The consumption module was identical to that of the 2000 and 2005 HIESs, while the consumption data were collected via two week recall, as well as by diary. Price data were collected using a questionnaire created for this pilot by the World Bank team and a survey company, DATA. Price data include 32 major food and nonfood items.

Table 11 shows the median prices from the price survey and unit values from the consumption module for the six most important food items by four strata (Dhaka rural, Khulna rural, Rajshahi rural, and Dhaka urban). The histograms of these numbers are shown in the appendix (figures A2.1–A2.4). Table 11 shows the differences between the median price and unit value as proportions of their average so that the size of relative difference can be easily discerned. If the relative difference is statistically significant, percentage difference is in bold with p-values less than 5 percent. Positive (negative) differences indicate that prices (unit values) are larger than unit values (prices).

According to table 11, medians of prices and unit values are usually very similar, but reflecting small movements in both values, SEs are small, and even small differences in median prices and unit values become significant. More specifically, in Dhaka rural, the prices and unit values for coarse rice and chicken meat are statistically significantly different, but the relative differences of the two items are only 6 and 3 percent, respectively. In Khulna rural, the noticeable differences in medians between unit values and prices are found in medium size rice and chicken meat, although only the difference for chicken meat is statistically significant. In Rajshahi rural, medium rice is statistically significantly different, but the relative difference is only 2 percent. In Dhaka urban, median prices of coarse rice and beef meat are statistically significantly different, and the relative differences are 8 and 4 percent, respectively.

Overall, medians of unit values can be similar to those of prices if the price surveys were collected from each stratum. This implies that unit values from the subsample might be quite reliable even though the sample size is limited. Also, although we need to see whether the results hold when expanding the sample to the whole country, this analysis suggests that switching to price data might have minimal impacts on price adjustments and poverty estimates.

Bangladesh poverty measurement uses unit values from consumption data of the HIES to estimate inflation rates and poverty lines. Therefore, if consumption data are collected from only a subsample of the HIES data, estimation of inflation rates and poverty lines, and thus the resulting poverty estimates, can be noisy and unreliable. However, the above analysis shows, at least in the case of Bangladesh’s 2000 and 2005 data, that using only a subsample of 2005 data does not increase noise in estimated inflation rates, poverty lines, or poverty headcount rates.

Price data collection is simple and less costly, but raises the possibility of incomparability between price data and unit values, which would make future poverty estimates incomparable with past poverty estimates. However, analysis based on the pilot survey reviewed here suggests that price data are comparable to unit values from consumption data. These results suggest that inflation rates and poverty lines can be accurately updated with a subsample of HIES data or additional price surveys. This is certainly encouraging for the Subsample Imputation approach, and even for the Subsample Direct Estimation.

# **V. Cost Analysis of Three Proposed Approaches and Effects of Switching from Diary- to Recall-Based Data Collection**

Results here show that even if the Bangladesh economy did not experience a large economic crisis between 2000 and 2005, the subsample approaches provide better results for 2005 than the Full Sample Imputation that builds consumption models from the previous round of the HIES, that is, from the 2000 HIES data. But, as discussed earlier, collecting consumption data is costly, even for a subsample. Also, 20