simpler estimation method used for the following 1,000 replications. This is the case for SEs of poverty estimates in both urban and rural areas.

**Table 6: Comparison of Full Sample Direct Estimation between Only Households Included in both the 2000 and 2005  HIESs and All** **<u>Households in the 2005 HIES</u>**

|Area/Level|Poverty estimates from<br>“new” full sample|Poverty estimates from all<br>households in the 2005<br>HIES|
|---|---|---|
|National|39.8<br>(1.0)|40.0<br>(1.1)|
|Rural|43.6<br>(1.2)|43.8<br>(1.3)|
|Urban|28.4<br>(1.6)|28.4<br>(1.9)|

_Source_ : Authors’ estimations using data from the 2000 and 2005 HIESs. _Note_ : Numbers in parentheses are SEs. Because of the mismatch of location codes between 2000 and 2005, 860 samples, 60 (8.1 percent of samples in 2005) were dropped for the “new” full sample estimates. Cluster-robust SEs were used.

# **Full Sample Imputation**

Using the ELL method as in Christiaensen et al. (2012), the parameters for consumption models and errors were estimated from the 2000 HIES data and then applied to the 2005 HIES to impute household expenditure, from which poverty headcount rates for 2005 were estimated. To reflect differences in consumption patterns between urban and rural areas, we prepared separate consumption models for urban and rural areas. As discussed above, a critical assumption of the Full Sample Imputation is that the estimated consumption models are stable between 2000 and 2005.

Because of changes in subdistrict boundaries between the two years, a small portion (8.1 percent) of samples in 2005 cannot be used, because we could not match some subdistricts in 2000 to those in 2005. The poverty rates and SEs in 2005 were obtained using the remaining 91.9 percent of the 2005 sample. However, as shown in table 6, the poverty rates in the “new” 2005 full sample are very close to those estimated from all households in the 2005 HIES.

Models with and without ownership of household durables were estimated to test “asset drift.” As explained earlier, the relationship between asset ownership and income or consumption likely changes over time, so Full Sample Imputation with household durables could be affected or biased if an asset drift exists. Household durables are important correlates of income or consumption, so not including these variables could reduce the model’s fit and thus increase the SEs of poverty estimates. To see the potential impact of the asset drift, we compared poverty estimates with and without household durables in consumption models.

**Table 7: Poverty Estimates from Full Sample Imputation (%)**

|Area/Level|With d|urables|Without|durables|Full sample direct<br>estimation<sup>a</sup>|
|---|---|---|---|---|---|
|National<sup>b</sup>|38.2|(4.0)<sup>c</sup>|38.9|(3.7)|39.8|
|Rural|41.8|(5.0)|43.1|(4.6)|43.6|
|Urban|26.0|(3.1)|24.3|(3.6)|28.4|

_Source_ : Authors’ estimations using data from the 2000 and 2005 HIESs.

- _Notes_ : Numbers in parentheses are SEs. Povmap2 Software is used for the small area estimation. a. Full sample refers to the number of households in the 2005 HIES that belong to subdistricts in both the 2000 and 2005  HIES data.

- b. Weighted average of rural and urban estimators.

- c. SEs are calculated assuming poverty rates of urban and rural areas are not correlated.

Table 7 summarizes the results of the Full Sample Imputation. Inclusion of ownership of durable goods has less impact on the national poverty rate, but it has a noticeable impact on urban and rural poverty rates. Compared with the true poverty rates (or Full Sample Direct Estimation), inclusion of durable 14