Writings

2009
Computational Social Science
David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, and Marshall Van Alstyne. 2009. “Computational Social Science.” Science, 323, Pp. 721-723.Abstract

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Article
Gary King and Langche Zeng. 2009. “Empirical versus Theoretical Claims about Extreme Counterfactuals: A Response.” Political Analysis, 17, Pp. 107-112.Abstract

In response to the data-based measures of model dependence proposed in King and Zeng (2006), Sambanis and Michaelides (2008) propose alternative measures that rely upon assumptions untestable in observational data. If these assumptions are correct, then their measures are appropriate and ours, based solely on the empirical data, may be too conservative. If instead and as is usually the case, the researcher is not certain of the precise functional form of the data generating process, the distribution from which the data are drawn, and the applicability of these modeling assumptions to new counterfactuals, then the data-based measures proposed in King and Zeng (2006) are much preferred. After all, the point of model dependence checks is to verify empirically, rather than to stipulate by assumption, the effects of modeling assumptions on counterfactual inferences.

The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation
Kosuke Imai, Gary King, and Clayton Nall. 2009. “The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation.” Statistical Science, 24, Pp. 29–53.Abstract
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 the resulting efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, many other studies avoid pairing, in part because of claims in the literature, echoed by clinical trials standards organizations, that this matched-pair, cluster-randomization design has serious problems. We argue that all such claims are unfounded. We also prove that the estimator recommended for this design in the literature is unbiased only in situations when matching is unnecessary and and its standard error is also invalid. To overcome this problem without modeling assumptions, we develop a simple design-based estimator with much improved statistical properties. We also propose a model-based approach that includes some of the benefits of our design-based estimator as well as the estimator in the literature. Our methods also address individual-level noncompliance, which is common in applications but not allowed for in most existing methods. We show that from the perspective of bias, efficiency, power, robustness, or research costs, and in large or small samples, pairing should be used in cluster-randomized experiments whenever feasible and 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.
Article
The Future of Political Science: 100 Perspectives
Gary King, Kay Schlozman, and Norman Nie. 2009. The Future of Political Science: 100 Perspectives. New York: Routledge Press.
Matched Pairs and the Future of Cluster-Randomized Experiments: A Rejoinder
Kosuke Imai, Gary King, and Clayton Nall. 2009. “Matched Pairs and the Future of Cluster-Randomized Experiments: A Rejoinder.” Statistical Science, 24, Pp. 64–72.Abstract

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 the resulting efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, many other studies avoid pairing, in part because of claims in the literature, echoed by clinical trials standards organizations, that this matched-pair, cluster-randomization design has serious problems. We argue that all such claims are unfounded. We also prove that the estimator recommended for this design in the literature is unbiased only in situations when matching is unnecessary and and its standard error is also invalid. To overcome this problem without modeling assumptions, we develop a simple design-based estimator with much improved statistical properties. We also propose a model-based approach that includes some of the benefits of our design-based estimator as well as the estimator in the literature. Our methods also address individual-level noncompliance, which is common in applications but not allowed for in most existing methods. We show that from the perspective of bias, efficiency, power, robustness, or research costs, and in large or small samples, pairing should be used in cluster-randomized experiments whenever feasible and 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.

Article
From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data
Myron P Gutmann, Mark Abrahamson, Margaret O Adams, Micah Altman, Caroline Arms, Kenneth Bollen, Michael Carlson, Jonathan Crabtree, Darrell Donakowski, Gary King, Jaret Lyle, Marc Maynard, Amy Pienta, Richard Rockwell, Lois Rocms-Ferrara, and Copeland H Young. 2009. “From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data.” Library Trends, 57, Pp. 315–337.Abstract

Social science data are an unusual part of the past, present, and future of digital preservation. They are both an unqualified success, due to long-lived and sustainable archival organizations, and in need of further development because not all digital content is being preserved. This article is about the Data Preservation Alliance for Social Sciences (Data-PASS), a project supported by the National Digital Information Infrastructure and Preservation Program (NDIIPP), which is a partnership of five major U.S. social science data archives. Broadly speaking, Data-PASS has the goal of ensuring that at-risk social science data are identified, acquired, and preserved, and that we have a future-oriented organization that could collaborate on those preservation tasks for the future. Throughout the life of the Data-PASS project we have worked to identify digital materials that have never been systematically archived, and to appraise and acquire them. As the project has progressed, however, it has increasingly turned its attention from identifying and acquiring legacy and at-risk social science data to identifying on going and future research projects that will produce data. This article is about the project's history, with an emphasis on the issues that underlay the transition from looking backward to looking forward.

Article
Preserving Quantitative Research-Elicited Data for Longitudinal Analysis.  New Developments in Archiving Survey Data in the U.S.
Mark Abrahamson, Kenneth A Bollen, Myron P Gutmann, Gary King, and Amy Pienta. 2009. “Preserving Quantitative Research-Elicited Data for Longitudinal Analysis. New Developments in Archiving Survey Data in the U.S.” Historical Social Research, 34, 3, Pp. 51-59.Abstract

Social science data collected in the United States, both historically and at present, have often not been placed in any public archive -- even when the data collection was supported by government grants. The availability of the data for future use is, therefore, in jeopardy. Enforcing archiving norms may be the only way to increase data preservation and availability in the future.

