Volume 2, Issue 2, April 2001
The Research Agenda: Dynamic Models of Crime and Punishment, by Antonio Merlo
An important phenomenon of the last decade has been the sharp and steady
decline in crime. In the United States, the crime rate per 100 inhabitants
was equal to 5.95 in 1980 and dropped to 5.09 in 1996. While this general
trend has been observed for most categories of crime, the most noticeable
decline has been observed for property crimes (that is, burglary, larceny,
robbery, and motor vehicle theft), which account for over 90% of all
crimes. The property crime rate per 100 inhabitants in the United States
went down 17% from 5.60 in 1980 to 4.65 in 1996.
Antonio Merlo is the Lawrence R. Klein Associate Professor of Economics at
the University of Pennsylvania and is Director of the Penn Institute for
Economic Research. His field is political economy, in particular
bargaining, political stability, and crime. Merlo's RePEc/IDEAS
What accounts for this decline? Both the popular press and the academic
literature have been searching for answers to this important question (See
for example the article "Crime in America: Defeating the bad guys" in The
Economist of October 3, 1998 and the collection of articles in the 1998
Summer issue of the Journal of Criminal Law and Criminology). Several main
factors have been identified as possible explanations for this phenomenon.
The first is related to demographics. It is well documented that most
crimes are committed by youths. Their fraction in the population has being
declining in the 1990s. For instance, the fraction of people between the
ages of 15 and 25 was 20.5% in 1980 and went down to 15.1% in 1996.
Another key factor is related to law enforcement. Expenditures on police
protection have increased from 0.6% of GDP in 1980 to 0.7% of GDP in 1996.
Also, many initiatives to change the "style of policing" have been
implemented in many U.S. cities. As a result, the clearance rate (i.e.,
the fraction of crimes cleared by arrest) has been increasing. For
example, in 1980 the clearance rate for property crimes was equal to 16.8.
In 1996, it increased to 18.5. At the same time, the "severity" of
punishment has remained pretty much constant. For example, the expected
punishment for property crimes (measured by the average length of prison
sentences multiplied by the fraction of offenders sentenced to prison) was
equal to 12.5 and 12.3 months in 1980 and 1995, respectively.
There are also other important phenomena that have been taking place in
the 1990s that must be taken into consideration when trying to account for
what is happening to crime. In particular, changes in the structure of
earnings, employment opportunities, and the skill composition of the work
force are likely to be intimately related to changes in the level of
criminal activity. The following observations all seem to point to a
reduction in crime. Real earnings have been increasing. Average real
earnings increased by approximately 10% between 1980 and 1996. At the same
time, aggregate unemployment has been decreasing and so has the fraction
of unskilled individuals in the labor force. For example, the fraction of
individuals in the labor force with less than a high school degree has
declined substantially between 1980 and 1996.
Other observations, however, point in the direction of an increase in
crime. Income inequality has been increasing. By virtually any measure,
the distribution of real earnings has become substantially more unequal
over the past twenty years. In addition, youth unemployment has been
increasing. For example, the unemployment rate for people between the ages
of 15 and 19 was equal to 17.1 in 1980 and rose to 17.8 in 1996.
These observations raise important questions. First, are these factors
sufficient to explain the observed decline in property crime evidenced
between 1980 and 1996? Second, what is the quantitative effect of each one
of these factors on property crime? Third, what is the relation between
individual economic opportunities, public policies, and property crime?
Providing answers to these questions is one of the main goals of my
research agenda, conducted in collaboration with Ayse Imrohoroglu
(University of Southern California) and Peter Rupert (Federal Reserve Bank
of Cleveland). The emphasis on property crime is justified by the fact
that unlike violent crimes, property crimes are typically motivated by the
prospect of direct pecuniary gain. Economic considerations are therefore
most likely to guide individual decisions of engaging in this type of
The main ideas presented here come from a working paper, "What Accounts
for the Decline in Crime?" (Imrohoroglu, Merlo, and Rupert (2001)). Some
of the ideas are also drawn from an article recently published in the
International Economic Review entitled "On the Political Economy of Income
Redistribution and Crime" (Imrohoroglu, Merlo, and Rupert (2000)).
