I received my Ph.D. from Harvard University in 2021. My research studies how electoral politics translates into democratic policymaking, especially in modern American Politics. I also develop statistical software to improve the measurement of public opinion and electoral behavior. My postdoctoral work at Stanford will include research with Professor Doug Rivers on new data and survey methods for describing the microgeography of electoral behavior. From July 2022, I will join the faculty at Yale University as Assistant Professor of Political Science.

Stanford Advisors

All Publications

  • Unrepresentative big surveys significantly overestimated US vaccine uptake. Nature Bradley, V. C., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X., Flaxman, S. 2021


    Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias:an instance of the Big Data Paradox1. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi-Facebook2,3 (about 250,000 responses per week) and Census Household Pulse4 (about 75,000 every two weeks). In May 2021, Delphi-Facebook overestimated uptake by 17 percentage points (14-20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11-17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios-Ipsosonline panel5 with about 1,000 responses per week following survey research best practices6 provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework1 to explain the inaccuracy in the three surveys. Wethen analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of thepopulation mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition.

    View details for DOI 10.1038/s41586-021-04198-4

    View details for PubMedID 34880504

  • The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census. Science advances Kenny, C. T., Kuriwaki, S., McCartan, C., Rosenman, E. T., Simko, T., Imai, K. 2021; 7 (41): eabk3283


    [Figure: see text].

    View details for DOI 10.1126/sciadv.abk3283

    View details for PubMedID 34613778

  • Congressional Representation: Accountability from the Constituent's Perspective AMERICAN JOURNAL OF POLITICAL SCIENCE Ansolabehere, S., Kuriwaki, S. 2021

    View details for DOI 10.1111/ajps.12607

    View details for Web of Science ID 000645132500001

  • The "Math Prefresher" and the Collective Future of Political Science Graduate Training PS-POLITICAL SCIENCE & POLITICS King, G., Kuriwaki, S., Park, Y. 2020; 53 (3): 537-541
  • Wealth, Slaveownership, and Fighting for the Confederacy: An Empirical Study of the American Civil War AMERICAN POLITICAL SCIENCE REVIEW Hall, A. B., Huff, C., Kuriwaki, S. 2019; 113 (3): 658-673