

  • GDP = Domestically produced consumption + Imported consumption + Domestically produced investment + Imported investment + Government spending on domestically produced stuff + Government spending on imported stuff + Exports – Imports

So you can see that while imports are subtracted from GDP at the end of this equation, they’re also added to the earlier parts of the equation. In other words, imports are first added to GDP and then subtracted out again. So the total contribution of imports on GDP is zero.”

That is an excerpt from a lovely little write-up by Noah Smith on his Substack, and one that I’ll be using whenever I teach macro. It’s lovely for many reasons, but most of all for the reason that the bullet point goes a very long way towards making the point that a lot of folks miss: you don’t get rich by importing less.

When I say “you”, I mean the country in question – and this equation, written out this way, helps us understand why. If you’re a student of macro, and are under the impression that India will get richer if only we imported lesser, think about the definition of GDP:

Gross domestic product (GDP) is the total monetary or market value of all the finished goods and services produced within a country’s borders in a specific time period.

If you think about it, how can imports possibly qualify as being produced within a country’s borders? As Noah says, the equation can also be written like this:

GDP = Domestically produced consumption + Domestically produced investment + Government spending on domestically produced stuff + Exports

Read the rest of Noah’s post, especially if you are a student of macroeconomics. It should help clear up a lot of basic, but important and often misunderstood ideas about GDP calculations.

Russia has stopped publishing detailed monthly trade statistics. But figures from its trading partners can be used to work out what is going on. They suggest that, as imports slide and exports hold up, Russia is running a record trade surplus.
On May 9th China reported that its goods exports to Russia fell by over a quarter in April, compared with a year earlier, while its imports from Russia rose by more than 56%. Germany reported a 62% monthly drop in exports to Russia in March, and its imports fell by 3%. Adding up such flows across eight of Russia’s biggest trading partners, we estimate that Russian imports have fallen by about 44% since the invasion of Ukraine, while its exports have risen by roughly 8%.

Think about the previous section, and try and answer this question: is Russia poorer or richer or unchanged because Russia isn’t importing as much, as measured by GDP and changes in GDP?

Well, Russia may be worse off, and Russians may be worse off. It’s leader?

As a result, analysts expect Russia’s trade surplus to hit record highs in the coming months. The iif reckons that in 2022 the current-account surplus, which includes trade and some financial flows, could come in at $250bn (15% of last year’s gdp), more than double the $120bn recorded in 2021. That sanctions have boosted Russia’s trade surplus, and thus helped finance the war, is disappointing, says Mr Vistesen. Ms Ribakova reckons that the efficacy of financial sanctions may have reached its limits. A decision to tighten trade sanctions must come next.
But such measures could take time to take effect. Even if the eu enacts its proposal to ban Russian oil, the embargo would be phased in so slowly that the bloc’s oil imports from Russia would fall by just 19% this year, says Liam Peach of Capital Economics, a consultancy. The full impact of these sanctions would be felt only at the start of 2023—by which point Mr Putin will have amassed billions to fund his war. (Emphasis added)

Macro is hard! But it also matters, especially at times such as these.

What is a Doom Loop?

Is a global recession imminent?

Probably. Macroeconomic forecasting is the stupidest of sports, but it is looking quite likely, yes.

How will recession start, how will it play out, and how long will it last? I don’t have the faintest idea, and trust me, nobody knows for sure.

But certain channels of both cause and effect (and sometimes both at the same time, because macro is hard) can be readily identified. And one such channel in today’s day and age is that of a ‘doom loop’.

A country is at risk of a doom loop when a shock to one part of its economic system is amplified by its effect on another. In rich countries, central banks should have the power to halt such a vicious cycle by standing behind government debt, stabilising financial markets or cutting interest rates to support the economy. But in the euro zone, the ECB can only do this to a degree for individual countries.

I haven’t taught international macro for a while now, but when I used to, I would explain this to my students by calling it the Mamata Banerjee/Narendra Modi/Raj Thackeray problem. I hope your curiosity is piqued!

