[House Hearing, 111 Congress]
[From the U.S. Government Publishing Office]
BUILDING A SCIENCE OF
ECONOMICS FOR THE REAL WORLD
=======================================================================
HEARING
BEFORE THE
SUBCOMMITTEE ON INVESTIGATIONS AND
OVERSIGHT
COMMITTEE ON SCIENCE AND TECHNOLOGY
HOUSE OF REPRESENTATIVES
ONE HUNDRED ELEVENTH CONGRESS
SECOND SESSION
__________
JULY 20, 2010
__________
Serial No. 111-106
__________
Printed for the use of the Committee on Science and Technology
Available via the World Wide Web: http://www.science.house.gov
______
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COMMITTEE ON SCIENCE AND TECHNOLOGY
HON. BART GORDON, Tennessee, Chair
JERRY F. COSTELLO, Illinois RALPH M. HALL, Texas
EDDIE BERNICE JOHNSON, Texas F. JAMES SENSENBRENNER JR.,
LYNN C. WOOLSEY, California Wisconsin
DAVID WU, Oregon LAMAR S. SMITH, Texas
BRIAN BAIRD, Washington DANA ROHRABACHER, California
BRAD MILLER, North Carolina ROSCOE G. BARTLETT, Maryland
DANIEL LIPINSKI, Illinois VERNON J. EHLERS, Michigan
GABRIELLE GIFFORDS, Arizona FRANK D. LUCAS, Oklahoma
DONNA F. EDWARDS, Maryland JUDY BIGGERT, Illinois
MARCIA L. FUDGE, Ohio W. TODD AKIN, Missouri
BEN R. LUJAN, New Mexico RANDY NEUGEBAUER, Texas
PAUL D. TONKO, New York BOB INGLIS, South Carolina
STEVEN R. ROTHMAN, New Jersey MICHAEL T. MCCAUL, Texas
JIM MATHESON, Utah MARIO DIAZ-BALART, Florida
LINCOLN DAVIS, Tennessee BRIAN P. BILBRAY, California
BEN CHANDLER, Kentucky ADRIAN SMITH, Nebraska
RUSS CARNAHAN, Missouri PAUL C. BROUN, Georgia
BARON P. HILL, Indiana PETE OLSON, Texas
HARRY E. MITCHELL, Arizona
CHARLES A. WILSON, Ohio
KATHLEEN DAHLKEMPER, Pennsylvania
ALAN GRAYSON, Florida
SUZANNE M. KOSMAS, Florida
GARY C. PETERS, Michigan
JOHN GARAMENDI, California
VACANCY
------
Subcommittee on Investigations and Oversight
HON. BRAD MILLER, North Carolina, Chair
STEVEN R. ROTHMAN, New Jersey PAUL C. BROUN, Georgia
LINCOLN DAVIS, Tennessee BRIAN P. BILBRAY, California
CHARLES A. WILSON, Ohio VACANCY
KATHY DAHLKEMPER, Pennsylvania
ALAN GRAYSON, Florida
BART GORDON, Tennessee RALPH M. HALL, Texas
DAN PEARSON Subcommittee Staff Director
EDITH HOLLEMAN Subcommittee Counsel
JAMES PAUL Democratic Professional Staff Member
DOUGLAS S. PASTERNAK Democratic Professional Staff Member
KEN JACOBSON Democratic Professional Staff Member
TOM HAMMOND Republican Professional Staff Member
C O N T E N T S
July 20, 2010
Page
Witness List..................................................... 2
Hearing Charter.................................................. 3
Opening Statements
Statement by Representative Brad Miller, Chairman, Subcommittee
on Investigations and Oversight, Committee on Science and
Technology, U.S. House of Representatives...................... 7
Written Statement............................................ 8
Statement by Representative Paul C. Broun, Ranking Minority
Member, Subcommittee on Investigations and Oversight, Committee
on Science and Technology, U.S. House of Representatives....... 9
Written Statement............................................ 10
Witnesses:
Dr. Robert M. Solow, Professor Emeritus, Massachusetts Institute
of Technology
Oral Statement............................................... 12
Written Statement............................................ 14
Dr. Sidney G. Winter, Deloitte and Touche Professor Emeritus of
Management, The Wharton School of the University of
Pennsylvania
Oral Statement............................................... 15
Written Statement............................................ 17
Dr. Scott E. Page, Leonid Hurwicz Collegiate Professor of Complex
Systems, Political Science, and Economics, University of
Michigan
Oral Statement............................................... 27
Written Statement............................................ 29
Dr. V.V. Chari, Paul W. Frenzel Land Grant Professor of Liberal
Arts, University of Minnesota
Oral Statement............................................... 32
Written Statement............................................ 34
Dr. David C. Colander, Christian A. Johnson Distinguished
Professor of Economics, Middlebury College
Oral Statement............................................... 38
Written Statement............................................ 39
Discussion....................................................... 45
BUILDING A SCIENCE OF ECONOMICS FOR THE REAL WORLD
----------
TUESDAY, JULY 20, 2010
House of Representatives,
Subcommittee on Investigations and Oversight,
Committee on Science and Technology,
Washington, DC.
The Subcommittee met, pursuant to call, at 10:07 a.m., in
Room 2318 of the Rayburn House Office Building, Hon. Brad
Miller [Chairman of the Subcommittee] presiding.
hearing charter
COMMITTEE ON SCIENCE AND TECHNOLOGY
SUBCOMMITTEE ON INVESTIGATIONS AND OVERSIGHT
U.S. HOUSE OF REPRESENTATIVES
Building a Science of Economics
for the Real World
tuesday, july 20, 2010
10:00 a.m.-12:00 p.m.
2318 rayburn house office building
Purpose
The Subcommittee on Investigations and Oversight will hold a
hearing on July 20, 2010, to examine the promise and limits of modern
macroeconomic theory in light of the current economic crisis. The
Subcommittee has previously looked at how the global financial meltdown
of 2008 may have been caused or abetted by financial risk models, many
of which are rooted in the same assumptions upon which today's
mainstream macroeconomic models are based.\1\ But the insights of
economics, a field that aspires to be a science and for which the
National Science Foundation (NSF) is the major funding resource in the
Federal Government, shape far more than what takes place on Wall
Street. Economic analysis is used to inform virtually every aspect of
domestic policy. If the generally accepted economic models inclined the
Nation's policy makers to dismiss the notion that a crisis was
possible, and then led them toward measures that may have been less
than optimal in addressing it, it seems appropriate to ask why the
economics profession cannot provide better policy guidance. Further, in
an effort to improve the quality of economic science, should the
Federal Government consider supporting new avenues of research through
the NSF?
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\1\ Hearing of the Subcommittee on Investigations and Oversight of
the House Committee on Science and Technology on ``The Risks of
Financial Modeling: VaR and the Economic Meltdown,'' September 10,
2009, serial no. 111-48.
Background
The implosion of the subprime mortgage market came as almost a
total surprise to most mainstream economists. Five weeks after the
investment house Lehman Brothers had filed for bankruptcy protection,
former Federal Reserve Board Chairman Alan Greenspan called the
financial crisis ``much broader than anything [he] could have
imagined.'' \2\ The chief steward of the U.S. economy from 1987 to 2006
said he was in a state of ``shocked disbelief'' because he had ``found
a flaw in the model that [he] perceived [to be] the critical
functioning structure that defines how the world works.'' \3\ Adherence
to this model had prevented him from envisioning a critical
eventuality: that the ``modern risk management paradigm,'' seen by
Greenspan as ``a critical pillar to market competition and free
markets,'' could ``break down.'' \4\
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\2\ Hearing of the House Committee on Oversight and Government
Reform, ``The Financial Crisis and the Role of Federal Regulators,''
Oct. 23, 2008, preliminary transcript, p. 16, http://
oversight.house.gov/images/stories/documents/20081024163819.pdf (last
visited on July 14, 2010).
\3\ Ibid., p. 37.
\4\ Ibid., p. 18 and p. 34 respectively.
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Greenspan's crumbled ``intellectual edifice'' depends on the
``efficient market hypothesis'' and the assumptions that underlie
it.\5\ This hypothesis holds that the price of a financial asset traded
on an exchange must indicate its true value because the market's
efficiency is such that the price at any given moment reflects all
pertinent information about the asset.\6\ It assumes that those trading
on the market are considered to have rational expectations, which means
that each possesses all available information about the market--indeed,
all available information about the world--and makes optimal use of it.
The basis for the efficient market hypothesis, the ``rational
expectations hypothesis,'' is a standard feature of modern
macroeconomic models, which are concerned with the overall economy and
its most important forces: growth, unemployment, inflation, monetary
and fiscal policy, and the business cycle. ``Whether we are talking
about models of financial markets or of the real economy, our models
are based on the same fundamental building blocks,'' writes the
economist Alan Kirman.\7\
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\5\ Ibid., p. 18.
\6\ This assumption, it will be noted, would rule out the
possibility of a price bubble on the exchange. The Subcommittee held a
hearing on asset valuation issues in the wake of the Wall Street
meltdown and the subsequent rescue packages. That hearing, held May 19,
2009, was titled ``The Science of Insolvency,'' serial no. 111-27.
\7\ Alan Kirman, ``The Economic Crisis is a Crisis for Economic
Theory,'' February 2010 version, p. 2, http://www.econ.ed.ac.uk/papers/
A-Kirman.pdf (last visited on July 14, 2010).
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The dominant macro model has for some time been the Dynamic
Stochastic General Equilibrium model, or DSGE, whose name points to
some of its outstanding characteristics. ``General'' indicates that the
model includes all markets in the economy. ``Equilibrium'' points to
the assumptions that supply and demand balance out rapidly and
unfailingly, and that competition reigns in markets that are
undisturbed by shortages, surpluses, or involuntary unemployment.
``Dynamic'' means that the model looks at the economy over time rather
than at an isolated moment. ``Stochastic'' corresponds to a specific
type of manageable randomness built into the model that allows for
unexpected events, such as oil shocks or technological changes, but
assumes that the model's agents can assign a correct mathematical
probability to such events, thereby making them insurable. Events to
which one cannot assign a probability, and that are thus truly
uncertain, are ruled out.
The agents populating DSGE models, functioning as individuals or
firms, are endowed with a kind of clairvoyance. Immortal, they see to
the end of time and are aware of anything that might possibly ever
occur, as well as the likelihood of its occurring; their decisions are
always instantaneous yet never in error, and no decision depends on a
previous decision or influences a subsequent decision. Also assumed in
the core DSGE model is that all agents of the same type--that is,
individuals or firms--have identical needs and identical tastes, which,
as ``optimizers,'' they pursue with unbounded self-interest and full
knowledge of what their wants are. By employing what is called the
``representative agent'' and assigning it these standardized features,
the DSGE model excludes from the model economy almost all consequential
diversity and uncertainty--characteristics that in many ways make the
actual economy what it is. The DSGE universe makes no distinction
between system equilibrium, in which balancing agent-level
disequilibrium forces maintains the macroeconomy in equilibrium, and
full agent equilibrium, in which every individual in the economy is in
equilibrium. In so doing, it assumes away phenomena that are
commonplace in the economy: involuntary unemployment and the failure of
prices or wages to adjust instantaneously to changes in the relation of
supply and demand. These phenomena are seen as exceptional and call for
special explanation.
To what extent is this model, a highly theoretical construct that
appears to bear little resemblance to everyday life, used in shaping
policy that affects people and events in the real world? Prominent
economists disagree. As long as a decade ago, John Taylor stated that
it had migrated beyond the walls of the academy: ``[A]t the practical
level, a common view of macroeconomics is now pervasive in policy
research projects at universities and central banks around the world.
This view evolved gradually since the rational expectations revolution
of the 1970s and has solidified during the 1990s. It differs from past
views, and it explains the growth and fluctuations of the modern
economy; it can thus be said to represent a modern view of
macroeconomics.'' \8\ In 2006 V.V. Chari and Patrick Kehoe, academic
economists who are advisers to the Federal Reserve Bank of Minneapolis,
echoed Taylor's claim in an article titled ``Modern Macroeconomics in
Practice: How Theory is Shaping Policy.'' \9\
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\8\ John B. Taylor, ``Teaching Modern Macroeconomics at the
Principles Level,'' p. 1, from a speech delivered Jan. 7, 2000, http://
citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.7891 (last visited
on July 14, 2010).
\9\ Journal of Economic Perspectives, Vol. 20, No. 4 (Fall 2006),
Pp. 3-28.
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Similarly, Michael Woodford argued in 2008 that there had been a
convergence in the macro models used in the academic and policy
spheres. He cited a number of central banks in the industrialized world
that were using ``fully coherent DSGE models reflecting the current
methodological consensus,'' adding that, in the cases of Canada and New
Zealand, ``these were not mere research projects, but models routinely
used for practical policy deliberations.'' \10\ The Federal Reserve
Board's main policy model, FRB/US, was developed before the recent
trend toward DSGE, but the Fed had ``departed sharply from [its]
previous generation'' of models and had incorporated numerous
assumptions and features consistent with DSGE. \11\
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\10\ Michael Woodford, ``Convergence in Macroeconomics: Elements of
the New Synthesis,'' p. 17, from a speech delivered on Jan. 4, 2008,
http://www.aeaweb.org/articles.php?doi=10.1257/mac.1.1.267 (last
visited on July 14, 2010).
\11\ Ibid., p. 16.
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A different view of the influence of the DSGE model outside
academia has been put forward by Gregory Mankiw, who was chairman of
the President's Council of Economic Advisers from 2003 to 2005. ``The
sad truth is that macroeconomic research of the past three decades has
had only minor impact on the practical analysis of monetary or fiscal
policy,'' he wrote in 2006. Still, despite this apparent expression of
regret, he added: ``The fact that modern macroeconomic research is not
widely used in practical policymaking is prima facie evidence that it
is of little use for this purpose.'' \12\
---------------------------------------------------------------------------
\12\ Gregory Mankiw, ``The Macroeconomist as Scientist and
Engineer,'' May 2006, p. 19, http://www.economics.harvard.edu/files/
faculty/
40-Macroeconomist-as-Scientist.pdfm
(last visited on July 14, 2010).
---------------------------------------------------------------------------
What, then, are the opportunities in the U.S. for realistic
macroeconomic policy guidance at this precarious time in the history of
the national economy? Kirman, who is among the critics of modern macro
models, suggests: ``If the DSGE proponents have got it right, then they
should be able to explain why their models do not allow for the
possibility of a crisis of the sort that we are currently facing.
Indeed this applies to all macroeconomic models, for if major crises
are a recurrent feature of the economy then our models should
incorporate this possibility.''\13\
---------------------------------------------------------------------------
\13\ Kirman, op. cit., p. 2.
Questions
Today's troubled economic landscape is overflowing with ready tests
of any model's relevance to the real world.
Last month's G20 summit in Toronto produced a broad
policy consensus behind ``austerity'' plans designed to reduce
public debt. Practically speaking, that means governments made
commitments to slash their public spending. The recovery is
still shaky, and the possibility of a double-dip recession
looms on the horizon. What might be the consequences of cutting
government spending now? How can we determine when austerity
policies make economic sense?
The basic unemployment rate in the United States has
been hovering at just below ten percent. Adding in the long-
term unemployed who have become too discouraged to continue
looking for work, as well as those who are working part time
but would like to work full time, pushes the percentage of
unemployed above 16 percent.\14\ Yet not so long ago the
consensus figure among economists for the U.S. ``natural rate
of unemployment'' was stable at between four and five percent.
How do economists explain this high and lingering unemployment
rate? What can and should be done about it?
---------------------------------------------------------------------------
\14\ U.S. Bureau of Labor Statistics, Household Data Table A-15
``Alternative measures of labor underutilization, http://data.bls.gov/
cgi-bin/print.pl/news.release/empsit.t15.htm (last visited on July 15,
2010).
It has been suggested that one reason so many are
staying unemployed is that they are lazy and enjoy receiving
unemployment benefits. What can economics tell us about whether
unemployment benefits have a large perverse effect of
increasing the unemployment rate? If that is so, why was the
``natural rate'' of unemployment thought to be closer to four
---------------------------------------------------------------------------
percent just a few years ago?
Japan has been stuck in a deflationary spiral for
almost 20 years. Relatively high unemployment, weak
productivity gains and slack demand appear to have become
permanent features of its economy. Some observers point to
signs that a similar condition could await the United States.
How do macroeconomists explain Japan's lingering deflationary
situation? Is the U.S. in danger of falling into a similar
trap, and what might be done to avoid it?
The mortgage housing bubble that expanded throughout
the first years of this century was anything but inconspicuous.
Why weren't more economists able to identify it and to
recognize its potential for doing broad damage to the U.S. and
world economies? If economics cannot currently identify
emerging conditions that could threaten the Nation's economic
well-being, what kind of work do we need to fund to receive
such insights.
Policy makers wrestle with these issues every day. Does the current
state of economic research offer reliable, robust answers? Is the
reigning macroeconomic model trustworthy for policy-making purposes? If
not, should the government consider funding different kinds of research
that may provide more useful insights to real economic outcomes?
Witnesses
Dr. Robert M. Solow, Professor Emeritus, Department of Economics,
MIT
Dr. Sidney G. Winter, Deloitte and Touche Professor Emeritus of
Management, The Wharton School of the University of Pennsylvania
Dr. Scott E. Page, Leonid Hurwicz Collegiate Professor of Complex
Systems, Political Science, and Economics, University of Michigan
Dr. David C. Colander, Christian A. Johnson Distinguished Professor
of Economics, Middlebury College
Dr. V.V. Chari, Paul W. Frenzel Land Grant Professor of Liberal
Arts, University of Minnesota
Chairman Miller. This hearing will now come to order.
Good morning, and welcome to today's hearing entitled
``Building a Science of Economics for the Real World.'' I know
that economists must think that politicians are impossible to
please. Harry Truman complained that he wanted a one-handed
economist, and now we are complaining that we got very
confident, unequivocal economic advice in the last decade or so
but that one-handed advice proved to be wrong.
Unemployment is hovering at just under ten percent, more
than 16 percent when you include the folks who have given up
looking for work or who are working part time when they really
want a full-time job. Banks have cash but aren't lending. The
Federal Reserve can't lower interest rates any more without
paying banks to take the money. There is worried talk of a
deflationary spiral like the one that has dogged Japan for
almost two decades now, and arguments about whether it is
better to stimulate the economy or cut the deficit appear
backed more by ideology--almost theology--gut feeling and
election-year politics than by any evidence and honest
analysis.
It would be great to have some reliable guidance to lead us
out of this mess, but what we thought was authoritative
guidance failed to see the mess coming and may actually have
helped create the mess to begin with. Expert models of finance
and the economy led to risk-taking at our largest financial
firms and failed to warn leading economic policymakers that
doom lurked in the housing market.
Because our experts' way of looking at the economy left
them blind to the crisis that was building, we were unprepared
to deal with the crisis. A few weeks after Lehman Brothers went
bust, former Fed Chairman Alan Greenspan, the steward of our
economy during the 20 years that culminated in the housing
bubble, told our colleagues on the House Oversight and
Government Reform Committee that his reaction to the financial
crisis was ``shocked disbelief.'' He had ``found a flaw,'' as
he put it, ``in the model that [he] perceived [to be] the
critical functioning structure that defines how the world
works.''
Greenspan's fallen model of the market shares many
assumptions with the model that is favored today from academe
to the world's central banks. The macroeconomic model is called
the Dynamic Stochastic General Equilibrium model, mercifully
called DSGE for short. According to the model's most devoted
acolytes, the model's insights rival the perfect knowledge that
Paul described in the First Letter to the Corinthians, but
unlike the knowledge Paul described, DSGE's insights are
supposedly available to us in the here and now. That overstates
the case some, but if politicians can't exaggerate, who can?
To be fair, DSGE and similar macroeconomic models were
conceived as theorists' tools, but why, then, do we continue to
rely upon them for so many critical decisions, so much
practical policy advice? And what has caused them to become,
and to stay, so firmly entrenched? And, finally, the most
important question of all: How do we get out of the mess we are
in? What economic models, what tools are at our disposal to
give us useful advice to deal with our urgent economic
problems? If this approach to economics is useless for the
purposes of advising policymakers to lead to better economic
outcomes, what are we getting out of the economic research we
fund through NSF?
Besides raising these questions about the dominant model,
we plan to have a look at the competition. What kinds of
alternative models exist, and do we need to generate more
still? Should we be using a variety of models in concert rather
than relying on only one model or one kind of model, much the
way meteorologists use a variety of models? Should the Federal
Government use its funding of economic science to encourage the
development of those alternative approaches?
We do have a very distinguished panel today to help us
consider these issues. Dr. Robert Solow will tell us what is in
the DSGE model, where it parts from the realities of the world,
and what kind of advice it tends to deliver. Dr. Solow very
modestly is not wearing today for this hearing his Nobel
medallion. Dr. Sidney Winter will talk about the economic
realities that DSGE and its macroeconomic cousins fail to take
into account and about how to look for policy advice when there
are important features of the economy that don't lend
themselves to modeling. Dr. Scott Page will provide a glimpse
of a new form of model that advanced computing power has made
possible, the agent-based model, and make a case for the use of
many and varied models. Dr. David Colander will explain why
DSGE has achieved such a monopoly and outline a plan designed
to open the floor to a broader spectrum of ideas. And Dr. V.V.
Chari will state that while DSGE models are definitely capable
of improvement, many of the criticisms leveled against them are
inaccurate and, in any case, there is no other game in town.
And I note that Dr. Chari is the minority witness and is a very
useful addition to this panel today.
[The prepared statement of Chairman Miller follows:]
Prepared Statement of Chairman Brad Miller
I know that economists must think politicians are impossible to
please. Harry Truman complained that he wanted a one-handed economist.
And now we're complaining that we got very confident economic advice in
the last decade, but that one-handed advice proved to be wrong.
Unemployment is hovering at just under 10 percent--more than 16
percent when you include the folks who have given up looking for work
or who are working part time when it's a full-time job they really
want. Banks have cash but aren't lending, and the Federal Reserve can't
lower interest rates any more without paying banks to take the money.
There's worried talk of a deflationary spiral like the one that's
dogged Japan for almost two decades. And arguments about whether it is
better to stimulate the economy or cut the deficit appear backed more
by ideology, gut feeling and election-year politics, than by honest
evidence.
It would be great to have some reliable guidance to lead us out of
this mess. But what we thought was authoritative guidance failed to see
the mess coming and may actually have helped create the mess to begin
with. Expert models of finance and the economy led to risk-taking at
our largest financial firms and failed to warn our leading economic
policy makers that doom lurked in the housing market.
Because our experts' way of looking at the economy left them blind
to the crisis that was building, we were unprepared to deal with the
crisis. A few weeks after Lehman Brothers went bust, Former Fed
Chairman Alan Greenspan, the steward of our economy during the 20 years
that culminated in the housing bubble's growth, told our colleagues on
the House Oversight and Government Reform Committee that his reaction
to the financial crisis was one of ``shocked disbelief.'' He had
``found a flaw,'' as he put it, ``in the model that [he] perceived [to
be] the critical functioning structure that defines how the world
works.''
Greenspan's fallen model of the market shares many assumptions with
the model that's favored today, from academe to the world's central
banks. The macroeconomic model is called the Dynamic Stochastic General
Equilibrium model mercifully called DSGE for short. According to the
model's most devoted acolytes, the model's insights rival the perfect
knowledge Paul described in the First Letter to the Corinthians; but
unlike the knowledge Paul described, DSGE's insights are available in
the here and now.
To be fair, DGSE and similar macroeconomic models were first
conceived as theorists' tools. But why, then, are they being relied on
as the platform upon which so much practical policy advice is
formulated? And what has caused them to become, and to stay, so firmly
entrenched? And, finally, the most important question of all: What do
we get when we apply the various tools at our disposal to the urgent
economic problems we're facing today?
If this approach to economics is useless for the purposes of
advising policy makers to lead to better economic outcomes, what are we
getting out of the economic research funded through the NSF?
Besides raising these questions about the dominant model, we plan
to have a look at the competition. What kinds of alternative models
exist, and do we need to generate still others? Should we be using a
variety of models in concert rather than relying on only one type?
Should the Federal Government use its funding of economic science to
encourage the development of these alternative approaches?
We have a distinguished panel to help us delve into these issues.
Dr. Robert Solow will tell us what is in the DSGE model, where it parts
from the realities of the world, and what kind of advice it tends to
deliver. Dr. Sidney Winter will talk about the economic realities that
DSGE and its macroeconomic cousins fail to take into account and about
how to look for policy advice when there are important features of the
economy that don't lend themselves to modeling. Dr. Scott Page will
provide a glimpse of a new form of model that advanced computing power
has made possible, the agent-based model, and make a case for the use
of many and varied models. Dr. David Colander will explain why DSGE has
achieved such a monopoly and outline a plan designed to open the floor
to a broader spectrum of ideas. And Dr. V.V. Chari will state that
while DSGE models are definitely capable of improvement, many of the
criticisms leveled against them are inaccurate and, in any case,
``there is no other game in town.''
