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

    • 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; 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.
    • 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
      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

Ecological Regression with Partial Identification

Ecological Regression with Partial Identification
Wenxin Jiang, Gary King, Allen Schmaltz, and Martin A. Tanner. Working Paper. “Ecological Regression with Partial Identification”.Abstract

 

Ecological inference is the process of learning about individual behavior from aggregate data. We study a partially identified linear contextual effects model for ecological inference and describe how to estimate the district level parameter averaging over many precincts in the presence of the non-identified parameter of the contextual effect. This may be regarded as a first attempt in this venerable literature to limit the scope of the key form of non-identifiability in ecological inference. To study the operating characteristics of our methodology, we have amassed the largest collection of data with known ground truth ever applied to evaluate solutions to the ecological inference problem. We collect and study 459 datasets from a variety of fields including public health, political science and sociology. The datasets contain a total of 2,370,854 geographic units (e.g., precincts), with an average of 5,165 geographic units per dataset. Our replication data are publicly available via the Harvard Dataverse (Jiang et al. 2018) and may serve as a useful resource for future researchers. For all real data sets in our collection that fit our proposed rules, our methodology reduces the width of the Duncan and Davis (1953) deterministic bound, on average, by about 45%, while still capturing the true district level parameter in excess of 97% of the time.

 

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A New Model for Industry-Academic Partnerships

Indaca
Gary King and Nathaniel Persily. Working Paper. “A New Model for Industry-Academic Partnerships”.Abstract

The mission of the academic social sciences is to understand and ameliorate society’s greatest challenges. The data held by private companies holds vast potential to further this mission. Yet, because of its interaction with highly politicized issues, customer privacy, proprietary content, and differing goals of firms and academics, these data are often inaccessible to university researchers. We propose here a new model for industry-academic partnerships that addresses these problems via a novel organizational structure: Respected scholars form a commission which, as a trusted third party, receives access to all relevant firm information and systems, and then recruits independent academics to do research in specific areas following standard peer review protocols organized and funded by nonprofit foundations. We also report on a partnership we helped forge under this model to make data available about the extremely visible and highly politicized issues surrounding the impact of social media on elections and democracy. In our partnership, Facebook will provide privacy-preserving data and access; seven major politically and substantively diverse nonprofit foundations will fund the research; and the Social Science Research Council will oversee the peer review process for funding and data access.

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Use of a Social Annotation Platform for Pre-Class Reading Assignments in a Flipped Introductory Physics Class

Use of a Social Annotation Platform for Pre-Class Reading Assignments in a Flipped Introductory Physics Class
Kelly Miller, Brian Lukoff, Gary King, and Eric Mazur. 3/2018. “Use of a Social Annotation Platform for Pre-Class Reading Assignments in a Flipped Introductory Physics Class.” Frontiers in Education, 3, 8, Pp. 1-12. Publisher's VersionAbstract
In this paper, we illustrate the successful implementation of pre-class reading assignments through a social learning platform that allows students to discuss the reading online with their classmates. We show how the platform can be used to understand how students are reading before class. We find that, with this platform, students spend an above average amount of time reading (compared to that reported in the literature) and that most students complete their reading assignments before class. We identify specific reading behaviors that are predictive of in-class exam performance. We also demonstrate ways that the platform promotes active reading strategies and produces high-quality learning interactions between students outside class. Finally, we compare the exam performance of two cohorts of students, where the only difference between them is the use of the platform; we show that students do significantly better on exams when using the platform.
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An Improved Method of Automated Nonparametric Content Analysis for Social Science

An Improved Method of Automated Nonparametric Content Analysis for Social Science
Connor T. Jerzak, Gary King, and Anton Strezhnev. Working Paper. “An Improved Method of Automated Nonparametric Content Analysis for Social Science”.Abstract

Computer scientists and statisticians are often interested in classifying textual documents into chosen categories. Social scientists and others are often less interested in any one document and instead try to estimate the proportion falling in each category. The two existing types of techniques for estimating these category proportions are parametric "classify and count" methods and "direct" nonparametric estimation of category proportions without an individual classification step. Unfortunately, classify and count methods can sometimes be highly model dependent or generate more bias in the proportions even as the percent correctly classified increases. Direct estimation avoids these problems, but can suffer when the meaning and usage of language is too similar across categories or too different between training and test sets. We develop an improved direct estimation approach without these problems by introducing continuously valued text features optimized for this problem, along with a form of matching adapted from the causal inference literature. We evaluate our approach in analyses of a diverse collection of 73 data sets, showing that it substantially improves performance compared to existing approaches. As a companion to this paper, we offer easy-to-use software that implements all ideas discussed herein.

