Knowledge that Transforms

Why is prosperity distributed so unevenly across America's metropolitan areas? While population growth has gone disproportionately towards the Sunbelt, high-skill areas have experienced the strongest income growth since 1970. Gaps between more and less educated areas were modest forty years ago, but they have become quite large, and far larger than would be predicted solely by the general rise in the returns to skill. Unemployment rates during the recent recession were also strongly correlated with area level education. This essay reviews Enrico Moretti's The New Geography of Jobs, which both describes and explains these significant regional trends.

This essay will discuss the criticisms of the economic approach to markets offered by Michael Sandel's What Money Can't Buy. After reviewing the main arguments, the essay looks at these from three main angles. First, it relates them to different traditions of thinking about markets and their achievements that have been developed by economists. Second, it discusses the idea that markets can change values as argued by Sandel in light of recent related literature in economics. Third, it discusses some of the literature on alternatives to using the market to allocate resources and the pros and cons of these.

Neuroeconomics aims to discover mechanisms of economic decision, and express them mathematically, to predict observed choice. While the contents of neuroeconomic models and evidence are obviously different than in traditional economics, (some of the) goals are identical: to explain and predict choice, the effects of comparative statics, and perhaps make interesting new welfare judgments that are defensible. To this end, Paul Glimcher's important book carefully describes how economics, psychological, and neural levels of explanation can be linked (a structure which has been successful in visual neuroscience). As Glimcher shows, the neural evidence is quite strong for a process of learning valuations through prediction error, and a simple model of neural valuation and comparison that corresponds to random utility (though subject to normalization, which produces menu effects). There is also rapidly growing evidence for more complicated constructs in behavioral economics, including prospect theory's account of risky choice, hyperbolic time discounting, level-k models of games, and social preferences corresponding to internal reward based on what happens to other agents.

Harstad and Selten's article in this forum performs a valuable service by highlighting the dominance of optimization-based models over boundedly rational models in modern microeconomics, and questioning whether optimization-based models are a better way forward than boundedly rational models. This article complements Rabin's response to Harstad and Selten, focusing on modeling strategic behavior. I consider Harstad and Selten's examples and proposed boundedly rational models in the light of modern behavioral economics and behavioral game theory, commenting on the challenges that remain and the most promising ways forward.

Academic economists appear to be intensely interested in rankings of journals, institutions, and individuals. Yet there is little discussion of the uncertainty associated with these rankings. To illustrate the uncertainty associated with citations-based rankings, I compute the standard error of the impact factor for all economics journals with a five-year impact factor in the 2011 Journal Citations Report. I use these to derive confidence intervals for the impact factors as well as ranges of possible rank for a subset of thirty journals. I find that the impact factors of the top two journals are well defined and set these journals apart in a clearly defined group. An elite group of 9–11 mainstream journals can also be fairly reliably distinguished. The four bottom ranked journals are also fairly clearly set apart. For the remainder of the distribution, confidence intervals overlap and rankings are quite uncertain.

Transportation is a vital sector of the U.S. economy based on consumers', firms', and government's enormous expenditures in money and time and on its effect on virtually all other sectors in the economy. I assess the performance of the transportation system and consider how it could be improved by analyzing whether the United States has the optimal mix of public and private provision. The empirical evidence indicates that our hugely important transportation system has been compromised by various government policies and the significant welfare costs motivate either vastly improving public provision or expanding the role of the private sector.

Research in experimental economics has cogently challenged the fundamental precept of neoclassical economics that economic agents optimize. The last two decades have seen elaboration of boundedly rational models that try to move away from the optimization approach, in ways consistent with experimental findings. Nonetheless, the collection of alternative models has made little headway supplanting the dominant paradigm. We delineate key ways in which neoclassical microeconomics holds continuing and compelling advantages over bounded-rationality models, and suggest, via a few examples, the sorts of further, difficult pushes that would be needed to redress this state of affairs. Closer collaboration between theoretic modeling and experiments is clearly seen to be necessary.

The choice of an overall discount rate for climate change investments depends critically on how different components of investment payoffs are discounted at differing rates reflecting their underlying risk characteristics. Such underlying rates can vary enormously, from ≈ 1 percent for idiosyncratic diversifiable risk to ≈ 7 percent for systematic nondiversifiable risk. Which risk-adjusted rate is chosen can have a huge impact on cost-benefit analysis. In this expository paper, I attempt to set forth in accessible language with a simple linear model what I think are some of the basic issues involved in discounting climate risks. The paper introduces a new concept that may be relevant for climate-change discounting: the degree to which an investment hedges against the bad tail of catastrophic damages by insuring positive expected payoffs even under the worst circumstances. The prototype application is calculating the social cost of carbon.

Harstad and Selten (this forum) raise interesting questions about the relative promise of optimization models and bounded-rationality models in making progress in economics. This article builds from their analysis by indicating the potential for using neoclassical (broadly defined) optimization models to integrate insights from psychology on the limits to rationality into economics. I lay out an approach to making (imperfect and incremental) improvements over previous economic theory by incorporating greater realism while attempting to maintain the breadth of application, the precision of predictions, and the insights of neoclassical theory. I then discuss how many human limits to full rationality are, in fact, well understood in terms of optimization.

This paper provides a survey of business cycle facts, updated to take account of recent data. Emphasis is given to the Great Recession, which was unlike most other postwar recessions in the United States in being driven by deleveraging and financial market factors. We document how recessions with financial market origins are different from those driven by supply or monetary policy shocks. This helps explain why economic models and predictors that work well at some times do poorly at other times. We discuss challenges for forecasters and empirical researchers in light of the updated business cycle facts.