Ecological Inference

Inferring individual behavior from group-level data: The first approach to incorporate both unit-level deterministic bounds and cross-unit statistical information, methods for 2x2 and larger tables, Bayesian model averaging, applications to elections, software.

Methods

Ecological Inference: New Methodological Strategies
Summarizes the explosion of research in ecological inference that has occurred in the previous eight years, all following the key insight of models that include both deterministic and statistical information. King, Gary, Ori Rosen, Martin Tanner, Gary King, Ori Rosen, and Martin A Tanner. 2004. Ecological Inference: New Methodological Strategies. New York: Cambridge University Press.Abstract
Ecological Inference: New Methodological Strategies brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half decade has witnessed an explosion of research in ecological inference – the attempt to infer individual behavior from aggregate data. The uncertainties and the information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but such inferences are required in many academic fields, as well as by legislatures and the courts in redistricting, by businesses in marketing research, and by governments in policy analysis.
Binomial-Beta Hierarchical Models for Ecological Inference
An extension of the work in the above book to use MCMC technology, making models for larger tables possible. King, Gary, Ori Rosen, and Martin A Tanner. 1999. Binomial-Beta Hierarchical Models for Ecological Inference. Sociological Methods and Research 28: 61–90.Abstract
The authors develop binomial-beta hierarchical models for ecological inference using insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King’s ecological inference model. The new approach reveals some features of the data that King’s approach does not, can easily be generalized to more complicated problems such as general R x C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the significance of the covariates. It may also be better suited to cases in which the observed aggregate cells are estimated from very few observations or have some forms of measurement error. This article also provides an example of a hierarchical model in which the statistical idea of "borrowing strength" is used not merely to increase the efficiency of the estimates but to enable the data analyst to obtain estimates.
Outlines some of the history of ecological inference research, and introduces this new book. King, Gary, Ori Rosen, and Martin Tanner. 2004. Information in Ecological Inference: An Introduction. In Ecological Inference: New Methodological Strategies, Gary King, Rosen, Ori, and Tanner, Martin. New York: Cambridge University Press.
This article uses MCMC technology, and a quicker approximation, to make ecological inferences using deterministic and statistical information in larger tables. Rosen, Ori, Wenxin Jiang, Gary King, and Martin A Tanner. 2001. Bayesian and Frequentist Inference for Ecological Inference: The RxC Case. Statistica Neerlandica 55: 134–156.Abstract
In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R x C contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta hierarchical model developed by King, Rosen and Tanner (1999) from the 2 x 2 case to the R x C case, the inferential procedure employs Markov chain Monte Carlo (MCMC) methods. As such the resulting MCMC analysis is rich but computationally intensive. The frequentist approach, based on first moments rather than on the entire likelihood, provides quick inference via nonlinear least-squares, while retaining good frequentist properties. The two approaches are illustrated with simulated data, as well as with real data on voting patterns in Weimar Germany. In the final section of the paper we provide an overview of a range of alternative inferential approaches which trade-off computational intensity for statistical efficiency.
Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data
Related research on aggregation, revealing the logical inconsistency of some popularly used models. Alt, James E, Gary King, and Curtis Signorino. 2001. Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data. Political Analysis 9: 21–44.Abstract
Binary, count and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only single theoretical process exists for which know statistical methods can estimate the same parameters - and it is generally used only for count and duration data. The result is that seemingly trivial decisions abut which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time independence. We also derive a set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of understanding and avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available. We discuss these issues and suggest an agenda for political methodologists interested in this very large class of aggregation problems.
Did Illegal Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?
Details of an application conducted for the New York Times, including extensions of ecological inference to Bayesian model averaging. Imai, Kosuke, and Gary King. 2004. Did Illegal Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?. Perspectives on Politics 2: 537–549.Abstract
Although not widely known until much later, Al Gore received 202 more votes than George W. Bush on election day in Florida. George W. Bush is president because he overcame his election day deficit with overseas absentee ballots that arrived and were counted after election day. In the final official tally, Bush received 537 more votes than Gore. These numbers are taken from the official results released by the Florida Secretary of State's office and so do not reflect overvotes, undervotes, unsuccessful litigation, butterfly ballot problems, recounts that might have been allowed but were not, or any other hypothetical divergence between voter preferences and counted votes. After the election, the New York Times conducted a six month long investigation and found that 680 of the overseas absentee ballots were illegally counted, and no partisan, pundit, or academic has publicly disagreed with their assessment. In this paper, we describe the statistical procedures we developed and implemented for the Times to ascertain whether disqualifying these 680 ballots would have changed the outcome of the election. The methods involve adding formal Bayesian model averaging procedures to King's (1997) ecological inference model. Formal Bayesian model averaging has not been used in political science but is especially useful when substantive conclusions depend heavily on apparently minor but indefensible model choices, when model generalization is not feasible, and when potential critics are more partisan than academic. We show how we derived the results for the Times so that other scholars can use these methods to make ecological inferences for other purposes. We also present a variety of new empirical results that delineate the precise conditions under which Al Gore would have been elected president, and offer new evidence of the striking effectiveness of the Republican effort to convince local election officials to count invalid ballots in Bush counties and not count them in Gore counties.

