# **6. Results and discussion**

**_Overall effects_**

Table 1 shows results from our 2SLS estimation using, as a dependent variable, the two measures of livelihood diversification – diversification of activities as a primary and secondary occupation (Panel A), and the two measures of agricultural commercialization – sales of crop and livestock products (Panel B). The table also presents OLS estimates (with controls) for comparison. Both dependent variables and refugee inflow are Inverse Hyperbolic Sine (IHS) transformed, so the coefficients can be interpreted as elasticities.<sup>24</sup> The OLS estimate results are lower in magnitude than the 2SLS, indicating that the OLS estimate would underestimate the effect of refugee presence.<sup>25</sup> Here, we will discuss the estimates from the 2SLS.

The 2SLS results show statistically significant and intuitively signed coefficients for diversification of activities as a secondary occupation and sale of livestock products, both with and without controls. The estimates with and without controls are similar, showing the results are robust to the exclusion of controls.

Regarding livelihood diversification, we find that a 1% increase in refugee inflow leads to more than 1% (2.5 and 2.7% without and with controls, respectively) increase in diversification of activities as a secondary occupation. We find no effect on diversification of activities as a primary occupation across model specifications. Putting the results together suggests that refugee presence significantly increases availability of livelihood activities and households in host communities that pursue these activities as a secondary occupation (because of the positive elasticity effect on secondary occupation) while retaining their primary activity (because of the robust null effect on primary occupation). This can be attributed to the nature of primary and secondary activities in the study areas, where many individuals engage in activities that require skills or livelihood assets as a primary occupation rather than a secondary occupation. For instance, about 7.6% of the individuals who have a primary occupation engage in salaried employment (comparable to 1.5% of individuals who have salary employment as a secondary occupation), which requires knowledge of the local working culture and some level of education (see Appendix D). Hence, people tend to keep their primary activity and either take on or diversify in a secondary activity. While less skilled and/or asset-intensive (secondary) occupations face fierce competition from immigrants or refugees as observed by Morales (2017) in Colombia and Ruiz and Vargas-Silva (2018) in Tanzania, such competition in host communities in Ethiopia is absent as refugees in Ethiopia, except for some Eritrean refugees, experience restricted movement out of camps and are not permitted to work according to the Ethiopian refugee law prior to 2019. This precludes the argument that households engage in more activities as a coping strategy to maintain their existing living standards.

> 24 The elasticities are calculated from the regression coefficients following Bellemare and Wichman (2020) to account for distortions in actual elasticities due to IHS transformation. If a percentage change in refugee presence leads to a more than a percentage change in our outcome variable of interest (i.e., the coefficient is greater than one), the observed effect is elastic. In contrast, if a percentage change in refugee presence leads to a less than a percentage change in outcome variables of interest (i.e., the coefficient is less than one), the observed effect is inelastic).

> 25 This could be for several reasons, but the major one is our main explanatory variable of interest – refugee inflow – is not randomized and hence, the estimates from OLS are less consistent and more biased.

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