Iterative Approaches to R x C Ecological Inference Problems: Where They Can Go Wrong and One Quick Fix

This article argues that a key step in King's iterative approachto R x C ecological inference problems—the aggregationof groups into broad conglomerate categories—can introduceproblems of aggregation bias and multimodality into data, inducingmodel violations. As a result, iterative EI estimates can beconsiderably biased, even when the original data conform tothe assumptions of the model. I demonstrate this problem intuitivelyand through simulations, show the conditions under which itis likely to arise, and illustrate it with the example of Colouredvoting during the 1994 elections in South Africa. I then proposean easy fix to the problem, demonstrating the usefulness ofthe fix both through simulations and in the specific South Africancontext.