Complexity in Monetary Policy: The Mechanisms Do Matter

Beware manufactured stability: "expectations management" is inherently fragile

A recent working paper adds to the discussion on monetary policy and growth.  In the model, monetary policy affects growth by allowing credit-intensive industries to engage in larger projects.  It does so by easing liquidity needs and, thus, giving firms the "breathing space" to invest.  The paper finds evidence for this by looking at industry-level financial constraint variables as well as the performance of the industry in response to monetary policy.  The key finding is that countercyclical monetary policy can have a significant positive impact on long run productivity growth, especially for recessions.  This result is robust to controlling for:

...the interaction between these measures of financial constraints and country-level economic variables such as inflation, financial development, and the size of government which are likely to affect the country’s ability to pursue more countercyclical macroeconomic policies (5).

Moreover, the regressions shed light on another unique internal link from monetary policy to growth: countercyclical monetary policy promotes higher levels of R+D spending.  While the model explains it through liquidity needs, the concept of "signal-processing" causing firms to invest inefficiently likely applies.  This suggests that if we really are in a "great stagnation" of growth and innovation, a stable nominal economy vis-a-vis monetary policy will be increasingly important.

To extend the model, if monetary policy exerts a diverse range of effects on what is considered money through safe asset creation, the effect of countercyclical monetary policy is probably stronger than what the interest rate would state.  By increasing liquidity through countercyclical policy, this allows firms to invest more:

The intuition for this proposition is simple. Firms need to hoard liquidity in order to weather liquidity shocks if the aggregate state is bad. This liquidity hoarding is costly...because of the lack of commitment of consumers. Reducing interest rates in bad times lowers the amount of hoarded liquidity, by increasing the ability of firms to leverage their net worth. This effect is weaker when the aggregate state is good because in that state, short-term profits are enough to cover reinvestment needs so that no liquidity needs to be hoarded to weather liquidity shocks that occur in that aggregate state of the world. Hence a higher marginal benefit of reducing interest rates in bad times relative to good times. This effect is strong enough to overcome a countervailing effect arising from the fact that lowering interest rates in bad times leads to an implicit subsidy from consumers to entrepreneurs, explaining that optimal interest rate policy is countercyclical (14, my emphasis).

And now the Nassim Nicholas Taleb homonculus starts screaming into my ear.

To what extent does this mechanism of monetary policy just create more interlocking fragilities?  I've previously argued that one of the problems with NGDP targeting is that it hides the complexity of the ecology of markets.  In this model, the world is encouraged to increase complexity because of a monetary regime that promotes more stable growth.  The firms with "unshakeable" expectations can leverage themselves to the hilt to try to maximize future growth.  But finance is fragile; what would happen if an unseen risk arises?  More seriously, although a debt crisis would wipe these firms out, nobody would be able to tell in the short run while those debt instruments are still there.

The issue here is that "average growth" is nowhere near as important as "variant growth".  While growth is stable most of the time, the impact of tail events rises as markets become more interconnected.  Leverage is inherently dangerous in an interconnected world because it enables complex cascading effects that go beyond the ability of our models to predict.  This uncertainty is heightened by the fact that even slight miscalibration errors can cause monstruous miscalculations.  These problems aren't solved by monetary policy either because financial crises are aggregate supply problems.  If credit mediation crashes, resources are no longer allocated efficiently, raising unit costs for all factors of production.

A possible way to get around this is if we can commit to more equity instead of leverage.  From Taleb's 10 principles for a Black-Swan free society:

5. Counter-balance complexity with simplicity. Complexity from globalisation and highly networked economic life needs to be countered by simplicity in financial products. The complex economy is already a form of leverage: the leverage of efficiency. Such systems survive thanks to slack and redundancy; adding debt produces wild and dangerous gyrations and leaves no room for error. Capitalism cannot avoid fads and bubbles: equity bubbles (as in 2000) have proved to be mild; debt bubbles are vicious.

