Archive for September 2016

 
 

How did we end up here?

I’ve finally had a chance to read Paul Romer’s critique of macroeconomics, and it’s every bit as interesting as you might expect.  I’m going to focus on a single issue, which in my view lies at the heart of what’s gone wrong in recent decades—identification.  This issue has been the main focus of my blogging over the past 7 1/2 years, so it’s very dear to my heart. By late 2008, it was clear to me that not only did economists not know how to identify monetary shocks, but also that they were very far off course, and didn’t even understand that fact. Indeed this misunderstanding actually became highly destructive to progress in both economic science and economic policymaking.  One of the two the worst contractionary monetary shocks of my lifetime is generally regarded as “easy money”.  So how did we get here?

1. The earliest monetary shocks were seen as involving the price of money.  Coin debasement was a common example.  No one knew the money supply, and banks did not yet exist. This policy tool was used by FDR in 1933, but today has fallen out of fashion in big economies. Small countries like Singapore still use the price of money (exchange rates) as a policy instrument, but they do not drive the research agenda.  I’m trying to bring it back with NGDP futures targeting.

2.  Although the monetarist approach to identification (i.e. the money supply) dates back at least to Hume, it really came into its own with fiat money, especially during hyperinflationary fiat regimes.  Milton Friedman preferred the M2 money supply as a monetary indicator, at least during part of his career.

3.  Then for some strange reason the profession shifted from the money supply to interest rates, as an indicator of monetary shocks.  But why?

Perhaps you are thinking that you know the answer.  Maybe it had something to do with the early 1980s, when velocity was unstable and monetarism was “discredited”.  If that is indeed what you are thinking, then it merely illustrates that you are even more confused than you know.  Yes, velocity is unstable.  And yes, that means Friedman’s 4% money growth rule might not be a good idea.  But that has absolutely no bearing on the argument for replacing the money supply with interest rates, as an indicator of the stance of monetary policy.

The relationship between i and NGDP is just as unstable as the relationship between M2 and NGDP, probably more unstable.  At least with M2, we generally can assume that an increase means an easier monetary policy, and a reduction means a tighter policy.  We don’t even know that much about the relationship between interest rates and NGDP. Right now, markets expect about a 1% fed funds rate in 2019. Suppose you had a crystal ball that told you that the fed funds rate in 2019 would be 3%, not 1%.  There’s a classic “monetary shock”. The stance of monetary policy changed unexpectedly.  But which way—is that easier than expected policy, or tighter?  I have no idea, and you don’t know either.  Even worse, my best guess would be “easier” but the official model says “tighter.”

Paul Romer says we know that monetary shocks are really important.  I agree.  And he says the Volcker disinflation proves that.  I agree, and could cite many other examples, probably even more than Romer could cite.  So I’m completely on board with his general critique of those who claim we don’t know whether monetary shocks are important.  But Romer then claims that the real interest rate is a useful measure of the stance of monetary policy, and it isn’t—not even close.

Am I denying that if the Fed suddenly raised the real interest rate by 200 basis points, money would be tighter on that particular day or week?  No, I agree that that statement is true.  But it’s also true that if the Fed suddenly raised the nominal interest rate by 200 basis points, money would be tighter on that particular day or week.  Or if the Fed suddenly cut M2 by 10%, money would be tighter on that particular day or week.

So why don’t we use M2 to measure the stance of monetary policy?  Because over longer periods of time, movements in M2 do not reliably signal easier or tighter monetary policy.  But that’s also true of movements in nominal interest rates. If you have a highly contractionary policy, then inflation and nominal rates will fall in the long run.  Hence low rates don’t mean easy money.  And this argument also applies to real interest rates.  If the Fed adopts a tight money policy that drives the economy into a depression, then real interest rates will decline, even as policy is effectively contractionary.  This actually happened in 1929-33 and 2008-09.

All of the traditional indicators are unreliable.  The smarter New Keynesians will say that money is tight when the interest rate is above its Wicksellian equilibrium rate. But how do we know when that is the case?  After all, the Wicksellian equilibrium rate cannot be directly observed.  You need to look at outcomes; Wicksell said interest rates were above equilibrium when prices were falling, and vice versa. But that means we can only identify easy and tight money by looking at outcomes; are prices rising or falling?

