(Henderson et al., 2011, 2012) and at lower levels of aggregation (Harari and La Ferrara, 2013; Michalopoulos et al., 2014; Bundervoet et al., 2015; Alesina et al., 2016). Specifically, related to our paper the work of Bertinelli and Strobl (2013) and Elliott et al. (2015) have shown, respectively for the Caribbean and for China, that not only are night lights a reliable proxy of the economic damages brought about by natural disasters, but that it is night lights measured at lower levels of aggregation which provide the most reliable proxy. In section 6 we present our own tests of the relationship between night lights and economic activity at the subnational level in Mexico.

NOAA publicly provides composites in yearly frequency covering the 1992 to 2012 period. These satellite-year datasets have a spatial resolution of approximately 30 arc seconds, that is, each pixel roughly represents a one kilometer square cells. Each pixel has an associated digital number (DN) which represents the intensity of lights normalized across satellites in a scale ranging from 0 (no light) to 63 (maximum light). Given the operational lifespan of the satellites we will use data from the F16 satellite for the 2004 to 2009 period, and from the F18 satellite for the 2010 to 2012 period. Following Henderson et al. (2011) our subnational measure of night lights will be constructed by summing the DN across all pixels that are within the subnational boundary, dividing the sum by the area of the observed pixels, and then taking the log of the previous number. We will calculate this measure of brightness at both the state and municipal level, and refer to it from this point on as log night lights. The key outcome variable will be the change in log night lights between the year the disaster takes place and the following year.

In addition to the publicly available imagery, NOAA has produced, especially for this paper, monthly frequency composites for the 2004 to 2013 period. These satellite-month datasets are derived using the same process and raw images as the annual composites. In addition to calculating log night lights at the municipal and state level as we did with the annual composites. We will construct two additional sets of night light measures from monthly composites. The first will take advantage of the overlapping coverage of the F15 and F16 satellite during the 2004 the 2007 period, and use as source data pixel level averages from the two satellites. The second will calculate our log night lights measure from composites where we have excluded top coded pixels, that is, pixels with a DN of 63.

Unfortunately, late sunset during summer months leads to missing log night lights data for some parts of Mexico, every year, between June and August. In addition in 2009 due to degradation of the sensor on board the F16 satellite we have up to 7 months of missing night light information. In order to overcome the constrains created by missing data, we will take averages over monthly log night lights. In our preferred specification the key outcome variable is the log difference between the average for the 12 months before the disaster and the average at various points in the post disaster period. One important implication of using these averages in conjunction with stable night light imagery is that our results will not be affected by transient phenomena 7