the extent to which saturation weakens the suitability of nightlight as a proxy for local economic activity in the Caribbean.

Table 1 shows the frequency distribution of DNs across pixels for Caribbean islands, along with the information on land areas, population density, and GDP per capita based on purchasing power parity (PPP). In most small states, a large land area (or number of pixels)<sup>10</sup> has a DN value of 0—that is, it has no artificial lights for the entire 12-year sample period. In Haiti, for example, which is relatively more exposed to hurricane strikes, 70 percent of pixels are unlit, while in Cuba, The Bahamas, and the Dominican Republic, more than a quarter of pixels are unlit. Following Bertinellli and Strobl (2013) and Elliott, Strobl and Sun (2015), who also use nightlight data to study the economic impact of tropical storms on local activity, this study assumes there is no economic activity in pixels with a DN value of 0 for the entire sample period and therefore excludes them from the econometric analysis. They make up about 67 percent of the total sample.

Table 1 also shows the number of top-coded pixels (with a DN value of 63) and the mean DN across the sample. The percentages of censored pixels are zero or close to zero, suggesting that nightlight saturation in urban centers might not be a major issue in the Caribbean. By contrast, the average DN value shows some variance across the 29 countries and territories. The island of Sint Maarten/St. Martin, which is half Dutch and half French territory, has the region’s highest average DN (with 53.67 and 46.02, respectively); this is followed by Bermuda (36.24) and Aruba (36.08), countries with the highest income level in the Caribbean. In some way, the correlation between nightlight and income levels could be proved for these countries. However, nightlight might not be a reliable proxy for income levels due to cultural differences in the use of lights and light saturation in large cities (Ghosh et al. 2010). Instead, nightlight data work better as a proxy of economic growth (Henderson, Storeygard and Weil 2012). For this reason, our empirical strategy (see Section 3) centers on variations in nightlight intensity across pixels and over years, after controlling for time-invariant and time-specific effects, to isolate the impact of hurricanes on local economic activity.

Mean nightlight by Caribbean country or territory conveys different shapes of trends (Annex 1). In 2000, Haiti, Dominica, Montserrat, and Cuba presented the lowest DN value in the region (< 5), while St. Martin, Sint Maarten, Bermuda, and Puerto Rico, showed a value of 38–48, with 63 being the maximum value of nightlight. Surprisingly, five years later, in 2005, the islands with the lowest DN value at the beginning of the period had decreased their luminosity by approximately 2 points, while those with the highest DN values had decreased by around 8 points. In other words, the low-income islands reduced their value of nightlights by a lower number than the high-income islands. Overall, mean nightlight in the 29 countries and territories fell from 7.73 in 2000 to 6.12 in 2015. The impact of hurricanes and tropical storms on their local economies during that period could be a reason for this decline.

By 2010, DN values in almost all Caribbean islands had increased by 2–16 points, with the exception of Aruba, where it had reduced by 1 point. Trinidad and Tobago exhibited the highest growth in luminosity, from 13.51 to 29.32 points, followed by St. Martin and Martinique with a rise of 11–12 points. At the end of the studied period, in 2012, average nightlight on all the islands had dropped by 1 point. Five countries or territories— Aruba; Bonaire, Sint Eustatius, and Saba; Cayman Islands; Curaçao; and St. Martin showed a 1–3 point increase, and in Haiti, St. Kitts and Nevis, and St. Lucia, the DN value remained the same. Resilience to natural disasters on these islands may have improved the last decades. Figure 1 presents the map of nightlight intensity in the Caribbean Islands during the 2000–12 period.

> 10 A country’s number of pixels is proportional to its land area.

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