For an economic union of political entities to work, there are (very broadly speaking) four things that must be present:

  1. A monetary union (which the EU has)
  2. A fiscal union (this is the Mamata Banerjee angle, explained below)
  3. Capital mobility (Narendra Modi)
  4. Labor mobility (Raj Thackeray)

Now, bear in mind that my examples are from a while back. I am referring to Mamata Banerjee’s first stint as Chief Minister, and the version of Narendra Modi I have in mind is the Chief Minister of Gujarat.

But back when Mamata Banerjee became Chief Minister of Bengal for the first time, one of the first things she did was to ask the Centre for help given West Bengal’s precarious finances. The point is not about whether it was given or not (as far as this blogpost is concerned), the point is that states routinely ask for, and sometimes get, aid from the centre. This may be because of natural disasters, or man made ones, financial ones or otherwise. The point is that the central government has the ability to ‘help’ out states if necessary. It is, of course, more complicated than that, and a fiscal union also implies the ability to raise and share taxes, but the central point is the fact there is help available, if needed.

But the ability of the European Union to do so is severely constrained, because you will need a lot of good luck to convince, for example, German voters that their taxes might be used to help the Spanish economy in its time of need. And for somewhat similar reasons, you can make more or less the same argument for the inability of the European Central Bank to chip in when necessary.

Or consider Narendra Modi’s invitation to Ratan Tata, to have his Tata Nano factory be relocated from West Bengal to Sanand in Gujarat. That’s an example of capital mobility, and again, this is much easier to achieve within a country.

And finally, Raj Thackeray, and his opposition to workers from outside Maharashtra ‘taking’ jobs within the state – that is a great way to understand what (lack of) labor mobility means.

The point is that an economic union must necessarily have these four things in place for it to be a meaningful, stable and well-functioning European Union. The idea isn’t new, of course – Robert Mundell‘s idea has been around since the late 1950’s, and there have been others who have worked on related ideas. Also read Paul Krugman on the topic.

But the point is that if a crisis strikes the EU, they have a limited range of weaponry that they can deploy.

Please read the rest of the article to get a sense of how linkages between European governments and its banks, the banks and the broader economy, and the broader economy and the European governments can both cause and exacerbate a crisis.

And as usual, the concluding paragraph for your perusal:

The euro zone is at less risk from doom loops than it was ten years ago, thanks to reforms to the banking system, the ECB’s commitment to preserve the euro and some embryonic fiscal integration. But the danger has not disappeared. And reforms to the euro zone’s architecture that would further reduce the risk have stalled⁠—in part because in 2012 the ECB boldly stepped in, easing the pressure on governments to make difficult decisions. As the ECB once again intervenes, the prospects for deep euro-zone reform look increasingly remote.

Links for 22nd December, 2018

  1. On why inequality matters.
  2. Macro is hard interesting.
  3. On the continued success of Abenomics.
  4. Carbon pricing from a global perspective.
  5. What are people saying about immigration?

David Warsh’s Take on Inflation

One of the sentences I have most enjoyed reading and internalizing is this one, by Scott Sumner: Never Reason From A Price Change.

I’ve capitalized each word in that sentence because it really is a sentence that makes you think until your head hurts. Here’s an early (perhaps the first) blog post from Scott in which he explains what he’s getting at:

My suggestion is that people should never reason from a price change, but always start one step earlier—what caused the price to change. If oil prices fall because Saudi Arabia increases production, then that is bullish news. If oil prices fall because of falling AD in Europe, that might be expansionary for the US. But if oil prices are falling because the euro crisis is increasing the demand for dollars and lowering AD worldwide; confirmed by falls in commodity prices, US equity prices, and TIPS spreads, then that is bearish news.

At its simplest – although there is always more to it than that – never reason from a price change means that the price might have changed because of demand, or supply or both. The headaches begin when you try to think through which of these might be more dominant, and the headache acquires splitting migraine status when you realize that you need to also ask about what else might be at play.

If you are a student of macroeconomics, a useful way to spend a morning is by clicking through this set of links and reading other posts by Scott Sumner on this topic. Remember, as always, the point is not to necessarily agree with Scott, but to read and ask how and why he arrives at his conclusions, and if you disagree with him, why do you do so. Best way to learn, especially if you can find a friend nerdy enough to do the exercise with you.

