prepared and brought an official letter explaining this survey is part of the official data collection.

To test the performance of this proposed approach, a pilot was conducted in Serbia. This pilot focuses on (i) whether the S2S method works well with telephone interviews, or more specifically, whether responses to a questionnaire can differ between face-to-face interviews (F2F) and CATI, and (ii) how cost-effective the telephone data collection is. Serbia was chosen for this pilot because the SORS had an established telephone call center and extensive experience collecting data via telephone interviews, in particular for the Labor Force Survey, and the SORS showed interest in participating in this experiment.

# **4 Design of the Pilot**

This section explains how the pilot was designed. It begins by how the formula was developed, followed by the description of how samples are selected for F2F and CATI groups. It also describes training for enumerators and other logistical arrangements. The results of this pilot will be discussed in the next section.

## **4.1 Model Selection**

In this pilot, consumption data are not collected, but are projected from 10 or 15 simple questions using a formula that is developed using an S2S imputation approach. The S2S assumes that following relationship between household expenditure and non-monetary indicators: where the dependent variable ��� is (log) per capita consumption of household _h_ in location _c_ , ���� is a vector of household explanatory variables, ��� is a vector of location specific variables, � and � are vectors of coefficients, and � is a constant. The stochastic error term ��� can be divided into a location-specific effect �� and a household-specific effect ��� such that ��� = �� + ��� . Hetroskedasticity of the household-specific effect ��� is allowed in this specification.

The model selection involved an iterative process. At first, we tried the variables that are commonly available in the S2S imputation and poverty map literature. We included four categories for candidate explanatory variables: (1) demographics and education, (2) durable asset ownership, (3) current economic status (employment status, main income source, etc.), and (4) location variables (stratum fixed effects). However, the model with the four kinds of variables could not predict the change in poverty rates across time.

Next, we included variables that more sensitive to changing economic conditions and in turn candidates for picking up short-term changes in poverty rates. We added variables on recent purchases, that is, indicators of whether any household member purchased particular items, such as clothes and shoes, during the last three months. Secondly, we included subjective perception questions such as whether monthly income satisfies a household’s monthly needs for your households and how a household’s current situation is compared to one year ago. Lastly, we included more employment-related variables based on all of economically active household members, in addition to household head’s employment-related variables, in order to be able to explain the change in the economic situation. In the end, about 40 variables are used in the model and statistically significant variables are selected using the backward stepwise selection. The list of variables and coefficients from the identified prediction model using the 8