To guide our quantitative investigation of the major determinants of
observed patterns of property crime, we specify a dynamic equilibrium
model with heterogeneous agents. The agents in our model differ ex ante
with respect to their income earning abilities. In each period of their
finite life, agents receive a stochastic employment opportunity. After
knowing their employment status, they decide how much to save and whether
to engage in criminal activities in that period. Criminal activities
amount to stealing from other agents in the economy. If agents choose to
commit a crime, they may be apprehended and punished.
There is a long tradition of economic models of crime initiated by Becker
(1968), see for example Harris (1970), Stigler (1970), Ehrlich (1973), and
Polinsky and Shavell (1984). Our model shares many of the features of
existing models and embeds Becker's paradigm in a dynamic equilibrium
framework. The dynamic nature of our model allows us to investigate
individual decisions to engage in criminal activities over the life cycle.
The equilibrium aspect of our model allows us to investigate the response
of the aggregate crime rate to a variety of factors. We calibrate our
model using U.S. data for 1980 so as to reproduce the observed property
crime rate. We then use 1996 data to evaluate the effect of changes in
demographics, police activities, the distribution of wages, employment
opportunities, and the skill composition of the work force on crime.
Our main findings can be summarized as follows. First, the model is
capable of reproducing the drop in crime between 1980 and 1996. In
particular, the combined effect of the changes in unemployment rates,
earnings profiles, age distribution of the population, shares by human
capital type, and the ability of the police to capture criminals that have
occurred between 1980 and 1996 can account for about 90% of the observed
decline in property crime.
Second, the most important factors that account for the observed decline
in property crime are (in order of importance): the higher apprehension
probability, the stronger economy, and the aging of the population. In
particular, the higher apprehension probability alone would have amounted
to a 43% decrease in the crime rate, the higher income to a 20% decrease,
and the smaller fraction of youth in the population to a 11% decrease.
Third, the effect of unemployment on crime is negligible. This finding is
mostly due to the following two factors. First, even though the overall
unemployment rate is lower in 1996 as opposed to 1980, youth unemployment
rates were actually higher in 1996. Second, the overwhelming majority of
criminals in our economy are employed.
Fourth, the increased inequality prevented an even larger decline in
property crime. In fact, holding everything else constant, the increase in
income inequality between 1980 and 1996 would have caused a 59% increase
in property crime. This result is due to the fact that when income
inequality increases relatively more people find it profitable to engage
in criminal activities.
These results indicate that the two most important determinants of the
crime rate are the apprehension probability and income inequality. The
higher apprehension probability lowers the crime rate by 43% and the
higher income inequality increases the crime rate by 59%. The relative
magnitude of these opposing effects plays a very important role in the
resulting crime rate.
The satisfactory performance of the model in accounting for the drop in
crime observed between 1980 and 1996 raises an obvious question. Can the
model successfully account for the behavior of the time series of property
crime rates over a longer time period? Over the past quarter century the
property crime rate in the United States has displayed some interesting
patterns. In fact, the decline during the 1990s is only one of the
interesting features of this time series. Property crime peaked in 1980,
fell sharply during the first half of the 1980s, rose again during the
second half of the 1980s (although not back to its 1980 level), and is
currently at its lowest level in a quarter of a century. Can our analysis
also account for these patterns?
The experiments we perform to answer this question can be described as
follows. Take the calibrated model (which generates a crime rate equal to
the one observed in 1980), and input data relative to unemployment rates,
earnings profiles, age distribution of the population, shares by human
capital type, the ability of the police to capture criminals, and the
length of the prison term for a different year. For 1975, 1985, 1990 and
1996, compute the steady-state equilibrium of the model and compare the
crime rate generated by the model to the one in the data.
