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 U.S. Social Security Administration 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.
    • 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.
    • 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
      Statistical methods to accommodate missing information in data sets due to scattered unit nonresponse, missing variables, or cell 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
      "Anchoring Vignette" methods for when different respondents (perhaps from different cultures, countries, or ethnic groups) understand survey questions in different ways; an approach to developing theoretical definitions of complicated concepts apparently definable only by example (i.e., "you know it when you see it"); how surveys work.
    • 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

booc.io: An Education System with Hierarchical Concept Maps

booc.io: An Education System with Hierarchical Concept Maps
Michail Schwab, Hendrik Strobelt, James Tompkin, Colin Fredericks, Connor Huff, Dana Higgins, Anton Strezhnev, Mayya Komisarchik, Gary King, and Hanspeter Pfister. Forthcoming. “booc.io: An Education System with Hierarchical Concept Maps.” IEEE Transactions on Visualization and Computer Graphics. Abstract

Information hierarchies are difficult to express when real-world space or time constraints force traversing the hierarchy in linear presentations, such as in educational books and classroom courses. We present booc.io, which allows linear and non-linear presentation and navigation of educational concepts and material. To support a breadth of material for each concept, booc.io is Web based, which allows adding material such as lecture slides, book chapters, videos, and LTIs. A visual interface assists the creation of the needed hierarchical structures. The goals of our system were formed in expert interviews, and we explain how our design meets these goals. We adapt a real-world course into booc.io, and perform introductory qualitative evaluation with students.

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Scoring Social Security Proposals: Response from Kashin, King, and Soneji

Scoring Social Security Proposals: Response from Kashin, King, and Soneji
Konstantin Kashin, Gary King, and Samir Soneji. 2016. “Scoring Social Security Proposals: Response from Kashin, King, and Soneji.” Journal of Economic Perspectives, 2, 30: 245-248. Publisher's Version Abstract

This is a response to Peter Diamond's comment on two paragraph comment on a passage in our article, Konstantin Kashin, Gary King, and Samir Soneji. 2015. “Systematic Bias and Nontransparency in US Social Security Administration Forecasts.” Journal of Economic Perspectives, 2, 29: 239-258. 

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Preface: Big Data is Not About the Data!

Preface: Big Data is Not About the Data!
Gary King. 2016. “Preface: Big Data is Not About the Data!.” In Computational Social Science: Discovery and Prediction, edited by R. Michael Alvarez. Cambridge: Cambridge University Press. Abstract

A few years ago, explaining what you did for a living to Dad, Aunt Rose, or your friend from high school was pretty complicated. Answering that you develop statistical estimators, work on numerical optimization, or, even better, are working on a great new Markov Chain Monte Carlo implementation of a Bayesian model with heteroskedastic errors for automated text analysis is pretty much the definition of conversation stopper.

Then the media noticed the revolution we’re all apart of, and they glued a label to it. Now “Big Data” is what you and I do.  As trivial as this change sounds, we should be grateful for it, as the name seems to resonate with the public and so it helps convey the importance of our field to others better than we had managed to do ourselves. Yet, now that we have everyone’s attention, we need to start clarifying for others -- and ourselves -- what the revolution means. This is much of what this book is about.

Throughout, we need to remember that for the most part, Big Data is not about the data....

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Effectiveness of the WHO Safe Childbirth Checklist Program in Reducing Severe Maternal, Fetal, and Newborn Harm: Study Protocol for a Matched-Pair, Cluster Randomized Controlled Trial in Uttar Pradesh, India

Effectiveness of the WHO Safe Childbirth Checklist Program in Reducing Severe Maternal, Fetal, and Newborn Harm: Study Protocol for a Matched-Pair, Cluster Randomized Controlled Trial in Uttar Pradesh, India
Katherine Semrau, Lisa R. Hirschhorn, Bhala Kodkany, Jonathan Spector, Danielle E. Tuller, Gary King, Stuart Lisptiz, Narender Sharma, Vinay P. Singh, Bharath Kumar, Neelam Dhingra-Kumar, Rebecca Firestone, Vishwajeet Kumar, and Atul Gawande. 2016. “Effectiveness of the WHO Safe Childbirth Checklist Program in Reducing Severe Maternal, Fetal, and Newborn Harm: Study Protocol for a Matched-Pair, Cluster Randomized Controlled Trial in Uttar Pradesh, India.” Trials, 17, 576: 1-10. Abstract

Background: Effective, scalable strategies to improve maternal, fetal, and newborn health and reduce preventable morbidity and mortality are urgently needed in low- and middle-income countries. Building on the successes of previous checklist-based programs, the World Health Organization (WHO) and partners led the development of the Safe Childbirth Checklist (SCC), a 28-item list of evidence-based practices linked with improved maternal and newborn outcomes. Pilot-testing of the Checklist in Southern India demonstrated dramatic improvements in adherence by health workers to essential childbirth-related practices (EBPs). The BetterBirth Trial seeks to measure the effectiveness of SCC impact on EBPs, deaths, and complications at a larger scale.

