is helpful to utilize a previous survey from the same or a similar population to assign a few key items to the core module. In the best case, the selected items are still highly consumed and will improve the estimates of poverty. In the worst case, the selected items are no longer important in which case they will hardly affect the time savings compared to a design with zero core items and also equal its performance. In countries with large variation in diet across regions, subnational core modules can potentially improve estimates even further.

This paper also demonstrates the difficulty of achieving convincing results using alternative methods. Simply removing items from the consumption aggregate to create time savings, but without adjusting the poverty line, can lead to considerable bias in the poverty estimates. Better results can be achieved by adjusting the poverty line based on a previous survey in order to accommodate the reduced number of items, but without conserving the shape of the consumption distribution measured by the Gini. Furthermore, the potential for large changes in consumption patterns, which cannot be determined under this approach, creates considerable uncertainty in the resulting estimates. Similarly, a survey based only on covariates and their structural relationship with poverty estimates from a previous survey introduces a large bias in the estimates, at least in the studied case with a 10-year gap between surveys. The proposed rapid approach outperforms all these approaches, only at the cost of increased complexity.

The rapid approach introduces additional complexity into both the questionnaire design and the estimation of poverty. The capacity of enumerators is often low in developing countries. While the rapid approach increases the complexity of the questionnaire, CAPI technology easily solves this problem. Survey software can automatically compile a single consumption module based on the core and optional modules for each household, without making the partition explicit to the enumerator or demanding the execution of complex skip patterns. Furthermore, advanced CAPI technology can be used to generate the questionnaire automatically based on the assignment of the household to an optional module. While enumerators should be made aware that different households will be asked for different items, administering a rapid questionnaire does not require any additional training of enumerators beyond the standard skills for consumption questionnaires.

Conversely, the analysis of data using the rapid approach requires high analytical capacity, something that is usually lacking in developing countries. While the general concept of the assignment of optional consumption modules to households can usually be explained to local partners, poverty analysis based on a bootstrapped sample of the consumption distribution can potentially be too demanding for local capacity. However, even standard poverty analysis often surpasses the limits of local capacity, especially in conflict or post-conflict settings. Therefore, capacity building tends to focus on data collection skills with the long-term perspective of creating data analysis capacity. In addition, the rapid approach might be the only possibility to create poverty estimates in certain areas. For example in the case of Somalia, the rapid approach limited overall questionnaire administering time to less than 60 minutes for more than 90 percent of households as required by security considerations for enumerators (Pape and Mistiaen 2018; Pape and Wollburg 2019).

The rapid approach administers different consumption modules to different households. In theory, this can create a response bias if households report differently on a consumed item depending on the type 16