There are some features of this proposed approach that are worth noting. First of all, this approach does not collect consumption or income data directly. As mentioned, the L2L study recommends that a telephone survey should not have too many questions; otherwise, a telephone interview is likely to be unfinished due to a sudden loss of connection or interviewees’ refusal to answer more questions. This is problematic if consumption or income data need to be collected because a telephone interview often requires one hour or more.

Instead, the above procedure uses an S2S approach. This approach does not collect consumption or income data, but collects simple questions, like ownership of assets, housing conditions, employment and education level of household members, and household size, from which each household’s consumption or income data are projected using a formula. The formula is developed by running regressions using the latest round of a household survey that includes both consumption/income and other questions. Literature suggests that the number of questions needed for the S2S imputation approach is as few as 10 to 20; as a result, collection of these variables usually takes only 5 to 10 minutes. Therefore, even telephone interviews can collect enough information necessary to estimate household expenditure or income.

However, there are several challenges for the S2S approach. First, the formula needs to be stable over time. This can be a strong assumption because people might change consumption patterns. Therefore, when we developed the formula in the first step, we conducted several tests to check whether the formula is stable over time using the past data.

Second, reporting patterns can differ across interview modes. As discussed in the literature review, for the S2S approach to work, households need to respond to questions in exactly the same way as when they responded to the household survey used for developing the formula. But, this is not necessarily the case. To minimize the reporting bias, we use CATI for telephone interviews because L2L and Croke et al. (2012) show that the reporting bias of CATI appears much less than SMS or IVR. Therefore, we select CATI in the fourth step.

The second step is also important to ensure the national representativeness of data collected by telephone interviews. Telephone ownership is still skewed in a richer segment of population in many developing countries. If we simply call households randomly, the likelihood to select richer households is much higher than the proportion of their population. If most of the poor do not have telephones, we might not get any information from the poor. As a result, any statistics from the telephone survey might not be representative at the national level. To avoid this, prior to telephone interviews, we conduct a careful sampling exercise, select a nationally representative sample, and carry out a baseline survey with the select households to collect their telephone numbers.

To minimize the high non-response rate, the third step is introduced based on recommendations from the staff in the Statistical Office of the Republic of Serbia (SORS). According to pilots in the L2L study, non-response rates could reach more than 50 percent, and the non-response rate could be not random. As a result, even with post-enumeration adjustments, it is difficult to produce poverty and inequality statistics that are representative at the national level. However, surveys in the L2L study were carried out by a private sector, not a national statistics office. Households do not have any obligation to respond to the surveys.

The SORS argued that if a survey were conducted without an official letter of request to select households, the non-response rate would be very high in Serbia. This is a reason the SORS staff 7