# **Data**

We applied this method to recent household consumption data from Kenya. Kenya’s source for official poverty estimates is the Kenyan Integrated Household Budget Survey (KIHBS). The two last rounds were implemented in 2005/6 and 2015/16. The 2005/6 round used a representative sample of households in Kenya stratified by 7 provinces and 69 districts split into urban and rural. The sample size of the cleaned data set includes 12,695 households in 1,338 clusters. The 2015/16 round used a representative sample of households in Kenya stratified by 47 counties split into urban and rural. The sample size of the cleaned data set includes 21,585 households in 2,387 clusters. For both surveys, data collection was carried out over a period of 12 months.

The 2015/16 round was accompanied by a CAPI pilot implementing the rapid approach. The pilot used the same sampling frame as the 2015/16 round and interviewed up to an additional 6 households in the same clusters, resulting in a sample size of 12,662 households (as not all clusters and all intended households were interviewed due to non-response).<sup>8</sup> The questionnaire was derived from the KIHBS 2015/16 questionnaire but was considerably simplified across all modules. Specifically, the consumption module was administered according to the rapid approach. We are using the data set that was constructed with 5 food and 5 non-food items in the core (selected based on the highest consumption share in KIHBS 2005/6), with the remaining 128 food and 76 non-food items partitioned into 3 optional modules.<sup>9</sup> Thus, the expected time saving was about 30 percent.

In the remainder of the paper, we use KIHBS 2015/16 with the full consumption module as a benchmark. The previous round of 2005/6 is used to define the core items, and for two of the alternative approaches it is used to adjust the poverty line and to build the consumption model for the cross-survey imputation. The 2015/16 pilot is used as a real-world example for the implementation of the rapid approach. We harmonize the data sets to ensure comparability across the different surveys. The harmonized data sets include 133 food and 81 non-food items. The consumption aggregate is exclusively based on these 214 items. In addition, we use harmonized location and household characteristics for the various models, including a binary and a categorical location variable as well as 6 additional binary, 9 additional categorical and 9 continuous variables for household characteristics (Table 5 in the Appendix).

Consumption shares differ markedly between the 2005/6 and 2015/16 surveys, which is unsurprising given the 10-year gap (Kenya National Bureau of Statistics 2018).<sup>10</sup> For example, the top 10 food items in 2005/6 capture 58 percent of the food consumption share, but only 39 percent of the consumption share in 2015/16. Non-food consumption changed to a lesser degree. The top 10 items in 2005/6 represent 64 percent of non-food consumption shrinking to 59 percent in 2015/16. These differences will impact the

> 8 Balance mean tests are shown in Table 6 indicating similar households in both surveys with similar although not always statistically indistinguishable characteristics.

> 9 The assignment of optional modules to households was balanced with 4,222 households assigned to module 1, 4,192 to optional module 2, and 4,248 to optional module 3.

> 10 See Table 4 for the shares of the items with the top 20 consumption shares for KIHBS 2005/6 and KIHBS 2015/16.

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