Abstract. King's “solution” works with some data sets and fails with others. As a theoretical matter, inferring the behavior of subgroups from aggregate data is generally impossible: The relevant parameters are not identifiable. Unfortunately, King's diagnostics do not discriminate between probable successes and probable failures. Caution would seem to be in order.

Introduction

King (1997) proposed a method for ecological inference and made sweeping claims about its validity. According to King, his method provided realistic estimates of uncertainty, with diagnostics capable of detecting failures in assumptions. He also claimed that his method was robust, giving correct inferences even when the model is wrong.

Our review (Freedman, Klein, Ostland, and Roberts 1998 [Chapter 5]) showed that the claims were exaggerated. King's method works if its assumptions hold. If assumptions fail, estimates are unreliable: so are internally-generated estimates of uncertainty. His diagnostics do not distinguish between cases where his method works and where it fails. King (1999) raised various objections to our review. After summarizing the issues, we will respond to his main points and a few of the minor ones. The objections have little substance.

Model comparisons

Our review compared King's method to ecological regression and the neighborhood model. In our test data, the neighborhood model was the most accurate, while King's method was no better than ecological regression. To implement King's method, we used his software package EZIDOS, which we downloaded from his web site.