theoretical guidance regarding variables to be included in imputation models to ensure comparability and reliability of imputed poverty data. They recommend including covariates that change over time, but call for excluding variables whose rates of return are likely to change markedly in the face of evolving economic conditions. This argument makes sense in theory, but it is difficult to identify which variables would satisfy these conditions. Stifel and Christiaensen (2007) included several household durables in their imputation models, but Harttgen, Klasen and Vollmer (2012) criticized this decision because of the so-called “asset drift” effect—where the pace of improvement in asset ownership is much faster than the pace of income growth. As a result, the relationship between asset ownership and income or consumption could well be changing over time. In the end, which variables satisfy the recommendations of Stifel and Christiaensen (2007) is intrinsically an empirical question. At the same time, ascertaining empirically how best to proceed is difficult because actual household consumption data are needed. Remember, imputation methods are being employed precisely because actual household consumption data are not available.

Christiaensen et al. (2012) examined some of the arguments raised by Stifel and Christiaensen (2007) through an experiment. They collected multiple rounds of cross-sectional data including both household consumption and nonconsumption variables from four developing countries, namely rural China, Kenya, the Russian Federation, and Vietnam. They created imputation models from one round and imputed household consumption expenditure into other rounds that also had actual consumption data. They illustrated with their four case studies that imputation models can reproduce poverty statistics generated from directly collected consumption data. The results support the notion that imputation models constructed from past surveys can predict household consumption data of future rounds. Such results are encouraging and give us a certain level of confidence in the stability of imputation model coefficients over time. However, this exercise is still experimental. Validation is possible only in those settings where imputed estimates can be compared to actual estimates. In practice, the imputation approach needs to be used in those circumstances where actual consumption data are not available, and therefore, analysts must be willing to assume that the imputation model is accurately predicting household expenditure and poverty rates.

This paper explores a hybrid approach—implementation of a survey that collects a large sample of nonconsumption data, which can be collected easily and cheaply, but also collects a small subsample of both consumption and nonconsumption data. Collection of household consumption data in the subsample provides several advantages. First, the data can be used to estimate poverty rates directly. Because of the small size of the subsample, this direct estimation will not be precise: sampling errors can be significant. However, direct estimation of the poverty rate from the subsample might still provide a good ball park figure. Second, using the subsample, which includes both consumption and nonconsumption data, we can create imputation models and apply them to the full (and larger) sample to impute consumption for all sample households. This hybrid approach will result in an overall poverty estimate with the sampling error associated with the large household survey and a modeling error associated with our having imputed consumption into the large survey. Encouragingly, literature on small area estimation (for example, Elbers, Lanjouw, and Lanjouw [2003], hereafter ELL) suggests that model errors need not be excessive if proper care is taken in specifying and estimating the imputation model.

This hybrid approach can address the problems faced by both Stifel and Christiaensen (2007) and Christiaensen et al. (2012). Both papers create imputation models from past data and apply them to the latest household survey data. As discussed above, we cannot test whether the imputation models are still valid or reflect the relationship among household consumption and other correlates. In the hybrid approach, the imputation models are created from the subsample of the latest household survey, and so, by construction, the models reflect the latest relationship among household consumption and other correlates.

Despite these advantages, the hybrid approach is not perfect. First, collecting consumption data is costly, even if it is collected only for a subsample. We know that the larger the subsample size, the more accurate 3