macroeconomics

All posts tagged macroeconomics

In this post, I make some conjectures about U.S. economic growth over the next year or two.

Many people expect a depression, due to the current high unemployment numbers. But depressions aren’t caused by unemployment – that’s a symptom, with little predictive power.

The main cause of poor economic growth has been an inability to alter wages so that the supply of and demand for labor are in balance. That typically means deflation, or a large, unexpected decline in the inflation rate, combined with sticky wages.

So I’ll mostly focus on guessing whether we’ll have inflation or deflation.

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I recently made a bet with Robin Hanson that US COVID-19 deaths will be less than 250,000 by Jan 1, 2022 (details hiding in these Facebook comments).

I gave a few hints here about my reasons for optimism (based on healthweather.us). I’ll add some more thoughts here, but won’t try to fully explain my intuitions. Note that these are more carefully thought out than my reasoning at the time of the bet, and the evidence has been steadily improving between then and now.

First, a quick sanity check. Metaculus has been estimating about 2 million deaths from COVID-19 worldwide this year. It also predicts that diagnosed cases will decline each quarter from this quarter through at least Q4 2020, and stabilize in Q1 2021 at 1/10 the rate of the current quarter, suggesting that most deaths will occur this year.

U.S. population is roughly 4% of the world, suggesting a bit over 80k deaths if the U.S. is fairly average. The U.S. looks about a factor of 5 worse than average as measured by currently confirmed deaths, but a bit of that is due to a few countries doing a poorer job of confirming the deaths that happen (Iran?), and more importantly, the Metaculus forecasts likely anticipate that countries such as India, Brazil, and Indonesia will eventually have a much higher fraction of the world’s deaths than is the case now. So I’m fairly comfortable with betting that the U.S. will end up well within a factor of 3 of the world per capita average.

I was about 75% confident in late March that R0 had dropped below 1, and my confidence has been slowly increasing since then.

Note a contrary opinion here. It appears to produce results that are slightly pessimistic, due to assuming that testing effort hasn’t increased.

Yet even if it’s currently a little bit above 1, there’s still a fair amount of reason for hope.

Many people have been talking as if strict shelter-in-place rules (lockdowns) are the main tools for keeping R0 < 1. That’s a misleading half-truth. Something like those rules may have been critical last month for generating quick coordination around some drastic and urgent changes. But the best longer-term strategies are less drastic and more effective.

One obstacle to lowering R0 is that hospitals are a source of infection. I’m pretty sure that will be solved, on a lousy schedule that’s unconnected with the lockdowns.

Within-home transmission likely has a significant effect on R0. Lockdowns didn’t cause any immediate drop in that transmission, but that transmission drops a good deal as the fraction of people who have been staying at home for 2+ weeks rises, so R0 is likely declining now due to that effect.

Most buildings that are open to the public should soon require good masks for anyone to enter. It wasn’t feasible to include such a rule in the initial lockdown orders, but there’s a steady move toward following that rule.

I expect those 3 changes to reduce R0 at least 20%, and probably more, between late March and late April.

Robin is right to be concerned about the competence of institutions that we relied on to prevent the pandemic. Yet I see modest reasons for optimism that the U.S. will mostly use different institutions for test and trace: Google, Apple, LabCorp, etc., and they’re moderately competent. Also, most institutions are more competent at handling problems which they recall vividly than they are at handling problems which have been insignificant in the lifetimes of most executives.

We can be pretty sure based on China’s results that R0 < 1 is not a narrow target. Wuhan got R0 lower than the key threshold by a factor of something like two. They did that in roughly the worst weather conditions – most of the time, warmer (or occasionally colder) weather will modestly reduce R0. So we’ll be able to survive a fair amount of incompetence.

But there’s still plenty of uncertainty about whether next week’s R0 will be just barely acceptable, or comfortably below 1.

Deliberate Infection?

The challenges of adapting to the most likely scenarios took nearly all of my attention in March. So I had no remaining slack to adequately prepare for a scenario that looked unlikely to me, but which looked likely to Robin. For one thing, I ought to have evaluated the possibility that money will be significantly more valuable to me if Robin wins the bet than if he loses.