Today we would substitute above or below 2% inflation, or 4% NGDP growth, but the basic idea is the same.  Money is tight when it’s too tight to hit your target, and vice versa. Ben Bernanke got this right in 2003, and then lost track of this concept when he joined the Fed:

Do contemporary monetary policymakers provide the nominal stability recommended by Friedman? The answer to this question is not entirely straightforward. As I discussed earlier, for reasons of financial innovation and institutional change, the rate of money growth does not seem to be an adequate measure of the stance of monetary policy, and hence a stable monetary background for the economy cannot necessarily be identified with stable money growth. Nor are there other instruments of monetary policy whose behavior can be used unambiguously to judge this issue, as I have already noted. In particular, the fact that the Federal Reserve and other central banks actively manipulate their instrument interest rates is not necessarily inconsistent with their providing a stable monetary background, as that manipulation might be necessary to offset shocks that would otherwise endanger nominal stability.

Ultimately, it appears, one can check to see if an economy has a stable monetary background only by looking at macroeconomic indicators such as nominal GDP growth and inflation. On this criterion it appears that modern central bankers have taken Milton Friedman’s advice to heart.

Others will object that New Keynesians understand that it’s the level of interest rates relative to the Wicksellian equilibrium rate that matters.  For instance, a recent paper by Vasco Curdia shows that money was actually quite contractionary, during and after the Great Recession.

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Yes, but that paper was written in 2015.  Back in late 2008 and throughout 2009, market monetarists were just about the only people claiming that monetary policy was highly contractionary—and that was the period when we most needed clear thinking.  Others were lulled by meaningless indicators like low nominal and real interest rates, as well as a ballooning monetary base.

How did we end up using interest rates as an indicator of the stance of monetary policy?  Romer provides one possible clue in his paper:

By rejecting any reliance on central authority, the members of a research field can coordinate their independent efforts only by maintaining an unwavering commitment to the pursuit of truth, established imperfectly, via the rough consensus that emerges from many independent assessments of publicly disclosed facts and logic; assessments that are made by people who honor clearly stated disagreement, who accept their own fallibility, and relish the chance to subvert any claim of authority, not to mention any claim of infallibility.

I fear that economists have deferred too much to the “central authority” of central banks.  When I talk to macroeconomists, they seem to think it’s natural to use interest rates in their monetary models because the central banks actually target short-term interest rates.  But that’s a lousy reason.

Another problem may be that some economists are infected by a popular prejudice—that low interest rates are a “good thing” for the economy.  We visualize that we would be more likely to buy a new house if interest rates fell, and extrapolate from that to the claim that low rates would boost GDP.  That’s obviously an example of the fallacy of composition.  Yes, I’d be more inclined to borrow money if interest rates fell, ceteris paribus.  But some other guy is less inclined to lend me the money if interest rates fell, ceteris paribus.  Of course ceteris is not paribus if interest rates fall, and it all depends on whether they fall because of an increase in the money supply (expansionary) or more bearish expectations from the public (contractionary.)

Elsewhere I call this “reasoning from a price change”, and even Nobel laureates do it:

Real interest rates have turned negative in many countries, as inflation remains quiescent and economies overseas struggle.

Yet, these negative rates haven’t done much to inspire investment, and Nobel laureate economist Robert Shiller is perplexed as to why.

“If I can borrow at a negative interest rate, I ought to be able to do something with that,” he tells U.K. magazine MoneyWeek. “The government should be borrowing, it would seem, heavily and investing in anything that yields a positive return.”

But, “that isn’t happening anywhere,” Shiller notes. “No country has that. . . . Even the corporate sector, you might think, would be investing at a very high pitch. They’re not, so something is amiss.”

And what is that?

“I don’t have a complete story of why it is. It’s a puzzle of our time,” he maintains.

Actually there is no puzzle.  Shiller seems unaware that it’s normal for the economy to be weak during periods of low interest rates, and strong during periods of high interest rates.  He seems to assume the opposite.  In fact, interest rates are usually low precisely during those periods when the investment schedule has shifted to the left.  Shiller’s mistake would be like someone being puzzled that oil consumption was low during 2009 “despite” low oil prices.

I know what commenters will say—I’m a pigmy throwing stones at Great Men. They are right.  Guilty as charged.  Look, I’ve made the mistake of reasoning from a price change numerous times—it’s easy to do.  But that won’t stop me from criticizing the ideas of people much more famous than I am.  In Paul Romer I’ve found a kindred spirit.

PS.  Since I’m nearly 6’4″, perhaps I should be PC and add, “Not that there’s anything wrong with being a pigmy”.

PPS.  This link has videos to the recent Mercatus/Cato conference on monetary policy rules.

