Recent Papers

Preprints and Works in Progress

• Matching for Causal Inference Without Balance Checking by Stefano M. Iacus, Gary King, and Giuseppe Porro, Version: 7/1/08. (Paper: PDF) We address a major discrepancy in matching methods for causal inference in observational data. Since these data are typically plentiful, the goal of matching is to reduce bias and only secondarily to keep variance low. However, most matching methods seem designed for the opposite problem, guaranteeing sample size ex ante but limiting bias by controlling for covariates through reductions in the imbalance between treated and control groups only ex post and only sometimes. (The resulting practical difficulty may explain why most published applications do not check whether imbalance was reduced and so may not even be decreasing bias.) We introduce a new class of "Monotonic Imbalance Bounding" (MIB) matching methods that enables one to choose a fixed level of maximum imbalance, or to reduce maximum imbalance for one variable without changing the maximum imbalance for the others. We then discuss a specific MIB method called "Coarsened Exact Matching" (CEM) which, unlike most existing approaches, also explicitly bounds through ex ante user choice both the degree of model dependence and the treatment effect estimation error, eliminates the need for a separate procedure to restrict data to common support, meets the congruence principle, is robust to measurement error, works well with modern methods of imputation for missing data, is computationally efficient even with massive data sets, and is easy to understand and use. This method can improve causal inferences in a wide range of applications, and may be preferred for simplicity of use even when it is possible to design superior methods for particular problems. We also make available open source software which implements all our suggestions.
• Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler by Gary King, Ori Rosen, Martin Tanner, and Alexander F. Wagner, Version: 8/1/2007. (Paper: PDF) The enormous Nazi voting literature rarely builds on modern statistical or economic research. By adding these approaches, we find that the most widely accepted existing theories of this era cannot distinguish the Weimar elections from almost any others in any country. Via a retrospective voting account, we show that voters most hurt by the depression, and most likely to oppose the government, fall into separate groups with divergent interests. This explains why some turned to the Nazis and others turned away. The consequences of Hitler's election were extraordinary, but the voting behavior that led to it was not. Forthcoming in Journal of Economic History 68, 4 (December, 2008) (The methods used here were developed in this article.)
• The Changing Evidence Base of Political Science Research by Gary King. (Paper: PDF) An essay to appear in Gary King, Norman Nie, and Kay Scholzman, eds., The Future of Political Science: 100 Perspectives, New York: Routledge Press, 2009. (a volume in honor of Sidney Verba).
• The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation by Kosuke Imai, Gary King, and Clayton Nall. Version: 4/9/08. (Paper: PDF) A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals --- such as households, communities, firms, medical practices, schools, or classrooms --- even when the individual is the unit of interest. To recoup some of the resulting efficiency loss, many studies pair similar clusters and randomize treatment within pairs. Other studies (including almost all published political science field experiments) avoid pairing, in part because some prominent methodological articles claim to have identified serious problems with this ``matched-pair cluster-randomized'' design. We prove that all such claims about problems with this design are unfounded. We then show that the estimator for matched-pair designs favored in the literature is appropriate only in situations where matching is not needed. To address this problem without modeling assumptions, we generalize Neyman's (1923) approach and propose a simple new estimator with much improved statistical properties. We also introduce methods to cope with individual-level noncompliance, which most existing approaches often assume away. We show that from the perspective of, among other things, bias, efficiency, power, or robustness, pairing should be used in cluster-randomized experiments whenever feasible; failing to do so is equivalent to discarding a considerable fraction of one's data. We develop these techniques in the context of a randomized evaluation we are conducting of the Mexican Universal Health Insurance Program.
• Extracting Systematic Social Science Meaning from Text by Daniel Hopkins and Gary King. Version: 4/15/08. (Paper: PDF) We develop a method of automated content analysis that gives approximately unbiased estimates of quantities of theoretical interest to social scientists. With a small sample of documents hand coded into investigator-chosen categories, our method can give accurate estimates of the proportion of documents in each category in a larger population. Existing methods allow for the possibility of substantial bias in estimating the category proportions that are often of interest to social scientists. We first show how to correct the bias for any existing classifier, and then go further to estimate the proportions without the intermediate step of individual document classification and with greatly reduced assumptions. We also introduce a statistical correction for the less-than-perfect levels of inter-coder reliability that typically characterize human document classification. These methods allow us to measure the classical conception of public opinion as those views that are actively and publicly expressed, rather than the attitudes or non-attitudes of the populace as a whole. Specifically, we track the daily opinions of thousands of people about President Bush using a massive data set of online blogs we develop and make available with this article. We also offer easy-to-use software that implements our methods, and we demonstrate its effectiveness with several other text categorization problems.
