# **IV. Concluding remarks**

Reweighting procedures do not fully correct the bias in poverty estimation, and the selection of reweighting procedures listed above does not make much difference in correcting the bias. However, our findings show that a combination of reweighting and the SWIFT-based poverty projection approach effectively eliminates the bias in poverty estimation in all four case studies. The literature proposes several weight adjustment procedures to reduce sampling bias in phone or web surveys. However, as far as we know, there has been no previous attempt that examined the role of reweighting on poverty projections.

To monitor the impacts of the COVID-19 pandemic in a frequent and timely manner, the World Bank launched the COVID-19 HFPS surveys in around 80 countries. The COVID-19 HFPS surveys offer valuable and timely information on the living conditions, livelihoods, coping mechanisms, and social assistance during the pandemic, but they also have serious limitations. First, these surveys do not have direct poverty or inequality measures. Second, the data collected often suffers from sampling and nonresponse bias due to limited phone ownership in many developing countries and nonresponses associated with phone interviews. To address the first limitation, SWIFT methodology was adopted. SWIFT utilizes a machine-learning-based technique to impute poverty and inequality statistics using 10 to 15 simple questions included in the HFPS surveys (Yoshida et al., 2022a). However, the second limitation, namely, bias arising from sampling and nonresponses, can lead to bias in the poverty statistics estimated by SWIFT. To address this issue, this paper explores some widely used reweighting techniques and examines if they could alleviate these bias in poverty projections.

Specifically, this paper identifies a few variants of propensity score weightings (PSWs), including 1) PSWs and 2) a combination of PSW and non-PSW techniques. To assess the performance of reweighting procedures and the SWIFT-based poverty projections, we conduct experiments by drawing subsamples of phone owners from reference surveys in Rwanda, St Lucia, Uganda (refugees), and Ethiopia. In the experiment in Ethiopia, we excluded households who did not own a phone from the ESS4 survey and further excluded those who did not respond to the Ethiopia High-Frequency Phone Survey, which leads to both sampling bias and nonresponse bias by construction. Using these artificially biased subsamples, we test whether reweighting procedures and the SWIFT poverty projections combined can reduce the sampling and nonresponse bias in poverty estimation.

Interestingly, the contribution of reweighting procedures and the SWIFT-based poverty projection technique in reducing the bias of the poverty estimates differ by context. In the experiments of Rwanda EICV5 and Uganda Refugee Household Survey, the SWIFT poverty projections are the main contributor to reducing the bias in poverty estimation. In the experiments with Ethiopia ESS4 and St Lucia 2016 HBS surveys, reweighting procedures are the main contributor to reducing the bias in poverty estimation.

How does the SWIFT approach to poverty projection play a role in minimizing sampling bias? Existing literature indicates that while reweighting can mitigate sampling bias, it cannot completely remove it. This paper's experiments also confirm that poverty rates derived from actual consumption data are still subject to bias. In contrast, the poverty rates imputed by SWIFT, post-reweighting, have almost no sampling bias in all four examined cases. What accounts for this difference?

A plausible explanation lies in the limitations of reweighting. Even though it balances many observed variables between a biased subsample and the full sample, reweighting may not balance certain unobserved variables correlated with household expenditures and poverty. Consequently, poverty rates based on actual consumption data, which are correlated with the unobserved but unbalanced variables, could retain the sampling bias. In contrast, SWIFT's poverty projections are not influenced by these unobserved household characteristics in imputing household-level consumption. Thus, if all regression variables in the SWIFT models are effectively balanced through reweighting, the resulting poverty rates should be unbiased. However, further theoretical and empirical studies are essential for definitive conclusions. ` 27