in the region, as well as a brief description of hurricane data and our proxy for potential damage from hurricanes. Section 3 explains our empirical estimation approach, and Section 4 presents our results. Section 5 concludes the paper.

# **2. Data and statistics**

To quantify the impact of hurricane strikes on local economic activity in the Caribbean, we used three main data sets: nightlight data as a proxy for local economic activity; hurricane windstorm hazard data from a wind field model; and global mangroves databases.

# **2.1.Nightlight application**

The Defense Meteorological Satellite Program (DSMP) – Operational Linescan System (OLS) nighttime light (NTL) data are primary data on economic activity, available at local, regional and global scales (Bertinellli and Strobl 2013; Henderson, Storeygard and Weil 2012; Chen and Nordhaus 2011). Compared to other remote sensing satellite observations, these data are particularly useful for evaluating policies with highly localized effects, or in countries with poor or non-existent subnational GDP data (Lowe 2014). With their global coverage and long temporal span, DSMP NTL data have also been extensively used in studies on electricity consumption, socioeconomic activities, light pollution, urban ecosystems, and urban extent mapping (Li et al. 2020).

Scientists at the NOAA developed a method to remove cloud-obscured pixels from raw nightlight imageries, as well as sources of transient lights such as the bright half of the local cycle, auroral activity, forest fires, and other events. Since 1992, they have processed raw daily nightlight imageries from around the world, delivering yearly cloud-free nightlight composites that essentially capture nocturnal human activity (Elvidge et al. 2010). NTL data records have digital number (DN) values, ranging from 0 (no light) to 63 (maximum light); the spatial resolution is 30 arc-seconds, and there is near-global coverage of 180oW to 180oE longitude and 65oS to 75o latitude (Li et al. 2020).

For this study, we use imagery recorded by satellite between 2000 and 2012, covering 29 Caribbean countries and territories: Anguilla; Antigua and Barbuda; Aruba; The Bahamas; Barbados; Bermuda; Bonaire, Sint Eustatius and Saba; British Virgin Islands; Cayman Islands; Cuba; Curaçao; Dominica; the Dominican Republic; Grenada; Guadeloupe; Haiti; Jamaica; Martinique; Montserrat; Puerto Rico; St. Kitts and Nevis; St. Lucia; St. Vincent and the Grenadines; St. Barthélemy; St. Martin; Sint Maarten; Trinidad and Tobago; Turks and Caicos Islands; and Virgin Islands (U.S.). Although some studies—such as Bertinellli and Strobl (2013) and Andreano et al. (2020)—use yearly frequency nightlight data to predict local economic activities and map poverty in the Caribbean, this paper is one of the first to use and exploit yearly NOAA frequency data to assess the role of mangroves as natural protection against the damaging effects of hurricane strikes on local economic activities in the Caribbean.

Many of the economists who use nightlights as a proxy for economic activity—such as Doll, Muller and Morley (2006) and Keola, Andersson and Hall (2015)—pay great attention to nightlight saturation in urban centers. Henderson, Storeygard and Weil (2012), however, who use a fixed-effects specification for a panel of 188 countries over 17 years to test whether sensor saturation impairs the capacity of nightlight to predictive GDP, find that the estimated elasticity of nightlight with respect to GDP and remains unchanged after controlling for the number of top-coded pixels.<sup>9</sup> In the same way, this study examines RR<sup>2</sup>

> 9 The R-squared reflects the contribution of lights to explaining within-country and within-year variation in income.

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