Paul Romer on the identification crisis

LK Beland directed me to a paper by Paul Romer, which I’ve only had time to skim.  But the abstract is great:

For more than three decades, macroeconomics has gone backwards. The treatment of identification now is no more credible than in the early 1970s but escapes challenge because it is so much more opaque. Macroeconomic theorists dismiss mere facts by feigning an obtuse ignorance about such simple assertions as “tight monetary policy can cause a recession.” Their models attribute fluctuations in aggregate variables to imaginary causal forces that are not influenced by the action that any person takes. A parallel with string theory from physics hints at a general failure mode of science that is triggered when respect for highly regarded leaders evolves into a deference to authority that displaces objective fact from its position as the ultimate determinant of scientific truth.

He focuses on RBC theory, but the problems go far deeper.  New Keynesian models also fail at identification.  Nick Rowe has an excellent recent post on this problem:

Suppose we model monetary policy as M(t) = bX(t) + e(t), where M is the money supply, X is some vector of macroeconomic variables, and e is some random shock. Or, if you prefer, as i(t) = bX(t) + e(t), where i is a nominal interest rate. We estimate (somehow) that monetary policy reaction function, and call our estimate of e(t) the “monetary shock”.

Let us suppose, heroically, that our estimate of the monetary policy reaction function is correct. The econometrician, by sheer luck, gets it exactly right. Whatever that means. And then we use that estimate of monetary shocks to see what percentage of macroeconomic fluctuations (somehow defined) was caused by those “monetary shocks”, and what percentage was caused by other shocks. And suppose, again heroically, we get it right.

This is nonsense. We are making exactly the same mistake that the people were making in my Gold Standard examples above. If the central bank had been following the monetary policy reaction function exactly (or if the econometrician had a complete data set and correct model of the central bank’s behaviour so the estimated reaction function fitted exactly) then by definition there would have been no “monetary shocks”. And so “monetary shocks” would explain 0% of anything, because there weren’t any. 100% of macroeconomic fluctuations were caused by other, non-monetary shocks. Any deterministic monetary policy will have zero “monetary shocks”, by that definition, and any organisation’s behaviour is deterministic, if we understand it fully enough. That is not a useful definition of “monetary shocks”.

Monetary shocks are not the e(t). Monetary shocks mean the central bank chose the wrong monetary policy reaction function. It’s the choice of parameter b, and the choice of which variables belong in the vector X.

People get sick of me always talking about “the stance of monetary policy”.  But the misidentification of easy and tight money is THE central problem in macroeconomics.  Everything else is a footnote.

On a related topic, check out my new post at Econlog—I’m interested in feedback on my graph.

NGDP Advisers

There’s a new post by James Alexander, Benjamin Cole, Justin Irving, Marcus Nunes describing their consulting firm, called NGDP-Advisers:

After a six-year run, during which Historinhas helped spread the Market Monetarist approach, this blog will undergo a metamorphosis, becoming NGDP-Advisers. The blog will continue but be augmented by new products that will be available via subscription.”

.  .  .

At NGDP Advisers, we hope not only to continue our examination of the global economy, but also to recognize realities and advise accordingly. We’ll yell from the cliff tops ‘what should be’, but we’ll also help you get ready for what ‘will be’.

Please join us at ngdp-advisers.com, the best is yet to come. The Historinhas blog will stay up but dormant, and recent and all future posts will be freely available here ngdp-advisers.com/blog/ 

This is good to see.  Ideas are taken more seriously when they move beyond academia and out into the marketplace.  I’ve added them to my blog roll and look forward to reading what they have to say.  If I’m not mistaken, the four participants reside in 4 different continents–so I expect an international perspective.

Speaking of NGDP, I found a new SSRN working paper (by Jonathan Benchimol and Fourçans André), with the following abstract:

Since the beginning of the financial crisis, a lively debate has emerged regarding which monetary policy rule the Fed (and other central banks) should follow, if any. To clarify this debate, several questions must be answered. Which monetary policy rule fits best the historical data? Which monetary policy rule best minimizes economic uncertainty and the Fed’s loss function? Which rule is best in terms of household welfare? Among the different rules, are NGDP growth or level targeting rules a good option, and when? Do they perform better than Taylor-type rules? To answer these questions, we use Bayesian estimations to test the Smets and Wouters (2007) model under nine different monetary policy rules with US data from 1955 to 2015 and over three different sub-periods. We find that when considering only the central bank’s loss function, the estimates generally indicate the superiority of NGDP level targeting rules, whatever the period. However, if other criteria are considered, the central bank’s objectives are not consistently met by a single rule for all periods.

I was pleasantly surprised by their findings, as traditional loss function criteria are biased against NGDP targeting, by assuming that inflation instability is what matters, whereas it’s actually NGDP growth instability that is the problem.

 

 

Our Trumpian Treasury

In Washington DC, all the best and the brightest in both parties are horrified by the prospects of a Trump Presidency.  But perhaps we owe Trump an apology, as in some respects he already controls our trade policy.  Here’s a report from 4 months ago:

The U.S. government is sending a message to countries it believes are manipulating their currencies: We’re watching you.

