These latter examples show that cross-survey imputation methods can be used effectively to improve both the levels of _disaggregation_ and the _frequency_ at which statistics of interest may be obtained. This is of great relevance to both research and policy making. Imagine a country that may have been affected by the 2008 global financial crisis and the 2001 crisis before that. Suppose however that the only official poverty estimates that are available over this extended period are for the years 2001 and 2007, while the next official poverty estimates will not become available until 2014. This means that until 2014 policy makers will have no data on how poverty has evolved post-2007, i.e. there is no way to tell whether the recent global crisis has had any impact on poverty. By 2014, when the new official poverty estimates are published, the effects of the 2008 crisis may not be visible any longer and the window for response may have passed. The same argument can be applied to macroeconomic shocks that have occurred prior to 2007, which include the 2001 financial crisis. Relying solely on official poverty estimates derived from these household surveys does not allow us to identify any responses in poverty between survey years, which can be far apart. And poverty may exhibit important seasonal fluctuations that annual poverty estimates cannot reveal.<sup>4</sup> In this paper, we build on the existing cross-survey imputation literature to improve the _frequency_ of poverty estimates, i.e. to provide up-to-date estimates of poverty when official estimates are deemed outdated. We do this using quarterly Labor Force Surveys (LFSs) from Morocco, a country that represents an ideal natural experiment. Morocco implemented consumption surveys in 2001 and 2007 and is expecting to complete the next survey in 2013. Note however that much has happened since 2007 as well as between 2001 and 2007; two global financial crisis (2001 and 2008) and a number of domestic shocks which include favorable rainfall that has boosted agricultural output. Any one of these events may have had a significant impact on poverty levels. However, policy makers in Morocco do not have the data needed to verify whether poverty rates have indeed changed. The question we wish to address is whether we can use quarterly Labor Force Surveys (LFSs) to fill these gaps and, by doing so, connect the dots of poverty estimates in Morocco for the period between the last two consumption surveys (2001-2007) and beyond. To our knowledge, this is the first comprehensive experiment of crosssurvey imputation that uses LFS data to estimate a series of quarterly poverty rates spanning a decade. Note that if the proposed methodology proves successful, it can be applied to countries worldwide, wherever there is a need for it, since LFSs are standard surveys that are conducted at least annually if not quarterly in almost every country.

The application of this methodology to Morocco shows encouraging results. We estimated quarterly poverty rates with LFSs for the period 2001-2007 using separately a consumption model estimated from 2001 consumption data and a consumption model estimated from 2007 consumption data. Despite the fact that the two models are 6 years apart, we found that the models produced nearly identical poverty trends over the period under consideration. We also estimated the 2001 poverty rate using the 2007

> 4 A few countries have attempted to address this problem by producing statistics on poverty at an infra-annual level. For example, Peru experimented with the administration of quarterly consumption surveys while Mexico produces a proxy of income poverty every month using Labor Force Surveys. However, collecting survey data quarterly is evidently very expensive while many countries do not collect income data together with labor data making these efforts difficult to replicate elsewhere.

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