# <u>Closing the data gap when surveys cannot be collected</u>

Measuring monetary poverty by survey-to-survey imputation<sup>12</sup> Many countries operate in an environment where household consumption data are partially missing. This includes situations when: i) consumption data are available at one point in time but are not available in the second point in time, or ii) situations when consumption data exist in all points in time but are not comparable. “Imputation” is the key method to provide poverty estimates in the absence of consumption data.

## <u>Method</u>

Measuring monetary poverty is possible only when information about consumption or income is available. When consumption data are not comparable across two survey rounds or do not exist in one of the periods, (survey-to-survey) imputation methods can be used. The necessary condition is availability of at least one HBS that includes consumption data. Key to the survey-to-survey imputation is the estimation of a model that explains consumption or income as a function of different social-economic and geographic characteristics for the same year consumption or income data are available. This model is then used to predict consumption in the second year of the survey, using the same explanatory variables. As a result, each household in the second year will get “imputed” consumption data, making it possible to construct a poverty rate. Detailed technical reviews of the survey-to-survey imputation methodology can be found in Dang et al. (2014) and Dang et al. (2019).

Several countries applied this methodology in the MENA region. Dang et al. (2004) used Jordan’s Household Income and Expenditure (HIES) surveys from 2008 to 2010 to impute consumption to the Labor Force Survey during the same period. They validated the results using actual poverty rates from the HIES. The results were encouraging, with imputation-based poverty estimates not showing statistically significant differences from true poverty rates.

Douidich et al. (2013) used the method in Morocco, and Cuesta and Lara Ibarra (2017) simulated poverty in Tunisia using household budget and labor force surveys (LFSs). Using the latest available HBSs to impute consumption and calculate poverty using LFSs in each case, the authors found that the approach accurately reproduced actual official poverty statistics.

## <u>Data requirements and pitfalls</u>

Survey-to-survey imputation methods rely on several key assumptions, which may not necessarily hold— a fact that should be taken into consideration before applying them (Dang et al. 2014): First, the relationship between consumption and other household characteristics in the household consumption survey used in the base period is assumed to remain stable over time. This assumption is not likely to be accurate in countries experiencing structural changes, such as those that occur during conflict or due to a significant economic shock. Assuming stability of household characteristics over time

> 12 Here we focus on the methods which allow getting missing consumption at the household level, but there is another method to get missing poverty rate at the aggregate level by using forecasting techniques. This method is used by the World Bank to track the corporate goal of reducing extreme poverty. The method and its shortcomings are discussed in the annex.

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