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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 
(II)

COMMITTEE ON SCIENCE AND TECHNOLOGY 

HON. BART GORDON, Tennessee, Chair 

JERRY F. COSTELLO, Illinois 

EDDIE BERNICE JOHNSON, Texas 

LYNN C. WOOLSEY, California 

DAVID WU, Oregon 

BRIAN BAIRD, Washington 

BRAD MILLER, North Carolina 

DANIEL LIPINSKI, Illinois 

GABRIELLE GIFFORDS, Arizona 

DONNA F. EDWARDS, Maryland 

MARCIA L. FUDGE, Ohio 

BEN R. LUJA ́ N, New Mexico 

PAUL D. TONKO, New York 

STEVEN R. ROTHMAN, New Jersey 

JIM MATHESON, Utah 

LINCOLN DAVIS, Tennessee 

BEN CHANDLER, Kentucky 

RUSS CARNAHAN, Missouri 

BARON P. HILL, Indiana 

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 

RALPH M. HALL, Texas 

F. JAMES SENSENBRENNER JR., 

Wisconsin 

LAMAR S. SMITH, Texas 

DANA ROHRABACHER, California 

ROSCOE G. BARTLETT, Maryland 

VERNON J. EHLERS, Michigan 

FRANK D. LUCAS, Oklahoma 

JUDY BIGGERT, Illinois 

W. TODD AKIN, Missouri 

RANDY NEUGEBAUER, Texas 

BOB INGLIS, South Carolina 

MICHAEL T. MCCAUL, Texas 

MARIO DIAZ-BALART, Florida 

BRIAN P. BILBRAY, California 

ADRIAN SMITH, Nebraska 

PAUL C. BROUN, Georgia 

PETE OLSON, Texas 

SUBCOMMITTEE ON INVESTIGATIONS AND OVERSIGHT

HON. BRAD MILLER, North Carolina, Chair 

STEVEN R. ROTHMAN, New Jersey 

LINCOLN DAVIS, Tennessee 

CHARLES A. WILSON, Ohio 

KATHY DAHLKEMPER, Pennsylvania 

ALAN GRAYSON, Florida 

BART GORDON, Tennessee 

PAUL C. BROUN, Georgia 

BRIAN P. BILBRAY, California 

VACANCY 

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 
(III)

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

(1)

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.
2
3

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. 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. 5 Ibid., p. 18. 6This 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. 

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? 

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

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—
4

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). 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). 9Journal of Economic Perspectives, Vol. 20, No. 4 (Fall 2006), Pp. 3–28. 10Michael 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). 

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

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

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, 
5

11 Ibid., p. 16. 12Gregory 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). 13Kirman, op. cit., p. 2. 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).

but the Fed had ‘‘departed sharply from [its] previous generation’’ of models and had 

incorporated numerous assumptions and features consistent with DSGE. 11

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

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

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? 

• 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 
6

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
7

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 
8

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 onehanded 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.’’
9

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. 
10

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 men-
11

tioned 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. 
12

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. 
13

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. 
14

[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-ownerconsumer-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 
15

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 descrip-
16

tion, 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, 
17

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. 
18

1To 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. 

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

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 
19

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. 

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

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, 
20

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 
21

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). 4The 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. 5See 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. 

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. 

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

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

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 pre-
22

6For 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. 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. 8 I participated myself in one significant effort of that general kind (Phelps 1970). 

sented 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

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 longmaintained 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.’’

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. 

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 some-
23

times suggested.) Behavioral understanding has been furthered by experimental economics and by the work of the small band of researchers following the recentlyopened 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 
24

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’’). 

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

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 theoryblinded) 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 
25

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.
26

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.
27

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 
28

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 
29

1Miller, J. and S. Page (2008) Complex Adaptive Systems: An Introduction to Computational 

Models of Social Life, Princeton University Press. 

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. 

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 sys-
30

2See Bednar (2009) The Robust Federation, Cambridge University Press. 3 Aristotle, Politics (trans. Benjamin Jowett), Book Three, Part XV, available at http://

classics.mit.edu/Aristotle/politics.3.three.html 4See Page, S (2007) The Difference: How the Power of Diversity Creates Better Groups, Firms, 

Schools, and Societies, Princeton University Press. 

tems 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

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

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. 

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. 
31

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. 6Farmer, D and D. Foley (2009) ‘‘The Economy Needs Agent Based Modelling’’ Nature 460: 

685 686/

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

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

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 re-
32

sponses 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-ofthe-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 
33

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 
34

1The views expressed herein are those of the author and not necessarily those of the Federal 

Reserve Bank of Minneapolis or the Federal Reserve System. 

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

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. 
35

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 de-
36

regulating 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.
37

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. 
38

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-wherethe-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. 
39

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 
40

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 
41

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.) 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) 

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. 

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. 

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 
42

3Gerard 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. 

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

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 
43

4For 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. 5Keynes 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.’’

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) 

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. 

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 agentbased 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 econ-
44

omy 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.
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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

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Opponents Speak Out on the Current Controversy in Macroeconomics, Lanham, 

Maryland. Rowman and Littlefield Publishers.

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(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 

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172–178.

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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 
46

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 
47

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. 
48

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 diver-
49

1For 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.’’

sity 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.

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 halfcentury, 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 
50

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 Represent-
51

ative 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. 
52

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, al-
53

2For 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.’’

though 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.

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 
54

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 inter-
55

esting 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. 
56

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 
57

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 
58

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. 
59

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 cliche ́s are right; and I think that costbenefit 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 
60

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 naı ̈vely 

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 
61

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 
62

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-
63

3For the purpose of clarification, Dr. Colander has requested that the word ‘‘macroeconomists’’ 

in this sentence be corrected to read ‘‘macroeconomic theorists.’’

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, 
64

‘‘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. 

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 
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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 
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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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