So my advice to you is to prepare for a lively discussion, and with
that I yield back my time and call on the Ranking Member, Dr. Broun,
for his opening statement.
Chairman Miller. I yield back my time--actually I had no
time to yield back and I now recognize the ranking member, Dr.
Broun, for his opening statement.
Mr. Broun. Thank you, Mr. Chairman.
Let me welcome the witnesses today, and I greatly
appreciate you all being here with us.
Today's hearing on macroeconomic modeling continues this
Committee's work on the role of science in economics. Not
surprisingly, several of the topics addressed at our previous
two hearings are also relevant today as we discuss
macroeconomic modeling. Understanding the purpose and
limitations of models is just as important in macroeconomic
models as it is in financial risk modeling.
In general, modeling is also a theme this Committee has
addressed several times in the past. Whether it is in regard to
climate change, chemical exposures, pandemics, determining
spacecraft survivability or attempting to value complex
financial instruments, models are only as good as the data and
assumptions that go into them. Ultimately, decisions have to be
made based on a number of variables which should include
scientific models but certainly not exclusively. As the
witnesses of previous hearings have stated, ``Science
describes; it does not prescribe.'' No model will ever relieve
a banker, trader, risk manager or policymaker of the
responsibility of making difficult decisions.
This Committee struggles enough with the complexities of
modeling, risk assessment and risk management regarding the
physical sciences. Attempting to adapt these concepts to
economics is even more complex. Despite the attempts of many to
develop a scientific panacea for informing economic decisions,
models are only a tool employed by decision makers and
economists. They add another layer of insight but they are not
crystal balls. Appreciation of this complexity and
understanding the limitation and intended purpose of the
economic models is just as important as what the models tell
us.
We have an esteemed panel of witnesses here today who will
discuss the appropriate roles and limitations of models such as
the Dynamic Stochastic General Equilibrium, DSGE model. Mr.
Chairman, maybe they will explain why they picked such a name.
But I look forward to you all's testimony, and I yield back the
balance of my time. Thank you.
[The prepared statement of Mr. Broun follows:]
Prepared Statement of Representative Paul C. Broun
Thank you Mr. Chairman.
Let me welcome the witnesses here today and thank them for
appearing. Today's hearing on macroeconomic modeling continues this
Committee's work on the role of science in economics. Not surprisingly,
several of the topics addressed at our previous two hearings are also
relevant today as we discuss macroeconomic modeling. Understanding the
purpose and limitations of models is just as important in macroeconomic
models as it is in financial risk modeling.
In general, modeling is also a theme this Committee has addressed
several times in the past. Whether it is in regard to climate change,
chemical exposures, pandemics, determining spacecraft survivability, or
attempting to value complex financial instruments, models are only as
good as the data and assumptions that go into them. Ultimately,
decisions have to be made based on a number of variables which should
include scientific models, but certainly not exclusively. As a witness
at a previous hearing stated, ``science describes, it does not
prescribe.'' No model will ever relieve a banker, trader, risk manager,
or policy maker of the responsibility to make difficult decisions.
This Committee struggles enough with the complexities of modeling,
risk assessment, and risk management regarding physical sciences.
Attempting to adapt these concepts to economics is even more complex.
Despite the attempts of many to develop a scientific panacea for
informing economic decisions, models are only a tool employed by
decision-makers and economists. They add another layer of insight, but
are not crystal balls. Appreciating this complexity, and understanding
the limitations and intended purpose of economic models is just as
important as what the models tell you.
We have an esteemed panel of witnesses here today who will discuss
the appropriate roles and limitations of models such as the Dynamic
Stochastic General Equilibrium (DSGE) model. I look forward to their
testimony and yield back my time.
Thank you.
Chairman Miller. The reason I said it out loud was that we
would all be forgiven that and could all just say DSGE going
forward.
I now ask unanimous consent that all additional opening
statements submitted by members will be included in the record.
Without objection, so ordered.
It is now my pleasure now to introduce our witnesses. Dr.
Robert Solow is Institute Professor Emeritus at MIT, where he
has been a Professor of Economics since 1949, and is currently
Foundation Scholar at the Russell Sage Foundation as well as
the President of the Cournot Center for Economic Study. I had
more about you, sir. Dr. Solow did receive the Nobel Prize for
Economics, as I mentioned earlier, in 1987, and the National
Medal of Sciences in 1999. He is a member of the National
Academy of Science. Dr. Solow was the Chairman of the Board of
the Federal Reserve Bank of Boston for three years. Earlier in
this career, he served as the Senior Economist on the Council
of Economic Advisors during President Kennedy's Administration.
Dr. Sidney Winter is the Deloitte and Touche Professor of
Management Emeritus at the Wharton School of the University of
Pennsylvania. Before joining Wharton in 1933, he served for
four years as chief economist of the U.S. General Accounting
Office, now called the Government Accountability Office, our
friends at GAO in Washington. He taught for more than two
decades in the economics departments of Yale University and the
University of Michigan.
Dr. Scott Page is the Leonid Hurwicz Collegiate Professor
of Complex Systems, Political Science and Economics at the
University of Michigan, and an External Faculty Member of the
Santa Fe Institute. Dr. Page, you might want to come up with a
shorter way to describe your job, just like DSGE is so handy.
He is the author of three books and more than 50 scientific
research papers and has won awards for teaching and service at
four major universities.
Dr. V.V. Chari is the Paul W. Frenzel Land Grant Professor
of Liberal Arts, Professor of Economics and Founding Director
of the Heller-Hurwicz Institute at the University of Minnesota.
He has served the Federal Reserve Bank of Minneapolis, for
which he now consults as a Senior Research Officer and Economic
Advisor. He has been elected Fellow of the Econometric Society.
And then finally, Dr. David Colander has been the Christian
A. Johnson Distinguished Professor of Economics at Middlebury
College since 1982. He has authored, co-authored or edited more
than 40 books and 150 articles on a wide range of topics. His
books include a Principles of Macroeconomics text and
Intermediate Macro text. He is the former President of the
History of Economic Thought Society.
Our witnesses should know that you each have five minutes
for your spoken testimony. Your written testimony will be
included in the record of the hearing. When you have completed
your spoken testimony, we will begin with questions. Each
member will have five minutes to question.
It is the practice of the Subcommittee, since this is an
investigations and oversight subcommittee, to take testimony
under oath. It does seem very unlikely that there would be any
perjury prosecutions coming out of today's hearing. The
prosecutor, the U.S. Attorney, would have to prove that you
knew the truth and consciously deviated from it. Do any of you
have any objection to taking an oath? The record should reflect
that all the witnesses indicated that they had no objection.
You also have the right to be represented by counsel. Do any of
you have counsel here? Surprisingly enough, the record should
reflect that none of the witnesses, or, all the witnesses
indicated they did not have counsel. Please now stand and raise
your right hand. Do you swear to tell the truth and nothing but
the truth? The record should reflect that all of the witnesses
did take the oath.
We will now start with Dr. Robert Solow. Dr. Solow, you are
recognized for five minutes. I think you may need to turn on
your--is your microphone on?
STATEMENT OF ROBERT M. SOLOW, PROFESSOR EMERITUS, MASSACHUSETTS
INSTITUTE OF TECHNOLOGY
Mr. Solow. Well, I start by thanking you and Dr. Broun for
inviting me to this hearing. It is a little odd to be
discussing an abstract question like how the macroeconomy works
under circumstances like this, but it is pretty urgent. Here we
are near the bottom, as the chairman said, of a deep and
prolonged recession and the immediate future is very uncertain.
We are in desperate need of jobs, and the approach to
macroeconomics that dominates the elite universities of the
country and many central banks and other influential policy
circles, that approach seems to have essentially nothing to say
about the problem. It doesn't offer any guidance or insight and
it really seems to have nothing useful to say. And my goal in
the next few minutes is to try to explain why it has failed and
is sort of intrinsically bound to fail.
But before I go on, there is something preliminary that I
want to make clear. I am generally a quite traditional,
mainstream economist. I think that the body of economic
analysis that we have built up over the years and teach to our
students is pretty good. There is no need to overturn it in any
wholesale way and there is no acceptable suggestions for doing
that. It goes without saying that there are big gaps in our
understanding of the economy and there are plenty of things we
know that ain't true. That is almost inevitable. The national
economy is a fearfully complex thing and it is changing all the
time, so there is no chance that anyone is ever going to get it
right once and for all. So it is all the more important to
catch foolishness when you see it.
When it comes to things as important as macroeconomics, I
think that every proposition has to pass a smell test: Does it
really make sense? And I don't think that the currently popular
DSGE models--I can say Dynamic Stochastic General Equilibrium
without a lapse--I don't think that those models pass the smell
test. They take it for granted that the whole economy can be
thought of as if it were a single, consistent person or dynasty
carrying out a rationally designed, long-term plan,
occasionally disturbed by unexpected shocks but adapting to
them in a rational, consistent way. I don't think that that
picture passes the smell test. And the protagonists of this
idea make a claim to respectability by asserting that it is
founded on what we know about microeconomic behavior; but I
really think that this claim is generally phony. The advocates
believe what they say, there is no doubt, but they seem to have
stopped sniffing or to have lost their sense of smell
altogether.
So most economists are willing to believe that individual
agents, consumers, investors, borrowers, lenders, workers,
employers all make their decisions so as to do roughly the best
that they can for themselves, given their possibilities and
their information. They don't always behave in that fairly
rational way, and systematic deviations are well worth
studying. But it is not a bad first approximation in many
cases.
The DSGE model populates its simplified economy with
exactly one single combined worker, owner, consumer, everything
else who plans ahead carefully, lives forever; and one
important consequence of this representative-agent assumption
is that there are no conflicts of interest, no incompatible
expectations, no deceptions. This all-purpose decision maker
essentially runs the economy according to its own preferences--
not directly, of course, the economy has to operate through
generally well-behaved markets and prices. Under pressure from
skeptics and from the need to deal with actual data, DSGE
modelers have worked hard to allow for various market frictions
and imperfections like rigid prices and wages, asymmetries of
information, time lags and so on. This is all to the good, and
they have done very good work. But the basic story always
treats the whole economy as if it were like a person trying
consciously and rationally to do the best it can on behalf of
the representative agent, given its circumstances. This cannot
be an adequate description of a national economy, which is
pretty conspicuously not pursuing a consistent goal. A
thoughtful person faced with that economic policy based on that
kind of idea might reasonably wonder what planet he or she is
on.
The most obvious example is that the DSGE story has no real
room for unemployment of the kind we see most of the time and
especially now: unemployment that is pure waste. There are
competent workers willing to work at the prevailing wage or
even a bit less, but the potential job is stymied by a market
failure. The economy is simply unable to organize a win-win
situation that is apparently there for the taking. This sort of
outcome is incompatible with the notion that the economy is in
rational pursuit of an intelligible goal. The only way the DSGE
and related models can cope with unemployment is to make it
somehow voluntary, a choice of current leisure or a desire to
retain flexibility for the future or something like that. But
that is exactly the sort of explanation that does not pass the
smell test.
To the extent that the observed economy is actually doing
the best it can given the circumstances, it is already adapting
optimally to whatever expected or unexpected disturbances come
along. It cannot do better. It follows that conscious public
policy can only make things worse. If the government has better
information than the representative agent has, then all the
government has to do is to make the information public. If
prices are imperfectly flexible, then the government can make
them more flexible by attacking monopolies and weakening
unions, and actually even that proposition is dubious on its
own.
The point that I am making is that the DSGE model has
nothing useful to say about anti-recession policy because it
has built into its essentially implausible assumption the
conclusion that there is nothing for macroeconomic policy to
do. I think we have just seen how untrue that is for an economy
attached to a highly leveraged, weakly regulated financial
system, as the chairman pointed out, but I think it was just as
visibly false in earlier recessions and in episodes of
inflationary overheating that followed quite different
patterns. There are other traditions in macroeconomics that
provide better ways to do macroeconomics, and I hope we will
get a chance to talk about that soon. Thank you.
[The prepared statement of Dr. Solow follows:]
Prepared Statement of Robert Solow
It must be unusual for this Committee, or any Congressional
Committee, to hold a hearing that is directed primarily at an
analytical question. In this case, the question is about
macroeconomics, the study of the growth and fluctuations of the broad
national aggregates--national income, employment, the price level, and
others--that are basic to our country's standard of living. How are
these fundamental aggregates determined, and how should we think about
them? While these are tough analytical questions, it is clear that the
answers have a direct bearing on the most important issues of public
policy.
It may be unusual for the Committee to focus on so abstract a
question, but it is certainly natural and urgent. Here we are, still
near the bottom of a deep and prolonged recession, with the immediate
future uncertain, desperately short of jobs, and the approach to
macroeconomics that dominates serious thinking, certainly in our elite
universities and in many central banks and other influential policy
circles, seems to have absolutely nothing to say about the problem. Not
only does it offer no guidance or insight, it really seems to have
nothing useful to say. My goal in the next few minutes is to try to
explain why it has failed and is bound to fail.
Before I go on, there is something preliminary that I want to make
clear. I am generally a quite traditional mainstream economist. I think
that the body of economic analysis that we have piled up and teach to
our students is pretty good; there is no need to overturn it in any
wholesale way, and no acceptable suggestion for doing so. It goes
without saying that there are important gaps in our understanding of
the economy, and there are plenty of things we think we know that
aren't true. That is almost inevitable. The national--not to mention
the world--economy is unbelievably complicated, and its nature is
usually changing underneath us. So there is no chance that anyone will
ever get it quite right, once and for all. Economic theory is always
and inevitably too simple; that can not be helped. But it is all the
more important to keep pointing out foolishness wherever it appears.
Especially when it comes to matters as important as macroeconomics, a
mainstream economist like me insists that every proposition must pass
the smell test: does this really make sense? I do not think that the
currently popular DSGE models pass the smell test. They take it for
granted that the whole economy can be thought about as if it were a
single, consistent person or dynasty carrying out a rationally
designed, long-term plan, occasionally disturbed by unexpected shocks,
but adapting to them in a rational, consistent way. I do not think that
this picture passes the smell test. The protagonists of this idea make
a claim to respectability by asserting that it is founded on what we
know about microeconomic behavior, but I think that this claim is
generally phony. The advocates no doubt believe what they say, but they
seem to have stopped sniffing or to have lost their sense of smell
altogether.
This is hard to explain, but I will try. Most economists are
willing to believe that most individual ``agents''--consumers
investors, borrowers, lenders, workers, employers--make their decisions
so as to do the best that they can for themselves, given their
possibilities and their information. Clearly they do not always behave
in this rational way, and systematic deviations are well worth
studying. But this is not a bad first approximation in many cases. The
DSGE school populates its simplified economy--remember that all
economics is about simplified economies just as biology is about
simplified cells--with exactly one single combination worker-owner-
consumer-everything-else who plans ahead carefully and lives forever.
One important consequence of this ``representative agent'' assumption
is that there are no conflicts of interest, no incompatible
expectations, no deceptions.
This all-purpose decision-maker essentially runs the economy
according to its own preferences. Not directly, of course: the economy
has to operate through generally well-behaved markets and prices. Under
pressure from skeptics and from the need to deal with actual data, DSGE
modellers have worked hard to allow for various market frictions and
imperfections like rigid prices and wages, asymmetries of information,
time lags, and so on. This is all to the good. But the basic story
always treats the whole economy as if it were like a person, trying
consciously and rationally to do the best it can on behalf of the
representative agent, given its circumstances. This can not be an
adequate description of a national economy, which is pretty
conspicuously not pursuing a consistent goal. A thoughtful person,
faced with the thought that economic policy was being pursued on this
basis, might reasonably wonder what planet he or she is on.
An obvious example is that the DSGE story has no real room for
unemployment of the kind we see most of the time, and especially now:
unemployment that is pure waste. There are competent workers, willing
to work at the prevailing wage or even a bit less, but the potential
job is stymied by a market failure. The economy is unable to organize a
win-win situation that is apparently there for the taking. This sort of
outcome is incompatible with the notion that the economy is in rational
pursuit of an intelligible goal. The only way that DSGE and related
models can cope with unemployment is to make it somehow voluntary, a
choice of current leisure or a desire to retain some kind of
flexibility for the future or something like that. But this is exactly
the sort of explanation that does not pass the smell test.
Working out a story like this is not just an intellectual game,
though no doubt it is a bit of that too. To the extent that the
observed economy is actually doing the best it can, given the
circumstances, it is already adapting optimally to whatever expected or
unexpected disturbances come along. It can not do better. It follows
that conscious public policy can only make things worse. If the
government has better information than the representative agent has,
then all it has to do is to make that information public. If prices are
imperfectly flexible, then the government can make them more flexible
by attacking monopolies and weakening unions. Actually this proposition
is dubious on its own.
The point I am making is that the DSGE model has nothing useful to
say about anti-recession policy because it has built into its
essentially implausible assumptions the ``conclusion'' that there is
nothing for macroeconomic policy to do. I think we have just seen how
untrue this is for an economy attached to a highly-leveraged, weakly-
regulated financial system. But I think it was just as visibly false in
earlier recessions (and in episodes of inflationary overheating) that
followed quite different patterns. There are other traditions with
better ways to do macroeconomics.
One can find other, more narrowly statistical, reasons for
believing that the DSGE approach is not a good way to understand
macroeconomic behavior, but this is not the time to go into them. An
interesting question remains as to why the macroeconomics profession
led itself down this particular garden path. Perhaps we can come to
that later.
Chairman Miller. Thank you, Dr. Solow.
Dr. Winter, you are recognized for five minutes.
STATEMENT OF SIDNEY G. WINTER, DELOITTE AND TOUCHE PROFESSOR
EMERITUS OF MANAGEMENT, THE WHARTON SCHOOL OF THE UNIVERSITY OF
PENNSYLVANIA
Mr. Winter. Thank you. Mr. Chairman and members of the
subcommittee, this hearing explores some fundamental and
relatively neglected questions related to the recent financial
crisis, and I am pleased and honored to be asked to
participate. And I am honored to be following Bob Solow on this
panel because Bob was once my college honors examiner and not
long after that my boss, but that was a long time ago.
As you mentioned, Mr. Chairman, I moved to the management
department at the Wharton School of the University of
Pennsylvania after previously having spent two decades teaching
microeconomic theory at the Ph.D. level. One of the reasons
that I shifted to a management department is that it offered me
a more supportive environment for my research, which is more
concerned than is the economics discipline with the realities
of business behavior and of organizational behavior generally.
The concern of this hearing, the shortcomings of the DSGE
model, represents the tip of a very large iceberg, an iceberg
which comprises by far the greatest proportion of model
building and theorizing in the discipline, both microeconomic
and macroeconomic.
A distinctive feature of economics among the sciences is
the degree to which most economists, especially most
theoretical economists, are oblivious to behavioral realities
at the levels of the fundamental units of the complex system
that they study: the business firms and households. Although
many economists defy that description, they remain few compared
to the mainstream and do not get much attention or carry much
weight.
I was asked to discuss what is left out of the DSGE model.
All theories must of course leave out almost everything, since
the point of theory is to simplify reality in a way that tells
the truth while not aspiring to tell the whole truth. But DSGE
is an extreme example of the tendency to analyze hyperstylized
versions of economic problems, thereby denying or suppressing
quite observable and verifiable realities.
If improving the model is the problem, the challenge is to
identify specific causal mechanisms in reality that should be
in the model but are now excluded. However, in my view,
improving model is not the only thing that deserves attention.
We should really be talking about how to organize ourselves to
meet the real needs for economic policy guidance, initially
leaving open the questions about models and empirical inquiry.
I attempt three things in the remainder of my time. First,
I mention a piece of economic reality that was fundamental to
the recent financial crisis but was not reflected in the DSGE
model or any macroeconomic model I know of. Second, I will
suggest the difficulties and prospects of adding this piece to
the prevailing models. And third, I will expand on the need to
extend the quest for policy advice beyond models and their
improvement.
The piece of reality I referred to is the process by which
the residential mortgage business in the United States evolved
into a system where nobody really cared whether the loans were
going to be repaid. This meant, as you know, not attending
carefully to the creditworthiness of borrowers and not
seriously appraising the collateral. These practices developed
slowly out of familiar mechanisms of self-interest, with
attendant thoughtful advocacy, until at the end lenders in the
traditional sense--with traditional lender incentives--had gone
almost extinct as an economic species. I review this story in
my written testimony. This is a pretty shocking thing in an
economy in which self-interest is regarded as a fundamental and
generally constructive guiding force. It may be particularly
shocking to economic theorists because it beautifully
illustrates the type of behavioral reality that most theorists
tend to deny, since it seems sharply at odds with conventional,
oversimplified images of economic rationality. What? Lenders
didn't care about loan repayment? Most theorists would be so
sure that couldn't happen that they wouldn't bother to check.
While there were other contributing factors, if you ask
what distinguished this event from other economic crises, it
becomes clear that residential mortgages and the business
practices related to them were central to this crisis and to
where the bailout money went. Without the mortgage-related
practices, there might still have been a crisis at some point,
but it would not have been much like the financial crisis of
2008 and it might have been a lot less severe.
So was this episode something that macroeconomic theory
could or should make room for? These are all major
macroeconomic events and they have a clear basis in long-
sustained patterns of economic behavior among private-sector
actors. So there might be a presumption that the causal
mechanisms do belong in the model, yet it is hard to imagine
that much of the story of mortgage-market evolution is eligible
for inclusion in macroeconomic theory as we conventionally
understand it. The mortgage market by itself is far more
complex than the DSGE represents the whole economy to be.
We could at least have a richer collection of partial
models to inform us and we need in particular models based on
business practice, an idea that does not appear in any
mainstream economic theory text that I know about. Other key
words to look for would include habit, organizational routines,
organizational capabilities, business systems, business
processes. They are all very much a part of the reality, and
they can produce social outcomes very different from those
anticipated in standard theory--and they are all absent from
the textbooks. This is probably because they are all in some
ways at odds with the theorists' assumption that businesses
reliably get the right answer to the problems that they face.
Finally, I will return to my suggestion that we may need to
look beyond the models and the theories to find the kinds of
adjustments that are needed and appropriate, given the very
large social stakes in macroeconomic problems. New research
initiatives are needed in the regulatory agencies as well as in
academe. Most fundamentally, we need to make sure that adequate
intellectual resources are applied to the task of understanding
what is happening in the economy as opposed to what is
happening in the models. Those seeking that understanding must
draw on the valuable body of knowledge that mainstream
economics has accumulated but also on much broader sources.
Historical perspective is particularly important. In the domain
of modeling, we need more models that seek to capture
systematic behavioral tendencies as they are and then assess
the implied outcomes in terms of the service to private and
social interests, rather than committing fully to the right
answer framework from the very start.
Thank you, Mr. Chairman. Thank you for your attention.
[The prepared statement of Dr. Winter follows:]
Prepared Statement of Sidney G. Winter
Mr. Chairman and members of the Committee, this hearing explores
some fundamental and relatively neglected questions related to the
recent financial crisis, and I am pleased and honored to be asked to
participate.
My name is Sidney Winter. I am the Deloitte and Touche Professor of
Management, Emeritus, at The Wharton School of the University of
Pennsylvania, where I spent 15 years in the Management Department. I am
trained as an economist, and I previously was a tenured faculty member
in two economics departments, those of Yale University (13 years) and
the University of Michigan (8). One of my central roles there was to
teach microeconomic theory at the Ph.D. level. Between Yale and
Wharton, I spent four years as the Chief Economist of what was then
called the U.S. General Accounting Office (now the U.S. Government
Accountability Office).
One of the reasons that I wound up in a management department is
that it offered me a more supportive environment for my kind of
research, which is more concerned than is the economics discipline with
the realities of business behavior, and of organizational behavior
generally. It should be clear that my background and research do not
qualify me as any sort of macroeconomist, theoretical or applied. What
I offer here is a different perspective, which I hope the Committee
will find useful in the context of this hearing.
The concern of this hearing, the shortcomings of the DSGE model,
represents the tip of a very large iceberg, an iceberg which comprises
almost all model-building and theorizing in the discipline, both
macroeconomic and microeconomic. A distinctive feature of economics
among the sciences is the degree to which most economists, especially
most theoretical economists, are oblivious to behavioral realities at
the levels of the fundamental units of the complex system they study.
In absolute number, there are many dissenters from that dominant
view, and much constructive work is done from different viewpoints. I
will take note of some it later on. One can reasonably argue that a
slow trend has favored the dissenters for a few decades now. Relatively
speaking, however, the dissenters are still few and their aggregate
research effort is a fraction of what the mainstream tradition mounts,
especially in core, policy-relevant domains like macroeconomics and
public finance. Relative to the mainstream, the dissenters do not get
much attention, and do not carry much weight.
