At night in the suburbs of San Francisco, some of us awake as the
hills echo and re-echo with the howls of the coyotes that have fed well
on Glenn Rudebusch's chickens. We then lie awake, worrying. We worry
why the Great Moderation in the U.S. business cycle on the real side
that we have seen since the mid-1980s has not carried a big reduction
in financial-side variability with it. We toss and turn, worrying that
the real-side volatility decline has been part good transitory luck and
part statistical illusion, all because people in financial markets
putting their money where their mouths were do not project the
continuation of the Great Moderation into the future.
Christina Wang's paper lets us sleep more easily, even if the
coyotes continue to prey upon the chickens of Federal Reserve Bank Vice
Presidents. It teaches us an important and valuable lesson: a financial
system that is doing a better job will be highly likely to have both
higher financial and lower real volatility.
When a firm goes bankrupt and defaults on its debt, it may be
because it has had bad luck, it may be because it was badly managed, or
it may be because it suffered from moral hazard--took account of the
fact that in the lower tail the losses are eaten not by the firm but by
the bank that loaned it the money. Banks that have a hard time
distinguishing between these possibilities will be averse to
lending--charge a high interest rate premium on loans--to firms seen as
having a high degree of undifferentiated idiosyncratic risk.
Improvements in data collection and analysis that allow firms to
differentiate will cause banks to fear undifferentiated firm-level
idiosyncratic risk less, and charge lower interest rate premiums for
such lending. Other things being equal, firms will smooth production
more, and smooth cash-borrowing requirements less, seeking to squeeze
out more productive efficiencies by taking on more financial risk. To
the extent that improvements in data collection and analysis reduce
banks' fixed costs of monitoring loans, other things being equal banks
will do more to diversify away firm-level idiosyncratic risk.
When a bank goes bankrupt and defaults on its debt, it may be
because it has had bad luck, it may be because it was badly managed, or
it may be because it suffered from moral hazard--took account of the
fact that in the lower tail the losses are eaten not by the banks'
shareholders but by those who hold or guarantee its liabilities.
Improvements in data collection and analysis by those to whom banks owe
their liabilities will allow them to better classify banks, and so the
cost to banks of portfolios with bank-level idiosyncratic risk will
fall. Other things being equal, banks will be willing to take on more
bank-level idiosyncratic risk.
Of course this function that Christina Wang identifies is the
primary job--one of the primary jobs--of financial markets: to
diversify away idiosyncratic risk, as was ably explicated by that
notable predecessor of Lintner and Markowitz, William Shakespeare. As
Shakespeare writes, Antonio, the Merchant of Venice, does not fear that
the lower tail of his portfolio return distribution extends far enough
down to the state in which his heart is cut out with a knife. Antonio
he has a properly-diversified portfolio. The banker lending him the
money uses the highest information technology of that day: wandering
down to Venice's Grand Canal, loitering on the High Bridge, and
gossiping. The banker concludes that Antonio has:
an argosy bound to Tripolis, another to the Indies; I
understand moreover, upon the Rialto, he hath a third at Mexico, a
fourth for England, and other ventures...
Here the analogy breaks down. Negative transitory systematic news
does indeed provoke a crisis in Antonio's affairs, but he is rescued
not by a competent, technocratic lender of last resort but by his bride
disguised as a teenage judge.
Christina Wang hopes that starting sometime in the mid-1980s we took
a jump toward the ideal financial world in which one of CAPM's cousins
holds, in which idiosyncratic risk is not priced because it is properly
diversified away, and in which as a result the real economy can grab
for all the production-smoothing efficiency benefits without worrying
about firm- or bank-level costs of default or illiquidity. This shift
could drive a reduction in real-side volatility coupled with an
increase or no change in financial-side volatility.
