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People adjust to the risks presented by natural disasters in a number of ways; they can move out of harms way, they can self protect, or they can insure. This paper uses Hurricane Andrew, the largest U.S. natural disaster prior to Katrina, to evaluate how people and housing markets respond to a large disaster. Our analysis combines a unique ex post database on the storm’s damage along with information from the 1990 and 2000 Censuses in Dade County, Florida where the storm hit. The results suggest that the economic capacity of households to adjust explains most of the differences in demographic groups’ patterns of adjustment to the hurricane damage. Low income households respond primarily by moving into low-rent housing in areas that experienced heavy damage. Middle income households move away to avoid risk, and the wealthy, for whom insurance and self-protection are most affordable, appear to remain. This pattern of adjustment with respect to income is roughly mean neutral, so an analysis based on measures of central tendency such as median income would miss these important adjustments. Copyright Springer Science + Business Media, LLC 2006Natural hazards, Economic adjustment, Hurricanes,

Efforts to measure people’s responses to spatially delineated risks confront the potential for correlation between these risks and other, unobserved characteristics of these locations. The possibility of correlation arises in part because individuals observe other locational attributes that can be expected to influence the hedonic equilibrium. One response to this problem is to use events from nature to exploit both temporal and spatial variation in the behavioral responses of interest. This paper evaluates the use of hurricanes as a source of new risk information to households in coastal counties potentially subject to the effects of these storms. We study the extent to which housing prices before and after hurricane Andrew, a hurricane with unprecedented property loss, reveal how Floridians responded to the risk information provided by the storm. Two counties are selected – one without and another with damage from the hurricane. To evaluate the plausibility of using quasi-random experiments for locations not directly affected by natural events, we compare Lee County’s results to those of Dade County, where the majority of the damage occurred. Our findings suggest, after controlling for ex post storm damage and changes in insurance markets, there is a reasonably high level of consistency in a repeat sales model’s ability to estimate the effects of the risk information conveyed by the storm for both counties. Copyright Springer 2006hurricane risk, repeat sales, hedonic models, Q51, Q54,

Seasonal climate forecasts are becoming more accurate and available for longer lead times. These improvements have significant implications for agricultural markets. Complex empirical linkages lead from climate forecasts to the information that growers and others use. There are equally complex linkages from climate information to market outcomes. Fundamental to modeling the effects of a climate forecast on agricultural markets is the direct agronomic impacts of climate on commodity production, but also important are the endogenous responses of growers, traders, and others. This paper examines market responses to climate information in a simple framework that highlights the most important issues while abstracting from most complications. We emphasize how trade policy affects commodity market responses and the value of climate information. The Value of Climate ForecastsResearch assessing the agricultural market effects and value of climate forecasts includes models and methods from meteorology, agronomy, and economics. Johnson and Holt review some of this literature and provide a summary table categorizing the methods used and industries studied (see also Mjelde, Hill, and Griffiths, which contains an extensive list of references). One approach in this literature (Mjelde, Thompson, and Nixon; Mjelde et al.) treats climate variables as inputs in the crop production process and obtains technological parameters through econometric methods or crop models. A Bayesian decision framework is then employed to model how growers or others update their behavior conditional on the The authors are Frank H. Buck, Jr., professor; graduate student; and research economist, Department of Agricultural and Resource Economics, University of California, Davis.We gratefully acknowledge funding from the U.S. National Oceanic and Atmospheric Administration.forecast. Individual producer supply functions derived in this manner depend on both the climate forecast and the realized climate event. To analyze market price, the individual supply functions are aggregated to the market relationships, and the demand side of the market is specified. The value of a forecast is computed as the change in expected welfare between what would have happened with the forecast and without the forecast. ENSO, Climate, and the Value of ForecastsThe El Ni¡ Oscillation (ENSO) refers to a quasi-periodic oscillation in sea surface temperature and barometric pressure over the tropical Pacific. ENSO is broadly separated into three phases: El Ni¡ (warm phase), neutral, and La Ni¡ (cold phase). An ENSO forecast is a forecast for sea surface temperature, barometric pressure, or some other ENSO index in the tropical Paci¡ Thus, an ENSO forecast does not directly predict precipitation or atmospheric temperature anomalies. However, evidence suggests that in certain regions an ENSO forecast is related to a forecast of the climate variables most relevant to crop production. For example, statistical shifts in precipitation probability dist¡ associated with the ENSO phases hav...