# _HIES to HIES imputation using additional variables_

The analysis so far has shown that differences in survey design bias imputations based on the LFS in urban areas, but shows almost no evidence that it affects the imputations for rural areas and estates. Then, what is causing the large difference between the HIES to LFS imputation and the direct estimation in rural areas? This sub-section investigates another potential cause of failure in survey-to-survey imputation – bias due to the inability of common variables to adequately track changes in household expenditure. We assess this by adding variables only available in the HIES to the model. This analysis builds on the HIES to HIES imputation in Table 7, which was limited to variables common to the LFS and the HIES.

Expanding the set of variables in the model is likely to reduce the bias. Many of the common variables, such as household demographics and education status, change little over the course of the three years. For example, the rural average household size, which is one of the strongest predictor of poverty, changed only from 4.35 to 4.25 in rural areas between the two rounds of HIES surveys. Even though employment variables can change rapidly in many contexts, they remained nearly constant in the data sets used in this analysis. As mentioned above, variables that are strong predictors of consumption in the cross-section will not be useful indicators of changes in poverty if they do not reflect changes in consumption over time.

This potential limitation can be addressed by adding additional variables to the HIES model that are not available in the LFS, which improves the accuracy of consumption models. The main variables excluded from the HIES to LFS imputation are ownership of consumer durables and housing conditions. These variables are known to be powerful predictors of poverty and household expenditures (see for example Stifel and Christiaensen, 2007, and Douidich et al. 2013). Collecting information on ownership of consumer durables and housing conditions is easy, quick, and cheap.

Tables 12, 13, and 14 show the results of HIES 2006/7 to HIES 2009/10 imputations with these additional variables. The final models still include measures of household demographics and employment status, which change little over time. They are included in the final models because they explain a large share of the cross-sectional variation in consumption, which remain important to distinguish the poor from the non-poor in the 2009/10 data. Ownership of key consumer durables and housing conditions became significantly more widespread over time. The improvements are particularly striking in rural areas and estates, which are the two sectors in which the previous models underestimated the decline in poverty.

The asset for which ownership expanded most rapidly is cell phone ownership. But, this rapid expansion may reflect reductions in cell phone prices and service fees. As a result, owning cell phones is likely to have very different financial consequences in 2009 than in 2006. Therefore, we follow previous studies that exclude cell phone ownership from consumption models (see Revilla et al.(2010) for Zambia and Allwine et al. (2013) for Lesotho).

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