[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?
---------------------------------------------------------------------------
    \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\
---------------------------------------------------------------------------
    \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.
---------------------------------------------------------------------------
    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\
---------------------------------------------------------------------------
    \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).
---------------------------------------------------------------------------
    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\
---------------------------------------------------------------------------
    \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.
---------------------------------------------------------------------------
    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\
---------------------------------------------------------------------------
    \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.
---------------------------------------------------------------------------
    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.)
---------------------------------------------------------------------------
    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.
---------------------------------------------------------------------------
    \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\
---------------------------------------------------------------------------
    \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.
---------------------------------------------------------------------------
    \3\ For the purpose of clarification, Dr. Colander has requested 
that the word ``macroeconomists'' in this sentence be corrected to read 
``macroeconomic theorists.''
---------------------------------------------------------------------------
    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.]

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