QnAs with Gary King

Abstract

Gary King, a professor of social science at Harvard University and a member of the National Academy of Sciences, fashions tools that harness the power of statistics, machine learning, and informatics to make sense of the numbers that matter to society. From evaluating the efficacy of a Mexican health policy reform to predicting the fate of the US Social Security trust fund, King’s sophisticated number crunching has important practical implications for disciplines as diverse as social, political, and health sciences. King tells PNAS how quantitative social science research can extend from academic journals into real-world scenarios. > PNAS:You designed a health policy experiment to evaluate the Mexican health care program, Seguro Popular , which was aimed at reducing health care costs for the poor. What was new about this experiment, and what did you find? > King:Most large-scale public policy experiments fail mainly because study participants prefer to be in the treatment group rather than in the control group. Also, there is often political opposition to the random assignment of people to different groups because that’s not how government projects are typically executed. After all, the job of politicians is to try to ensure that their constituents benefit from policy measures, not to conduct scientific research. However, breaking the randomization of subject assignment …