The results we obtain indicate that the factors identified in our analysis
as the main determinants of aggregate property crime rates can account for
the behavior of the time series of property crime rates between 1975 and
1996. In particular, not only can our analysis qualitatively account for
the increase in property crime rates in the 1970s, the drop observed in
the first half of the 1980s, the subsequent rise in the later part of the
decade and the sharp decline in the 1990s, but it can also reproduce the
quantitative changes in the time series.
So far, we have focused attention on the aggregate predictions of the
model. The model, however, can also generate implications with respect to
individual behavior and, in particular, the composition of the criminal
population. Focusing attention on the properties of the benchmark economy
calibrated to 1980, our model predicts that about 79% of the people
engaging in criminal activities are employed. This implies that
approximately 5% (16%) of the employed (unemployed) population engages in
criminal activities. This (perhaps surprising) implication of the model is
consistent with the data. According to the Bureau of Justice Statistics,
in 1979, 71% of all state prisoners were employed prior to their
conviction. Studies by Grogger (1998) and Witte and Tauchen (1994) that
use other data sets provide further evidence in support of this finding.
Next, we turn our attention to the composition of the criminal population
by age and educational attainment. Our model predicts that about 76% of
the people who commit property crimes are 18 years of age or younger.
According to the Federal Bureau of Investigation, in 1980, 47.7% of all
people arrested for property offenses were 18 years of age or younger.
While the figure in the data is much lower than the one generated by the
model, juvenile property offenders are often released without being
formally arrested and charged of a crime. Nevertheless, we believe the
model may overstate the amount of juvenile delinquency. Furthermore, the
model predicted fraction of criminals without a high school diploma is
equal to 46.1%. In 1979, 52.7% of the correctional population in state
prisons did not have a high school diploma. Hence, the model seems to be
capable of reproducing certain dimensions of the socio-demographic
composition of the criminal population fairly well.
Our model also has implications on the amount of recidivism present in the
economy. In our benchmark economy, 40% of all criminals had a prior
conviction. This percentage is lower than the one in the data. According
to the Bureau of Justice Statistics, in 1979, 61% of those admitted to
state prisons were recidivists.
Hence, a possible limitation of our model is that it may overstate the
amount of juvenile delinquency and understate the amount of recidivism
present in the economy. In our model described above, if agents choose to
commit a crime they may be apprehended and punished. The extent of
punishment amounts to a prison term. However, in reality, convicted
criminals may also be "stigmatized." That is, after a conviction,
individuals may face lower wages than if they had not been convicted. This
additional component of punishment is not legislated but occurs as a
societal outcome that stigmatizes the ex-prisoner. This stigma may force
the individual onto an earnings path that is lower than their
Several empirical studies have analyzed the effect of this type of stigma.
Waldfogel (1994) shows the decline in earnings to be roughly 10% and quite
persistent, taking eight years to get halfway back to pre-conviction
levels. Allgood, Mustard and Warren (1999) find a decline of 12% and that
effect did not disappear for the six years following release. Grogger
(1995) and Kling (1999), on the other hand, find only a small decline that
is quite temporary. Grogger (1995) finds a drop of only 4% lasting just
six quarters. Kling (1999) finds an even smaller effect when looking at
street criminals, but a larger effect when considering white-collar crime.
We model "stigma" as a permanent 2% reduction in wages following an
incarceration. Compared to our benchmark economy without stigma, the
presence of stigma induces a lower amount of juvenile delinquency (59.9
versus 76.1) and a higher amount of recidivism (75.0 versus 40.0) in the
economy. These two effects are obviously related. Holding the aggregate
crime rate constant, in an economy with relatively more recidivism
relatively more crimes are committed by older people (the recidivists).
The intuition for why stigma is associated with higher recidivism and
lower juvenile delinquency is rather subtle and interesting. By
essentially increasing the "severity" of punishment, stigma discourages
the involvement in criminal activities. The more persistent the effect of
stigma, the more severe is the relative increase in punishment for a young
individual relative to an older individual. Hence, ceteris paribus, the
presence of stigma discourages juvenile delinquency relatively more. In
addition, stigma has a direct effect on recidivism. By reducing
post-conviction wages, stigma reduces the opportunity cost of engaging in
criminal activities for individuals with a criminal record. This effect
Recall that in 1980, 47.7% of all people arrested for property offenses
were 18 years of age or younger. Moreover, the recidivism rate among state
prisoners in 1979 was equal to 61%. Thus, introducing stigma into the
analysis improves the overall ability of the model to match salient
features of the data.
