one hour or more. These might be reasons why, despite high expectations for telephone surveys for filling data gaps, there is no country (as of April 2016) that uses telephone interviews to collect data for estimating official poverty and inequality statistics.

This paper proposes a new procedure of telephone surveys that can address the aforementioned issues, and shows its performance in a pilot that was conducted jointly by the Statistical Office of the Republic of Serbia (SORS) and the World Bank. One of the most distinctive features of the new approach is the use of a formula for estimating poverty. Collection of consumption or income data directly is time-consuming and labor intensive. Instead, our proposal is to collect 10 to 15 simple questions and project household expenditure or income from them using a formula. This approach is often called a “Survey-to-Survey imputation approach” or S2S. It saves on interview time significantly and the literature shows that S2S can project poverty rates reasonably well under certain circumstances. But, so far, none has tested whether S2S works well in telephone surveys. This paper is one of the first studies that systematically analyzes whether S2S can be used for telephone surveys.

This rest of this paper is organized as follows. Section 2 provides a literature review. Section 3 introduces a new approach for telephone data collection. Section 4 explains the design of this pilot in detail. Section 5 presents the results of this pilot, and Section 6 concludes.

# **2 Literature Review**

## **2.1 S2S Method**

Given that it is difficult to collect consumption or income data directly via telephone interviews, a survey-to-survey (S2S) imputation technique is a good alternative for monitoring monetary poverty via telephone surveys. This subsection reviews the literature on the S2S method.

The first incidence when S2S played a key role for poverty estimation can be traced to Deaton and Dreze (2002) and Kijima and Lanjouw (2003). The Government of India made a slight change in the recall period in part of the food consumption module for its National Sample Survey Organization (NSSO) survey of 1999-2000, and it was widely argued that the resulting data likely overestimated consumption expenditure data. Since consumption data using the recall period consistent with the previous rounds for 1999-2000 were unavailable, S2S was needed to estimate changes in poverty. Deaton and Dreze (2002) created a model, using the portion of the consumption module in which recall remained unchanged, to project total household expenditure. Kijima and Lanjouw (2003) applied the same approach, but using non-consumption data. Whether using a portion of consumption or non-consumption characteristics leads to more accurate predictions remains open to debate. Subsequent analysis using consumption data from so called thin rounds of household surveys, which are collected between years with a large household survey but are not used to estimate official poverty rates, suggests that the finding of Kijima and Lanjouw (2003) seems more plausible. However, there no systematic test to examine the reliability of either approach was conducted.

Stifel and Christiaensen (2007) made one of the first attempts of conducting S2S for different household surveys. They created consumption models based on the 1997 Kenya Welfare Monitoring Survey (WMS) and applied the models to three consecutive rounds of the Demographic and Health Survey (DHS) between 1993 and 2003. The 1997 WMS has both consumption and non-consumption data while DHS has only non-consumption data.

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