which variables would satisfy these conditions. For example, Stifel and Christiaensen (2007) included ownership of several consumer durables in their imputation models, but Harttgen, Klasen and Vollmer (2012) later criticized this decision on the grounds that improvement in asset ownership outpaced income growth, creating “asset drift”. Identifying variables that satisfy the recommendations of Stifel and Christiaensen (2007) is intrinsically an empirical question, but the performance of the Kenya model could not be tested in the Kenya data because only one round of consumption data was available.

Christiaensen et al. (2012) moved one step further by analyzing a series of the past household budget surveys available in the Russian Federation, Vietnam, Kenya, and rural China. Their empirical strategy was to first create projection models using one round of household budget survey, impute household expenditure data into other rounds of household budget survey, and then compare projected poverty rates from the imputed expenditure with those estimated directly from actual consumption data. The comparison between projected poverty rates and directly estimated poverty rates validated the reliability of SSI projections. Models that included a comprehensive set of assets performed particularly well. These results are encouraging, and suggest that imputation models tend to remain stable over time.

Douidich et al. (2013) is the first attempt to validate the reliability of SSI using different types of surveys. They created consumption models in one round of the Moroccan Household Expenditure Survey (HES), imputed consumption data into more frequent Labor Force Surveys (LFS) and estimated poverty rates using the imputed data. To examine the accuracy of the projected poverty statistics, they compared them with poverty rates directly estimated from HIES consumption data collected in the same year as the LFS. The results are very encouraging. Projected poverty rates are very close to directly estimated poverty rates, irrespective of which round of HES is used to create consumption models. But, this raises the question of whether the results generalize to other countries and time periods. The objective of this paper is to use the same approach to test whether SSI between the HIES and LFS works in Sri Lanka.

# **II. Data**

# _Household Income and Expenditure Survey (HIES)_

We test the performance of SSI using the 2006 and 2009 rounds of the HIES and the LFS in Sri Lanka. Two rounds of the HIES were collected during this period, namely the 2006/7 and 2009/10 HIES surveys. HIES data are collected over a one-year period to capture seasonal changes in income and household expenditures. The 2006/7 HIES contains 18,544 households, while the 2009/10 HIES contains 19,958 households. The questionnaire includes questions on income, household expenditures, household and individual characteristics, housing conditions, and ownership of key consumer durables. Changes in the design of the questionnaire were minimal between two rounds. Due to the civil war, the 2006/7 HIES excludes the Northern and part of the Eastern Provinces, while the 2009/10 HIES covers the entire island (excluding only 3 districts from Northern Province) for the first time since 1983.

# _Labor Force Survey (LFS)_

The LFS is collected each quarter. The questionnaire includes household and individual characteristics and a wide variety of labor statistics. Roughly 18,000 households are interviewed each year. Key statistics are designed to be nationally representative every quarter and representative at the district level each year. In both years, LFS excluded Northern Province, and included Eastern Province only in 2009.

# _Comparability of the LFS and HIES_

To conduct SSI, the variables used in the consumption models need to be available in both the LFS and HIES data, which significantly restricts the set of available variables. For example, Christiaensen et al. (2012) found that ownership of consumer durables and housing conditions are correlated with household expenditures. But in Sri Lanka, only the HIES includes this information. As a result, for the HIES to LFS 5