In the first step, we select a set of comparable non-monetary indicators that are available in both the NLSS and GHS (Table _2_ ). The advantage of using the NLSS and GHS is that the questionnaires on household characteristics, assets ownership, consumption frequency and demographics is the same and asked using the same recall periods (see Table _4_ ). Moreover, we can ensure comparability between the different indicators using data for from the 2018/19 NLSS and 2018/19 GHS. While the two surveys differ in their data collection schedule – with the 2018/19 GHS being collected in two visits in July-September 2018 and January-February 2019 and the 2018/19 NLSS being collected over 12 months – they provide information for part of the same year making it possible to check if the assumptions behind the survey-to-survey imputations are plausible. Column 3 in Table _2_ shows the difference in means for each variable used in the survey-to-survey exercise and available in the 2018/19 NLSS and 2018/19 GHS. Zone population adjustments are applied to the 2018/19 GHS data to ensure that the zone-level population estimates match those from the 2018/19 NLSS. The indicators are overall highly comparable, with only a few variables – such as household head’s employment variables and consumption frequency of food-items – showing larger differences possibly due to seasonal variation. In Section 6, we test the robustness of the results to different specifications of the consumption model that do not include this set of variables. Variables to be included in the consumption model are selected using stepwise selection with an optimal p-value of 0.01.

In the second step, we develop a consumption model using the selected variables and consumption data from 2018/19 NLSS. Variables used in the consumption model include regional dummies, demographics of the household (dependency ratio), household head characteristics (gender, employment category), living conditions (main source of cooking fuel, toilet availability), consumption frequency dummies (food and non-food items), asset ownership (air conditioning, washing machine, cars and other vehicles, generator, microwave, TV set, computer). The model therefore includes variables that capture short-run variation – such as employment and a set of dummy variables for whether certain food and non-food items were being consumed – as well as more stable household characteristics – such as location, demographics, housing amenities, and ownership of assets.<sup>15</sup> The model relies on the assumption that these indicators are highly correlated with poverty and explain a relatively large share of variation in poverty: the R-squared in all of the consumption models presented in this paper is at least 0.50.

> 15 The analysis adopts a “SWIFT Plus” (Survey of Wellbeing via Instant and Frequent Tracking) approach to conduct the survey-to-survey imputation. This approach was developed to overcome possible limitations arising in case a large shock or crisis, as the 2015/16 oil price plunge in Nigeria, occurred between the baseline and target surveys. It consists of including variables that reflect households’ current welfare status, such as employment, in the consumption model used in the imputations.

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