|||weights)<br>||||
|---|---|---|---|---|---|
||**Reweig**|**hting Target Va**|**riables:**|||
|*†Household Size|4.39|4.69|4.46|4.45|4.40|
|Dependency Ratio<br>|0.43|0.39|0.42|0.42|0.43|
|*†Household Size Sq.|0.24|0.27|0.24|0.24|0.24|
|Urban/Rural|0.19|0.26|0.20|0.20|0.18|
|Head Age|44.70|43.25|44.44|44.42|44.79|
|†region==Kigali|0.15|0.20|0.16|0.15|0.14|
|region==Other urban|0.08|0.10|0.09|0.09|0.08|
|<br>region==Southern rural|0.21|0.18|0.21|0.21|0.21|
|*region==Western rural|0.18|0.17|0.18|0.18|0.19|
|*region==Northern rural<br>|0.14<br>|0.13<br>|0.13<br>|0.14<br>|0.14<br>|
|wall==mud/bricks/cement|0.69|0.74|0.69|0.69|0.69|
|wall==wood/stone/tree<br>|0.31|0.26|0.31|0.31|0.31|
|trunks/plastic/other||||||
|lighting==Elec/biogas/generator<br>|0.34|0.45|0.35|0.35|0.33|
|lighting==oil lamp/firewood/candle/|0.09|0.12|0.12|0.13|0.12|
|lantern||||||
|lighting==solar/batteries/phone|0.56|0.43|0.53|0.53|0.54|
|TV set|0.10|0.15|0.11|0.11|0.11|
|Bicycle|0.13|0.18|0.14|0.14|0.14|
|Decoder|0.07|0.11|0.08|0.08|0.07|
|Cupboard|0.10|0.15|0.12|0.11|0.11|
||**Additional S**|**WIFT model var**|**iables (rural)**|||
|†Head Sex<br>|0.25|0.20|0.24|0.24|0.25|
|head age (square/100)|22.41|20.73|22.09|22.11|22.54|
|head_mrstat==Widow or widower|0.17|0.13|0.16|0.16|0.17|
|†Worked at least 1h in last 7 days|0.86|0.88|0.87|0.87|0.87|
|†head_sec==2 Private farm|0.53|0.47|0.51|0.51|0.52|
|consume local/imported rice in the|0.37|0.42|0.40|0.40|0.40|
|<br>past 7 days||||||
||**Additional S**|**WIFT model Vari**|**ables (urban)**|||
|Head Age|44.70|43.25|44.44|44.42|44.79|
|consume beef meat in thepast 7 days|0.11|0.15|0.13|0.12|0.12|
|||**Others:**||||
|head_mrstat==Married|0.62|0.67|0.63|0.64|0.62|
|monogamously||||||
|Radio|0.45|0.56|0.53|0.53|0.53|
|Observations|14580|9589|9589|9589|9589|

Note: Variables marked with * 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) or in the first section where they show up (SWIFT model Variables-rural) but exclude them from subsequent sections (SWIFT model variables-urban). Source: authors’ estimation using EICV5 (2017).

# _III.2 Results from experiments with Saint Lucia HBS 2016 data_

For Saint Lucia’s experiment, the poverty estimate according to the official statistics is 25.0 percent, and the poverty rate among the subsample without any weight adjustment is 22.2 percent (Figure 3). The difference of 2.8 percentage points between the actual rates of the reference and phone surveys is not statistically significant at the five percent level. This small difference stems from widespread mobile phone ownership; in the Household Budget Survey 2016, 81.5 percent of households (1,214 out of 1,490) stated they own a mobile phone.

Like Rwanda EICV5 in the previous section, three types of weights were calculated to adjust for the sampling bias in the phone owners’ sample of HBS 2016 in Saint Lucia, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW. The first subfigure in Figure 3 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 22.2%, 23.5%, 24.2%, and 24.7%, respectively. Among the last three poverty rates with adjusted weights, the performance of 15