I was asked to discuss ``what is left out'' of the dominant DSGE
model. All theories must leave out almost everything, since the idea of
theory is to try to tell the truth while not aspiring to telling the
whole truth--because the latter ambition is hubris-ridden and
ultimately counterproductive. But DSGE is an extreme case of the
tendency to analyze hyper-stylized versions of economic problems,
thereby suppressing or denying quite observable realities. In the DSGE
case, the suppressed realities include the fact that economic actors
are diverse and have diverse interests. Like many other economists, I
would argue that the divergence of interests is one fundamental source
of the difficulty society has in settling on good rules for the
economic game. Macroeconomic dysfunctions like financial crises and
involuntary unemployment are among the problems that good rules could
help prevent--but for our difficulties in agreeing on enforceable ones.
On this view, representing the macroeconomic problem as one confronting
a single optimizing actor is an approach that is off target from the
start.\1\
---------------------------------------------------------------------------
\1\ To be fair, the economy-as-single-actor approach does have its
own substantial history in the discipline, as illustrated by
discussions of hypothetically perfect central planning.
---------------------------------------------------------------------------
It is useful to think of economic models as parables. True, the
great teachers of history did not typically use mathematical notation
when they used a parable to get a point across. Putting aside the
notation issue, and also the level of professed concern with logical
consistency, there are strong parallels between what those teachers
sought to do and what economic modelers seek to do. The objective is
not to tell ``the whole truth,'' but to get the point across. ``When
you think about this complex world we live in, or about how to get to
heaven as your next stop, you might find it helpful keep this in mind:
(insert parable here)''
Robert Solow put this very well when in characterizing his own
approach to economic theory:
``My general preference is for small, transparent, tailored
models, often partial equilibrium, usually aimed at
understanding some little piece of the (macro)-economic
mechanism.'' (Solow 2008).
Arguably, almost all of what economic theorists ``know'' today
about how the economy works can reasonably be thought of as a string of
logically tight parables, some with a degree of empirical grounding,
many not. The DSGE model is consistent with this broad approach to
understanding the economy, but stands out for the ambitious scope of
its subject matter, as well as for its high commitment to analyzing the
optimal behavior of a single, fictitious type of actor.
Thus, if improving the model is the problem, the challenge is to
locate the zone where there is an interesting case for an incremental
adjustment, identifying specific things that should be in but are now
excluded. However, in my view, improving the model is not the only
thing that deserves attention. We should really be talking about how to
organize ourselves to meet the real needs for economic policy guidance.
I attempt three things in this testimony. First, I will point to a
piece of economic reality that was fundamental to the recent financial
crisis but was not reflected in the DSGE model or in any macroeconomic
model I know of. Second, I will suggest the difficulties and prospects
of getting this piece of reality reflected in the models. Third, I will
expand on the need to extend the quest for policy guidance beyond
models and their improvement.
Building Toward Crisis: The Insidious Evolution of the U.S. Mortgage
Market
The reality I speak of is the process by which the residential
mortgage business evolved into a system where, when the loans were
being made, nobody really cared whether the loans were going to be
repaid. This meant not attending carefully to the credit-worthiness of
borrowers, and not seriously appraising the collateral. These practices
developed slowly, driven by familiar considerations of self-interest
and opportunity, with attendant thoughtful advocacy--until in the end,
traditional lenders, with traditional incentives, had almost gone
extinct as an economic species. Those who remained presumably still
cared, but they were largely replaced by new species of players who,
collectively, lost track of the problem of loan quality. At least some
of those new players suffered great financial losses as a consequence
of their errors, but the losses inflicted on the taxpayers and society
as a whole were, and continue to be, much larger.
That is a pretty shocking thing to happen in an economy in which
self-interest is regarded as a fundamental, and generally constructive,
guiding force. It may be particularly shocking to economic theorists,
because it beautifully illustrates a type of behavioral reality that
most theorists tend to deny, since it seems so sharply at odds with
conventional, oversimplified, images of economic rationality. What,
lenders didn't care about loan repayment? Most theorists would be so
sure it couldn't happen that they wouldn't bother to check.
This insidious transformation happened ``sensibly,'' at least until
quite a late stage (Jacobides 2005). Private sector actors responded to
incentives in a largely familiar way, though with an unusually strong
component of ``financial innovation.'' (While we tend instinctively to
celebrate ``innovation,'' it should be remembered that ``innovative''
often means ``untested and hazardous.'') Government authorities and
other observers commented on some of these developments, and there was
some questioning and some level of warning was heard. Authoritative
figures, however, largely pronounced the developments to be acceptable
or even benign. (See for example (Greenspan 2002).)
What was involved in the evolutionary transformation that brought
us to a regime where ``the lender doesn't care''? It is a complex
question for which I can only sketch an answer. Though there are some
gaps, many of the relevant facts are well known by now. There remains
in any case the problem of putting the facts in the required order to
help make sense of the crisis, and that is what I attempt here.
To unravel this complex story, it is simplest and also immediately
instructive to start with the role of the mortgage broker. The broker
is effectively an ``out-sourced'' sales arm for financial institutions
that originate mortgages, i.e., that advance the money in the context
of the actual sale of a property. The mortgage broker role did not
always exist; the job of finding and hand-holding mortgage customers
was formerly a task for employees of the financial institution that
made the loan. Brokers became particularly important to mortgage banks,
non-depository financial institutions that originated mortgage loans
and financed them through the capital markets. As of 1988, brokers were
involved in about 10% of loan originations by mortgage banks. There was
a jump to about 35% by 1991, partly because troubled savings and loan
institutions were cutting payrolls in the context of an industry
crisis. Released sales employees became independent contractors,
initially for former employers, but ultimately performed the brokerage
function in a wider market. By 1999 the broker-mediated fraction was
over 60% and has remained at similarly high levels since. (Jacobides
2005).\2\
---------------------------------------------------------------------------
\2\ An institution that used its employees in the sales function
rather than brokers would have superior opportunities to control loan
quality, but might choose to exert control in the ``wrong direction''--
a possibility dramatically illustrated in the case of Washington
Mutual, which not only complemented its thrift business with a mortgage
bank, but allowed the risky practices of securitized loans to become
the norm in the rest of the organization, as the recent Senate hearings
demonstrated.
---------------------------------------------------------------------------
Like most brokers, a mortgage broker is paid on commission, a
percentage of the value of the deal. Once the deal is done--meaning the
financing arranged and the house purchase closed--the broker takes a
commission and leaves that scene and looks to facilitate another deal.
This means that the direct self-interest of the broker is to facilitate
deals and collect commissions, and the quality of the collateral and
the probability of repayment do not enter into that directly. In this
sense, it is clear that the broker ``doesn't care''--at least in his or
her assigned theoretical role as a self-interested economic agent. (But
is the mortgage broker ``the lender''? Clearly not. We will look
further for a true lender, one who might still have cared, even in the
world with mortgage brokers.)
This is a huge example of what economists call an ``agency
problem''--the agent may not have the interest of the principal at
heart. The solution to the agency problem, if it is not available in
the incentives, is in controls. Mortgage applications typically involve
the completion of a lot of forms that are supposed to provide whatever
assurance is reasonably available about the collateral and the
creditworthiness of the borrower. From the viewpoint of the broker, the
problem is to get these forms completed, and completed in essentially
reassuring ways, so the financing can be arranged and the commission
can be collected. And that indeed was what happened, at least until a
late stage when even the nominal defenses of loan quality crumbled and
documentation-light loans became commonplace--all the way to the
extreme of the NINJA loan. (``No Income, Job or Assets''). To interpret
the evolution as a whole, it is important first to understand that if
something was going to resist the degradation of loan quality, it
emphatically was not the incentives operating on mortgage brokers.
We come next to the originator, the financial institution that
initially advances the money. If the originator were going to hold the
loan, there would be an incentive to actually read those forms
describing the loan and assess the prospects of repayment. Here is
where ``mortgage backed securities'' (MBS) and the ``originate and
sell'' business model enter the story. Many originators made money by
becoming, in effect, another kind of broker--taking a cut but not
holding a continuing interest, or very little. They forwarded the
mortgages to Wall Street firms, who packaged them into MBS. Thus the
originator did not retain an interest in the asset and, like the
broker, had little direct incentive to be concerned with loan quality.
If the forms that accompanied the application were supposed to defeat
the obvious agency problem at the broker level, we confront the
question of who had the incentive to actually attend to that
information. Under the ``originate and sell'' model, the originator is
not that party. In fact, intense local competition among originators
often deflected managerial attention away from loan quality and toward
the increase in volume.
The securitization of mortgages is an important financial
innovation. It has a substantial history that can for present purposes,
be dated from its introduction in the 1970s by the government sponsored
enterprises (GSEs), Ginnie Mae, Fannie Mae and Freddie Mac. Initially,
the loans themselves were made under governmental loan guarantee
programs (FHA, VA). That constraint was subsequently relaxed, and
private sector securitizers, mostly investment banks, followed the
governmental lead. All of this was widely celebrated for its benign
effects on housing finance, even by some conservative economists who
credited the government leadership with reducing informational
imperfections in the market. As the bubble peaked in 2006, private
sector securitization activity had risen above 40% of total
securitization As the crisis broke 2007-2008, it collapsed. Overall,
securitization played an increasing role in the mortgage finance system
over the long period, as Table 1 indicates.
The economic rationale of securitization is based on the reduction
of investment risk through diversification and the related capacity to
raise housing finance through the capital markets rather than
individual financial institutions. Because individual borrowers face
diverse circumstances affecting repayment, it is possible to improve
things by pooling risks and offering an investor the opportunity to
invest, in effect, in the average performance of the pool. The economic
logic is sound, provided certain conditions hold. Unfortunately, the
``certain conditions'' are not very certain at all, if by that one
means that it is objectively easy to determine the degree to which they
obtain. One condition is that the repayment histories of individual
loans do not respond too much to the causal factors they inevitably
share, such as influences on the general level of housing prices.
Another is that the quality of loans in MBS pools remains uncorrupted
by the feedback from the securitization itself. That feedback includes
not only a reduced incentive to look carefully at individual loans, but
also the learning of self-interested agents about the exploitable weak
spots in the control system. (The latter parallels a problem commonly
noted in the context of government regulation: Both public and private
``regulators'' have trouble staying ahead in their games with the
``regulatees.'')
In the end, of course, somebody has to be putting money at risk to
finance mortgage lending. It does not follow, however, that these
individuals or organizations are in a position to provide a secure
anchor for the chain of agency problems, effectively insisting that
everybody down the line to the mortgage broker has an eye on loan
quality. We can indeed locate, in the history of the crisis, some
people who seemingly had the ``right incentives'' and some of them
should, in retrospect, have been more careful. Nevertheless, most of
them are best called investors rather than lenders, because the actual
apparatus of loan-making was very far removed from them. In effect, the
parties who put up the money mostly had an investor interest comparable
to that of a typical stock market investor, a role which generally does
not entail delving into the question of whether, for example, corporate
management is making a good decision about the location of the next
plant the company builds. Similarly, investors in MBS and related
derivatives did not delve into the quality of the actual mortgage loans
behind those securities.
Their institutional distance from the action left most investors
poorly positioned to make good investment choices, and in many cases--
such as ordinary people with their retirement money invested through
funds of various kinds--they did not remotely have practical incentives
to attack the very large problem of understanding where their money
went. The big investors did not fare that much better, for they did not
get a lot of help with understanding what was happening to their money.
Their perceived ``needs''--to invest their money at a good return--were
met by waves of financial innovation that took the form of ever-more
complex repackaging of underlying mortgage debt, plus new ways to place
bets for or against particular securities.\3\ This process made the
information gulf widen until, it appears, it even swallowed some of the
parties who were creating it.
---------------------------------------------------------------------------
\3\ Varying levels of detail about collateralized debt obligations
(CDOs), synthetic CDOs credit default swaps (CDSs), tranches and the
like are available from sources at varying levels of readability. One
good source is (Pozen 2009). For the highly readable version, see
(Lewis 2010).
---------------------------------------------------------------------------
In sum: Between the investors, large and small, and the mortgage
originators, there were first the securitizers and then other
institutional actors who might possibly have played a role in
maintaining attention to loan quality--but didn't. In these layers, the
story became complex and even exotic, ultimately taking leave of the
domain of ``sensible'' economic motivation.
While much of this detail can be left aside, it is important to
take specific note of the role of the rating agencies. These for-profit
organizations exercised quasi-governmental authority by virtue of
regulatory requirements restricting insurance companies, pension funds
and other significant institutional investors to invest only in
``investment grade'' securities--a determination left to designated
rating agencies. These agencies, however, were customers of the
securitizers. They naturally tended to have ``customer satisfaction''
at heart, as any respectable for-profit actor in a market economy tends
to do. Like the mortgage broker role, the customer orientation of
rating agencies toward issuers was not always a feature of the system.
Here again we note the role of institutional evolution: The rating
agencies used to have investors as their customers, not issuers. The
very important change of the business model occurred in the early
1970s. (See (White 2009) on the evolution of the rating agencies.) \4\
---------------------------------------------------------------------------
\4\ The importance of that change as a factor in the crisis is
challenged by some who emphasize the overwhelming levels of demand for
the securities, itself the result of other factors. Charles Calomiris
points to the role of asset managers looking for yield on behalf of
their clients--and afflicted by yet another agency problem inherent in
the way they were rewarded (Calomiris 2008). Ultimately, it might be
difficult to disentangle the underlying strength of demand from the
influence of obfuscation and misrepresentation. An accurate forecast of
the events of September 2008 certainly would have discouraged a lot of
demand.
---------------------------------------------------------------------------
In retrospect, it appears that the rating agencies took customer
satisfaction a good deal too seriously. Their ratings, and the related
regulatory restrictions on investments, served to sustain the demand
for MBS and related derivatives in the face of disastrous weakness in
the underlying loans, with extremely adverse consequences for investors
in the U.S. and around the world.\5\
---------------------------------------------------------------------------
\5\ See Michael Lewis's best-selling book, The Big Short, for
particularly vivid testimony on the character and behavior of the
rating agencies, as well as other matters. While academic norms should
discourage me from citing a popular journalistic book as ``evidence,''
I see a lot of face validity in this testimony. Hence, if there is
genuine disagreement on its factual accuracy, it seems that it would be
useful for somebody to orchestrate an orderly confrontation on whatever
is said to be disputable. There are several excellent books on the
origin of the crisis to which the same remark applies.
---------------------------------------------------------------------------
We can thus explain how the insidious transformation happened, how
there gradually evolved a mortgage lending system that had lost track
of the loan quality issue. Traditional mortgage lenders with
traditional incentives became an endangered species as a consequence of
a series of incremental changes in institutions and industry
architecture, and hence in the operative incentives. Many of those
changes were of a readily identifiable, datable kind, or were marked by
measurable trends. Mortgage borrowers, and ``lending'' as an activity
concretely manifested at real estate closings, became far separated
from the investors who had the ultimate stake in loan principal. In
that gap there evolved layer upon layer of related business practices
that seemed to ``work'' in the prevailing context. Like most such
practices, they were retained while they worked, or perhaps a bit
longer.
It remains for me to place the business practices of the
residential mortgage sector in context among the candidate causes of
the crisis. One can find on the website of the Financial Crisis Inquiry
Commission a list of the 22 topics and substantive areas of concern to
the Commission, all of which can plausibly be colored as contributing
``causes'' of the crisis. Undoubtedly, it was a complex event, with
numerous factors involved. Assigning weights among multiple causes of a
complex event is intrinsically a difficult thing to do, and no one has
a credible claim to having sorted this one out completely.
If, however, we examine the aspects that distinguish this event
from other historical episodes of bubble-and-crisis, it is very clear
that residential mortgages and the practices related to them were
central to the distinctive features of THIS crisis--and to where the
bailout money went. The collapse of Bear Stearns, Lehman, AIG and
others largely resulted from practices related to mortgages and derived
securities. While excessive leveraging of investments in those
securities was a major factor, the risks of leverage depend in general
on the resistance to price decline presented by the leveraged assets.
Thus, when the fundamental weakness of the mortgage-related assets
became apparent, the havoc wrecked by the excessive leverage was all
the more extreme. Further back along the causal chain, laxity in
underwriting practices not only produced the loans that underpinned
flawed securities, but contributed to the housing bubble in a manner
similar to the role played by low interest rates--a causal factor
strongly emphasized by some economists (e.g., (Taylor 2009)). Because
loans were made that shouldn't have been made, there was more demand
for houses than there should have been, leading to higher prices, and
thus more home equity to borrow against, further delaying the day of
reckoning.\6\
---------------------------------------------------------------------------
\6\ For many homeowners, the threatened ``reckoning'' involved
upward adjustments in mortgage interest rates on adjustable rate
mortgages--with the result that the continued affordability of the
mortgage was dependent on a continuing increase in the price of the
house, generating equity that could be borrowed to pay the higher
interest.
---------------------------------------------------------------------------
To assess the ``cause'' of the crisis without reference to
mortgage-related business practices would seem to be a bold exercise in
hypothetical history. However sound and factual such an account might
be with respect to interest rates, asset bubbles, speculative
psychology and other matters, it has a weak claim to being about the
Financial Crisis of 2008. Without the mortgage-related practices, there
might still have been a crisis at some point, but it would not have
been much like the Financial Crisis of 2008. It might also have been a
lot less severe, and thus more in line with several previous crises in
U.S. financial markets.
Does the Residential Mortgage Sector Belong in Macroeconomics?
Is the foregoing story about things that macroeconomic theory
should or could make room for? The housing bubble, the financial crisis
and the great recession are major macroeconomic events and ones with a
clear (though partial) basis in long-maintained economic behavior
patterns of private sector actors. The events were not basically
``shocks'' from technology or misguided public policies, though both of
those did play a role.\7\ Given the importance of the events and their
sources in economic behavior, it might seem that there is a presumption
that the relevant mechanisms do ``belong in the model.''
---------------------------------------------------------------------------
\7\ Neither can the collapse be attributed to the occurrence of
some highly improbable event, which however was more probable than
previously expected because the relevant probability distributions had
``fat tails.'' Recognition of the empirical importance of fat-tailed
distributions is long overdue, and was effectively promoted by (Taleb
2007). But fat-tailed distributions have little to do with the crisis.
What happened was an extended process of ``more of the same, only
worse''--until in the end there was too much of the same, and it was
much worse, and the system collapsed. It seems that Taleb emphatically
agrees; see http://www.
fooledbyrandomness.com/imbeciles.htm.
---------------------------------------------------------------------------
Yet, it is hard to imagine that much of the story that I have
summarized here is eligible for inclusion in macroeconomic theory as we
conventionally understand it. Deferring my discussion of the broader
implications of that conclusion, I accept for the moment the
conventional framing where progress is achieved through the
accumulation of parables--partial models that each illuminates some
little piece of the economic mechanism.
In that perspective, there remains abundant opportunity to improve
macroeconomics by adding realism to the characterization of the
problems faced by the different sorts of economic actors. Though not
favored in the DSGE camp, this line has been vigorously pursued for a
long time.\8\ There is a wide range of possibilities as to how exactly
one goes about this; they differ particularly in the degree to which
they seek to reconcile realism with a standing commitment to the
traditional theoretical tools of optimization analysis.
---------------------------------------------------------------------------
\8\ I participated myself in one significant effort of that general
kind (Phelps 1970).
---------------------------------------------------------------------------
In my view, the best path to further progress of this general kind
is to develop models that are more securely grounded in an appreciation
of the behavioral phenomena at the micro-levels--business firms and
organizations, as well as individuals and households. By ``grounded in
an appreciation,'' I mean, ``attentive to the available evidence on the
phenomena and prepared to concede it presumptive validity.'' I
emphatically do not mean that it is possible to avoid the trouble of
thoughtful theorizing by somehow ``copying'' observed behavior directly
into a model.
With respect to individuals and to a lesser extent households,
there has been much progress of this kind in recent years. In their
recent book, (Akerlof and Shiller 2009) review a number of areas where
insights from behavioral research, combined with more conventional
economic research greatly illuminate issues of macroeconomic
significance--e.g., the origins of involuntary unemployment, saving
behavior, and the role of speculative psychology. (As noted above, both
speculative psychology and more considered speculative motives
undoubtedly played a role in the housing bubble, but perhaps were less
central to the eventual collapse than is sometimes suggested.)
Behavioral understanding has been furthered by experimental economics
and by the work of the small band of researchers following the
recently-opened paths to grounding behavioral understanding in human
neurophysiology.
With respect to business firms and organizations, however,
mainstream economics has shown little tendency to reach a modus vivendi
with relevant lines of research, even to the limited extent that this
is done with respect to behavioral research at the individual level. A
basic fact is omitted from the mainstream models. Where there are
plausible ways of dealing with this troublesome fact that are available
from heterodox economic approaches, management and organization
studies, and other social science disciplines, these opportunities tend
to be ignored by the mainstream discipline.
The basic fact is that, in almost all real decision situations,
neither the nature of the decision problem nor the list of available
options is presented at the start with anything like the clarity
posited in a mainstream model. Problems have to be discovered and
framed; options have to be invented and designed. Consequently, it is
far from the case that a mere optimization calculation (given some
criterion) is all that separates the actor from a good decision--as
mainstream modeling practice suggests. In the cases where this
generalization does not hold--and there are important examples--the
reason it does not hold is that the hard work has already been done in
the past, and the power of systematic optimization techniques can
readily be accessed to produce actionable results. Of course, that
investment of ``hard work'' was itself an application of human
ingenuity, and it may be flawed. The optimization may yield the right
answer to quite the wrong problem, or fall short because of
implementation difficulties within a frame that is basically sound.
The manifestations of the omitted fact are diverse, being quite
different in the domain of high-level strategic decisions than they are
in, say, pricing and inventory control in a department store or
supermarket. Empirical behavioral research at the strategic level is
often hampered by problems of access, and definitive results are also
elusive because of the fog of uncertainty and complexity that is
typical at that level. Lower in the hierarchy, however, the
opportunities for observation and understanding by researchers are much
greater.
It has been understood for a very long time that decisions about
things like hiring, production techniques, output levels and pricing--
the things featured in the economics texts as what firms decide about--
are often not the subject of high-level managerial attention on a
continuing basis (see. e.g., (Gordon 1948) or (Cyert and March 1963)).
At least, they are not handled that way in the large organizations that
account for the bulk of economic activity. It could hardly be
otherwise, for there are just too many such decisions to be made.
Of necessity, and for a variety of specific reasons, firms commit
for extended periods of time to systematic ways of doing things,
including ways of making the ``decisions'' classically featured in the
textbooks. These systematic ways often involve specialized equipment
and personnel--computers and software, engineers and HR managers, for
example. (Note that the personnel in these roles are ``agents'' as
distinguished from principals, and incentives are not necessarily well
aligned.) This decision apparatus is just as much an intermediate-term
``given'' in a typical firm as the plant and equipment is; it is open
to reconsideration, but only over time and as the occasions warrant.
For example, as noted previously, savings and loan institutions
embraced the mortgage broker system initially in the context of crisis,
as a cost control measure--not because it was identified as an
``optimal'' way to market mortgages. Once they had it in place, they
stuck with it, it evolved on its own, and it seemed to succeed. In the
financial markets, programmed trading provides an extreme example of
the reality of systemization and automation in domains that economic
theory treats as (intelligent? human?) ``decisions''.
To explore this basic reality, we need instructive models based on
``business practice''--an idea that does not appear in any mainstream
economic theory text that I know about. Other keywords to look for in
the index would include habits, skills, organizational routines,
organizational capabilities, business systems, business processes. Such
terms are commonplace in the discourse about business problems outside
of economics, but all seem to be virtually absent from the economics
texts. This is probably because they are in some ways at odds with the
theorist's standard assumption that businesses reliably get the right
answer to the problems they face, As illustrated in the evolution of
the mortgage market, business practices can produce social outcomes
very different from those anticipated in standard theory.
While extending the theoretical parables in the ``business
practice'' direction would be helpful, it remains true that parables
are by nature limited in aspiration and effectiveness relative to the
challenge of understanding the mechanism as a whole. The mechanism as a
whole is a complex system with many tightly interconnected parts, and
fragmentary analytical models are as unlikely to illuminate it fully as
they would be for a commercial airliner. You would not want to take the
inaugural flight in a new type of airliner where the relevant experts
explained merely that they believed they understood isolated fragments
of its mechanism. But that is the sort of flight the whole U.S. economy
took with its ``new'' mortgage market.
