:The hedonic property value model is among our foremost tools for evaluating the economic consequences of policies that target the supply of local public goods, environmental services, and urban amenities. We design a theoretically consistent and empirically realistic Monte Carlo study of whether omitted variables seriously undermine the method's ability to accurately identify economic values. Our results suggest that large gains in accuracy can be realized by moving from the standard linear specifications for the price function to a more flexible framework that uses a combination of spatial fixed effects, quasi-experimental identification, and temporal controls for housing market adjustment.

Zhao, and especially V. Kerry Smith. We are grateful to the editor, Holger Sieg, and two anonymous referees for insightful comments on previous drafts. We also thank participants of the 2010 AEA meetings, the 2010 W2133 meetings, and the 2010 Duke conference on housing market dynamics, and seminar participants at

Households "sort" across neighborhoods according to their wealth and their preferences for public goods, social characteristics, and commuting opportunities. The aggregation of these individual choices in markets and in other institutions influences the supply of amenities and local public goods. Pollution, congestion, and the quality of public education are examples. Over the past decade, advances in economic models of this sorting process have led to a new framework that promises to alter the ways we conceptualize the policy evaluation process in the future. These "equilibrium sorting" models use the properties of market equilibria, together with information on household behavior, to infer structural parameters that characterize preference heterogeneity. The results can be used to develop theoretically consistent predictions for the welfare implications of future policy changes. Analysis is not confined to marginal effects or a partial equilibrium setting. Nor is it limited to prices and quantities. Sorting models can integrate descriptions of how non-market goods are generated, estimate how they affect decision making and, in turn, predict how they will be affected by future policies targeting prices or quantities. Conversely, sorting models can predict how equilibrium prices and quantities will be affected by policies that target product quality, information, or amenities generated by the sorting process. These capabilities are just beginning to be understood and used in applied research. This survey article aims to synthesize the state of knowledge on equilibrium sorting, the new possibilities for policy analysis, and the conceptual and empirical challenges that define the frontiers of the literature.

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as ''economic epidemiology'' or ''epidemiological economics,'' the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.