**Table 3: Forward Imputation (HIES “2006” to LFS 2009 Imputation)**

||Direct esti<br>2006|mation<br>2009|Imputation<br>Variables<br>only from<br>LFS|-**2009**<br>With wage<br>data|
|---|---|---|---|---|
|National|15.3|8.6|12.3|11.1|
||(0.7)|(0.5)|(0.5)|(0.5)|
|Urban|7.4|6.1|4.4|4.1|
||(1.3)|(1.1)|(1.4)|(1.4)|
|Rural|15.4|8.9|12.3|10.8|
||(0.8)|(0.6)|(0.8)|(0.8)|
|Estate|33.6|10.4|31.5|31.7|
||(3.0)|(1.7)|(3.3)|(3.1)|

Source: Authors’ calculations using HIES 2006/7 and 2009/10 data. Notes: Tables A2-1 to A2-3 show the consumption models with means of these variables included in the final models for urban areas, rural areas and estates. The figures in parentheses are cluster-robust standard errors for the direct estimations and the standard errors that include the modeling and sampling errors for imputation.

The extent to which the imputed estimates overstate poverty varies by sector. In rural areas, the projected reduction in poverty was 3.1 percentage points, which accounts for less than half of the true reduction in poverty of 6.5 percentage points. In the estate sector, the differences are much larger. Projected poverty fell only 2 percentage points, while actual poverty fell 20 percentage points. On the other hand, the projection overestimates the reduction of poverty in urban areas. The projected reduction in poverty is 3 percentage points, while actual poverty fell only 1.3 percentage points.

# _Adding wage data_

The mediocre performance of the imputation from the 2006 HIES to the 2009 LFS is likely due to a limited number of variables that are common in HIES and LFS data. In particular, the LFS does not include powerful poverty predictor such as ownership of consumer durables and housing conditions. Stifel and Christiaensen (2007), which also faced the same problem, proposed a solution – adding areaspecific variables from other sources. In particular, they use rainfall data at the district level to improve the prediction of consumption models, in part because levels of rainfall vary over time. Of course, including area-specific variables can only explain variation across areas and not within them. Choosing the level of disaggregation therefore requires balancing the additional precision gained from using smaller areas, against the potential loss of precision in the data itself. For example, average wages at the village level may not be measured precisely enough to predict well in the model, while average wages at the national level will not be sufficiently disaggregated to be useful.

In Sri Lanka, area-specific wage data are not published. We therefore use national-level wage data disaggregated by sector. As explained above, this comes from a database of wage rates created by Department of Labor that captures monthly wage rates for four sectors.<sup>7</sup> The data set shows substantial improvements in wage rates for some sectors (see Table 4). These data, however, are only available at the national level and are not separately collected for the urban, rural and estate sectors.

7 Because the wage variable is measured at the sector level, the estimates should account for clustering at the level of the sector in addition the level of the PSU. Because neither ELL nor simple probit models are well set-up to cluster at multiple levels, the standard errors for the models that include wage data are likely to be underestimated.

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