• An Introduction to the Dataverse Network as an Infrastructure for Data Sharing by Gary King. Version: 7/8/07. (Paper: PDF) We introduce a set of integrated developments in web application software, networking, data citation standards, and statistical methods designed to put some of the universe of data and data sharing practices on somewhat firmer ground. We have focused on social science data, but aspects of what we have developed may apply more widely. The idea is to facilitate the public distribution of persistent, authorized, and verifiable data, with powerful but easy-to-use technology, even when the data are confidential or proprietary. We intend to solve some of the sociological problems of data sharing via technological means, with the result intended to benefit both the scientific community and the sometimes apparently contradictory goals of individual researchers. Published in, Sociological Methods and Research.
• Misunderstandings among Experimentalists and Observationalists about Causal Inference by Kosuke Imai, Gary King, and Elizabeth Stuart. Version: 9/19/07 (Paper: PDF) We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference in experimental and observational research. These issues concern some of the most basic advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization, and matching after treatment assignment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies, and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions better understand each other's inferential problems and attempted solutions. Journal of the Royal Statistical Association, Series A. Vol. 171, Part 2 (2008): 481-502.
• A `Politically Robust' Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program, by Gary King, Emmanuela Gakidou, Nirmala Ravishankar, Ryan T. Moore, Jason Lakin, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. Version: 1/24/2007 (Paper: PDF). We develop an approach to conducting large scale randomized public policy experiments intended to be more robust to the political interventions that have ruined some or all parts of many similar previous efforts. Our proposed design is insulated from selection bias in some circumstances even if we lose observations; our inferences can still be unbiased even if politics disrupts any two of the three steps in our analytical procedures; and other empirical checks are available to validate the overall design. We illustrate with a design and empirical validation of an evaluation of the Mexican Seguro Popular de Salud (Universal Health Insurance) program we are conducting. Seguro Popular, which is intended to grow to provide medical care, drugs, preventative services, and financial health protection to the 50 million Mexicans without health insurance, is one of the largest health reforms of any country in the last two decades. The evaluation is also large scale, constituting one of the largest policy experiments to date and what may be the largest randomized health policy experiment ever. Journal of Policy Analysis and Management, Vol. 26, Issue 3 (2007): 479--506.
• What to do about Missing Values in Time Series Cross-Section Data by James Honaker and Gary King. Version: 9/21/07 (Paper: PDF) Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in these fields have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation. First, we build a multiple imputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we build nonignorable missingness models by enabling analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, because these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also made it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing algorithms.
• Verbal Autopsy Methods with Multiple Causes of Death by Gary King and Ying Lu. Version: published. (Paper: PDF) Verbal autopsy procedures are widely used for estimating cause-specific mortality in areas without medical death certification. Data on symptoms reported by caregivers along with the cause of death are collected from a medical facility, and the cause-of-death distribution is estimated in the population where only symptom data are available. Current approaches analyze only one cause at a time, involve assumptions judged difficult or impossible to satisfy, and require expensive, time consuming, or unreliable physician reviews, expert algorithms, or parametric statistical models. By generalizing current approaches to analyze multiple causes, we show how most of the difficult assumptions underlying existing methods can be dropped. These generalizations also make physician review, expert algorithms, and parametric statistical assumptions unnecessary. With theoretical results, and empirical analyses in data from China and Tanzania, we illustrate the accuracy of this approach. While no method of analyzing verbal autopsy data, including the more computationally intensive approach offered here, can give accurate estimates in all circumstances, the procedure offered is conceptually simpler, less expensive, more general, as or more replicable, and easier to use in practice than existing approaches. We also show how our focus on estimating aggregate proportions, which are the quantities of primary interest in verbal autopsy studies, may also greatly reduce the assumptions necessary, and thus improve the performance of, many individual classifiers in this and other areas. As a companion to this paper, we also offer easy-to-use software that implements the methods discussed herein. forthcoming in Statistical Science, Vol. 23, No. 1 (February, 2008, forthcoming), .