To conclude, the results presented suggest that our analysis has
identified some key factors to help further our understanding of the
complex phenomenon of crime. At the same time, however, they clearly
display the limitations of our current analysis and help us identify
future avenues of research. In particular, a richer model is needed to
confront the micro evidence on participation rates in criminal activities
by different age and population groups identified by a variety of
demographic characteristics. Preliminary attempts to incorporate learning
and group-specific, history-dependent apprehension probabilities in our
model produced encouraging results. For example, incorporating into the
model learning-by-doing in criminal activities (i.e., the more an
individual engages in criminal activities the higher his returns from
these activities), not only produces results that are similar to the ones
induced by stigma (i.e., lower juvenile delinquency and higher recidivism
than in the baseline model), but can also account for heterogeneity in
participation rates by population groups. The increased flexibility,
however, comes with the difficult challenge of collecting the necessary
data to calibrate the additional components of the model.
Allgood, S., Mustard, D.B. and Warren, R.S. 1999. "The Impact of Youth
Criminal Behaviour on Adult Earnings." Manuscript, University of Georgia.
Becker, G. S. 1968. "Crime and Punishment: An Economic Approach."
Journal of Political Economy
, 78, 169-217.
Ehrlich, I. 1973. "Participation
in Illegitimate Activities: A
Theioretical and Empirical Investigation
." Journal of Political
, 81, 521-565.
Grogger, J. 1995. "The
Effects of Arrests on the Employment and Earnings
of Young Men
." Quarterly Journal of Economics
, 110, 51-71.
Grogger, J. 1998. "Market
Wages and Youth Crime
." Journal of Labor
, 16, 756-791.
Harris, J. R. 1970. "On
the Economics of Law and Order
." Journal of
, 78, 165-174.
Imrohoroglu, A, Merlo, A. and Rupert, P. 2000. "On the
of Income Redistribution and Crime
." International Economic
Imrohoroglu, A, Merlo, A. and Rupert, P. 2001. "What Accounts
the Decline in Crime?
." Penn Institute for Economic Research working
Kling, J. R. 1999 "The
Effect of Prison Sentence Length on the Subsequent
Employment and Earnings of Criminal Defendants
." Discussion Paper 208,
Woodrow Wilson School, Princeton University.
Polinsky, A. M. and Shavell, S. 1984. "The Optimal
of Fines and Punishment
." Journal of Public Economics
Stigler, G. J. 1970. "The
Optimum Enforcement of Laws
." Journal of
, 78, 526-536.
Waldfogel, J. 1994. "Does Conviction Have a Persistent Effect on Income
and Employment?" International Review of Law and Economics
Witte, A.D. amd Tauchen, H. 1994. "Work and
An Exploration Using Panel Data
." Public Finance
, 49, 155-167.
Interviews Harald Uhlig on Dynamic Contracts
Harald Uhlig is Professor of Economics at the Institute for Economic
Policy I at Humboldt University (Berlin, Germany). His interests lie
broadly in macroeconomics, with a focus on banking, business cycles and
numerical methods, among many others. Uhlig's RePEc/IDEAS
- EconomicDynamics: In your work on financial institutions, you show that
can drive themselves into ruin if unchecked. How can this be considering
that they are rational?
Harald Uhlig: Competition between financial intermediaries is indeed
interests me quite a bit, in particular regarding its consequences for the
functioning of the aggregate economy. And indeed, this type of competition
can lead to a "financial collapse", where no lending takes place in the
end. I have one paper with Hans Gersbach, the framework of which I also
used in an earlier paper of mine on "Transition and financial collapse"
and a somewhat related paper with Dirk Krueger. In the financial collapse
story, there are entrepreneurs seeking funding from banks, but who differ
in their qualities. Banks compete in the contracts they offer. Two forces
are at work. On the one hand, banks need to cross-subsidize the losses
they make on bad entrepreneurs with the profits they make on good ones. On
the other hand, competition means that other banks will try to lure away
the good entrepreneurs with better contracts, so that not too much profit
can be made on them. Under some circumstances, there is no equilibrium
where the banks break even, and the credit market collapses.
