Estimating uncertainty in spatial microsimulation approaches to small area estimation: A new approach to solving an old problem

Article history: Received 9 September 2015 Received in revised form 20 May 2016 Accepted 15 June 2016 Available online xxxx A wide range of user groups from policy makers to media commentators demand ever more spatially detailed information yet the desired data are often not available at fine spatial scales. Increasingly, small area estimation (SAE) techniques are called upon to fill in these informational gaps by downscaling survey outcome variables of interest based on the relationships seen with key covariate data. In the process SAE techniques both rely extensively on small area Census data to enable their estimation and offer potential future substitute data sources in the event of Census data becoming unavailable. Whilst statistical approaches to SAE routinely incorporate intervals of uncertainty around central point estimates in order to indicate their likely accuracy, the continued absence of such intervals from spatial microsimulation SAE approaches severely limits their utility and arguably represents their key methodological weakness. The present article presents an innovative approach to resolving this keymethodological gap based on the estimation of variance of the between-area error term from amultilevel regression specification of the constraint selection for iterative proportional fitting (IPF). The performance of the estimated credible intervals are validated against known Census data at the target small area and show an extremely high level of performance. As well as offering an innovative solution to this long-standing methodological problem, it is hopedmore broadly that the researchwill stimulate the spatial microsimulation community to adopt and build on these foundations so that we can collectivelymove to a positionwhere intervals of uncertainty are delivered routinely around spatial microsimulation small area point estimates. © 2016 Published by Elsevier Ltd.