(1998) and Stan (2005) to assess the accuracy of simulated data on wind speed. To further evaluate the accuracy of the results, model estimations were also compared with simultaneous in‐situ measurements collected during the passage of Hurricanes Jeanne and Frances in 2004 across Florida (Pita el al., 2015). These robustness checks provide important information about the accuracy of information generated by the model.

The use of this wind speed model is a key innovation in this study since it represents a completely exogenous input in the estimation of hurricanes’ potential destructive power at each geographical location, which is completely calibrated and validated for the Central America region. Strobl (2012) and Hsiang and Jina (2014) focus their papers on maximum wind speed using a more simplistic hurricane model not necessarily calibrated for our region of interest.

# 2.2. Hurricane Damage Indexes

Having precise windstorm hazard data is the first step in the study’s characterization of hurricane impact. What makes hurricanes destructive, and hence economically disruptive, is the strong wind speed coming in contact with exposed assets and buildings. In a storm, strong winds run into stationary objects (e.g., trees, buildings, cars, etc.). Some of these elements can safely absorb the energy carried in the moving air, while others cannot. To illustrate this connection, consider two hurricanes of the same magnitude, with the only difference being that one of them strikes a densely populated area, while the other one touches upon an isolated region. These two virtually identical hurricanes will have extremely different and opposite economic impacts. The consideration of exposure of assets and population is a key factor for the estimation of damages. To keep the exogeneity of the hurricane’s damages indexes represents a challenge, as it is difficult to link each economic unit with the level of wind by which it has been hit.<sup>7</sup> In this study we consider three approaches based on 3 different indexes, as outlined in the recent literature. The first one is simply using the maximum sustained wind speed (MSWS) as generated by the Pita et al. model, representing a fully exogenous measure of hurricane intensity. This variable being fully exogenous, comprises potentially high levels of measurement error.

The second approach, proposed by Hsiang and Jina (2014), is the Wind Exposure Index (WEI) describing a spatially‐weighted index by area exposed to hurricane wind speed within a sub‐national unit (see Equation 2). This index aims to recover the average effect of hurricane exposure on an average grid. As Hsiang and Jina (2014) suggest, intuitively the WEI index represents the exposure all units of land would have if the measure of a storm is “spread out” evenly across all locations in a country. If exposed area is correlated with socioeconomic damages, but the overall intensity of a storm is not correlated with populated or economically active regions or countries, measurement error is reduced.

> 7 Another aspect that needs to be addressed in the characterization of hurricanes is the temporal and spatial mismatch between socioeconomic data and wind speed data.  Socioeconomic data are often collected, calculated or released at an aggregate level by country or region on a yearly basis. Meanwhile, wind speed data are available at a finer resolution (1km<sup>2</sup> ) for each point in time. In the case of the GDP analysis (section 3), indexes are aggregated at the country level, while in the case of the household analysis (section 4), indexes are aggregated at the sub‐national level (regions).

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