and households fixed effects (Columns 3, 6, and 9). The perception and interaction indices are standardised to facilitate interpretation. We draw two conclusions from this table.

First, the correlation between the number of interactions that host respondents have with refugees is positive and statistically significant in most specifications. The magnitude of the association is moderate. In the Uganda sample, a one-standard-deviation increase in the interaction index is associated with a 0.076 to 0.1 standard-deviation increase in the perception index.<sup>15</sup> The association is somewhat stronger in the Nakivale settlement, both in terms of statistical significance and magnitude. With household fixed effects, the correlation is positive but not always significant with the Kampala sample. This might indicate that omitted variables at the household level drive the positive association between interactions and perceptions in the Kampala context.

Second, there is a strong correlation between respondents’ perception of refugees and the perception of other household members and neighbours. The association is statistically significant and large.<sup>16</sup> In the Uganda sample, a one-standard-deviation increase in the average perception index of other household members is associated with a 0.26 to 0.3 standarddeviation increase in the perception index. A one-standard-deviation increase in the average perception index of respondents’ neighbours is associated with a 0.33 standard-deviation increase in the perception index. The significance and magnitude of these correlations is stable across contexts and robust to various specification changes, for example, excluding the measures of perception among other household members and neighbours or considering other aggregation methods to construct the perception and interaction indices.

These correlations should not be interpreted as causal because of possible endogeneity issues. We believe that omitted variable bias is unlikely to fully explain the positive correlation observed in Nakivale as regression coefficients remain very stable when going from a regression without controls to a regression with a full set of control variables and household fixed effects (Altonji et al. 2005; Oster 2019). But omitted variable bias is possible in Kampala, as the inclusion of household fixed effects leads to insignificant results. Most importantly, reverse causality could bias regression coefficients in all contexts. And mismeasurement problems could lead to attenuation bias.

In Table 2, we use an IV approach to address endogeneity issues. The structure of Table 2 is similar to Table 1 with respect to samples and control variables. We consider two instruments: 1) the proportion of refugees living in respondents’ EA (Columns 1, 4, and 7), and 2) the average interaction index among other household members and neighbours

> 15With respect to the types of interaction, we find that sharing meals is somewhat more strongly associated with positive perceptions than simply having conversations or business exchanges, whether measured by number of meals/conversations/exchanges or through binary indicators of having participated in any of these exchanges. As the differences are small, and including the separate measures might induce multicollinearity, we keep the focus of this analysis to the overall interaction index.

> 16We explored whether this intra-cluster correlation in perceptions is notable also at higher geographical levels than neighbourhoods. When considering the opinions of peers in the six research sites or the three countries, there is no longer any correlation between the individuals’ and peers’ opinions. As such, individual opinions seem to be strongly influenced by the immediate and local networks, and less so by the wider society.

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