• Toward A Common Framework for Statistical Analysis and Development by Kosuke Imai, Gary King, and Olivia Lau. Version: 4/9/08. (Paper: PDF) We describe some progress toward a common framework for statistical analysis and software development built on and within the R language, including R's numerous existing packages. The framework we have developed offers a simple unified structure and syntax that can encompass a large fraction of statistical procedures already implemented in R, without requiring any changes in existing approaches. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R. forthcoming in the Journal of Computational and Graphical Statistics .
• The Future of Partisan Symmetry as a Judicial Test for Partisan Gerrymandering after LULAC v. Perry by Bernard Grofman and Gary King. Version: proofs. (Paper: PDF) While the Supreme Court in Bandemer v. Davis found partisan gerrymandering to be justiciable, no challenged redistricting plan in the subsequent 20 years has been held unconstitutional on partisan grounds. Then, in Vieth v. Jubilerer, five justices concluded that some standard might be adopted in a future case, if a manageable rule could be found. When gerrymandering next came before the Court, in LULAC v. Perry, we along with our colleagues filed an Amicus Brief (King et al., 2005), proposing the test be based in part on the partisan symmetry standard. Although the issue was not resolved, our proposal was discussed and positively evaluated in three of the opinions, including the plurality judgment, and for the first time for any proposal the Court gave a clear indication that a future legal test for partisan gerrymandering will likely include partisan symmetry. A majority of Justices now appear to endorse the view that the measurement of partisan symmetry may be used in partisan gerrymandering claims as “a helpful (though certainly not talismanic) tool” (Justice Stevens, joined by Justice Breyer), provided one recognizes that “asymmetry alone is not a reliable measure of unconstitutional partisanship” and possibly that the standard would be applied only after at least one election has been held under the redistricting plan at issue (Justice Kennedy, joined by Justices Souter and Ginsburg). We use this essay to respond to the request of Justices Souter and Ginsburg that “further attention … be devoted to the administrability of such a criterion at all levels of redistricting and its review.” Building on our previous scholarly work, our Amicus Brief, the observations of these five Justices, and a supporting consensus in the academic literature, we offer here a social science perspective on the conceptualization and measurement of partisan gerrymandering and the development of relevant legal rules based on what is effectively the Supreme Court’s open invitation to lower courts to revisit these issues in the light of LULAC v. Perry. Published in the Election Law Journal, January 2007.
• A Proposed Standard for the Scholarly Citation of Quantitative Data by Micah Altman and Gary King. Version: published. (Paper: PDF) An essential aspect of science is a community of scholars cooperating and competing in the pursuit of common goals. A critical component of this community is the common language of and the universal standards for scholarly citation, credit attribution, and the location and retrieval of articles and books. We propose a similar universal standard for citing quantitative data that retains the advantages of print citations, adds other components made possible by, and needed due to, the digital form and systematic nature of quantitative data sets, and is consistent with most existing subfield-specific approaches. Although the digital library field includes numerous creative ideas, we limit ourselves to only those elements that appear ready for easy practical use by scientists, journal editors, publishers, librarians, and archivists. Published in D-Lib Magazine, March, 2007
• Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference by Daniel E. Ho, Kosuke Imai, Gary King, and Elizabeth A. Stuart, Version: 5/4/2006. (Paper: PDF) Although political science articles rarely include causal estimates from more than a few model specifications, authors usually choose these from numerous trial runs readers never see. Given the typically large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that the author found it possible to find a specification that fits his or her favorite hypothesis? Matching methods, which offer the promise of causal inference with fewer assumptions, is one possible way forward, but the literature suffers from conflicting approaches to estimation, uncertainty, theoretical results, and practical advice. We propose a unified approach that makes it possible for researchers to preprocess data (see our easy-to-use software, Matchit) and then to apply whatever familiar parametric techniques they would have used anyway. Instead of replacing existing methods, we use matching to make parametric models work better by giving more accurate and considerably less model-dependent causal inferences. Published in Political Analysis.
• Demographic Forecasting by Federico Girosi and Gary King, (Book manuscript: PDFs) We introduce a framework for forecasting age-sex-country-cause-specific mortality rates that incorporates considerably more information, and thus has the potential to forecast much better, than any existing approach. Mortality forecasts are used in a wide variety of academic fields, and for global and national health policy making, medical and pharmaceutical research, and social security and retirement planning.
