communities in our sample are located close to, but not just at the border;<sup>22</sup> and (ii) most crossborder trade between host communities and refugee source countries occurs between major towns and most of our study kebeles are far away from these towns. Second, the reported number of conflicts in the neighboring regions could be associated with communication infrastructure in the place of origin. However, most neighboring regions in source countries are underdeveloped with very similar levels of communications infrastructure and accessibility, so we do not anticipate any significant systematic differences in the number of casualties reported. We, however, control for any potential effect of these two threats by including the nearest region of the neighboring countries fixed effect in our regression models so that a significant part of the effect of conflict can be attributed to refugee inflow.

We use two-stage least squares (2SLS)<sup>23</sup> to estimate the causal impact of refugee inflow on livelihood diversification, and commercialization. The 2SLS is implemented by estimating the following two equations as a first and second stage regression, respectively: R 1<sup>+ ββ</sup> 1<sup>I</sup> 1<sup>XX</sup> 1<sup>+ εε</sup> ii<sup>= αα</sup> 2 2R ii<sup>+ γγ</sup> 2 ii<sup>+ νν</sup> 2 ii1<sup>(3)</sup> � Where, ii R is the fitted refugee inflow from equation (3) and all the notations are similar to ıı ii ii2 YY = αα + ββ �+ γγ XX + νν + εε the ones in equation 1 and 2. The estimate 2 is the adjusted effect of refugee presence on the ii outcome variable of interest and can be interpreted as a causal effect assuming our instrument ββ (I ) is valid. The regression results and summary statistics of first-stage regressions, with and without controls, are presented in Appendix C. Both first-stage regressions have a significant statistics ( and with and without controls, respectively) and Kleibergen-Paap rk Wald statistic (1) = 19.02 and ), FF FF(1,112) = 18.283 = 19.838 , PP< 0.01 suggesting the instrument is a good predictor of refugee presence in the first stage regression. Kleibergen-Paap Wald rk F statistic is 18.81 and 20.94 with and without control, respectively, (XX<sup>2</sup> = 21.86 , PP< 0.01 both the critical value of 16.38 (for a 10% maximal IV size), suggesting our model is well identified.

We start our main estimation for overall effects using the full sample, without the covariates and then include covariates to check the robustness of our results. The set of controls include whether the household is a member of a community and social organization, the amount of loan for productive purpose (in _Birr_ ; _aeu_ adjusted), total land owned by the household (in hectares; _aeu_ adjusted), value of livestock owned (in _Birr_ ; _aeu_ adjusted), distance to allweather road (in Kilometers), level of education, gender, and marital status of the household head. In all models, we include kebele fixed effects to control for unobserved heterogeneity at the kebele level, and cluster standard errors at the village level.

> 22 The mean and median distances to the border are 44,966 and 39,888 meters, respectively, with the minimum of 37 meters, and maximum of 107,406 meters.

> 23 2SLS is an estimation technique used to estimate the causal impact of an explanatory variable of interest (i.e., refugee inflow) on outcome variable of interest (i.e., livelihood diversification and commercialization) in the presence of endogeneity threats. It is implemented with two regressions: the first regression estimates the value of the endogenous variable using an additional variable (i.e., potential refugee exposure) and the second regression uses the predicted values to estimate the impact of the endogenous variable.

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