An Incomplete Data Approach to the Ecological Inference Problem

In this paper, we propose to formulate ecological inference as a coarse data problem where only a subset of the complete-data sample space is observed. Applying the related assumptions and theoretical results of Heitjan and Rubin (1991), we formally identify three key factors that affect ecological inference; distributional, contextual and aggregation effects. Different modeling strategies are discussed to deal with distributional and contextual effects. While aggregation effects cannot be statistically adjusted, we show how to formally quantify the magnitude of such effects through the use of the Expectation-Maximization algorithm. The paper concludes with simulations and empirical applications that assess the performance of the proposed models. C-code used to implement the proposed method is available with easy-to-use R interface.