Gary King is the Weatherhead University Professor at Harvard University. He also serves as Director of the Institute for Quantitative Social Science. He and his research group develop and apply empirical methods in many areas of social science research. Full bio and CV

Research Areas

    • Anchoring Vignettes (for interpersonal incomparability)
      Methods for interpersonal incomparability, when respondents (from different cultures, genders, countries, or ethnic groups) understand survey questions in different ways; for developing theoretical definitions of complicated concepts apparently definable only by example (i.e., "you know it when you see it").
    • Automated Text Analysis
      Automated and computer-assisted methods of extracting, organizing, understanding, conceptualizing, and consuming knowledge from massive quantities of unstructured text.
    • Causal Inference
      Methods for detecting and reducing model dependence (i.e., when minor model changes produce substantively different inferences) in inferring causal effects and other counterfactuals. Matching methods; "politically robust" and cluster-randomized experimental designs; causal bias decompositions.
    • Event Counts and Durations
      Statistical models to explain or predict how many events occur for each fixed time period, or the time between events. An application to cabinet dissolution in parliamentary democracies which united two previously warring scholarly literature. Other applications to international relations and U.S. Supreme Court appointments.
    • 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.
    • Missing Data, Measurement Error, Differential Privacy
      Statistical methods to accommodate missing information in data sets due to survey nonresponse, missing variables, or variables measured with error or with error added to protect privacy. Applications and software for analyzing electoral, compositional, survey, time series, and time series cross-sectional data.
    • Qualitative Research
      How the same unified theory of inference underlies quantitative and qualitative research alike; scientific inference when quantification is difficult or impossible; research design; empirical research in legal scholarship.
    • Rare Events
      How to save 99% of your data collection costs; bias corrections for logistic regression in estimating probabilities and causal effects in rare events data; estimating base probabilities or any quantity from case-control data; automated coding of events.
    • Survey Research
      How surveys work and a variety of methods to use with surveys. Surveys for estimating death rates, why election polls are so variable when the vote is so predictable, and health inequality.
    • Unifying Statistical Analysis
      Development of a unified approach to statistical modeling, inference, interpretation, presentation, analysis, and software; integrated with most of the other projects listed here.
    • Evaluating Social Security Forecasts
      The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, government programs comprising greater than 50% of all federal government expenditures, industry decision making, and the evidence base of many scholarly articles. Forecasts are also essential for scoring policy proposals, put forward by both political parties. Because SSA makes public little replication information, and uses ad hoc, qualitative, and antiquated statistical forecasting methods, no one in or out of government has been able to produce fully independent alternative forecasts or policy scorings. Yet, no systematic evaluation of SSA forecasts has ever been published by SSA or anyone else. We show that SSA's forecasting errors were approximately unbiased until about 2000, but then began to grow quickly, with increasingly overconfident uncertainty intervals. Moreover, the errors all turn out to be in the same potentially dangerous direction, each making the Social Security Trust Funds look healthier than they actually are. We also discover the cause of these findings with evidence from a large number of interviews we conducted with participants at every level of the forecasting and policy processes. We show that SSA's forecasting procedures meet all the conditions the modern social-psychology and statistical literatures demonstrate make bias likely. When those conditions mixed with potent new political forces trying to change Social Security and influence the forecasts, SSA's actuaries hunkered down trying hard to insulate themselves from the intense political pressures. Unfortunately, this otherwise laudable resistance to undue influence, along with their ad hoc qualitative forecasting models, led them to also miss important changes in the input data such as retirees living longer lives, and drawing more benefits, than predicted by simple extrapolations. We explain that solving this problem involves using (a) removing human judgment where possible, by using formal statistical methods -- via the revolution in data science and big data; (b) instituting formal structural procedures when human judgment is required -- via the revolution in social psychological research; and (c) requiring transparency and data sharing to catch errors that slip through -- via the revolution in data sharing & replication.An article at Barron's about our work.