This way, the net worth of companies can be converted into equity stakes, and funding can be obtained this way.  Given the outsize role of large events, this shift to a more black swan free society should occur before the adoption of monetary policies that could increase fragility.  In this world, monetary policy would allow for increased efficiency of markets while also preventing Black Swans from coming to roost.

Correlations Across Time: How Stable are the Curves?

What is the Philips curve, and how do we know it's there?   It was originally discovered by Irving Fisher in 1926 when he noted the negative correlation between inflation and unemployment.  Of course, he was not the first to realize this connection between prices and employment, as Hume commented on this exact issue almost 200 years before:

In my opinion, it is only in the interval or intermediate situation, between the acquisition of money and the rise in prices, that the increasing quantity of gold or silver is favourable to industry. . . . The farmer or gardener, finding that their commodities are taken off apply themselves with alacrity to the raising of more. . . . It is easy to trace the money in its progress through the whole commonwealth; where we shall find that it must first quicken the diligence of every individual, before it increases the price of labour

For this reason, Milton Friedman often said that modern macroeconomics has made it just one derivative past Hume.  Instead of just focusing on the first derivative and changes in the price level, we now look at the second derivative and changes in the inflation rate.

Robert Hall took this one step further in his 1986 exposition on efficient monetary policy and, instead of looking at one more derivative, looked at one more parameter.  Instead of just looking at the levels of unemployment and inflation, he theorized on the relationship between the volatility of the two variables.  He hypothesized the existence of an efficient policy frontier, a trade-off between price stability and unemployment stability that would prevent both variables from settling down in the face of periodic random shocks.

But have either of these correlations held throughout time?  The Philip's curve worked originally very well in the 1960's to 1980's, but then broke down as stagflation struck and expected inflation shifted the "stable" Philip's curve.  Thus, there seems to be a severe issue with measuring the Philip's curve; where should one start and end the observation window?  The analysis can easily become utterly meaningless, as:

To see how meaningless correlation can be outside of Mediocristan, take a historical series involving two variables that are patently from Ex­ tremistan, such as the bond and the stock markets, or two securities prices, or two variables like, say, changes in book sales of children's books in the United States, and fertilizer production in China; or real-estate prices in New York City and returns of the Mongolian stock market. Measure correlation between the pairs of variables in different subperiods, say, for 1994, 1995, 1996, etc. The correlation measure will be likely to ex­hibit severe instability; it will depend on the period for which it was com­puted. Yet people talk about correlation as if it were something real, making it tangible, investing it with a physical property, reifying it. The same illusion of concreteness affects what we call "standard" deviations. Take any series of historical prices or values. Break it up into subsegments and measure its "standard" deviation. Surprised? Every sample will yield a different "standard" deviation. Then why do people talk about standard deviations? Go figure. 

Note here that, as with the narrative fallacy, when you look at past data and compute one single correlation or standard deviation, you do not notice such instability (Taleb, The Black Swan, my emphasis).

So, in this post, I want to look at the time series data and see how the correlation evolves over time.  This is important for both the Philip's curve and the efficient policy frontier, as one can see if either of those relationships actually holds across all time periods.

Monthly CPI and unemployment data are obtained from the St. Louis Federal Reserve website, and variabilities for each variable are measured by the standard deviation of the past year's worth of observations.  Correlations were then calculated in five year windows, such that a correlation coefficient on month t is the correlation between the variables of interest in months t-59 to t.  As the concept of a standard deviation is a bit abstract and not well understood, I took the logarithms of the standard deviations, to allow an explanation in terms of percentage increases in one variable leading to percent increases in another.

Below is a tool to gain a qualitative understanding of the evolution of the correlations.  Red denotes high numbers (strong positive correlation), while green denotes low numbers (strong negative correlation).  The black lines mark every 10 years to give a sense of scale in the colorful "time series".