Software

King, Gary, and Kenneth Benoit. 2003. EzI: A(n Easy) Program for Ecological Inference. Website Published as part of the Gauss Package by Aptech Systems, Kent, Washington, and as a stand-alone program called EzI: A(n Easy) Program for Ecological Inference, by Kenneth Benoit and me.

Data

Discussions and Extensions

The Future of Ecological Inference Research: A Reply to Freedman et al.
King, Gary. 1999. The Future of Ecological Inference Research: A Reply to Freedman et al.. Journal of the American Statistical Association 94: 352-355.Abstract
I appreciate the editor’s invitation to reply to Freedman et al.’s (1998) review of "A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data" (Princeton University Press.) I welcome this scholarly critique and JASA’s decision to publish in this field. Ecological inference is a large and very important area for applications that is especially rich with open statistical questions. I hope this discussion stimulates much new scholarship. Freedman et al. raise several interesting issues, but also misrepresent or misunderstand the prior literature, my approach, and their own empirical analyses, and compound the problem, by refusing requests from me and the editor to make their data and software available for this note. Some clarification is thus in order.
Adolph, Christopher, Gary King, Kenneth W Shotts, and Michael C Herron. 2003. A Consensus on Second Stage Analyses in Ecological Inference Models. Political Analysis 11: 86–94.Abstract
Since Herron and Shotts (2003a and hereinafter HS), Adolph and King (2003 andhereinafter AK), and Herron and Shotts (2003b and hereinafter HS2), the four of us have iterated many more times, learned a great deal, and arrived at a consensus on this issue. This paper describes our joint recommendations for how to run second-stage ecological regressions, and provides detailed analyses to back up our claims.
King, Gary. 2002. Isolating Spatial Autocorrelation, Aggregation Bias, and Distributional Violations in Ecological Inference. Political Analysis 10: 298–300.Abstract
This is an invited response to an article by Anselin and Cho. I make two main points: The numerical results in this article violate no conclusions from prior literature, and the absence of the deterministic information from the bounds in the article’s analyses invalidates its theoretical discussion of spatial autocorrelation and all of its actual simulation results. An appendix shows how to draw simulations correctly.
King, Gary. 2000. Geography, Statistics, and Ecological Inference. Annals of the Association of American Geographers 90: 601–606.Abstract
I am grateful for such thoughtful review from these three distinguished geographers. Fotheringham provides an excellent summary of the approach offered, including how it combines the two methods that have dominated applications (and methodological analysis) for nearly half a century– the method of bounds (Duncan and Davis, 1953) and Goodman’s (1953) least squares regression. Since Goodman’s regression is the only method of ecological inference "widely used in Geography" (O’Loughlin), adding information that is known to be true from the method of bounds (for each observation) would seem to have the chance to improve a lot of research in this field. The other addition that EI provides is estimates at the lowest level of geography available, making it possible to map results, instead of giving only single summary numbers for the entire geographic region. Whether one considers the combined method offered "the" solution (as some reviewers and commentators have portrayed it), "a" solution (as I tried to describe it), or, perhaps better and more simply, as an improved method of ecological inference, is not importatnt. The point is that more data are better, and this method incorporates more. I am gratified that all three reviewers seem to support these basic points. In this response, I clarify a few points, correct some misunderstandings, and present additional evidence. I conclude with some possible directions for future research.