Jaiswal and Wald (2008). The data provide the share of 99 different building types within a country separately for urban and rural areas. For Indonesia the building type information was compiled from a World Housing Encyclopedia (WHE) survey. The WHE survey uses fraction of population who lives or works in buildings of different types as their definition of how the building mass is split up. Without any other information available, we use this as an indication of the distribution of building types and mass, but, necessarily, assume that the distribution is homogenous within urban and rural areas.

Damage curves by building type are derived from the curves constructed by the Global Earthquake Safety Initiative (GESI) project; see (International and Regional Development 2001). More specifically, buildings are first divided into 9 different types across a wide range of building materials from wood and steel through to lightweight shacks. Each building type itself is then rated according to the quality of the design, the quality of construction, and the quality of materials. Total quality is measured on a scale of zero to seven, depending on the total accumulated points from all three categories. According to the type of building and the total points acquired through the quality classification, each building is then assigned one of nine damage curves which provides estimates of the percentage of building damage for a set of 28 peak ground acceleration intervals.

In order to use these damage curves, we first allocated each of the 99 building types given in the USGS building inventory to one of the 9 more aggregate categories of the GESI building classification. However, to assign a building type its quality‐specific damage curve we would further need to determine its quality in terms of design, construction, and materials, an aspect for which we unfortunately have no further information. We instead assume that building quality is homogenous across building type and construct eight different damage curves with different quality rating scenarios (ranging from 0 to 7). Initial analysis showed some effect based on the quality assumption, but lacking any meaningful information we decided to use a mean quality assumption of 4 across the countries.

To model estimated damage due to a particular earthquake event the data from the ShakeMaps and GESI are used. Then, one identifies the value of peak ground acceleration that each nightlight cell experiences by matching each earthquake point with its nearest nightlight cell. If the cell is further away than 1.5 kilometers or if it experiences shaking (PGA) of less than 0.05g the value is set to 0. In order to derive a cell _i_ specific earthquake damage index, _ED_ , the following equation is applied: where DR is the damage ratio according to the peak ground acceleration, pga , and the urban‐ rural qualification k of cell i , defined for a set of 8 different building quality q categories.

Once the damage index has been constructed, the different data sets are combined and a final data set with the nightlights, population and earthquake data are made, ref Figure 2. With this combined data set, we do a case study of the 2016 Aceh Earthquake and we run the fixed effect regression: 15