The residential mortgage system is far more complex than the DSGE
model represents the economy as a whole to be. The DSGE model does not
contain even a rudimentary representation of the financial sector at
the level of the ``IS-LM'' model that has long been a staple of the
macroeconomics textbooks, much less a reflection of the richer
representations of asset markets and financial intermediation to be
found in the broader research traditions of macroeconomic theory and
financial economics. The DSGE economy cannot be brought low by the
behavior of its brokers and bankers, because it doesn't have any.
In the world of contemporary practical affairs, and on into many
branches of pure science, extremely complex systems are effectively
managed by complex organizations that seek to leave nothing to chance.
Many of these systems are of extraordinary reliability--though we are
recently reminded that big disasters can happen. This reliability is an
accomplishment of social organization as much as it is of technology,
and it involves effective integration of many different specialized
skills and partial understandings. Although the stakes involved in
macroeconomic policy management are much higher than in, say, space
exploration, the ambition to surmount the challenge of complexity
appears to be largely missing.\9\
---------------------------------------------------------------------------
\9\ I must leave aside discussion of the applied side of
macroeconomics represented by the econometric forecasting models.
Although those models represent a higher ambition in terms of
addressing the complexity of the system by assembling understanding of
the pieces, the crisis of 2008 demonstrated that , for them too, far
too much was evidently left out of the model. In particular, the
dramatic events in the financial markets in the fall of 2008 were not
significantly reflected in model forecasts by December 2008--there was
only a continuation of a year-long trend toward a more pessimistic view
of 2009 (as shown in the changing ``Blue Chip consensus'').
---------------------------------------------------------------------------
I argue, therefore, that we are a long way from being able to
understand the economy and generate macroeconomic policy guidance at a
level commensurate with the stakes. The parables approach is
constructive, and it can be more helpful in the future, but it is not
adequate to the task. The discussion of the mortgage market and its
role in the crisis suggests that it will be very difficult to correct
this situation while staying within the frame of ``improving the
model.''
Meeting the Needs for Policy Guidance
I return to my suggestion that we may need to look beyond the
models and theories, and beyond academic economics as practiced now, to
find the kinds of adjustments that are fundamentally needed and
appropriate.
There are, to begin with, issues about research funding and
allocation, in particular, about the scale and character of projects
that deserve public support. To devote more attention to how the
system's pieces fit together, as well as to what the pieces actually
amount to in behavioral terms, we need research projects at a larger
scale than has been typical. We also need intense and sensible (i.e.,
not theory-blinded) attention to economic phenomena. And we need these
things on a continuing basis, enabling the tracking of the actual
evolution of the system.
A panel of experts convened by the Pew Foundation commented as
follows on the collective failure of the regulatory agencies to do that
sort of tracking in the years leading up to the crisis:
``The crisis revealed both gaps in regulation and
unanticipated interconnections among different types of
financial institutions and markets. Yet no one was charged with
understanding these interconnections, looking for gaps,
detecting early signs of systemic threats and acting to
mitigate them. During the years preceding the crisis, no
regulator was tasked with monitoring and understanding the
overall health of institutions and markets and the connections
between them across the entire breadth of the financial system.
Nor was any regulator charged with taking the lead in
responding to any early signs of systemic risks. So, for
example, several years ago there were widely recognized signs
of unusual credit expansion and increases in leverage
associated with an unprecedented rise in housing prices. These
developments signaled the beginning of a bubble with the
potential to destabilize the entire system. No action by any
government agency was taken to address this.'' (Pew 2009)
I argue that the economics discipline was complicit to a degree in
this regulatory shortfall, since the task of ``monitoring and
understanding the overall health of institutions and markets and the
connections between them across the entire breadth of the financial
system'' is certainly one in which economists should be productively
involved, but the prevailing research orientations of the discipline do
little to support the development of competence at such an ambitious
level. To improve the situation, change is needed not only in the
regulatory agencies, but in academe. The two change agendas are
inevitably closely related.
Given the highly individualistic way that economic research is
organized in universities, the regulatory agencies may in fact be the
most promising place to organize research of requisite scale and
continuity. Given that the new financial reform legislation implies
broadened responsibilities for the Federal Reserve, as well as the
creation of a new Federal Stability Oversight Council, it may be an
opportune time to reconsider the channels by which economic research
can usefully inform policy and practice at the Federal level.
This suggestion, however, begs a number of important questions
about the training, recruiting, pay and supervision of the government
economists who might participate in such initiatives. The universities
will continue to play the central role in the training of new
economists and in doing so they will continue to impart an image of
what is desirable in terms of style and focus in economic research. If,
as I argue, some adjustment in style and focus is needed, then some of
that adjustment must happen in universities or it will not happen at
all. Beyond that, the universities compete with the government in the
market for talent, and thereby constrain what agencies can do. My own
impression is that the academic research model is more influential than
it should be among economists in government, given that the latter
should be oriented toward different objectives. My own experience tells
me that this can be hard to resist, given the relative pay scales and
the role of the promised job content in the recruiting process.
Especially in the market for well trained economists from the elite
universities, there is a tendency to use the job perquisite of
``research freedom'' as a recruiting feature. In practice, this may
often mean freedom to try to lay the groundwork for a possible future
career in academe, and such ``freedom'' entails acceptance in the short
term of the research orientations of academe. Elsewhere in the
government, such as among young lawyers in the Antitrust Division of
DOJ, the use of government employment as a career stepping-stone seems
to produce acceptable results at a relatively low cost. While the
stepping-stone system is not necessarily a bad one in principle, I
think it works relatively poorly for economists. The divergence in job
content is too great, and would become even greater if my suggested
reorientations should come to pass. This again underscores the need for
some change on the academic side if there is to be any prospect of
significant change overall.
One way or another, we need to make sure that adequate intellectual
resources are applied to the task of understanding what is happening in
the economy, as opposed to what is happening in the models. Those
seeking that understanding must draw on the valuable body of knowledge
that mainstream economics has accumulated, but also on much broader
sources. Historical perspective is particularly important. In the
domain of modeling, we need more models that seek to capture systematic
behavioral tendencies as they are, and then assess the implied outcomes
in terms of service to private and social interests, rather than
committing fully to the ``right answer'' framework at the outset.
Once again, I thank the Committee for the opportunity to appear
here today, and for your attention.
References
Akerlof, G. A. and R. J. Shiller (2009). Animal Spirits: How Human
Psychology Drives the Economy and Why It Matters for Global
Capitalism. Princeton, Princeton University Press.
Calomiris, C. W. (2008). The subprime crisis: What's old, what's new,
and what's next. Maintaining Stability in a Changing Financial
System. Kansas City, MO, Federal Reserve Bank of Kansas City.
Cyert, R. M. and J. G. March (1963). A Behavioral Theory of the Firm.
Englewood Cliffs, NJ, Prentice-Hall.
Gordon, R. A. (1948). ``Short period price determination in theory and
practice.'' American Economic Review 38: 265-288.
Greenspan, A. (2002). International financial risk management: Remarks
before the Council on Foreign Relations, November 19, 2002.
Washington, Federal Reserve Board.
Jacobides, M. (2005). ``Industry change through vertical
disintegration: How and why markets emerged in mortgage
banking.'' Academy of Management Journal 48: 465-498.
Lewis, M. (2010). The Big Short: Inside the Doomsday Machine. New York,
Norton.
Pew (2009). Principles on Financial Reform: A Bipartisan Policy
Statement. T. F. o. F. Reform, Pew Charitable Trusts.
Phelps, E. S. (1970). Microeconomic Foundations of Employment and
Inflation Theory. New York, Norton.
Pozen, R. C. (2009). Too Big to Save? How to Fix the U.S. Financial
System. Hoboken, NJ, Wiley.
Solow, R. M. (2008). ``Comments: The state of macroeconomics.'' Journal
of Economic Perspectives 22(Winter 2008): 243-246.
Taleb, N. N. (2007). The Black Swan: The Impact of the Highly
Improbable. New York, Random House.
Taylor, J. B. (2009). Getting Off Track: How Government Actions and
Interventions Caused, Prolonged and Worsened the Financial
Crisis. Stanford, CA, Hoover Institution Press.
White, L. J. (2009). A brief history of credit rating agencies: How
financial regulation entrenched this industry's role in the
subprime mortgage debacle of 2007-2008. Mercatus on Policy, No.
59, Mercatus Center, George Mason University.
Chairman Miller. Thank you, Dr. Winter.
Dr. Page, you are recognized for five minutes.
STATEMENT OF SCOTT E. PAGE, LEONID HURWICZ COLLEGIATE PROFESSOR
OF COMPLEX SYSTEMS, POLITICAL SCIENCE, AND ECONOMICS,
UNIVERSITY OF MICHIGAN
Mr. Page. Thank you, Mr. Chairman, and thank you to the
committee for this opportunity to come and speak.
As mentioned, I am a Professor of Complex Systems at the
University of Michigan and the Santa Fe Institute. Complex
systems is probably unfamiliar to many of you, so I am going to
begin with a simple definition if I may. Complex systems
consist of diverse, connected, interdependent and adaptive
actors who collectively produce phenomena that are difficult to
explain or predict. So given this definition, an economy,
traffic on the Beltway or even the stuff that goes on inside
the Beltway, right, is going to be classified as ``complex.''
In my comments today, I want to describe the benefits of
having a variety of models when trying to understand a complex
system. And I am going to show how complex-systems models
themselves have an ability to generate insights that are going
to be of interest to this committee, including the pace of
innovation and market crashes.
So let me talk for a minute about the success of models as
predictors. Models have proven almost, as Dr. Broun mentioned,
almost unbelievably accurate in predicting some physical
phenomena, such as the patterns in which planets orbit the sun.
Yet as we all know, models have proven less adept at predicting
the economy, and that is because the economy is complex. The
solar system is complicated, it has got lots of connecting
parts, but those parts aren't very diverse, right? They are
little orbs, and they don't adapt, and because of that, it is
predictable. So if you take something like a complex system, a
single model can only cast so much light. Hence, we need
multiple models, and this is an idea that goes back to
Aristotle, who asserted that a multitude is a better judge than
any individual.
Now, that is not just an intuition, that is something we
can actually formalize. So my own work--I have written some
stuff that basically says if I have a crowd of models and take
the average of those predictions, then you can prove the
following: that the crowd of models' accuracy is going to equal
the average model's accuracy plus the model diversity. So this
mathematical identity that I have framed here verbally shows
the benefits of combining models. What you want is, you want a
lot of models and you want those models to be diverse. But that
is not to say that a group of models is accurately going to
forecast the economy. It probably won't. The economy is too
complex. But we can widen our lens and we can use a crowd of
models to predict bounds and the likely fluctuations in the
economy, and to anticipate unintended consequences and
riskiness of policy decisions--such as the expanding of use of
sophisticated financial instruments such as credit default
swaps.
So let me turn to my second point, the particular value of
complex-systems models to help understand and guide the
economy. This goes back to some of the things that Bob and Sid
have mentioned. The economy consists of over 300 million
people, 30 million organizations--and about 90 percent of those
seek profits--and tens of thousands of government agencies.
These actors are diverse. They have diverse beliefs and goals.
They adapt as circumstances change, and they don't do so in
lockstep. Some people spend, some save, some innovate, some
people seek the comfort of routine. It is the aggregated,
interdependent actions of these millions of actors--people,
organizations and governments--that produce the macroeconomic
patterns that we are trying to explain and predict.
So how do we model this? The neoclassical approach assumes
that individuals and firms make optimal choices subject to
constraints of budgets, technology and time. Actors, be they
firms, people or governments, accurately anticipate the future
effects of their actions and the government's actions. And, in
its simplest form, this model is going to produce a stable
equilibrium with balanced growth. Now, modern variants, which
Dr. Chari will probably talk about, of this model include
technological shocks that reverberate throughout the economy.
These variants also include frictions, such as wages that are
slow to fail. This stickiness exacerbates the depth and length
of the echoes caused by the shocks.
Now, this neoclassical model, this DSGE model, is stark. It
assumes no sectors of the economy, no unemployment, no physical
geography, no networks of connections, no learning--the agents
are always optimizing--and little or no heterogeneity of
income, wealth or behaviors. Further, almost all of the
responses by the actors tend to equilibrate the system: So, for
example, if you get an increase in demand for housing, this is
going to increase the price of housing, therefore causing a
reduction in future demand of housing. This is what we call in
complex systems a ``negative feedback.'' The more you get of
something, these negative feedbacks push things back. They tend
to stabilize systems, and they lie at the core of neoclassical
models.
Now, the complexity approach assumes individuals with
diverse incomes and abilities who are situated in place and
time. These actors don't necessarily maximize profits of
utility. Instead, what they do is they follow rules that have
survived or succeeded in the marketplace. So if a financial
firm with greater leverage, such as Morgan Stanley, is making
higher profits, other firms may follow their lead.
Now, note this effect: more leverage leads to greater
leverage. This is a ``positive feedback.'' Positive feedbacks
create what we call ``correlated behavior.'' Hence, systems
that contain them can exhibit clustered volatility in large
events like stock market crashes and home mortgage crises.
These could be avoided if the agents in the model were capable
of predicting the future and realizing they should be
optimizing, not following other people. But they are not, and,
unfortunately, neither are we. So I don't mean to imply that
complex systems can predict crashes. They probably can't. But
they can provide an alternative lens to enable us to design
rules, laws, incentives and institutions, as well as encourage
the development of productive social norms, and it might reduce
the likelihood and severity of financial collapses.
Complex systems also are analyzed using computational, what
are called ``agent-based techniques.'' This was mentioned.
These techniques are capable of including sector-level details:
financial markets, real estate markets and service markets. The
ability of complex-systems models to include realistic detail
creates the potential for new insights into causes and rates of
innovation. So, for example, from a complex-systems
perspective, the innovative potential of an economy depends on
its building blocks: the ideas, technologies and basic science
that sits out there that people work with. So innovation comes
about by combining and recombining those building blocks.
Lest I make agent-based models seem like a panacea, I
should add a word of warning: A model that contains too much
detail can be as perplexing as the real world it was built to
explain. Models should only include so much detail as necessary
and no more. So it is an open question what necessary detail
should be included in models of the economy, but I believe that
the financial sector, unemployment and heterogeneous consumers
probably fit the bill.
So to sum up, our goal is to understand an economy that is
increasing in complexity. The neoclassical approach emphasizes
optimization in the face of constraints. The complex-systems
paradigm emphasizes diversity, networks, interdependencies--
positive as well as negative--and adaptation.
So let me conclude with my first point. For non-complex
systems, we can use single models. We can, for example, just
multiply an object's mass by its acceleration and get a really
good approximation of force. But if you have a complex system
like an economy, no one model will likely work. We need a
crowd. We actually need a crowd of diverse models.
I thank you for this opportunity to speak to the committee.
[The prepared statement of Dr. Page follows:]
Prepared Statement of Scott E. Page
I thank you for this opportunity to address the committee.
My name is Scott E Page. I am the Leonid Hurwicz Collegiate
Professor of complex systems, political science, and economics at the
University of Michigan-Ann Arbor and an external faculty member of the
Santa Fe Institute. I study diversity in complex social systems.
Complex systems may be unfamiliar territory to many, so I begin
with a definition. Complex systems consist of diverse, connected,
interdependent, and adaptive actors who collectively produce patterns
that are difficult to explain or predict.\1\ Complex systems are
neither ordered nor chaotic. They lie in between.
---------------------------------------------------------------------------
\1\ Miller, J. and S. Page (2008) Complex Adaptive Systems: An
Introduction to Computational Models of Social Life, Princeton
University Press.
---------------------------------------------------------------------------
Complex systems interest scientists because they are capable of
producing emergent phenomena in which the whole differs in kind from
the parts that comprise it. A brain differs in kind from a neuron. A
society differs in kind from a person.
Given this definition, the economy, traffic on the Beltway, and the
goings on ``inside the Beltway'' are all complex. Trying to make sense
of and harness the complexity of the social world is what motivates my
research efforts.
In my comments today, I first describe how, when we're confronted
with complexity, we benefit by relying on a variety of models. I then
show how complex systems models, by including the diversity and
interconnectedness of the economy, have a special ability to generate
insights into phenomena of central interest to this committee,
including the pace of innovation and market crashes.
My points both relate diversity to complexity. First, I'm saying
that the economy is complex, not in some loose metaphorical way, but
according to formal scientific definitions of complexity. As a result,
we're never going to predict its future with much accuracy. Our best
approach will be to encourage the creation of diverse models.
Second, I'm saying that we need to develop richer complex systems
models of the economy because they embrace the diversity and
interconnectedness that drive fluctuations, and because they may enable
us to gain deeper insights into the causes of innovation. I'll argue
that these models are much more flexible than standard neoclassical
models.
I begin with a simple question: Why model? A standard response
would be that models enable us to explain and predict empirical data--
to make sense of the world. Models vary in their accuracy depending
upon the domain. For example, in predicting physical phenomena--the
rate at which objects fall, the patterns in which the planets orbit the
sun, and so on--they're almost absurdly accurate.
Yet, as we all know, models have proven less adept at predicting
the economy. That's because the economy is a complex system. The solar
system may be complicated, i.e., have lots of connected parts. But the
parts aren't that diverse, and they don't adapt. Hence, planetary
orbits are predictable.
Prediction is only one of many reasons to encourage model building
and interpretation. Models help us design policies and mechanisms. For
example, the FCC spectral auction provides an excellent example of how
models were used to anticipate shortcomings of traditional auction
mechanisms.
Models also inform data collection, produce bounds on outcomes,
explore counterfactuals, and explain whether a system will equilibrate,
cycle, produce chaos, or generate complexity.
And perhaps most importantly, models help us identify the important
parts and work through the logic of systems, especially complex,
unpredictable systems like the economy or political systems.\2\
---------------------------------------------------------------------------
\2\ See Bednar (2009) The Robust Federation, Cambridge University
Press.
---------------------------------------------------------------------------
The complexity of the economy provides almost endless grist for our
cognitive mills. An inquisitive person's head cannot help but develop
theories and construct analogies about the economy. Many of these
contain a grain of truth. Unfortunately, most also include logical
inconsistencies.
The advantage of models is that they identify truths and reveal
inconsistencies by forcing us to characterize the relevant parts of a
system and to understand how those parts relate to one another.
However, when applied to a complex system, a single model can only
cast light on some dimensions. Hence, we need multiple models. The
advantage of combining diverse models was recognized by Aristotle, who
asserted, ``a multitude is a better judge than any individual.'' \3\
---------------------------------------------------------------------------
\3\ Aristotle, Politics (trans. Benjamin Jowett), Book Three, Part
XV, available at http://classics.mit.edu/Aristotle/
politics.3.three.html
---------------------------------------------------------------------------
That's not just an intuition. With the help of a little
mathematics, the claim can be made formal: My research has shown that
if I have a crowd of models and take the average, then it follows that
Crowd of Models' Accuracy = Average Model Accuracy + Model Diversity.
The mathematical identity that I've framed verbally here shows the
benefits of combining models.\4\ I want to reiterate that by no single
model or even a group of models will accurately forecast the economy.
It's too complex.
---------------------------------------------------------------------------
\4\ See Page, S (2007) The Difference: How the Power of Diversity
Creates Better Groups, Firms, Schools, and Societies, Princeton
University Press.
---------------------------------------------------------------------------
We can widen our lens a bit, though. And we can use a crowd of
models to predict bounds on the likely fluctuations in the economy and
to anticipate unintended consequences of policy decisions, such as
allowing the expansion of sophisticated financial instruments.
I now turn to my second point: the particular value of complex
systems models to help understand and guide the economy.
The U.S. economy consists of over three hundred million people,
nearly thirty million organizations--about ninety percent of which seek
profits--and tens of thousands of government agencies. These actors
possess diverse beliefs and goals. They adapt as circumstances change,
though not in lock step. Some spend and some save. Some innovate. Some
seek the comfort of routine.
The aggregated interdependent actions of these millions of actors--
people, organizations, and governments--produce the macroeconomic
patterns that economists seek to explain and predict.
How then, do we model this? The neoclassical approach assumes that
individuals and firms make optimal choices subject to constraints on
budgets, technology, and time. Both sets of actors accurately
anticipate future effects of their actions and the government. In its
simplest form this model produces a stable equilibrium with balanced
growth.
Modern variants of this model include technological shocks that
reverberate through the economy. These variants also include frictions,
such as wages that are slow to fall. This stickiness exacerbates the
depth and length of the echoes caused by the shocks.
The neoclassical model is stark. It assumes no sectors of the
economy, no physical geography, no networks of connections, no learning
(agents always optimize), and little or no heterogeneity of income,
wealth, or behaviors.\5\
---------------------------------------------------------------------------
\5\ Narayana Kocherlakota, the President of the Minneapolis Fed,
has written that ``as far as I am aware, no central bank is using a
model in which heterogeneity among agents or firms plays a prominent
role''. Kocherlakota (2010) ``Modern Macroeconomic Models as Tools for
Economic Policy,'' Federal Reserve Bank of Minneapolis.
---------------------------------------------------------------------------
Oh yeah, and the only unemployment it includes is voluntary.
Further, almost all of the responses by the actors in the
neoclassical model tend to equilibrate the system. An increase in
demand for housing increases the price of housing, thereby causing a
reduction in future demand for housing. This is an example of a
``negative feedback.'' Negative feedbacks stabilize systems and lie at
the core of neoclassical economic models.
The complexity approach assumes individual agents with diverse
incomes and abilities who are situated in place and time. Their actions
influence those in their social and economic networks. These actors
don't optimize some hypothesized objective functions, be it a single
period's profits or lifetime's income. Instead, they follow rules that
have survived or are succeeding in the marketplace.
In a complex systems model, if financial firms with greater
leverage are making higher profits, other firms may follow their lead
even if the aggregate effect of all that leveraging is not sustainable.
This sort of effect--in which more leverage leads to even greater
leverage--is called a ``positive feedback.'' Positive feedbacks produce
correlation in observed behavior. Hence, systems that contain them can
exhibit both clustered volatility and large events, for instance, stock
market bubbles and home mortgage crises. These could be avoided if the
agents in the model were capable of predicting the future consequences
of their actions, but they are not. Neither are economists.
I do not mean to imply that complex systems models can predict
crashes. They cannot. What they can do is provide an alternative lens
to enable us to design rules, laws, incentives, and institutions--as
well as encourage the development of productive social norms--that
might reduce the likelihood and severity of financial collapses.
Adopting complex systems models requires a change in tools as well
as a change in paradigm. Complex systems models are often analyzed
using computational or what are called ``agent based'' techniques.
These techniques are capable of including sector level details--
financial markets, real estate markets, and service markets.\6\
---------------------------------------------------------------------------
\6\ Farmer, D and D. Foley (2009) ``The Economy Needs Agent Based
Modelling'' Nature 460: 685 686/
---------------------------------------------------------------------------
The ability of complex systems models to include realistic detail
has other advantages as well. It creates the potential for new insights
into causes and rates of innovation. The innovative potential of an
economy depends on its building blocks--ideas, technologies, and basic
science. Innovation comes about by combining and recombining those
building blocks.
Lest I make agent based models seem a panacea, I should add a word
of warning. A model that contains too much detail can be as perplexing
as the reality it was built to explain. Models should include only as
much detail as necessary and no more.
In 1922, Georgia O'Keefe wrote that ``details are confusing. It is
only by selection, by elimination, by emphasis that we get to the real
meaning of things.'' She was right. That's why standard macro models,
which leave out so much information, can still be of great value.
However, I would argue that to get at the real meaning of things in the
economy, the necessary details should include the financial sector,
unemployment, and heterogeneous consumers.
To sum up, our goal is to understand an economy that's increasing
in complexity. The neoclassical approach emphasizes optimization in the
face of constraints and responses to shocks, and sees macro level
patterns as the re-equilibration of those shocks. The complex systems
paradigm emphasizes diversity, networks, interdependencies (positive as
well as negative feedbacks), and adaptation. Neither is right. Neither
is wrong. They're both models. And both can be useful.
I'll conclude by reiterating my first point. For noncomplex
systems, we can use single models. We can, for example, just multiply
an object's mass by its acceleration to get a really good approximation
of force. But for a complex system, like an economy, no one model will
be accurate. We need a crowd, a crowd of diverse models.
I thank you for the opportunity to speak to the committee.
Chairman Miller. Thank you, Dr. Page.
Dr. Chari, you are recognized for five minutes.
STATEMENT OF V.V. CHARI, PAUL W. FRENZEL LAND GRANT PROFESSOR
OF LIBERAL ARTS, UNIVERSITY OF MINNESOTA
Mr. Chari. Thank you, Mr. Chairman.
It is an honor and a privilege to testify before this
Committee. Let me begin with a disclaimer. Nothing I say here
should be construed as reflecting the views of the Federal
Reserve Bank of Minneapolis or the Federal Reserve System.
