PhD Student, PhD Student
Department: Supply Chain and Information Systems, Laboratory for Economic Management and Auctions
Office: 483A Business Building
Bernardo (Bernie) Quiroga is a PhD Candidate (ABD) at the Smeal College of Business, affiliated with the Laboratory for Economics, Management and Auctions and the Department of Supply Chain and Information Systems.
Bernie is trained as a Business Economist (with expertise in Microeconometrics and Experimental Economics) and Management Scientist. Before joining Penn State as a graduate student, he was a faculty member at the Pontifical Catholic University of Chile and the University of the Andes, both in Santiago, Chile. He has taught Statistics, Econometrics, Managerial Decision Making, Data Analysis, Microeconomics, and Optimization Methods, at the graduate (MBA, M.Sc.) and undergraduate levels. Bernie is teaching Supply Chain Analytics at Smeal this Fall 2014.
Bernie's current research is devoted to Procurement Decision Making. In particular, his work explains bidding behavior in governmental procurement auction processes where price and quality-related dimensions are taken into consideration for contract assignment, combining laboratory experiments and game-theoretical structural estimation techniques.
In other research, he has studied procurement inventory decisions under competition, financial performance of public family-controlled firms, and hedonic valuation measures of housing attributes.
Auctions and Procurement
Ph.D Candidate (ABD), Business Administration, Penn State University, 2015
M.S.B.A., Supply Chain & Information Systems, Penn State University, 2012
M.A., Economics, Penn State University, 2010
Magister (M.S.), Economics, concentration in Public Policy, Pontificia Universidad Católica de Chile, 2006
Commercial Engineer, Economics, Pontificia Universidad Católica de Chile, 2005
Specialist Diploma, Applied Macroeconomics (PIMA), Pontificia Universidad Católica de Chile, 2004
Licentiate (B.Sc.), Economics and Management Sciences, Pontificia Universidad Católica de Chile, 2004
SCM 421, Supply Chain Analytics