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How to conquer partisan gerrymandering

How to conquer partisan gerrymandering
Gary King and Robert X Browning. 12/26/2017. “How to conquer partisan gerrymandering.” Boston Globe (Op-Ed), 292 , 179 , Pp. A10. Publisher's VersionAbstract
PARTISAN GERRYMANDERING has long been reviled for thwarting the will of the voters. Yet while voters are acting disgusted, the US Supreme Court has only discussed acting — declaring they have the constitutional right to fix the problem, but doing nothing. But as better data and computer algorithms are now making gerrymandering increasingly effective, continuing to sidestep the issue could do permanent damage to American democracy. In Gill v. Whitford, the soon-to-be-decided challenge to Wisconsin’s 2011 state Assembly redistricting plan, the court could finally fix the problem for the whole country. Judging from the oral arguments, the key to the case is whether the court endorses the concept of “partisan symmetry,” a specific standard for treating political parties equally in allocating legislative seats based on voting.
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Edited transcript of a talk on Partisan Symmetry at the 'Redistricting and Representation Forum'

Edited transcript of a talk on Partisan Symmetry at the 'Redistricting and Representation Forum'
Gary King. Forthcoming. “Edited transcript of a talk on Partisan Symmetry at the 'Redistricting and Representation Forum'.” Bulletin of the American Academy of Arts and Sciences, Winter, Pp. 55-58.Abstract

The origin, meaning, estimation, and application of the concept of partisan symmetry in legislative redistricting, and the justiciability of partisan gerrymandering. An edited transcript of a talk at the “Redistricting and Representation Forum,” American Academy of Arts & Sciences, Cambridge, MA 11/8/2017.

Here also is a video of the original talk.

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How the news media activate public expression and influence national agendas

How the news media activate public expression and influence national agendas
Gary King, Benjamin Schneer, and Ariel White. 11/10/2017. “How the news media activate public expression and influence national agendas.” Science, 358, Pp. 776-780. Publisher's VersionAbstract

We demonstrate that exposure to the news media causes Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting 48 mostly small media outlets, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimated the causal effect on proximal measures, such as website pageviews and Twitter discussion of the articles’ specific subjects, and distal ones, such as national Twitter conversation in broad policy areas. Our intervention increased discussion in each broad policy area by approximately \(\approx 62.7\%\) (relative to a day’s volume), accounting for 13,166 additional posts over the treatment week, with similar effects across population subgroups. 

On the Science website: AbstractReprintFull text, and a comment (by Matthew Gentzkow) "Small media, big impact".

 

 

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Presentations

Simplifying Matching Methods for Causal Inference, at National Taiwan University, Wednesday, May 30, 2018:
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 the most commonly used existing method, propensity score matching, should rarely be used in practice. Easy-to-use software is available to implement all methods discussed.
How the news media activate public expression and influence national agendas, at University of Vermont, Monday, April 30, 2018
This talk reports on the results of first large scale randomized news media experiment. We demonstrate that even small news media outlets can cause large numbers of Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting \(48\) mostly small media outlets, and working with them over \(5\) years, we chose groups of these outlets to write and publish articles on subjects we... Read more about How the news media activate public expression and influence national agendas
How the news media activate public expression and influence national agendas, at St. Louis Area Methods Meeting (SLAMM), Iowa State University, Friday, April 20, 2018:
This talk reports on the results of first large scale randomized news media experiment. We demonstrate that even small news media outlets can cause large numbers of Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting \(48\) mostly small media outlets, and working with them over \(5\) years, we chose groups of these outlets to write and publish articles on subjects we... Read more about How the news media activate public expression and influence national agendas
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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
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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