Economic Dynamics Newsletter

Volume 2, Issue 2 (April 2002)

The EconomicDynamics Newsletter is a free supplement to the Review of Economic Dynamics (RED). It is published twice a year in April and November.

In this issue

The Research Agenda: Antonio Merlo on Dynamic Models of Crime and Punishment

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 entry.

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.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 criminal activities.

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 pre-conviction path.

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 generates recidivism.

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 Economy, 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 Economics, 16, 756-791.
Harris, J. R. 1970. “On the Economics of Law and Order.” Journal of Political Economy, 78, 165-174.
Imrohoroglu, A, Merlo, A. and Rupert, P. 2000. “On the Political Economy of Income Redistribution and Crime.” International Economic Review, 41, 1-25.
Imrohoroglu, A, Merlo, A. and Rupert, P. 2001. “What Accounts for the Decline in Crime?.” Penn Institute for Economic Research working Paper 01-012.
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 Use of Fines and Punishment.” Journal of Public Economics, 24, 89-99.
Stigler, G. J. 1970. “The Optimum Enforcement of Laws.” Journal of Political Economy, 78, 526-536.
Waldfogel, J. 1994. “Does Conviction Have a Persistent Effect on Income and Employment?” International Review of Law and Economics, 14, 103-119.
Witte, A.D. amd Tauchen, H. 1994. “Work and Crime: An Exploration Using Panel Data.” Public Finance, 49, 155-167.

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 entry.
EconomicDynamics: In your work on financial institutions, you show that competing banks can drive themselves into ruin if unchecked. How can this be considering that they are rational?
Harald Uhlig: Competition between financial intermediaries is indeed something that 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“, NBER working paper 6952.
Alvarez, F., Atkeson, A. and Kehoe, P. 2000. “Money, interest rates, and exchange rates with endogenously segmented markets“, Minneapolis Fed Staff Report 278.
Gersbach, H. and Uhlig H. 1999. “On the Coexistence Problems of Financial Institutions.” Mimeo.
Gersbach, H. and Uhlig H. 1999. “Financial Institutions and Business Cycles.” Mimeo.
Gersbach, H. and Uhlig H. 1998. “Debt Contracts, Collapse and Regulation as Competition Phenomena.” Tilburg Center for Economic Research working paper.
Krueger, D. and Uhlig, H. 2000. “Competitive Risk-Sharing Contracts with One-Sided Commitment.” Mimeo, Stanford University.
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 paper.
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 Kehoe, an issue on “Families, Inequality and Growth,” edited by Raquel Fernández, Jeremy 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 RED. In 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 Boldrin (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 Antonio 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.

Gary Hansen
Coordinating Editor
Review of Economic Dynamics

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


Evans, C. W., and Honkapohja, S. 2001 “Learning and Expectations in Macroeconomics“, Princeton University Press.

EconomicDynamics 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