A Treasury report targets five countries in particular: China, Japan, Korea, Taiwan and Germany. Each meets at least two of the three criteria that “determine whether an economy may be pursuing foreign exchange policies that could give it an unfair competitive advantage against the United States.”

At a time when currency devaluation has become a major tool used by multiple countries to stimulate growth, the U.S. is looking to protect its own interests. The report is an outgrowth of the Trade Facilitation and Trade Enforcement Act of 2015, a bipartisan effort aimed at stemming the global race to the bottom.

The criteria to determine whether a country should be on the “Monitoring List” of countries using unfair currency practices are: a trade surplus of larger than $20 billion, or 0.1 percent of U.S. GDP; a trade surplus with the U.S. that is more than 3 percent of that country’s GDP; “persistent one-sided intervention,” defined as purchases of foreign currency amounting to more than 2 percent of the country’s GDP in a one-year period.

No country meets all three criteria, according to the report, though the five on the list meet at least two.

For those not familiar with open economy macroeconomics, those criteria reach almost Trumpian levels of ignorance.  Where to begin:

1.  If you worry about this sort of thing (and a few respectable economists do) then you would look at a completely different set of criteria.  For instance, the trade deficit is a meaningless data point, if anything, you’d look at current account deficits.

2.  Bilateral deficits with the US are stupidity squared, a data point of no conceivable relevance, even to the most mercantilist economist on Earth.  All that “matters” is overall surpluses or deficits, not bilateral.

3.  If you are looking for “villains”, you would certainly not look at overall surpluses; rather you’d focus on surpluses as a share of GDP, or some similar metric.  Otherwise you’d be biased against large countries.  The current account surplus for China is about $170 per capita, for Switzerland it’s over $8000 per capita.  Even as a share of GDP the Swiss surplus is far higher.

4.  “Intervention” should not be defined as purchases of foreign assets, but rather as high government saving rates.  All government saving tends to have the same effect on the CA balance, whether it is used to buy domestic or foreign assets.

5.  The Treasury singles out Asian countries in a Trumpian fashion, for no apparent reason.  Switzerland has a $71 billion CA surplus, and engages in massive purchases of foreign assets to hold down the value of the SF.  There’s two criteria right there.  Why did it not make the list?  I have no idea, its trade surplus is also well above $20 billion.  Lots of other northern European countries also have massive CA surpluses, and spend lots of money holding down the value of their currencies.  In contrast, China’s recently been trying to hold up the value of its currency.

6.  If you were an American mercantilist, I’d think that you’d be much more worried about Germany’s $300 billion CA surplus, than China’s $250 billion CA surplus.  Germany exports lots of capital goods that might otherwise be bought from America, whereas China tends to export less sophisticated goods, which might otherwise be produced in other Asian countries, or Mexico.  Norway, Sweden, the Netherlands and even Italy have CA surpluses far in excess of $20 billion.  Notice that the Treasury follows in the proud tradition Pat Buchanan and Donald Trump in pointing fingers mostly at Asian countries, even though the logic of their mercantilist argument would suggest we should target the northwest Europeans.  Perhaps Germany was put on the list to try to make the racism appear a bit less blatant.

PS.  I forgot to shed a few tears for the “victims” of all this evil currency manipulation.  There’s Australia, with its $62 billion deficit, and no recessions in 25 years.  The UK, with its $162 billion deficit and the US, with its $473 billion deficit.  Both countries have a 4.9% unemployment rate.  In contrast, those sneaky eurozone members with their $394 billion CA surplus keep stealing our jobs, which probably explains their 10.1% unemployment rate.

PPS.  I don’t put all this on the Treasury; it’s Congress that forces them to engage in this sort of nonsense.

PPPS.  Speaking of Trump, last May I had commenters earnestly informing me that I should support Trump because he favored low interest rates.  Now Trump is slamming Yellen for her low interest rate policy.  Trump reminds me of that anecdote about 100 monkeys typing away.  Yes, there’s a tiny chance they might randomly type out Hamlet, but I’d put my money on something far worse.  There are many more ways to screw up than there are ways to succeed.

And no, this is not “normal” in politics.  Normal politicians lie and change their views on occasion.  Hillary’s not much worse than average (although she is certainly worse.)  But Trump’s just completely off the charts in terms of policy ignorance and personal dishonesty.  I’ve never seen anything close to this in my life, and I’ve been following politics since the late 1960s.  Nixon might have been closest on the honesty criterion, but of course was far more knowledgeable.  And even Nixon tried to avoid statements that were obvious lies. He was a devious liar.  Trump just doesn’t care.  He’ll look you in the eye and tell you that he opposed the Iraq War.  And the reporter who asked the question will be too cowardly to call him a liar to his face.  I almost hope Trump wins.  We deserve him.