Likewise, markets in insurance contracts between competing insurers can
collapse in my paper with Dirk Krueger. It is rather similar to the
"market of lemons" phenomenon, which Akerlof described much earlier,
although my paper with Dirk Krueger does not even rely on asymmetric
information. These collapses can be the byproduct of rational agents
trading with each other. In the end, banks make neither profits nor
losses. What I do not model is the entry in that industry, but one could.
Now, if there is a sunk cost in entering the market, and banks could
foresee that they would make neither profits nor losses, once they do,
it would obviously be irrational to enter in the first place.
ED: What is your take on the current remodeling of the Basle accord, in
particular regarding the proposed self-assessment by banks of their
capital requirements? Is there a moral hazard problem looming?
HU: I should not comment too much on the Basle accord: there are much
greater experts than me. The remodeling has certainly become necessary:
all exchange rate crises of recent history are typically also banking
crises. Many think that much harm could have been avoided if the financial
institutions in these countries had been subject to more stringent
standards in the first place: that sounds right to me although there may
be important tradeoffs here. As for the self-assessment by banks: that may
be perfectly OK. Actually, all our theories of optimal mechanism design
rely on the "revelation principle" which essentially says, that one might
as well restrict attention on mechanisms in which participants truthfully
reveal their situation. Truthful revelation does not happen out of the
goodness of the heart, of course: instead, truthful revelation needs to be
incentive-compatible, it needs to be in the interest of the agent who does
the revealing, otherwise there is indeed a moral hazard issue. So that
should be the crucial issue: are the revelation rules in the new Basle
accord incentive compatible? Would a bank in trouble have enough
incentives to say that they are in trouble? Reputational considerations
surely matter here a lot, and it may be hard but also very interesting to
sort this all out.
ED: A smooth financial environment is generally credited for spurning
growth. But can the setup of financial institutions also influence the
business cycle, except for occasional credit crunches?
HU: In my paper on "transition and financial collapse", it actually is
possible that cycles arise precisely because of the presence of financial
intermediaries - more precisely, because of asymmetric information which
the financial intermediaries are there to solve. The story goes roughly as
follows. There are young entrepreneurs, who run small projects, and
middle-aged entrepreneurs, who can take small successful projects, which
they ran as a young entrepreneur previously, and turn them into large
successful projects. Imagine now that the current young generation of
entrepreneurs is flush with cash, but the previous young generation
was not. While the current young then find it easy to obtain additional
funding to finance their projects and therefore create lots of successful
ones, the current middle generation only has very few projects which they
can continue. As a result, the economy will not do too well currently,
wage earnings will be low, and the next generation of young entrepreneurs
will not have the chance to build up sufficient cash to run many projects.
But the currently young cash-flush entrepreneurs with their many projects
will create a booming economy next period, endowing the young
entrepreneurial generation two periods from now with sufficient wage
earnings to successfully start many projects.
One can imagine versions with more generations here, and an intriguing web
of interactions. I find it plausible that this mechanism actually does
play an important role in business cycle fluctuations. One could probably
tell this story without financial intermediation, but with asymmetric
information and thus financial intermediation, small effects of this type
can be vastly amplified. And that, I think, is the major key from this
literature for understanding business cycles.
ED: Why is it important to model contract relationships within a dynamic
general equilibrium framework?
HU: Putting contract relationships into a dynamic general equilibrium
framework imposes an enormous discipline, and that is why it has been so
hard to do. For example, aggregate information cannot be "hidden": in a
general equilibrium framework, there are typically many ways for agents to
observe it in some aggregate variable. Next, a static view of a credit
relationship can allow you to assume that agents or intermediaries will be
in utter misery in some states of the world, you can model payoffs pretty
much anyway you like. In a dynamic general equilibrium framework, agents
may try to intertemporally smooth consumption and circumvent some of the
forces imposed upon them in a two-period static partial equilibrium model.
