|*Household Size Sq.|0.13|0.14|0.13|0.14|0.13|
|---|---|---|---|---|---|
|*Sex of Head (1=male)|0.60|0.60|0.60|0.60|0.60|
|Internet access installed at home|0.38|0.43|0.40|0.38|0.39|
|Wall==plywood|0.17|0.16|0.17|0.17|0.17|
|Toilet==flush toilet|0.75|0.78|0.77|0.77|0.75|
|Land_owner==owned with title<br>|0.29|0.29|0.29|0.28|0.29|
|Smart television/television|0.82|0.85|0.84|0.84|0.82|
|*District==Castries City|0.15|0.15|0.15|0.15|0.15|
|*District==Castries Sub-Urban|0.25|0.27|0.26|0.26|0.25|
|*District==AnseLaRayeCanaries|0.05|0.04|0.04|0.05|0.05|
|District==Soufriere|0.05|0.05|0.05|0.05|0.05|
|District==Laborie|0.04|0.03|0.04|0.04|0.04|
|*District==Vieuxfort|0.09|0.08|0.08|0.08|0.09|
|District==Micoud|0.10|0.10|0.10|0.10|0.10|
|District==Dennery|0.07|0.07|0.08|0.08|0.07|
|District==Gros Islet|0.15|0.16|0.15|0.15|0.15|
||**Additio**<br>|**nal SWIFT mod**<br>|**el Variables**<br>|||
|Age of Head|52.26|49.85|50.51|50.44|50.70|
|you went without eating for a whole|0.07|0.05|0.05|0.05|0.06|
|day||||||
|your household ran out of food|0.20|0.18|0.18|0.18|0.18|
|because of a lack of money||||||
|Employment Status|0.65|0.72|0.70|0.70|0.70|
|||**Others:**||||
|Wall==wood/timber|0.13|0.11|0.11|0.11|0.12|
|Wall==blocks|0.51|0.54|0.53|0.52|0.52|
|Wall==wood/concrete|0.18|0.18|0.18|0.19|0.18|
|Number of rooms|3.57|3.63|3.61|3.60|3.60|
|Refrigerator|0.81|0.85|0.84|0.84|0.83|
|Freezer|0.13|0.15|0.14|0.14|0.14|
|Washing machine|0.56|0.62|0.60|0.59|0.58|
|Smart devices – tablet|0.21|0.25|0.23|0.23|0.23|
|Jewellery|0.14|0.16|0.16|0.15|0.15|
|Observations|1491|1214|1214|1213|1213|

Note: Variables marked with asterisks are used both as reweighting target variables and as variables included in the SWIFT poverty projection model. To avoid repetition, we only include these variables in the first section (reweighting target variables) but exclude them from the second section (SWIFT model variables). Source: authors’ estimation using HBS 2016.

# _III.3. Results from experiments with the_ _<mark>2018 URHS</mark>_

The poverty estimate of the 2018 URHS is 47.1 percent, and the actual poverty headcount rate of the phone owner subsample is 38.8 percent. The lower poverty rate with the unadjusted weights in the phone sample resonates with the findings in the existing literature that the phone owners are much richer than the full sample of refugees.

Like Rwanda EICV5 and Saint Lucia HBS 2016, three types of weights were calculated to adjust for the sampling bias in the phone owner sample of URHS 2018 in Uganda, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW.<sup>11</sup> The first subfigure in Figure 4 compares the poverty rates of the phone owner sample by applying the abovementioned weights to the consumption data. The poverty rates among the phone owners applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 38.8%, 43.6%, 43.0%, and 42.6%, respectively. While the last three poverty rates with adjusted weights are slightly but not significantly higher than the poverty rate of the phone owner sample with original weights (38.8%), they

> 11 External data sources on the share of refugees from each country of origin are used to calibrate the weights. Details of the non-PSW adjustments are available in Appendix 3.

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