I want to make three points in this testimony. The first
point is that macroeconomic research is a very big tent which
accommodates a very diverse area of perspectives and is open to
lots of different ways of thinking about the economy. The
second issue that I want to raise is, why did the current
generation of so-called DSGE models--there is not one, there
are many--fail to see the crisis coming and what should
macroeconomic research look like going forward in order to
forestall future crises? And the third is, what can the public
and Congress do to foster the kinds of macroeconomic research
that is needed to ensure that we don't have catastrophes like
the events of the last couple of years?
First, in terms of macroeconomic research: Macroeconomic
research, as I said, is a very big tent and accommodates a very
diverse set of viewpoints. There is a shared language and a
shared methodology but not necessarily a shared substance in
terms of policy issues. This openness and flexibility is best
summarized by an aphorism that macroeconomists often use: ``If
you have an interesting and a coherent story to tell, you can
do so within a DSGE model. If you cannot, it probably is
incoherent.''
Now, macroeconomic research has changed a lot in the last
25 years, and I want to emphasize the nature of that change and
I believe that much of that change constitutes progress. The
state-of-the-art DSGE model in, say, 1982 had a representative
agent, no unemployment, no financial factors, no sticky prices
and wages, no crises, no role for government. What do the
state-of-the-art DSGE models of today look like? They have
heterogeneity, all kinds of heterogeneity arising from income
fluctuations, unemployment and the like. They have
unemployment. They do have financial factors. They have sticky
prices and wages. They have crises. And they have a role for
government.
Given the limited amount of time, let me talk about
financial factors. The best way of thinking about the important
developments in theorizing about financial factors is to think
about the career and accomplishments of Ben Bernanke. We have
often heard the statement summarized: ``Academic
macroeconomists who are interested and active in policy
routinely write down models with no role for financial
factors.'' Nothing could be further from the truth. Ben devoted
his career to developing such models. Was he a bit player, a
heterodox person way outside on the sidelines of modern
macroeconomics? No, he was chairman of Princeton's economics
department. He was right at the center, the heart, of
macroeconomic debate and issues and improving models.
Second, let me talk about crises. Now, our brothers and
sisters in international macroeconomics who study the economies
of other countries have for the last decade or more been
routinely developing DSGE models, quantitative DSGE models with
crises. Why? Because they have been studying countries that
have been routinely buffeted by these kinds of crises, so it is
natural for them to study them. How about a role for the
government? Kareken and Wallace in the late 1970s emphasized
that deposit insurance together with government bailouts
possibly creates strong private incentives for excessive risk-
taking, and they emphasized the importance of government
regulation in order to prevent these incentives for excessive
risk-taking from going overboard. So macro models are very
different from what they were. They can analyze a wide variety
of policies. They are being used, particularly by central banks
to guide monetary policy. They have been used in policy circles
to analyze questions of fundamental tax reform, social security
reform, and I believe they can and should be used for other
policies and questions.
But all is not fine and dandy. Clearly, this class of
models failed to see the crisis coming. Why? I offer three
reasons. First, any model has got to be disciplined by
historical data. That is a necessity. Now, modelers of the U.S.
economy naturally tend to focus on the experience of the last
60 years, particularly of the United States. What has the
experience of the last 60 years been? Well, relative especially
to other countries, it has been remarkably stable except for
the recent crisis, and so, in that sense, those kinds of models
naturally tended to deemphasize these kinds of financial
crises.
The sad thing about this is that, as I said, there are
people in international macro writing down models, quantitative
DSGE models, of crisis. What should we have done? We should
have incorporated their insights. Why did we not? It is
natural. Whenever I read a paper about, say, Argentina, I am
tempted to say, ``Oh, well, that is Argentina, we are in the
United States, it can't happen here.'' What we have learned is,
it can happen here and it is clear going forward that we need
to incorporate those kinds of insights. It is clear going
forward we need to incorporate the insights from the banking
and deposit-insurance literature on incentives to take on
excessive risk. Those are elements that were thought
unimportant, they clearly are not unimportant. They are within
our tool kit and can be used.
Final issue: What, if anything at all, can the public and
Congress do about this? It is useful to put some numbers on the
table here. NSF funding for economics overall is roughly $27
million. Two point six million dollars of that goes to the
PSID, a very worthwhile activity. About ten percent of the
remainder goes to fund, in my judgment, my estimation,
macroeconomic research, so we are talking about $2.5 million.
Now, compare $2.5 million with the NSF's budget of roughly $7
billion and an overall basic research budget for the Federal
Government of the order of about $30 billion, and so we are
talking about less than peanuts. We are talking about a tiny
amount of money.
Now, would investing additional resources in macroeconomic
research of the kind that is being practiced in the best
universities and the best research departments across the
Nation add substantially to our welfare? In my judgment, yes.
All we have to do is reduce the probability of the next crisis
by 100th of one percent, and if we quadrupled the amount of
resources to NSF's macroeconomics research program, it would
pay for itself tenfold. That is the kind of return that we are
talking about. Now, can that return be realized? Not for sure.
No one can offer guarantees, but I think that the odds are that
we have got a bunch of very smart people, capable people who
are open to diverse ideas. They can do it.
So let me conclude by trying to summarize three basic
messages. First is a message to critics: These are not your
father's models. These models are very different from the
descriptions that critics often offer of these kinds of models,
and so it is not helpful to advance the debate on the future of
modern macro by caricaturing models from a generation ago.
Message to my fellow researchers: Yes, the United States is not
Argentina but we have a lot to learn from the experiences of
other countries.
Third, the message to the public and Congress is that
macroeconomic research of the kind that is being practiced at
leading departments offers a very gigantic bang for the buck.
Thank you.
[The prepared statement of Dr. Chari follows:]
Prepared Statement of V.V. Chari \1\
---------------------------------------------------------------------------
\1\ The views expressed herein are those of the author and not
necessarily those of the Federal Reserve Bank of Minneapolis or the
Federal Reserve System.
---------------------------------------------------------------------------
Mr Chairman, Ranking member and Honorable Members of the Committee.
It is an honor and a privilege to testify before you. The purpose of
this hearing, as I understand it, is to examine the promise and the
limits of modern macroeconomic theory in providing advice for policy.
In this testimony, I will make three major points. First, I will argue
that macroeconomics has made huge progress, especially in the last 25
years or so. Second, I will address why our models failed to see the
recent crisis coming and how our research in the future must change so
that we can forestall such crises. Third, I will argue that
macroeconomic research is severely underfunded and that devoting
greater resources to macroeconomic research will have huge social
benefits.
1. Progress since the early 1980s
I begin with a simple message about all models: Models are
purposeful simplifications that serve as guides to the real world, they
are not the real world.
This message comes from understanding that policymaking and policy
advice necessarily must use models. Policymakers need to understand the
rough quantitative magnitudes of the key tradeoffs and they need to
understand the economic forces that drive the tradeoffs. A hugely
complicated model that no one understands cannot convey an
understanding of the key tradeoffs. Large models simply have too many
moving parts. A macroeconomic model of monetary policy will surely
leave out the Cotton Exchange in Minneapolis! By construction, a model
is an abstraction which incorporates features of the real world thought
important to answer the policy question at hand and leaves out details
unlikely to affect the answer much. Abstracting from irrelevant detail
is essential given scarce computational resources, not to mention the
limits of the human mind in absorbing detail! Criticizing the model
just because it leaves out some detail is not just silly, it is a sure
fire indicator of a critic who has never actually written down a model.
All the interesting policy questions involve understanding how
people make decisions over time and how they handle uncertainty. All
must deal with the effects on the whole economy. So, any interesting
model must be a dynamic stochastic general equilibrium model (often
called a DSGE model). From this perspective, there is no other game in
town. Modern macroeconomic models, often called DSGE models ill macro
share common additional features. All of them make sure that they are
consistent with the National Income and Product Accounts. That is,
things must add up. All of them lay out clearly how people make
decisions. All of them are explicit about the constraints imposed by
nature, the structure of markets and available information on choices
to households, firms and the government. From this perspective DSGE
land is a very big tent. The only alternatives are models in which the
modeler does not clearly spell out how people make decisions. Why
should we prefer obfuscation to clarity? My description of the style of
modern macroeconomics makes it clear that modern macroeconomists use a
common language to formulate their ideas and the style allows for
substantial disagreement on the substance of the ideas. A useful
aphorism in macroeconomics is: ``If you have an interesting and
coherent story to tell, you can tell it in a DSGE model. If you cannot,
your story is incoherent.''
What progress have we made in modern macro? State of the art models
in, say, 1982, had a representative agent, no role for unemployment, no
role for financial factors, no sticky prices or sticky wages, no role
for crises and no role for government. What do modern macroeconomic
models look like?
The models have all kinds of heterogeneity in behavior and
decisions. This heterogeneity arises because people's objectives
differ, they differ by age, by information, by the history of their
past experiences. Please look at the seminal work by Rao Aiyagari, Per
Krusell and Tony Smith, Tim Kehoe and David Levine, Victor Rios Rull,
Nobu Kiyotaki and John Moore. All of them are (or were, in the case of
Rao, who is unfortunately deceased) prominent macroeconomists at
leading departments and much of their work is explicitly about models
without representative agents. Any claim that modern macro is dominated
by representative agent models is wrong.
In terms of unemployment, the baseline model used in the analysis
of labor markets in modern macroeconomics is the Mortensen-Pissarides
model. The main point of this model is to focus on the dynamics of
unemployment. It is specifically a model in which labor markets are
beset with frictions.
In terms of a role for financial factors, the career and
accomplishments of Ben Bernanke show that mainstream academics have
been intensively interested in financial factors. Starting with a
famous paper in the American Economic Review in 1983, through his work
with Mark Gertler in 1989 and subsequently also with Simon Gilchrist in
1999, he has devoted his career to incorporating financial frictions in
quantitative dynamic stochastic general equilibrium models. The famous
Bernanke Gertler paper was published two decades before the current
crisis. It was an attempt to understand the greatest economic crisis in
U.S. history: the Great Depression. Others, including Nobu Kiyotaki,
Hugo Hopenhayn and Tom Cooley have dramatically improved our
understanding of financial factors. Was Ben a heterodox, bit player on
the sidelines of modern macroeconomics? Absolutely not. He was chairman
of the Princeton economics department, a leading center of modern
macroeconomics. Mainstream macroeconomic models do have crises driven
by financial frictions. Any assertion to the contrary is false.
In terms of sticky prices and wages, the baseline DSGE model used
by the European Central Bank, the Federal Reserve and by other central
banks is the so-called New Keynesian model. The central features of
this model are sticky wages and prices.
In terms of financial crises, an important branch of modern macro
is international macroeconomics. A huge fraction of this literature led
by Tim Kehoe at Minnesota and Guillermo Calvo at Columbia has
explicitly focused on financial crises. In terms of domestic macro, Lee
Ghanian and Harold Cole explicitly attempt to develop DSGE models of
the Great Depression.
In terms of a role for government, let me use papers presented at
the recent meetings of The Society of Economic Dynamics held in
Montreal earlier this month as an example of the changes in
macroeconomic modeling. This society typically has a large number of
members who develop DSGE models. About 50 dealt specifically with
policy in macroeconomic models. In none of these 50 papers was the best
policy by the government to do nothing and simply get out of the way.
Critics who assert otherwise should get out of their ivory towers and
attend the SED conference, Minnesota macro week and the meetings of the
National Bureau of Economic Research's Economic Fluctuations and Growth
group. Also in terms of a role for the government, macroeconomic
theorists have long warned us of the bad side effects of deregulating
financial markets. In 1979, Kareken and Wallace at Minnesota pointed
that deregulated financial markets with explicit deposit insurance or
implicit government guarantees would lead to an orgy of risk taking.
Gary Stern, President of the Minneapolis Fed, inspired by Kareken and
Wallace and other researchers at Minnesota and elsewhere wrote a book
titled ``Too Big to Fail'' which laid out specific proposals to
regulate banks and financial markets.
Such improvements have made it possible for us to understand
macroeconomic forces much better. In spite of our difficulties in
conducting monetary policy during the recent crisis, I would argue
that, in general, the conduct of monetary policy has been much better
over the last two decades across the world than over the preceding two
decades. We have much better models for analyzing the consequences of
fundamental changes to the tax system, improved models to think of
pension reform, and better models to analyze the challenges of health
care reform. Obviously, we need to improve on these models, but we are
getting closer to an era of policymaking informed by a clearer
understanding of the quantitative consequences of alternative policies
and the key tradeoffs that must be made in formulating policy.
A common criticism of macroeconomic theory is that the actors in
our models are typically rational and forward looking. In the vast
majority of our models, individual actors are purposeful agents who do
not lightly forgo profit opportunities if they can profitably exploit
the opportunities given their constraints. There is nothing explicitly
in DSGE modeling that excludes the possibility that we can think of
individuals as little behavioral automatons who follow fixed decision
rules and routinely leave $1,000 bills on the sidewalk. The traditional
modeling style is certainly that people make the best decisions they
can, given their constraints and their information. The advantage of
the traditional modeling procedure is that it imposes discipline on the
modeler. Give me the freedom to make up decision rules based on dubious
evidence from psychology labs in which the subjects are college
sophomores and I can explain pretty much anything. The problem is that
my dubious model will surely give the wrong answer to any interesting
policy question.
Thomas Sargent, a distinguished macroeconomist has written a number
of papers modeling agents as learning about the economy over time in
otherwise conventional DSGE models. His style of modeling imposes
considerable discipline on the way people learn. Nothing in the
structure of the methodology forces one to use conventional rational
expectations as the only way of modeling belief formation. DSGE land
is, indeed, very welcoming to innovations.
Other criticisms fail to appreciate the extent to which historical
data plays, and should play, a central role in developing models. To
see this role, note that DSGE models in macro are designed to answer
quantitative questions. What would be the effect on GDP of changing tax
rates on capital income by 10 percentage points forever and raising
labor tax rates to make up for the revenue? What would be the
consequences of a monetary policy which raised the Federal Funds Rate
by 10 basis points if the stock market goes up by 1 percent? Answering
the first question requires in part pinning down elasticities of
intertemporal substitution in consumption for households and
intertemporal substitution in consumption for production of firms. We
pin down these parameters using historical time series and cross
sectional evidence. A variety of econometric methods, estimation,
calibration and the like are used to ensure that the model is
consistent with key features of the data. This methodology often
implies that the models are not well suited to analyze extremely rare
events. But then I know of no method that is well suited for this
purpose. Answering the second question requires developing quantitative
models of stock market fluctuations.
All is not, however, well in DSGE land. For example, we do not have
a satisfactory model to analyze the kinds of regulation of the
financial markets recently legislated by Congress. We do not fully
understand the sources of the various shocks that buffet the economy
over the business cycle. We do not know what would happen if we
required banks to hold T Bills to back all their deposits. So, how
should policy makers use advice from DSGE models. I would suggest that
they should do so in exactly the way that central bank policy makers
use the advice that their research departments give from such models.
It is one ingredient, and a very useful ingredient, in policy making.
It is a useful ingredient because it offers a disciplined way of
reasoning through the quantitative importance of various economic
forces. The reason that they do not rely exclusively on such models is
because they understand that the point of the models is to make a point
or teach a lesson, not to make policy in real time. As such the models
are guides to the real world but they are not the real world.
2. Why did we not see the crisis coming and what should be done?
Clearly DSGE models failed to predict the recent financial crisis.
More precisely, they failed to emphasize the risks to which the economy
was exposed in the period before the crisis. Was this failure because
we did not have the right tools in our toolbox? I will argue that we
had all the ingredients to see the problem. Macroeconomists who focus
on the economies of the rest of the world have long understood the need
to model financial crises and have actively been developing such
models. They have understood this need because many countries in the
rest of the world have been buffeted by financial crises. A second tool
we had was our understanding of how policy affects risk taking
incentives. At a theoretical level, since Kareken and Wallace's work in
the late 1970s, we have understood that with deposit insurance or the
prospects of government bailouts, private actors have strong incentives
to take on excessive risk. Excessive risk taking played a central role
in the recent crisis.
Why then did our models of the U.S. economy fail to incorporate the
insights from the study of other countries or the theoretical insights
from the literature on deposit insurance? I offer three reasons. First,
all useful models must be consistent with key features of the
historical data. The history of U.S. economic performance since World
War II is remarkable because economic fluctuations have been relatively
small and have not been dominated by severe fluctuations in financial
markets to the extent seen in the recent crisis. A focus on U.S.
historical performance leads modelers to develop models in which severe
financial crises are the exception, not the norm. The obvious
implication for academics is that we need to ensure that our models are
consistent not just with U.S. experience but the experience of
countries in the rest of the world.
The second reason is that we deemphasized the insights of the
theoretical literature on the perverse effects of government bailouts
because understanding these effects requires that we impute even more
rationality and foresight to economic agents than we currently impute.
The theoretical insight from the literature on deposit insurance is
that debt holders must rationally see that they will be protected in
the event of crises. They then have limited incentives to charge higher
prices for risk taking. Stockholders then have strong incentives to
reward managers of financial intermediaries to take on excessive risk.
Whenever I lay out this argument, many distinguished economists have
dismissed them because they are skeptical that financial market
participants are that sensitive to bailout prospects. The lesson of the
recent crisis is that financial markets are far smarter than economists
credited them to be. The lesson for academics is that we should be
skeptical of those who would argue that people are not very smart and
those who would argue that imposing irrationality on market actors is a
useful modeling device.
The third reason is that, as a society, we have devoted far too
little by way of resources to modern macroeconomics. We have too few
people working on modern macroeconomics, we have too few students and
we devote too little in the way of other resources to this area. I
would argue that the United States devotes shamefully little to
economic research. For example, the NSF's budget for economics is a
pitiful $27 million out of which $2.6 million goes to the worthwhile
activity of supporting the Panel Study on Income Dynamics. Twenty five
million dollars for an activity that is deemed fundamentally important
by the people of the United States? Out of that 25 million dollars, my
best estimate is that only about 10 percent goes to macroeconomics.
Compare $2.5 million to an overall NSF budget of $6 billion or to the
Federal Government support of basic research of roughly $30 billion. I
should emphasize that, in my judgment, the NSF's peer review process in
economics is exceptionally fair and thoughtful. Expanding resources to
the NSF's economics program will surely result in much better economic
research and will result in very little waste. Even if it does seem
like special interest pleading, I would argue that if we want to
prevent the next big crisis, the only way to do so is to devote
substantially more resources to modern macroeconomics so that we can
attract the best minds across the world to the study and development of
mainstream macroeconomics.
The recent crisis has raised, correctly, the question of how best
to improve modern macroeconomic theory. I have argued we need more of
it. After all, when the AIDS crisis hit, we did not turn over medical
research to acupuncturists. In the wake of the oil spill in the Gulf of
Mexico, should we stop using mathematical models of oil pressure?
Rather than pursuing elusive chimera dreamt up in remote corners of the
profession, the best way of using the power in the modeling style of
modern macroeconomics is to devote more resources to it.
Chairman Miller. Thank you, Dr. Chari.
Dr. Colander, you are recognized for five minutes.
STATEMENT OF DAVID C. COLANDER, CHRISTIAN A. JOHNSON
DISTINGUISHED PROFESSOR OF ECONOMICS, MIDDLEBURY COLLEGE
Mr. Colander. Thank you very much for this opportunity to
testify.
I am known in the economics profession as the economics
court jester because I am the person who says what everyone
knows but everyone knows better than to say in polite company.
As a court jester, I see it appropriate to start my testimony
with a well-known joke, a variation of a well-known joke. It
begins with a Congressman walking home late at night. He
notices an economist searching under the lamppost for his keys.
Recognizing that the economist is a potential voter, he stops
to help. After searching a while without luck, he asks the
economist where he lost his keys. The economist points over
into the dark abyss. The Congressman asks incredulously, ``Then
why the heck are you searching here?'' to which the economist
responds, ``This is where the light is.'' That well-known joke
is told by critics of economists a lot because it captures
economists' tendency to be highly mathematical and technical in
their research. On the surface, searching where the light is is
clearly a stupid strategy. The obvious place to search is where
you have lost the keys.
However, that in my view is the wrong lesson to take from
this joke. I would argue that for scientific research the
searching-where-the-light-is strategy is far from stupid. Where
else but in the light can you reasonably search to find your
keys or figure out in a scientific way what the system is? What
is stupid is if the person who is there searching thinks he is
going to find the keys under the lamppost. Searching where the
light is only makes good sense if the search is not to find the
keys--that is, to come up with practical policy recommendations
based directly on models--but rather to expand theoretical
knowledge: to understand the topography of the illuminated land
and how that lighted topography relates to the topography of
the dark, in the dark, where the keys were lost.
Most top economic theorists I talk to know that it is
stupid to directly search for policy keys in the light. Then
why do they often let people assume that is what they are
doing? Because they believe that if they didn't appear to be
doing so, they wouldn't get funded. The reality is that funders
of economic research, such as NSF, all too often want immediate
policy answers from abstract scientific models. Researchers
respond to incentives, and if the researcher's livelihood is
dependent on drawing policy conclusions from abstract formal
models, they will do it. So if the economist had answered the
Congressman honestly, he would have told him, ``I am searching
for the keys here because that is where you are funding me to
search.''
Keynes once said that policymakers are the slaves of some
defunct economist. Economists like that story, by the way. To
make the story complete, however, what he should have added is
that, in turn, economists are the slaves of some defunct
policymaker who established a funding system for research. The
incentives inherent in that funding system play a central role
in the kind of research that gets done.
The reason I am testifying here is I believe NSF can take
the lead in changing the institutional incentive structure by
implementing two structural changes in NSF programs funding
economics, and I think these will change economists'
incentives. The first proposal involves making diversity of the
reviewer pool an explicit goal of reviewing process of NSF
grants in social sciences. This would involve consciously
including what are dissenting economists as part of the peer-
reviewing pool, as well as reviewers outside of economics, such
as physicists, mathematicians, statisticians and individuals
from business and government who have real-world experience.
They could put some sense of what Professor Solow said: ``Does
it pass the smell test?'' Such a broader peer-review process
would likely encourage research on a much broader range of
models, promoting more creative work and providing that
commonsense feedback from the real world that you need to
figure out whether the topography of the models fits the
topography of the land that you are trying to search for in the
dark.
The second proposal involves increasing the number of
researchers trained in relating models to the real world as
opposed to just producing models. This can be done by
explicitly providing some research grants to interpret rather
than develop models. In a sense, what I am suggesting is an
applied science division of the National Science Foundation's
economics component. This division would fund work on analyzing
which of the many models are there being developed are
appropriate for the real world.
The applied science work would involve a quite different
set of skills than the standard scientific economics research
requires. It would require researchers to have a solid
consumer's knowledge of economic theory and econometrics but
not necessarily a producer's knowledge of that. You often find
that people who can be fantastic at producing models are not
very good at interpreting them and relating them to the real
world, and you can have more specialization than what we have.
In addition, it would require a knowledge of institutions,
methodology and previous literature as well as a sensibility of
how the system works, and I think, you know, there are
definitely economists who have that. Ben Bernanke I think does,
Alan Blinder. But interestingly, when they were at Princeton,
they weren't the one teaching macro theory: And when I talked
to students there when I taught there, they said, ``Oh, we
wouldn't take it from him; that wouldn't prepare us to write
articles.'' So they taught undergraduates as opposed to the
graduates, and that to me is crazy.
The skills involved in interpreting models are the skills
that are currently not taught in graduate economic programs. By
providing grants for interpretive work, the NSF would encourage
the development of a group of economists who specialize in
interpreting models and applying models to the real world,
making it less likely that fiascos like the financial crisis
would occur. Thank you.
[The prepared statement of Dr. Colander follows:]
Prepared Statement of David Colander
Mr. Chairman and Members of the Committee: I thank you for the
opportunity to testify. My name is David Colander. I am the Christian
A. Johnson Distinguished Professor of Economics at Middlebury College.
I have written or edited over forty books, including a top-selling
principles of economics textbook, and 150 articles on various aspects
of economics. I was invited to speak because I am an economist watcher
who has written extensively on the economics profession and its
foibles, and specifically, how those foibles played a role in
economists' failure to adequately warn society about the recent
financial crisis. I have been asked to expand on a couple of proposals
I made for NSF in a hearing a year and a half ago.
Introduction
I'm known in the economics profession as the Economics Court Jester
because I am the person who says what everyone knows, but which
everyone in polite company knows better than to say. As the court
jester, I see it as appropriate to start my testimony with a variation
of a well-known joke. It begins with a Congressman walking home late at
night; he notices an economist searching under a lamppost for his keys.
Recognizing that the economist is a potential voter, he stops to help.
After searching a while without luck he asks the economist where he
lost his keys. The economist points far off into the dark abyss. The
Congressman asks, incredulously, ``Then why the heck are you searching
here?'' To which the economist responds--``This is where the light
is.''
