of hurricanes on nightlight for each bin. Panel b plots the distribution of mangrove width for each bin, and panel c plots distance to the coast for each model and bin.

For example, the top two rows, colored with green bars, plot the impact of hurricanes on nightlight for each model 1 ( _q=2_ ) bin, where the bins correspond to cells with above and below median width (approximately 0.25 kilometers) on their path to the coast. It is possible to see that in below-median areas, hurricanes can significantly reduce nightlight. The estimated coefficient of -3.10 indicates that Category 3 hurricane winds ( f = 0.2 , wind speed of 203 kph) would reduce nightlight by 0.62 units ( ≈ −3.10 ∗0.2 ) equivalent to a 5.1 percent reduction, using the inverse of the average nightlight level in 2000.<sup>24</sup> In contrast, the above-median effect is smaller and would imply a 4.7 percent reduction in nightlight.<sup>25</sup> Rows 3–5 (orange) correspond to model 2 ( _q=3_ or the third tercile) where mangrove width is greater than 1 kilometer and an average width of 2 kilometers. Rows 6–9 (purple) correspond to model 3 (q=4 or the fourth quartile) where mangrove width is greater than 1.26 kilometers and an average width of 2.3 kilometers), respectively. Figure 5 shows that most—though not all—models show a decreasing pattern of hurricane damage as we move through the corresponding bins. For example, using model 3, the impact is reduced until the third bin, where the reduction in nightlights is equivalent to 1 percent,<sup>26</sup> instead of 6.5 percent in the first bin.<sup>27</sup> A similar result can be found in panel c, where, on average, a wider stretch of mangrove also implies a longer distance to the coast.

**Figure 5. Impact of hurricanes on nightlight by mangrove width, excluding Cuba**

<!-- Start of picture text --> 1 -3.10 1 0.15 1 5.89<br>2 -2.85 2 1.05 2 10.95<br>1 -3.66 1 0.10 1 4.92<br>2 -0.99 2 0.35 2 8.38<br>3 -4.29 3 1.33 3 11.93<br>1 -3.95 1 0.08 1 4.62<br>2 -1.60 2 0.23 2 7.43<br>3 -0.61 3 0.51 3 9.36<br>4 -5.28 4 1.54 4 12.39<br>-8 -3 2 0 2 4 0 5 10 15<br>a) Estimated impact of hurricanes b) Distribution of mangrove width (km) c) Distance to the coast (km)<br>on nightlights (NL)<br> Authors’ calculations based on data from LECZ database.<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from LECZ database.

_Notes:_ In models 1-3, from panel (a) we discretize the mangrove width variable into bins that correspond to its q-quantiles and estimate the impact of hurricanes on economic activity for each bin. Model 1 uses q=2 and is colored in green. Model 2 uses q=3 and is colored in orange. Model 3 uses q=4 and is colored in purple. Panel (b) plots the distribution of mangrove width for each bin, and panel (c) plots the distribution of distance to the coast for each bin. Regression coefficients are shown in panel (a) and the average value for panels (b) and (c).

Including Cuba in the sample (figure 6), mangrove width appears to mitigate the impact of hurricanes on nightlight in the first quantile of the models 2 and 3. In model 2 (orange), the estimated coefficient

> 24 The calculation was as follows: [ ≈ −3.10 ∗0.2 ] *[1/12.1]

> 25 Following the same procedure to find the percentage, using the coefficient -2.85.

> 26 Following the same procedure to find the percentage, using the coefficient -0.61.

> 27 Following the same procedure to find the percentage, using the coefficient -3.95.

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