     As it turns out, the tools we developed in pursuit of this goal also have broader statistical implications, in addition to their use for forecasting mortality or other variables with similar statistical properties. First, our methods make it possible to include different explanatory variables in a time series regression for each cross-section, while still borrowing strength from one regression to improve the estimation of all. Second, we show that many existing Bayesian (hierarchical and spatial) models with explanatory variables use prior densities that incorrectly formalize prior knowledge. Many demographers and public health researchers have fortuitously avoided this problem so prevalent in other fields by using prior knowledge only as an ex post check on empirical results, but this approach excludes considerable information from their models. We show how to incorporate this demographic knowledge into a model in a statistically appropriate way. Finally, we develop a set of tools useful for developing models with Bayesian priors in the presence of partial prior ignorance. This approach also provides many of the attractive features claimed by the empirical Bayes approach, but fully within th standard Bayesian theory of inference. YourCast software is available to implement all the methods in the book. Forthcoming, Princeton University Press
• The Dangers of Extreme Counterfactuals by Gary King and Langche Zeng. (Article: PDF) We address the problem that occurs when inferences about counterfactuals — predictions, "what if" questions, and causal effects — are attempted far from the available data. The danger of these extreme counterfactuals is that substantive conclusions drawn from statistical models that fit the data well turn out to be based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Yet existing statistical strategies provide few reliable means of identifying extreme counterfactuals. We offer a proof that inferences farther from the data are more model-dependent, and then develop easy-to-apply methods to evaluate how model-dependent our answers would be to specified counterfactuals. These methods require neither sensitivity testing over specified classes of models nor evaluating any specific modeling assumptions. If an analysis fails the simple tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on empirical evidence. Published in Political Analysis. Whatif software is available to implement all procedures described in this paper. For a less technical version of this paper, see the next paper.
• When Can History be Our Guide? The Pitfalls of Counterfactual Inference by Gary King and Langche Zeng, Version: published. (Paper: PDF) Inferences about counterfactuals are essential for prediction, answering "what if"' questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than empirical evidence. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, and so this problem can be hard to detect. We develop easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on genuine prior knowledge or empirical evidence. We use these methods to evaluate the extensive scholarly literatures on the effects of changes in the degree of democracy in a country (on any dependent variable) and separate analyses of the effects of UN peacebuilding efforts, and find evidence that many scholars are inadvertently drawing conclusions based more on modeling hypotheses than on their empirical evidence. For some research questions, history contains insufficient information to be our guide. International Studies Quarterly. Whatif software is available to implement all procedures described in this paper. For a technical version of this paper, see the previous paper.
• Death by Survey: Estimating Adult Mortality without Selection Bias from Sibling Survival Data by Emmanuela Gakidou and Gary King, Version: 2/21/06. (Paper: PDF). The widely used methods for estimating adult mortality rates from sample survey responses about the survival of siblings, parents, spouses, and others depend crucially on an assumption that we demonstrate does not hold in real data. We show that when this assumption is violated — so that the mortality rate varies with sibship size — mortality estimates can be massively biased. By using insights from work on the statistical analysis of selection bias, survey weighting, and extrapolation problems, we propose a new and relatively simple method of recovering the mortality rate with both greatly reduced potential for bias and increased clarity about the source of necessary assumptions. Demography.
• Understanding the Lee-Carter Mortality Forecasting Method by Federico Girosi and Gary King. (Paper: PDF) We demonstrate here several previously unrecognized or insufficiently appreciated properties of the Lee-Carter mortality forecasting approach, the dominant method used in both the academic literature and practical applications. We show that this model is a special case of a considerably simpler, and less often biased, random walk with drift model, and prove that the age profile forecast from both approaches will always become less smooth and unrealistic after a point (when forecasting forward or backwards in time) and will eventually deviate from any given baseline. We use these and other properties we demonstrate to suggest when the model would be most applicable in practice.
• Publication, Publication by Gary King, Version: published. (Article: PDF) I show herein how to write a publishable paper by beginning with the replication of a published article. This strategy seems to work well for class projects in producing papers that ultimately get published, helping to professionalize students into the discipline, and teaching them the scientific norms of the free exchange of academic information. I begin by briefly revisiting the prominent debate on replication our discipline had a decade ago and some of the progress made in data sharing since. Published in PS: Political Science and Politics. See also, Data Sharing and Replication.
