(734) 277-4894

Relevant Projects:

NIA, NIH, Sloan Foundation


(Umich): UMich Retirement Research Center (MRRC); Michigan Center on the Demography of Aging (MiCDA), Weiser Center for Europe and Eurasia’s Center for European Studies (WCEE-CES); American Economic Association (AEA), Econometric Society (ES), American Association for Public Opinion Research (AAPOR); European Economic Association (EEA), European Survey Association (ESA), Human Capital and Economic Opportunity Working Group (HCEO)

Pamela Giustinelli

Adjunct Professor

Survey Research Center for Social Research

Adjunct Research Assistant Professor, Survey Research Center, Institute for Social Research

Pamela Giustinelli, is an Adjunct Research Assistant Professor in the Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.

Pamela is interested in modeling, empirical, and counterfactual policy analysis of individual and multilateral decision making under uncertainty-ambiguity, especially as it applies to the family and human capital contexts. She is also interested in survey methodology, particularly as it relates to this line of research. Here are some important questions in her research agenda:

  • How do preferences, beliefs, choice sets, and other elements of a choice situation determine what choices people make and also how they make those choices? (That is, the “decision rules,” “decision protocols,” or “modes of interactions” they use.) And how are those elements formed?
  • What information do individuals and groups have or use when making decisions under uncertainty? And what information is or is not shared among decision makers in multilateral settings?
  • What are the implications of the above points for policy?
  • To inform modeling, identification, and prediction of choice behaviors, what components of individuals’ and groups’ decision processes can we sensibly measure in surveys? From whom? And in what formats?

Data science methodology: Survey design for elicitation of components of human decision processes and interactions under uncertainty/ambiguity

Data science applications: Human capital (school choice, labor supply, end-of-life living arrangements)