while the fifth column shows poverty estimates using the full sample of 2005 HIES data to estimate poverty lines and inflation rates. Like before, the simplified projection method slightly underestimates the national and rural poverty rates, and SEs are slightly higher than when the full-fledged ELL is applied. The difference in poverty rates between the first and the fifth columns is minimal, no more than 1 percentage point for national, rural, and urban poverty rates. This is the case for both sample sizes, 320 and 640. This makes sense, because both inflation rates and poverty lines estimated from a subsample of 2005 HIES data are very similar to those from the full 2005 HIES sample. Also, it is interesting to see that SEs of all poverty rates do not change when a subsample of 2005 HIES data are used to estimate inflation rates and poverty lines. The results of Direct Estimation of poverty rates are also similar.

This analysis confirms that using data from the 2000 and 2005 HIESs, even if only a subsample of 2005 HIES data is used, does not change the inflation rates and poverty lines much. As a result, the results of the Subsample Imputation do not change much. This is very encouraging for the imputation approach.

# **Using Price Survey Data**

Even though estimating inflation rates and poverty lines using a subsample of 2005 HIES data worked well, unit values are known to be noisy and vulnerable to outliers. Price data are often argued to be more reliable than unit values. One of the issues with using price data, which are currently collected for the CPI, is that these data do not have enough observations to create stratum-specific inflation rates. Therefore, the BBS is currently using unit values from HIES data to estimate stratum-specific food price inflation rates. However, the lack of observations can be overcome with relatively limited additional costs. If price data are collected in all PSUs of a hybrid survey, there will be enough observations to create inflation rates that are representative at the stratum level. Furthermore, collecting price data in this manner saves transportation and lodging costs because enumerators are in PSUs anyway to collect other data and collecting price data is not time consuming.

Even though price data can be collected at minimal additional cost, switching to price data can be problematic. Switching to price data can make new poverty estimates incomparable with past estimates if the price data are not comparable to unit values.

To determine the quality and comparability of price data, as well as the cost of collecting price data, the BBS and a World Bank team carried out a pilot survey, which collected price data and consumption data from 60 PSUs and 720 households (12 households in each PSU) in four strata: Dhaka urban, Dhaka rural, Khulna rural, and Rajshahi rural. The pilot was conducted in June and July 2011 with trained enumerators.

|**Table 11: Dif**|**ferenc**|**e betw**<br>D|**een Me**<br>haka rura<br>|**dian U**<br>l<br>|**nit Val**<br>K|**ues and **<br>hulna rur<br>|**Prices**<br>al<br>|**for Sel**<br>Raj|**ect Ite**<br>shahi rur<br>|**ms**<br>al<br>|D|haka urb<br>|an<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Items|Unit|Price|Unit<br>value|%<br>diff|Price|Unit<br>value|%<br>diff|Price|Unit<br>value|%<br>diff|Price|Unit<br>value|%<br>diff|
|Rice, medium|Kg|35|35|0|35|33|6|32|31|**2**|36|36|1|
|Rice, coarse|Kg|32|34|**-6**|31|31|0|29|30|-3|32|35|**-8**|
|Beef meat|Kg|250|250|0|250|250|0|250|250|0|250|259|**-4**|
|Chicken meat|Kg|145|150|**-3**|131|141|**-7**|140|143|-2|145|147|-1|
|Milk, liquid|L|40|40|0|30|30|0|30|30|0|50|50|-1|
|Soybean oil|L|118|120|-2|120|120|0|120|120|0|120|120|0|

_Source_ : Authors’ estimations using pilot survey data. _Note_ : Bold numbers indicate the medians are statistically different between survey prices and unit values with p-values less than 5 percent. Shares of differences between survey and unit values = (survey price - unit value) / mean (survey price, unit value). All prices are reported in Bangladesh taka.

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