It is certainly possible to imagine circumstances where deliberate coronavirus infection is quite valuable. But it looks rather low value in the scenario I think we’re in.

I don’t have much hope of getting a sensible program of deliberate infection in a society that couldn’t even stockpile facemasks in February.

I also see only a small chance that talking about deliberate infection now will help in a future pandemic. I expect this to be humanity’s last major natural pandemic (note: I’m too lazy today to evaluate the relevance of bioterrorist risks). I don’t know exactly how we’ll deal with future pandemics, but the current crisis is likely to speed up some approaches that could prevent a future virus from becoming a crisis. Some conjectures about what might be possible within a decade:

  • Better approaches to vaccination, such that vaccines could become widely available within a week of identifying the virus.
  • Medical tricorders that are as ubiquitous as phones, and which can be quickly updated to detect any new virus.

Still, I do think deliberate infection should be tried in a few places, in case the situation is as desperate as Robin believes. I’ll suggest Australia as a top choice. It has weather-related reasons for worrying that the peak will come in a few months. It has substantial tuberculosis vaccination, which may reduce the death rate among infected people by a large margin (see Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19: an epidemiological study).

Note that tuberculosis vaccination looks a good deal more promising than deliberate infection, so it should be getting more attention.

Other odds and ends

Some of the concerns about a lasting economic slowdown are due to expectations that the restaurant industry will be shut down for years. I expect many other businesses to reopen within months with strict requirements that everyone wear masks, but it’s rather hard to eat while wearing a mask. So I see a large uncertainty about which year the restaurant business will return to normal. Yet I also don’t see people who used to rely on restaurants putting up with cooking at home for long. I see plenty of room for improvement in providing restaurant-like food to the home.

Current apps for delivery from restaurants seem like clumsy attempts to tack on a service as an afterthought. There’s plenty of room to redesign food preparation around home delivery, in ways that more efficiently and conveniently handle more of the volume that restaurants were handling before.

We have significant unemployment among restaurant workers, combined with food being hard to acquire for reasons which often boil down to labor shortages (combined with rules against price gouging). That’s not the kind of disruption that causes a lasting depression. The widespread opposition to price gouging is slowing down the adjustments a bit, but even so, it shouldn’t be long before the unemployed food service workers manage to become redeployed in whatever roles are appropriate to this year’s food preparation and delivery needs.

Finally, what should we think about this news: SuperCom Ships Coronavirus Quarantine Compliance Technology for Immediate Pilot?

Book(?) review: The Great Stagnation: How America Ate All The Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better, by Tyler Cowen.

Tyler Cowen wrote what looks like a couple of blog posts, and published them in book form.

The problem: US economic growth slowed in the early 1970s, and hasn’t recovered much. Median family income would be 50% higher if the growth of 1945-1970 had continued.

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Scott Sumner asks whether those of us[1] who talked about a housing bubble are predicting another one now.

Sumner asks “Is it possible that the housing boom was not a bubble?”.

It’s certainly possible to define the word bubble so that it wasn’t. But I take the standard meaning of bubble in this context to mean something like a prediction that prices will be lower a few years after the time of the prediction.

Of course, most such claims aren’t worth the electrons they’re written on, for any market that’s moderately efficient. And we shouldn’t expect the news media to select for competent predictions.

Sumner’s use of the word “bubble” isn’t of much use to me as an investor. If prices look like a bubble for a decade after their peak, that’s a good reason to have sold at the peak, regardless of what happens a decade later.

If I understand Sumner’s definition correctly, he’d say that the 1929 stock market peak looked for 25 years like it might have been a bubble, then in the mid 1950s he would decide that it had been shown not to be a bubble. That seems a bit strange.

Even if I intended to hold an investment for decades, I’d care a fair amount about the option value of selling sooner.

2.

The U.S. is not currently experiencing a housing bubble. I can imagine a small housing bubble developing in a year or two, but I’m reasonably confident that housing prices will be higher 18 months from now than they are today.

Several signs from 2005/2006 that I haven’t seen recently:

I mostly used to attribute the great recession to the foolish leverage of the banking system and homebuyers, who underestimated the risks of a significant decline in housing prices.

