the poverty estimates will be. But, we also know that as the subsample size increases, the cost of implementing the hybrid survey also rises. So, a key policy question is how much will a reduction in the size of the subsample increase the standard errors (SEs) of the poverty estimates. Furthermore, consumption data need to be comparable over time.

Second, the hybrid approach calls for regular re-estimation of the underlying imputation models. Developing reliable imputation models has become simpler since the World Bank Development Research Group created the PovMap 2 software for this purpose, but it remains a not insignificant task and a formidable challenge in those settings where statisticians have limited experience with these methods.

Third, the hybrid approach will produce the current value of imputed expenditure; therefore, some adjustments for intertemporal and spatial price differences need to be made. Such price adjustments often use unit values from household surveys, but the reliability of unit values will be limited since the size of subsample is small. In contrast, the approach proposed by Christiaensen et al. (2012) is attractive because (i) imputation models will be estimated only for the year in which a big household survey with a full sample of consumption data was carried out; (ii) imputed expenditure/income is already adjusted to prices of the big household survey year and, as a result, we do not have to make any additional price adjustments for the following years; and (iii) except for the big household survey year, we do not have to collect consumption data at all.

The objective of this paper is to compare the following three different poverty estimation approaches: (i) estimating poverty rates from consumption data in a subsample (“Subsample Direct Estimation”); (ii) estimating poverty rates using household expenditure imputed from models created from a subsample (“Subsample Imputation”); and (iii) estimating poverty rates using imputed household expenditure based on models created from a previous round of a large household survey (“Full Sample Imputation,” used by Christiaensen et al. [2012]).

This paper conducts the above analysis using the 2000 and 2005 Bangladesh Household Income and Expenditure Survey (HIES) data. Bangladesh was chosen because Bangladesh Bureau of Statistics expressed interest on the subsample imputation approach and also offered help to carry out additional surveys to see the effects of slight modification of survey modules on survey implementation costs. The 2000 and 2005 HIES data were chosen although a more recent round of HIES data (the 2010 HIES) is available because the poverty lines are slightly modified for the 2010 datasets. The modification of poverty lines in addition to price adjustments usually causes additional challenge to “Full Sample Imputation.” Therefore, the 2000 and 2010 HIES were selected. However, its robustness against changes in methodology is also can be seen as an advantage of the subsample approach.<sup>2</sup> This paper also studies to what extent the limited sample size affects price adjustments, poverty lines, and poverty estimates. For situations where unit values from a subsample are too noisy to produce reliable poverty estimates, we also evaluated another way of conducting price adjustments using price data from an independent price survey. An issue here is that if unit values and prices are too different, poverty lines updated by price data would be very different and produce poverty estimates that are not comparable to the previous official poverty rates, which were estimated with price adjustments based on unit values. This paper examines whether differences between prices and unit values, both collected in a pilot survey, are statistically significant.

Finally, this paper examines the cost implications of collecting consumption data for a subsample, as well as the reliability of price data collected from price surveys. Collection of consumption expenditure data for a subsample might be useful to improve and ensure the statistical accuracy of imputed expenditure data; however, if it is costly, annual collection of such data might not be a realistic option for developing countries. We therefore examine the costs of collecting consumption data for a subsample after reviewing the current data collection process adopted by the Bangladesh Bureau of Statistics (BBS).

> 2 This insight was drawn from discussions with Salman Zaidi.

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