# **4. Results and discussion**

In this section, we first evaluate the impact of hurricanes on economic activity and assess the mitigating role of mangroves against hurricanes in the Caribbean region. Using the hurricane windstorm model— and the nightlight data set to quantify the causal effects of the hurricane windstorm model on local economic activities—we categorize three hurricane damage indexes, which provides a higher level of detail. In a second step, we include mangrove data and characteristics to evaluate its potential mitigation effect of the impact of hurricanes on economic activity. The main descriptive statistics for the variables in the sample used in this section can be found in Annex 4.

# **4.1.Impact of hurricanes on nightlight**

We initially explore the impact of hurricanes on nightlight using data for 29 Caribbean countries and territories. This comprises more than 2 million observations due to the grid-cell measurement for the 2000–12 period.<sup>19</sup> The econometric results show a negative, statistically significant, association between nightlight and the hurricane damage index. Table 3 provides estimates for the two-way fixed effect equation described in Section 3 and using three specifications (columns 1 to 3) to include a potential lagged effect of hurricanes on nightlight and to discriminate the effect in low-elevation coastal zones. Particularly, the size for the estimate for , which measures the impact of hurricanes on nightlight, shows that nightlight in cells that experience Category 3 hurricane winds ( , wind speed of 203 kilometers per hour) decrease by 0.35 units ( ≈ −1.75 ∗0.2ββ ).<sup>20</sup> Given that the average level of nightlight intensity in 2000 was 12.1,<sup>21</sup> the effect is equivalent to a 2.9 percent decrease in local economic activity. ff= 0.2 Table 3 shows that the negative association between nightlights and the hurricane damage index seems to be short-lived. In column 2, it is possible to evidence that, consistent with previous literature (Elliott, Strobl and Sun 2015; Del Valle et al. 2018), hurricanes have a short-lived effect on local economic activity. To evaluate this, we included a lagged term for the hurricane damage index and found that, while the contemporaneous coefficient remains negative and statistically significant, the lagged coefficient is positive and statistically significant. This may be related to implementing rehabilitation and reconstruction stages following a disaster shock.

The effect of windstorms is more negative in storm surge-prone areas, measured as low-elevation coastal zones. Column 3 provides estimates to assess whether hurricane impact is dissimilar in coastal lowland zones. For this purpose, we included dummy variables to identify these areas and create interaction terms with the damage index. The estimated coefficient for non-storm-prone areas is positive and statistically significant, while the coefficient for storm-prone areas is negative and statistically significant as well. This suggests that the effect of hurricanes is considerably greater in low-elevation coastal zones. Specifically, ff using average nightlight levels for 2000 (as in column 1), we found that the negative effect of Category 3 hurricane winds is associated with a 16.1 percent decrease in local economic activity.<sup>22</sup>

> 19 For the regression analysis, we excluded data from Cuba, as they seem to be driven by a positive association between nightlight and the damage index, perhaps due to limited data density but large coverage (see Annex 5). 20 Recall that our specification is a log-linear regression model. Therefore, to find the coefficient that is multiplied by the damage index ( ), the following calculation is performed: _[exp (-0.0177)-1] *100._ 21 Records in nightlight image data are the DN values, ranging from 0 (no light) to 63 (maximum light). 22 The calculation was as follows:  The calculation was as follows: ff _[[[exp (-0.0103)-1] *100] *(0.2)]*[1/12.1]_ .

> 22 The calculation was as follows:  The calculation was as follows: _[[[exp (-0.0103)-1] *100] *(0.2)]*[1/12.1]_ .

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