# **VI. Concluding remarks**

Survey-to-survey imputation, when applied to existing data, can be a cost-effective and powerful tool to produce frequent and timely estimates of trends in poverty. This paper proposes several tests to examine whether this technique can be used to increase the frequency of poverty estimates in Sri Lanka, where an expenditure survey is conducted every three years while a labor force survey is conducted every quarter. Douidich et al. (2013) explored this approach for Morocco, where an LFS is conducted every quarter but Household Expenditure Surveys (HES) are conducted every seven years. That study showed that in Morocco, imputations based on LFS data can produce statistically reliable poverty rates in each quarter. In contrast, this paper presents strong evidence that unless the sampling and questionnaire of the LFS are changed or a new household survey is created, survey-to-survey imputation in Sri Lanka will not work.

The analysis demonstrates two potential pitfalls to successful survey-to-survey imputation. First, the wording of the questionnaires of the two surveys and households’ responses to the questions can differ between the two surveys. Second, imputing from one survey to another restricts the prediction model to the set of variables available in both surveys, which may not accurately capture changes in household expenditure per capita. In addition, if the two surveys use different sampling strategies, validation tests can make the imputation appear to be less accurate than it actually is.

The analysis shows that in urban areas, SSI produces biased estimates of poverty rates due to the differences in wording of the employment question in the two surveys. In addition, differences in the sampling design, while not causing bias, contributed to the observed discrepancy between the two surveys. In the rural and estate sectors, however, the estimated poverty rates were biased because the limited set of proxies common to both surveys do not accurately reflect changes in household expenditure. As a result, both forward and backward projections severely underestimate the decline in poverty observed during this period. In urban Sri Lanka, labor market outcomes such as employment rates, sectors, and wages track changes in living standard fairly well. On the other hand, in part because self-employment income and agricultural income is difficult to collect, the labor market outcomes collected in the labor force survey did not adequately capture the substantial improvements in welfare enjoyed by rural and estate households.

Unless the LFS expands its questionnaire to include ownership of consumer durables and housing conditions, and makes its sampling design and questionnaire consistent with the HIES, the HIES to LFS imputation cannot produce reliable poverty estimates in the rural and estate sectors. In the estate sector in particular, the only possible solution would be to add additional variables not currently collected in the HIES or the LFS, which could, for example, measure short-term fluctuations in the wages of estate workers.

In Sri Lanka, creating a new household survey is a feasible option to generate frequent and reliable poverty estimates in a cost effective way. While it would also be possible to alter the questionnaire of the labor force survey to ensure consistency with the HIES, this would require a substantial revision that would also create a structural break in Sri Lanka’s labor statistics. To implement a new household survey, a short questionnaire can be fielded that includes the variables necessary to impute household expenditures accurately. If such a survey utilizes mobile technology and Computer Assisted Personal Interview (CAPI) techniques, survey implementation costs can be minimized while improving the precision of the survey-to-survey imputations.

An important lesson from this analysis is that there is no guarantee that survey-to-survey imputation works, and we know very little a priori about what variables are useful to explain both cross-sectional and inter-temporal variation. Theory can offer only broad guidance about which variables track changes in poverty or living standards closely, and it is therefore impossible to predict ex-ante whether and under what conditions survey-to-survey imputation performs well. We now know that imputing poverty based on labor market outcomes and asset indicators worked well for a particular period in Morocco, but that 24