Methods: This matched-pair, cluster-randomized controlled, adaptive trial will be conducted in 120 facilities across 24 districts in Uttar Pradesh, India. Study sites, identified according to predefined eligibility criteria, were matched by measured covariates before randomization. The intervention, the SCC embedded in a quality improvement program, consists of leadership engagement, a 2-day educational launch of the SCC, and support through placement of a trained peer “coach” to provide supportive supervision and real-time data feedback over an 8-month period with decreasing intensity. A facility-based childbirth quality coordinator is trained and supported to drive sustained behavior change after the BetterBirth team leaves the facility. Study participants are birth attendants and women and their newborns who present to the study facilities for childbirth at 60 intervention and 60 control sites. The primary outcome is a composite measure including maternal death, maternal severe morbidity, stillbirth, and newborn death, occurring within 7 days after birth. The sample size (n = 171,964) was calculated to detect a 15% reduction in the primary outcome. Adherence by health workers to EBPs will be measured in a subset of births (n = 6000). The trial will be conducted in close collaboration with key partners including the Governments of India and Uttar Pradesh, the World Health Organization, an expert Scientific Advisory Committee, an experienced local implementing organization (Population Services International, PSI), and frontline facility leaders and workers

Discussion: If effective, the WHO Safe Childbirth Checklist program could be a powerful health facilitystrengthening intervention to improve quality of care and reduce preventable harm to women and newborns, with millions of potential beneficiaries.

Trial registration: BetterBirth Study Protocol dated: 13 February 2014; ClinicalTrials.gov: NCT02148952; Universal Trial Number: U1111-1131-5647. 

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Comment on 'Estimating the Reproducibility of Psychological Science'

Comment on 'Estimating the Reproducibility of Psychological Science'
Daniel Gilbert, Gary King, Stephen Pettigrew, and Timothy Wilson. 2016. “Comment on 'Estimating the Reproducibility of Psychological Science'.” Science, 6277, 351: 1037a-1038a. Publisher's Version Abstract

recent article by the Open Science Collaboration (a group of 270 coauthors) gained considerable academic and public attention due to its sensational conclusion that the replicability of psychological science is surprisingly low. Science magazine lauded this article as one of the top 10 scientific breakthroughs of the year across all fields of science, reports of which appeared on the front pages of newspapers worldwide. We show that OSC's article contains three major statistical errors and, when corrected, provides no evidence of a replication crisis. Indeed, the evidence is consistent with the opposite conclusion -- that the reproducibility of psychological science is quite high and, in fact, statistically indistinguishable from 100%. (Of course, that doesn't mean that the replicability is 100%, only that the evidence is insufficient to reliably estimate replicability.) The moral of the story is that meta-science must follow the rules of science.

Replication data is available in this dataverse archive. See also the full web site for this article and related materials, and one of the news articles written about it.

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The C-SPAN Archives as The Policymaking Record of American Representative Democracy: A Foreword

The C-SPAN Archives as The Policymaking Record of American Representative Democracy: A Foreword
Gary King. 2016. “The C-SPAN Archives as The Policymaking Record of American Representative Democracy: A Foreword.” In Exploring the C-SPAN Archives: Advancing the Research Agenda, edited by Robert X Browning. West Lafayette, IN: Purdue University Press. Abstract

Almost two centuries ago, the idea of research libraries, and the possibility of building them at scale, began to be realized. Although we can find these libraries at every major college and university in the world today, and at many noneducational research institutions, this outcome was by no means obvious at the time. And the benefits we all now enjoy from their existence were then at best merely vague speculations.

How many would have supported the formation of these institutions at the time, without knowing the benefits that have since become obvious? After all, the arguments against this massive ongoing expenditure are impressive. The proposal was to construct large buildings, hire staff, purchase all manner of books and other publications and catalogue and shelve them, provide access to visitors, and continually reorder all the books that the visitors disorder. And the libraries would keep the books, and fund the whole operation, in perpetuity. Publications would be collected without anyone deciding which were of high quality and thus deserving of preservation—leading critics to argue that all this effort would result in expensive buildings packed mostly with junk.  . . .

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Urban observatories: City data can inform decision theory

Aristides A. N. Patrinos, Hannah Bayer, Paul W. Glimcher, Steven Koonin, Miyoung Chun, and Gary King. 3/19/2015. “Urban observatories: City data can inform decision theory.” Nature, 519: 291. Publisher's Version Abstract

Data are being collected on human behaviour in cities such as London, New York, Singapore and Shanghai, with a view to meeting city dwellers' needs more effectively. Incorporating decision-making theory into analyses of the data from these 'urban observatories' would yield further valuable information.

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Presentations

Simplifying Matching Methods for Causal Inference, at Harvard School of Public Health, Kresge Room G2, Thursday, January 26, 2017:

This talk introduces methods of matching for causal inference that are simpler, more powerful, and easier to understand than prior approaches. Software is available to implement everything discussed. Copies of my articles on the subject are available at my website.

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at University of Wisconsin-Madison, Monday, January 23, 2017:

This talk is based on this paper (forthcoming in the American Political Science Review), by me, Jennifer Pan, and Margaret Roberts, along with a brief summary of our previous work. Here's an abstract: The Chinese government has long been suspected of hiring as many as 2,000,000 people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people.

An Improved Method of Automated Nonparametric Content Analysis for Social Science, at Texas A&M Inaugural STATA Lecture, Thursday, January 19, 2017:

A vast literature in computer science and statistics develops methods to automatically classify textual documents into chosen categories. In contrast, social scientists are often more interested in aggregate generalizations about populations of documents --- such as the percent of social media posts that speak favorably of a candidate's foreign policy. Unfortunately, trying to maximize the percent of individual documents correctly classified often yields biased estimates of statistical aggregates.

Big Data is Not About the Data!, at Shanghai Jiao Tong University, Wednesday, January 4, 2017:

The spectacular progress the media describes as "big data" has little to do with the 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.

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