Applied Microeconomics
Applied Microeconomics
The Applied Microeconomics research group unites researchers working on a broad array of topics within such areas as labour economics, economics of education, health economics, family economics, urban economics, environmental economics, and the economics of science and innovation. The group operates in close collaboration with the CAGE Research Centre.
The group participates in the CAGE seminar on Applied Economics, which runs weekly on Tuesdays at 2:15pm. Students and faculty members of the group present their ongoing work in two brown bag seminars, held weekly on Tuesdays and Wednesdays at 1pm. Students, in collaboration with faculty members, also organise a bi-weekly reading group in applied econometrics on Thursdays at 1pm. The group organises numerous events throughout the year, including the Research Away Day and several thematic workshops.
Our activities
Work in Progress seminars
Tuesdays and Wednesdays 1-2pm
Students and faculty members of the group present their work in progress in two brown bag seminars. See below for a detailed scheduled of speakers.
Applied Econometrics reading group
Thursdays (bi-weekly) 1-2pm
Organised by students in collaboration with faculty members. See the Events calendar below for further details
People
Academics
Academics associated with the Applied Microeconomics Group are:
Research Students
Events
CRETA Talk - Eva Vivalt (ANU)
Title: How Do Policymakers Update Their Beliefs?
Abstract: Evidence-based policymaking requires not only evidence, but also for policymakers to update their beliefs based on that evidence. We examine how policymakers, researchers, and development practitioners update in response to results from academic studies, using a unique opportunity to run an experiment on policymakers. We find evidence of 鈥渧ariance neglect鈥�, a bias similar to extension neglect in which confidence intervals are ignored. We also find evidence of asymmetric updating on good news relative to one鈥檚 prior beliefs. Finally, we test whether providing different types of information can mitigate the observed biases.