• The Supreme Court During Crisis: How War Affects only Non-War Cases by Lee Epstein, Daniel E. Ho, Gary King, and Jeffrey A. Segal, Version: Published. (Article: PDF) Does the U.S. Supreme Court curtail rights and liberties when the nation's security is under threat? In hundreds of articles and books, and with renewed fervor since September 11, 2001, members of the legal community have warred over this question. Yet, not a single large-scale, quantitative study exists on the subject. Using the best data available on the causes and outcomes of every civil rights and liberties case decided by the Supreme Court over the past six decades and employing methods chosen and tuned especially for this problem, our analyses demonstrate that when crises threaten the nation's security, the justices are substantially more likely to curtail rights and liberties than when peace prevails. Yet paradoxically, and in contradiction to virtually every theory of crisis jurisprudence, war appears to affect only cases that are unrelated to the war. For these cases, the effect of war and other international crises is so substantial, persistent, and consistent that it may surprise even those commentators who long have argued that the Court rallies around the flag in times of crisis. On the other hand, we find no evidence that cases most directly related to the war are affected.
     We attempt to explain this seemingly paradoxical evidence with one unifying conjecture: Instead of balancing rights and security in high stakes cases directly related to the war, the justices retreat to ensuring the institutional checks of the democratic branches. Since rights-oriented and process-oriented dimensions seem to operate in different domains and at different times, and often suggest different outcomes, the predictive factors that work for cases unrelated to the war fail for cases related to the war. If this conjecture is correct, federal judges should consider giving less weight to legal principles established during wartime for ordinary cases, and attorneys should see it as their responsibility to distinguish cases along these lines. Published New York University Law Review. Winner of the 2005 Pi Sigma Alpha Award, the 2005 Robert H. Durr Award, and the 2006 McGraw-Hill Award, and Honorable Mention for the 2005 American Judicature Society Award and Law and Society Association Prize.
• Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes by Gary King and Jonathan Wand, Version: 11/3/2006. (Paper: PDF) When respondents use the ordinal response categories of standard survey questions in different ways, the validity of analyses based on the resulting data can be biased. Anchoring vignettes is a survey design technique, introduced by King, Murray, Salomon, and Tandon (2004), intended to correct for some of these problems. We develop new methods both for evaluating and choosing anchoring vignettes, and for analyzing the resulting data. With surveys on a diverse range of topics in a range of countries, we illustrate how our proposed methods can improve the ability of anchoring vignettes to extract information from survey data, as well as saving in survey administration costs. Political Analysis
• Enhancing the Validity and Cross-cultural Comparability of Survey Research by Gary King, Christopher J.L. Murray, Joshua A. Salomon, and Ajay Tandon, Version: Published. (Article: PDF) We address two long-standing problems in survey research: measuring complicated concepts, such as political freedom or efficacy, that researchers define best with reference to examples; and what to do when respondents interpret identical survey questions in different ways. Scholars have long addressed these problems with approaches to reduce incomparability, such as writing more concrete questions -- with uneven success. Our alternative is to measure directly whatever incomparability exists and to correct for it. We measure incomparability via respondents' assessments, on the same scale as the self-assessments to be corrected, of hypothetical individuals described in short vignettes. Since actual levels of the vignettes are invariant over respondents, variability in vignette answers reveals incomparability. Our corrections require either simple recodes or a statistical model designed to save survey administration costs. With analysis, simulations, and cross-national surveys, we show how these problems can drastically mislead survey researchers and how our approach can fix them. You may also be interested in the Anchoring Vignettes website, which includes information about conferences on the subject, a FAQ, software, example vignettes, and other materials. Published in American Political Science Review.
• Finding New Information for Ecological Inference Models: A Comment on Jon Wakefield, "Ecological Inference in 2x2 Tables." (Paper: PDF) To appear in the Journal of the Royal Statistical Society, Series A.
• Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski by Nathaniel Beck, Gary King, and Langche Zeng, Version: Corrected Proofs. (Article: PDF) Published in the American Political Science Review.
• A Consensus on Second Stage Analyses in Ecological Inference Models by Christopher Adolph and Gary King, with Michael C. Herron and Kenneth W. Shotts. (Article: PDF) Published in Political Analysis.
