district, or village—and can be drawn from any data source that includes all locations in the sample. All parameters regarding the regression coefficients (β , γ)<sup>and distributions of the error terms are estimated</sup> by feasible generalized least squares (FGLS).

In the second stage, the consumption model is used to impute household expenditure using a data set that contains the explanatory and location specific variables but lacks information on consumption. Poverty estimates and their standard errors can be calculated based on the imputed expenditure data.

ˆ The estimated regression coefficients β<sup>ˆ</sup> , γ and the error terms η _c_ and ε _ch_ are stochastic, and ELL proposes a way to account for this when calculating poverty estimates and their standard errors. An imputed value of expenditure for each household is a sum of predicted log expenditure �Xch′ β̂ + Zc′γ�� and error terms (η�c and ε̃ch) that are drawn randomly from the empirical distributions of the error terms, η _c_ and ε _ch_ . This random draw of error terms is repeated 100 times and for each round, household expenditures and corresponding poverty rates are estimated. The mean across the 100 simulations of a poverty indicator provides a point estimate of poverty, and the standard deviation is treated as a standard error of the point estimate of poverty rate. Because of the clear differences in consumption patterns between urban, rural, and estate sectors, we estimate separate consumption models for each sector.

# **IV. Survey-to-survey imputations between LFS and HIES**

This section examines whether the SSI imputation analysis between the LFS and the HIES produces reliable poverty estimates. We test the performance of the SSI by comparing poverty rates imputed by SSI with those estimated from the actual consumption data available in the HIES. More specifically, we create two consumption models, one based on the 2006 HIES and the other on the 2009 HIES. These consumption models are then used to impute household expenditure into the LFS of the other survey year. Thus, the imputation exercise is conducted both backward and forward.

# _Forward projections_

The forward projection means that we first estimate projection models using the 2006 HIES data and then impute household expenditures into households in the 2009 LFS data. To allow for regional differences, we create consumption models for urban areas, rural areas, and estates, separately. The third column of table 3 shows the results. Compared with the poverty rates estimated directly from the 2009 consumption data, imputing into the 2009 LFS overestimates the poverty rates for rural areas and estates and underestimates the poverty rate for urban area. As a whole, the forward imputation overestimates the poverty rate of 2009 at the national level, which is 3 percentage points higher than the direct estimation.<sup>6</sup>

> 6 Various specifications were tested, and province or district dummy variables as the location-specific variables are not included in the final model in order to decrease the number of time-invariant variables such as these variables and increase time-variant variables that would be able to explain the change across time. The estimation results are robust with or without these location dummy variables since the estimates and standard errors do not change as shown in Table A4 in Appendix. In addition, the final specifications do not include a cluster-specific error since the estimated cluster-specific variation was very small with the final model specification. This was measured by the ratio of the estimated cluster-level variance to the mean squared error, and the ratios are less than 0.03 in urban and rural sectors. In the estate sector, this ratio was about 0.08, but we could include the error in the estimate sector due to a problem in the software package (Povmap 2) as of now.

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