She has a nice theoretical costly-state-verification model of the
effects of improved data collection and analysis technologies. She has
a very interesting theoretical Dixit-Stiglitz-based three-period model
of the joint determination of real and financial volatility. The key
insight is a very good one: that production-smoothing has not just
manufacturing-side and labor-side efficiency benefits but
financial-side efficiency costs: only if banks are confident in their
ability to monitor firms and large depositors confident in their
ability to monitor banks will firms be able to easily and cheaply
borrow the money they need in recession to enable a
production-smoothing corporate strategy. The fact that times of
recession are times when a firm's free cash is likely to be uniquely
valuable and not to be best invested in building up inventories is a
potentially powerful explanation of why we have, historically, seen the
reverse of production-smoothing in the American economy. She has
interesting empirical results that suggest that banks and firms have
reacted to a likely information-driven fall in the cost of
idiosyncratic financial risk to take on more of it. The theory is sound
and convincing. The micro empirics are interesting and suggestive.
But how much can this channel add up to on the macro level? How,
exactly, does ICT help bankers? Working for the original J.P. Morgan,
Charlie Coster was on the boards of 88 railroads at the turn of the
last century and died of overwork--Morgan is reputed to have recruited
Coster's successor while they were together carrying Coster's coffin to
its grave. What would today's ICT have done to increase Coster's
contribution to Morgan's bottom line, exactly?
And how much of the Great Moderation in real-side economic
volatility can this channel account for? Recall the size of the Great
Moderation: a 40% fall in the standard deviation of the cyclical
component of GDP, more or less the same however you choose to measure
it. A fall in spite of the fact that technology and cost shocks have in
all likelihood been quantitatively greater in the past ten years than
in any other post-WWII decade save possibly the 1970s.
As Christina Wang says, her paper as written can't do the job. It
can only do about a third of the job--although Doug Elmendorf said half
last hour. The model as extended quite possibly could.
In this literature, the game that is being hunted is the positive
correlation between production and inventory investment that we saw in
the past. In a standard production-smoothing model inventory investment
should be relatively high when production is relatively low, and sales
are very low. Instead--back before 1985--inventory investment was high
when production was high. This shift could be possibly traced to
Christina Wang's mechanisms. But it can account, in my
back-of-the-envelope guess, for not a 40% but a 15% decline in the
standard deviation of the cyclical component, whatever that is.
The big game for this model--as Chistina Wang says in her
conclusion--will, I think, come from applications of models like this
to the household sector. It's not just firms that have benefitted from
the application of information technology to credit screening. I have
gotten three offers of VISA cards and two offers of what were described
as "guaranteed low interest" home-equity loans so far this week. Plus
the people behind the counter at my most local Starbucks have started
asking me if I'm interested in a no-annual-fee Starbucks VISA that will
come with $25 of free caffeinated drinks. I don't know whether they are
doing this to everybody or whether there is something special in my
file. The smoothing-out of household durables purchases will, I think,
be an important part of the Great Moderation when we finally nail it
down. And I think that's where the high returns from Christina Wang's
model will come.
Last, the smoothing out of residential construction--if it indeed
stays smoothed-out--may well turn out to be the heart of the matter.
One branch of the conventional wisdom is that the smoothing-out of
residential construction is a result of good luck that is about to end:
that America's banks have been offered too much rice wine by the
People's Bank of China, and have responded by lending like drunken
bankers: $600,000 zero-down floating-rate loans to single-earner
middle-class families buying three-bedroom houses in Vallejo, CA: and
we will be sorry.
Christina Wang's paper suggests a second possible explanation. That
recent residential investment financed by so-called "non standard"
mortgage loans is a result at least in part not of the inebriation of
the banking sector but of the ability to more finely calculate risk and
return than was possible in the days when your mortgage had to be
30-year-fixed, 20% down, with amortization plus real estate taxes
amounting to no more than 33% of last year's household income. That was
an inadequate screen. What, really, are the current screens? How good
are they? The application of models like this to residential financing
may be the real big game here.
