satellite images for fishery management. In short, the VBD algorithm detects spikes in the illumination from offshore areas, at the same time controls for background noise radiance due to moonlight, and filters out lighting and energetic particles in the ionosphere.

For the purpose of this analysis, we use the monthly-aggregate VBD products published by NOAA for the period between 2013 and 2016.<sup>13</sup> We then aggregate the number of monthly boats detected into 10-mile-square geo-grid cells that, together, spans Vietnam’s entire Maritime Exclusive Economic Zone.<sup>14</sup> The chosen resolution of 10-mile-square grid allows us to better capture the effect for different fishing grounds across the entire country’s coastal area. It covers a marine space granular enough to detect micro changes in fishing activity’s patterns (e.g. within-province fishing grounds’ migration). Nevertheless, this footprint level still spans a sufficiently large sea segment, which allows us to be less concerned with issues about spatial autocorrelation or spurious boat detection.

The second panel (Panel II) in Table 1 presents fundamental statistics of two dependent variables related to fishing intensity that are made possible by VIIRS: (1) _fishing prevalence_ , i.e. boat detection likelihood, which is the probability that at least a boat was detected inside a particular grid in a given month and, thus, provides information regarding the _extensive_ margin of the disaster’s impact; and (2) _fishing density_ , i.e. the number of boats detected in each grid, which captures the _intensive_ margin. The three-year baseline gridmonth statistics from VIIRS 2013-2015 suggests that fishery actions are considerably more intense in the central coast. Near-shore ( _≤_ 20 nautical mile) fishery activity has a 59 percent average monthly coverage, while off-shore fishing covers over two-third of the marine pixels.<sup>15</sup> The respective boat-detection likelihoods in the control provinces are 40 (near-shore) and 55 (off-shore) percent. On average, there are approximately 5 boats detected per month in each of the 10-mile-square pixel in the treatment provinces, similar across both near-shore and offshore fishing grounds. It is also noted that these statistics on fishing intensity are consistent with the LFS labor-market information; recall in Panel I of the table that saltwater fishers in the central ( _Formosa_ -affected) provinces have a greater baseline average workload relative to those in the control provinces.

> 13 As subsequently discussed, our main empirical setting employs three full years of pre-treatment observations (2013-2015) to capture potential seasonality effects in fishing activities in different coastal regions. It is noticed that 2013 is the first year with available VIIRS data for all months.

> 14 According to Elvidge et al. (2018), the monthly temporal aggregation addresses each of the three criteria that could be potential concerns for higher-frequency intervals: lunar cycle effect, seasonal variation, and cloud cover. The monthly aggregation VBD mitigates lunar cycle effects and improve the cloud-free boat-detection capability. It should also be noted that monthly temporal aggregation is widely used in economic analyses to mitigate seasonal effects on economic and fisheries data (Burkhauser et al., 2000; Garza-Gil et al., 2006; Neidell, 2004).

> 15 We cap the off-shore marine space to be the pixels located between 20nm and 80 nm from the shoreline.

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