I’ve somewhat changed my mind after reading Sumner’s writings, and I now think the Fed had the power to prevent most of the decline in gdp, unless it was constrained by some unannounced limit on the size of its balance sheet. But I still think it’s worth asking why we needed unusual Fed actions. The fluctuations in leverage caused unusual changes in demand for money, and the Fed would have needed to cause unusual changes in the money supply to handle that well. So I think the housing bubble provides a good explanation for the timing of the recession, although that explanation is incomplete without some reference to the limits to either the Fed’s power or the Fed’s competence.

[1] – he’s mainly talking about pundits who blamed the great recession on the housing bubble. I don’t think I ever claimed there was a direct connection between them, but I did imply an indirect connection via banking system problems.

Book review: The Midas Paradox: Financial Markets, Government Policy Shocks, and the Great Depression, by Scott B Sumner.

This is mostly a history of the two depressions that hit the U.S. in the 1930s: one international depression lasting from late 1929 to early 1933, due almost entirely to problems with an unstable gold exchange standard; quickly followed by a more U.S.-centered depression that was mainly caused by bad labor market policies.

It also contains some valuable history of macroeconomic thought, doing a fairly good job of explaining the popularity of theories that are designed for special cases (such as monetarism and Keynes’ “general” theory).

I was surprised at how much Sumner makes the other books on this subject that I’ve read seem inadequate.
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[See my previous post for context.]

I started out to research and write a post on why I disagreed with Scott Sumner about NGDP targeting, and discovered an important point of agreement: targeting nominal wages forecasts would probably be better than targeting either NGDP or CPI forecasts.

One drawback to targeting something other than CPI forecasts is that we’ve got good market forecasts of the CPI. It’s certainly possible to create markets to forecast other quantities that the Fed might target, but we don’t have a good way of predicting how much time and money those will require.

Problems with NGDP targets

The main long-term drawback to targeting NGDP (or other measures that incorporate the quantity of economic activity) rather than an inflation-like measure is that it’s quite plausible to have large changes in the trend of increasing economic activity.

We could have a large increase in our growth rate due to a technology change such as uploaded minds (ems). NGDP targeting would create unpleasant deflation in that scenario until the Fed figured out how to adjust to new NGDP targets.

I can also imagine a technology-induced slowdown in economic growth, for example: a switch to open-source hardware for things like food and clothing (3-d printers using open-source designs) could replace lots of transactions with free equivalents. That would mean a decline in NGDP without a decline in living standards. NGDP targeting would respond by creating high inflation. (This scenario seems less likely and less dangerous than the prior scenario).

Basil Halperin has some historical examples where NGDP targeting would have produced similar problems.

Problems with inflation forecasts?

Critics of inflation targeting point to problems associated with oil shocks or with strange ways of calculating housing costs. Those cause many inflation measures to temporarily diverge from what I want the Fed to focus on, which is the problem of sticky wages interacting with weak nominal wages to create unnecessary unemployment.

Those problems with measuring inflation are serious if the Fed uses inflation that has already happened or uses forecasts of inflation that extend only a few months into the future.

Instead, I recommend using multi-year CPI forecasts based on several different time periods (e.g. in the 2 to 10 year range), and possibly forecasts for time periods that start a year or so in the future (this series shows how to infer such forecasts from existing markets). In the rare case where forecasts for different time periods say conflicting things about whether the Fed is too tight or loose, I’d encourage the Fed to use its judgment about which to follow.

The multi-year forecasts have historically shown only small reactions to phenomena such as the large spike in oil prices in mid 2008. I expect that pattern to continue: commodity price spikes happen when markets get evidence of their causes/symptoms (due to market efficiency), not at predictable future times. The multi-year forecasts typically tell us mainly whether the Fed will persistently miss its target.

Won’t using those long-term forecasts enable the Fed to make mistakes that it corrects (or over-corrects) for shorter time periods? Technically yes, but that doesn’t mean the Fed has a practical way to do that. It’s much easier for the Fed to hit its target if demand for money is predictable. Demand for money is more predictable if the value of money is more predictable. That’s one reason why long-term stability of inflation (or of wages or NGDP) implies short-term stability.