As expected, the correlation coefficients fluctuated throughout history. For the Philips curve, old Keynesian theory would predict a negative correlation.  However, if there's a supply shock, both inflation and unemployment move in the same direction.  This makes sense as the two major supply shocks in recent history were the negative aggregate supply oil shock in the mid 1980's, as well as the positive aggregate supply shock in the 1990's.

With this in mind, we see that the Philip's curve relationship was actually quite stable up until the 1990's.  Although the oil price shock did force the correlation positive for a short period, it quickly reverted to a negative value.  However, from about 1990 on, the correlation between unemployment and inflation became consistently, if only weakly, positive.  Since both inflation and unemployment rose in that time period, this is another piece of evidence that suggests much of the aggregate supply gains in the 1990's were steadily reversed in the 2000's.

However, the relationship between the two volatilities was not as clear cut.  A log-log regression of the unemployment volatility versus the inflation volatility over the entire 60 years yields a slope of 0.44, with a 95% confidence interval between 0.346 and 0.540, suggesting that 1% increase in inflation volatility resulted in about a 0.44% increase in unemployment volatility.  Yet this general correlation masks the variance.  Around the 1980's and 2010, the correlation was incredibly positive, while in the 1970's and 2000's the correlation is very negative.

From this, general conclusions can be made.  First, policy is not efficient.  Even if there were an efficient policy frontier, we're not on it.  The many zones of positive correlation indicate that there's much more monetary policy can do to limit volatility in the two variables.  Second, that there are interesting things going on with transmission mechanisms that would cause uncertain inflation to translate to uncertain output.  Third, if there are severe risks to inflation volatility, it may be in our interest to lower unemployment volatility as well.  Moderating the relationship between these two variables may become one of the biggest benefits of NGDP targeting, as uncertainty along the Philips curve may cause movement towards higher levels of volatility.

Nominal GDP Targeting and Complexity

The Complexity View

I've recently started rereading passages of The Black Swan: The Impact of the Highly Improbable and I find it fascinating. The prose is fluid, and the arguments are powerful. Much of the book mocks economic theory, as models tend to minimize the role of large shocks that defy normal distributions.  In the book, Taleb inserts the following chart that shows how much these "outliers" influence the stock market.

Taleb places the blame for these large swings in the market on the shoulders of the Federal Reserve.  He argues that stabilization policy actually makes the economy more fragile, making it more likely to go down in a dramatic fashion once the "big one" hits.  He sums up this argument in the following quote from a section titled "Beware Manufactured Stability" in a supplementary essay.

...fear of volatility, leading to interference with nature to impose "regularity" makes us more fragile across so many domains.  Preventing small forest fires sets the grounds for more extreme ones; giving out antibiotics when it is not very necessary makes us more vulnerable to severe epidemics... 

Which brings me to another organism: economic life. Our aversion to variability and desire for order and our acting on it has helped precipitate severe crises... Another thing we saw in the 2008 debacle: the U.S. government (or, rather, the Federal Reserve) had been trying for years to iron out the business cycle, making us exposed to a severe disintegration. This is the sort of reasoning I have against "stabilization" policies and manufacturing a nonvolatile environment ...

In a sense, the reduction of volatility in the Great Moderation was only an illusion of stability.  We were, as Taleb would say, "sitting on a pile of dynamite," unaware of the risk that lay underneath.

Impact on NGDP Targeting Policy

This kind of critique seems rather damning against nominal GDP targeting.  The typical analysis of NGDP targeting hinges on the assertion that low volatility implies high stability.  But what if this isn't true?  What if these times of low volatility are just times of high fragility?  Some analysis of the arguments for NGDP targeting even suggest mechanisms by which this is the case.  Debt problems are waved away because NGDP is stable, financial opacity becomes a non-issue because monetary policy compartmentalizes it,  perceptions of "safe" assets  change because expectations of nominal growth are maintained.  Stable expectations permit these innovations because agents can plan ahead, allowing for higher growth.

However, this higher efficiency comes at the cost of redundancy.  Taleb jokes in an interview with Russ Roberts that:

An economist would never design a human being with two lungs and two kidneys. It's wasteful. Deadweight loss.  