The model needs to have some stability properties to work, so that means
that returns etc cannot be too crazy. A dynamic general equilibrium
framework makes it possible to meaningfully talk about monetary policy,
and how it interacts with these issues: understanding that interaction
seems to me to be crucial for understanding how and where monetary policy
can have an effect. Finally, a dynamic general equilibrium framework
allows one to investigate the quantitative aspects of the whole issue, to
see whether the numbers come out about right. That is not done often in
this literature, here it is still a wide-open research field.
ED: Why are contracts with limited commitment so little studied with
dynamic general equilibrium models?
HU: The literature is still evolving, so I think we will see more of that
kind of research in the future. It is certainly one of the interesting
frontiers. Now, in some ways, the models in the literature are already set
in a dynamic general equilibrium framework, but the real test would be to
also allow for aggregate uncertainty. This is generally hard to do because
contracts allow to condition on all available information at some point in
time. So part of the game in the literature is to keep that information
down to a minimum, e.g. one or two agent-specific state variables. Once
aggregate information is allowed, the contracts could become a lot more
complicated, and the models much harder to keep track of. Then one either
needs heavy numerical tools or one needs to find clever tricks to keep
things manageable. I think Andrew Atkeson and Pat Kehoe, for example, have
successfully used such a strategy to explain the volatility of exchange
rates: their trick has been to rig things so that there is no trade in
equilibrium. Also, Fernando Alvarez and Urban Jermann and more recently
Hanno Lustig are using limited commitment contracts to explain asset
pricing facts, so they use some form of aggregate uncertainty as well. But
all this is still at the level of tailoring things to a specific issue and
keeping things that happen at the aggregate level very much under control.
It would be nice if we had an elegant way of putting these things into
standard stochastic dynamic general equilibrium models routinely and would
be able to meaningfully address important issues that way. So some clever
people have to come up with a way to make that happen. If not, the
frontier will probably move someplace else.
Alvarez, F. and Jermann, U. 1999. "Quantitative
Asset Pricing Implications of Endogenous Solvency Constraints
working paper 6952.
Alvarez, F., Atkeson, A. and Kehoe, P. 2000. "Money,
interest rates, and exchange rates with endogenously segmented
", Minneapolis Fed Staff Report 278.
Gersbach, H. and Uhlig H. 1999. "On the
Coexistence Problems of Financial Institutions
Gersbach, H. and Uhlig H. 1999. "Financial
Institutions and Business Cycles
Gersbach, H. and Uhlig H. 1998. "Debt
Contracts, Collapse and Regulation as Competition Phenomena
Center for Economic Research working paper.
Krueger, D. and Uhlig, H. 2000. "Competitive
Risk-Sharing Contracts with One-Sided Commitment
." Mimeo, Stanford
Lustig, H. 2000. "Understanding Endogenous Borrowing Constraints and Asset
Prices." Mimeo, Stanford University.
Uhlig, H. 1995. "Transition
and Financial Collapse
." Tilburg Center for Economic Research working
Review of Economic Dynamics:
News from the Editor
The official journal of the Society for Economic Dynamics is now in its
fourth year of publication and we are delighted about its progress. If you
are not currently subscribing to the journal, you should take a look at the
table of contents from recent issues to see what you have been missing
We are also planning some exciting
special issues that are forthcoming over the next year or so. These include
an issue on "Great Depressions" edited by Ed Prescott and Tim
an issue on "Families, Inequality and Growth," edited by Raquel Fernández,
Greenwood and Victor
Rios-Rull, and an issue on "Productivity Growth:
A New Era?" edited by Boyan Jovanovic. Also, there have been some recent
changes to the Editorial Board that I would like to tell you about.