Critics of economists like this joke because it nicely captures
economic theorists' tendency to be, what critics consider, overly
mathematical and technical in their research. Searching where the light
is (letting available analytic technology guide one's technical
research), on the surface, is clearly a stupid strategy; the obvious
place to search is where you lost the keys.
That, in my view, is the wrong lesson to take from this joke. I
would argue that for pure scientific economic research, the ``searching
where the light is'' strategy is far from stupid. The reason is that
the subject matter of social science is highly complex--arguably far
more complex than the subject matter of most natural sciences. It is as
if the social science policy keys are lost in the equivalent of almost
total darkness, and you have no idea where in the darkness you lost
them. In such a situation, where else but in the light can you
reasonably search in a scientific way?
What is stupid, however, is if the scientist thinks he is going to
find the keys under the lamppost. Searching where the light is only
makes good sense if the goal of the search is not to find the keys, but
rather to understand the topography of the illuminated land, and how
that lighted topography relates to the topography in the dark where the
keys are lost. In the long run, such knowledge is extraordinarily
helpful in the practical search for the keys out in the dark, but it is
only helpful where the topography that the people find when they search
in the dark matches the topography of the lighted area being studied.
What I'm arguing is that it is most useful to think of the search
for the social science policy keys as a two-part search, each of which
requires a quite different set of skills and knowledge set. Pure
scientific research--the type of research the NSF is currently designed
to support--ideally involves searches of the entire illuminated domain,
even those regions only dimly lit. It should also involve building new
lamps and lampposts to expand the topography that one can formally
search. This is pure research; it is highly technical; it incorporates
the latest advances in mathematical and statistical technology. Put
simply, it is rocket (social) science that is concerned with
understanding for the sake of understanding. Trying to draw direct
practical policy conclusions from models developed in this theoretical
search is generally a distraction to scientific searchers.
The policy search is a search in the dark, where one thinks one has
lost the keys. This policy search requires a practical sense of real-
world institutions, a comprehensive knowledge of past literature,
familiarity with history, and a well-tuned sense of nuance. While this
search requires a knowledge of what the cutting edge scientific
research is
telling researchers about illuminated topography, the knowledge
required is a consumer's knowledge of that research, not a producer's
knowledge.
How Economists Failed Society
In my testimony last year, I argued that the economics profession
failed society in the recent financial crisis in two ways. First, it
failed society because it over-researched a particular version of the
dynamic stochastic general equilibrium (DSGE) model that happened to
have a tractable formal solution, whereas more realistic models that
incorporated purposeful forward looking agents were formally
unsolvable. That tractable DSGE model attracted macro economists as a
light attracts moths. Almost all mainstream macroeconomic researchers
were searching the same lighted area. While the initial idea was neat,
and an advance, much of the later research was essentially dotting i's
and crossing is of that original DSGE macro model. What that meant was
that macroeconomists were not imaginatively exploring the multitude of
complex models that could have, and should have, been explored. Far too
small a topography of the illuminated area was studied, and far too
little focus was given to whether the topography of the model matched
the topography of the real world problems.
What macroeconomic scientific researchers more appropriately could
have been working on is a multiple set of models that incorporated
purposeful forward looking agents. This would have included models with
multiple equilibria, high level agent interdependence, varying degrees
of information processing capacity, true uncertainty rather than risk,
and non-linear dynamics, all of which seem intuitively central in
macroeconomic issues, and which we have the analytical tools to begin
dealing with.\1\ Combined, these models would have revealed that
complex models are just that--complex, and just about anything could
happen in the macro-economy. This knowledge that just about anything
could happen in various models would have warned society to be prepared
for possible crises, and suggested that society should develop a
strategy and triage policies to deal with possible crises. In other
words, it would have revealed that, at best, the DSGE models were of
only limited direct policy relevance, since by changing the assumptions
of the model slightly, one would change the policy recommendation of
the model. The economics profession didn't warn society about the
limitations of its DSGE models.
---------------------------------------------------------------------------
\1\ I have called this research into more complex economic models,
Post Walrasian macroeconomics, and have spelled out what is involved in
Colander, 1996, 2006.)
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The second way in which the economics profession failed society was
by letting policy makers believe, and sometimes assuring policy makers,
that the topography of the real-world matched the topography of the
highly simplified DSGE models, even though it was obvious to anyone
with a modicum of institutional knowledge and educated common sense
that the topography of the DSGE model and the topography of the real-
world macro economy generally were no way near a close match. Telling
policy makers that existing DSGE models could guide policy makers in
their search in the dark was equivalent to telling someone that
studying tic-tac toe models can guide him or her in playing 20th
dimensional chess. Too strong reliance by policy makers on DSGE models
and reasoning led those policy makers searching out there in the dark
to think that they could crawl in the dark without concern, only to
discover there was a cliff there that they fell off, pulling the U.S.
economy with it.
Economists aren't stupid, and the macro economists working on DSGE
models are among the brightest. What then accounts for these really
bright people continuing working on simple versions of the DSGE model,
and implying to policy makers that these simple versions were useful
policy models? The answer goes back to the lamppost joke. If the
economist had answered honestly, he would have explained that he was
searching for the keys in one place under the lamppost because that is
where the research money was. In order to get funding, he or she had to
appear to be looking for the keys in his or her research. Funders of
economic research wanted policy answers from the models, not wild
abstract research that concluded with the statement that their model
has little to no direct implications for policy.
Classical economists, and followers of Classical economic
methodology, which included economists up through Lionel Robbins (See
Colander, 2009), maintained a strict separation between pure scientific
research, which was designed to be as objective as possible, and which
developed theorems and facts, and applied policy research, which
involved integrating the models developed in science to real world
issues.\2\ That separation helped keep economists in their role as
scientific economists out of policy.
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\2\ Nassau Senior, the first Classical economist to write on method
put the argument starkly. He writes. ``(the economist's) conclusions,
whatever be their generality and their truth, do not authorize him in
adding a single syllable of advice. That privilege belongs to the
writer or statesman who has considered all the causes which may promote
or impede the general welfare of those whom he addresses, not to the
theorist who has considered only one, though among the most important
of those causes. The business of a Political Economist is neither to
recommend nor to dissuade, but to state general principles, which it is
fatal to neglect, but neither advisable, nor perhaps practicable, to
use as the sole, or even the principle, guides in the actual conduct of
affairs.'' (Senior 1836: 2-3)
---------------------------------------------------------------------------
It did not prevent them from talking about, or taking positions on,
policy. It simply required them to make it clear that, when they did
so, they were not speaking with the certitude of economic science, but
rather in their role as an economic statesman. The reason this
distinction is important is that being a good scientist does not
necessarily make one a good statesman. Being an economic statesman
requires a different set of skills than being an economic scientist. An
economic statesman needs a well-tuned educated common sense. He or she
should be able to subject the results of models to a ``sensibility
test'' that relates the topography illuminated by the model to the
topography of the real world. Some scientific researchers made good
statesmen; they had the expertise and training to be great policy
statesmen as well as great scientists. John Maynard Keynes, Frederick
Hayek, and Paul Samuelson come to mind. Others did not; Abba Lerner and
Gerard Debreu come to mind.\3\
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\3\ Gerard Debreu is a great economic scientist who is clear about
his work having no direct policy relevance; he did not try to play the
role of policy statesman. Abba Lerner was less clear about keeping the
two roles separate. This lead Keynes to remark about Lerner ``He is
very learned and has an acute and subtle mind. But it is not easy to
get him to take a broad view of a problem and he is apt to lack
judgment and intuition, so that, if there is any fault in his logic,
there is nothing to prevent it from leading him to preposterous
conclusions.'' (Keynes, 1935: 113) There are also economists whom I
consider great statesmen, but not great scientists. Herbert Stein and
Charles Goodhart come to mind.
---------------------------------------------------------------------------
The need to separate out policy from scientific research in social
science is due to the complexity of economic policy problems. Once one
allows for all the complexities of interaction of forward looking
purposeful agents and the paucity of data to choose among models, it is
impossible to avoid judgments when relating models to policy.
Unfortunately, what Lionel Robbins said in the 1920s remains true
today, ``What precision economists can claim at this stage is largely a
sham precision. In the present state of knowledge, the man who can
claim for economic science much exactitude is a quack.'' (Robbins,
1927, 176)
Why Economists Failed Society
One of J.M. Keynes's most famous quotes, which economists like to
repeat, highlights the power of academic economists. He writes, ``the
ideas of economists and political philosophers, both when they are
right and when they are wrong, are more powerful than is commonly
understood. Indeed, the world is ruled by little else. Practical men,
who believe themselves to be quite exempt from any intellectual
influences, are usually the slaves of some defunct economist. Madmen in
authority, who hear voices in the air, are distilling their frenzy from
some academic scribbler of a few years back.'' (Keynes, 1936: 135) What
this quotation misses is the circularity of the idea generating
process. The ideas of economists and political philosophers do not
appear out of nowhere. Ideas that succeed are those that develop in the
then existing institutional structure. The reality is that academic
economists, who believe themselves quite exempt from any practical
influence, are in fact guided by an incentive structure created by some
now defunct politicians and administrators.
Bringing the issue home to this committee, what I am saying is that
you will become the defunct politicians and administrators of the
future. Your role in guiding research is pivotal in the future of
science and society. So, when economists fail, it means that your
predecessors have failed. What I mean by this is that when, over
drinks, I have pushed macroeconomic researchers on why they focused on
the DSGE model, and why they implied, or at least allowed others to
believe, that it had policy relevance beyond what could reasonably be
given to it, they responded that that was what they believed the
National Science Foundation, and other research support providers,
wanted.
That view of what funding agencies wanted fits my sense of the
macroeconomic research funding environment of the last thirty years.
During that time the NSF and other research funding institutions
strongly supported DSGE research, and were far less likely to fund
alternative macroeconomic research. The process became self-fulfilling,
and ultimately, all macro researchers knew that to get funding you
needed to accept the DSGE modeling approach, and draw policy
conclusions from that DSGE model in your research. Ultimately,
successful researchers follow the money and provide what funders want,
even if those funders want the impossible. If you told funders it is
impossible, you did not stay in the research game.
One would think that competition in ideas would lead to the
stronger ideas winning out. Unfortunately, because the macroeconomy is
so complex, macro theory is, of necessity, highly speculative, and it
is almost impossible to tell a priori what the strongest ideas are. The
macro economics profession is just too small and too oligopolistic to
have workable competition among supporters of a wide variety of ideas
and alternative models. Most top researchers are located at a small
number of interrelated and inbred schools. This highly oligopolistic
nature of the scientific economics profession tends to reinforce one
approach rather than foster an environment in which a variety of
approaches can flourish. When scientific models are judged by their
current policy relevance, if a model seems temporarily to be matching
what policy makers are finding in the dark, it can become built in and
its premature adoption as ``the model'' can preclude the study of other
models. That is what happened with what economists called the ``great
moderation'' and the premature acceptance of the DSGE model.
Most researchers; if pushed, fully recognize the limitations of
formal models for policy.\4\ But more and more macroeconomists are
willing to draw strong policy conclusions from their DSGE model, and
hold them regardless of what the empirical evidence and common sense
might tell them. Some of the most outspoken advocates of this approach
are Vandarajan Chari, Patrick Kehoe and Ellen McGrattan. They admit
that the DSGE model does not fit the data, but state that a model
neither ``can nor should fit most aspects of the data'' (Chari, Kehoe
and McGratten, 2009, pg 243). Despite their agreement that their model
does not fit the data, they are willing to draw strong policy
implications from it. For example, they write ``discretionary policy
making has only costs and no benefits, so that if government
policymakers can be made to commit to a policy rule, society should
make them do so.'' (Chari and Kehoe, 2006; pg 7, 8)
---------------------------------------------------------------------------
\4\ For example, Robert Lucas one of the originators of the DSGE
modeling approach, in some of his writings, was quite explicit about
its policy limitations long before the crisis. He writes ``there's a
residue of things they (DSGE models) don't let us think about. They
don't let us think about the U.S. experience in the 1930s or about
financial crises and their real consequences in Asian and Latin
America; they don't let us think very well about Japan in the 1990's.''
(Lucas, 2004) Even earlier (Klamer, 1983) Lucas stated that if he were
appointed to the Council of Economic Advisors, he would resign.
---------------------------------------------------------------------------
While they slightly qualify this strong conclusion slightly later
on, and agree that unforeseen events should allow breaking of the rule,
they provide no method of deciding what qualifies as an unforeseen
event, nor do they explain how the possibility of unforeseen events
might have affected the agent's decisions in their DSGE model, and
hence affected the conclusions of their model. Specifying how agents
react to unexpected events in uncertain environments where true
uncertainty, not just risk, exists is hard. It requires what Robert
Shiller and George Akerlof call an animal spirits model; the DSGE model
does not deal with animal spirits.
Let's say that the U.S. had followed their policy advice against
any discretionary policy, and had set a specific monetary policy rule
that had not taken into account the possibility of financial collapse.
That fixed rule could have totally tied the hands of the Fed, and the
U.S. economy today would likely be in a depression.
Relating this discussion back to the initial searching in the light
metaphor, the really difficult problem is not developing models; they
really difficult policy problem is relating models to real world
events.\5\ The DSGE model is most appropriate for a relatively smooth
terrain. When the terrain out in the dark where policy actually is done
is full of mountains and cliffs, relying on DSGE model to guide policy,
even if that DSGE model has been massaged to make it seem to fit the
terrain, can lead us off a cliff, as it did in the recent crisis. My
point is a simply one: Models can, and should, be used in policy, but
they should be used with judgment and common sense.
---------------------------------------------------------------------------
\5\ Keynes recognized this. He wrote (1938) ``Economics is a
science of thinking in terms of models joined to the art of choosing
models which are relevant to the contemporary world. It is compelled to
be this, because, unlike the typical natural science, the material to
which it is applied is, in too many respects, not homogeneous through
time. The object of a model is to segregate the semi-permanent or
relatively constant factors from those which are transitory or
fluctuating so as to develop a logical way of thinking about the
latter, and of understanding the time sequences to which they give rise
in particular cases. Good economists are scarce because the gift for
using ``vigilant observation'' to choose good models, although it does
not require a highly specialized intellectual technique, appears to be
a very rare one.''
---------------------------------------------------------------------------
DSGE supporter's primary argument for using the DSGE model over all
other models is based on their model having what they call micro
foundations. As we discuss in Colander, et al. (2008) what they call
micro foundations are totally ad hoc micro foundations. As almost all
scientists, expect macroeconomic scientists, fully recognize, when
dealing with complex systems such as the economy, macro behavior cannot
be derived from a consideration of the behavior of the components taken
in isolation. Interaction matters, and unless one has a model that
captures the full range of agent interaction, with full inter-agent
feedbacks, one does not have an acceptable micro foundation to a macro
model. Economists are now working on gaining insight into such
interactive micro foundations using computer generated agent-based
models. These agent based models can come to quite different
conclusions about policy than DSGE models, which calls into question
any policy conclusion coming from DSGE models that do not account for
agent interaction.
If one gives up the purely aesthetic micro foundations argument for
DSGE models, the conclusion one arrives at is that none of the DSGE
models are ready to be used directly in policy making. The reality is
that given the complexity of the economy and lack of formal statistical
evidence leading us to conclude that any particular model is definitely
best on empirical grounds, policy must remain a matter of judgment
about which reasonable economists may disagree.
How the Economics Profession Can Do Better.
I believe the reason why the macroeconomics profession has arrived
in the situation it has reflects serious structural problems in the
economics profession and in the incentives that researchers face. The
current incentives facing young economic researchers lead them to both
focus on abstract models that downplay the complexity of the economy
while overemphasizing the direct policy implications of their abstract
models.
The reason I am testifying today is that I believe the NSF can take
the lead in changing this current institutional incentive structure by
implementing two structural changes in the NSF program funding
economics. These structural changes would provide economists with more
appropriate incentives, and I will end my testimony by outlining those
proposals.
Include a wider range of peers in peer review
The first structural change is a proposal to make diversity of the
reviewer pool an explicit goal of the reviewing process of NSF grants
to the social sciences. This would involve consciously including what
are often called heterodox and other dissenting economists as part of
the peer reviewer pool as well as including reviewers outside of
economics. Along with economists on these reviewer panels for economic
proposals one might include physicists, mathematicians, statisticians,
and individuals with business and governmental real world experience.
Such a broader peer review process would likely encourage research on a
much wider range of models, promote more creative work, and provide a
common sense feedback from real world researchers about whether the
topography of the models matches the topography of the real world the
models are designed to illuminate.
Increase the number of researchers trained to interpret models
The second structural change is a proposal to increase the number
of researchers explicitly trained in interpreting and relating models
to the real world. This can be done by explicitly providing research
grants to interpret, rather than develop, models. In a sense, what I am
suggesting is an applied science division of the National Science
Foundation's social science component. This division would fund work on
the appropriateness of models being developed for the real world.
This applied science division would see applied research as true
``applied research'' not as ``econometric research.'' It would not be
highly technical and would involve a quite different set of skills than
currently required by the standard scientific research. It would
require researchers who had a solid consumer's knowledge of economic
theory and econometrics, but not necessarily a producer's knowledge. In
addition, it would require a knowledge of institutions, methodology,
previous literature, and a sensibility about how the system works--a
sensibility that would likely have been gained from discussions with
real-world practitioners, or better yet, from having actually worked in
the area.
The skills involved in interpreting models are skills that
currently are not taught in graduate economics programs, but they are
the skills that underlie judgment and common sense. By providing NSF
grants for this interpretative work, the NSF would encourage the
development of a group of economists who specialize in interpreting
models and applying models to the real world. The development of such a
group would go a long way towards placing the necessary warning labels
on models, making it less likely that fiascos, such as the recent
financial crisis would happen again.
Bibliography
Chari, V.V., and P. Kehoe, 2006. Modern macroeconomics in practice: How
theory is shaping policy. Journal of Economic Perspectives
20(4), 3-28.
Chari, V.V., P. Kehoe and E. McGrattan, 2009. ``New Keynesian Models:
Not Yet Useful for Policy Analysis'' Macroeconomics, (AEA) vol
1. No. 1
Colander, David, 1996. (ed.) Beyond Microfoundations: Post Walrasian
Economics, Cambridge, UK. Cambridge University Press.
Colander, David, 2006. (ed.) Post Walrasian Macroeconomics: Beyond the
Dynamic Stochastic General Equilibrium Model, Cambridge, UK.
Cambridge University Press.
Colander, David, 2009. ``What was `It' that Robbins was Defining?''
Journal of the History of Economic Thought, December. Vol.
31:4, 437-448.
David Colander, Peter Howitt, Alan Kirman, Axel Leijonhufvud, and Perry
Mehrling, 2008. ``Beyond DSGE Models: Toward an Empirically
Based Macroeconomics'' American Economic Review, 98:2, 236-24.
Keynes, John Maynard, 1935. Letter to Lionel Robbins 1st May, 1935.
Reprinted in Colander, David and Harry Landreth, 1997. The
Coming of Keynesianism to America, Cheltenham, England. Edward
Elgar.
Keynes, John Maynard, 1936. The General Theory of Employment, Interest
and Money, London. Macmillan.
Keynes, J.M., (1938). Letter to Roy Harrod. 4, July. http://
economia.unipv.it/harrod/edition/editionstuff/rfh.346.htm
Klamer, Arjo, 1984. Conversations with Economists: New Classical
Economists and Opponents Speak Out on the Current Controversy
in Macroeconomics, Lanham, Maryland. Rowman and Littlefield
Publishers.
Lucas, Robert, 2004. ``My Keynesian Education'' in M. De Vroey and K.
Hoover (eds.) The IS'LM Model: Its rise, Fall and Strange
Persistence, Annual Supplement to Vol. 36 of History of
Political Economy, Durham, NC, Duke University Press
Robbins, Lionel, 1927. ``Mr. Hawtrey on the Scope of Economics''
Economica. Vol. 7, 172-178.
Senior, Nassau William, 1836. (1951). An Outline of the Science of
Political Economy, New York. Augustus M. Kelly.
Solow, Robert, 2008. ``The state of macroeconomics'' Journal of
Economic Perspectives 22(1), 243-249.
Chairman Miller. Thank you, Dr. Colander.
I now recognize Dr. Broun for a motion.
Mr. Broun. Thank you, Mr. Chairman. I ask unanimous consent
that Ms. Biggert, a member of the Full Committee, participate
in this Subcommittee as if she were a member of this
Subcommittee.
Chairman Miller. And my colleague on the Financial Services
Committee. Without objection, that is so ordered.
Mr. Broun. Thank you, Mr. Chairman.
Chairman Miller. We will now begin with questions, the
first round of questions, and I now recognize myself for five
minutes.
Dr. Chari, you testified that 30 years ago the models
assumed no government action could improve things, and that was
no longer the assumption, but there seems to be some
disagreement among the panel about the extent to which policy
can improve things. My own experience in financial crisis as a
policymaker has been very much with a narrow, micro kind of
point of view. When I was first elected to Congress in 2003,
the advice I got was that most Members of the House--unlike the
Senate, where they can hold forth on all matters--that most
Members of the House labored in obscurity and I should pick
some technical issue no one cared about or was paying any
attention to. I would probably never be heard from again, but
if I picked an issue that there was no one from my party with
my point of view who had already claimed that issue, I would be
doing useful work. And the issue I picked was mortgage lending.
My experience in dealing with mortgage lending was that the
loans, the individual loans, were horrific. Dr. Solow talked
about the assumptions that there were no conflicts of interest
and no lack of information, no lack of knowledge; and middle-
class homeowners and, really, subprime mortgage lending was not
to purchase homes, it was people who owned homes and needed to
borrow money. They were refinances overwhelmingly. They were
handed a sheaf of documents, small print written by the bank's
lawyer--by someone else's lawyer, not by their lawyer--and they
were getting advice from a mortgage broker who was actually
being paid by the lender in addition to what they were being
paid by the borrower, and were getting paid more by the lender
the worse the loans were for the borrower. And the borrowers,
the homeowners, were relying upon that advice and being told
that ``this is very complicated, I will help you through this,
I am a mortgage professional.'' Almost everything I said should
be changed that with the argument, ``Oh, that will result in
unintended consequences,'' and I would say, ``What kind of
unintended consequences?'' ``Well, we don't know, that is the
point: they are unintended.'' And it kind of became an
epistomological test, you know: How could you possibly do
anything without knowing that there would not be unintended
consequences?
Do all of you agree that the models do now assume that
there is government action, that government action can help?
And how do we overcome the concern about unintended
consequences, God only knows what they are? Any of you? Dr.
Chari, since you were the one who said 30 years there has been
a change from your father's economic models?
Mr. Chari. Sure. Here is one way of illustrating the nature
of that change: Earlier this month, the Society for Economic
Dynamics, a hotbed of DSGE-style modeling, held its meetings.
About 400 papers were presented. I flipped through the program.
About 50 of those papers dealt with policy in macroeconomic
models. Guess what? In none of the 50 was the best role for the
government to get out of the way and stay out of the way. In
every one of those 50 papers, every one of them analyzed
ranging from monetary policy to fiscal policy to innovation,
all across the line, they all had a role for policy. In terms
of sort of thinking through the mortgage market, I think it is
really important to understand that if you look at the people
who held the debt issued by major financial intermediaries, ex
post--that is now, after all these events--they have suffered
very small losses, primarily because of various bailout
programs. These debts that were issued by major financial
intermediaries were backed in substantial part by subprime and
other kinds of mortgages. It is rational in a world like that
that the people who issue subprime mortgages will pay very
little attention to the risk characteristics of the borrowers.
I am not saying that is the only factor, but that is an
important factor. Understanding the importance of that factor
has obvious implications for the nature of financial regulation
going forward. That is the kind of insight that comes out of
thinking through a model in which even if everybody
counterfactually behaves very rationally, you can create very
perverse incentives, but you can create very bad outcomes and
you can create an important role for government policy.
Chairman Miller. Any of the others want to address the role
of government under any of these models or whether--to the
extent of which the models assume that government can play a
useful role? Dr. Solow, you certainly touched upon this as you
did, Dr. Winter. Dr. Solow?
Mr. Solow. Yes. Thank you. I have a lot of empathy for Dr.
Chari and what he is trying to explain. This is very difficult
to do. It is easy to say ``you should include this aspect of
reality, you should include that aspect of reality.'' To try to
do it in a logically tight way is extremely difficult. But when
he says that the more recent vintages of DSGE models have a
role for government or allow for unemployment, then I think
that is really a little misleading in the sense that, if you
were to look closely at a DSGE model with unemployment and ask
how does unemployment happen and what does it mean, it wouldn't
be the kind of unemployment that you see in your district, for
instance. It wouldn't be the case where there are workers who
are unemployed workers competent to do a job because they did
it six months ago or a year ago, prepared to work for a little
less than the going wage, and no one will employ them because
there is no market available for their output. Instead, if you
read the pages on unemployment in a DSGE model, it is full of
explaining little glitches in the labor market, little
inefficiencies here and there, and there is a tendency to
underestimate the cost.