• What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation by Jeff Gill and Gary King. (Article: PDF) What should a researcher do when statistical analysis software terminates before completion with a message that the Hessian is not invertible? The standard textbook advice is to respecify the model, but this is another way of saying that the researcher should change the question being asked. Obviously, however, computer programs should not be in the business of deciding what questions are worthy of study. Although noninvertible Hessians are sometimes signals of poorly posed questions, nonsensical models, or inappropriate estimators, they also frequently occur when information about the quantities of interest does exist in the data, through the likelihood function. We explain the problem in some detail and lay out two preliminary proposals for ways of dealing with noninvertible Hessians without changing the question asked. Published in Sociological Methods and Research. (Also available to implement the ideas in the paper is Software in: Gauss or R).
• The Future of Replication. (Article: PDF) Since I proposed the replication standard for political science research, more journals have required or encouraged authors to make data available, and more authors have shared their data. The calls for continuing this trend are more persistent than ever, and the agreement among the journal editors in this Symposium to adopt a unified replication policy continues this trend. In this article, I offer a vision of a possible future for the replication movement. The plan is to implement this vision via the Virtual Data Center project, which — by automating the process of citing, verifying, finding, sharing, archiving, subsetting, converting, analyzing, and distributing data — may greatly facilitate adherence to the replication standard. Published in a symposium on the subject in International Studies Perspectives; this article begins at p.100 in the pdf of the symposium. (You may be interested my "Replication, Replication" article, where the original proposal appeared, and related information.)
• Armed Conflict as a Public Health Problem by Christopher J.L. Murray; Gary King, Alan D. Lopez, Niels Tomijima, and Etienne Krug. (Article: PDF) Armed conflict is a major cause of injury and death worldwide, but we need much better methods of quantification before we can accurately assess its effect.
     Armed conflict between warring states and groups within states have been major causes of ill health and mortality for most of human history. Conflict obviously causes deaths and injuries on the battlefield, but also health consequences from the displacement of populations, the breakdown of health and social services, and the heightened risk of disease transmission. Despite the size of the health consequences, military conflict has not received the same attention from public health research and policy as many other causes of illness and death. In contrast, political scientists have long studied the causes of war but have primarily been interested in the decision of elite groups to go to war, not in human death and misery.
     We review the limited knowledge on the health consequences of conflict, suggest ways to improve measurement, and discuss the potential for risk assessment and for preventing and ameliorating the consequences of conflict. Published in the BMJ. Note: The BMJ used to be called the British Medical Journal. Also available is the BMJ's Interactive version.
• Did Illegally Counted Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election? by Kosuke Imai and Gary King, Revised Proofs: 7/20/2004. (Article: PDF) 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. (You might also be interested in the slides (PDF) we use to present this material.) Published in Perspectives on Politics.
• An Automated Information Extraction Tool For International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design by Gary King and Will Lowe, Version: 11/1/01. (Article: PDF) Despite widespread recognition that aggregated summary statistics on international conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or occasionally monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the last two decades. We offer some reason to change this view, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by 'reading" news stories without human coders. We design a method that makes it feasible for the first time to evaluate these programs when they are applied in areas with the particular characteristics of international conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large scale data collections, the program dominates human coding. We believe our new evaluative method will be of use in international relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We also believe that the data created by programs similar to the one we evaluated should see dramatically increased use in international relations research. To facilitate this process, we are also releasing with this article 3.7 million coded international events, covering the entire world for the last decade. Published in International Organization.
