imputation, we cannot include ownership of consumer durables and housing conditions in consumption models.

The time period of the LFS and HIES also should match. The LFS collects data from January to December, while the HIES data is fielded from July to June. To ensure consistency between the two surveys, we restricted the data to the common six month periods in 2006 and 2009 (i.e., July to December of each year) so that poverty rates projected from the SSI are comparable to those estimated from actual consumption data in the HIES.

Finally, we ensure that the geographic coverage is consistent across the surveys and across time. Table 1 shows missing provinces and districts in each survey, compared with HIES 2009/10 which covered the entire island. As mentioned above, Northern Province was excluded in every survey except the 2009/10 HIES. In addition, the 2006 LFS did not include Eastern Province. Furthermore, three districts in the Estate sector are not covered in the LFS. Given these differences in coverage, the subsequent analysis is restricted to those areas covered by both HIES and LFS in both 2006 and 2009, so that the two surveys cover identical time periods and districts.

**Table 1: Geographic areas not covered by HIES and LFS in 2006 and 2009**

||2006|2009|
|---|---|---|
|HIES|N|None|
|LFS|N,E,NC*,D*|N,NC*,D*|

Notes: N=Northern Province, E=Eastern Province, NC=North Central Province; D= Three districts (Gampaha, Hambantota and Puttalam). * Only missing in the estates sector.

<mark>Despite all these efforts to make these survey data comparable, incompatibilities between the surveys remain. In particular, both the reference period for employment statistics and the sampling strategies of the HIES and LFS differ, which make the two data sources not be directly comparable. This is a concern, given that differences in questionnaire design can have large and systematic effects on efforts to elicit labor force statistics in developing countries (Beegle, et. al., 2010). We discuss these issues below in Section V in greater detail.</mark>

# _Other variables_

Variables that are not collected in either the HIES or LFS can also be included in the model, as long as they are aggregated at a regional level. For example, Stifel and Christiaensen (2007) include regionally aggregated rainfall data in their consumption models. In a similar vein, we include one additional regionally aggregated variable in the prediction model, namely industry level wage data published by the Central Bank of Sri Lanka. This wage data is based on information on monthly wage rate provided annually by the Ministry of Labor, which collects nominal wages for different trades fixed by Wage Boards and the Salaries and Wages in Public Administration Circulars in Sri Lanka. The Central Bank annually publishes separate real and nominal wage rate indices for each major industry and sector such as Workers in Agriculture, Workers in Industry and Commerce, Workers in Services, Central Government Employees and Government School Teachers. This wage data, even if they are aggregated to the industry level, are strong predictors of household expenditures.

# _Poverty trends_

According to the official poverty statistics, the prevalence of poverty declined significantly between 2006 and 2009 in all three sectors (see the first main column in Table 2). The official statistics include all households in the HIES, but as mentioned above, we evaluate the performance of SSI using only geographic areas and time-periods common to the HIES and LFS data. The third to sixth columns in 6