and number of items previously asked. Unfortunately, we cannot estimate such a response bias in the available data. However, implementation of the rapid approach with an enhanced design with different optional modules varying in their comprehensiveness of items can in general shed light on this bias. Comparison between responses for the same item in a comprehensive and a non-comprehensive list can also indicate a lower bound for response bias. Assuming that the context of a comprehensive list is a better estimate, the response bias could be corrected for. However, it is expected that this type of response bias is very small in comparison to general measurement and estimation errors.

The main source of bias for the rapid approach is created by the assumption of zero co-variance between optional modules. Further research can help to estimate co-variance between modules within the survey and adjust the consumption estimates accordingly. Using a random assignment of items to optional modules, the co-variance between groups of items within an optional module can potentially be used to estimate the co-variance between optional modules. Or administering optional modules that share items might also be helpful to estimate the co-variance between modules.

The rapid approach reduces administering time considerably. While this creates opportunities to include additional questionnaire modules on different topics (e.g. remittances or health), it also has the potential to reduce the non-response rate (A. Deaton and Grosh 2000). The KIHBS 2015/16 survey suffered from a high non-response rate specifically in wealthier areas. For example, the capital city Nairobi had a response rate of 77 percent compared to 92 percent in the rest of the country. The highest non-response rates were specifically observed in clusters in wealthier areas within Nairobi. A high correlation of non-response with welfare status can lead to biased poverty estimates. The considerably shorter pilot survey – which not only used the rapid approach for consumption but generally shortened the questionnaire across modules and was carried out using tablets rather than paper – did not show the same pattern of lower response rates in wealthier areas. The pilot response rate of 99 percent in Nairobi was considerably higher than the standard KIHBS and was the same as in the rest of the country.<sup>14</sup> Thus, the rapid approach can help to contribute to shorter questionnaires mitigating concerns around low response rates, especially if correlated with welfare status. In addition, the rapid approach is likely to reduce enumerator and respondent fatigue based on the documented impact of fatigue on (consumption) estimates in the literature (Rolstad, Adler, and Rydén 2011; Diehr et al. 2005; Snyder et al. 2007; Fiedler and Mwangi 2016; Finn and Ranchhod 2015).<sup>15</sup> The rapid approach might also be particularly useful in the context of evaluating shock or project impacts on poverty. In these cases, reliance on structural models estimated between surveys is dangerous. Shocks are likely to distort structural relationships between household characteristics and poverty. For example, a light shock is often mitigated by the household reducing its consumption, rather than selling assets or moving into another dwelling. A structural model estimated before the shock will not be able to capture the reduced consumption, thereby underestimating the impact of the shock. Similarly, project impacts cannot be adequately estimated using a structural model from before the project. For example, the

> 14 The change in the response rate is unlikely to be explained by the transition from PAPI to CAPI (Banks and Laurie 2000; Schräpler, Schupp, and Wagner 2010).

> 15 Estimating the reduction of fatigue on measurement error would require a specifically designed survey.

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