    • Incumbency Advantage
      Proof that previously used estimators of electoral incumbency advantage were biased, and a new unbiased estimator. Also, the first systematic demonstration that constituency service by legislators increases the incumbency advantage.
    • Chinese Censorship
      Reverse engineering Chinese information controls -- the most extensive effort to selectively control human expression in the history of the world. We show that this massive effort to slow the flow of information paradoxically also conveys a great deal about the intentions, goals, and actions of the leaders. We downloaded all Chinese social media posts before the government could read and censor them; wrote and posted comments randomly assigned to our categories on hundreds of websites across the country to see what would be censored; set up our own social media website in China; and discovered that the Chinese government fabricates and posts 450 million social media comments a year in the names of ordinary people and convinced those posting (and inadvertently even the government) to admit to their activities. We found that the goverment does not engage on controversial issues (they do not censor criticism or fabricate posts that argue with those who disagree with the government), but they respond on an emergency basis to stop collective action (with censorship, fabricating posts with giant bursts of cheerleading-type distractions, responding to citizen greviances, etc.). They don't care what you think of them or say about them; they only care what you can do.
    • Mexican Health Care Evaluation
      An evaluation of the Mexican Seguro Popular program (designed to extend health insurance and regular and preventive medical care, pharmaceuticals, and health facilities to 50 million uninsured Mexicans), one of the world's largest health policy reforms of the last two decades. Our evaluation features a new design for field experiments that is more robust to the political interventions and implementation errors that have ruined many similar previous efforts; new statistical methods that produce more reliable and efficient results using fewer resources, assumptions, and data, as well as standard errors that are as much as 600% smaller; and an implementation of these methods in the largest randomized health policy experiment to date. (See the Harvard Gazette story on this project.)
    • Presidency Research; Voting Behavior
      Resolution of the paradox of why polls are so variable over time during presidential campaigns even though the vote outcome is easily predictable before it starts. Also, a resolution of a key controversy over absentee ballots during the 2000 presidential election; and the methodology of small-n research on executives.
    • Informatics and Data Sharing
      Replication Standards New standards, protocols, and software for citing, sharing, analyzing, archiving, preserving, distributing, cataloging, translating, disseminating, naming, verifying, and replicating scholarly research data and analyses. Also includes proposals to improve the norms of data sharing and replication in science.
    • International Conflict
      Methods for coding, analyzing, and forecasting international conflict and state failure. Evidence that the causes of conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but are large, stable, and replicable wherever the ex ante probability of conflict is large.
    • Legislative Redistricting
      The definition of partisan symmetry as a standard for fairness in redistricting; methods and software for measuring partisan bias and electoral responsiveness; discussion of U.S. Supreme Court rulings about this work. Evidence that U.S. redistricting reduces bias and increases responsiveness, and that the electoral college is fair; applications to legislatures, primaries, and multiparty systems.
    • Mortality Studies
      Methods for forecasting mortality rates (overall or for time series data cross-classified by age, sex, country, and cause); estimating mortality rates in areas without vital registration; measuring inequality in risk of death; applications to US mortality, the future of the Social Security, armed conflict, heart failure, and human security.
    • Teaching and Administration
      Publications and other projects designed to improve teaching, learning, and university administration, as well as broader writings on the future of the social sciences.