He follows up with:

So, the opposite of spare parts would be debt. And nature doesn't like debt. Nature likes redundancies. This mechanism of overreaction is redundancy.

And this is what terrifies me about NGDP targeting.  The incredibly stable regime creates an environment in which redundancy is eschewed in favor of fragility.  Perhaps it would be better to have a more resilient economy that wouldn't be able to accumulate as much capital, but one that has lower levels of debt.  The cost of a mistake in an NGDP targeting world would be incredible.  Even if, theoretically, under a stable monetary regime, there are no demand-side recessions, would you be willing to bet the stability of the entire global financial system on it?  Even if it were true, can you guarantee the Fed will be able to maintain a "stable monetary regime" for perpetuity?

I'm not trying to say the current monetary system is ideal; the dismal employment numbers firmly reject that view.  But when we look onto NGDP targeting as the solution to the global economic malaise, we need to be careful that we don't put all of our eggs into one basket.  NGDP targeting is an incredible tool for monetary policy; but it can't be a panacea for all of these troubles.  

This critique of NGDP targeting brings up another key issue for the design of policy.  Optimal policy has to do more than maximize welfare, it must also be robust to errors.  While in the game playing, platonic world of models NGDP targeting should create incredible reductions in volatility and instability, what are the possible effects on global fragility?  Policy engineering needs to take into account Murphy's Law: "If anything can go wrong, it will."  The only question is how we prepare.

NGDP Targeting: What if it fails?

Recently in the economics blogosphere, the monetary paradigm of nominal GDP level targeting (NGDPLT) has been gaining steam.  NGDP targeting takes a departure from the classic regime of inflation targeting by the growth rate in NGDP, allowing for balance between employment and inflation.  This then leads to a wide variety of benefits, as the new regime is robust to supply shocks, can craft stable expectations of overall future growth, and can reduce fears of any specific industry going through a crisis.  It's particularly attractive for financial crises, as if NGDP growth is stable, previously sustainable levels of debt are less likely to become unsustainable.  If the economy's productive capacity is constant, there's no reason for it to be less able to service its debt.

However, this view seems almost too simplistic.  Even though the US economy was incredibly stable during the over the 20 year Great Moderation, it all came down to a screeching halt with the 2008 financial crisis.  Similarly, even though Britain managed to stay out of a major recession for 16 years, NGDP fell by about 4.7% during the crisis.  Given that there had been such a long legacy of stability, how did expectations suddenly become unanchored?  Even if the US Federal Reserve made a bad policy decision at that point to focus on oil prices and other supply shocks to the detriment of nominal stability, why did the expectations of prudent policy in the future not "solve back" the concerns?  In the end, the crisis culminated into the worst disruption since the Great Depression: hardly a desired result for a responsible regime.

The unhappy ending in 2008 seems to suggest that responsible policy can break down into chaos given a large enough of an exogenous shock.  This problem is very close to what Nicolas Nassim Taleb discusses in The Black Swan: in exchange for low volatility, the economy goes along with high fragility, such that one large shock can cause non-linear, disproportionate harm.  So in the end, the question is about credibility.  How is it established?  How is it maintained?  If decades of prudent monetary policy were not enough to anchor expectations, why should we expect the Federal Reserve to be considered "credible" when the next large financial bubble appears?  If shadow banking markets start to grow shadows and systemic risk goes through the roof, why should we expect the Federal Reserve to be considered "credible"?  Especially if non-monetary factors as posited by Bernanke play a key role in recessions, why would nominal stability be enough?  And when everything crashes down, how will we deal with the mess of debt and contracts that were only sustainable under the old regime?  NGDP targeting seems to play on circular logic.  Boost aggregate demand to hold the expectation; with the expectation there's no need to boost aggregate demand.

So when a policy maker messes up, and lets a NGDP crisis unfold, the crisis emerges.  This is where the black swan hides, cloaked by the rhetoric of stable expectations and the "perfect" monetary policy.