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

  • Applications
    • 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.
    • Information Control by Authoritarian Governments
      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.
  • Methods
    • Automated Text Analysis
      Automated and computer-assisted methods of extracting, organizing, understanding, conceptualizing, and consuming knowledge from massive quantities of unstructured text.
    • 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").
    • 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
      Statistical methods to accommodate missing information in data sets due to scattered unit nonresponse, missing variables, or values or variables measured with error. Easy-to-use algorithms and software for multiple imputation and multiple overimputation for surveys, time series, and time series cross-sectional data. Applications to electoral, and other compositional, 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.

Recent Papers

The Generalization in the Generalized Event Count Model, With Comments on Achen, Amato, and Londregan

The Generalization in the Generalized Event Count Model, With Comments on Achen, Amato, and Londregan
Gary King and Curtis S Signorino. 1996. “The Generalization in the Generalized Event Count Model, With Comments on Achen, Amato, and Londregan.” Political Analysis, 6, Pp. 225–252.Abstract
Article
We use an analogy with the normal distribution and linear regression to demonstrate the need for the Generalize Event Count (GEC) model. We then show how the GEC provides a unified framework within which to understand a diversity of distributions used to model event counts, and how to express the model in one simple equation. Finally, we address the points made by Christopher Achen, Timothy Amato, and John Londregan. Amato's and Londregan's arguments are consistent with ours and provide additional interesting information and explanations. Unfortunately, the foundation on which Achen built his paper turns out to be incorrect, rendering all his novel claims about the GEC false (or in some cases irrelevant).
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CEM: Coarsened Exact Matching in Stata

CEM: Coarsened Exact Matching in Stata
Matthew Blackwell, Stefano Iacus, Gary King, and Giuseppe Porro. 2009. “CEM: Coarsened Exact Matching in Stata.” The Stata Journal, 9, Pp. 524–546.Abstract
Article
In this article, we introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In coarsened exact matching, users temporarily coarsen their data, exact match on these coarsened data, and then run their analysis on the uncoarsened, matched data. Coarsened exact matching bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is monotonic imbalance bounding (so that reducing the maximum imbalance on one variable has no effect on others), does not require a separate procedure to restrict data to common support, meets the congruence principle, is approximately invariant to measurement error, balances all nonlinearities and interactions in sample (i.e., not merely in expectation), and works with multiply imputed datasets. Other matching methods inherit many of the coarsened exact matching method’s properties when applied to further match data preprocessed by coarsened exact matching. The cem command implements the coarsened exact matching algorithm in Stata.
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How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science

How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science
Gary King. 1986. “How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science.” American Journal of Political Science, 30, Pp. 666–687.Abstract
Article
This article identifies a set of serious theoretical mistakes appearing with troublingly high frequency throughout the quantitative political science literature. These mistakes are all based on faulty statistical theory or on erroneous statistical analysis. Through algebraic and interpretive proofs, some of the most commonly made mistakes are explicated and illustrated. The theoretical problem underlying each is highlighted, and suggested solutions are provided throughout. It is argued that closer attention to these problems and solutions will result in more reliable quantitative analyses and more useful theoretical contributions.
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The Significance of Roll Calls in Voting Bodies: A Model and Statistical Estimation

The Significance of Roll Calls in Voting Bodies: A Model and Statistical Estimation
Gary King. 1986. “The Significance of Roll Calls in Voting Bodies: A Model and Statistical Estimation.” Social Science Research, 15, Pp. 135–152.Abstract
In the long history of legislative roll call analyses, there continues to exist a particularly troubling problem: There is no satisfactory method for measuring the relative importance or significance of individual roll calls. A measure of roll call significance would be interesting in and of itself, but many have realized that it could also substantially improve empirical research. The consequence of this situation is that hundreds of researchers risk heteroskedastic disturbances (resulting in inefficient estimates and biased standard errors and test statistics), are unable to appropriately choose the roll calls most suited to their theory (resulting in analyses that may not correctly test their theory), and often use methods that create more problems than they solve (resulting in selection bias, unrealistic weighting schemes, or relatively subjective measures). This article introduces a new method designed to meet these problems. Based on an application of Box-Tiao intervention analysis, the method extracts from observed voting participation scores the "revealed preferences" of legislators as a measure of roll call significance. Applying this method to roll calls from the U.S. Senate demonstrates the success of the method and suggests its utility in applied research.
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The Development of Political Activists: A Model of Early Learning

