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
      We reverse engineer 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

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
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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Seats, Votes, and Gerrymandering: Measuring Bias and Representation in Legislative Redistricting

Seats, Votes, and Gerrymandering: Measuring Bias and Representation in Legislative Redistricting
Robert X Browning and Gary King. 1987. “Seats, Votes, and Gerrymandering: Measuring Bias and Representation in Legislative Redistricting.” Law and Policy, 9, Pp. 305–322.Abstract
The Davis v. Bandemer case focused much attention on the problem of using statistical evidence to demonstrate the existence of political gerrymandering. In this paper, we evaluate the uses and limitations of measures of the seat-votes relationship in the Bandemer case. We outline a statistical method we have developed that can be used to estimate bias and the form of representation in legislative redistricting. We apply this method to Indiana State House and Senate elections for the period 1972 to 1984 and demonstrate a maximum bias 6.2% toward the Republicans in the House and a 2.8% bias in the Senate.
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Political Parties and Foreign Policy: A Structuralist Approach

Political Parties and Foreign Policy: A Structuralist Approach
Gary King. 1986. “Political Parties and Foreign Policy: A Structuralist Approach.” Political Psychology, 7, Pp. 83–101.Abstract
This article introduces the theory and approach of structural anthropology and applies it to a problem in American political science. Through this approach, the "bipartisan foreign policy hypothesis" and that "two presidencies hypothesis" are reformulated and reconsidered. Until now participants in the debate over each have only rarely built on, or even cited, the other’s research. An additional problem is that the widespread conventional wisdom in support of the two hypotheses is inconsistent with systematic scholarly analyses. This paper demonstrates that the two hypotheses are drawn from the same underlying structure. Each hypothesis and the theoretical model it implies is conceptually and empirically extended to take into account the differences between congressional leaders and members. Then, historical examples and statistical analyses of House roll call data are used to demonstrate that the hypotheses, while sometimes supported for the congressional members, are far more applicable to leadership decision making. Conclusions suggest that conventional wisdom be revised to take these differences into account.
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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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Presidential Appointments to the Supreme Court: Adding Systematic Explanation to Probabilistic Description

Presidential Appointments to the Supreme Court: Adding Systematic Explanation to Probabilistic Description
Gary King. 1987. “Presidential Appointments to the Supreme Court: Adding Systematic Explanation to Probabilistic Description.” American Politics Quarterly, 15, Pp. 373–386.Abstract
Three articles, published in the leading journals of three disciplines over the last five decades, have each used the Poisson probability distribution to help describe the frequency with which presidents were able to appoint United States Supreme Court Justices. This work challenges these previous findings with a new model of Court appointments. The analysis demonstrates that the number of appointments a president can expect to make in a given year is a function of existing measurable variables.
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News story: "Entrepreneurial Academia with Gary King"

Presentations

Is Survey Instability Due to Respondents who Don't Understand Politics or Researchers Who Don't Understand Respondents? (Caltech), at California Institute of Technology, Wednesday, March 13, 2024:
For over 75 years, survey researchers have observed disturbingly large proportions of respondents changing answers when asked the same question again later, even if no material changes have taken place. This “survey instability” is central to substantive debates in many scholarly fields and, more generally, for choosing the data generation process underlying all survey data analysis methods. By building on developments in neuroscience, cognitive psychology, and statistical measurement, we construct an encompassing model of the survey response, narrow competing hypotheses to a single data... Read more about Is Survey Instability Due to Respondents who Don't Understand Politics or Researchers Who Don't Understand Respondents? (Caltech)
How American Politics Ensures Electoral Accountability in Congress (UCLA), at UCLA, Tuesday, March 12, 2024:
An essential component of democracy is the ability to hold legislators accountable via the threat of electoral defeat, a concept that has rarely been quantified directly. Well known massive changes over time in indirect measures -- such as incumbency advantage, electoral margins, partisan bias, partisan advantage, split ticket voting, and others -- all seem to imply wide swings in electoral accountability. In contrast, we show that the (precisely calibrated) probability of defeating incumbent US House members has been surprisingly constant and remarkably high for two-thirds of a century. We... Read more about How American Politics Ensures Electoral Accountability in Congress (UCLA)
Correcting Measurement Error Bias in Conjoint Survey Experiments (Harvard Experiments Working Group), at Harvard Experiments Working Group, Friday, February 9, 2024:
Conjoint survey designs are spreading across the social sciences due to their unusual capacity to estimate many causal effects from a single randomized experiment. Unfortunately, by their ability to mirror complicated real-world choices, these designs often generate substantial measurement error and thus bias. We replicate both the data collection and analysis from eight prominent conjoint studies, all of which closely reproduce published results, and show that a large proportion of observed variation in answers to conjoint questions is effectively random noise. We then discover a common... Read more about Correcting Measurement Error Bias in Conjoint Survey Experiments (Harvard Experiments Working Group)
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Books

Designing Social Inquiry: Scientific Inference in Qualitative Research, New Edition

Designing Social Inquiry: Scientific Inference in Qualitative Research, New Edition
Gary King, Robert O. Keohane, and Sidney Verba. 2021. Designing Social Inquiry: Scientific Inference in Qualitative Research, New Edition. 2nd ed. Princeton: Princeton University Press. Publisher's VersionAbstract
"The classic work on qualitative methods in political science"

Designing Social Inquiry presents a unified approach to qualitative and quantitative research in political science, showing how the same logic of inference underlies both. This stimulating book discusses issues related to framing research questions, measuring the accuracy of data and the uncertainty of empirical inferences, discovering causal effects, and getting the most out of qualitative research. It addresses topics such as interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. The book only uses mathematical notation to clarify concepts, and assumes no prior knowledge of mathematics or statistics.

Featuring a new preface by Robert O. Keohane and Gary King, this edition makes an influential work available to new generations of qualitative researchers in the social sciences.
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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
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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An Interview with Gary

Gary King on Twitter