Which is a nice way to segue into our topic du jour: inflation.

A candy bar that cost a nickel in 1950 today costs $1.25 or so, depending on where you buy it. That, in a paper wrapper, is the price revolution of the twentieth century. Why did it happen? The answer usually given is that the quantity of money increased – too much paper money chasing too few candy bars.
A more satisfying explanation, casual though it may be, is to recognize that the global economy has grown considerably more complex since 1950, and the system of money, banking, and credit more complex along with it. The price of the candy bar wasn’t going to return to its previous level, no matter what the Fed or the candy-manufacturers did.

So begins a lovely little ruminative essay by David Warsh on how to think about inflation. It is lovely, but the emphasis in the previous sentence should be on the word “little”. I wish it was ten times longer!

But students used to textbook definitions of inflation might have their curiosity piqued after reading the second paragraph from the extract: what might complexity have to do with inflation?

A somewhat cryptic answer is given in the very next line that follows the end of the extract, where David Warsh refers to a book he wrote in 1984, called The Idea of Economic Complexity. I haven’t read the book, but I remember being told about it – alas, I can no longer remember who recommended it to me! But the idea of the book, from what I can recollect of the discussion, is as follows:

If you were to manufacture a Nokia 3310 today, odds are that you would be able to manufacture it at a fraction of the price that it commanded when it was first launched. Duh, you might think: so far, so obvious. Warsh’s point in the book is that this doesn’t necessarily mean that phones have become cheaper. In fact, as we can all attest, they go up in terms of price every year. The exact same thing might become cheaper, sure, but we keep making stuff more complex as we go along, and it is this increasing complexity that adds to inflation.

Now, bear in mind that I am treading on extremely thin ice over here! I’m describing a book to you that I haven’t read (strike one), on the basis of a conversation about the book that took place many years ago (strike two), and I’m now about to speculate on what else might be at play where this idea is concerned (strike three!).

All those CYA disclaimers aside, I’d like to think that complexity need not be just about the product itself, but could also be about the way it is manufactured, where all it is manufactured, where it is assembled, and how it is sold. Not to mention how all of this is financed!

As I’ve said before on these pages, macro is hard!

In Economic Development and the Price Level, in 1962, Geoffrey Maynard argued the opposite: that money generally adjusts to trade, rather than trade to money. In very different formats, the argument continues today.
“Development” is a bland word with which to describe the difference between the world economy in the time of Columbus and the world today. Economic philosopher David Ellerman has suggested that diversity describes the key difference, grounding his description in information theory; I proposed complexity in that 1984 book. But what is it that has become more diverse or complex? Not until I read “Increasing Returns an Economic Progress” (1928), by Allyn Young, did it occur to me that the growing complexity I had been thinking about were increases, of one sort or another, in the division of labor.

What a lovely excerpt, no? So much to add to the “To Read” list, but also how wonderful to pause and ponder on what the link might be between a Smithian division of labor and inflation. I hope you pause and think about this, much as I did when I read Warsh’s post, and again while drafting this paragraph right now.

To be clear: you’d expect division of labor to make systems more efficient, and therefore things cheaper. But Warsh suggests that there might be a way to link division of labor to complexity, and complexity to inflation!

Warsh ends his post in enigmatic fashion:

Are you comfortable with the too-much-money-chasing-too-few-goods story? Do you believe that the Fed could have prevented the rise in its price? And if wasn’t “inflation,” then what was it? The depreciation of money, relative to goods?
As with the sixteenth-century voyages of discovery, money follows development and development follows money. If you have only the quantity theory of money to rely on, you don’t know what is going on.

And that, I’d argue, is A Good Thing. A Good Thing because it allows us to prioritize reading The Idea of Economic Complexity, and allows to think about what David Warsh might be hinting at. An incentive (a carrot) to read the book, in other words, and one that I plan to use in the coming weeks.

Inflation: Oh ’tis problematic. Or is it?

A student messaged last week, asking some questions about inflation and its measurement in India. In particular, they wanted to know about food and its impact on inflation right now.