Similarly, with the role of government, I said those models
have lots of room for government. What the government should do
is try to make the world more like the neat model by
eliminating inflexibilities and rigidities and elements of
imperfection and whatnot. The notion that the government
might--when there is unemployment and excess capacity because
there is not enough demand for goods and services to employ the
whole economy at a reasonable level--that the government should
try to find ways to fill that gap. That doesn't appear even in
recent-vintage DSGE models because there is no gap, there is
not a gap of that kind.
Chairman Miller. Thank you, Dr. Solow.
Dr. Winter, do you feel moved to----
Mr. Winter. Yes, I would like to just comment briefly on
what Dr. Chari said. I think in the attempt to understand what
happened in the financial crisis, we should recognize that
there are different lines of explanation for the behaviors we
see up and down the system, and then Wall Street in particular.
Enormous losses were inflicted on Wall Street firms and
enormous personal losses on some Wall Street players, and there
is a question of whether that happened because they didn't
understand the system that they had participated in creating or
whether they were in some more rational way responding to the
incentives of the system that presented themselves.
So that, in fact, is, I think, an important research
question, you know: What exactly was the basis for the kinds of
decisions that created these enormous financial
vulnerabilities? And there are voices out there which I
consider to be credible voices that say, basically, Wall Street
confused itself in the end and created a system which it in
turn did not understand at all. Now, I don't know what the
right answer is, but it is a very important question, and what
I would argue for in the domain of economic research is that we
try to do better at resolving some of those questions on a
factual basis when they turn up. There is a whole list of
questions like that about the financial crisis which could be
investigated with high academic standards and systematically.
It would tell us a lot about how the system failed us.
Chairman Miller. Dr. Page.
Mr. Page. Yeah, I guess one quick comment, and this gets
to, I think, some of the stuff David had said and also Chari as
well. I agree that these DSGE models are powerful models and
they have definitely changed over the last 20, 30 years, but
there is this fundamental question of how do we think of the
economy. So if I give you one word to describe the economy and
you could choose between ``equilibrium'' or ``complex,'' you
would probably vote ``complex,'' right? But yet where the
streetlight is, as David was saying, and where we have built up
all this knowledge, is in these equilibrium models. Now, there
has been attempts, and I think they have been extremely
successful, to introduce, you know, volatility. So there is
this notion there is a shock to the equilibrium, and then,
because of frictions and heterogeneity, that shock echoes
through the system creating these complex patterns. But this
equilibrium mindset, I think, can be complemented by a mindset
that instead thinks of the economy as something we are probably
never going to understand, but we will see that different sets
of policies create different types of incentives, creating
certain types of positive and negative feedback. So we did see
this giant rush.
If you are just monitoring the economy and you don't think
it is perfectly working and you suddenly see this huge rise in
refinancing, a little bell should go off and you should say,
``Let us think through the repercussions of this thing.'' And
what the finance people would have told you, they would have
said, ``Look, we are bundling all this risk, it is all going to
be fine.'' And they would have said looking back that--you
know, past data--``this bundling is going to work.'' But then
you realize you are placing a lot of faith on a particular
assumption about bundling that in fact didn't hold true. So I
think that there is a fundamental question of this notion
between do we think of the economy as an equilibrium? And if
you do, then you are imposing a lot of logical coherence; and
if you do want that logical coherence, then you are sort of
stuck with something like DSGE.
There is an alternative approach which is based more on
sort of complexity theory, which is not as advanced, which
thinks of the economy constantly in flux. And one of the things
that has been great about the NSF, I should say, is they have
funded a lot of this very exploratory research into complex
systems, right, to try and create alternative models of the
economy.
Chairman Miller. We have now gloriously exceeded my time,
but Dr. Colander, you appear to be longing to address this
question as well.
Mr. Colander. You know, I would like to reiterate, you
know, what Scott just said, that is, really is, the point. DSGE
models are wonderful and you can expand them and everything
else, they are impressive, but they are one particular point of
equilibrium that you are looking at. Then you are relating it
to this world out there, which is extraordinarily complicated,
which has this complexity, this diversity going on. And, yes,
you can squeeze and push these DSGE models to make them explain
things, but it is like telling people here, ``yes, we can get a
little roughness in the topography,'' when there is actually a
gigantic cliff, or there might be. You need a variety of other
models. Now, I am not saying I know what other models are
there, and the emphasis is that one needs a lot of diversity
within mainstream macroeconomics, and I consider myself a
mainstream macroeconomist too but, you know, within that sense,
everyone knew, and I think Dr. Chari has written, you have to
start with a DSGE as your foundation so you have to start from
this point and then move out as opposed to allowing you to
search the entire lighted area and that just hasn't
happened.\1\ And that, I think, is what Dr. Solow is saying.
There is nothing wrong with DSGE models, but there is a lot of
topography out there, and we need more of that diversity--and,
somehow, within the way academia works, it has not allowed that
to happen. And that, I think, is sad.
---------------------------------------------------------------------------
\1\ For the purpose of clarification, Dr. Colander has requested
that his testimony from ``Now, I am not saying I know. . .'' in this
paragraph to ``. . .that just hasn't happened'' be corrected to read as
follows:
``Now, I am not saying that I know what the other models
are. What I am saying is that one needs a lot of diversity
within mainstream macroeconomics. I consider myself a
mainstream macroeconomist too but do not believe, as Dr.
Chari has written, that you have to start with a DSGE as
your foundation. I believe mainstream economists should be
out there searching the entire lighted area, and that just
hasn't happened.''
Chairman Miller. My time has expired, and I apologize to
the other members of this Committee, and I will try to be
reasonably lenient with others' time as well.
Dr. Broun is recognized for five minutes.
Mr. Broun. Mr. Chairman, I ask unanimous consent that we
let Ms. Biggert go out of order.
Chairman Miller. Without objection.
Ms. Biggert. Thank you so much, Mr. Chairman and Ranking
Member Broun. I had to come to this Committee because Dr. Solow
was here, and I just wanted to say that he is my hero. I have
been a longtime member of the Science Committee and a strong
proponent of research and development, and so I have
frequently, probably very frequently, reminded my colleagues of
the importance of research and development, of the scientific
and technological investments in the future of our economic
competitiveness and security. So one of the ways that I have
done this is always to remind them that science-driven
technologies accounted for more than 50 percent of the growth
of the U.S. economy in the last half-century, and this was a
quote that I always used from Dr. Solow. So I really appreciate
being here, and all of you, this has been a very interesting
discussion.
And I am hope I am not straying off of the subject too
much, but I would like to ask Dr. Solow about the factors that
you see contributing to the U.S. growth in the first half of
this century. And do you believe that scientific and
technological investments will continue to contribute at the
same level now that we are in another century?
Mr. Solow. Thank you. I don't know how a hero is supposed
to respond. Before I taught at MIT, I was a Technical Sergeant
in the U.S. Army, and if you just call me Sarge, I will settle
for that. That is the way I spent my youth.
I do think that science and technology--first of all, let
me say I don't believe that the current crisis and the long
recession will fundamentally impair the long-run growth
potential of the U.S. economy, although it is going to take a
long time to shake off those effects. But they remain there
because, just as you said, the basic sources of that growth are
in innovation of various kinds. What I think may have changed
for the longer run is where in the economy that innovation
takes place. We may have suffered from an excessive innovation
in the financial services sector of the economy. I love Paul
Volcker's remark that the best financial engineering innovation
of all was the ATM machine. But I think, for instance, that the
Committee possibly ought to think that if from now on--I
suspect to be true--that the weight of the service sector in
our economy is permanently bigger than it was in the first half
of the 20th century or even in the second half of the 20th
century, what does that say for the character of innovation
that can affect the economy? I used to tell students and others
that services are just like goods, the only difference being
you can't inventory a service. I can't get three haircuts so
that I don't have to go back again. But there may be
differences in the way, in the kinds of science, the kinds of
innovation that generate productivity in the service sector.
There may be differences in the reception that service-sector
firms can give to technological innovation, and I think that is
a good subject for the Committee on Science and Technology to
pursue.
I have no doubt that those potentialities for long-run
growth or productivity are still there. During the buildup of
the great stock of computers in the United States, most of
those computers were being bought in the service sector: in
retail and wholesale trade, in financial services and
elsewhere. And some service sectors exhibit very rapid growth
in productivity, but that is the kind of long-run change in the
economy that may affect the role of science and technology. But
that role remains fundamental for growth, that it is the
generator of long-term growth I think is undoubtedly still
true. Thank you.
Ms. Biggert. Thank you very much, Sarge. Now I have to
return to Financial Services, where there is another hearing
going on and I am due to be there. Thank you very much.
And thank you again for your indulgence.
Chairman Miller. Thank you, Ms. Biggert.
I now recognize Ms. Dahlkemper for five minutes.
Ms. Dahlkemper. Thank you, Mr. Chair, and thank you all for
your testimony today. It is a fascinating subject. I am a new
Member of Congress, so it has been truly a fascinating time to
join this wonderful body and I appreciate all of your
expertise.
I wanted to ask you: I come from northwestern Pennsylvania,
an area actually that has been suffering economically for a
long time; but the Nation's current number of long-term
unemployed is estimated at 6.8 million jobless, certainly a
number that we have never seen before. So what do
macroeconomics or any field or subfield have to say about the
effects of the economy of extending unemployment benefits for
these people versus just simply letting them fall off the
rolls? And I don't know who might like to address that. Dr.
Winter? I don't know. Maybe not. Dr. Chari? Who would like to--
whoever would like to----
Mr. Chari. Since you are from northwestern Pennsylvania and
I received my Ph.D. in economics from Carnegie Mellon, I
suppose I should be the person to try and help out. Let me just
say one thing before I start. Professor Solow is not just a
hero to Representative Biggert. He is a hero to all
macroeconomists, modern or otherwise.
So the kinds of models of unemployment that people have
written down, most notably the kinds of models that Mortensen
and--Dale Mortensen at Northwestern and Chris Pissarides at the
London School of Economics--have written down and a whole bunch
of other people have written down emphasize the key tradeoff.
The tradeoff is that we don't have very good insurance markets
to protect people when they do get unemployed and so therefore
there is a role for government policy in providing unemployment
compensation because those markets are missing. The tradeoff is
that providing unemployment benefits does tend to discourage
people from looking as intensively for jobs. That is one
effect. The second effect, which I think research has
demonstrated is much more important, is that it tends to make
them more unwilling to accept jobs when they do come up, and so
that is the tradeoff. Now, making this decision requires
understanding the quantitative assessments of those kinds of
tradeoffs. Those quantitative assessments suggest that in times
of relative prosperity, Congress has sensibly decided not to
extend unemployment benefits for too long. In times of greater
difficulty, Congress has done it.
So we have models that give us numbers. I don't have them
immediately offhand, but we can certainly talk about it and I
can communicate those kinds of numbers. But the ultimate
decision about the size of those tradeoffs, how important is it
to protect people from prolonged periods of economic hardship
versus the effect on their own incentives, is something that
Congress has to make the difficult decision on. The guidance
that modern macro kinds of models can offer is a sense of the
quantitative magnitudes of the tradeoffs. Both those effects
are present. That is what the research seems to demonstrate
fairly unequivocally, and so we need to balance those effects.
But I don't have immediately offhand the exact numbers that--
the latest version, of course, they are going to differ from
model to model.
Ms. Dahlkemper. Would anyone else like to address that? Dr.
Winter, do you want to comment? Dr. Solow, after Dr. Winter.
Dr. Winter.
Mr. Winter. Okay. Well, I just want to say again that in
that area, there is a lot of rhetoric that goes around in the
economics discipline about the causes of unemployment and the
role of the incentives provided by unemployment insurance--and,
unfortunately, I don't think this rhetoric moves forward very
much over the years. It could be improved by closer study of
these phenomena through research, and that would be a valuable
thing to do. But on the face of it, it seems to me that, when
you have the very dramatic changes in levels of unemployment
that we have experienced and when you have the regional
diversity that we have in levels of unemployment, that it is
quite implausible that they are somehow fixed considerations of
human nature underlying the phenomena that you are looking at.
I think that people become unemployed because of circumstances
beyond their control to a very large extent.
So I think again if there is doubt about that, and
sometimes one hears comments that suggest there is doubt about
it, I think it can be looked into carefully and should be.
Ms. Dahlkemper. Dr. Solow, would you like to comment?
Mr. Solow. Thank you. I just wanted to add to what Sidney
just said, with which I agree, that if we simply look at
northwest Pennsylvania, your area, there are lot of long-term
unemployed people for a number of reasons--for at least two big
reasons, none of which has to do with a matter of refined
incentives that are provided to them by the unemployment
insurance system. One is that in every recession that follows a
financial crisis, there is a tendency for unemployment to be
very prolonged because the blow to business confidence and the
blow to the confidence of lenders in the creditworthiness of
businesses is such as to make businesses unwilling to make
long-term commitments to employment. But secondly, western
Pennsylvania is not an area full of rising industries, so that
long-term unemployment, I suspect, has been a problem there
that is antecedent to the financial crisis.
I think one has to ask, what are in fact the--and no simple
model is going to do this for you--you have to ask, what are
the possibilities for northwest Pennsylvania or for any other
particular region of the country? What are the development
possibilities that are there? If there are development
possibilities, those are the things to pursue. And, in the
meantime, I think it is simply a matter of humanity to support
the 50-plus-year-old workers who clearly are not laying down on
job search because they are getting UI benefits. They are not
searching so actively because they know damn well there is
nothing there to find. But one ought to focus, a) on the
economic development of the region, and b), once that is in
hand, or in the head at least, on preparing the labor force of
the area for whatever kind of development seems promising. But
looking at piddling little incentives, I think, is not going to
help us at all.
Ms. Dahlkemper. Thank you. That is sort of the sense I get
from talking to people in my district all the time.
Thank you very much. My time is expired.
Chairman Miller. Thank you, Ms. Dahlkemper.
Dr. Broun is recognized for five minutes.
Mr. Broun. Thank you, Mr. Chairman.
The famous philosopher Voltaire once said that ``the
perfect is the enemy of the good.'' We use models such as
weather forecasts every day in society knowing that they are
not guaranteed to be correct all the time but accept this based
on the importance of the utility of the information that the
models produce.
Dr. Solow in his testimony argues that ``the DSGE model has
nothing useful to say.'' I hope I quoted you correctly. I think
so. Should the economists throw away the DSGE model approach
outright or cautiously use the model with the understanding
that there are limitations and shortcomings? For the panel. Dr.
Solow, to begin with.
Mr. Solow. I think it is the latter. First of all, a lot
depends on what you mean by the DSGE approach. If you simply
mean that particular collection of assumptions, then I think
there is nothing holy about that, and those assumptions can be
discarded the way any set of assumptions can be discarded. If
you mean by the DSGE approach being dynamic, being stochastic
and being interested in general equilibrium, then I think that
approach can be pursued, although I share Scott Page's view--
and, I suppose, the view of most of us here--that the
presumption of equilibrium is a little excessive.
But I think there is a lot--I don't want to discard DSGE.
The people who do it are among the brightest macroeconomists we
have. They are not foolish. I do think they are neatness freaks
like me and tend to be pushed by their neatness freakery into
looking further at this, but I do think it has to loosen up.\2\
I do think that one wants to give up the representative-agent
presumption; I think that one has to give up the devotion to
equilibrium and keep the broader methodology. And in the
course--let me just add one more thing--in the course of
meeting criticisms from sourpusses like me and from the data,
the DSGE people have made a lot of modifications, and in the
course of doing that they have done a lot of good work. I would
like to focus on that and give up some more of the more
egregious assumptions.
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\2\ For the purpose of clarification, Dr. Solow has requested that
his testimony from ``into looking further. . .'' in this paragraph to
``. . .to loosen up.'' be corrected to read as follows:
``. . .into foolish conclusions. They can pursue a much
looser version if they want to make sense.''
Mr. Broun. Dr. Chari, do you want to comment?
Mr. Chari. I think Professor Solow is exactly right. All
the interesting policy questions inherently involved
understanding the behavior of individuals who are confronted
with making decisions that are inherently dynamic in the sense
that, if I want to buy a refrigerator or a car or if I am a
businessperson planning to invest in plant and equipment, I am
making a decision about incurring cost today in return for
future benefits. Dynamics is essential to lots and lots of
decisions. Stochastic in the sense of handling uncertainty as
we all know is central to decision-making. General equilibrium
just means making sure that stuff adds up and things are
consistent across the way. These are innocuous terms. It is
hard at a conceptual level to disagree. Quite frankly, I don't
know what the alternative is. There isn't any out there.
Now, within the class of DSGE models, there are a bunch of
assumptions that people have made. As I illustrated earlier,
early generations made very stringent assumptions. We have
thrown away most of those kinds of assumptions. What Scott is
asking for, for example, is that we describe individuals as
computer programs that react in predictable ways. There is
nothing in the flexibility of the methodology that
automatically precludes that, no. It is open to that kind of
thing. Here is the problem that modelers confront, and here is
the difficulty: The difficulty is that every time we add
another ingredient into the model, we want to try and make sure
that it is disciplined in some fashion--that is, it is
disciplined by historical evidence of the United States, other
countries, things like that--both at the micro level and at the
macro level. Absent that discipline, you have just got a bunch
of idle theorists doing completely worthless stuff--and I am an
idle theorist too, so I am all for idle theorists. But it is
not the kind of stuff that is going to lead to fundamental
improvements.
So every time we add a complication to the model, we
discipline that complication by focusing on an additional piece
of data. So, for example, early generations of these models had
a representative agent. Modern generations have all kinds of
heterogeneity. How do we discipline that heterogeneity? Well,
we have data on the wealth of individuals, how the wealth of
individuals evolves over time, how the cross-sectional
distribution of income evolves over time. That is the kind of
stuff that we use to discipline that kind of activity. Early
versions didn't have sticky prices or wages. Thanks to
important work that Pete Klenow, who was at the Minneapolis
Fed, did with Mark Bils, we now have disciplined, interesting
ways of introducing sticky prices and sticky wages. Right.
Okay. So these kinds of things, every time things are added, as
long as they are added with focus on data, with some discipline
in the process of doing it, in general my reading of the
macroeconomics profession is, ``come on board, the water is''--
``come on into the water, it is fine.''
The main thing I complain about in my field is, you know,
it is amazing how few of us there are. By my count, there are
roughly 200--if I was being very generous and included a lot of
people, 500--economists who are actively engaged in the
production and the consumption and the interpretation of DSGE
models. We are talking about--this is an economy of 300 million
people with, you know, hundreds of thousands of economists. We
are devoting a tiny fraction of our energy to this. Would we be
better off if we could quadruple it? Absolutely. Would I be
better off I had four times as many students? Yes. What holds
me back? I just don't have the money. I wish I did.
Mr. Page. Can I just follow up very quickly and say that
Chari is raising a really good point, in that there has been
this push on these DSGE models to include all sorts of things,
including heterogeneity and stickiness and frictions and that
sort of thing? And I think this relates to your opening
comments as well about studying climate-change models. I mean,
one thing I think we really need is a lot--we don't need one
big model. Every model is going to make mistakes, and one big
model is just going to be really hard to understand and it is
going to screw up. What we need is, we need lots of models that
each include different parts of things. And then we need people
who have really sound judgment who can say, ``Okay, here is our
15 models of the economy, this one is really focusing on
heterogeneity, this one has got lots of frictions, this one has
got much better sort of firm-level detail, right, with sectors
of the economy.'' And of all these models in play. . . And then
use sort of our judgment and our wisdom and sort of be willing
to abandon the notion of stationarity--that the world next week
is going to look like it was the week before--and have a dialog
between those models to make good choices.
I think when we lock into a single model, which I think we
have done with climate change and I think sometimes the Fed
does--for political reasons, it makes a lot more sense to have
the Federal Reserve Bank of Minneapolis or to have the IPCC
say, ``this is what is going to happen.'' And that is silly
because it is going to be wrong: You are better off saying
``here is a whole suite of models and there is a lot of
disagreement about it because this thing is complex.'' I think
we just have to be willing to accept that. I think that is
politically difficult to do because people want answers.
Mr. Broun. My time is expired, but Mr. Chairman, I would
like to make a comment if I may. These models are certainly
interesting from a theoretical perspective and you have
different economic theories, whether it is Keynesian or supply
side, et cetera, and as policymakers, unfortunately we don't
base the policies that we create and administrations both
Republican and Democrat try to do the best they can to use
whatever tools that they have to try to make this country a
better place, and I appreciate you all's effort and what you
all are doing in trying to give us some modeling and give us
some kind of idea about how to proceed. And unfortunately, I
think Republicans and Democrats alike don't pay as much
attention to unintended consequences of decision-making that we
make and hopefully in this Committee, the Science Committee, we
have all agreed that science can't create policy but policy can
be created based on scientific evidence and best evidence, and
that is what I do as a physician, so I want to thank you all,
and my time has run out and I will yield back.
Chairman Miller. Thank you, Dr. Broun.
Dr. Broun quoted a philosopher as saying that the enemy of
the good is not the bad but the perfect, which is probably the
most quoted quotation from a philosopher in Washington, but
never with attribution. It was Voltaire, and what politician
wants to admit to quoting from a French philosopher?
Mr. Broun. Mr. Chairman, I want to remind you, I just did.
Chairman Miller. But not with attribution, never with
attribution.
We are seeing the President sign tomorrow a new financial
reform bill that will include agencies with new duties,
including the Consumer Financial Protection Bureau and a
Systemic Risk Council that is supposed to see systemic risk
coming--although Alan Greenspan always thought that you could
not recognize a bubble while you are in it, you could only
recognize a bubble after it had burst. Obviously, we want to
improve upon that and recognize bubbles as they are forming and
not after they burst.
Dr. Winter, what sort of research will those new regulatory
agencies need, would you urge them to undertake, to inform
their decisions?
Mr. Winter. The problem illustrated in the history of the
crisis is that the institutional arrangements of the economy,
and particularly in the mortgage markets, changed quite
dramatically over a period of, well, some decades, and then
rather rapidly in the final decade of the episode. And,
basically, the regulatory agencies were to some extent unaware
of the extent of these changes and did not have the capacity to
try to estimate or understand what the implications of those
changes would be. There was this--the Pew Foundation sponsored
a very high-level expert task force on financial regulatory
reform, which delivered a short report and remarked on this
point. And I actually quote that remark in my written
testimony, saying basically that the system changed a great
deal, the regulators were not aware of the extent of this
change and its implications. Now, to be aware of those changes,
they would have had to had a lot of very high-quality economic
research going on someplace, and presumably in-house, because I
don't think it is very likely that that kind of research would
be adequately supported elsewhere.
So this would bring me back to Dave Colander's remarks
about the need to have some greater strength in the domains of
applied economics, to use the understandings that we accumulate
in academic research to address the really significant
problems. So I think and suggest in my written testimony that
these agencies--and I would include particularly the Fed staff
in this respect--needs to have economists who have more of an
orientation to the institutional context and the way that it is
changing and are interested in trying to estimate the
implications of that so that they can provide some useful
guidance.
Chairman Miller. Thank you.
Dr. Winter, I have heard Ben Bernanke say, not so much in
defense of why they didn't see the bubble forming but why they
did not act to adopt consumer protections--which the Fed had
the authority to do since 1994--as saying that the abuses that
became enormous happened fairly quickly: 2004 to 2006. Subprime
lending went from eight percent of all mortgages to 28 percent
of all mortgages in two years, and it was an almost entirely
unregulated corner of the market that they didn't see
happening. It was not depository institutions that were
initiating the loans. They were a conduit to the securitization
market, which was almost entirely unregulated, which were
investment banks that were not depository institutions. And no
one, no regulator, actually really saw what was happening. So
your testimony just now is very consistent with what Chairman
Bernanke has said as well.
Anyone else wish to----
Mr. Winter. May I comment on that?
Chairman Miller. Yes, Dr. Winter.
Mr. Winter. Thank you. So I am familiar with the sequence
of events in the Fed on the matter of subprime lending, and I
think an important part of what happened was that there was
discussion--there was not action but there was discussion--
about the need to protect the borrowers in the context of that
episode. Now, I think what was very largely missed was the fact
that we had created a system in which the lenders were making
dumb loans, that the effect of the securitization process and
the general losing track of what was happening to the relevant
information, created not only the abuse of the borrowers but a
great vulnerability in the lenders.