It would be a bit safer to target nominal wage rate forecasts rather than CPI forecasts if we had equally good markets forecasting both. But I expect it to be easier to convince the public to trust markets that are heavily traded for other reasons, than it is to get them to trust a brand new market of uncertain liquidity.

NGDP targeting has been gaining popularity recently. But targeting market-based inflation forecasts will be about as good under most conditions [1], and we have good markets that forecast the U.S. inflation rate [2].

Those forecasts have a track record that starts in 2003. The track record seems quite consistent with my impressions about when the Fed should have adopted a more inflationary policy (to promote growth and to get inflation expectations up to 2% [3]) and when it should have adopted a less inflationary policy (to avoid fueling the housing bubble). It’s probably a bit controversial to say that the Fed should have had a less inflationary policy from February through July or August of 2008. But my impression (from reading the stock market) is that NGDP futures would have said roughly the same thing. The inflation forecasts sent a clear signal starting in very early September 2008 that Fed policy was too tight, and that’s about when other forms of hindsight switch from muddled to saying clearly that Fed policy was dangerously tight.

Why do I mention this now? The inflation forecast dropped below 1 percent two weeks ago for the first time since May 2008. So the Fed’s stated policies conflict with what a more reputable source of information says the Fed will accomplish. This looks like what we’d see if the Fed was in the process of causing a mild recession to prevent an imaginary increase in inflation.

What does the Fed think it’s doing?

  • It might be relying on interest rates to estimate what it’s policies will produce. Interest rates this low after 6.5 years of economic expansion resemble historical examples of loose monetary policy more than they resemble the stereotype of tight monetary policy [4].
  • The Fed could be following a version of the Taylor Rule. Given standard guesses about the output gap and equilibrium real interest rate [5], the Taylor Rule says interest rates ought to be rising now. The Taylor Rule has usually been at least as good as actual Fed policy at targeting inflation indirectly through targeting interest rates. But that doesn’t explain why the Fed targets interest rates when that conflicts with targeting market forecasts of inflation.
  • The Fed could be influenced by status quo bias: interest rates and unemployment are familiar types of evidence to use, whereas unbiased inflation forecasts are slightly novel.
  • Could the Fed be reacting to money supply growth? Not in any obvious way: the monetary base stopped growing about two years ago, M1 and MZM growth are slowing slightly, and M2 accelerated recently (but only after much of the Fed’s tightening).

Scott Sumner’s rants against reasoning from interest rates explain why the Fed ought to be embarrassed to use interest rates to figure out whether Fed policy is loose or tight.

Yet some institutional incentives encourage the Fed to target interest rates rather than predicted inflation. It feels like an appropriate use of high-status labor to set interest rates once every few weeks based on new discussion of expert wisdom. Switching to more or less mechanical responses to routine bond price changes would undercut much of the reason for believing that the Fed’s leaders are doing high-status work.

The news media storytellers would have trouble finding entertaining ways of reporting adjustments that consisted of small hourly responses to bond market changes. Whereas decisions made a few times per year are uncommon enough to be genuinely newsworthy. And meetings where hawks struggle against doves fit our instinctive stereotype for important news better than following a rule does. So I see little hope that storytellers will want to abandon their focus on interest rates. Do the Fed governors follow the storytellers closely enough that the storytellers’ attention strongly affects the Fed’s attention? Would we be better off if we could ban the Fed from seeing any source of daily stories?

Do any other interest groups prefer stable interest rates over stable inflation rates? I expect a wide range of preferences among Wall Street firms, but I’m unaware which preferences are dominant there.

Consumers presumably prefer that their banks, credit cards, etc have predictable interest rates. But I’m skeptical that the Fed feels much pressure to satisfy those preferences.

We need to fight those pressures by laughing at people who claim that the Fed is easing when markets predict below-target inflation (as in the fall of 2008) or that the Fed is tightening when markets predict above-target inflation (e.g. much of 2004).

P.S. – The risk-reward ratio for the stock market today is much worse than normal. I’m not as bearish as I was in October 2008, but I’ve positioned myself much more cautiously than normal.

