Depending on available data, we attempt to address these challenges with varying degrees of success. First, we account for location characteristics that are correlated with refugee (and IDP) location choice, such as population. Second, we explore alternate specifications for the density variable (defined as the share of Syrian refugees—and IDPs in KRI— relative to the host community), at the enumeration area, sub-district, and district level. Third, where possible, we also include variables that measure pre-existing economic conditions in the local area.

The results discussed below are based on three alternative models relating outcomes to the size of the influx of forced migrants into local markets for services. In one of the specifications, levels of outcomes in 2015 are regressed on levels of outcomes in 2010, density measures at various geographical units that are taken as definitions of local markets, and interactions between both: 0 1 2 3<sup>+ θθ</sup> (1) ii,uu,2015 ii,uu,2010 uu ii,uu,2010 uu ii,uu,2015 gg yy = ββ + ββ yy + ββ dd + ββ yy dd + γγXX<sup>TT</sup> ii,uu,2015 where denotes the outcome in+ εε for host-community individual living in locality in 2010, is the regional measure of refugee inflow, ii,uu,tt yy tt∈{2010, 2015} ii vector of exogenous variables at the individual or regional level, are governorate fixed uu ii,uu,2015<sup>is the</sup> effects and uu is an unobservable error term. For Lebanon, dd is a scalar with the XX<sup>TT</sup> gg θθ density of Syrian refugees, and for Kurdistan, it is a vector including the density of IDPs ii,uu,2015 uu in a given locality.εε<sup>7</sup> In (1), 2 captures the partial effect of an increase of 100 percentage dd points in the density measure for a host-community individual such that = 0, and 3 is the incremental difference for the sub-population that satisfies ββ = 1.<sup>8</sup> ii,uu,2010 ii yy Since the absolute effect on the latter group is also an interesting parameter, all tables that ii,uu,2010 present estimates from this specification also depict p-values from a test with the null ββ yy hypothesis that 2 3 = 0. To explore heterogeneity with respect to forcibly displaced in and outside camps in Kurdistan, the density variables are broken down into a sum of non-camp and camp density defined analogously. ββ + ββ For continuous dependent variables observed in 2010 (changes in perceptions about health and education services, for instance) it is less meaningful to capture effects on the group satisfying = 0, and the following model is estimated: ii,uu,2010 Δ yy 0 1<sup>+ θθ</sup> ii,uu,2015−2010 uu ii,uu,2015 gg<sup>+ Δεε</sup> ii,uu,2015−2010<sup>(2)</sup> where yy Δ = ββ is the change in the relevant outcome between 2010 and 2015 and + ββ dd + +γγXX<sup>TT</sup> all the other variables follow the same notation as model (1). ii,uu,2015−2010 yy Also, when past information on the outcome is not provided by the survey, the following model is estimated using contemporary outcomes:

> 7 Since SR and IDPs sorted into different regions in KRI, all specifications allow the partial effects to be different for both populations. For the same reason, density measures are orthogonal and including both in the same regression yields coefficients that are very similar to the ones obtained in models that include each of them separately.

> 8 For example, in the regressions where equals 1 if host-community individual is employed and 0 otherwise, it is an estimate of the effect on the sub-population of being unemployed in 2010. ii,uu,2015 yy ii

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