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Education and Scholarship by Video
Gary King. 2021. “Education and Scholarship by Video”. [Direct link to paper]Abstract

When word processors were first introduced into the workplace, they turned scholars into typists. But they also improved our work: Turnaround time for new drafts dropped from days to seconds. Rewriting became easier and more common, and our papers, educational efforts, and research output improved. I discuss the advantages of and mechanisms for doing the same with do-it-yourself video recordings of research talks and class lectures, so that they may become a fully respected channel for scholarly output and education, alongside books and articles. I consider innovations in video design to optimize education and communication, along with technology to make this change possible.

See related blog post here in APSAEducate and my recorded videos are here.

Participant Grouping for Enhanced Interactive Experience (4th)
Gary King, Brian Lukoff, and Eric Mazur. 1/26/2021. “Participant Grouping for Enhanced Interactive Experience (4th).” United States of America 10,902,031 B2 (U.S Patent and Trademark Office).Abstract
Representative embodiments of a method for grouping participants in an activity include the steps of: (i) defining a grouping policy; (ii) storing, in a database, participant records that include a participant identifier, a characteristic associated with the participant, and/or an identifier for a participant's handheld device; (iii) defining groupings based on the policy and characteristics of the participants relating to the policy and to the activity; and (iv) communicating the groupings to the handheld devices to establish the groups.
Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India
Rockli Kim, Avleen S. Bijral, Yun Xu, Xiuyuan Zhang, Jeffrey C. Blossom, Akshay Swaminathan, Gary King, Alok Kumar, Rakesh Sarwal, Juan M. Lavista Ferres, and S.V. Subramanian. 2021. “Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India.” Proceedings of the National Academy of Sciences, 118, 18, Pp. 1-11. Publisher's VersionAbstract
There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.
Survey Data and Human Computation for Improved Flu Tracking
Stefan Wojcik, Avleen Bijral, Richard Johnston, Juan Miguel Lavista, Gary King, Ryan Kennedy, Alessandro Vespignani, and David Lazer. 2021. “Survey Data and Human Computation for Improved Flu Tracking.” Nature Communications, 12, 194, Pp. 1-8. Publisher's VersionAbstract
While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users’ online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.
UnbiasedPrivacy
Georgina Evans, Gary King, Margaret Schwenzfeier, and Abhradeep Thakurta. 2021. “UnbiasedPrivacy”.
Building an International Consortium for Tracking Coronavirus Health Status
Eran Segal, Feng Zhang, Xihong Lin, Gary King, Ophir Shalem, Smadar Shilo, William E. Allen, Yonatan H. Grad, Casey S. Greene, Faisal Alquaddoomi, Simon Anders, Ran Balicer, Tal Bauman, Ximena Bonilla, Gisel Booman, Andrew T. Chan, Ori Cohen, Silvano Coletti, Natalie Davidson, Yuval Dor, David A. Drew, Olivier Elemento, Georgina Evans, Phil Ewels, Joshua Gale, Amir Gavrieli, Benjamin Geiger, Iman Hajirasouliha, Roman Jerala, Andre Kahles, Olli Kallioniemi, Ayya Keshet, Gregory Landua, Tomer Meir, Aline Muller, Long H. Nguyen, Matej Oresic, Svetlana Ovchinnikova, Hedi Peterson, Jay Rajagopal, Gunnar Rätsch, Hagai Rossman, Johan Rung, Andrea Sboner, Alexandros Sigaras, Tim Spector, Ron Steinherz, Irene Stevens, Jaak Vilo, Paul Wilmes, and CCC (Coronavirus Census Collective). 8/2020. “Building an International Consortium for Tracking Coronavirus Health Status.” Nature Medicine, 26, Pp. 1161-1165. Publisher's VersionAbstract
Information is the most potent protective weapon we have to combat a pandemic, at both the individual and global level. For individuals, information can help us make personal decisions and provide a sense of security. For the global community, information can inform policy decisions and offer critical insights into the epidemic of COVID-19 disease. Fully leveraging the power of information, however, requires large amounts of data and access to it. To achieve this, we are making steps to form an international consortium, Coronavirus Census Collective (CCC, coronaviruscensuscollective.org), that will serve as a hub for integrating information from multiple data sources that can be utilized to understand, monitor, predict, and combat global pandemics. These sources may include self-reported health status through surveys (including mobile apps), results of diagnostic laboratory tests, and other static and real-time geospatial data. This collective effort to track and share information will be invaluable in predicting hotspots of disease outbreak, identifying which factors control the rate of spreading, informing immediate policy decisions, evaluating the effectiveness of measures taken by health organizations on pandemic control, and providing critical insight on the etiology of COVID-19. It will also help individuals stay informed on this rapidly evolving situation and contribute to other global efforts to slow the spread of disease. In the past few weeks, several initiatives across the globe have surfaced to use daily self-reported symptoms as a means to track disease spread, predict outbreak locations, guide population measures and help in the allocation of healthcare resources. The aim of this paper is to put out a call to standardize these efforts and spark a collaborative effort to maximize the global gain while protecting participant privacy.
Computational social science: Obstacles and opportunities
David M. J. Lazer, Alex Pentland, Duncan J. Watts, Sinan Aral, Susan Athey, Noshir Contractor, Deen Freelon, Sandra Gonzalez-Bailon, Gary King, Helen Margetts, Alondra Nelson, Matthew J. Salganik, Markus Strohmaier, Alessandro Vespignani, and Claudia Wagner. 8/28/2020. “Computational social science: Obstacles and opportunities.” Science, 369, 6507, Pp. 1060-1062. Publisher's VersionAbstract
The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.
Do Nonpartisan Programmatic Policies Have Partisan Electoral Effects? Evidence from Two Large Scale Experiments
Kosuke Imai, Gary King, and Carlos Velasco Rivera. 1/31/2020. “Do Nonpartisan Programmatic Policies Have Partisan Electoral Effects? Evidence from Two Large Scale Experiments.” Journal of Politics, 81, 2, Pp. 714-730. Publisher's VersionAbstract