Notes:

[1] – They appear to produce nearly identical advice under most conditions that the U.S. has experienced recently.

I expect inflation targeting to be modestly safer than NGDP targeting. I may get around to explaining my reasons for that in a separate post.

[2] – The link above gives daily forecasts of the 5 year CPI inflation rate. See here for some longer time periods.

The markets used to calculate these forecasts have enough liquidity that it would be hard for critics to claim that they could be manipulated by entities less powerful than the Fed. I expect some critics to claim that anyway.

[3] – I’m accepting the standard assumption that 2% inflation is desirable, in order to keep this post simple. Figuring out the optimal inflation rate is too hard for me to tackle any time soon. A predictable inflation rate is clearly desirable, which creates some benefits to following a standard that many experts agree on.

[4] – providing that you don’t pay much attention to Japan since 1990.

[5] – guesses which are error-prone and, if a more direct way of targeting inflation is feasible, unnecessary. The conflict between the markets’ inflation forecast and the Taylor Rule’s implication that near-zero interest rates would cause inflation to rise suggests that we should doubt those guesses. I’m pretty sure that equilibrium interest rates are lower than the standard assumptions. I don’t know what to believe about the output gap.

Book review: Getting Off Track: How Government Actions and Interventions Caused, Prolonged, and Worsened the Financial Crisis, by John B. Taylor.

This book contains a few good ideas, expressed simply and clearly. It explains some of what caused the 2008 financial crisis. But he exaggerates a good deal when he claims he has “provided empirical proof that monetary policy was a key cause of the boom”.

He provides clear evidence that counterparty risk was a more important problem than lack of liquidity, and has some hints about how counterparty risk could have been handled better. But that doesn’t say much about whether policies to deal with liquidity problems were mistaken – I doubt that many of the people pushing those liquidity related policies were denying that counterparty risk was a problem.

I’ve been convinced since before the crisis that having the Fed follow the Taylor Rule would have been better than the monetary policy that we actually had. But the details of the rule seem somewhat arbitrary, and I’m disappointed that the book doesn’t provide much explanation of why the Taylor Rule is better than alternative rules.

I’ve found a link on Taylor’s blog to an article (The Taylor Rule and QE2 By David Papell) which compares it to some alternatives which seem motivated primarily by a desire to rationalize more monetary or fiscal stimulus. But what I want to know is whether it’s possible to create a rule that is more countercyclical without being more inflationary.

I have an intuition that it’s not too hard to improve on the inflation component of the rule. The CPI seems to have many drawbacks, such as being slow to reflect changes. I suspect Taylor’s inflation coefficient of 1.5 is larger than what an ideal rule would use in order to make up for the delays associated with using the CPI. When I’m estimating inflation for my investment decisions, I pay more attention to the ISM price index, the money supply (MZM), commodity prices, and stock prices. And the ISM Purchasing Managers Index should provide more up to date evidence of the “output gap” than GDP figures. A version of the Taylor Rule which emphasized those should react more quickly to changes in the economy.

Book review: This Time is Different: Eight Centuries of Financial Folly by Carmen M. Reinhart and Kenneth S. Rogoff.

This book documents better than any prior book the history of banking and government debt crises. Most of it is unsurprising to those familiar with the subject. It has more comprehensive data than I’ve seen before.

It is easier reading than the length would suggest (it has many tables of data, and few readers will be tempted to read all the data). It is relatively objective. That makes it less exciting than the more ideological writings on the subject.

The comparisons between well governed and poorly governed countries show that governments can become mature enough that defaults on government debt and hyperinflation are rare or eliminated, but there is little different in banking crises between different types of government / economies.

They claim that international capital mobility has produced banking crises, but don’t convince me that they understand the causality behind the correlation. I’d guess that one causal factor is that the optimism that produces bubbles causes more investors to move money into countries they understand less well than their home country, which means their money is more likely to end up in reckless institutions.

The book ends with tentative guesses about which countries are about to become mature enough to avoid sovereign debt crises. Among the seven candidates is Greece, which is now looking like a poor guess less than a half year after it was published.

