# **2. Methodology**

## **Overview**

The rapid approach being proposed here applies a split-questionnaire design to the consumption module of a household survey, thereby generating systematically absent data that can be conveniently imputed.<sup>3</sup> Instead of having all households report on all consumption items, important items are assigned to a core module and the remaining items are split into two or more optional modules.<sup>4</sup> Each household then answers the questions in the core module and in only one of the optional modules. This approach reduces average interview time considerably, down to 45 to 60 minutes per household for a standard household consumption survey. The cost of this efficiency gain is that data are deliberately absent for those optional modules that were not administered to certain households. We can however offset this cost by estimating the deliberately absent data for each household based on the data collected from other households for that module. While this approach utilizes a structural model for the imputation of the deliberately absent data, the model is estimated within the survey rather than between two surveys, thereby circumventing the problem of biased structural parameters due to having different sample populations or considering the same population at different points in time.

The rapid approach starts by defining the number of core items and the number of optional modules for the non-core consumption items. The smaller the number of core items and the greater the number of optional modules used, the faster the questionnaire can be administered, as fewer items need to be asked for each household. However, having fewer core items and more optional modules also increases the uncertainty in the estimation as less consumption information is available. Thus, the choice of these two parameters can be informed by simulations on a previous or similar survey to gauge the performance of the estimation vis-à-vis the time savings in administering the questionnaire. Another consideration is that it is beneficial to balance the number of households for each optional module, ideally at the cluster level of the survey.

The next step is to select core consumption items. Although consumption in any given country will exhibit some variability, data on a few dozen key items will usually be sufficient to capture the majority of consumption. Important consumption items can be identified using average consumption share per household or across households, as estimated by previous consumption surveys in the same context or recorded consumption shares in neighboring and/or similar countries. While a good choice of core items will improve the performance of the estimation, the methodology still works if no core items are used, e.g. in a context without any prior information. The identified key items are then assigned to the core module that will be administered to all households.

Finally, non-core items are randomly partitioned into optional modules. It is important to note that the conceptual distinction between core and optional items should not be reflected in the layout of the questionnaire. Instead, all items per household need to be grouped into categories of consumption items

> 3 While the split-questionnaire design is more popular in other disciplines such as psychology (e.g. Graham, Hofer, and MacKinnon 1996), the approach has not yet been applied to large-scale household-based surveys, nor with the goal of reducing the time required to estimate consumption or poverty.

> 4 As is shown below, the core module is not strictly necessary, further reducing the interview time.

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