A vast literature demonstrates that voters around the world who benefit from their governments' discretionary spending cast more ballots for the incumbent party than those who do not benefit. But contrary to most theories of political accountability, some suggest that voters also reward incumbent parties for implementing "programmatic" spending legislation, over which incumbents have no discretion, and even when passed with support from all major parties. Why voters would attribute responsibility when none exists is unclear, as is why minority party legislators would approve of legislation that would cost them votes. We study the electoral effects of two large prominent programmatic policies that fit the ideal type especially well, with unusually large scale experiments that bring more evidence to bear on this question than has previously been possible. For the first policy, we design and implement ourselves one of the largest randomized social experiments ever. For the second policy, we reanalyze studies that used a large scale randomized experiment and a natural experiment to study the same question but came to opposite conclusions. Using corrected data and improved statistical methods, we show that the evidence from all analyses of both policies is consistent: programmatic policies have no effect on voter support for incumbents. We conclude by discussing how the many other studies in the literature may be interpreted in light of our results.

Evaluating COVID-19 Public Health Messaging in Italy: Self-Reported Compliance and Growing Mental Health Concerns
Soubhik Barari, Stefano Caria, Antonio Davola, Paolo Falco, Thiemo Fetzer, Stefano Fiorin, Lukas Hensel, Andriy Ivchenko, Jon Jachimowicz, Gary King, Gordon Kraft-Todd, Alice Ledda, Mary MacLennan, Lucian Mutoi, Claudio Pagani, Elena Reutskaja, Christopher Roth, and Federico Raimondi Slepoi. 2020. “Evaluating COVID-19 Public Health Messaging in Italy: Self-Reported Compliance and Growing Mental Health Concerns”. Publisher's VersionAbstract

Purpose: The COVID-19 death-rate in Italy continues to climb, surpassing that in every other country. We implement one of the first nationally representative surveys about this unprecedented public health crisis and use it to evaluate the Italian government’ public health efforts and citizen responses. 
Findings: (1) Public health messaging is being heard. Except for slightly lower compliance among young adults, all subgroups we studied understand how to keep themselves and others safe from the SARS-Cov-2 virus. Remarkably, even those who do not trust the government, or think the government has been untruthful about the crisis believe the messaging and claim to be acting in accordance. (2) The quarantine is beginning to have serious negative effects on the population’s mental health.
Policy Recommendations: Communications focus should move from explaining to citizens that they should stay at home to what they can do there. We need interventions that make staying at home and following public health protocols more desirable. These interventions could include virtual social interactions, such as online social reading activities, classes, exercise routines, etc. — all designed to reduce the boredom of long term social isolation and to increase the attractiveness of following public health recommendations. Interventions like these will grow in importance as the crisis wears on around the world, and staying inside wears on people.

Replication data for this study in dataverse