that household characteristics from the survey relate to consumption the same way in clusters covered by the survey as in clusters not covered by the survey.

In a different scenario, a recent census and only dated survey data may be available. Reliable predictions of poverty measures at the time of the recent census can only be obtained under the additional strong assumption of non-changing structural parameters (including the regression parameters linking explanatory variables to consumption) over time (e.g., Kijima and Lanjouw, 2003). This holds for both ELL and our estimator. If both structural parameters and the distribution of the explanatory variables change over time, ELL results in biased estimates. In contrast, linking census covariate means to predict survey consumption would remain a valid method to generate a poverty map at the time of the survey. In the remainder of this paper, we will focus on the practically more relevant case of a dated census and a recent survey.

Although monitoring poverty over time is of eminent interest to economists (see, for instance, Deaton and Kozel, 2005), little attention has been paid to updating small area estimation approaches which combine dated census and recent survey data. Emwanu et al. (2006) require panel data with one wave collected at the time of the census. While structural changes in the explanatory variables may be detected and tackled by weighting procedures in such a setting, the remaining assumptions of the ELL method as described above are still required. Furthermore, availability of panel data over a longer time span without substantial attrition is rare, especially in developing countries. The National Statistical Coordination Board of the Philippines (2009) uses explanatory variables deemed time-invariant to estimate intercensal poverty measures. Whether the distribution of variables changes over time is not assessed formally but rather based on _impromptu_ assumption. This approach still relies on similar assumptions as the ELL method, even though changes in the distribution of the explanatory variables are ruled out by choosing time-invariant variables. One may also test whether the distribution of potential predictors changed over time and then restrict the set of predictors in the first stage to only those that exhibit no drift.<sup>2</sup> However, severe shocks and extended time periods between survey and census will tend to quickly exhaust the set of viable predictors to do so. And it is exactly in those settings in which the demand for an updated poverty map is likely to be high. Isidro (2010) and Isidro et al. (2016) propose to fit a model on simultaneously collected survey and census data first, for instance by ELL, and update the resulting estimates using a more recent survey. Their Extended Structure Preserving Estimation (ESPREE) approach does not require panel data but contemporaneous surveys and census collection with common variables. The ESPREE method relies on updating multi-way contingency tables which is computationally tractable only for a limited number of categorical explanatory variables and an outcome indicator which is a proportion, for instance the number of people who live below the poverty line. A more general updating

> 2This has been suggested for an update of the Bangladeshi poverty maps by researchers from The Bangladesh Bureau of Statistics, The World Bank and The United Nations World Food Programme (2010).

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