Chairman Miller. The loans would not have been dumb if
housing had continued to appreciate the way it was
appreciating, but that was. . . Because even if a borrower
couldn't pay it back when the reset came after just two or
three years--and they had to pay 30 to 50 percent more in a
monthly payment, which they couldn't begin to do, and then they
had to pay a pre-payment in order to get out of that mortgage--
if the house had appreciated 15 percent in those two years,
there was no realistic possibility they wouldn't pay the full
loan. They could either refinance or they could sell their
house. But when it stopped appreciating, the music stopped.
My time has expired. I now recognize Dr. Broun for five
minutes.
Mr. Broun. Thank you, Mr. Chairman.
In Dr. Solow's testimony, he said there are other
traditions with better ways to do macroeconomics. If
macroeconomic models like the DSGE are insufficient and should
not be relied upon, what other tools should economists use? I
will throw that out.
Mr. Solow. Thank you. I am glad to take a crack at that. In
our discussion here, possibly because it is set up as a
discussion of DSGE models, we tend to think and talk as if the
choices are you can do DSGE or you can do something else. And
Dr. Chari suggested, ``Oh, well, DSGE can be a big raft, climb
aboard.'' I don't want to climb aboard. I would rather have the
DSGE people take a swim off the raft. There have been, and
still are, long traditions of work, theoretical and applied, in
macroeconomics which have had their ups and downs, but they are
not all novelties or things that we ought to try for the first
time.
My hero in macroeconomics, now alas dead, was James Tobin,
Professor James Tobin of Yale University and a very dear and
close friend of mine. Jim Tobin did macroeconomics in a way
that paid, I think, just about the right attention to the
microeconomic foundations of macroeconomics. He made sure that
everything he did was compatible with the truth that what
happens macroeconomically is the aggregation of millions of
firms and individuals, and one wants only to say things that
are compatible with that, and that give reasonable results that
pass the smell test. Tobin, and not only Tobin but many others
besides, left a large body of work which is now all of a sudden
forgotten or ignored, and I think that is a terrible mistake.
We should simply go--one of the things we should do is continue
those traditions. They all worked in terms of aggregate supply
and demand in one way or another, and they focused their
research on learning more about the components of aggregate
demand or learning more about the components of aggregate
supply, and what happens in markets that fail to match demand
and supply. And that is a perfectly sound tradition. It is not
something new or untried. We could go back to do that or do
more of that. I would like to see us do more of that. One of
the problems is that the DSGE model is so attractive, as I
said, to the neatness freak in many economists that it is very
hard for anything else to get any traction, and I hope that we
can change that. I hope that the misbehavior of the economic
system in the past four years will help to change that.
But we don't have to do something brand new, although I am
game to give agent-based models a run. I am all for that. There
are some new things, but there are some basic existing
traditions that could be revived.
Mr. Broun. Dr. Chari?
Mr. Chari. I want to go back to my aphorism: If you have
got an interesting story to tell and you think it is a coherent
story, nothing easier than or harder than to try to put it into
a model and see if it comes out. If it does, then it is a
coherent story. If it does not, then you haven't thought
through the economic problem quite as well. These are very
flexible models.
Let me give you an illustration of the sense in which they
are very flexible. There is work by Mike Woodford that is now
about seven or eight years old. And a lot of that work has been
recently addressed by very prominent macroeconomists--John
Taylor, Larry Christiano, Marty Eichenbaum, a whole bunch of
people--all of whom were interested in the following question:
when interest rates are very low--we are in a zero-interest-
rate world, so to speak, as we are now--what would be the
consequences of big increases in government expenditures, what
would be the consequences of various kinds of shocks to the
economy? And those models delivered answers that I suspect
Professor Solow had in mind when he said: ``Can they generate
environments in which there is unemployment because of
insufficient demand?'' Certainly, if you look at the output of
those models, it looks that way. Those are models where
apparently even wasteful expenditures in government can in
certain circumstances be desirable. So that is the sense in
which is an open, flexible tool. I would argue it does produce
the kind of unemployment that Professor Solow thinks he sees.
So all that is all fair game.
When I said ``come on board,'' what I meant was that if
people have an idea--and Scott, for example, has been talking
about agent-based modeling, and I think it is intriguing and
interesting, and, as he recognizes, subject to computational
limitations--there is really nothing in the logic of the basic
method that prevents you from putting those kinds of features
in. What are the kinds of things that then are left out? It is
only ideas that are incoherent, arguments that are intended to
obfuscate rather than clarify. It is a way of thinking about
the world. It does not start off with any presupposition about
what the outcomes are.
A final observation that is worthwhile keeping in mind
whenever you think about all these things is, there are terms
like bubble, involuntary unemployment, a variety of different
kinds of things, which are theoretical constructs. They can be
useful theoretical constructs, but they can also impede us from
thinking through what the fundamental issue is. So the
fundamental issue is the following: Housing prices in the
United States rose dramatically over about a decade-long period
and then collapsed dramatically. Is that a bubble or not? That
is a hard question to tell. But an inarguable question to tell
is, to address is, do you have a model that can produce
dramatic increases in housing prices? If you do not have a
model that can produce dramatic increases in housing prices,
don't come and tell me how we should regulate the housing
market. You can't have something interesting to say. Now, in
that model is that dramatic increase in housing prices a
bubble? It may or may not be. It depends on the details of the
particular model.
So it is very important for models to be consistent with
the data. It is very important for us to be able to write down
models where the unemployment rate is sometimes 11 percent and
sometimes four percent. It is less interesting, I think, to ask
in that model, is that unemployed person involuntarily
unemployed or voluntary unemployed? Those are hard questions.
It is valid and legitimate to ask yourself the following
question: If you asked the actors in the model, ``would you
accept a job at the prevailing wage?'' you better get a
situation where sometimes 11 percent of them say yes and
sometimes four percent of them say yes. So the way I describe
this somewhat more succinctly is, we should judge models by the
outcomes that they proceed, not by the particular language that
we use to decide whether the outcomes are the outcomes that we
want from the models.
Mr. Broun. Thank you. My time is expired.
Chairman Miller. Ms. Dahlkemper is recognized for five
minutes.
Ms. Dahlkemper. Dr. Solow, your work highlighted the
importance of technology as a key driver of economic growth,
and Ms. Biggert had alluded to that earlier in her questioning.
And we look for budget savings and, you know, we talked about
western Pennsylvania where I am from and manufacturing-based
economy in large part. How should we prioritize spending to
guarantee that what we spend represents the best investment for
our future?
Mr. Solow. You really want me to tell you how to do that?
Ms. Dahlkemper. We are looking for answers.
Chairman Miller. You can use your full five minutes.
Mr. Solow. Are you speaking primarily of expenditures on
science and technology or expenditures broadly?
Ms. Dahlkemper. Well, expenditures broadly.
Mr. Solow. I think that you ought to--there are two aspects
to public expenditures, especially now, in the next couple of
years. One is that public expenditures of any kind will provide
some employment and some secondary expenditure as well and,
perhaps at longer-term costs in terms of accumulating debt,
would certainly improve the economy, would put idle resources
to work. That is what I am trying to get at.
The second aspect of public expenditures, which would be
true even if there were no idle resources to put to work, are
that you have to think in terms: What is the social benefit?
What is the benefit to society of spending money on object A as
against spending an equivalent amount of resources, of real
resources, on B? And in other words, sometimes the cliches are
right; and I think that cost-benefit analysis, although a
cliche, is the right answer to this question when all resources
are reasonably fully employed. And in my view, and I have no
personal interest in this, I think that expenditures on the
promotion of innovation in science and technology, including
social science and organization and things like that is, are
resources well spent in our economy. On the other hand, right
now, in 2010, I think that almost the dominant fact about
public expenditures is that they have a good shot, when
interest rates are low and staying low, of putting idle
resources to work. That doesn't obviate the cost-benefit
analysis, because you can put resources to work in one way or
in another, and your job as a legislator is to make that
judgment for people. Was it Dr. Broun that quoted someone, I
don't know with attribution or without, that science describes
but doesn't prescribe? Well, the same is true of economists and
economics. It is your judgment what is the most socially
beneficial use of a real dollar's worth or a real million
dollars' worth of labor and capital in our economy, and that
changes from time to time. So there is no reason to expect
there to be an answer that is valid for five years.
Ms. Dahlkemper. Thank you.
Dr. Page?
Mr. Page. And one thing we have all mentioned is that we
think of the economy as the sum of these 300 million people,
right? And when you talk about the effects of some of these
policies, you are taking about aggregating up over those 300
million people--saying if we spend, you know, X million dollars
or a billion dollars, we are going to get a one percent, two
percent, three percent change in things. And I think it is
incredibly important to focus not just on the mean but also on
the variants. As we know, this economic downturn has affected
people differently across groups by race, by age, by region, as
you alluded to. It has been very different. And I think that we
can sort of, with all good intentions, sort of naively assume
sort of a linear view of the world: that, you know, if we put
this amount of money in, we will get this nice, clean linear
effect. But if I look through a tighter focused lens and look
at the level of particular communities, whether it is by region
or age group or by race, you can see that communities can fall
apart, right? So there can be these sort of non-linear
threshold-type phenomena where entire communities, regions,
groups of people can really suffer, right? And so I think that
it is very important we think about policy, not just to think
at the macro level but also ask, ``How are these things
targeted in such a way to prevent sort of cataclysmic events at
the micro scale?'' We tend to focus on these sort of large
events at the macro scale, but these same things are happening
at the community level and at the group level, and we need to
think of policy, I think, through a finer lens as opposed to
just through these broad macro models.
Ms. Dahlkemper. Would anyone else like to comment? Dr.
Winter.
Mr. Winter. Yes, sort of the management perspective on the
spending question. You know, one of the difficulties that we
face in a circumstance like this, where we are asking what
useful lines of government expenditure might be, is that some
of them take a lot more time to plan and to get implementation
than others do. So it is not just sort of a cost-benefit
analysis in the abstract. You really have to look at the time
phasing of the impacts and the requisite levels of
administrative investment in order to make it happen. And that
line of thinking brings me back to your question about the
unemployment benefits, which is a seemingly very reasonable
line of expenditure to pursue: to renew those benefits with the
administrative apparatus to do that already in place and not
requiring to be invented.
A similar point would apply, I think, to state and local
government expenditures, where the state and local governments
are doing many things that were deemed to be worthwhile in the
past and they probably are worthwhile now, and the reason they
are being cut back is because these governments are so strapped
for revenue. So that is another area where in very short term
and with very little new administrative investment you could
make important things happen.
Ms. Dahlkemper. Thank you. My time is expired.
Chairman Miller. Thank you.
We will now have a third round of questions, and I
recognize myself for five minutes.
Dr. Solow, you used the phrase, in answer to a question,
``the weight of the service sector,'' and I am not sure you
intended that phrase the way I took it the moment that you said
it: that, obviously, there are many parts of the service sector
that make a useful contribution, but some does just feel like a
weight. As a brand-new Member of Congress hearing Alan
Greenspan testify, one Member asked him the question, ``Do you
think it is important that our economy make things?'' And like
Ms. Dahlkemper, I represent a district where we lost a lot of
manufacturing jobs so I listened intently, and he said he did
not think it was important necessarily that we make things but
the economy add value. And the distinction that I thought he
was drawing was to bring in the service sector but also
specifically the financial sector: that it didn't matter quite
so much if we lost a lot of textile jobs if we were the world's
financial capital and we were adding value. Being the world's
financial capital now does not seem like such a great deal for
us.
And some economists have pointed to the growth of the
financial sector as a symptom of something wrong. It has gone
from four percent to eight percent, but most notably the
increase in the profitability of having gone from between 5 and
15 percent of all corporate profits to more than 40 with
compensation almost twice what most Americans, the average
American worker, is making. Should we regard that as simply the
result of self-correcting forces in the current equilibrium or
as symptomatic of something wrong? I am inclined to view it the
way a doctor would view a swollen organ in the body. Anyone?
Dr. Solow?
Mr. Solow. Yes. So am I. In fact, I was going to interject
something along those lines earlier and then I thought I was
talking too much so I shut up. I think the doctor's point of
view is the right point of view here but I think there is a
much broader point, especially in connection with financial
services. When we were talking about what kind of research new
regulatory bodies ought to do, one of the things that I would
like, one of the kinds of research I would like to see done, is
this: We have drifted into the habit of talking about the
financial services sector as if it justified itself, as if to
know and love and earn a lot of money in the financial sector
is its own reward. The fact is, God created the financial
sector to help the real economy, not to help itself, and one of
the kinds of research I would like to see is a much deeper
analysis of the way the financial part of the economy is
related to the real economy, to the economy of employment and
production and consumption and all that. I suspect, as it seems
as if the chairman may suspect, that the financial services
sector has grown relatively to the point where it is not even
adding value to the real economy. It may be adding compensation
to its members but it is not improving the efficiency or
productivity of the real economy.
There are clear ways in which financial activity can and
does do that. We know from lots of empirical study that, for
economies at a lower stage of development than the United
States and western Europe, financial depth really promotes
economic growth. It allocates resources better. It allocates
risk better. But I have the feeling, as you seem to have the
feeling, that we have got to the point where the financial
services sector is creating risk rather than allocating it. So
I would like to see research aimed, as I said, at the relation
between finance and the real economy, and particularly at what
is the productivity in terms of the real economy of resources
devoted to financial activity.
When I spoke earlier of the weight of the financial sector,
I was really simply thinking of the service sector. I was
really just thinking of the amount of employment that it
generates and the fraction of GDP that it generates. They are
both very large and increasing. But I would not take for
granted that because the financial sector is large and growing,
that it must be profitable in the sense of beneficial to the
efficiency and productivity of the real economy.
Chairman Miller. Well, my time is expired, but I do have
one other question that actually seems pertinent to the work of
the Committee. If we assume that one of the reasons, perhaps
the principal reason, we hold hearings is to inform the
decisions that actually may come before us, one of the things
that the Federal Government does is fund economic research. Dr.
Solow just discussed some of the additional research he thought
would be useful in informing economic decisions. Dr. Colander
in his testimony talked about how we might reallocate the $27
million that we spent in NSF for economic research, which
doesn't seem like that much in the scheme of things, given the
importance of the issues. Do any of you have a view as well on
how we might reallocate those resources? Dr. Solow?
Mr. Solow. I will defer to anyone else who wants to say
anything about this.
In a way, I have to disagree with David about his
recommendation. Let me just say that first. I am not--in fact,
I am a little appalled at the idea of appointing physicists and
mathematicians and statisticians to review committees for the
economics part of NSF. In my excessively long experience,
physicists and mathematicians are capable of infinitely more
stupidity about the economy than economists are, and not only
capable of but they exercise that capacity frequently. I
would--but I do have a small, tentative, extremely tentative
suggestion of a way in which funds devoted to NSF's economics
division might be profitably employed that I think David might
agree with.
I spoke of earlier traditions in macroeconomics. It seems
to me that as of now those traditions are most carefully
practiced not in universities but in organizations, some of
them for-profit firms, which do model building in order to do
consulting for private businesses and for government agencies.
I know the names of several of those, but that is unimportant.
And the Council of Economic Advisors, the Federal Reserve
Board, the Congressional Budget Office all make use of those
commercially or otherwise-maintained macroeconomic models. I
wonder whether NSF some--you might provide some funds for NSF
that it could spend making grants for basic research done by
some of those model-building enterprises. And there I would
even like to see the selection committee have representatives
from the Council of Economic Advisors, the Federal Reserve
Board, the Congressional Budget Office, which have an idea of
how to use these models, how they are used, how they might be
improved, perhaps by the addition of altogether different
agent-based sorts of things. I think that might be a useful
thing to do because, as I said, this is where the older
traditions in macroeconomics seem to be most practiced these
days. Thank you.
Chairman Miller. Dr. Chari?
Mr. Chari. I have served on the NSF's review panel in
economics and I have been a frequent reviewer for various kinds
of proposals at the NSF. I have also performed similar tasks at
the University of Minnesota, Northwestern University, for
university-wide grant-making activity. I have also participated
in some private kinds of things. The NSF's process for
allocating funds beats everything else I have seen by a mile.
It is exceptionally balanced. It is very fair, very thoughtful.
The most striking thing, and I think everybody who has served
on an NSF panel always says, is the bent is always in two
directions. The bent is young people. We have to make sure that
young people who are coming into the profession get funded and
so therefore we have to take big risks. The second bent is, we
have to be sensitive to people who have ideas that seem outside
the box. We should not be locked into our way of thinking about
things. Of course we need to apply standards, but we do need to
do that.
Given my experience dealing with the National Science
Foundation, and in any event, given the approximate $2.5
million for macroeconomic research, I don't think there is much
to be done by way of reallocation. I would, however, reiterate
the case I made in my original spoken testimony. I think given
the importance of the issues at hand, given the centrality of
these issues for important policymaking, this is an area--at
the risk of being a pleader for special interests, this is an
area where I do think the social returns to modest increases in
funding are likely to be very substantial.
Chairman Miller. Dr. Winter.
Mr. Winter. First I would like to second that last remark
of Dr. Chari's. I think given the stakes in these issues, I
think it is very hard to imagine that we couldn't reasonably
try to devote more resources to useful kinds of economic
research.
But to follow up on Bob Solow's comment about the physicist
and mathematicians, an interesting note is that younger
generations of those physicists and mathematicians that he
spoke of became the Wall Street quants, and so the model
building that was going on in Wall Street was informed by very
high levels of technical skill but very little of the broader
perspective which one would at least hope that economists might
have brought to that situation. And I am more sympathetic to
Dave Colander's proposal about the review. I think he probably
did not have in mind the physicists and mathematicians, but
perhaps he did, but there are a lot of social scientists and
even management scholars out there who think they know
something about the way the world works, and I think their
voices and some of those review processes would be helpful.
Chairman Miller. Thank you.
Dr. Colander to defend yourself.
Mr. Colander. Yes. Let me just say that what I was trying
to emphasize, I think you need diversity within there. I think
as Dr. Chari said, there are 200 people, loosely, that are
considered macroeconomists.\3\ If we really got down to it, if
we were talking over beers, he would say there is probably
about 12 that we take seriously and the rest, well, you know,
they work on the edges. What happens is, it is a very small
area, which means it can get inbred in the sense of here
everyone starts to move in lockstep, and that is what I think
Dr. Page is emphasizing. Diversity in and of itself is good,
and somehow one needs a way of thinking, ``How can we bring in
as many diverse views as possible?'' So I can say, ``Well,
there is a whole tradition in macroeconomics from Axel
Leijonhufvud on coordination failures that all got moved out,
that really didn't get funded.'' There is work being done now
on saying, ``Look, our knowledge of the macroeconomy is so
minute that these models, that are formal models, are not
helping us, so they use fractally cointegrated vector
autoregression models.'' Now, you can sort of add that and sort
of have that. But they are essentially statistical models. They
say, ``Let's pull everything we can from the statistics and use
our judgment about theory to add in, and not have formal
models.'' So there is differences in methodology, and I think
is that differences in methodology that hasn't been allowed the
diversity. Once you accept the methodology, I fully--and I have
written that I think the profession is very diverse. You know,
they don't hold any particular views but you have got to accept
this methodology.
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\3\ For the purpose of clarification, Dr. Colander has requested
that the word ``macroeconomists'' in this sentence be corrected to read
``macroeconomic theorists.''
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If you don't accept this methodology, you are not part of
the economics profession. That is what I am saying is wrong,
because the methodology is itself questionable, and the
question is really here: It has to have microfoundation if it
is going to be there, but of a particular type, of an
equilibrium type. In other words, if you think of things
broadly, every economist believes incentives are important. But
the way they approach it is, here within the system that is
created by the macroeconomy, you are operating within that--in
which case the microfoundations have macrofoundations which
have microfoundations, and they feed back, and you are not sure
where it is. So therefore you have no distinct methodology that
you allow.
Now, I don't know what is right, but what I am saying is,
the way to sort of have the system arrive at that is to
maintain the diversity and that the current system is not
allowing for that diversity. Thanks.
Chairman Miller. Thank you, Dr. Colander. If we start using
those other models that you describe or mention, could you come
up with some initials for those?
I now recognize Dr. Broun for five minutes.
Mr. Broun. I thank you, Mr. Chairman. Just for the sake of
expediency, I am going to submit my further questions to the
panel, and I appreciate you all's written response to that, so
I will----
Chairman Miller. I am sorry. What was----
Mr. Broun. I am going to submit my further questioning for
written response to the panel and I will just yield back.
Chairman Miller. Thank you. I think we are--Dr. Page, did
you have anything to add to that last question. It is something
that actually is within the jurisdiction of this committee, so
perhaps we should talk about that a little bit if you have a
thought on the direction in which the government-funded
economics research should head.
Mr. Page. Yeah, I think it is always risky to sort of ask
people off the top of their head, you know, ``What should we do
within an agency?'' I think you always get better decisions if
you bring in--if you sort of task people to think about it
beforehand and bring sort of people with different vested
interests and different training to think about it. But I think
one thing that is interesting is that we haven't within
economics thought about could there be, you know, sort of big
new funding initiatives, you know. One thing that the NSF did
that I think has been very successful is these IGERT grants,
these Integrated Graduate Education Research Training grants,
where you set of up sort of these interdisciplinary graduate
training programs across the sciences. And these, I think,
really transform graduate education in a lot of ways. We
haven't done similar things within economics. I mean, there is
some--I have one of these, which is partially within the
economics department. The University of Michigan actually has
two.
But we haven't sort of issued anything of, like a grand
challenge or just real opportunities to say to the economics,
you know, division within the NSF, ``Suppose we gave you $10
million, right, for a one-time-only event, how would you use
the money.'' So rather than just sort of come up with a
program, give them an opportunity and let economists and
sociologists and people who know financial markets and people
who study organizations, you know, task an interesting, diverse
group of people with coming up with things--``What do you think
we could give, and what would the bang for the buck be, and how
large are the stakes?''--as opposed to sort of, you know,
someone getting on a hobbyhorse and saying, ``This is my idea,
let's do it.'' Because I think that there is a lot of really
bright people who care deeply about these things, and I think
we have the resources--we could be very innovative. The reason
we are not being as innovative as we could be now is, it is a
very small pie and there is a lot of incredibly talented people
and there is a lot of people with, you know, strong agendas.
And I think that the economics division, like most of the
divisions, does a very decent job of dividing that up, but
there hasn't been a sense of: ``Here is something interesting,
here is an opportunity, you know, here is a pile of money, what
could you do?'' And if you brought, you know, some, I think,
bright, diverse minds together, something interesting would
probably come out.
Chairman Miller. Thank you.
I think we are now to the end of our hearing.
Mr. Broun. May I make a comment, Mr. Chairman?
Chairman Miller. Dr. Broun.
Mr. Broun. Just to go back to what Dr. Page was saying, I
bet if we gave each one of you $10 million, the money would be
utilized in some manner or another, so I trust that that would
happen.
Thank you, Mr. Chairman.
Chairman Miller. Thank you, Dr. Broun. Dr. Broun, I will
make an agreement with you that if you do not tell voters in
North Carolina that I have gone to Washington and started
quoting French philosophers, I will not tell voters in Georgia
that you have as well.
Mr. Broun. Are you asking for a unanimous consent request?
Chairman Miller. I will agree. Thank you.
The hearings that I have grown to like the best are when
there are really smart, thoughtful people whose testimony
agrees with what I already think. I don't claim expertise in
economics, but a great legal philosopher, Oliver Wendell
Holmes, wrote that the life of the law has not been logic, the
life of the law has been experience. And the lesson that I take
from your testimony is that we do need logic as we need logic
in law, but if experience pulls us up short for where logic
appears to lead us, we should listen to the logic, to the
experience, whether it is the smell test that Dr. Solow--
whether we call it a ``smell test'' or otherwise. If the
logical, the result of the application of logic leads to an
unacceptable result or one that does not make sense, then we
should pause over it. And certainly a lot that went on in the
economy in the last ten years, looked at in isolation, made no
sense. And explaining that it could not be looked at in
isolation, had to be looked at as a broader, part of a broader
macroeconomy--in which the little pieces all made sense and
there were self-correcting forces and there was an equilibrium
and we couldn't tamper with it or we would be tampering with
mysterious forces that were beyond our knowledge--that proved
not to be a good policy course. We would have been better
dealing with the injustices, the things that in isolation
appeared to make no sense.
I do appreciate very much this very distinguished panel
coming today. It would be great if you could continue to be
available to us as we have questions in this area. I appreciate
all of you being here.
I now have a script for the closing. Before we bring the
hearing to a close, I want to thank our witnesses for
testifying--I think I already said that extemporaneously, but
now let me also say it from the script--before our Subcommittee
today. Under the rules of the Committee, the record will remain
open for two weeks for additional statements from the members
and for answers to any follow-up questions the Subcommittee may
have for the witnesses. And having heard the testimony, I do
not think that there is any possibility of any problems with
possible perjured testimony.
The witnesses are excused and the hearing is now adjourned.
[Whereupon, at 12:20 p.m., the Subcommittee was adjourned.]