• Improving Forecasts of State Failure by Gary King and Langche Zeng, Version: Published. ( Article: PDF). We offer the first independent scholarly evaluation of the claims, forecasts, and causal inferences of the State Failure Task Force and their efforts to forecast when states will fail. State failure refers to the collapse of the authority of the central government to impose order, as in civil wars, revolutionary wars, genocides, politicides, and adverse or disruptive regime transitions. States that sponsor international terrorism or allow it to be organized from within their borders are all failed states. This task force, set up at the behest of Vice President Gore in 1994, has been led by a group of distinguished academics working as consultants to the U.S. Central Intelligence Agency. State Failure Task Force reports and publications have received attention in the media, in academia, and from public policy decision-makers. In this article, we identify several methodological errors in the task force work that cause their reported forecast probabilities of conflict to be too large, their causal inferences to be biased in unpredictable directions, and their claims of forecasting performance to be exaggerated. However, we also find that the task force has amassed the best and most carefully collected data on state failure in existence, and the required corrections which we provide, although very large in effect, are easy to implement. We also reanalyze their data with better statistical procedures and demonstrate how to improve forecasting performance to levels significantly greater than even corrected versions of their models. Although still a highly uncertain endeavor, we are as a consequence able to offer the first accurate forecasts of state failure, along with procedures and results that may be of practical use in informing foreign policy decision making. We also describe a number of strong empirical regularities that may help in ascertaining the causes of state failure. Published in World Politics. You may also be interested in the last state failure task force report (PDF), the state failure data we used to write this paper, or the related paper, Nathaniel Beck, Gary King, and Langche Zeng. "Improving Quantitative Studies of International Conflict: A Conjecture," American Political Science Review, Vol. 94, No. 1 (March, 2000): 21-36. Winner of the Gosnell Prize. (Article: PDF | Abstract: HTML)
• The Rules of Inference by Lee Epstein and Gary King, Version: Published. (Article: PDF) Although the term "empirical research" has become commonplace in legal scholarship over the past two decades, law professors have, in fact, been conducting research that is empirical -- that is, learning about the world using quantitative data or qualitative information -- for almost as long as they have been conducting research. For just as long, however, they have been proceeding with little awareness of, much less compliance with, the rules of inference, and without paying heed to the key lessons of the revolution in empirical analysis that has been taking place over the last century in other disciplines. The tradition of including some articles devoted to exclusively to the methodology of empirical analysis -- so well represented in journals in traditional academic fields -- is virtually nonexistent in the nation's law reviews. As a result, readers learn considerably less accurate information about the empirical world than the studies' stridently stated, but overconfident, conclusions suggest. To remedy this situation both for the producers and consumers of empirical work, this Article adapts the rules of inference used in the natural and social sciences to the special needs, theories, and data in legal scholarship, and explicate them with extensive illustrations from existing research. The Article also offers suggestions for how the infrastructure of teaching and research at law schools might be reorganized so that it can better support the creation of first-rate empirical research without compromising other important objectives. Published in The University of Chicago Law Review 69, 1 (Winter, 2002): 1--209, with comments from six scholars and a response (PDF) by us. (An abbreviated version of our discussion of infrastrucure (PDF) (Section IX, Pp.114-133) is published in the Journal of Legal Education, along with comments from five law school deans.
• Measuring Total Health Inequality: Adding Individual Variation to Group-Level Differences by Emmanuela Gakidou and Gary King, Version: Published. (Article: PDF) Background: Studies have revealed large variations in average health status across social, economic, and other groups. No study exists on the distribution of the risk of ill-health across individuals, either within groups or across all people in a society, and as such a crucial piece of total health inequality has been overlooked. Some of the reason for this neglect has been that the risk of death, which forms the basis for most measures, is impossible to observe directly and difficult to estimate. Methods: We develop a measure of total health inequality - encompassing all inequalities among people in a society, including variation between and within groups - by adapting a beta-binomial regression model. We apply it to children under age two in 50 low- and middle-income countries. Our method has been adopted by the World Health Organization and is being implemented in surveys around the world; preliminary estimates have appeared in the World Health Report (2000). Results: Countries with similar average child mortality differ considerably in total health inequality. Liberia and Mozambique have the largest inequalities in child survival, while Colombia, the Philippines and Kazakhstan have the lowest levels among the countries measured. Conclusions:Total health inequality estimates should be routinely reported alongside average levels of health in populations and groups, as they reveal important policy-related information not otherwise knowable. This approach enables meaningful comparisons of inequality across countries and future analyses of the determinants of inequality. Published in International Journal for Equity in Health.
• A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data by James Honaker, Jonathan Katz, and Gary King, Version: 10/2001. (Article: PS , PDF) Katz and King (1999) develop a model for predicting or explaining aggregate electoral results in multiparty democracies. This model is, in principle, analogous to what least squares regression provides American politics researchers in that two-party system. Katz and King applied this model to three-party elections in England and revealed a variety of new features of incumbency advantage and where each party pulls support from. Although the mathematics of their statistical model covers any number of political parties, it is computationally very demanding, and hence slow and numerically imprecise, with more than three. The original goal of our work was to produce an approximate method that works quicker in practice with many parties without making too many theoretical compromises. As it turns out, the method we offer here improves on Katz and King's (in bias, variance, numerical stability, and computational speed) even when the latter is computationally feasible. We also offer easy-to-use software (Amelia) that implements our suggestions in Political Analysis. You may also be interested in the two predecessors to this paper, Katz and King's "A Statistical Model for Multiparty Electoral Data," American Political Science Review, 93, 1 (March, 1999): 15-32 (Replication Dataset: ICPSR 1190) | Abstract: HTML) Winner of the Pi Sigma Alpha Award, and King, Honaker, Joseph, and Scheve's Analyzing Incomplete Political Science Data, American Political Science Review, 95, 1 (March, 2001): 49-69 (Abstract: HTML) Named most cited by ISI's Essential Science Indicators). (To draw ternary diagrams, see the Stata module TRIPLOT, by Nicholas Cox, or our Gauss procedures.)