The Development of Political Activists: A Model of Early Learning
Gary King and Richard Merelman. 1986. “The Development of Political Activists: A Model of Early Learning.” Social Science Quarterly, 67, Pp. 473–490.Abstract
An analysis of panel data reveals the unique importance of early learning to the development of political activism among Americans. A combination of two learning models– the frequently used crystallization model and the rarely analyzed sensitization model– is advanced as most appropriate for understanding political socialization and the development of political activism. The findings contribute to research on elite behavior and on the process of political socialization.
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Democratic Representation and Partisan Bias in Congressional Elections

Democratic Representation and Partisan Bias in Congressional Elections
Gary King and Robert X Browning. 1987. “Democratic Representation and Partisan Bias in Congressional Elections.” American Political Science Review, 81, Pp. 1252–1273.Abstract
Article
The translation of citizen votes into legislative seats is of central importance in democratic electoral systems. It has been a longstanding concern among scholars in political science and in numerous other disciplines. Through this literature, two fundamental tenets of democratic theory, partisan bias and democratic representation, have often been confused. We develop a general statistical model of the relationship between votes and seats and separate these two important concepts theoretically and empirically. In so doing, we also solve several methodological problems with the study of seats, votes and the cube law. An application to U.S. congressional districts provides estimates of bias and representation for each state and deomonstrates the model’s utility. Results of this application show distinct types of representation coexisting in U.S. states. Although most states have small partisan biases, there are some with a substantial degree of bias.
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All writings

Presentations

Big Data is Not About the Data!, at Nina Zipser's Freshman Seminar "Models of the World: Explaining the Past and Predicting the Future", Tuesday, November 13, 2018:
Slides
The spectacular progress the media describes as "big data" has little to do with the growth of data.  Data, after all, is becoming commoditized, less expensive, and an automatic byproduct of other changes in organizations and society. More data alone doesn't generate insights; it often merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. I illustrate these points with a wide range of examples from research I've participated in, including ... Read more about Big Data is Not About the Data!
Simplifying Matching Methods for Causal Inference, at University of Western Ontario, Thursday, November 8, 2018:
Slides
We show how to use matching methods for causal inference to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce methods that are simpler, more powerful, and easier to understand than existing approaches. We also show that propensity score matching, an enormously popular approach, often accomplishes the opposite of its intended goal -- increasing imbalance, inefficiency, model dependence, and bias -- and should be replaced with other matching methods in applications.  See ... Read more about Simplifying Matching Methods for Causal Inference
Sequential Experiments and News Media Effects, at Harvard Psychology Graduate Student Methods Dinner, Tuesday, October 30, 2018:
Slides

We report on the results of first large scale randomized news media experiment, with a special focus on the sequential experimental design we used. Instead of fixing your n ahead of time and finding out about your p-value post hoc, you can choose your p-value (or CI) ahead of time and discover the n needed. Your experiment should not risk collecting more data than necessary (wasting your time, grant resources, and research subjects' patence) or less than you need to draw firm conclusions.

... Read more about Sequential Experiments and News Media Effects
Simplifying Matching Methods for Causal Inference, at Dartmouth University Program in Quantitative Social Science, Friday, October 19, 2018:
Slides
We show how to use matching in causal inference to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand than existing approaches. We also show that propensity score matching, an enormously popular method, often accomplishes the opposite of its intended goal -- increasing imbalance, inefficiency, model dependence, and bias -- and should not be used in applications.  See ... Read more about Simplifying Matching Methods for Causal Inference
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Books

Demographic Forecasting

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.

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Ecological Inference: New Methodological Strategies

Ecological Inference: New Methodological Strategies
Gary King, Ori Rosen, Martin Tanner, Gary King, Ori Rosen, and Martin A Tanner. 2004. Ecological Inference: New Methodological Strategies. New York: Cambridge University Press.Abstract
Complete Book
Ecological Inference: New Methodological Strategies brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half decade has witnessed an explosion of research in ecological inference – the attempt to infer individual behavior from aggregate data. The uncertainties and the information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but such inferences are required in many academic fields, as well as by legislatures and the courts in redistricting, by businesses in marketing research, and by governments in policy analysis.
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