Ethiopia conducted twelve rounds of HFPS between April 2020 and June 2021. Round-on-round attrition was high due to (i) enumerator and respondent fatigue, (ii) challenges with network connectivity, particularly in rural Ethiopia; and (iii) a conflict that erupted in the North of Ethiopia and that prevented calls among households in the Tigray regions after the seventh round. Round seven, which included a SWIFT module, was conducted between October 19 and November 10, 2020, and interviewed 2,534 households (715 rural and 1,819 urban). HFPS round seven (HFPS7) data is the main data set used for poverty and inequality analysis and profiling of the poor.

The sample of the HFPS7, similar to all other rounds of the Ethiopia HFPS, is subject to both sampling bias and non-responses bias. The selection of phone owners from the ESS4 reference survey leads to sampling bias, and the random digit dial process as part of the HFPS4 further leads to nonresponse bias. Therefore, by construction, this subsample is subject to both types of biases.

# <u>Results</u>

In this Ethiopia example, the poverty estimate from the reference sample is 23.5 percent, and the poverty rate among the subsample of phone owners without any weight adjustment is 16.6 percent (Figure 5). The difference of approximately seven percentage points between the actual rate of the reference survey and that of the phone survey is not statistically significant at the five percent level.

Like previous experiments, three types of weights were calculated to adjust for the sampling bias in the subsample of ESS4, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW. The first subfigure in Figure 5 compares the poverty rates of the phone owner sample by applying the abovementioned weights to the consumption data. The poverty rates among the biased HFPS subsample applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 16.6%, 22.3%, 23.0%, and 22.6%, respectively. These results show that different types of reweighting procedures reduce the bias due to sampling and nonresponses but do not fully eliminate the bias.

Figure 5 (subfigure 2) shows that the SWIFT model trained with the full sample of the reference data, when combined with reweighting techniques, accurately projects the national poverty rate, but does not show almost any clear improvement compared with the results with only reweighting. The SWIFT-based poverty rate using original weights is 18.7%, and the SWIFT-based poverty rate applying PSW and non-PSW weights is 21.4%. These rates are not as close to the poverty rate in the reference survey (23.5%) as the poverty rate estimates using reweighting procedures. This suggests that once reweighting alleviates sampling and nonresponse bias, the SWIFT projection method does not make additional improvements.

Lastly, Table 5 displays the summary statistics of variables included in the reweighting procedure, variables included in the SWIFT projection model, and other untargeted indicators not included in either procedure. All reweighting techniques produce close estimates for all the above-mentioned indicators, and the difference between reweighting procedures is minimal.

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