effective in reducing the bias. In fact, our study finds that the performance of reweighting in estimating poverty can be improved when combined with poverty imputation methods such as SWIFT.

# _Experiments_

This paper examines whether, and if so, to what extent, a combination of reweighting techniques and the SWIFT poverty projection methodology can eliminate sampling biases in poverty estimates based on biased survey samples. To see this, we first use reference household surveys in Rwanda, St Lucia, and Uganda and construct subsamples by selecting households with at least a mobile or landline phone. These samples are subject to sampling bias by construction. Without reweighting and SWIFT poverty projections, the poverty rates in these subsamples of phone owners are lower than those in the full samples. We then examine whether reweighting and SWIFT poverty projections can correct for the abovementioned bias.

Phone and web survey data collections face sampling and nonresponse biases, but the abovementioned experiments only focus on sampling biases that arise from uneven phone ownership. To understand the ability of the SWIFT and reweighting techniques to adjust for nonresponse bias, this paper conducts an additional experiment using the sample of Ethiopia High-Frequency Phone Survey round 7 (HFPS7), which is a subsample of the 2018/19 Ethiopia Socioeconomic Survey round 4 (ESS4). Since this subsample of ESS4 includes only phone owners, it is subject to sampling bias. Also, since it includes only households of ESS4 that responded to the HFPS7, it is also subject to nonresponse bias. Using this subsample, we conduct the same analysis as above to assess whether reweighting techniques and SWIFT can correct the bias in poverty estimates arising from both sampling and nonresponse biases.

This paper is organized as follows. Section II introduces the SWIFT poverty projection methodology and reweighting techniques, and Section III displays results from four experimental studies (Saint Lucia, Rwanda, Uganda, and Ethiopia). Section IV concludes with an assessment of a combination of reweighting and the SWIFT-based poverty projection model in eliminating the bias in poverty estimation based on the four case studies.

# **II. SWIFT and Reweighting Methodology**

# _II.1. SWIFT poverty projection methodology_

SWIFT is an application of Survey-to-Survey imputation techniques (S2S) to monitor poverty rapidly. SWIFT trains an imputation model in a training dataset by regressing household expenditures/incomes on poverty proxies. Household expenditures and poverty rates are then imputed in another dataset, called “output data,” by plugging poverty proxies of the output data into the model. Figure 1 illustrates the process.

There are two key assumptions in the standard SWIFT methodology. First, the relationship between household income or expenditure and poverty correlates in the output data can be expressed in equation (1): l (1) llwhere hoo = xxhoo′ββoo + uuhoo ) where l refers to a natural logarithm of the household income or expenditure of household uuhoo~NN(0, σσoo _h_ in the output data _o_ . is a vector of poverty correlates of household _h_ in the output data, _o_ . is a vector hoo of coefficients of poverty correlates (ll ). refers to a residual and is often assumed to follow a normal hoo oo distribution of xx (kk× 1) ) .<sup>1</sup> The output data includes the poverty proxy data }h=1 but does not include ββ (kk× 1) hoo hoo xx uu HH oo hoo NN(0, σσ {xx

> 1 This normal distribution and linearity can be relaxed. For the sake of exposition, normal distribution is assumed.

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