• Proper Nouns and Methodological Propriety: Pooling Dyads in International Relations Data, Version: Published. (Article: PDF) This article is the concluding comment in the symposium on the analysis of dyadic international conflict data, to be published in International Organization. The participants' included Donald Green, Soo Yeon Kim, and David Yoon, and their original paper, "Dirty Pool," and responses by John Oneal and Bruce Russett, "Clear and Clean: The Fixed Effects of the Liberal Peace," and Nathaniel Beck and Jonathan Katz, "Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon."
• Bayesian and Frequentist Inference for Ecological Inference: The R x C Case by Ori Rosen, Wenxin Jiang, Gary King, and Martin A. Tanner, Version: Published. (Article: PDF) 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. As in the 2 x 2 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. Published in Statistica Neerlandica. You may also be interested in Gary King, Ori Rosen, and Martin Tanner's "Binomial-Beta Hierarchical Models for Ecological Inference" Sociological Methods and Research Vol. 28, No. 1 (August, 1999): 61-90 ( Abstract: HTML ) which was our first effort in this line of research, or my book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data.
• Rethinking Human Security by Gary King and Christopher J.L. Murray, Version: Published. (Article: PDF In the last two decades, the international community has begun to conclude that attempts to ensure the territorial security of nation-states through military power have failed to improve the human condition. Despite astronomical levels of military spending, deaths due to military conflict have not declined. Moreover, even when the borders of some states are secure from foreign threats, the people within those states do not necessarily have freedom from crime, enough food, proper health care, education, or political freedom. In response to these developments, the international community has gradually moved to combine economic development with military security and other basic human rights to form a new concept of "human security". Unfortunately, by common assent the concept lacks both a clear definition, consistent with the aims of the international community, and any agreed upon measure of it. In this paper, we propose a simple, rigorous, and measurable definition of human security: the expected number of years of future life spent outside the state of "generalized poverty". Generalized poverty occurs when an individual falls below the threshold in any key domain of human well-being. We consider improvements in data collection and methods of forecasting that are necessary to measure human security and then introduce an agenda for research and action to enhance human security that follows logically in the areas of risk assessment, prevention, protection, and compensation. This paper has appeared in Political Science Quarterly.
• Logistic Regression in Rare Events Data by Gary King and Langche Zeng. ( Article: PDF) Rare events are binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros ("nonevents"). In many literatures, rare events have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of non-events (peace). This enables scholars to save as much as 99% of their (non-fixed) data collection costs, or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed. You may also be interested in the slides used in giving talks on this paper and part of the previous one. This paper has appeared in Political Analysis. We have also written a companion piece to this article for International Organization, "Explaining Rare Events in International Relations" (PDF), which overlaps this one, excludes most technical material, and includes empirical examples and pedagogical material. The next paper is related too.
• Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies by Gary King and Langche Zeng. (Article: PDF) Classic, or "cumulative," case-control sampling designs do not admit inferences about quantities of interest other than risk ratios, and then only by making the rare events assumption. Probabilities, risk differences, and other quantities cannot be computed without knowledge of the population incidence fraction. Similarly, density, or "risk set," case-control sampling designs do not allow inferences about quantities other than the rate ratio. Rates, rate differences, cumulative rates, risks, and other quantities cannot be estimated unless auxiliary information about the underlying cohort such as the number of controls in each full risk set is available. Most scholars who have considered the issue recommend reporting more than just the relative risks and rates, but auxiliary population information needed to do this is not usually available. We address this problem by developing methods that allow valid inferences about all relevant quantities of interest from either type of case-control study when completely ignorant of or only partially knowledgeable about relevant auxiliary population information. Published in Statistics in Medicine.

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