# **Introduction**

This paper examines whether survey-to-survey imputation (SSI) methods produce reliable poverty estimates in Sri Lanka.<sup>1</sup> SSI methods aim to predict changes in the distribution of household per capita expenditure, and therefore poverty rates, using information contained in other surveys that are conducted more frequently. In Sri Lanka, a Labor Force Survey (LFS) is fielded quarterly, while the Household Income and Expenditure Survey (HIES) is carried out once every three years. Therefore, survey-to-survey imputation has the potential to generate much more frequent estimates using existing HIES and LFS data, at negligible additional cost. Douidich et al. (2013) applied this approach to Moroccan data, and found that projected poverty rates based on the LFS data closely matched official poverty rates estimated from actual consumption data.

Two key assumptions must be satisfied for SSI to produce reliable poverty estimates. First, the two surveys must ask the same questions. If the common variables in the two surveys measure different versions of a concept, then the coefficients estimated using the expenditure survey will be inconsistent with the poverty proxies taken from the LFS data. Second, the prediction model must be fixed over time. For this latter assumption to hold in a strict sense, changes in the variables present in both surveys must explain all of the average change in household expenditures. The weaker the model’s ability to explain changes in the household consumption distribution, the greater the bias in predicted changes in poverty that will result from assuming that the model’s coefficients are fixed. In Sri Lanka, the common set of variables in both surveys excludes key factors that track changes in consumption in rural areas, such as accurate measures of local wages and profits, housing characteristics, and assets. Therefore, the model’s predictions severely underestimate the extent of poverty reduction in the rural and estate sectors. For SSI to successfully estimate changes in poverty, the common set of variables used in the model must not only be strong and stable predictors of consumption in a cross-section, but also need to explain a large share of the intertemporal variation in expenditure.

We use the 2006 and 2009 rounds of the HIES and LFS surveys to test whether SSI can explain changes in observed headcount poverty rates, following Douidich et al. (2013).<sup>2</sup> First we estimate prediction formulas using the 2006 HIES data, and apply those formulas to data from the 2009 LFS to estimate household expenditure and poverty in 2009. Then, we check how close the projected poverty rates are to actual poverty rates computed from the household expenditure data collected in the 2009 HIES. We also repeat this exercise in the reverse direction, by estimating formulas using the 2009 HIES data, and using them to impute household expenditures into the 2006 LFS data. The projected poverty rates of 2006 are then compared with those estimated directly from household expenditures in the 2006 HIES.

In Sri Lanka, we find major discrepancies between the predicted estimates of poverty and actual poverty rates. In particular, predicted poverty substantially underestimates the decline in poverty in the rural and estate sectors, which contain about 85 percent of the country’s population. In the rural sector, when imputing backwards to obtain estimates of poverty in 2006, the difference between the projected and actual poverty rates is 5 percentage points, and the model predicted less than a quarter of the actual change in poverty. This finding stands in stark contrast to the Moroccan analysis, which showed that the models predicted changes in poverty well. In Morocco, unlike in Sri Lanka, the poverty rates projected by

> 1 Two examples of survey-to-survey imputation methods applied to projecting poverty in developing countries are Stifel and Christiaensen (2007) and Christiaensen et al. (2012)

> 2 HIES surveys were carried out in 2006/7 and 2009/10, not in 2006 and 2009. As a result, these surveys are called the 2006/7 and 2009/10 HIES surveys. However, to match the survey periods between the LFS surveys and the HIES surveys, we use only the common period of both rounds of HIES and LFS surveys, July to December in 2006 and 2009. As a result, when we are running the SSI between HIES and LFS, we call HIES surveys the 2006 and 2009 HIES surveys. However, later when we use all 12 months data of HIES surveys, we call the surveys the 2006/7 and 2009/10 HIES surveys.

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