In this Book

  • A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data
  • Book
  • Gary King
  • 2013
  • Published by: Princeton University Press
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summary

This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem.


King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice.


King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem.

Table of Contents

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  1. Cover
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  1. Title Page, Copyright, Dedication
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  1. Contents
  2. pp. vii-x
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  1. Figures
  2. pp. xi-xii
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  1. Tables
  2. pp. xiii-xiv
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  1. Preface
  2. pp. xv-xxii
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  1. I. Introduction
  2. pp. 1-2
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  1. 1. Qualitative Overview
  2. pp. 3-27
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  1. 2. Formal Statement of the Problem
  2. pp. 28-34
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  1. II. Catalog of Problems to Fix
  2. pp. 35-36
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  1. 3. Aggregation Problems
  2. pp. 37-55
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  1. 4. Non-Aggregation Problems
  2. pp. 56-74
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  1. III. The Proposed Solution
  2. pp. 75-76
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  1. 5. The Data: Generalizing the Method of Bounds
  2. pp. 77-90
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  1. 6. The Model
  2. pp. 91-122
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  1. 7. Preliminary Estimation
  2. pp. 123-140
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  1. 8. Calculating Quantities of Interest
  2. pp. 141-157
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  1. 9. Model Extensions
  2. pp. 158-196
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  1. IV. Verification
  2. pp. 197-198
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  1. 10. A Typical Application Described in Detail: Voter Registration by Race
  2. pp. 199-216
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  1. 11. Robustness to Aggregation Bias: Poverty Status by Sex
  2. pp. 217-225
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  1. 12. Estimation without Information: Black Registration in Kentucky
  2. pp. 226-234
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  1. 13. Classic Ecological Inferences
  2. pp. 235-246
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  1. V. Generalizations and Concluding Suggestions
  2. pp. 247-248
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  1. 14. Non-Ecological Aggregation Problems
  2. pp. 249-262
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  1. 15. Ecological Inference in Larger Tables
  2. pp. 263-276
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  1. 16. A Concluding Checklist
  2. pp. 277-292
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  1. VI. Appendices
  2. pp. 293-294
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  1. Appendix A: Proof That All Discrepancies Are Equivalent
  2. pp. 295-300
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  1. Appendix B: Parameter Bounds
  2. pp. 301-303
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  1. Appendix C: Conditional Posterior Distribution
  2. pp. 304-306
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  1. Appendix D: The Likelihood Function
  2. pp. 307-308
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  1. Appendix E: The Details of Nonparametric Estimation
  2. pp. 309-310
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  1. Appendix F: Computational Issues
  2. pp. 311-312
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  1. Glossary of Symbols
  2. pp. 313-316
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  1. References
  2. pp. 317-336
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  1. Index
  2. pp. 337-342
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  1. About the Author
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