First, however, I want to urge all of you to submit your work to
particular, I hope that those of you who have participated in the Society's
conferences in the past, as well as those who will be attending the 2001
Meetings in Sweden, will seriously consider RED as a publication outlet
for the work you are presenting at these meetings. One motivation for
establishing the Review of Economic Dynamics was to provide such an outlet,
so I hope everyone will take advantage of this opportunity.
We have recently created a new Editorial Advisory Board that consists of
three past presidents of the Society and past editors of the journal: Dale
T. Mortensen, Edward C. Prescott, and Thomas J. Sargent. These individuals
have been and continue to be extremely important to the Society by providing
leadership and helping to establish this journal. The Review of Economic
Dynamics is very fortunate that they have agreed to continue to provide
leadership over the coming years. The advisory board members will not be
handling the review process for submitted manuscripts, but will be involved
in helping choose the editors who will be. They will be routinely consulted
on whatever policy issues that may come up regarding the journal.
Next, I want to announce two new Editors of the journal, Michele
(University of Minnesota) and Richard
Rogerson (University of Pennsylvania).
Both have been Associate Editors since the beginning, and have now agreed to
take on an expanded role. In addition, we welcome Raquel Fernández (New
York University), Lars Ljungqvist (Stockholm School of Economics), and
Merlo (University of Pennsylvania), who have recently joined the
journal as Associate Editors.
I also want to acknowledge those who have recently stepped down as editors
for their service to the journal. These include Ramon Marimon, who has
taken a position with the Ministry of Science and Technology in Spain, and
Edward Prescott, who has
switched from being an Editor to being a member of
the Editorial Advisory Board. In addition, Fernando Alvarez has finished
his term as Associate Editor. These individuals have all worked hard on the
behalf of RED, and the journal has benefited greatly from their efforts.
Review of Economic Dynamics
Review: Evans & Honkapohja's Learning and Expectations in Macroeconomics
Much of modern macroeconomics relies on the rational expectations (RE)
hypothesis, but such theories are often silent on whether the equilibria
are actually attainable by people who do not initially have RE. This
books sets out conditions on the learnability of the equilibrium. This has
important implications. First, a RE equilibrium is moot if it cannot be
learned. Second, learnability may help select among multiple equilibria.
Third, learning dynamics themselves may be of interest, for example after
major economic events or policy shifts. In fact, learning dynamics can
lead to endogenous cycles quite similar to those in the data.
Evans and Honkapohja emphasize econometric learning, that is agents use
versions of recursive least squares to update their beliefs about the
structure of the economy. Actual aggregate outcomes are in turn influenced
by such beliefs. They provide conditions under which a given RE
equilibrium is learnable. The first four chapters of the book offer a
non-technical overview that is quite useful for just curious about
macroeconomic learning. The following chapters are more rigorous and
discuss many variations of econometric learning, such as learning
with misspecified models, sunspots, multivariate and nonlinear models.
A more detailed review by James
Bullard is available at http://www.stls.frb.org/research/econ/bullard/.
Evans, C. W., and Honkapohja, S. 2001 "Learning and Expectations in
Macroeconomics", Princeton University Press.
Links: Agent-based computational economics (ACE)
Managed by Leigh
Tesfatsion at Iowa State University, the ACE web site
offers an extensive set of resources for researchers on this emerging
field of computational economics. ACE, in a nutshell, studies model
economies with evolving, interacting agents and tries to explain how such
complex, decentralized systems can replicate various features of modern
economies: fiat money, cycles, technological innovation, trade networks.
Then, the impact of various socio-economic structures on individual
behavior and social welfare can be studied. For example, how do price
dispersion and buyer loyalty emerge? What is the impact of competing
currencies? What imply different auction rules on oligopolistic markets?
This is a very computationally intensive field that really emerged with
object-oriented programming. Like Dynamic General Equilibrium theory,
tools appear to be an important prerequisite for any research. The ACE
web site offer lots of such tools, along with various surveys and
announcements. But even people outside of the narrow ACE field should find
lots of interesting material, be it in game theory, industrial
organization, money search, political economy, finance and others.
The ACE web site is at http://www.econ.iastate.edu/tesfatsi/ace.htm
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