A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data

I examine a recently proposed solution to the ecological inference problem (King 1997). It is asserted that the proposed model is able to reconstruct individual-level behavior from aggregate data. I discuss in detail both the benefits and limitations of this model. The assumptions of the basic model are often inappropriate for instances of aggregate data. The extended version of the model is able to correct for some of these limitations. However, it is difficult in most cases to apply the extended model properly.

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A New Approach to the Study of Ticket Splitting

American Political Science Review ◽

10.2307/2585479 ◽

1998 ◽

Vol 92 (3) ◽

pp. 533-544 ◽

Cited By ~ 77

Author(s):

Barry C. Burden ◽

David C. Kimball

Keyword(s):

Survey Data ◽

Aggregate Data ◽

Divided Government ◽

Ecological Fallacy ◽

Ecological Inference ◽

High Quality ◽

Inference Problem ◽

New Approach ◽

Congressional Districts ◽

Individual Level

A new solution to the ecological inference problem is used to examine split-ticket voting patterns across states and congressional districts in the 1988 elections. Earlier studies of ticket splitting used either aggregate data, which suffer from the “ecological fallacy” and threaten individual-level inferences, or survey data from small, unrepresentative samples. We produce more accurate estimates of the proportions of voters splitting their ballots in each state and district, which enables us to examine variations across geographical units. We also clarify the connection between ticket splitting and divided government and test several competing theories about the causes of both. We find, contrary to balancing arguments, that voters are not intentionally splitting tickets to produce divided government and moderate policies. In most cases split outcomes are a by-product of lopsided congressional campaigns that feature well-funded, high-quality candidates versus unknown competitors.

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Cross‐Level/Ecological Inference

10.1093/oxfordhb/9780199286546.003.0024 ◽

2009 ◽

Cited By ~ 3

Author(s):

Wendy Tam Cho ◽

Charles F. Manski

Keyword(s):

Recent Work ◽

Partial Identification ◽

Aggregate Data ◽

Statistical Problem ◽

Ecological Inference ◽

Inference Problem ◽

Individual Behaviour ◽

Methodological Approaches

This article reports the main methodological approaches to the statistical problem. It describes the fundamental indeterminacy of the problem. It also provides a framework that coherently binds the variety of approaches that have been proposed to address this problem. Then, an overview of these various approaches and their respective contributions are mentioned. The ecological inference problem within the literature of partial identification and the recent work generalizing the use of logical bounds on possible solutions as an identification region for the general r × c problem are explained. It finally covers some admonitions about this fascinating problem that has enthralled decades of scholars from varied disciplines. The analysis by Duncan and Davis made clear that aggregate data only partially reveal the structure of individual behaviour. However, their contribution has largely been viewed as limited and an appreciation for the idea of bounds or an identification region has yet to fully emerge.

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