In this paper, Stifel and Christiaensen (2007) provide theoretical guidance regarding the choice of variables to be included in imputation models, so as to maintain comparability and reliability of imputed poverty data. They recommend including variables that change over time, but call for the exclusion of variables whose rates of return are likely to change markedly in the face of evolving economic conditions. This argument makes sense in theory, but it is difficult to identify which variables would satisfy these conditions. For example, Stifel and Christiaensen (2007) include ownership of several consumer durables in their imputation models, but Harttgen, Klasen and Vollmer (2012) criticize this decision because of the so-called “asset drift” effect— where the pace of improvement in asset ownership is much faster than of income growth. Unfortunately, this could not be tested in the Kenya data because only one round of consumption data was available although in the end, which variables satisfy the recommendations of Stifel and Christiaensen (2007) is intrinsically an empirical question.

Christiaensen et al. (2012) moved one step further by conducting an experiment using a series of the past household budget surveys available in the Russian Federation, Vietnam, Kenya, and rural China. Their empirical strategy is to first create projection models using one round of household budget survey, impute household expenditure data into other rounds of household budget survey, and then compare poverty rates projected from the imputed expenditure with those estimated directly from actual consumption data. The comparison between projected poverty rates and directly estimated poverty rates can give us a sense of how reliable the S2S is. The results provide suggestive support to the notion that imputation models constructed from the past rounds can predict household consumption data of future rounds. Such results are encouraging and give us a certain level of confidence on the stability of imputation model coefficients over time.

Douidich et al. (2013) is the first attempt of carrying out a test on reliability of S2S between different surveys. They created consumption models in one round of Household Expenditure Survey (HES), imputed consumption data into more frequent Labor Force Surveys (LFS) and estimated poverty rates using the imputed data in Morocco. To examine the accuracy of the projected poverty statistics, they compared them with poverty rates directly estimated from consumption data of HES conducted in the same year as the LFS. The results are very encouraging. Projected poverty rates are very close to directly estimated poverty rates, irrespective of which round of HES is used to create the consumption models. But, now, a question is whether this is also the case for other countries and other times. The methodology was examined further in Newhouse et al. (2014) and Dang et al. (2014).

As shown above, the S2S method has been tested in many different settings, but whether it works well with telephone interviews remains an open question. The S2S is attractive for telephone interviews because it needs only 10 to 15 simple questions to estimate poverty rates. But telephone interviews are not flawless and some potential shortcomings of telephone interviews might severely bias poverty estimates produced by the S2S method. The next subsection reviews the literature on the telephone interviews.

# **2.2 Telephone Data Collection**

The literature on the telephone data collection suggests several potential pitfalls of telephone interviews. They include (i) high attrition and non-response rates, and lack of representativeness; (ii) response bias; and (iii) lack of welfare indicators necessary to monitor monetary poverty. This subsection summarizes findings on the three key issues.

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