**Table 9: Median duration of interviews (minutes)**

||Urban|Rural|Pooled|
|---|---|---|---|
|F2F|10|12|10|
|CATI pre-interview|5|7|5|
|CATI interview|4:44|5:49|5:05|

Source: The authors’ calculation using data collected during the pilot.

survey was in the field between late June and early July 2013. The final data was delivered by the end of July 2013. In total, this entire pilot was prepared and implemented in five months.

## **5.4 Caveats**

While this experiment provides some evidence that differences between F2F and CATI are likely to be negligible, several other challenges exist for S2S imputation methods to succeed. One of the central assumptions with this approach is that the imputation models are stable across time; so one must be alert to potential shifts in the model coefficients. Models might change over time, particularly during a crisis and as the period between the model calibration and prediction year widens.

Furthermore, this approach is not a substitute for the collection of high quality consumption or income data. The full multi-module household surveys allow for more in-depth analysis of poverty and distributional analysis and are vital to expanding the evidence base to inform policy decisions. Also, to develop reliable and accurate imputation models, we need to have rich and reliable multi-topic household surveys. Therefore, it is critical for the S2S approach that high quality multi-topic household surveys are implemented every few years.

As for the role of telephone surveys, they can offer several advantages in terms of time, costs, and flexibility but they also pose several challenges for sampling to obtain nationally representative estimates, particularly in the context of developing countries. First, telephone coverage is not 100 percent in most developing countries. If telephone signals do not reach some areas, populations in the areas will not be included in telephone surveys; as a result, they are not fully nationally representative. This concern is particularly serious in rural areas of developing countries. To overcome this, we might need to carry out a household survey with mixed interview modes – F2F for areas without telephone signals and CATI for areas with telephone signals. In this way, we can maintain representativeness of data while taking advantage of cost-effectiveness of telephone data collection. Needless to say, the findings of this paper are critical for this mixed interview approach.

Second, even if some areas are covered by telephone networks, poor households tend to be the last group of people that own telephones. Given that our main interest is on poverty estimation, this is a potentially serious pitfall. Once poor households are selected and they do not own telephones, it is necessary to provide cell phones to them for representativeness of data to be maintained.

# **6 Conclusion**

The results of this experiment suggest that conducting phone interviews to collect nonconsumption data to predict poverty using an S2S method would not lead to any systematic bias. Indeed, we found that differences in interview mode are not likely to lead to large differences in responses and in turn bias poverty projections. Although these results do not 21