Identifying common variables between the surveys and the census The Small Area Estimation (SAE) process entails constructing a statistical model for predicting the outcomes, which in this case are per capita consumption when estimating monetary poverty and height for age Z scores when estimating stunting. Ultimately, this model, which is trained using survey data to forecast monetary poverty, is utilized for poverty prediction within the census dataset. However, this is only feasible if there is a shared set of explanatory variables for the relevant outcome in both the survey (EPM21-22), DHS, and the 2018 census. Therefore, the initial step in the SAE process involves recognizing common variables suitable for prediction. In this context, "common" denotes variables measuring the same indicators and thus should exhibit similar distributions across these two data sources.

Identifying common variables involved three primary stages. Firstly, we sought variables that gauge the same phenomena. This was achieved through a careful review of the questionnaires from EPM2122, DHS 2021, and the census to verify if the wording of the questions matched. Secondly, we compared the response categories for categorical variables to determine their alignment. In cases where differences were observed, we assessed if these categories could be merged for comparability. The third step involved comparing the means of the common variables. Drawing from previous SAE estimations, we categorized the prediction variables into three main groups:

- Demographic characteristics of the household head, including age, gender, marital status, and education level.

- Dwelling characteristics, encompassing materials used for the roof, walls, floor, etc.

- Household assets, which includes whether the household possesses common assets like bicycles, cars, radios, etc.

For instance, in the context of education, the first candidate variable is whether the household head attended school, a question posed similarly in both the census and EPM21-22. Thus, no significant transformation was necessary. The second candidate variable regarding education pertained to the highest level of education attained. However, there was a distinction in the response categories, with EPM21-22 offering more detailed options compared to the census. Consequently, the survey variables were adjusted to consolidate categories to match those in the census. Following this transformation process, the distribution of categories (the proportion in each category) was compared, and the variable was deemed acceptable due to its similar distribution in both the survey and census. This general process was replicated to identify the remaining variables, which were subsequently applied to the DHS dataset.

# Statistical methodology

The statistical procedure used is the empirical best predictor model developed by Molina and Rao (2010) based on the work of Battese, Harter, and Fuller (1988). Since this method requires that the error terms be distributed normally, household per capita consumption is log transformed to better approximate normality. Sampling weights are applied in the consumption prediction model following Guadarrama et al (2018), as described in Skarke and Kreutzmann (2020). As is standard, household size was used as population weights when aggregating poverty headcount estimates across households in the census data.

The model used to generate the estimates can be described as follows: 7 | P a g e