Recent Papers

A Theory of Statistical Inference for Matching Methods in Causal Research

A Theory of Statistical Inference for Matching Methods in Causal Research
Stefano M. Iacus, Gary King, and Giuseppe Porro. 2019. “A Theory of Statistical Inference for Matching Methods in Causal Research.” Political Analysis, 27, 1, Pp. 46-68.Abstract

Researchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties these theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. Properties of estimators based on this theory are much easier to understand and can be satisfied without the unattractive properties of existing theories, such as assumptions hidden in data analyses rather than stated up front, asymptotics, unfamiliar estimators, and complex variance calculations. Our theory of inference makes it possible for researchers to treat matching as a simple form of preprocessing to reduce model dependence, after which all the familiar inferential techniques and uncertainty calculations can be applied. This theory also allows binary, multicategory, and continuous treatment variables from the outset and straightforward extensions for imperfect treatment assignment and different versions of treatments.

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How Censorship in China Allows Government Criticism but Silences Collective Expression

How Censorship in China Allows Government Criticism but Silences Collective Expression
Gary King, Jennifer Pan, and Margaret E Roberts. 2013. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review, 107, 2 (May), Pp. 1-18.Abstract

We offer the first large scale, multiple source analysis of the outcome of what may be the most extensive effort to selectively censor human expression ever implemented. To do this, we have devised a system to locate, download, and analyze the content of millions of social media posts originating from nearly 1,400 different social media services all over China before the Chinese government is able to find, evaluate, and censor (i.e., remove from the Internet) the large subset they deem objectionable. Using modern computer-assisted text analytic methods that we adapt to and validate in the Chinese language, we compare the substantive content of posts censored to those not censored over time in each of 85 topic areas. Contrary to previous understandings, posts with negative, even vitriolic, criticism of the state, its leaders, and its policies are not more likely to be censored. Instead, we show that the censorship program is aimed at curtailing collective action by silencing comments that represent, reinforce, or spur social mobilization, regardless of content. Censorship is oriented toward attempting to forestall collective activities that are occurring now or may occur in the future --- and, as such, seem to clearly expose government intent.

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How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It

How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It
Gary King and Margaret E. Roberts. 2015. “How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It.” Political Analysis, 23, 2, Pp. 159–179. Publisher's VersionAbstract

"Robust standard errors" are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, with or without robust standard errors, will still bias estimators of all but a few quantities of interest. The resulting cavernous gap between theory and practice suggests that considerable gains in applied statistics may be possible. We seek to help researchers realize these gains via a more productive way to understand and use robust standard errors; a new general and easier-to-use "generalized information matrix test" statistic that can formally assess misspecification (based on differences between robust and classical variance estimates); and practical illustrations via simulations and real examples from published research. How robust standard errors are used needs to change, but instead of jettisoning this popular tool we show how to use it to provide effective clues about model misspecification, likely biases, and a guide to considerably more reliable, and defensible, inferences. Accompanying this article is open source software that implements the methods we describe. 

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Empirical versus Theoretical Claims about Extreme Counterfactuals: A Response

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.

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Amelia II: A Program for Missing Data

Amelia II: A Program for Missing Data
James Honaker, Gary King, and Matthew Blackwell. 2011. “Amelia II: A Program for Missing Data.” Journal of Statistical Software, 45, 7, Pp. 1-47.Abstract

Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. It also includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for different cross-sections. A full set of graphical diagnostics are also available. The program is easy to use, and the simplicity of the algorithm makes it far more robust; both a simple command line and extensive graphical user interface are included.

Amelia II software web site

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Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data

Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data
Edward Goldstein, Benjamin J Cowling, Allison E Aiello, Saki Takahashi, Gary King, Ying Lu, and Marc Lipsitch. 2011. “Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data.” PLoS ONE, 6, 8, Pp. e23380.Abstract

We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.

Link to PLoS version

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Presentations

How to Measure Legislative District Compactness If You Only Know it When You See it (Department of Biomedical Informatics, Harvard Medical School), at Department of Biomedical Informatics, Harvard Medical School, Tuesday, March 16, 2021:

To deter gerrymandering, many state constitutions require legislative districts to be "compact." Yet, the law offers few precise definitions other than "you know it when you see it," which effectively implies a common understanding of the concept. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct -- that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding, with high reliability (in data where...

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Statistically Valid Inferences from Privacy Protected Data (Interagency Arctic Research Policy Committee), at Interagency Arctic Research Policy Committee, Thursday, November 19, 2020:
Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about privacy violations. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data, statistical guarantees for researchers seeking insights from it, and protection for society from some fallacious scientific conclusions. We build on the standard of "... Read more about Statistically Valid Inferences from Privacy Protected Data (Interagency Arctic Research Policy Committee)
Empowering Social Science to Understand and Ameliorate Major Challenges of Human Society (Federal Interagency Conference on Social Science and Big Data), at Federal Interagency Conference on Social Science and Big Data, Wednesday, September 30, 2020:

Social scientists can understand and ameliorate some of the major challenges of human society by making new connections across academia, government, and industry; developing new methods of analyzing data, rather than merely watching big data get bigger; and ensuring they have the flexibility to ask new questions that arise in data analysis rather than sticking to the original ones posed. I make these points in discussions of research projects I've been lucky to lead across the US, China, Mexico, and Italy.  

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