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Identifying and Measuring Conditional Policy Preferences: The Case of Opening Schools During a Pandemic (WP-20-55)

Jonathan Green, Matthew Baum, James Druckman, David Lazer, Katherine Ognyanova, Matthew Simonson, Roy Perlis, and Mauricio Santillana

An individual’s issue preferences are non-separable when they depend on other issue outcomes (Lacy 2001a), presenting measurement challenges for traditional survey research. The researchers extend this logic to the broader case of conditional preferences, in which policy preferences depend on the status of conditions with inherent levels of uncertainty—and are not necessarily policies themselves. They demonstrate new approaches for measuring conditional preferences in two large-scale survey experiments regarding the conditions under which citizens would support reopening schools in their communities during the COVID-19 pandemic. By drawing on recently developed methods at the intersection of machine learning and causal inference, the authors identify which citizens are most likely to have school reopening preferences that depend on additional considerations. The results highlight the advantages of using such approaches to measure conditional preferences, which represent an underappreciated and general phenomenon in public opinion.

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Jonathan Green, Postdoctoral Research Fellow, Network Science Institute, Northeastern University

Matthew Baum, Marvin Kalb Professor of Global Communications, Harvard Kennedy School

James Druckman, Payson S. Wild Professor of Political Science and IPR Fellow, Northwestern University

David Lazer, Distinguished Professor of Political Science and Computer and Information Science, Northeastern University

Katherine Ognyanova, Assistant Professor of Communication, Rutgers University

Matthew Simonson, Network Science Institute, Northeastern University

Roy Perlis, Professor of Psychiatry, Harvard Medical School

Mauricio Santillana, Assistant Professor of Pediatrics and Epidemiology, Harvard University

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