# (i) High attrition and non-response rates, and lack of representativeness

For example, the “Listening to LAC” report (World Bank 2013), which is called L2L hereafter, reported that in one of their pilots, nearly 60 percent of households who agreed to participate in a subsequent telephone survey did not respond to the telephone interviews. Furthermore, it found the non-responses are not non-random. Croke et al. (2012) also show that in their Tanzania survey, on average, only 62 percent of households in the baseline survey and 75 percent of households with telephone access responded to telephone interviews. Leo et al. (2015), which used IVR for collecting data in four countries,<sup>2</sup> show a connection rate (the number of respondents over the number of telephone calls) ranges between 15 percent and 31 percent.

Such low response rates make it difficult to create a sample representative for a population of interest. Croke et al. (2012) found the distribution of a wealth indicator is largely different between the baseline survey and the subsequent telephone survey. Leo et al. (2015) show that although their surveys are designed to be nationally representative, the distributions of key demographic variables differ significantly from those of a Demographic and Health Survey, which is nationally representative.

There are several ways to reduce the biases. For example, even if the non-responses or attritions are not random, as long as they are associated with observable characteristics of respondents, the bias can be addressed by reweighting the remaining respondents by the inverse of the probability of attrition (see more details of this argument in Croke et al. 2012). Leo et al. (2015) follow an iterative proportional fitting algorithm developed by Bergmann (2011). A challenge for this type of adjustment is that it is difficult to find an adjustment in weight so that all variables become representative because each variable shows different rates of disparity from the nationally representative numbers. As a result, to make one variable consistent with the nationally representative number makes others inconsistent.<sup>3</sup> (ii) Lack of welfare indicators necessary to monitor monetary poverty It is often argued that telephone surveys can be used to collect poverty data in a cost-effective and timely manner. However, we did not find any telephone survey that collects consumption or income data necessary to estimate monetary poverty indicators. Croke et al. (2012) collected information on asset ownership and constructed a wealth indicator, but they did not collect consumption or income data that are necessary to estimate monetary poverty indicators. L2L also collected poverty correlates but did not collect consumption or income data directly. Demombynes et al. (2013) also did not collect consumption or income data via telephone surveys although they linked the telephone surveys with the latest Household Budget Survey so that the poverty status of a sampled household is known.

Most telephone surveys did not collect consumption or income data likely because collection of consumption or income data is time-consuming. To estimate a good quality monetary poverty 2 Afghanistan, Ethiopia, Mozambique, and Zimbabwe.

3 Even if a sample is not representative for a population of interest, Alderman et al. (2001) argued that key parameter estimates are often not affected. In other words, as long as we are interested in some regression parameters, even if the sample is not representative, we will be able to get estimates of the parameters that are not statistically significantly different from those estimated from a representative sample.

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