I said “almost”, I’m not quite there yet.  :)

China: What’s at stake

I see many biased stories on China, so it’s nice to read something sensible.  Here’s Alex Frew McMillan:

Write off China at your peril.

That’s the message coming out of Tuesday’s economic data, which were considerably stronger than expected. The data show that the second half of 2016 will produce growth similar to that of the first six months of the year.

Just as the sun is shining on China’s east coast today, the numbers dispel the gloom cast over China’s economy last year, when the most-pessimistic of pundits predicted a crash landing.

August industrial output rose 6.3% year on year, beating a 6.2% estimate and up from 6.0% the previous month. Retail sales were the real shock, rising to 10.6% from 10.2% the previous month, a number economists had anticipated would be repeated. Infrastructure investment was also strong, after a sharp drop in July.

People’s reactions to such numbers are all over the place. I just received a report in response to the figures from SocGen: “Concerns over bubbles, not growth.” Some people are just never happy!

Longer term, growth is slowing—which is appropriate:

Yes, China is growing at its slowest annual pace since 1990. But a $9 trillion economy is not going to continue producing double-digit gains, as it did from 2003 through 2010. The economy missed an 8% growth rate only twice between 1990 and 2012, but may never hit such a pace again.

China has grown up. Or it is at least in adolescence. The sunniest outcome of this set of figures is that it likely gives Beijing the boost necessary to pick up the pace in overhauling its overproducing, underperforming, bloated state-owned enterprises.

China’s economy grew 6.9% last year, and is on track to hit around 6.5% to 7% in 2016. That’s the figure Beijing “predicted” in March — and China’s leaders are rarely (let’s say never!) wrong when they can turn the stimulus tap at will. Failing that, they’ll “massage” the numbers anyway, as I explained in April.

CLSA says in response to the latest numbers that China is on pace for 6.7% this year. “Overall, China’s August activity data were reassuring for investors,” the brokerage said, one that I think has some of the most sensible research on Asia.

And a sober reminder that there is still a lot at stake:

But it is far from all roses in China’s garden. The vast divergence between China’s humming, and wealthy, east coast and the rest of the country was only reinforced by the recent tragic tale of a woman in rural northwestern China.

Yang Gailan, 28, had been farming wheat, peas and potatoes in Agu Shan Village in Gansu province, as outlined in this story from The New York Times. She supported her four children, all under 7, on the $500 per year that her husband sent home from his migrant job in a nearby town.

Since welfare benefits are only granted in China to people living on $350 per year, local officials removed her payments. One day, the young mother told relatives she was taking her children to go tend the family’s sheep. Then she poisoned them with pesticide, attacked them with an axe, killed them, and killed herself by drinking the pesticide. This month, her husband came back from work and killed himself with poison.

The Chuck Schumers and Donald Trumps of the world favor policies that would lead to more of these sad stories (which thank God are far less frequent than a few decades ago–due to economic growth.)

The story has gained prominence in China, and has rammed home the vast gap in wealth between China’s haves — the country’s GDP per capita is $6,471 while the United Nations defines poverty as living on less than $1 per day — and rural have-nots. Many families are ripped apart by economic forces that drive parents into migrant jobs where they lack the hukou residence permit that grants them rights as a local citizen. . . .

That’s in strong contrast to the young bucks and hinds foraging for tech jobs amid the gleaming office towers of Shenzhen, just across the border from me here in Hong Kong. It’s the city with the highest per-capita income in China. Although it is a former fishing village with one of China’s last stretches of mangroves, and it is Cantonese given its location in Guangdong province, you will struggle to find someone who understands the dialect. Mandarin-speaking, motivated youngsters head there for top-paying jobs from all around China. . . .

The estimate is that China needs to sustain growth above 5% to create enough jobs and wealth to avoid unrest and demonstrations. And if China can continue along that line, making the transition from an agrarian straight into a post-tech world, ripping through its industrial revolution at record pace, I will be happy.

So will 1.4 billion people.

All over the world, politics is becoming increasingly stupid.  How much longer before these xenophobic trends impact rapid growth in Asia?  Let’s hope that we can hold back the barbarians for a few more electoral cycles, until the poor in China don’t feel a need to kill the children that they cannot support.

PS.  I have new posts at Econlog on Fed officials struggling with the concept of “struggle”, and another on our latest failed fiscal experiment.