that food price inflation is estimated with unit values calculated from consumption data in the HIES. Unit values are used because there is a concern that price data collected for constructing the Consumer Price Index (CPI) have limited sample sizes at the stratum level, resulting in potentially large sampling errors. Unit values can fill the data gaps because they can be calculated from consumption data in the HIES, which has a large enough sample for each stratum. Two issues in the use of unit values are: (i) unit values are not available for most nonfood items because households do not have to report quantities for nonfood consumption, and (ii) unit values are known to be quite noisy. As a compromise, the Bangladesh Bureau of Statistics (BBS) uses an urban-rural breakdown of nonfood CPI data to estimate nonfood inflation, and estimates food inflation using unit values to construct a Tornqvist price index while carefully controlling for outliers.<sup>3</sup> Price adjustment for food items is a challenge when using the Subsample Imputation approach. The hybrid survey does not collect consumption data, except for the subsample; as a result, unit values can be calculated only from food consumption data in the subsample. Since the size of the subsample is restricted to just 640 households, or less, the number of unit values for each stratum can become very small, resulting in a potentially large sampling error.

This section tests two approaches for conducting price adjustments. First, we followed the BBS’s approach using the subsample, and quantified the extent of the potential bias. Second, we collected price data for food items and compared them with unit values. Compared to unit values, price data are often argued to be more reliable. For this experiment, we collected price data and consumption data in a few villages and compared them with unit values.

**Using Unit Values in Subsamples**

We tested the effect of updating poverty lines using subsamples of poverty estimates and SEs obtained by bootstrapping 100 subsamples. For each subsample, we calculated food inflation rates for 16 strata based on unit values available in the subsample. We then updated 16 poverty lines along with nonfood CPI, and implemented the Subsample Imputation. We compared the resulting poverty rates with those estimated from the Subsample Imputation using the official poverty lines of 2005 based on the full sample of 2005 HIES data. To determine the impact of sample size, we conducted this test with two subsample sizes: 640 and 320 households.

The food price inflation rates were calculated in four steps: first, we defined 13 food groups and calculated average budget shares of these groups for all 16 strata and both the 2000 and 2005 HIES j j rounds, say w0k and w5k. Second, we calculated unit values of a representative item (k) in each of 13 j food groups and selected the median value across all 16 strata (j) and for both rounds (0 or 5), say p0k and j p5k. Third, the stratum-specific food price inflation rates were calculated following Tornqvist: Finally, stratum-specific inflation rates were calculated by taking averages of the food price inflation rates and the nonfood inflation rates (estimated from nonfood CPI) weighted with average budget shares of food and nonfood items.

In the simulations to follow, the 2005 budget shares and median unit values were estimated from a subsample, although those of 2000 were estimated from its full sample. For the official update of the poverty lines, the 2005 budget shares and median unit values were estimated from the full sample of the 2005 HIES data.

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