Article
Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme
Gary King, Emmanuela Gakidou, Kosuke Imai, Jason Lakin, Ryan T Moore, Clayton Nall, Nirmala Ravishankar, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. 2009. “Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme.” The Lancet, 373, Pp. 1447-1454.Abstract

Background: We assessed aspects of Seguro Popular, a programme aimed to deliver health insurance, regular and preventive medical care, medicines, and health facilities to 50 million uninsured Mexicans. Methods: We randomly assigned treatment within 74 matched pairs of health clusters–-i.e., health facility catchment areas–-representing 118,569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August 2005, to September 2005) and follow-up survey 10 months later (July 2006, to August 2006) in 50 pairs (n=32 515). The treatment consisted of encouragement to enrol in a health-insurance programme and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. We estimated intention to treat and complier average causal effects non-parametrically. Findings: Intention-to-treat estimates indicated a 23% reduction from baseline in catastrophic expenditures (1·9% points and 95% CI 0·14-3·66). The effect in poor households was 3·0% points (0·46-5·54) and in experimental compliers was 6·5% points (1·65-11·28), 30% and 59% reductions, respectively. The intention-to-treat effect on health spending in poor households was 426 pesos (39-812), and the complier average causal effect was 915 pesos (147-1684). Contrary to expectations and previous observational research, we found no effects on medication spending, health outcomes, or utilisation. Interpretation: Programme resources reached the poor. However, the programme did not show some other effects, possibly due to the short duration of treatment (10 months). Although Seguro Popular seems to be successful at this early stage, further experiments and follow-up studies, with longer assessment periods, are needed to ascertain the long-term effects of the programme.

Article
Replication Data for: Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme
Gary King, Emmanuela Gakidou, Kosuke Imai, Jason Lakin, Ryan T Moore, Clayton Nall, Nirmala Ravishankar, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. 2009. “Replication Data for: Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme”. Publisher's Version
2008
Demographic Forecasting
Federico Girosi and Gary King. 2008. Demographic Forecasting. Princeton: Princeton University Press.Abstract

We introduce a new 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 the standard Bayesian theory of inference.

The Effects of International Monetary Fund Loans on Health Outcomes
Megan Murray and Gary King. 2008. “The Effects of International Monetary Fund Loans on Health Outcomes.” PLoS Medicine, 5.Abstract
A "Perspective" article that discusses an article by David Stuckler and colleagues showing that, in Eastern European and former Soviet countries, participation in International Monetary Fund economic programs have been associated with higher mortality rates from tuberculosis.
Article
The Future of Partisan Symmetry as a Judicial Test for Partisan Gerrymandering after LULAC v. Perry
Bernard Grofman and Gary King. 2008. “The Future of Partisan Symmetry as a Judicial Test for Partisan Gerrymandering after LULAC v. Perry.” Election Law Journal, 6, 1, Pp. 2-35.Abstract

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.

Brief PDF Article
How Not to Lie Without Statistics
Gary King and Eleanor Neff Powell. 2008. “How Not to Lie Without Statistics”.Abstract
We highlight, and suggest ways to avoid, a large number of common misunderstandings in the literature about best practices in qualitative research. We discuss these issues in four areas: theory and data, qualitative and quantitative strategies, causation and explanation, and selection bias. Some of the misunderstandings involve incendiary debates within our discipline that are readily resolved either directly or with results known in research areas that happen to be unknown to political scientists. Many of these misunderstandings can also be found in quantitative research, often with different names, and some of which can be fixed with reference to ideas better understood in the qualitative methods literature. Our goal is to improve the ability of quantitatively and qualitatively oriented scholars to enjoy the advantages of insights from both areas. Thus, throughout, we attempt to construct specific practical guidelines that can be used to improve actual qualitative research designs, not only the qualitative methods literatures that talk about them.
Article
Misunderstandings Among Experimentalists and Observationalists about Causal Inference
Kosuke Imai, Gary King, and Elizabeth Stuart. 2008. “Misunderstandings Among Experimentalists and Observationalists about Causal Inference.” Journal of the Royal Statistical Society, Series A, 171, part 2, Pp. 481–502.Abstract

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.

Article
Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler
Gary King, Ori Rosen, Martin Tanner, and Alexander Wagner. 2008. “Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler.” Journal of Economic History, 68, 4, Pp. 996.Abstract

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.

Article
Toward A Common Framework for Statistical Analysis and Development
Kosuke Imai, Gary King, and Olivia Lau. 2008. “Toward A Common Framework for Statistical Analysis and Development.” Journal of Computational Graphics and Statistics, 17, Pp. 1–22.Abstract
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.
Article
VA: Verbal Autopsies
Gary King and Ying Lu. 2008. “VA: Verbal Autopsies”. Publisher's Version
Verbal Autopsy Methods with Multiple Causes of Death
Gary King and Ying Lu. 2008. “Verbal Autopsy Methods with Multiple Causes of Death.” Statistical Science, 23, Pp. 78–91.Abstract
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.
Article
2007
Anchors: Software for Anchoring Vignettes Data
Johnathan Wand, Gary King, and Olivia Lau. 2007. “Anchors: Software for Anchoring Vignettes Data”. Publisher's Version
Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes
Gary King and Jonathan Wand. 2007. “Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes.” Political Analysis, 15, Pp. 46-66.Abstract

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.

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