community organizations, amount of loan for productive purposes, household head’s gender, marital status, and education. Following Angelsen et al. (2014), all assets shared by household members in production and consumption (e.g., land holding) as well as all production and income values were divided by adult equivalent units ( _aeu_ ) of a household, to adjust for household size and allow inter-household comparisons.

The coefficient β, in equation (1), can be interpreted as causal if refugee inflow is exogeneous, i.e., among others, there are no excluded variables in the model that affect the outcome variable of interest as the distance to and (or) the number refugees in the nearest refugee camp change(s). This is highly unlikely to hold as the location of refugee camps as well as refugees’ choice of camps or allocation of refugees to the camps, is not random for two reasons. First, refugee camps are often located along international borders for logistical purposes and easy repatriation of the refugees when the situation in the source countries become stable. Second, refugees travel long distance on foot and would like to stay in the closest refugee camps to their origin country (Baez 2011; Ruiz and Vargas-Silva 2018). Consequently, refugee inflow is endogenous and the estimated β from equation 1 is likely to be biased. We instrument refugee inflow with an Instrumental Variable, I , which is constructed as: I = P <u>Ps</u> s (2) ii XXCCPPCC Where, P s is the population density (in 2010) of the nearest region in the nearest source ii DDssrr country to the refugee camp, s is the intensity of conflict (between 2001 and 2018) in the nearest region in the nearest source country to the refugee camps, and PP is the distance of the camp to the nearest border (of the nearest region or source country). CCPPCC<sup>20</sup> Subscripts and ssrr DD stand for region in source country, refugee camp, and border to the nearest source country, respectively. In principle, the constructed potential refugee inflow is a valid instrument as ss, rr, _(i)_ bb most refugee camps in Ethiopia are located close to the border,<sup>21</sup> and people seeking refugees are allocated to the nearest refugee camp given the capacity of the camp and _(ii)_ the magnitude of refugee flow depends on the population density and intensity of conflicts in the source country (Baez 2011; Ruiz and Vargas-Silva 2018; Fallah et al. 2019). In addition, potential refugee inflow, I , is less likely to affect the outcome variables of interest directly except through a household’s exposure to actual refugee inflow, R ii (>10) in most of the first stage regression (see below and Appendix C; E). Previous studies ii<sup>. This is confirmed by F-statistics</sup> (e.g., Fallah et al. 2019; Baez 2011; Alix-Garcia, J., 2018) employed similar instruments to estimate the causal impact of refugees on host communities.

However, we anticipate two potential threats to our identification strategy. First, the intensity of conflict in the nearest region of source countries could affect economic activities and livelihoods in refugee host communities if there is significant cross-border trade between host communities and refugee source countries. But this is less likely because: (i) most of the host

> 20 Following the literature of the impact of refugees (see e.g., Baez et al. 2019), we prefer to use the composite index, rather than its components, as an instrument because the composite index gives us variation at the refugees’ camp level. The two components (population density and intensity of conflicts) mainly vary at refugee host regions and does not provide sufficient variation to explain refugee exposure if used individually.

> 21 The mean and median distances to the border are 53,921 and 48,924 meters, respectively, with the minimum of 3,439 meters, and maximum of 138,489 meters.

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