Book review: Meltdown: A Free-Market Look at Why the Stock Market Collapsed, the Economy Tanked, and Government Bailouts Will Make Things Worse by Thomas E. Woods Jr.

This book describes the Austrian business cycle theory (ABCT) in a more readable form than it’s usually presented. Its basic idea that malinvestment creates business cycles, and that central bank manipulation of interest rates can cause malinvestment, is correct. But when Woods tries to argue that only errors by a government can cause business cycles, his ideological blinders become obvious. He’s mostly right when he complains about government mistakes, and mostly wrong when he denies the existence of other problems.

He asks why businesses made a “cluster of errors” that added up to a big problem rather than independent errors which mostly canceled each other out. The only answer he can find is misleading signals sent by the Fed’s manipulation of interest rates. He doesn’t explain why businessmen fail to learn from the frequent and widely publicized patterns of those Fed actions. It’s unclear why groupthink needs a strong cause, but one obvious possibility that Woods ignores is that most people saw a persistent trend of rising housing prices, and didn’t remember large drops in housing prices over a region as large as the U.S.

He shows no understanding of the problems associated with sticky wages which are a key part of the better arguments for Keynesian approaches.

He wants to credit ABCT with having predicted this downturn. If you try to figure out when was the last time it didn’t predict a downturn (the early 1920s?), this seems less impressive than, say, Robert Shiller’s track record for predicting when bubbles burst.

His somewhat selective use of historical evidence carefully avoids anything that might present a picture more complex than government being the sole villain. He describes enough U.S. economic expansions to present a clear case that credit expansion contributed to the ensuing bust, and usually points to a government activity which one can imagine caused excessive credit expansion. But he’s unusually vague about the causes of the expansion that led to the panic of 1857. Could that be because he wants to overlook the role that new gold mining in California played in that inflationary cycle?

He mostly denies that free market approaches have been tested for long enough to see whether we would avoid business cycles under a true free market. He points to a few downturns when he says the government followed a wise laissez faire policy, and compares the shortness of those downturns with a few longer downturns where the government made some attempts to solve the downturns. When doing this, he avoids mention of the downturns where massive government actions were followed by mild recessions. Any complete survey comparing the extent of government action with the ensuing economic conditions would provide a much murkier picture of the relative contributions of government and market error than Woods is willing to allow.

The most interesting claim that I hadn’t previously heard is that a large decrease in the money supply in 1839-1843 coincided with healthy GNP growth, which, if true, is hard to explain without assuming Keynesian and monetarist theories explain a relatively small fraction of business cycle problems. My attempts to check this yielded a report at http://www.measuringworth.org/usgdp/ saying GDP in 2005 dollars rose from $31.37 in 1839 to $34.84 in 1843, but GDP per capita in 2005 dollars dropped from $1884 in 1839 to $1869 in 1843. Declining GDP per capita doesn’t sound very prosperous to me (although it’s a mild enough decline to provide little support for Keynesians/monetarists).

He tries to blame the “mistakes” of credit rating agencies on an SEC-created cartel of rating agencies. That “cartel” does have some special privileges, but he doesn’t say what stops bloggers from expressing opinions on bond risks and developing reputations that lead to investors using those opinions in addition to the “cartel”‘s ratings (Freerisk is a project which is planning a sophisticated alternative). I say that anyone who understands markets would expect the yield on the bonds to provide as good an estimate of risk as any alternative. Credit rating agencies must be performing some other function in order to thrive. An obvious function is to mislead bosses and/or regulators who don’t understand markets into thinking that the people making investment decisions are making choices that are safer than they actually are. It appears that the agencies performed that function well, and helped many people avoid being fired for poor choices.

His discussion of whether WWII spending cured the Great Depression points out that mainstream theories falsely predicted a return to depression in 1946. But it’s unclear whether all versions of Keynesianism make that mistake, and it’s unclear how ABCT could predict the U.S. would be much more prosperous in 1946 than at the start of the war.
Here’s an alternative explanation that lies in between those theories: wages were being kept too high for supply and demand to balance through 1941. Inflation and changes in government policy toward wage levels during WW2 eliminated the causes of that imbalance.

Arnold Kling has a good quasi-Austrian alternative here and here.