SSI were only about 1 percentage point different from those from actual household expenditure data, and the difference is statistically insignificant at the national and regional levels.

We investigate why predicted poverty in Sri Lanka fails to track estimated poverty rates. In urban areas, the main problem is incompatibility between the HIES and the LFS in terms of the surveys’ questionnaire. Differences in the employment question between the LFS and HIES leads to biased predictions in the LFS. The difference in the two surveys’ sampling strategy, on the other hand, does not cause bias, but it does increases the discrepancy between predicted and directly estimated poverty rates. To see the effect of the differences in questionnaire and sample design, we impute poverty based on welfare indicators and sampling weights taken from the HIES instead of the LFS, using only variables that are common to both surveys. When the predictor variables and the sampling weights both come from the HIES, estimated poverty rates correspond closely to those directly estimated from household expenditures. This confirms that the main problem with the use of SSI in urban areas is differences between the HIES and LFS in survey design.

In rural areas and the estate sector, however, differences in the values of the common variables and sampling weights between the LFS and HIES are negligible. Instead, the errors in the predictions arise mainly because the common variables fail to capture intertemporal variation in consumption. The set of common variables used in the model mainly consists of pre-determined variables such as education and location that, while highly correlated with household consumption in the cross-section, vary little over time. Furthermore, the labor market outcomes included in the model, such as the households’ source of income and the head’s sector of work, do not capture welfare changes in the rural and estate sector as well as they do in urban areas. Therefore, these common variables explain little of the observed changes in consumption, and predicted consumption greatly understates the increase in actual consumption.

In rural areas, which account for roughly 80 percent of the population, including additional variables largely fixes the problem. In particular, when imputing within the HIES, adding ownership of consumer durables and housing conditions to the model generates large improvements in performance. This is because as households become better off, they purchase assets and more expensive housing materials, meaning that these variables better reflect changes in consumption. In the estate sector as well, adding more variables improves the accuracy of SSI, but the projected poverty rates remain far from those estimated from actual household expenditures.

To generate accurate and frequent poverty estimates in Sri Lanka based on alternative survey data, either the design of the LFS needs to be revised, or a new welfare proxy monitoring survey should be created. Although it is typically cost-effective to use existing data to estimate poverty, ensuring that the LFS uses sampling and questionnaires that are comparable to the HIES may be a challenging endeavor, and would introduce a structural break in the measurement of labor market trends. It may be possible in the Sri Lankan context to create a new welfare proxy monitoring survey, which would collect only those variables needed to project household expenditure and poverty accurately. Recent technological advances, such as rapid improvements in computer assisted personal interviews using tablet devices, have lowered the cost of conducting such a survey.

This paper consists of seven sections. Section II reviews the literature, Section III describes the data, and Section IV explains the imputation method developed by Elbers, Lanjouw and Lanjouw (2003). Section V describes the results of the HIES to LFS imputation and Section VI conducts analyses to investigate the sources of biases in SSI. Section VII concludes.

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