Well, outsourcing is always and everywhere a good idea, and Vivek Kaul had already answered the question at great length:

What this means is that, despite the end consumers of food paying a higher price, the farmers are largely not benefitting from this rise in food prices, given that they sell their produce at the wholesale level.
This difference can be because of a few reasons.

a) A collapse in supply chains has led to what is being sold at the wholesale level not reaching the consumers at the retail level, thus, leading to higher prices for the consumer.

b) This could also mean those running the supply chains hoarding stuff, in order to increase their profit.

Having said that, the former reason makes more sense given that stuff like vegetables, egg, fish and meat, etc., cannot really be hoarded. Also, hoarding stuff like pulses, needs a specialized storage environment which India largely lacks.

The entire article is worth reading (and so is subscribing to Vivek’s blog, so please do so!). And if you think 2020 isn’t depressing enough already, do read this article, also written by him. A short excerpt follows:

To conclude, the Indian economy will contract during the second half of the financial year. There is a slim chance of growth being flat for the period January to March 2021. Inflation, even though it might come down a little, is likely to remain high due to the spread of the covid pandemic. Hence, India will see conflation through 2020-21.

From a reading-the-tea-leaves perspective, it would seem the RBI actually isn’t that worried about inflation right now (and rightly so!). Here’s an excerpt from an excellent newsletter, Anticipating the Unanticipated that makes this point:

But the RBI wants to signal it is willing to live with inflation running above ‘comfortable’ level in the coming days. The MPC report last week claimed almost 80 per cent of the increase in inflation beyond the 4 per cent target can be attributed to supply chain disruptions and increase in fuel prices. This it believes is a short-term phenomenon and inflation will be in the 5 per cent range next year. This is underlined to give comfort to bond investors to buy government securities without the fear of a near-term interest rate hike to contain inflation. Further, the other step announced by RBI in extending the HTM (hold-to-maturity) limits by another year to March 2022 is to protect any bondholder from the volatility of prices and booking losses on account of it. The overall RBI signal is it doesn’t want the worry of rising inflation and a consequent rate increase to come in the way of growth. It’s focus now is on improving the transmission of rate cuts to the borrowers to stimulate growth.

… and here is Anantha Nageswaran making the same point, but by utilizing a different analysis:

This exercise generates the hypothesis that there is little or no intersection of the household inflation expectations formation and the monetary policy regime. Two, high inflation expectations peaked in September 2014. Similarly, the current high inflation expectations should peak as supply disruptions ease. So, in my view, RBI is betting correctly that the rate of inflation would ease and project policy on hold for the next few quarters. Three, inflation generation process should matter only to the extent that it affects medium-term output and employment generation. For now, other indicators suggest that it is not as disruptive as it was in 2011-13. Therefore, there is no need to turn it into a fetish. The new MPC and the central bank have done well and done good. They should be pleased.

And for the data nerds among you, here is the Inflation Expectations Survey of Households by the RBI (do keep in mind the point Ananta Nageswaran makes about trimmed means in his article). Note that currently at least, not too many people seem to be too worried about persistently high food inflation.

Side note: Jason Furman’s podcast with Tyler Cowen contained this interesting snippet:

FURMAN: GDP could be more meaningful if we measured it better. The inflation rate gets harder and harder to measure over time. So I think the one that probably has deteriorated in meaningfulness is the measure of inflation. Number one, we don’t measure it well, and number two, it’s low enough that it’s hard to get that excited about it.

COWEN: Is that a quality-of-goods problem? Or how we do chaining over time? Where are we going wrong in measuring inflation?

FURMAN: Just more and more of the economy is in areas that are harder to measure the quality of, healthcare being the most notorious.

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

Finally, here are past EFE articles on inflation.

Links for 27th October, 2018

  1. GDP and productivity (god, macro is hard)
  2. A review of a book I’m struggling with (and enjoying the struggle!)
  3. On Volitional Philanthropy.
  4. The unfortunate kind of stubborn attachments.
  5. The world’s largest food market.

I’m traveling to France in November. There will be some links pertinent to my trip that I’ll increasingly start putting up in the daily links. Also, please feel free to mail me whatever links you think might help me learn more about France – thanks (