I am delighted to be able to present this lecture before so many people. I’m also very happy when I get to work with models inhabited by many people. That is the key to the framework for which Ed Prescott and I were cited by the Nobel Committee: Individuals are introduced explicitly in the models. Their decision problems are fully dynamic—they are forward looking. That is one of the prerequisites for what we ultimately seek, which is a framework we can use to evaluate economic policy. The eminent researcher and 1995 Nobel laureate in economics, Bob Lucas, from whom I have learned a great deal, wrote: “One of the functions of theoretical economics is to provide fully articulated, artificial economic systems that can serve as laboratories in which policies that would be prohibitively expensive to experiment with in actual economies can be tested out at much lower cost ... (Lucas, 1980, p. 696). Our task, as I see it ... is to write a FORTRAN program that will accept specific economic policy rules as ‘input’ and will generate as ‘output’ statistics describing the operating characteristics of time series we care about, which are predicted to result from these policies” (pp. 709–10). The desired environments to which Lucas refers would make use of information on “individual responses [that] can be documented relatively cheaply ... by means of ... censuses, panels [and] other surveys ...” (p. 710). Lucas seems to suggest that economic researchers place people in desired model environments and record how they behave under alternative policy rules. In practice, that is easier said than done. The key tool macroeconomists use is the computational experiment. With its help, the researcher performs exactly what I just described—places the model’s people in the desired environment and records their behavior. But the purpose of the computational experiment is broader than only to evaluate policy rules. The computational experiment is useful for answering a host of quantitative questions, that is, those for which we seek numerical answers. When evaluating government policy, the policy is stated in the form of a rule that specifies how the government will behave—what action to take under various contingencies—today and in the indefinite future. That is one reason it would be so difficult and prohibitively expensive to perform the alternative Lucas mentions, namely, to test the policies in actual economies.

American Economic Association Universal Academic Questionnaire Summary Statistics by Charles E. Scott and John J. Siegfried. Published in volume 96, issue 2, pages 527-530 of American Economic Review, May 2006

This paper uses data from a randomized social experiment in Mexico to estimate and validate a dynamic behavioral model of parental decisions about fertility and child schooling, to evaluate the effects of the PROGRESA school subsidy program, and to perform a variety of counterfactual experiments of policy alternatives. Our method of validation estimates the model without using post-program data and then compares the model’s predictions about program impacts to the experimental impact estimates. The results show that the model’s predicted program impacts track the experimental results. Our analysis of counterfactual policies reveals an alternative subsidy schedule that would induce a greater impact on average school attainment at similar cost to the existing program.

Social Value of Public Information: Comment: Morris and Shin (2002) Is Actually Pro-Transparency, Not Con by Lars E. O. Svensson. Published in volume 96, issue 1, pages 448-452 of American Economic Review, March 2006

Helping Infant Economies Grow: Foundations of Trade Policies for Developing Countries by Bruce Greenwald and Joseph E. Stiglitz. Published in volume 96, issue 2, pages 141-146 of American Economic Review, May 2006

The Impact of State Licensing Regulations on Low-Skilled Immigrants: The Case of Vietnamese Manicurists by Maya N. Federman, David E. Harrington and Kathy J. Krynski. Published in volume 96, issue 2, pages 237-241 of American Economic Review, May 2006

In recent decades, U.S. foreign trade grew much faster than GDP, but there is no consensus why. Notably lacking is an understanding of the role of multinational corporations (MNCs), which mediate over half of world trade. We use Bureau of Economic Analysis data on U.S. MNCs to study the rapid growth of MNC-based trade from 1983 to 1996. Using a model of U.S. MNCs and Canadian affiliates, we decompose this growth by source. Tariff reductions can largely explain increases in arms-length MNC-based trade. But intra-firm trade growth is attributed mostly to “technical change.” We present additional evidence suggesting just-in-time production facilitated intra-firm trade.

In an experiment, players' ability to learn to cooperate in the repeated prisoner's dilemma was substantially diminished when the payoffs were noisy, even though players could monitor one another's past actions perfectly. In contrast, in one-time play against a succession of opponents, noisy payoffs increased cooperation, by slowing the rate at which cooperation decays. These observations are consistent with the robust observation from the psychology literature that partial reinforcement (adding randomness to the link between an action and its consequences while holding expected payoffs constant) slows learning. This effect is magnified in the repeated game: when others are slow to learn to cooperate, the benefits of cooperation are reduced, which further hampers cooperation. These results show that a small change in the payoff environment, which changes the speed of individual learning, can have a large effect on collective behavior. And they show that there may be interesting comparative dynamics that can be derived from careful attention to the fact that at least some economic behavior is learned from experience.

Inequality Aversion, Efficiency, and Maximin Preferences in Simple Distribution Experiments: Comment by Gary E. Bolton and Axel Ockenfels. Published in volume 96, issue 5, pages 1906-1911 of American Economic Review, December 2006

The recent explosion of patenting and patent disputes has sparked a growing literature on the economics of patent litigation. Generally, models in this literature take the existence of a dispute as given. This assumption is troubling because it hampers the interpretation of empirical studies of patent litigation and the assessment of many patent policy reforms. Disputes would not arise if all technology adopters obtained ex ante licenses from patent owners. This suggests that two stories could explain the origin of patent disputes. In one, the technology adopter observes a patented technology, but chooses to imitate, “inventing around” and/or hiding the infringement. In the other, the adopter develops his own technology and is unaware of another firm’s putative patent rights. This kind of innocent infringement occurs because patent rights often have uncertain boundaries or questionable validity. In addition, the sheer number of patents facing a typical innovator makes careful assessment quite burdensome. Furthermore, patent claims are often hidden (sometimes strategically) until after firms have made technology investments. These two accounts suggest that a model of disputes should consider the decisions of patent owners to invent, to patent, and to monitor use of the patented technology by others; and the decisions of potential infringers to monitor extant patents, and develop and adopt new technology. Claude Crampes and Corinne Langinier (2002) endogenize disputes by focusing on a patent owner’s monitoring activity and imitative behavior by potential infringers. Our model includes this behavior, but it also includes defendants who “invent around” a patent, and defendants who are unaware of the patented technology. We find that this richer model generates testable implications that better match empirical evidence on patent litigation.